  We present a map of the spiral structure of the Galaxy, as traced by
molecular CS emission associated with IRAS sources which are believed to be
compact HII regions. The CS line velocities are used to determine the kinematic
distances of the sources, in order to investigate their distribution in the
galactic plane. This allows us to use 870 objects to trace the arms, a number
larger than that of previous studies based on classical HII regions. The
distance ambiguity of the kinematic distances, when it exists, is solved by
different procedures, including the latitude distribution and an analysis of
the longitude-velocity diagram. The well defined spiral arms are seen to be
confined inside the co-rotation radius, as is often the case in spiral
galaxies. We identify a square-shaped sub-structure in the CS map with that
predicted by stellar orbits at the 4:1 resonance (4 epicycle oscillations in
one turn around the galactic center). The sub-structure is found at the
expected radius, based on the known pattern rotation speed and epicycle
frequency curve. An inner arm presents an end with strong inward curvature and
intense star formation that we tentatively associate with the region where this
arm surrounds the extremity of the bar, as seen in many barred galaxies.
Finally, a new arm with concave curvature is found in the Sagitta to Cepheus
region of the sky.


  The magnetic properties and Mott transition of the Hubbard model on the
square lattice with frustration are studied at half-filling and zero
temperature by the variational cluster approximation. When the on-site
repulsion $U$ is large, magnetically disordered state is realized in highly
frustrated region between the N\'eel and collinear phases, and no
imcommensurate magnetic states are found there. As for the Mott transition, in
addition to the Mott gap and double occupancy, which clarify the nature of the
transition, the structure of the self-energy in the spectral representation is
studied in detail below and above the Mott transition point. The spectral
structure of the self-energy is almost featureless in the metallic phase, but
clear single dispersion, leading to the Mott gap, appears in the Mott insulator
phase.


  We present orbital fits and dynamical masses for HIP 113201AB and HIP
36985AB, two M1 + mid-M dwarf binary systems monitored as part of the SPHERE
SHINE survey. To robustly determine ages via gyrochronology, we undertook a
photometric monitoring campaign for HIP 113201 and for GJ 282AB, the two wide K
star companions to HIP 36985, using the 40 cm Remote Observatory Atacama Desert
(ROAD) telescope. We adopt ages of 1.2$\pm$0.1 Gyr for HIP 113201AB and
750$\pm$100 Myr for HIP 36985AB. To derive dynamical masses for all components
of these systems, we used parallel-tempering Markov Chain Monte Carlo sampling
to fit a combination of radial velocity, direct imaging, and Gaia and Hipparcos
astrometry. Fitting the direct imaging and radial velocity data for HIP 113201
yields a primary mass of 0.54$\pm$0.03 M$_{\odot}$, fully consistent with its
M1 spectral type, and a secondary mass of 0.145$\pm$ M$_{\odot}$. The secondary
masses derived with and without including Hipparcos/Gaia data are more massive
than the 0.1 M$_{\odot}$ estimated mass from the photometry of the companion.
An undetected brown dwarf companion to HIP 113201B could be a natural
explanation for this apparent discrepancy. At an age $>$1 Gyr, a 30 M$_{Jup}$
companion to HIP 113201B would make a negligible ($<$1$\%$) contribution to the
system luminosity, but could have strong dynamical impacts. Fitting the direct
imaging, radial velocity, and Hipparcos/Gaia proper motion anomaly for HIP
36985AB, we find a primary mass of 0.54$\pm$0.01 M$_{\odot}$ and a secondary
mass of 0.185$\pm$0.001 M$_{\odot}$ which agree well with photometric estimates
of component masses, the masses estimated from $M_{K}$-- mass relationships for
M dwarf stars, and previous dynamical masses in the literature.


  We investigate the entanglement features of the quantum states employed in
quantum algorithms. In particular, we analyse the multipartite entanglement
properties in the Deutsch-Jozsa, Grover and Simon algorithms. Our results show
that for these algorithms most instances involve multipartite entanglement.


  We discuss an infinite class of metabelian Von Neumann rho-invariants. Each
one is a homomorphism from the monoid of knots to the real line. In general
they are not well defined on the concordance group. Nonetheless, we show that
they pass to well defined homomorphisms from the subgroup of the concordance
group generated by anisotropic knots. Thus, the computation of even one of
these invariants can be used to conclude that a knot is of infinite order. We
introduce a method to give a computable bound on these invariants. Finally we
compute this bound to get a new and explicit infinite set of twist knots which
is linearly independent in the concordance group and whose every member is of
algebraic order 2 .


  The Juno mission has provided measurements of Jupiter s gravity field with an
outstanding level of accuracy, leading to better constraints on the interior of
the planet. Improving our knowledge of the internal structure of Jupiter is key
to understanding its formation and evolution but is also important in the
framework of exoplanet exploration. In this study, we investigated the
differences between the state-of-the-art equations of state and their impact on
the properties of interior models. Accounting for uncertainty on the hydrogen
and helium equation of state, we assessed the span of the interior features of
Jupiter. We carried out an extensive exploration of the parameter space and
studied a wide range of interior models using Markov chain Monte Carlo (MCMC)
simulations. To consider the uncertainty on the equation of state, we allowed
for modifications of the equation of state in our calculations. Our models
harbour a dilute core and indicate that Jupiter s internal entropy is higher
than what is usually assumed from the Galileo probe measurements. We obtain
solutions with extended dilute cores, but contrary to other recent interior
models of Jupiter, we also obtain models with small dilute cores. The dilute
cores in such solutions extend to 20% of Jupiter s mass, leading to better
agreement with formation evolution models. We conclude that the equations of
state used in Jupiter models have a crucial effect on the inferred structure
and composition. Further explorations of the behaviour of hydrogen helium
mixtures at the pressure and temperature conditions in Jupiter will help to
constrain the interior of the planet, and therefore its origin.


  In this paper, we describe how we extended a distributed key-value store
called Anna into an elastic, multi-tier service for the cloud. In its extended
form, Anna is designed to overcome the narrow cost-performance limitations
typical of current cloud storage systems. We describe three key aspects of
Anna's new design: multi-master selective replication of hot keys, a vertical
tiering of storage layers with different cost-performance tradeoffs, and
horizontal elasticity of each tier to add and remove nodes in response to load
dynamics. Anna's policy engine uses these mechanisms to balance service-level
objectives around cost, latency and fault tolerance. Experimental results
explore the behavior of Anna's mechanisms and policy, exhibiting orders of
magnitude efficiency improvements over both commodity cloud KVS services and
research systems.


  We look at the work of Oleg Ivrii connected with the dimension of
quasicircles for asymptotically small quasiconformality parameter $k$. We
intend to make this work more easily accessible. Our main focus is the integral
means spectrum associated with normalized conformal mappings of the exterior
disk which have quasiconformal extensions to the whole plane with small
dilatation parameter $k$. Moreover, we address the estimates from above only,
not the sharpness from below.


  The existence of a biplane with parameters $(121,16,2)$ is an open problem.
Recently, it has been proved by Alavi, Daneshkhah and Praeger that the order of
an automorphism group of a of possible biplane ${\mathcal D}$ of order $14$
divides $2^7\cdot3^2\cdot5\cdot7\cdot11\cdot13$. In this paper we show that
such a biplane do not have an automorphism of order $11$ or $13$, and thereby
establish that $|Aut({\mathcal D})|$ divides $2^7\cdot3^2\cdot5\cdot7.$
Further, we study a possible action of an automorphism of order five or seven,
and some small groups of order divisible by five or seven, on a biplane with
parameters $(121,16,2)$.


  This paper proposes a new method for estimating high-dimensional binary
choice models. The model we consider is semiparametric, placing no
distributional assumptions on the error term, allowing for heteroskedastic
errors, and permitting endogenous regressors. Our proposed approaches extend
the special regressor estimator originally proposed by Lewbel (2000). This
estimator becomes impractical in high-dimensional settings due to the curse of
dimensionality associated with high-dimensional conditional density estimation.
To overcome this challenge, we introduce an innovative data-driven dimension
reduction method for nonparametric kernel estimators, which constitutes the
main innovation of this work. The method combines distance covariance-based
screening with cross-validation (CV) procedures, rendering the special
regressor estimation feasible in high dimensions. Using the new feasible
conditional density estimator, we address the variable and moment (instrumental
variable) selection problems for these models. We apply penalized least squares
(LS) and Generalized Method of Moments (GMM) estimators with a smoothly clipped
absolute deviation (SCAD) penalty. A comprehensive analysis of the oracle and
asymptotic properties of these estimators is provided. Monte Carlo simulations
are employed to demonstrate the effectiveness of our proposed procedures in
finite sample scenarios.


  Normally the issue or question of the time of arrival of light rays at an
observer coming from a given source is associated with Fermat's Principle of
Least Time which yields paths of extremal time. We here investigate a related
but different problem. We consider an observer receiving light from an extended
source that has propagated in an arbitrary gravitational field. It is assumed
from the start that the propagation is along null geodesics. Each point of the
extended source is sending out a light-cones worth of null rays and the
question arises which null rays from the source arrive first at the observer.
Stated in an a different fashion, a pulse of light comes from the source with a
wave-front as the leading edge, which rays are associated with that leading
edge. In vacuum flat-space we have, from Huygen's principle, that the rays
normal to the source constitute the leading edge and hence arrive first at an
observer. We here investigate this issue in the presence of a gravitational
field. Though it is not obvious, since the rays bend and are focused by the
gravitational field and could even cross, in fact it is the normal rays that
arrive earliest. We give two proofs both involving the extemization of the time
of arrival, one based on an idea of Schrodinger for the derivation of
gravitational frequency shifts and the other based on V.I. Arnold's theory of
generating families.


  Machine learning models of accelerator systems (`surrogate models') are able
to provide fast, accurate predictions of accelerator physics phenomena.
However, approaches to date typically do not include measured input
diagnostics, such as the initial beam distributions, which are critical for
accurately representing the beam evolution through the system. In addition,
these inputs often vary over time, and models that can account for these
changing conditions are needed. Simulations are in some cases needed to provide
sufficient training data. These typically represent the designed machine before
construction; however, the behavior of the installed components may be quite
different due to changes over time or static differences that were not modeled.
Therefore, surrogate models that can leverage both simulation and measured data
successfully are needed. We introduce an approach based on convolutional neural
networks that uses the drive laser distribution and scalar settings as inputs
for the Linac Coherent Light Source II injector frontend. The model is able to
predict scalar beam parameters and the transverse beam distribution downstream,
taking into account the impact of time-varying non-uniformities in the initial
transverse laser distribution. We also introduce and evaluate a transfer
learning procedure for adapting the surrogate model from the simulation domain
to the measurement domain, to account for differences between the two. Applying
this approach to our test case results in a model that can predict test sample
outputs within a mean absolute percent error of 7.6%. This is a substantial
improvement over the model trained only on simulations, which has an error of
112.7% when applied to measured data. While we focus on the LCLS-II Injector
frontend, these approaches for improving ML-based online modeling of injector
systems could be easily adapted to other accelerator facilities.


  Model-based reinforcement learning (MBRL) has recently gained immense
interest due to its potential for sample efficiency and ability to incorporate
off-policy data. However, designing stable and efficient MBRL algorithms using
rich function approximators have remained challenging. To help expose the
practical challenges in MBRL and simplify algorithm design from the lens of
abstraction, we develop a new framework that casts MBRL as a game between: (1)
a policy player, which attempts to maximize rewards under the learned model;
(2) a model player, which attempts to fit the real-world data collected by the
policy player. For algorithm development, we construct a Stackelberg game
between the two players, and show that it can be solved with approximate
bi-level optimization. This gives rise to two natural families of algorithms
for MBRL based on which player is chosen as the leader in the Stackelberg game.
Together, they encapsulate, unify, and generalize many previous MBRL
algorithms. Furthermore, our framework is consistent with and provides a clear
basis for heuristics known to be important in practice from prior works.
Finally, through experiments we validate that our proposed algorithms are
highly sample efficient, match the asymptotic performance of model-free policy
gradient, and scale gracefully to high-dimensional tasks like dexterous hand
manipulation. Additional details and code can be obtained from the project page
at https://sites.google.com/view/mbrl-game


  Influence maximization is a well-studied problem that asks for a small set of
influential users from a social network, such that by targeting them as early
adopters, the expected total adoption through influence cascades over the
network is maximized. However, almost all prior work focuses on cascades of a
single propagating entity or purely-competitive entities. In this work, we
propose the Comparative Independent Cascade (Com-IC) model that covers the full
spectrum of entity interactions from competition to complementarity. In Com-IC,
users' adoption decisions depend not only on edge-level information
propagation, but also on a node-level automaton whose behavior is governed by a
set of model parameters, enabling our model to capture not only competition,
but also complementarity, to any possible degree. We study two natural
optimization problems, Self Influence Maximization and Complementary Influence
Maximization, in a novel setting with complementary entities. Both problems are
NP-hard, and we devise efficient and effective approximation algorithms via
non-trivial techniques based on reverse-reachable sets and a novel "sandwich
approximation". The applicability of both techniques extends beyond our model
and problems. Our experiments show that the proposed algorithms consistently
outperform intuitive baselines in four real-world social networks, often by a
significant margin. In addition, we learn model parameters from real user
action logs.


  A new algorithm for computing a point on a polynomial or rational curve in
B\'{e}zier form is proposed. The method has a geometric interpretation and uses
only convex combinations of control points. The new algorithm's computational
complexity is linear with respect to the number of control points and its
memory complexity is $O(1)$. Some remarks on similar methods for surfaces in
rectangular and triangular B\'{e}zier form are also given.


  The planned rhenium beta-decay experiment MARE might probe the absolute mass
scale of neutrinos with the same sensitivity as the tritium beta-decay
experiment KATRIN, which will start data taking in 2011 and will proceed for
five years. We present the energy distribution of emitted electrons for the
first unique forbidden beta-decay of 187Re. It is found that the p-wave
emission of electron dominates over the s-wave. By assuming mixing of three
neutrinos the Kurie function for the rhenium beta-decay is derived. It is shown
that the Kurie plot near the endpoint is within a good accuracy linear in the
limit of massless neutrinos like the Kurie plot of the superallowed beta-decay
of 3H.


  We study the following question: for given $d\geq 2$, $n\geq d$ and $k \leq
n$, what is the largest value $c(d,n,k)$ such that from any set of $n$ unit
vectors in $\mathbb{R}^d$, we may select $k$ vectors with corresponding signs
$\pm 1$ so that their signed sum has norm at least $c(d,n,k)$? The problem is
dual to classical vector sum minimization and balancing questions, which have
been studied for over a century. We give asymptotically sharp estimates for
$c(d,n,k)$ in the general case. In several special cases, we provide stronger
estimates: the quantity $c(d,n,n)$ corresponds to the $\ell_p$-polarization
problem, while determining $c(d, n, 2)$ is equivalent to estimating the
coherence of a vector system, which is a special case of $p$-frame energies.
Two new proofs are presented for the classical Welch bound when $n = d+1$. For
large values of $n$, volumetric estimates are applied for obtaining fine
estimates on $c(d,n,2)$. Studying the planar case, sharp bounds on $c(2, n, k)$
are given. Finally, we determine the exact value of $c(d,d+1,d+1)$ under some
extra assumptions.


  We present a preliminary experimental study of the dependence on optical
depth of slow and stored light pulses in Rb vapor. In particular, we
characterize the efficiency of slow and stored light as a function of Rb
density; pulse duration, delay and storage time; and control field intensity.
Experimental results are in good qualitative agreement with theoretical
calculations based on a simplified three-level model at moderate densities.


  A HD-like isotopic dipole moment is proposed as a sensible probe for
molecular environments, in particular for electrostatic fields and polarizable
(reactive) sites of molecules. Fictitious nuclear masses are chosen in order to
yield a rigid dipole with appropriate magnitude. Upon subtracting the
Born-Oppenheimer energy, the interaction is reduced to the Field-dipole-like
and the dipole-polarizability-like terms, the last one being particularly
informative since connected to potentially reactive sites. The Field strength
and orientation are easily obtained by identifying the minimum Field-dipole
energy configuration and flipping the dipole from it. In this case the method
appears to have a superior accuracy in comparison with ab initio approaches. In
tests with hydrogen, water, benzene and chlorobenzene molecules and with a
frustrated Lewis pair, the potential of the method is assessed.


  Bayesian optimization (BO) is a powerful framework for optimizing black-box,
expensive-to-evaluate functions. Over the past decade, many algorithms have
been proposed to integrate cheaper, lower-fidelity approximations of the
objective function into the optimization process, with the goal of converging
towards the global optimum at a reduced cost. This task is generally referred
to as multi-fidelity Bayesian optimization (MFBO). However, MFBO algorithms can
lead to higher optimization costs than their vanilla BO counterparts,
especially when the low-fidelity sources are poor approximations of the
objective function, therefore defeating their purpose. To address this issue,
we propose rMFBO (robust MFBO), a methodology to make any GP-based MFBO scheme
robust to the addition of unreliable information sources. rMFBO comes with a
theoretical guarantee that its performance can be bound to its vanilla BO
analog, with high controllable probability. We demonstrate the effectiveness of
the proposed methodology on a number of numerical benchmarks, outperforming
earlier MFBO methods on unreliable sources. We expect rMFBO to be particularly
useful to reliably include human experts with varying knowledge within BO
processes.


  We describe general methods for enumerating subsemigroups of finite
semigroups and techniques to improve the algorithmic efficiency of the
calculations. As a particular application we use our algorithms to enumerate
all transformation semigroups up to degree 4. Classification of these
semigroups up to conjugacy, isomorphism and anti-isomorphism, by size and rank,
provides a solid base for further investigations of transformation semigroups.


  The knots in solar prominences are often observed to fall with nearly
constant velocity, but the associated physical mechanism is currently not well
understood. In this letter, we presented a prominence observed by New Vacuum
Solar Telescope (NVST) in H-alpha wavelength. Knots that rose within the
prominence appear to have been preferentially located at higher altitude,
whereas those that fell were found throughout the entire prominence structure.
The descending speed of the knots near the solar surface was higher than that
far away from the solar surface. We noted that the knots near the solar surface
may run along a set of coronal loops observed from the Atmospheric Imaging
Assembly. Elsewhere, the majority of knots are interpreted to have descended
across more horizontal magnetic field with a nearly constant speed. This lack
of acceleration indicates that the liberated gravitational potential energy may
not manifest as an increase in kinetic energy. Assuming instead that the
descending knots were capable of exciting Alfven waves that could then
dissipate within the local corona, the gravitational potential energy of the
knots may have been converted into thermal energy. Assuming a perfectly elastic
system, we therefore estimate that the gravitational energy loss rate of these
observed knots amounts to 1/2000 of that required to heat the entire quiet-Sun,
increasing to 1/320 when considering possibly further downward motions of the
knots having disappeared in the H-alpha observations. This result suggests such
a mechanism may contribute to the heating of the corona local to these
prominences.


  In terms of the exaxt angular momentum projection, properties of the three
dimensional cranked HFB (3d-CHFB) states are analyzed quantitatively in the
context of the relation between the signature of an intrinsic symmetry and the
parity of angular momentum, (-1)^I. We found that the tilted states have
favorable features to describe states involved with high-K quantum number
and/or odd total angular momentum. This implies that 3d-CHFB can describe
properly the backbending phenomena like a "t-band and g-band" crossing, which
is suggested in N=106 isotopes.


  Control barrier functions (CBFs) enable guaranteed safe multi-agent
navigation in the continuous domain. The resulting navigation performance,
however, is highly sensitive to the underlying hyperparameters. Traditional
approaches consider fixed CBFs (where parameters are tuned apriori), and hence,
typically do not perform well in cluttered and highly dynamic environments:
conservative parameter values can lead to inefficient agent trajectories, or
even failure to reach goal positions, whereas aggressive parameter values can
lead to infeasible controls. To overcome these issues, in this paper, we
propose online CBFs, whereby hyperparameters are tuned in real-time, as a
function of what agents perceive in their immediate neighborhood. Since the
explicit relationship between CBFs and navigation performance is hard to model,
we leverage reinforcement learning to learn CBF-tuning policies in a model-free
manner. Because we parameterize the policies with graph neural networks (GNNs),
we are able to synthesize decentralized agent controllers that adjust parameter
values locally, varying the degree of conservative and aggressive behaviors
across agents. Simulations as well as real-world experiments show that (i)
online CBFs are capable of solving navigation scenarios that are infeasible for
fixed CBFs, and (ii), that they improve navigation performance by adapting to
other agents and changes in the environment.


  The two-dimensional free-boundary problem describing steady gravity waves
with vorticity on water of finite depth is considered. Bounds for stream
functions as well as free-surface profiles and the total head are obtained
under the assumption that the vorticity distribution is a locally Lipschitz
function. It is also shown that wave flows have counter-currents in the case
when the infimum of the free surface profile exceeds a certain critical value.


  A post-CME current sheet (CS) is a common feature developed behind an
erupting flux rope in CME models. Observationally, white light observations
have recorded many occurrences of a thin ray appearing behind a CME eruption
that closely resembles a post-CME CS in its spatial correspondence and
morphology. UV and X-ray observations further strengthen this interpretation by
the observations of high temperature emission at locations consistent with
model predictions. The next question then becomes whether the properties inside
a post-CME CS predicted by a model agree with observed properties. In this
work, we assume that the post-CME CS is a consequence of Petschek-like
reconnection and that the observed ray-like structure is bounded by a pair of
slow mode shocks developed from the reconnection site. We perform
time-dependent ionization calculations and model the UV line emission. We find
that such a model is consistent with SOHO/UVCS observations of the post-CME CS.
The change of Fe XVIII emission in one event implies an inflow speed of ~10
km/s and a corresponding reconnection rate of M_A ~ 0.01. We calculate the
expected X-ray emission for comparison with X-ray observations by Hinode/XRT,
as well as the ionic charge states as would be measured in-situ at 1 AU. We
find that the predicted count rate for Hinode/XRT agree with what was observed
in a post-CME CS on April 9, 2008, and the predicted ionic charge states are
consistent with high ionization states commonly measured in the interplanetary
CMEs. The model results depend strongly on the physical parameters in the
ambient corona, namely the coronal magnetic field, the electron density and
temperature during the CME event. It is crucial to obtain these ambient coronal
parameters and as many facets of the CS properties as possible by observational
means so that the post-CME current sheet models can be scrutinized more
effectively.


  We propose a way to understand the 3 fermion generations by the algebraic
structures of noncommutative geometry, which is a promising framework to unify
the standard model and general relativity. We make the tensor product extension
and the quaternion extension on the framework. Each of the two extensions alone
keeps the action invariant, and we consider them as the almost trivial
structures of the geometry. We combine the two extensions, and show the
corresponding physical effects, i.e., the emergence of 3 fermion generations
and the mass relationships among those generations. We define the coordinate
fiber space of the bundle of the manifold as the space in which the classical
noncommutative geometry is expressed, then the tensor product extension
explicitly shows the contribution of structures in the non-coordinate base
space of the bundle to the action. The quaternion extension plays an essential
role to reveal the physical effect of the structure in the non-coordinate base
space.


  This paper presents our pioneering effort for emotion recognition in
conversation (ERC) with pre-trained language models. Unlike regular documents,
conversational utterances appear alternately from different parties and are
usually organized as hierarchical structures in previous work. Such structures
are not conducive to the application of pre-trained language models such as
XLNet. To address this issue, we propose an all-in-one XLNet model, namely
DialogXL, with enhanced memory to store longer historical context and
dialog-aware self-attention to deal with the multi-party structures.
Specifically, we first modify the recurrence mechanism of XLNet from
segment-level to utterance-level in order to better model the conversational
data. Second, we introduce dialog-aware self-attention in replacement of the
vanilla self-attention in XLNet to capture useful intra- and inter-speaker
dependencies. Extensive experiments are conducted on four ERC benchmarks with
mainstream models presented for comparison. The experimental results show that
the proposed model outperforms the baselines on all the datasets. Several other
experiments such as ablation study and error analysis are also conducted and
the results confirm the role of the critical modules of DialogXL.


  We consider machine learning applications that train a model by leveraging
data distributed over a trusted network, where communication constraints can
create a performance bottleneck. A number of recent approaches propose to
overcome this bottleneck through compression of gradient updates. However, as
models become larger, so does the size of the gradient updates. In this paper,
we propose an alternate approach to learn from distributed data that quantizes
data instead of gradients, and can support learning over applications where the
size of gradient updates is prohibitive. Our approach leverages the dependency
of the computed gradient on data samples, which lie in a much smaller space in
order to perform the quantization in the smaller dimension data space. At the
cost of an extra gradient computation, the gradient estimate can be refined by
conveying the difference between the gradient at the quantized data point and
the original gradient using a small number of bits. Lastly, in order to save
communication, our approach adds a layer that decides whether to transmit a
quantized data sample or not based on its importance for learning. We analyze
the convergence of the proposed approach for smooth convex and non-convex
objective functions and show that we can achieve order optimal convergence
rates with communication that mostly depends on the data rather than the model
(gradient) dimension. We use our proposed algorithm to train ResNet models on
the CIFAR-10 and ImageNet datasets, and show that we can achieve an order of
magnitude savings over gradient compression methods. These communication
savings come at the cost of increasing computation at the learning agent, and
thus our approach is beneficial in scenarios where communication load is the
main problem.


  We investigate hadroproduction of $B_c$ mesons from initial gluons using QCD
perturbation theory and nonrelativistic bound state approximations. Our results
obtained by two completely independent calculations are confronted with
existing results which contradict each other. Moreover, we examine the
approximation based on heavy quark fragmentation and determine the range of
validity of it. Predictions for cross sections and differential distributions
at Tevatron and LHC energies are presented.


  I describe ongoing work developing Bayesian methods for flexible modeling of
arrival time series data without binning, aiming to improve detection and
measurement of X-ray and gamma-ray pulsars, and of pulses in gamma-ray bursts.
The methods use parametric and semiparametric Poisson point process models for
the event rate, and by design have close connections to conventional
frequentist methods currently used in time-domain astronomy.


  Pairing correlations have a strong influence on nuclear level densities.
Empirical descriptions and theoretical models have been developed to take these
effects into account. The present article discusses cases, where descriptions
of nuclear level densities are inconsistent or in conflict with the present
understanding of nuclear properties. Phenomenological approaches consider a
back-shift parameter. However, the absolute magnitude of the back-shift, which
actually corresponds to the pairing condensation energy, is generally not
compatible with the observation that stable pairing correlations are present in
essentially all nuclei. It is also shown that in the BCS model pairing
condensation energies and critical pairing energies are inconsistent for light
nuclei. A modification to the composite Gilbert-Cameron level-density
description is proposed, and the use of more realistic pairing theories is
suggested.


  A novel description of Josephson vortices (JVs) crossed by the pancake
vortices (PVs) is proposed on the basis of the anisotropic London theory. The
field distribution of a JV and its energy have been calculated for both dense
($a<\lambda_J$) and dilute ($a>\lambda_J$) PV lattices with distance $a$
between PVs, and the nonlinear JV core size $\lambda_J$. It is shown that the
``shifted'' PV lattice (PVs displaced mainly along JVs in the crossing vortex
lattice structure), formed in high out-of-plane magnetic fields transforms into
the PV lattice ``trapped'' by the JV sublattice at a certain field, lower than
$\Phi_0/\gamma^2s^2$, where $\Phi_0$ is the flux quantum, $\gamma$ is the
anisotropy parameter and $s$ is the distance between CuO$_2$ planes.
  With further decreasing $B_z$, the free energy of the crossing vortex lattice
structure (PV and JV sublattices coexist separately) can exceed the free energy
of the tilted lattice (common PV-JV vortex structure) in the case of $\gamma
s<\lambda_{ab}$ with the in-plane penetration depth $\lambda_{ab}$ if the low
($B_x<\gamma\Phi_0/\lambda_{ab}^2$) or high ($B_x\gtrsim \Phi_0/\gamma s^2$)
in-plane magnetic field is applied. It means that the crossing vortex structure
is realized in the intermediate field orientations, while the tilted vortex
lattice can exist if the magnetic field is aligned near the $c$-axis and the
$ab$-plane as well. In the intermediate in-plane fields
$\gamma\Phi_0/\lambda_{ab}^2\lesssim B_x \lesssim \Phi_0/\gamma s^2$, the
crossing vortex structure with the ``trapped'' PV sublattice seems to settle in
until the lock-in transition occurs since this structure has the lower energy
with respect to the tilted vortex structure in the magnetic field ${\vec H}$
oriented near the $ab$-plane.


  Recent measurements of the force versus extension curves in stretched single
stranded DNA, under conditions where the hydrogen bonding between complementary
bases is inhibited, provide a new handle for the study of self-avoiding effects
in a flexible polymer. We report in this paper upon analytic computations of
the force versus extension curves within a continuous version of the freely
joining chain model, with monomer-monomer repulsive interactions. The problem
is formulated as a Statistical Field Theory model, endowed with a fixed cutoff
associated with the curvature of the extension versus force curve, in the free
polymer limit. Using a Field Theory version of the Hartree-Fock approximation,
the self-avoiding single polymer problem reduces to the solving of a
one-dimension self-consistent integral equation. Taking the short range
potential limit, we obtain an explicit analytical solution, involving the roots
of an algebraic equation. In the low force regime, we find that the slope of
the relative extension at zero force increases steadily with the total monomer
number N, while it stays constant for a free polymer. We compare, for N=100,
our analytic results with those obtained by Monte Carlo simulations of a
discrete freely joining chain, in presence of excluded volume effects. Despite
a difference between the repulsive potential ranges, an appropriate choice of
our single free parameter leads to a good agreement between the two
computations.


  We present a sampling strategy suitable for optimization problems
characterized by high-dimensional design spaces and noisy outputs. Such outputs
can arise, for example, in time-averaged objectives that depend on chaotic
states. The proposed sampling method is based on a generalization of Arnoldi's
method used in Krylov iterative methods. We show that Arnoldi-based sampling
can effectively estimate the dominant eigenvalues of the underlying Hessian,
even in the presence of inaccurate gradients. This spectral information can be
used to build a low-rank approximation of the Hessian in a quadratic model of
the objective. We also investigate two variants of the linear term in the
quadratic model: one based on step averaging and one based on directional
derivatives. The resulting quadratic models are used in a trust-region
optimization framework called the Stochastic Arnoldi's Method (SAM). Numerical
experiments highlight the potential of SAM relative to conventional
derivative-based and derivative-free methods when the design space is
high-dimensional and noisy.


  The regularities of laser-induced damage of anisotropic materials, such as
LiNbO3 and KDP dielectric single crystals, are experimentally studied. It is
revealed that the shape of laser-induced damage in the dielectric crystals
depends on the elastic symmetry of crystal and the propagation direction of the
laser beam. When the beam propagates along the optic axis of crystals, the
figures of the laser damage are six-path stars for LiNbO3 and four-path ones
for KDP crystals. For the direction parallel to X and Y axes in KDP crystal,
the damage has initially cross-like configuration, with further splitting of
Z-oriented crack into two cracks in the process of damage evolution, leading to
transformation of orthogonal-type damage to a hexagonal-type one.


  Based on the study of cellular aging using the single-cell model organism of
budding yeast and corroborated by other studies, we propose the Emergent Aging
Model (EAM). EAM hypothesizes that aging is an emergent property of complex
biological systems, exemplified by biological networks such as gene networks.
An emergent property refers to traits that a system has at the system level but
which its low-level components do not. EAM is based on a quantitative
definition of aging using the mortality rate. A biological entity with a
constant mortality rate is considered non-aging which is equivalent to a
first-order chemical reaction. Aging can be quantitatively defined as an
increasing mortality rate over time, corresponding to an organism's increasing
chance of dying over time. EAM posits that biological aging can arise at the
system level of an organism, even if the system is composed of only non-aging
components. EAM is consistent with the observation that aging is largely
stochastic, influenced by numerous genes and epigenetic factors, with no single
gene or factor known as the bona fide cause of aging. A parsimonious version of
EAM can predict the Gompertz model of biological aging, the Strehler-Mildvan
correlation, and the trade-off between initial reproductive fitness (asexual
reproductive fitness) and late-life survival. EAM has been applied to
experimental results of the replicative lifespan of the budding yeast and can
potentially offer new insights into the aging process of other biological
species.


  The interpretation of the intensity and polarization of the spectral line
radiation produced in the atmosphere of the Sun and of other stars requires
solving a radiative transfer problem that can be very complex, especially when
the main interest lies in modeling the spectral line polarization produced by
scattering processes and the Hanle and Zeeman effects. One of the difficulties
is that the plasma of a stellar atmosphere can be highly inhomogeneous and
dynamic, which implies the need to solve the non-equilibrium problem of the
generation and transfer of polarized radiation in realistic three-dimensional
(3D) stellar atmospheric models. Here we present PORTA, an efficient multilevel
radiative transfer code we have developed for the simulation of the spectral
line polarization caused by scattering processes and the Hanle and Zeeman
effects in 3D models of stellar atmospheres. The numerical method of solution
is based on the non-linear multigrid iterative method and on a novel
short-characteristics formal solver of the Stokes-vector transfer equation
which uses monotonic B\'ezier interpolation. Therefore, with PORTA the
computing time needed to obtain at each spatial grid point the self-consistent
values of the atomic density matrix (which quantifies the excitation state of
the atomic system) scales linearly with the total number of grid points.
Another crucial feature of PORTA is its parallelization strategy, which allows
us to speed up the numerical solution of complicated 3D problems by several
orders of magnitude with respect to sequential radiative transfer approaches,
given its excellent linear scaling with the number of available processors. The
PORTA code can also be conveniently applied to solve the simpler 3D radiative
transfer problem of unpolarized radiation in multilevel systems.


  Following on after two previous papers discussing the formation of primordial
black holes in the early universe, we present here results from an in-depth
investigation of the extent to which primordial black hole formation in the
radiative era can be considered as an example of the critical collapse
phenomenon. We focus on initial supra-horizon-scale perturbations of a type
which could have come from inflation, with only a growing component and no
decaying component. In order to study perturbations with amplitudes extremely
close to the supposed critical limit, we have modified our previous computer
code with the introduction of an adaptive mesh refinement scheme. This has
allowed us to follow black hole formation from perturbations whose amplitudes
are up to eight orders of magnitude closer to the threshold than we could do
before. We find that scaling-law behaviour continues down to the smallest black
hole masses that we are able to follow and we see no evidence of shock
production such as has been reported in some previous studies and which led
there to a breaking of the scaling-law behaviour at small black-hole masses. We
attribute this difference to the different initial conditions used. In addition
to the scaling law, we also present other features of the results which are
characteristic of critical collapse in this context.


  In this paper, we delve into the computations performed at a node within a
message-passing algorithm. We investigate low complexity/latency multi-input
structures that can be adopted by the node for computing outgoing messages y =
(y1, y2, . . . , yn) from incoming messages x = (x1, x2, . . . , xn), where
each yj , j = 1, 2, . . . , n is computed via a multi-way tree with leaves x
excluding xj . Specifically, we propose two classes of structures for different
scenarios. For the scenario where complexity has a higher priority than
latency, the star-tree-based structures are proposed. The complexity-optimal
ones (as well as their lowest latency) of such structures are obtained, which
have the near-lowest (and sometimes the lowest) complexity among all
structures. For the scenario where latency has a higher priority than
complexity, the isomorphic-directed-rooted-tree-based structures are proposed.
The latency-optimal ones (as well as their lowest complexity) of such
structures are obtained, which are proved to have the lowest latency among all
structures.


  We study the quantum Zeno effect (QZE) and quantum anti-Zeno effect (QAZE) of
the multimode quantum Rabi model(MQRM). We derive an analytic expression for
the decay rate of the survival probability where cavity modes are initially
prepared as thermal equilibrium states. A crossover from QZE to QAZE is
observed due to the energy backflow induced by high frequency cavity modes. In
addition, we apply a numerically exact method based on the thermofield
dynamics(TFD) theory and the matrix product states(MPS) to study the effect of
squeezing of the cavity modes on the QZE of the MQRM. The influence of the
squeezing angle, squeezing strength and temperature on the decay rate of the
survival probability are discussed.


  This paper studies the continuous-time pre-commitment KMM problem proposed by
Klibanoff, Marinacci and Mukerji (2005) in incomplete financial markets, which
concerns with the portfolio selection under smooth ambiguity. The decision
maker (DM) is uncertain about the dominated priors of the financial market,
which are characterized by a second-order distribution (SOD). The KMM model
separates risk attitudes and ambiguity attitudes apart and the aim of the DM is
to maximize the two-fold utility of terminal wealth, which does not belong to
the classical subjective utility maximization problem. By constructing the
efficient frontier, the original KMM problem is first simplified as an one-fold
expected utility problem on the second-order space. In order to solve the
equivalent simplified problem, this paper imposes an assumption and introduces
a new distorted Legendre transformation to establish the bipolar relation and
the distorted duality theorem. Then, under a further assumption that the
asymptotic elasticity of the ambiguous attitude is less than 1, the uniqueness
and existence of the solution to the KMM problem are shown and we obtain the
semi-explicit forms of the optimal terminal wealth and the optimal strategy.
Explicit forms of optimal strategies are presented for CRRA, CARA and HARA
utilities in the case of Gaussian SOD in a Black-Scholes financial market,
which show that DM with higher ambiguity aversion tends to be more concerned
about extreme market conditions with larger bias. In the end of this work,
numerical comparisons with the DMs ignoring ambiguity are revealed to
illustrate the effects of ambiguity on the optimal strategies and value
functions.


  Previous work on calculating energy spectra from Compton scattering events
has either neglected considering the pulsed structure of the incident laser
beam, or has calculated these effects in an approximate way subject to
criticism. In this paper, this problem has been reconsidered within a linear
plane wave model for the incident laser beam. By performing the proper Lorentz
transformation of the Klein-Nishina scattering cross section, a spectrum
calculation can be created which allows the electron beam energy spread and
emittance effects on the spectrum to be accurately calculated, essentially by
summing over the emission of each individual electron. Such an approach has the
obvious advantage that it is easily integrated with a particle distribution
generated by particle tracking, allowing precise calculations of spectra for
realistic particle distributions in collision. The method is used to predict
the energy spectrum of radiation passing through an aperture for the proposed
Old Dominion University inverse Compton source. Many of the results allow easy
scaling estimates to be made of the expected spectrum.


  The essential quantum many-body physics of an ultracold quantum gas relies on
the single-particle Green's functions.\ We demonstrate that it can be extracted
by the spectrum of electromagnetically induced transparency (EIT).\ The
single-particle Green's function can be reconstructed by the measurements of
frequency moments in EIT spectroscopy.\ This optical measurement provides an
efficient and nondestructive method to reveal the many-body properties, and we
propose an experimental setup to realize it.\ Finite temperature and finite
size effects are discussed, and we demonstrate the reconstruction steps of
Green's function for the examples of three-dimensional Mott-insulator phase and
one-dimensional Luttinger liquid.


  Accretion bursts from low-mass young stellar objects (YSOs) are known for
many decades. In recent years, the first accretion bursts of massive YSOs
(MYSOs) have been observed. These phases of intense protostellar growth are of
particular importance for studying massive star formation. Bursts of MYSOs are
accompanied by flares of Class II methanol masers (hereafter masers), caused by
an increase in exciting mid-infrared (MIR) emission. The G323.46$-$0.08
(hereafter G323) event extends the small sample of known MYSO bursts. Maser
observations of the MYSO G323 show evidence of a flare, which was presumed to
be caused by an accretion burst. This should be verified with IR data. We used
time-dependent radiative transfer (TDRT) to characterize the heating and
cooling timescales for eruptive MYSOs and to infer the main burst parameters.
The G323 accretion burst is confirmed. It reached its peak in late 2013/early
2014 with a Ks-band increase of 2.5mag. TDRT indicates that the duration of the
thermal afterglow in the far-infrared (FIR) can exceed the burst duration by
years. The latter was proved by SOFIA observations, which indicate a flux
increase of $(14.2\pm4.6)$% at $70\, \rm \mu m$ and $(8.5\pm6.1)$% at $160\,
\mu$m in 2022 (2 years after the burst end). A one-sided light echo emerged
that was propagating into the interstellar medium. The G323 burst is probably
the most energetic MYSO burst observed so far. Within $8.4 \rm \, yrs$, an
energy of $(0.9\pm_{0.8}^{2.5}) \times 10^{47}\,\rm erg$ was released. The
short timescale points to the accretion of a compact body, while the burst
energy corresponds to an accumulated mass of at least
$(7\pm_{6}^{20})\,M_{Jup}$ and possibly even more if the protostar is bloated.
In this case, the accretion event might have triggered protostellar pulsations,
which give rise to the observed maser periodicity.


  We investigate the existence of closed planar loops with prescribed
curvature. Our approach is variational, and relies on a Hardy type inequality
and its associated functional space.


  Matrix square roots and their inverses arise frequently in machine learning,
e.g., when sampling from high-dimensional Gaussians $\mathcal{N}(\mathbf 0,
\mathbf K)$ or whitening a vector $\mathbf b$ against covariance matrix
$\mathbf K$. While existing methods typically require $O(N^3)$ computation, we
introduce a highly-efficient quadratic-time algorithm for computing $\mathbf
K^{1/2} \mathbf b$, $\mathbf K^{-1/2} \mathbf b$, and their derivatives through
matrix-vector multiplication (MVMs). Our method combines Krylov subspace
methods with a rational approximation and typically achieves $4$ decimal places
of accuracy with fewer than $100$ MVMs. Moreover, the backward pass requires
little additional computation. We demonstrate our method's applicability on
matrices as large as $50,\!000 \times 50,\!000$ - well beyond traditional
methods - with little approximation error. Applying this increased scalability
to variational Gaussian processes, Bayesian optimization, and Gibbs sampling
results in more powerful models with higher accuracy.


  In this study we attempt to explore the consequences of surfactant coating on
the electrohydrodynamic manipulation of a drop motion in a plane Poiseuielle
flow. In addition we consider bulk insoluble surfactants and a linear
dependency of the surface tension on the surfactant concentration. Subsequently
a double asymptotic perturbation method is used in terms of small electric
Reynolds number and capillary number in the limit of a diffusion-dominated
surfactant transport mechanism. Also going beyond the widely employed
axisymmetric framework, the coupled system of governing differential equations
in three dimensions are then solved by adopting the `generalized Lamb solution
technique'. The expressions of key variables suggest that the flow curvature of
the external flow, the electric field effects and the surfactant effects are
coupled in a non-trivial manner, well beyond a linear superposition. A careful
investigation shows that surfactant-induced Marangoni stresses interacts with
the electrohydrodynamic stresses in a highly coupled fashion. Owing to this,
under different combinations of electrical conductivity and permittivity
ratios, the Mason number and the applied electric field direction, the
surfactants affect differently on the longitudinal as well as cross-stream
migration velocity of the drop. The present results may be of utmost importance
in providing a deep insight to the underlying complex physical mechanisms. Most
importantly the ability of surfactants in selectively controlling the drop
motion in different directions, makes them suitable for achieving a new degree
of freedom in the electrical actuation of droplets in the microfluidic devices.


  While M- and F-theory compactifications describe a much larger class of vacua
than perturbative string compactifications, they typically need singularities
to generate non-abelian gauge fields and charged matter. The physical
explanation involves M2-branes wrapped on vanishing cycles. Here we seek an
alternative explanation that could address outstanding issues such as the
description of nilpotent branches, stability walls, frozen singularities and so
forth. To this end we use a model in which the three-form is related to the
Chern-Simons form of a bundle. The model has a one-form non-abelian gauge
symmetry which normally eliminates all the degrees of freedom associated to the
bundle. However by restricting the transformations to preserve the bundle along
the vanishing cycles, we may get new degrees of freedom associated to
singularities, without appealing to wrapped M2-branes. The analysis can be
simplified by gauge-fixing the one-form symmetry using higher-dimensional
instanton equations. We explain how this mechanism leads to the natural
emergence of phenomena such as enhanced ADE gauge symmetries, nilpotent
branches, charged matter fields and their holomorphic couplings.


  We show that one dimensional (1D) topological superconductivity can be placed
in the context of phenomena associated with strongly correlated electron
systems. Here we propose a system consisting of a one-dimensional chain of
strongly correlated fermions placed on a superconducting (SC) substrate that
exhibits a spin-singlet extended $s$-wave pairing. Strong electron correlation
is shown to transform an extended $s$-wave SC into a topological SC that hosts
Majorana fermions. In contrast to the approaches based on mean-field
treatments, no Zeeman or exchange magnetic field is needed to produce such an
effect.


  Fully Field Programmable Gate Array (FPGA)based digitizer for high-resolution
time and energy measurement is an attractive low cost solution for the readout
electronics in positron emission computed tomography (PET)detector. In recent
years, the FPGA based time-digital converter (FPGA-TDC) has been widely used
for time measurement in the commercial PET scanners. Yet, for the energy
measurement, few studies have been reported on a fully FPGA based, large
dynamic range and high resolution alternative to the commercial analog-digital
converter (ADC). Our previous research presents a 25 Ms/s FPGA-TDC based
free-running ADC (FPGA-ADC), and successfully employed it in the readout
electronics for PET detector. In this work-in-progress study, by means of the
time-interleaved strategy, a 50 Ms/s FPGA-ADC is presented. With only two
off-chip resistors, both the A/D conversion and energy measurement are achieved
on a Xilinx Kintex-7 FPGA. Therefore, this method has great advantages
inimproving system integration. Initial performance tests are also presented,
and we hope it can give us a possibility to develop a new FPGA-only front-end
digitizer for PET in future.


  We study the stability and the dynamics of many-soliton molecules in
dispersion-managed (DM) optical fibers with focus on five-and seven-soliton
molecules by analytical and numerical means. In particular we calculate the
binding force, pulse durations and equilibrium separations using a variational
approach. Predicted pulse shapes are in good agreement with those found by
numerical simulations of the underlying nonlinear Schr\"odinger equation.
Within limitations soliton molecules with up to seven solitons possibly allow
to encode three bits of data per clock period.


  Measurements of the cosmic microwave background (CMB) radiation provide a
unique opportunity for a direct study of the primordial cosmic plasma at
redshift z ~1000. The angular power spectra of temperature and polarisation
fluctuations are powerful observational objectives as they encode information
on fundamental cosmological parameters and on the physics of the early
universe. A large number of increasingly ambitious balloon-borne and
ground-based experiments have been carried out following the first detection of
CMB anisotropies by COBE-DMR, probing the angular power spectrum up to high
multipoles. The recent data from WMAP provide a new major step forward in
measurements percision. The ESA mission Planck Surveyor, to be launched in
2007, is the third-generation satellite devoted to CMB imaging. Planck is
expected to extract the full cosmological information from temperature
anisotropies and to open up new fronteers in the CMB field.


  In this paper we introduce squarefree vertex cover algebras. We study the
question when these algebras coincide with the ordinary vertex cover algebras
and when these algebras are standard graded. In this context we exhibit a
duality theorem for squarefree vertex cover algebras.


  The phenomenon of branched flow, visualized as a chaotic arborescent pattern
of propagating particles, waves, or rays, has been identified in disparate
physical systems ranging from electrons to tsunamis, with periodic systems only
recently being added to this list. Here, we explore the laws governing the
evolution of the branches in periodic potentials. On one hand, we observe that
branch formation follows a similar pattern in all non-integrable potentials, no
matter whether the potentials are periodic or completely irregular. Chaotic
dynamics ultimately drives the birth of the branches. On the other hand, our
results reveal that for periodic potentials the decay of the branches exhibits
new characteristics due to the presence of infinitely stable branches known as
superwires. Again, the interplay between branched flow and superwires is deeply
connected to Hamiltonian chaos. In this work, we explore the relationships
between the laws of branched flow and the structures of phase space, providing
extensive numerical and theoretical arguments to support our findings.


  The Adam optimization algorithm has proven remarkably effective for
optimization problems across machine learning and even traditional tasks in
geometry processing. At the same time, the development of equivariant methods,
which preserve their output under the action of rotation or some other
transformation, has proven to be important for geometry problems across these
domains. In this work, we observe that Adam $-$ when treated as a function that
maps initial conditions to optimized results $-$ is not rotation equivariant
for vector-valued parameters due to per-coordinate moment updates. This leads
to significant artifacts and biases in practice. We propose to resolve this
deficiency with VectorAdam, a simple modification which makes Adam
rotation-equivariant by accounting for the vector structure of optimization
variables. We demonstrate this approach on problems in machine learning and
traditional geometric optimization, showing that equivariant VectorAdam
resolves the artifacts and biases of traditional Adam when applied to
vector-valued data, with equivalent or even improved rates of convergence.


  Measurements of primordial non-Gaussianity ($f_{NL}$) open a new window onto
the physics of inflation. We describe a fast cubic (bispectrum) estimator of
$f_{NL}$, using a combined analysis of temperature and polarization
observations. The speed of our estimator allows us to use a sufficient number
of Monte Carlo simulations to characterize its statistical properties in the
presence of real world issues such as instrumental effects, partial sky
coverage, and foreground contamination. We find that our estimator is optimal,
where optimality is defined by saturation of the Cramer Rao bound, if noise is
homogeneous. Our estimator is also computationally efficient, scaling as
$O(N^{3/2})$ compared to the $O(N^{5/2})$ scaling of the brute force bispectrum
calculation for sky maps with $N$ pixels. For Planck this translates into a
speed-up by factors of millions, reducing the required computing time from
thousands of years to just hours and thus making $f_{NL}$ estimation feasible
for future surveys. Our estimator in its current form is optimal if noise is
homogeneous. In future work our fast polarized bispectrum estimator should be
extended to deal with inhomogeneous noise in an analogous way to how the
existing fast temperature estimator was generalized.


  Strategies based on the active targeting of tumor cells are emerging as smart
and efficient nanomedical procedures. Folic acid (FA) is a vitamin and a
well-established tumor targeting agent because of its strong affinity for the
folate receptor (FR), which is an overexpressed protein on the cell membranes
of the tumor cells. FA can be successfully anchored to several nanocarriers,
including inorganic nanoparticles (NPs) based on transition metal oxides. Among
them, TiO$_{2}$ is extremely interesting because of its excellent
photoabsorption and photocatalytic properties, which can be exploited in
photodynamic therapy. However, it is not yet clear in which respects direct
anchoring of FA to the NP or the use of spacers, based on polyethylene glycol
(PEG) chains, are different and whether one approach is better than the other.
In this work, we combine Quantum Mechanics (QM) and Classical Molecular
Dynamics (MD) to design and optimize the FA functionalization on bare and
PEGylated TiO$_{2}$ models and to study the dynamical behavior of the resulting
nanoconjugates in pure water environment and in physiological conditions. We
observe that they are chemically stable, even under the effect of increasing
temperature (up to 500 K). Using the results from long MD simulations (100 ns)
and from free energy calculations, we determine how the density of FA molecules
on the TiO$_{2}$ NP and the presence of PEG spacers impact on the actual
exposure of the ligands, especially by affecting the extent of FA-FA
intermolecular interactions, which are detrimental for the targeting ability of
FA towards the folate receptor. This analysis provides a solid and rational
basis for experimentalists to define the optimal FA density and the more
appropriate mode of anchoring to the carrier, according to the final purpose of
the nanoconjugate.


  This paper addresses an optimal guidance problem concerning the vertical
landing of a lunar lander with the objective of minimizing fuel consumption.
The vertical landing imposes a final attitude constraint, which is treated as a
final control constraint. To handle this constraint, we propose a nonnegative
small regularization term to augment the original cost functional. This ensures
the satisfaction of the final control constraint in accordance with
Pontryagin's Minimum Principle. By leveraging the necessary conditions for
optimality, we establish a parameterized system that facilitates the generation
of numerous optimal trajectories, which contain the nonlinear mapping from the
flight state to the optimal guidance command. Subsequently, a neural network is
trained to approximate such mapping. Finally, numerical examples are presented
to validate the proposed method.


  We consider propagation of density wave packets in a Bose-Einstein
condensate. We show that the shape of initially broad, laser-induced, density
perturbation changes in the course of free time evolution so that a shock wave
front finally forms. Our results are well beyond predictions of commonly used
zero-amplitude approach, so they can be useful in extraction of a speed of
sound from experimental data. We discuss a simple experimental setup for shock
propagation and point out possible limitations of the mean-field approach for
description of shock phenomena in a BEC.


  The formulation of the variational problems for the solute transport in a
fluid layer in presence of double-diffusive thermal convection is discussed. It
is shown that the variational functional obtained by Strauss can be generalized
and the general functional leads to accurate upper bounds on the solute
transport for the case of small and intermediate values of the Rayleigh number.
The general functional however is a non-homogeneous one but for asymptotically
large Rayleigh numbers it converges to the Strauss approximation. Thus for
small and intermediate values of the Rayleigh numbers one should use the
general functional and for vary large values of the Rayleigh numbers one can
use the functional of Strauss.


  As in arXiv: math. 0809.2365 we consider classical system of interacting
particles $\mathcal{P}_1, ..., \mathcal{P}_n$ on the line with only neighboring
particles involved in interaction. On the contrast to arXiv: math. 0809.2365
now {\it periodic boundary conditions} are imposed onto the system, i.e.
$\mathcal{P}_1$ and $\mathcal{P}_n$ are considered neighboring. Periodic Toda
lattice would be a typical example. We study possibility to control periodic
multiparticle systems by means of forces applied to just few of its particles;
mainly we study system controlled by single force. The free dynamics of
multiparticle systems in periodic and nonperiodic case differ substantially. We
see that also the controlled periodic multiparticle system does not mimic its
non-periodic counterpart.
  Main result established is global controllability by means of single
controlling force of the multiparticle system with ageneric potential of
interaction. We study the nongeneric potentials for which controllability and
accessibility properties may lack. Results are formulated and proven in
Sections~2,3.


  Qubit graph states are essential computational resources in measurement-based
quantum computations (MBQC). The most well-known method to generate graph
states in optics is to use fusion gates, which in many cases require expensive
entangled resource states. In this work, we propose an alternative approach to
generate graph states based on the graph picture of linear quantum networks
(LQG picture), through which we can devise schemes that generate caterpillar
graph states with boson subtractions. These subtraction schemes correspond to
efficient heralded optical setups with single-photon sources and more flexible
measurement elements than fusion gates. Caterpillar graph states encompass
various useful graph structures for one-way quantum computing, such as linear
graphs, star graphs, and networks of star graphs. We can exploit them as
resources for generating cluster states using conventional Type II fusion
gates. Our results demonstrate that the boson subtraction operator is a more
general concept that encompasses and can therefore optimize fusion gates.


  Given a hierarchical plan (or schedule) with uncertain task times, we propose
a deterministic polynomial (time and memory) algorithm for estimating the
probability that its meets a deadline, or, alternately, that its {\em makespan}
is less than a given duration. Approximation is needed as it is known that this
problem is NP-hard even for sequential plans (just, a sum of random variables).
In addition, we show two new complexity results: (1) Counting the number of
events that do not cross deadline is \#P-hard; (2)~Computing the expected
makespan of a hierarchical plan is NP-hard. For the proposed approximation
algorithm, we establish formal approximation bounds and show that the time and
memory complexities grow polynomially with the required accuracy, the number of
nodes in the plan, and with the size of the support of the random variables
that represent the durations of the primitive tasks. We examine these
approximation bounds empirically and demonstrate, using task networks taken
from the literature, how our scheme outperforms sampling techniques and exact
computation in terms of accuracy and run-time. As the empirical data shows much
better error bounds than guaranteed, we also suggest a method for tightening
the bounds in some cases.


  With the increasing deployment of deep neural networks in safety-critical
applications such as self-driving cars, medical imaging, anomaly detection,
etc., adversarial robustness has become a crucial concern in the reliability of
these networks in real-world scenarios. A plethora of works based on
adversarial training and regularization-based techniques have been proposed to
make these deep networks robust against adversarial attacks. However, these
methods require either retraining models or training them from scratch, making
them infeasible to defend pre-trained models when access to training data is
restricted. To address this problem, we propose a test time Data-free
Adversarial Defense (DAD) containing detection and correction frameworks.
Moreover, to further improve the efficacy of the correction framework in cases
when the detector is under-confident, we propose a soft-detection scheme
(dubbed as "DAD++"). We conduct a wide range of experiments and ablations on
several datasets and network architectures to show the efficacy of our proposed
approach. Furthermore, we demonstrate the applicability of our approach in
imparting adversarial defense at test time under data-free (or data-efficient)
applications/setups, such as Data-free Knowledge Distillation and Source-free
Unsupervised Domain Adaptation, as well as Semi-supervised classification
frameworks. We observe that in all the experiments and applications, our DAD++
gives an impressive performance against various adversarial attacks with a
minimal drop in clean accuracy. The source code is available at:
https://github.com/vcl-iisc/Improved-Data-free-Test-Time-Adversarial-Defense


  Motion forecasting plays a significant role in various domains (e.g.,
autonomous driving, human-robot interaction), which aims to predict future
motion sequences given a set of historical observations. However, the observed
elements may be of different levels of importance. Some information may be
irrelevant or even distracting to the forecasting in certain situations. To
address this issue, we propose a generic motion forecasting framework (named
RAIN) with dynamic key information selection and ranking based on a hybrid
attention mechanism. The general framework is instantiated to handle
multi-agent trajectory prediction and human motion forecasting tasks,
respectively. In the former task, the model learns to recognize the relations
between agents with a graph representation and to determine their relative
significance. In the latter task, the model learns to capture the temporal
proximity and dependency in long-term human motions. We also propose an
effective double-stage training pipeline with an alternating training strategy
to optimize the parameters in different modules of the framework. We validate
the framework on both synthetic simulations and motion forecasting benchmarks
in different domains, demonstrating that our method not only achieves
state-of-the-art forecasting performance, but also provides interpretable and
reasonable hybrid attention weights.


  The close connection of electricity and magnetism is one of the cornerstones
of modern physics. This connection plays crucial role from the fundamental
point of view and in practical applications, including spintronics and
multiferroic materials. A breakthrough was a recent proposal that in magnetic
materials called spin ice the elementary excitations have a magnetic charge and
behave as magnetic monopoles. I show that, besides magnetic charge, there
should be an electric dipole attached to each magnetic monopole. This opens new
possibilities to study and to control such monopoles by electric field. Thus
the electric--magnetic analogy goes even further than usually assumed: whereas
electrons have electric charge and magnetic dipole (spin), magnetic monopoles
in spin ice, while having magnetic charge, also have electric dipole.


  A device for absorbing and storing short duration impulses in an initially
uncompressed one-dimensional granular chain is presented. Simply stated, short
regions of sufficiently soft grains are embedded in a hard granular chain.
These grains exhibit long-lived standing waves of predictable frequencies
regardless of the timing of the arrival of solitary waves from the larger
matrix. We explore the origins, symmetry, and energy content of the soft region
and its intrinsic modes.


  Neural network (NN) compression via techniques such as pruning, quantization
requires setting compression hyperparameters (e.g., number of channels to be
pruned, bitwidths for quantization) for each layer either manually or via
neural architecture search (NAS) which can be computationally expensive. We
address this problem by providing an end-to-end technique that optimizes for
model's Floating Point Operations (FLOPs) or for on-device latency via a novel
$\frac{\ell_1}{\ell_2}$ latency surrogate. Our algorithm is versatile and can
be used with many popular compression methods including pruning, low-rank
factorization, and quantization. Crucially, it is fast and runs in almost the
same amount of time as single model training; which is a significant training
speed-up over standard NAS methods. For BERT compression on GLUE fine-tuning
tasks, we achieve $50\%$ reduction in FLOPs with only $1\%$ drop in
performance. For compressing MobileNetV3 on ImageNet-1K, we achieve $15\%$
reduction in FLOPs, and $11\%$ reduction in on-device latency without drop in
accuracy, while still requiring $3\times$ less training compute than SOTA
compression techniques. Finally, for transfer learning on smaller datasets, our
technique identifies $1.2\times$-$1.4\times$ cheaper architectures than
standard MobileNetV3, EfficientNet suite of architectures at almost the same
training cost and accuracy.


  Autonomous multi-robot systems, where a team of robots shares information to
perform tasks that are beyond an individual robot's abilities, hold great
promise for a number of applications, such as planetary exploration missions.
Each robot in a multi-robot system that uses the shared-world coordination
paradigm autonomously schedules which robot should perform a given task, and
when, using its worldview--the robot's internal representation of its belief
about both its own state, and other robots' states. A key problem for operators
is that robots' worldviews can fall out of sync (often due to weak
communication links), leading to desynchronization of the robots' scheduling
decisions and inconsistent emergent behavior (e.g., tasks not performed, or
performed by multiple robots). Operators face the time-consuming and difficult
task of making sense of the robots' scheduling decisions, detecting
de-synchronizations, and pinpointing the cause by comparing every robot's
worldview. To address these challenges, we introduce MOSAIC Viewer, a visual
analytics system that helps operators (i) make sense of the robots' schedules
and (ii) detect and conduct a root cause analysis of the robots' desynchronized
worldviews. Over a year-long partnership with roboticists at the NASA Jet
Propulsion Laboratory, we conduct a formative study to identify the necessary
system design requirements and a qualitative evaluation with 12 roboticists. We
find that MOSAIC Viewer is faster- and easier-to-use than the users' current
approaches, and it allows them to stitch low-level details to formulate a
high-level understanding of the robots' schedules and detect and pinpoint the
cause of the desynchronized worldviews.


  In this paper preconditioners for the Conjugate Gradient method are studied
to solve the Newton system with symmetric positive definite Jacobian. In
particular, we define a sequence of preconditioners built by means of SR1 and
BFGS low-rank updates. We develop conditions under which the SR1 update
maintains the preconditioner SPD. Spectral analysis of the SR1 preconditioned
Jacobians shows an improved eigenvalue distribution as the Newton iteration
proceeds. A compact matrix formulation of the preconditioner update is
developed which reduces the cost of its application and is more suitable for
parallel implementation. Some notes on the implementation of the corresponding
Inexact Newton method are given and numerical results on a number of model
problems illustrate the efficiency of the proposed preconditioners.


  We report the measurement of the acoustic quality factor of a gram-scale,
kilo-hertz frequency superfluid resonator, detected through the parametric
coupling to a superconducting niobium microwave cavity. For temperature between
400mK and 50mK, we observe a $T^{-4}$ temperature dependence of the quality
factor, consistent with a 3-phonon dissipation mechanism. We observe Q factors
up to $1.4\cdot10^8$, consistent with the dissipation due to dilute $^3$He
impurities, and expect that significant further improvements are possible.
These experiments are relevant to exploring quantum behavior and decoherence of
massive macroscopic objects, the laboratory detection of continuous wave
gravitational waves from pulsars, and the probing of possible limits to
physical length scales.


  High resolution transit spectroscopy has proven to be a reliable technique
for the characterization of the chemical composition of exoplanet atmospheres.
Taking advantage of the broad spectral coverage of the CARMENES spectrograph,
we initiated a survey aimed at characterizing a broad range of planetary
systems. Here, we report our observations of three transits of \tplanet with
CARMENES in search of \het\ absorption. On one of the nights, the He~{\sc i}
region was heavily contaminated by OH$^-$ telluric emission and, thus, it was
not useful for our purposes. The remaining two nights had a very different
signal-to-noise ratio (S/N) due to weather. They both indicate the presence of
\het\ absorption in the transmission spectrum of \tplanet, although a
statistically valid detection can only be claimed for the night with higher
S/N. For that night, we retrieved a 1.5$\pm$0.3\% absorption depth, translating
into a $R_p(\lambda)/R_p = 1.15\pm 0.14$ at this wavelength.
Spectro-photometric light curves for this same night also indicate the presence
of extra absorption during the planetary transit with a consistent absorption
depth. The \het\ absorption is modeled in detail using a radiative transfer
code, and the results of our modeling efforts are compared to the observations.
We find that the mass-loss rate, \mlr, is confined to a range of
3\,$\times\,10^{10}$\,\gs\ for $T$ = 6000\,K to 10\,$\times\,10^{10}$\,\gs\ for
$T$ = 9000\,K. We discuss the physical mechanisms and implications of the
He~{\sc i} detection in \tplanet and put it in context as compared to similar
detections and non-detections in other Neptune-size planets. We also present
improved stellar and planetary parameter determinations based on our visible
and near-infrared observations.


  I review the characteristics of planetary systems accessible to imaging. I
show that, beyond the basic angular resolution and dynamics performances of an
optical ``architecture'', other performances such as photometric precision,
spectral range or timing are necessary to access some physical characteristics
of the planets.


  We improve on Gonek-Montgomery's quantitative version of Kronecker's
approximation theorem.


  In synchrotron radiation there is a paradox whether or not the pitch angle of
a radiating charge varies. The conventional wisdom is that the pitch angle does
not change during the radiation process. The argument is based on Larmor's
radiation formula, where in a synchrotron case the radiation power is along the
instantaneous direction of motion of the charge. Then the momentum loss will
also be parallel to that direction and therefore the pitch angle of the charge
would remain unaffected. The accordingly derived formulas for energy losses of
synchrotron electrons in radio galaxies are the standard text-book material for
the last 50 years. However, if we use the momentum transformation laws from
special relativity, then we find that the pitch angle of a radiating charge
varies. While the velocity component parallel to the magnetic field remains
unaffected, the perpendicular component does reduce in magnitude due to
radiative losses, implying a change in the pitch angle. This apparent paradox
is resolved when effects on the charge motion are calculated not from Larmor's
formula but from Lorentz's radiation reaction formula. We derive the exact
formulation by taking into account the change of the pitch angle due to
radiative losses. From this we first time derive the characteristic decay time
of synchrotron electrons over which they turn from highly relativistic into
mildly relativistic ones.


  Using $\sim$427 ks of Chandra observations, we present a study of shock
heating and ICM cooling in the galaxy cluster RBS 797. We discover three nested
pairs of weak shocks at roughly 50 kpc, 80 kpc and 130 kpc from the center. The
total energy associated with the shocks is $\sim6\times10^{61}$ erg, with the
central AGN driving a pair of weak shocks every 20-30 Myr with a power
$P_{sh}\approx10^{46}$ erg s$^{-1}$. Based on its morphology and age ($\sim$30
Myr), the inner cocoon shock is associated with the four equidistant X-ray
cavities previously discovered. From the thermodynamic analysis of the inner 30
kpc, we find evidence for ICM condensation into colder gas between and behind
the X-ray cavities. The total AGN mechanical power (cavities and shocks) of
$3.4\times10^{46}$ erg s$^{-1}$ can balance the ICM radiative losses, estimated
as $L_{cool} = 2.3\times10^{45}$ erg s$^{-1}$. By building plots of $P_{cav}$
vs. $L_{cool}$, $P_{shock}$ vs. $L_{cool}$ and $P_{tot}$ vs. $L_{cool}$ for RBS
797 and 14 other galaxy clusters, groups and elliptical galaxies where both
cavities and shocks are detected, we verify that the most powerful outbursts
are found in the strongest cooling systems. Ultimately, we observe that the
mechanical power of the AGN exceeds the gas radiative losses by a factor that
is different for FR I and FR II radio galaxies, being less than a few tens for
FR Is (as RBS 797) and more than roughly a hundred for FR IIs.


  We have identified a clustering of red galaxies from deep optical/IR images
obtained as part of the Institute for Astronomy Deep Survey. Photometric
spectral-energy distributions indicate that most of these galaxies comprise
nearly pure old stellar populations at a redshift near 1.4, and Keck
spectroscopy of the three brightest red galaxies confirm this interpretation
and give redshifts ranging from 1.335 to 1.338. Four of the galaxies are close
together on the sky and less than 30" from an R=13.5 star, and we have obtained
deep adaptive-optics imaging of this group. Detailed analysis and modeling of
the surface-brightness profiles of these galaxies shows that two are normal
ellipticals, one is an S0, and one appears to be an essentially pure disk of
old stars, with no significant bulge. All four are highly relaxed, symmetric
systems. While the old, bulgeless disk galaxy represents a type that is rare at
the present epoch, the other three galaxies have structural parameters that are
essentially indistinguishable from those of present-day galaxies and differ
only in the age of their stellar populations.


  The elliptic flow (v2) pattern in terms of hadron mass and transverse
momentum pT is qualitatively described for pT < 2 GeV/c by ideal hydrodynamics
in Au + Au collisions at RHIC. In addition, for pT = 2 - 6 GeV/c the measured
v2 follow a universal scaling by the number of quarks explained by quark
coalescence/recombination models. These observations suggest that a partonic
collectivity develops in the matter in early stage of heavy ion collisions.
Centrality as well as system size and energy dependence of the v2 is important
to shed light on the underlying collision dynamics in heavy ion collisions. We
present the measurements of centrality dependence of v2 at sqrt{sNN} = 200 and
62.4 GeV in Au + Au and Cu + Cu collisions for K0S, phi, Lambda, Xi and Omega
at STAR experiment. We focus on the recent Cu + Cu results and discuss the
centrality dependence of v2 as well as the number of quark scaling as a
function of transverse kinetic energy at different system size and energies. We
also discuss the eccentricity scaled v2 for identified hadrons and implications
that ideal hydrodynamical limit has not been reached at RHIC.


  This short paper presents a work on the design of low noise microwave
amplifiers using particle swarm optimization (PSO) technique. Particle Swarm
Optimization is used as a method that is applied to a single stage amplifier
circuit to meet two criteria: desired gain and desired low noise. The aim is to
get the best optimized design using the predefined constraints for gain and low
noise values. The code is written to apply the algorithm to meet the desired
goals and the obtained results are verified using different simulators. The
results obtained show that PSO can be applied very efficiently for this kind of
design problems with multiple constraints.


  In this paper, chaotic block image permutation and XOR operation are
performed to achieve image encryption. The studied method of encryption makes
use of chaotic systems properties for secure and speed image encryption.
Firstly, the original image is divided into blocks of equal size. Then, two
chaotic maps are used to generate two key streams which are permuted among
themselves to produce one key steam. The image blocks are then shuffled using
part of key stream. Finally, scrambled image is diffused by XOR operation with
the key stream to get the encrypted image. The experimental results of several
performance analyses about the pixel correlation, various statistical analysis,
information entropy analysis, differential analysis, the key space and key
sensitivity analysis, show that the algorithm can resist several know attacks
effectively and has the advantages of large key space, high security, and high
speed, assuring safety performance and secure image encryption.


  Femtosecond optical pulses at mid-infrared frequencies have opened up the
nonlinear control of lattice vibrations in solids. So far, all applications
have relied on second order phonon nonlinearities, which are dominant at field
strengths near 1 MVcm-1. In this regime, nonlinear phononics can transiently
change the average lattice structure, and with it the functionality of a
material. Here, we achieve an order-of-magnitude increase in field strength,
and explore higher-order lattice nonlinearities. We drive up to five phonon
harmonics of the A1 mode in LiNbO3. Phase-sensitive measurements of atomic
trajectories in this regime are used to experimentally reconstruct the
interatomic potential and to benchmark ab-initio calculations for this
material. Tomography of the Free Energy surface by high-order nonlinear
phononics will impact many aspects of materials research, including the study
of classical and quantum phase transitions.


  One of the greatest challenges in applying compressive sensing (CS) signal
processing techniques to electromagnetic imaging applications is designing a
sensing matrix that has good reconstruction capabilities. Compressive reflector
antennas (CRA) are a class of antennas that have been shown to provide enhanced
image reconstruction performance over traditional reflector antennas (TRA) when
CS techniques are employed. In this paper, we present a unified CRA design
method, which considers both the sensing capacity and efficiency of the
antenna, and can be used for both compressive imaging and multiple-input
multiple-output (MIMO) communication applications. The unified design method is
assessed for a CRA configuration in which dielectric scatterers are added to
the surface of a TRA. The design results demonstrate the ability of the unified
design method to enhance the CS reconstruction capabilities of the CRA.


  We study probability measures defined by the variation of the sum of digits
in the Zeckendorf representation. For $r\ge 0$ and $d\in\mathbb{Z}$, we
consider $\mu^{(r)}(d)$ the density of integers $n\in\mathbb{N}$ for which the
sum of digits increases by $d$ when $r$ is added to $n$. We give a
probabilistic interpretation of $\mu^{(r)}$ via the dynamical system provided
by the odometer of Zeckendorf-adic integers and its unique invariant measure.
We give an algorithm for computing $\mu^{(r)}$ and we deduce a control on the
tail of the negative distribution of $\mu^{(r)}$, as well as the formula
$\mu^{(F_{\ell})} = \mu^{(1)}$ where $F_{\ell}$ is a term in the Fibonacci
sequence. Finally, we decompose the Zeckendorf representation of an integer $r$
into so-called "blocks" and show that when added to an adic Zeckendorf integer,
the successive actions of these blocks can be seen as a sequence of mixing
random variables.


  The design of efficient representations is well established as a fruitful way
to explore and analyze complex or large data. In these representations, data
are encoded with various visual attributes depending on the needs of the
representation itself. To make coherent design choices about visual attributes,
the visual search field proposes guidelines based on the human brain perception
of features. However, information visualization representations frequently need
to depict more data than the amount these guidelines have been validated on.
Since, the information visualization community has extended these guidelines to
a wider parameter space.
  This paper contributes to this theme by extending visual search theories to
an information visualization context. We consider a visual search task where
subjects are asked to find an unknown outlier in a grid of randomly laid out
distractor. Stimuli are defined by color and shape features for the purpose of
visually encoding categorical data. The experimental protocol is made of a
parameters space reduction step (i.e., sub-sampling) based on a machine
learning model, and a user evaluation to measure capacity limits and validate
hypotheses. The results show that the major difficulty factor is the number of
visual attributes that are used to encode the outlier. When redundantly
encoded, the display heterogeneity has no effect on the task. When encoded with
one attribute, the difficulty depends on that attribute heterogeneity until its
capacity limit (7 for color, 5 for shape) is reached. Finally, when encoded
with two attributes simultaneously, performances drop drastically even with
minor heterogeneity.


  Detecting offensive language on social media is an important task. The
ICWSM-2020 Data Challenge Task 2 is aimed at identifying offensive content
using a crowd-sourced dataset containing 100k labelled tweets. The dataset,
however, suffers from class imbalance, where certain labels are extremely rare
compared with other classes (e.g, the hateful class is only 5% of the data). In
this work, we present Dager (Data Augmenter), a generation-based data
augmentation method, that improves the performance of classification on
imbalanced and low-resource data such as the offensive language dataset. Dager
extracts the lexical features of a given class, and uses these features to
guide the generation of a conditional generator built on GPT-2. The generated
text can then be added to the training set as augmentation data. We show that
applying Dager can increase the F1 score of the data challenge by 11% when we
use 1% of the whole dataset for training (using BERT for classification);
moreover, the generated data also preserves the original labels very well. We
test Dager on four different classifiers (BERT, CNN, Bi-LSTM with attention,
and Transformer), observing universal improvement on the detection, indicating
our method is effective and classifier-agnostic.


  We make an attempt to dynamically study, in four space-time dimensions, the
classical gravitational collapse of a macroscopic circular fundamental string,
by a truncation of the Einstein equations that suppresses retarded features but
keeps the main self-gravity peculiarities of the relativistic string dynamics,
and allows the investigation of a possible infinite red-shift. The numerical
solution of the string evolution in the self-induced metric shows an infinite
red shift at a macroscopic size of the string, when the string reaches the
velocity of light. We further include the back-reaction of the radiation of
gravitons which induces energy dissipation: now the velocity of light is not
reached, the infinite red-shift does not form and the string simply shrinks
with damped oscillations.


  We study the accuracy of analytical wave function based many-body methods
derived by energy minimization of a Jastrow-Feenberg ansatz for electrons
(`Fermi hypernetted chain / Euler Lagrange' approach). Approximations to avoid
the complexity of the fermion problem are chosen to parallel successful boson
theories and be computationally efficient. For the three-dimensional
homogeneous electron gas, we calculate the correlation energy, the pair
distribution function and the static structure function in comparison with
simulation results. We also present a new variant of theory, which is
interpreted as approximate, self-consistent sum of ladder and ring diagrams of
perturbation theory. The theory performs particularly well in the highly dilute
density regime.


  Porous electrodes are fast emerging as essential components for next
generation supercapacitors. Using porous structures of Co3O4, Mn3O4, alpha
Fe2O3, and carbon, their advantages over the solid counterpart is unequivocally
established. The improved performance in porous architecture is linked to the
enhanced active specific surface and direct channels leading to improved
electrolyte interaction with the redox active sites. A theoretical model
utilizing the Fick's law is proposed, that can consistently explain the
experimental data. The porous structures exhibit nearly 50 - 80 percentage
increment in specific capacitance, along with high rate capabilities and
excellent cycling stability due to the higher diffusion coefficients.


  We consider superstrings on the pure-Ramond-Ramond $AdS_3\times S^3\times
T^4$ background. Using the recently-proposed dressing factors for the
worldsheet S matrix, we formulate the string hypothesis for the mirror
Bethe-Yang equations, and use it to derive the canonical mirror thermodynamic
Bethe ansatz (TBA) equations of the model. For the first time, these equations
account for all massive and massless modes of the model, and do not resort to
any limit or special kinematics. We also discuss the simplified mirror TBA
equations and the Y-system of the model.


  In this short note we argue why the conclusions made by the authors of
arXiv:1908.04675, related to arXiv:1805.05184 and in particular
arXiv:1602.03666 given therein, are incorrect. Nevertheless, we think that the
advocated Anti-Quark Nuggets are good candidates for the dark Universe, and
therefore they deserve further attention.


  The Khovanov homology of a link in $S^3$ and the Heegaard Floer homology of
its branched double cover are related through a spectral sequence constructed
by Ozsv\'ath and Szab\'o. This spectral sequence has topological applications
but is difficult to compute. We build an isomorphic spectral sequence whose
underlying filtered complex is as simple as possible: it has the same rank as
the Khovanov chain group. We show that this spectral sequence is not isomorphic
to Szab\'o's combinatorial spectral sequence, which Seed and Szab\'o
conjectured to be equivalent to Ozsv\'ath-\Szabo's. The discrepancy leads us to
define a variation of Szab\'o's theory for links embedded in a thickened
annulus. We conclude with a refinement of Seed and Szab\'o's conjecture for the
new theory.


  Interfaces between (110) and (111)SrTiO3 (STO) single crystalline substrates
and amorphous oxide layers, LaAlO3 (a-LAO), Y:ZrO2 (a-YSZ), and SrTiO3 (a-STO)
become conducting above a critical thickness tc. Here we show that tc for a-LAO
is not depending on the substrate orientation, i.e. tc (a-LAO/(110)STO) ~
tc(a-LAO/(111)STO) interfaces, whereas it strongly depends on the composition
of the amorphous oxide: tc(a-LAO/(110)STO) < tc(a-YSZ/(110)STO) <
tc(a-STO/(110)STO). It is concluded that the formation of oxygen vacancies in
amorphous-type interfaces is mainly determined by the oxygen affinity of the
deposited metal ions, rather than orientational-dependent enthalpy vacancy
formation and diffusion. Scanning transmission microscopy characterization of
amorphous and crystalline LAO/STO(110) interfaces shows much higher amount of
oxygen vacancies in the former, providing experimental evidence of the distinct
mechanism of conduction in these interfaces.


  In this paper, we propose a new proof of the Jensen formula in 1895. We also
derive some formulas similar to those in Pitman and Yor, 2003. Besides, a new
formula of the generalized Bernoulli function is also derived. At the end of
the paper, the probability density functions of sinh and tanh are studied
briefly for general cases.


  Suppose each user $i$ holds a private value $x_i$ in some metric space $(U,
\mathrm{dist})$, and an untrusted data analyst wishes to compute $\sum_i
f(x_i)$ for some function $f : U \rightarrow \mathbb{R}$ by asking each user to
send in a privatized $f(x_i)$. This is a fundamental problem in
privacy-preserving population analytics, and the local model of differential
privacy (LDP) is the predominant model under which the problem has been
studied. However, LDP requires any two different $x_i, x'_i$ to be
$\varepsilon$-distinguishable, which can be overly strong for
geometric/numerical data. On the other hand, Geo-Privacy (GP) stipulates that
the level of distinguishability be proportional to $\mathrm{dist}(x_i, x_i')$,
providing an attractive alternative notion of personal data privacy in a metric
space. However, existing GP mechanisms for this problem, which add a uniform
noise to either $x_i$ or $f(x_i)$, are not satisfactory. In this paper, we
generalize the smooth sensitivity framework from Differential Privacy to
Geo-Privacy, which allows us to add noise tailored to the hardness of the given
instance. We provide definitions, mechanisms, and a generic procedure for
computing the smooth sensitivity under GP equipped with a general metric. Then
we present three applications: one-way and two-way threshold functions, and
Gaussian kernel density estimation, to demonstrate the applicability and
utility of our smooth sensitivity framework.