  We analyze the sensitivity of $\beta$-decay rates in 78 Ni and 100,132 Sn to
a correction term in Skyrme energy-density functionals (EDF) which modifies the
radial shape of the nucleon effective mass. This correction is added on top of
several Skyrme parametrizations which are selected from their effective mass
properties and predictions about the stability properties of 132 Sn. The impact
of the correction on high-energy collective modes is shown to be moderate. From
the comparison of the effects induced by the surface-peaked effective mass in
the three doubly magic nuclei, it is found that 132 Sn is largely impacted by
the correction, while 78 Ni and 100 Sn are only moderately affected. We
conclude that $\beta$-decay rates in these nuclei can be used as a test of
different parts of the nuclear EDF: 78 Ni and 100 Sn are mostly sensitive to
the particle-hole interaction through the B(GT) values, while 132 Sn is
sensitive to the radial shape of the effective mass. Possible improvements of
these different parts could therefore be better constrained in the future.


  We extend the event-chain Monte Carlo algorithm from hard-sphere interactions
to the micro-canonical ensemble (constant potential energy) for general
potentials. This event-driven Monte Carlo algorithm is non-local,
rejection-free, and allows for the breaking of detailed balance. The algorithm
uses a discretized potential, but its running speed is asymptotically
independent of the discretization. We implement the algorithm for the cut-off
linear potential, and discuss its possible implementation directly in the
continuum limit.


  We consider the trajectory of a tracer that is the solution of an ordinary
differential equation $\dot\bbX(t)=\bbV(t, \bbX(t)),\ X(0)=0$, with the right
hand side, that is a stationary, zero-mean, Gaussian vector field with
incompressible realizations. It is known, see [K-F;C-X;K-L-O], that
$\bbX(t)/\sqrt{t}$ converges in law, as $t\to+\infty$, to a normal, zero mean
vector, provided that the field $V(t,x)$ is Markovian and has the spectral gap
property. We wish to extend this result to the case when the field is not
Markovian and its covariance matrix is given by a completely monotone Bernstein
function.


  We present an analysis of the X-ray emission for a complete sample of 288
Abell clusters spanning the redshift range 0.016<= z <= 0.09 from the ROSAT
All-Sky Survey. This sample is based on our 20cm VLA survey of nearby Abell
clusters. We find an X-ray detection rate of 83%. We report cluster X-ray
fluxes and luminosities and two different flux ratios indicative of the
concentration and extent of the emission. We examine correlations between the
X-ray luminosity, Abell Richness, and Bautz-Morgan and Rood-Sastry cluster
morphologies. We find a strong correlation between Lx and cluster richness
coupled to a dependence on the optical morphological type. These results are
consistent with the observed scatter between X-ray luminosity and temperature
and a large fraction of cooling flows. For each cluster field we also report
the positions, peak X-ray fluxes, and flux-ratios of all X-ray peaks above
3-sigma significance within a box of 2x2 Mpc centered on Abell's position.


  The paper briefly introduces multiple classifier systems and describes a new
algorithm, which improves classification accuracy by means of recommendation of
a proper algorithm to an object classification. This recommendation is done
assuming that a classifier is likely to predict the label of the object
correctly if it has correctly classified its neighbors. The process of
assigning a classifier to each object is based on Formal Concept Analysis. We
explain the idea of the algorithm with a toy example and describe our first
experiments with real-world datasets.


  The problem of defining and locating free will (FW) in physics is studied. On
basis of logical paradoxes, we argue that FW has a meta-theoretic character,
like the concept of truth in Tarski's undefinability theorem. Free will exists
relative to a base theory if there is freedom to deviate from the deterministic
or indeterministic dynamics in the theory, with the deviations caused by
parameters (representing will) in the meta-theory. By contrast, determinism and
indeterminism do not require meta-theoretic considerations in their
formalization, making FW a fundamentally new causal primitive. FW exists
relative to the meta-theory if there is freedom for deviation, due to
higher-order causes. Absolute free will, which corresponds to our intuitive
introspective notion of free will, exists if this meta-theoretic hierarchy is
infinite. We argue that this hierarchy corresponds to higher levels of
uncomputability. In other words, at any finitely high order in the hierarchy,
there are uncomputable deviations from the law at that order. Applied to the
human condition, the hierarchy corresponds to deeper levels of the subconscious
or unconscious mind. Possible ramifications of our model for physics,
neuroscience and artificial intelligence (AI) are briefly considered.


  The process of baryon number transfer due to string junction propagation in
rapidity is considered. It has a significant effect in the net baryon
production in $pp$ collisions at mid-rapidities and an even larger effect in
the forward hemisphere in the cases of $\pi p$ and $\gamma p$ interactions. The
results of numerical calculations in the framework of the Quark-Gluon String
Model are in reasonable agreement with the data with the same parameter values
for different energies.


  Upon specifying an equation of state, spherically symmetric steady states of
the Einstein-Euler system are embedded in 1-parameter families of solutions,
characterized by the value of their central redshift. In the 1960's Zel'dovich
[50] and Wheeler [22] formulated a turning point principle which states that
the spectral stability can be exchanged to instability and vice versa only at
the extrema of mass along the mass-radius curve. Moreover the bending
orientation at the extrema determines whether a growing mode is gained or lost.
We prove the turning point principle and provide a detailed description of the
linearized dynamics. One of the corollaries of our result is that the number of
growing modes grows to infinity as the central redshift increases to infinity.


  Mobile ad hoc networks (MANETs) are self-configuring wireless networks that
lack permanent infrastructure and are formed among mobile nodes on demand.
Rapid node mobility results in dramatic channel variation, or fading, that
degrades MANET performance. Employing channel state information (CSI) at the
transmitter can improve the throughput of routing and medium access control
(MAC) protocols for mobile ad hoc networks. Several routing algorithms in the
literature explicitly incorporate the fading signal strength into the routing
metric, thus selecting the routes with strong channel conditions. While these
studies show that adaptation to the time-variant channel gain is beneficial in
MANETs, they do not address the effect of the outdated fading CSI at the
transmitter. For realistic mobile node speeds, the channel gain is rapidly
varying, and becomes quickly outdated due the feedback delay. We analyze the
link throughput of joint rate adaptation and adaptive relay selection in the
presence of imperfect CSI. Moreover, for an 802.11 network that employs
geographic opportunistic routing with adaptive rate and relay selection, we
propose a novel method to reduce the effect of the feedback delay at the MAC
layer in the presence of Rayleigh fading. This method exploits channel
reciprocity and fading prediction and does not require significant modification
to the existing 802.11 frame structure. Extensive network simulations
demonstrate that the proposed approach significantly improves the throughput,
delay, and packet delivery ratio for high mobile velocities relative to
previously proposed approaches that employ outdated CSI at the transmitter.


  As environments involving both robots and humans become increasingly common,
so does the need to account for people during planning. To plan effectively,
robots must be able to respond to and sometimes influence what humans do. This
requires a human model which predicts future human actions. A simple model may
assume the human will continue what they did previously; a more complex one
might predict that the human will act optimally, disregarding the robot;
whereas an even more complex one might capture the robot's ability to influence
the human. These models make different trade-offs between computational time
and performance of the resulting robot plan. Using only one model of the human
either wastes computational resources or is unable to handle critical
situations. In this work, we give the robot access to a suite of human models
and enable it to assess the performance-computation trade-off online. By
estimating how an alternate model could improve human prediction and how that
may translate to performance gain, the robot can dynamically switch human
models whenever the additional computation is justified. Our experiments in a
driving simulator showcase how the robot can achieve performance comparable to
always using the best human model, but with greatly reduced computation.


  Federated Learning (FL) has emerged as a prominent privacy-preserving
technique for enabling use cases like confidential clinical machine learning.
FL operates by aggregating models trained by remote devices which owns the
data. Thus, FL enables the training of powerful global models using
crowd-sourced data from a large number of learners, without compromising their
privacy. However, the aggregating server is a single point of failure when
generating the global model. Moreover, the performance of the model suffers
when the data is not independent and identically distributed (non-IID data) on
all remote devices. This leads to vastly different models being aggregated,
which can reduce the performance by as much as 50% in certain scenarios.
  In this paper, we seek to address the aforementioned issues while retaining
the benefits of FL. We propose MultiConfederated Learning: a decentralized FL
framework which is designed to handle non-IID data. Unlike traditional FL,
MultiConfederated Learning will maintain multiple models in parallel (instead
of a single global model) to help with convergence when the data is non-IID.
With the help of transfer learning, learners can converge to fewer models. In
order to increase adaptability, learners are allowed to choose which updates to
aggregate from their peers.


  Motivated by a problem arising from Density Functional Theory, we provide the
sharp next-order asymptotics for a class of multimarginal optimal transport
problems with cost given by singular, long-range pairwise interaction
potentials. More precisely, we consider an $N$-marginal optimal transport
problem with $N$ equal marginals supported on $\mathbb R^d$ and with cost of
the form $\sum_{i\neq j}|x_i-x_j|^{-s}$. In this setting we determine the
second-order term in the $N\to\infty$ asymptotic expansion of the minimum
energy, for the long-range interactions corresponding to all exponents $0<s<d$.
We also prove a small oscillations property for this second-order energy term.
Our results can be extended to a larger class of models than power-law-type
radial costs, such as non-rotationally-invariant costs. The key ingredient and
main novelty in our proofs is a robust extension and simplification of the
Fefferman-Gregg decomposition (Fefferman 1985, Gregg 1989), extended here to
our class of kernels, and which provides a unified method valid across our full
range of exponents. Our first result generalizes a recent work of Lewin, Lieb
and Seiringer (2017), who dealt with the second-order term for the Coulomb case
$s=1,d=3$, by different methods.


  Passive intelligent reconfigurable surfaces (IRS) are becoming an attractive
component of cellular networks due to their ability of shaping the propagation
environment and thereby improving the coverage. While passive IRS nodes
incorporate a great number of phase-shifting elements and a controller entity,
the phase-shifts are typically determined by the cellular base station (BS) due
to its computational capability. Since the fine granularity control of the
large number of phase-shifters may become prohibitive in practice, it is
important to reduce the control overhead between the BS and the IRS controller.
To this end, in this paper we propose a low-rank approximation of the
near-optimal phase-shifts, which would incur prohibitively high communication
overhead on the BS-IRS controller links. The key idea is to represent the
potentially large IRS phase-shift vector using a low-rank tensor model. This is
achieved by factorizing a tensorized version of the IRS phase-shift vector,
where each component is modeled as the Kronecker product of a predefined number
of factors of smaller sizes, which can be obtained via tensor decomposition
algorithms. We show that the proposed low-rank models drastically reduce the
required feedback requirements associated with the BS-IRS control links. Our
simulation results indicate that the proposed method is especially attractive
in scenarios with a strong line of sight component, in which case nearly the
same spectral efficiency is reached as in the cases with near-optimal
phase-shifts, but with a drastically reduced communication overhead.


  The transport of the nuclear cosmic ray flux in the atmosphere is studied and
the atmospheric corrections to be applied to the measurements are calculated.
The contribution of the calculated corrections to the accuracy of the
experimental results are discussed and evaluated over the kinetic energy range
10-10$^{3}$ GeV/n. The Boron (B) and Carbon (C) elements system is used as a
test case. It is shown that the required corrections become largely dominant at
the highest energies investigated. The results are discussed.


  Recently many studies have been conducted on the topic of relation
extraction. The DrugProt track at BioCreative VII provides a manually-annotated
corpus for the purpose of the development and evaluation of relation extraction
systems, in which interactions between chemicals and genes are studied. We
describe the ensemble system that we used for our submission, which combines
predictions of fine-tuned bioBERT, sciBERT and const-bioBERT models by majority
voting. We specifically tested the contribution of syntactic information to
relation extraction with BERT. We observed that adding constituentbased
syntactic information to BERT improved precision, but decreased recall, since
relations rarely seen in the train set were less likely to be predicted by BERT
models in which the syntactic information is infused. Our code is available
online [https://github.com/Maple177/drugprot-relation-extraction].


  Due to the divergence-instability, low-order conforming finite element
methods (FEMs) for nearly incompressible elasticity equations suffer from the
so-called locking phenomenon as the Lam\'e parameter $\lambda\to\infty$ and
consequently the material becomes more and more incompressible. For the
piecewise linear case, the error in the $L^2$-norm of the standard Galerkin
conforming FEM is bounded by $C_\lambda h^2$. However, $C_\lambda \to \infty$
as $\lambda \to \infty$, resulting in poor accuracy for practical values of $h$
if $\lambda$ is sufficiently large. In this short paper, we show that for 2D
problems the locking phenomenon can be controlled by replacing $\lambda$ with
$\lambda^\alpha$ in the stiffness matrix, for a certain choice of
$\alpha=\alpha_*(h,\lambda)$ in the range $0<\alpha\le 1$. We prove that for
this optimal choice of $\alpha$, the error in the $L^2$-norm is bounded by $Ch$
where $C$ does not depend on $\lambda$. Numerical experiments confirm the
expected convergence behaviour and show that, for practical meshes, our
locking-free method is more accurate than the standard method if the material
is nearly incompressible. Our analysis also shows that the error in the
$H^1$-norm is bounded by $Ch^{1/2}$, but our numerical experiments suggest that
this bound is not sharp.


  We present new deep UBVRI images and high-resolution multi-object optical
spectroscopy of the young (~ 6 - 10 Myr old), relatively nearby (800 pc) open
cluster IC 2395. We identify nearly 300 cluster members and use the photometry
to estimate their spectral types, which extend from early B to middle M. We
also present an infrared imaging survey of the central region using the IRAC
and MIPS instruments on board the Spitzer Space Telescope, covering the
wavelength range from 3.6 to 24 microns. Our infrared observations allow us to
detect dust in circumstellar disks originating over a typical range of radii ~
0.1 to ~ 10AU from the central star. We identify 18 Class II, 8 transitional
disk, and 23 debris disk candidates, respectively 6.5%, 2.9%, and 8.3% of the
cluster members with appropriate data. We apply the same criteria for
transitional disk identification to 19 other stellar clusters and associations
spanning ages from ~ 1 to ~ 18 Myr. We find that the number of disks in the
transitional phase as a fraction of the total with strong 24 micron excesses
([8] - [24] > 1.5) increases from 8.4 +\- 1.3% at ~ 3 Myr to 46 +\- 5% at ~ 10
Myr. Alternative definitions of transitional disks will yield different
percentages but should show the same trend.


  Introduced by Kraitchik and Lehmer, an $x$-pseudosquare is a positive integer
$n\equiv1\pmod 8$ that is a quadratic residue for each odd prime $p\le x$, yet
is not a square. We use bounds of character sums to prove that pseudosquares
are equidistributed in fairly short intervals. An $x$-pseudopower to base $g$
is a positive integer which is not a power of $g$ yet is so modulo $p$ for all
primes $p\le x$. It is conjectured by Bach, Lukes, Shallit, and Williams that
the least such number is at most $\exp(a_g x/\log x)$ for a suitable constant
$a_g$. A bound of $\exp(a_g x\log\log x/\log x)$ is proved conditionally on the
Riemann Hypothesis for Dedekind zeta functions, thus improving on a recent
conditional exponential bound of Konyagin and the present authors. We also give
a GRH-conditional equidistribution result for pseudopowers that is analogous to
our unconditional result for pseudosquares.


  Exclusive heavy quarkonium photoproduction is one of the most popular
processes in EIC, which has a large cross section and a simple final state. Due
to the gluonic nature of the exchange Pomeron, this process can be related to
the gluon distributions in the nucleus. The momentum transfer dependence of
this process is sensitive to the interaction sites, which provides a powerful
tool to probe the spatial distribution of gluons in the nucleus. Recently the
problem of the origin of hadron mass has received lots of attention in
determining the anomaly contribution $M_{a}$. The trace anomaly is sensitive to
the gluon condensate, and exclusive production of quarkonia such as J/$\psi$
and $\Upsilon$ can serve as a sensitive probe to constrain it. In this paper,
we present the performance of the ECCE detector for exclusive J/$\psi$
detection and the capability of this process to investigate the above physics
opportunities with ECCE.


  In this work, we are interested in the behaviour of a single ferromagnetic
mono--domain particle submitted to an external field with a stochastic
perturbation. This model is a step toward the mathematical understanding of
thermal effects on ferromagnets. In a first part, we discuss modelling issues
and propose several ways to integrate a random noise in the deterministic
model. Then, among all these approaches, we focus on the more natural one and
study its long time behaviour. We prove that the system converges to the unique
stable equilibrium of the deterministic model and make precise the L^p rate of
the convergence. Finally, we illustrate the theoretical results by numerical
simulations.


  We construct some explicit quasihomogeneous algebraic solutions to the
associativity (WDVV) equations by using analytical methods of the finite gap
integration theory. These solutions are expanded in the uniform way to
non-semisimple Frobenius manifolds.


  Analog quantum simulation offers a hardware-specific approach to studying
quantum dynamics, but mapping a model Hamiltonian onto the available device
parameters requires matching the hardware dynamics. We introduce a paradigm for
quantum Hamiltonian simulation that leverages digital decomposition techniques
and optimal control to perform analog simulation. We validate this approach by
constructing the optimal analog controls for a superconducting transmon device
to emulate the dynamics of an extended Bose-Hubbard model. We demonstrate the
role of control time, digital error, and pulse complexity, and we explore the
accuracy and robustness of these controls. We conclude by discussing the
opportunity for implementing this paradigm in near-term quantum devices.


  We investigate properties of solutions of the scalar wave equation and
Maxwell's equations on Minkowski space with helical symmetry. Existence of
local and global solutions with this symmetry is demonstrated with and without
sources. The asymptotic properties of the solutions are analyzed. We show that
the Newman--Penrose retarded and advanced scalars exhibit specific symmetries
and generalized peeling properties.


  We consider the problem of estimating piecewise regular functions in an
online setting, i.e., the data arrive sequentially and at any round our task is
to predict the value of the true function at the next revealed point using the
available data from past predictions. We propose a suitably modified version of
a recently developed online learning algorithm called the sleeping experts
aggregation algorithm. We show that this estimator satisfies oracle risk bounds
simultaneously for all local regions of the domain. As concrete instantiations
of the expert aggregation algorithm proposed here, we study an online mean
aggregation and an online linear regression aggregation algorithm where experts
correspond to the set of dyadic subrectangles of the domain. The resulting
algorithms are near linear time computable in the sample size. We specifically
focus on the performance of these online algorithms in the context of
estimating piecewise polynomial and bounded variation function classes in the
fixed design setup. The simultaneous oracle risk bounds we obtain for these
estimators in this context provide new and improved (in certain aspects)
guarantees even in the batch setting and are not available for the state of the
art batch learning estimators.


  Tungsten is the main candidate material for plasma-facing armour components
in future fusion reactors. Bombardment with energetic fusion neutrons causes
collision cascade damage and defect formation. Interaction of defects with
helium, produced by transmutation and injected from the plasma, modifies defect
retention and behaviour. Here we investigate the residual lattice strains
caused by different doses of helium-ion-implantation into tungsten and
tungsten-rhenium alloys. Energy and depth-resolved synchrotron X-ray
micro-diffraction uniquely permits the measurement of lattice strain with
sub-micron 3D spatial resolution and ~10-4 strain sensitivity. Increase of
helium dose from 300 appm to 3000 appm increases volumetric strain by only ~2.4
times, indicating that defect retention per injected helium atom is ~3 times
higher at low helium doses. This suggests that defect retention is not a simple
function of implanted helium dose, but strongly depends on material composition
and presence of impurities. Conversely, analysis of W-1wt% Re alloy samples and
of different crystal orientations shows that both the presence of rhenium, and
crystal orientation, have comparatively small effect on defect retention. These
insights are key for the design of armour components in future reactors where
it will be essential to account for irradiation-induced dimensional change when
predicting component lifetime and performance.


  The quantum stabilizer formalism was originally introduced to describe
quantum error correction codes more conveniently and now are also playing an
important role in many other fields, e.g., quantum computing and quantum
foundation. In this dissertation, we first introduce relevant background and
necessary basic knowledge, then introduce the definition of quantum stabilizer
and its application in quantum system evolution and measurement. Finally, we
try to extend the quantum stabilizer formalism to qubit-qutrit and
qubit-ququart systems which not defined before, and further define quantum
stabilizers of arbitrary composite systems.


  We introduce a canonical form for reduced bases of integral closures of
discrete valuation rings, and we describe an algorithm for computing a basis in
reduced normal form. This normal form has the same applications as the Hermite
normal form: identification of isomorphic objects, construction of global bases
by patching local ones, etc. but in addition the bases are reduced, which is a
crutial property for several important applications. Except for very particular
cases, a basis in Hermite normal form cannot be reduced.


  Variations in the equivalent width of the neutral potassium line at 7699A are
reported, during ingress and into totality of the current eclipse of the
enigmatic eclipsing binary epsilon Aurigae. The increase and plateaus of line
strength are correlated with new system parameters and interferometric imaging
constraints, plus ancillary data being reported contemporaneously. Together,
these data reveal structural details of the transiting disc, never before
measured. Measured ring and gap placements do not immediately fit any proposed
model using simple tidal estimation. However, we predict egress times of
interest and urge continued monitoring of this star during the balance of its
eclipse, and encourage theoretical treatment of the disc substructure to be
pursued.


  It is known that a smooth LTB model cannot have a positive apparent central
acceleration. Using a local Taylor expansion method we study the low-redshift
conditions to obtain an apparent negative deceleration parameter $q^{app}(z)$
derived from the luminosity distance $D_L(z)$ for a central observer in a LTB
space, confirming that central smoothness implies a positive central
deceleration. Since observational data is only available at redshift greater
than zero we find the critical values of the parameters defining a centrally
smooth LTB model which give a positive apparent acceleration at $z>0$,
providing a graphical representation of the conditions in the
$q_0^{app},q_1^{app}$ plane, which are respectively the zero and first order
terms of the central Taylor expansion of $q^{app}(z)$. We finally derive a
coordinate independent expression for the apparent deceleration parameter based
on the expansion of the relevant functions in red-shift rather than in the
radial coordinate.
  We calculate $q^{app}(z)$ with two different methods to solve the null
geodesic equations, one based on a local central expansion of the solution in
terms of cosmic time and the other one using the exact analytical solution in
terms of generalized conformal time. %The expansion of the solution in terms of
cosmic time is quite useful also for other applications requiring foliation %of
space-time in space-like hyper-surfaces, such as spatial averaging, which is
much more difficult to study using the %analytical solution in terms of the
generalized conformal time coordinate.


  We propose an expression for a local planetesimal formation rate proportional
to the instantaneous radial pebble flux. The result --- a radial planetesimal
distribution --- can be used as initial condition to study the formation of
planetary embryos. We follow the idea that one needs particle traps to locally
enhance the dust-to-gas ratio sufficiently such that particle gas interactions
can no longer prevent planetesimal formation on small scales. The location of
these traps can emerge everywhere in the disk. Their occurrence and lifetime is
subject of ongoing research, thus they are implemented via free parameters.
This enables us to study the influence of the disk properties on the formation
of planetesimals, predicting their time dependent formation rates and location
of primary pebble accretion. We show that large $\alpha$-values of $0.01$
(strong turbulence) prevent the formation of planetesimals in the inner part of
the disk, arguing for lower values of around $0.001$ (moderate turbulence), at
which planetesimals form quickly at all places where they are needed for
proto-planets. Planetesimals form as soon as dust has grown to pebbles
($\sim\mathrm{mm}$ to $\mathrm{dm}$) and the pebble flux reaches a critical
value, which is after a few thousand years at $2-3\,$AU and after a few hundred
thousand years at $20-30\,$AU. Planetesimal formation lasts until the pebble
supply has decreased below a critical value. The final spatial planetesimal
distribution is steeper compared to the initial dust and gas distribution which
helps to explain the discrepancy between the minimum mass solar nebula and
viscous accretion disks.


  We present the results of an analysis of Wide-field Infrared Survey Explorer
(WISE) observations on the full 2500 deg^2 South Pole Telescope (SPT)-SZ
cluster sample. We describe a process for identifying active galactic nuclei
(AGN) in brightest cluster galaxies (BCGs) based on WISE mid-infrared color and
redshift. Applying this technique to the BCGs of the SPT-SZ sample, we
calculate the AGN-hosting BCG fraction, which is defined as the fraction of
BCGs hosting bright central AGNs over all possible BCGs. Assuming {\bf an
evolving} single-burst stellar population model, we find statistically
significant evidence (>99.9%) for a mid-IR excess at high redshift compared to
low redshift, suggesting that the fraction of AGN-hosting BCGs increases with
redshift over the range of 0 < z < 1.3. The best-fit redshift trend of the
AGN-hosting BCG fraction has the form (1+z)^(4.1+/-1.0). These results are
consistent with previous studies in galaxy clusters as well as field galaxies.
One way to explain this result is that member galaxies at high redshift tend to
have more cold gas. While BCGs in nearby galaxy clusters grow mostly by dry
mergers with cluster members, leading to no increase in AGN activity, BCGs at
high redshift could primarily merge with gas-rich satellites, providing fuel
for feeding AGNs. If this observed increase in AGN activity is linked to
gas-rich mergers, rather than ICM cooling, we would expect to see an increase
in scatter in the P_cav vs L_cool relation at z > 1. Lastly, this work confirms
that the runaway cooling phase, as predicted by the classical cooling flow
model, in the Phoenix cluster is extremely rare and most BCGs have low
(relative to Eddington) black hole accretion rates.


  Although the recent experimental efforts to improve the observation of
Ultra-High Energy Cosmic Rays (UHECRs) above $10^{18}$ eV, the origin and the
composition of such particles is still unknown. In this work, we present the
novel Monte Carlo code (HERMES) simulating the propagation of UHE nuclei, in
the energy range between $10^{16}$ and $10^{22}$ eV, accounting for propagation
in the intervening extragalactic and Galactic magnetic fields and nuclear
interactions with relic photons of the extragalactic background radiation. In
order to show the potential applications of HERMES for astroparticle studies,
we estimate the expected flux of UHE nuclei in different astrophysical
scenarios, the GZK horizons and we show the expected arrival direction
distributions in the presence of turbulent extragalactic magnetic fields. A
stable version of HERMES will be released in the next future for public use
together with libraries of already propagated nuclei to allow the community to
perform mass composition and energy spectrum analysis with our simulator.


  Topological insulators (TIs) constitute a new class of materials with an
energy gap in the bulk and peculiar metallic states on the surface. To date,
most experiments have focused on probing the surface electronic structure of
these materials. Here, we report on new and potentially interesting features
resulting from the bulk electronic structure. Our findings are based on a
comprehensive nuclear magnetic resonance (NMR) study of $^{77}$Se on
Bi$_2$Se$_3$ and Cu$_{0.15}$Bi$_2$Se$_3$ single crystals. First, we find two
resonance lines and show that they originate from the two inequivalent Se
lattice sites. Second, we observe unusual field-independent linewidths, and
attribute them to an unexpectedly strong internuclear coupling mediated by bulk
electrons. These results call for a revision of earlier NMR studies and add
insight into the bulk electronic properties of TIs.


  In this work, we investigate the importance of ethnicity in colorectal cancer
survivability prediction using machine learning techniques and the SEER cancer
incidence database. We compare model performances for 2-year survivability
prediction and feature importance rankings between Hispanic, White, and mixed
patient populations. Our models consistently perform better on single-ethnicity
populations and provide different feature importance rankings when trained in
different populations. Additionally, we show our models achieve higher Area
Under Curve (AUC) score than the best reported in the literature. We also apply
imbalanced classification techniques to improve classification performance when
the number of patients who have survived from colorectal cancer is much larger
than who have not. These results provide evidence in favor for increased
consideration of patient ethnicity in cancer survivability prediction, and for
more personalized medicine in general.


  $k$-means algorithm is one of the most classical clustering methods, which
has been widely and successfully used in signal processing. However, due to the
thin-tailed property of the Gaussian distribution, $k$-means algorithm suffers
from relatively poor performance on the dataset containing heavy-tailed data or
outliers. Besides, standard $k$-means algorithm also has relatively weak
stability, $i.e.$ its results have a large variance, which reduces its
credibility. In this paper, we propose a robust and stable $k$-means variant,
dubbed the $t$-$k$-means, as well as its fast version to alleviate those
problems. Theoretically, we derive the $t$-$k$-means and analyze its robustness
and stability from the aspect of the loss function and the expression of the
clustering center, respectively. Extensive experiments are also conducted,
which verify the effectiveness and efficiency of the proposed method. The code
for reproducing main results is available at
\url{https://github.com/THUYimingLi/t-k-means}.


  Let $\Omega$ be a domain in $\mathbb R^N$, where $N \ge 2$ and
$\partial\Omega$ is not necessarily bounded. We consider nonlinear diffusion
equations of the form $\partial_t u= \Delta \phi(u)$. Let $u=u(x,t)$ be the
solution of either the initial-boundary value problem over $\Omega$, where the
initial value equals zero and the boundary value equals 1, or the Cauchy
problem where the initial data is the characteristic function of the set
$\mathbb R^N\setminus \Omega$.
  We consider an open ball $B$ in $\Omega$ whose closure intersects
$\partial\Omega$ only at one point, and we derive asymptotic estimates for the
content of substance in $B$ for short times in terms of geometry of $\Omega$.
Also, we obtain a characterization of the hyperplane involving a stationary
level surface of $u$ by using the sliding method due to Berestycki, Caffarelli,
and Nirenberg. These results tell us about interactions between nonlinear
diffusion and geometry of domain.


  In this and the associated article 'BioBlender: Fast and Efficient All Atom
Morphing of Proteins Using Blender Game Engine', by Zini et al., we present
BioBlender, a complete instrument for the elaboration of motion (Zini et al.)
and the visualization (here) of proteins and other macromolecules, using
instruments of computer graphics. The availability of protein structures
enables the study of their surfaces and surface properties such as
electrostatic potential (EP) and hydropathy (MLP), based on atomic
contribution. Recent advances in 3D animation and rendering software have not
yet been exploited for the representation of proteins and other biological
molecules in an intuitive, animated form. Taking advantage of an open-source,
3D animation and rendering software, Blender, we developed BioBlender, a
package dedicated to biological work: elaboration of proteins' motions with the
simultaneous visualization of chemical and physical features. EP and MLP are
calculated using physico-chemical programs and custom programs and scripts,
organized and accessed within BioBlender interface. A new visual code is
introduced for MLP visualization: a range of optical features that permits a
photorealistic rendering of its spatial distribution on the surface of the
protein. EP is represented as animated line particles that flow along field
lines proportional to the total charge of the protein. Our system permits EP
and MLP visualization of molecules and, in the case of moving proteins, the
continuous perception of these features, calculated for each intermediate
conformation. Using real world tactile/sight feelings, the nanoscale world of
proteins becomes more understandable, familiar to our everyday life, making it
easier to introduce "un-seen" phenomena (concepts) such as hydropathy or
charges.


  One of the most challenging scenarios for smart speakers is multi-talker,
when target speech from the desired speaker is mixed with interfering speech
from one or more speakers. A smart assistant needs to determine which voice to
recognize and which to ignore and it needs to do so in a streaming, low-latency
manner. This work presents two multi-microphone speech enhancement algorithms
targeted at this scenario. Targeting on-device use-cases, we assume that the
algorithm has access to the signal before the hotword, which is referred to as
the noise context. First is the Context Aware Beamformer which uses the noise
context and detected hotword to determine how to target the desired speaker.
The second is an adaptive noise cancellation algorithm called Speech Cleaner
which trains a filter using the noise context. It is demonstrated that the two
algorithms are complementary in the signal-to-noise ratio conditions under
which they work well. We also propose an algorithm to select which one to use
based on estimated SNR. When using 3 microphone channels, the final system
achieves a relative word error rate reduction of 55% at -12dB, and 43\% at
12dB.


  In this paper, we study the real hypersurfaces $M$ in $\mathbb C^2$ at points
$p\in M$ of infinite type. The degeneracy of $M$ at $p$ is assumed to be the
least possible, namely such that the Levi form vanishes to first order in the
CR transversal direction. A new phenomenon, compared to known normal forms in
other cases, is the presence of resonances as roots of an universal polynomial
in the $7$-jet of the defining function of $M$. The main result is a complete
(formal) normal form at points $p$ with no resonances. Remarkably, our normal
form at such infinite type points resembles closely the Chern-Moser normal form
at Levi-nondegenerate points. For a fixed hypersurface, its normal forms are
parametrized by $S^1\times \mathbb R^*$, and as a corollary we find that the
automorphisms in the stability group of $M$ at $p$ without resonances are
determined by their $1$-jets at $p$. In the last section, as a contrast, we
also give examples of hypersurfaces with arbitrarily high resonances that
possess families of distinct automorphisms whose jets agree up to the resonant
order.


  We describe a novel method to measure the surface charge densities on optical
fibers placed in the vicinity of a trapped ion, where the ion itself acts as
the probe. Surface charges distort the trapping potential, and when the fibers
are displaced, the ion's equilibrium position and secular motional frequencies
are altered. We measure the latter quantities for different positions of the
fibers and compare these measurements to simulations in which unknown charge
densities on the fibers are adjustable parameters. Values ranging from $-10$ to
$+50$ e/$\mu$m$^2$ were determined. Our results will benefit the design and
simulation of miniaturized experimental systems combining ion traps and
integrated optics, for example, in the fields of quantum computation,
communication and metrology. Furthermore, our method can be applied to any
setup in which a dielectric element can be displaced relative to a trapped
charge-sensitive particle.


  In recent years, the field of neuroscience has gone through rapid
experimental advances and a significant increase in the use of quantitative and
computational methods. This growth has created a need for clearer analyses of
the theory and modeling approaches used in the field. This issue is
particularly complex in neuroscience because the field studies phenomena across
a wide range of scales and often requires consideration of these phenomena at
varying degrees of abstraction, from precise biophysical interactions to the
computations they implement. We argue that a pragmatic perspective of science,
in which descriptive, mechanistic, and normative approaches each play a
distinct role in defining and bridging levels of abstraction will facilitate
neuroscientific practice. This analysis leads to methodological suggestions,
including selecting a level of abstraction that is appropriate for a given
problem, identifying transfer functions to connect models and data, and the use
of models themselves as a form of experiment.


  In this work we consider the question of whether a simple diffusive model can
explain the scent tracking behaviours found in nature. For this behaviour to
occur, both the concentration of a scent and its gradient must be above some
threshold. Applying these conditions to the solutions of various diffusion
equations, we find that a purely diffusive model cannot simultaneously satisfy
the tracking conditions when parameters are in the experimentally observed
range. This demonstrates the necessity of modelling odor dispersal with full
fluid dynamics, where non-linear phenomena such as turbulence play a critical
role.


  A wide range of dark matter candidates have been proposed and are actively
being searched for in a large number of experiments, both at high (TeV) and low
(sub meV) energies. One dark matter candidate, a deeply bound $uuddss$
sexaquark, $S$, with mass $\sim 2$ GeV (having the same quark content as the
hypothesized H-dibaryon, but long lived) is particularly difficult to explore
experimentally. In this paper, we propose a scheme in which such a state could
be produced at rest through the formation of $\bar{p}-^3$He antiprotonic atoms
and their annihilation into $S$ + $K^+K^+\pi^-$, identified both through the
unique tag of a S=+2, Q=+1 final state, as well as through full kinematic
reconstruction of the final state recoiling against it.


  The present paper illustrates the utility of Brauer relations, Galois covers
of curves and the theory of regulator constants in the context of studying
isogenies between Jacobians and their relevance to the parity conjecture. This
framework presents a unified approach, enabling the reconstruction of a diverse
array of classical isogenies and the derivation of local expressions for Selmer
rank parities, drawing from an extensive body of existing literature. These
include the local expressions found in the works of Mazur--Rubin (dihedral
extensions), Coates--Fukaya--Kato--Sujatha ($p^g$ isogenies), Kramer--Tunnell
(quadratic twists of elliptic curves), Dokchitser--Maistret (Richelot
isogenies), and Docking (prym construction).


  A multi-way communication network with three nodes and a relay is considered.
The three nodes in this so-called Y-channel, communicate with each other in a
bi-directional manner via the relay. Studying this setup is important due to
its being an important milestone for characterizing the capacity of larger
networks. A transmit strategy for the Gaussian Y-channel is proposed, which
mimics a previously considered scheme for the deterministic approximation of
the Y-channel. Namely, a scheme which uses nested-lattice codes and lattice
alignment is used, to perform network coding. A new mode of operation is
introduced, named `cyclic communication', which interestingly turns out to be
an important component for achieving the capacity region of the Gaussian
Y-channel within a constant gap.


  Let $\pi:X\to Y$ be an $n$-dimensional iterated Kodaira fibration with fiber
of genus $g$ and injective monodromy. Llosa Isenrich and Py proved that we can
pass to a finite index subgroup of $\pi_1(X)$ to get the base space of an
n+1-dimensional iterated Kodaira fibration with injective monodromy and they
asked about bounding the index of such a group. We provide a bound on this
index.


  The Bethe approximation, or loopy belief propagation algorithm is a
successful method for approximating partition functions of probabilistic models
associated with a graph. Chertkov and Chernyak derived an interesting formula
called Loop Series Expansion, which is an expansion of the partition function.
The main term of the series is the Bethe approximation while other terms are
labeled by subgraphs called generalized loops. In our recent paper, we derive
the loop series expansion in form of a polynomial with coefficients positive
integers, and extend the result to the expansion of marginals. In this paper,
we give more clear derivation for the results and discuss the properties of the
polynomial which is introduced in the paper.


  We apply three optical coherence tomography (OCT) image analysis techniques
to extract morphometric information from OCT images obtained on peripheral
nerves of rat. The accuracy of each technique is evaluated against histological
measurements accurate to +/-1 um. The three OCT techniques are: 1) average
depth resolved profile (ADRP); 2) autoregressive spectral estimation (AR-SE);
and, 3) correlation of the derivative spectral estimation (CoD-SE). We
introduce a scanning window to the ADRP technique which provides transverse
resolution, and improves epineurium thickness estimates - with the number of
analysed images showing agreement with histology increasing from 2/10 to 5/10
(Kruskal-Wallis test, {\alpha} = 0.05). A new method of estimating epineurium
thickness, using the AR-SE technique, showed agreement with histology in 6/10
analysed images (Kruskal-Wallis test, {\alpha} = 0.05). Using a tissue sample
in which histology identified two fascicles with an estimated difference in
mean fibre diameter of 4 um, the AR-SE and CoD-SE techniques both correctly
identified the fascicle with larger fibre diameter distribution but incorrectly
estimated the magnitude of this difference as 0.5um. The ability of OCT signal
analysis techniques to extract accurate morphometric details from peripheral
nerve is promising but restricted in depth by scattering in adipose and neural
tissues.


  The classical approach to design a system is based on a deterministic
perspective where the assumption is that the system and its environment are
fully predictable, and their behaviour is completely known to the designer.
Although this approach may work fairly well for regular design problems, it is
not satisfactory for the design of highly sensitive and complex systems where
significant resources and even lives are at risk. In addition it can results in
extra costs of over-designing for the sake of safety and reliability. In this
paper, a risk-based design framework using Simulation Based Probabilistic Risk
Assessment (SIMPRA) methodology is proposed. SIMPRA allows the designer to use
the knowledge that can be expected to exist at the design stage to identify how
deviations can occur; and then apply these high-level scenarios to a rich
simulation model of the system to generate detailed scenarios and identify the
probability and consequences of these scenarios. SIMPRA has three main modules
including Simulator, Planner and Scheduler, and it approach is much more
efficient in covering the large space of possible scenarios as compared with,
for example, biased Monte Carlo simulations because of the Planner module which
uses engineering knowledge to guide the simulation process. The value-added of
this approach is that it enables the designer to observe system behaviour under
many different conditions. This process will lead to a risk-informed design in
which the risk of negative consequences is either eliminated entirely or
reduced to an acceptable range. For illustrative purposes, an earth observation
satellite system example is introduced.


  We present a unified band structure model to explain magnetic ordering in
Mn-doped semiconductors. This model is based on the $p$-$d$ and $d$-$d$ level
repulsions between the Mn ions and host elements and can successfully explain
magnetic ordering observed in all Mn doped II-VI and III-V semiconductors such
as CdTe, GaAs, ZnO, and GaN. This model, therefore, provides a simple guideline
for future band structure engineering of magnetic semiconductors.


  Hot gas trapped in a dark matter halo will produce a decrement in the surface
brightness of the microwave background, the Sunyaev-Zel'dovich (SZ) effect.
While massive clusters produce the strongest central SZ decrements, we point
out that a local galaxy halo, specifically the halo of M31, may be one of the
brightest integrated SZ sources in the sky. For various realistic gas
distributions consistent with current X-ray limits, we show that the integrated
SZ decrement from M31 will be comparable to decrements already detected in more
distant sources, provided its halo contains an appreciable quantity of hot gas.
A measurement of this decrement would provide direct information on the mass,
spatial distribution and thermodynamic state of hot gas in a low-mass halo, and
could place important constraints on current models of galaxy formation.
Detecting such an extended (~ 10 degree), low-amplitude signal will be
challenging, but should be possible with all-sky SZ maps from satellite
missions such as the Wilkinson Microwave Anisotropy Probe or the Planck
Surveyor.


  The stationary distributions of sums of positions of trajectories generated
by the logistic map have been found to follow a basic renormalization group
(RG) structure: a nontrivial fixed-point multi-scale distribution at the
period-doubling onset of chaos and a Gaussian trivial fixed-point distribution
for all chaotic attractors. Here we describe in detail the crossover
distributions that can be generated at chaotic band-splitting points that
mediate between the aforementioned fixed-point distributions. Self affinity in
the chaotic region imprints scaling features to the crossover distributions
along the sequence of band splitting points. The trajectories that give rise to
these distributions are governed first by the sequential formation of
phase-space gaps when, initially uniformly-distributed, sets of trajectories
evolve towards the chaotic band attractors. Subsequently, the summation of
positions of trajectories already within the chaotic bands closes those gaps.
The possible shapes of the resultant distributions depend crucially on the
disposal of sets of early positions in the sums and the stoppage of the number
of terms retained in them.


  The Matuyama-Brunhes reversal of Earth's magnetic dipole field took place
0.78 Ma ago, and detailed temporally resolved paleomagnetic data are available
for this period. A geomagnetic reversal is expected to impact the cosmic ray
flux, which in turn might impact atmospheric ionization rates. In this study a
model that yields atmospheric ionization for the entire globe based on an input
magnetic field is presented. Taking the time dependent paleomagnetic data as
input, a 3D time series of the atmospheric ionization rates during the reversal
is produced. We show, that as the dipole field weakens, the atmospheric
ionization increases at low latitudes. The increase is ca. 25% at the surface
and up to a factor of 5 in the upper atmosphere. Globally, ionization rates
increase around 13% at the surface and up to a factor of 2 in the upper
atmosphere, whereas polar regions are largely unaffected. Finally, the change
in ionization due to the solar 11-year cycle is greatly affected by the
reversal. The relative change in atmospheric ionization between solar-minimum
and solar-maximum varies between 2 and two orders of magnitude. All atmospheric
ionization data is made available for download.


  It has often been the case in history that the laws of physics have been used
as the framework for understanding and implementing information processing. The
tacit assumption is that the laws of physics are fundamental and that the
notion of information is derived from these laws. Here we take the opposite
view: the laws of physics are an application of the rules for processing
information. In this dissertation we apply the Entropic Dynamics (ED) framework
to construct a quantum dynamics for scalar fields in space-time. We begin by
considering a toy model consisting of many interacting particles, resulting in
the familiar Schrodinger equation for non-relativistic particles. Using a
similar methodology, we construct a theory of quantum scalar fields in flat
space-time that is relativistic, but not manifestly so. Here we also discuss a
novel way in which the ED of quantum scalar fields appears to evade the
so-called Wallstrom objection. To go further towards constructing a manifestly
covariant quantum ED of fields on a curved space-time, both fixed and
dynamical, we borrow from the "many-time" approaches of P. Weiss, P. Dirac, K.
Kuchar, and C. Teitelboim. For a fixed background the result is a manifestly
covariant ED of scalar fields that is in the spirit of the covariant quantum
theories proposed by S. Tomonaga and J. Schwinger. However, the formalism is
sufficiently flexible so as to allow for the possibility of modeling the back
reaction of the quantum matter fields on a fully dynamical classical
background. The simplest realization of this classical-quantum interaction
shares some formal similarity to semi-classical gravity models, and the
semi-classical Einstein equations, in particular. We consider such a theory and
discuss its plausibility as a candidate for a quantum gravity theory.


  The set of benchmark solutions used in the thermal radiative transfer
community suffer some coverage gaps, in particular nonlinear, non-equilibrium
problems. Also, there are no non-equilibrium, optically thick benchmarks. These
shortcomings motivated the development of a numerical method free from the
requirement of linearity and easily able to converge on smooth optically thick
problems, a moving mesh Discontinuous Galerkin (DG) framework that utilizes an
uncollided source treatment. Having already proven this method on time
dependent scattering transport problems, we present here solutions to
non-equilibrium thermal radiative transfer problems for familiar linearized
systems together with more physical nonlinear systems in both optically thin
and thick regimes, including both the full transport and the $S_2$/$P_1$
solution. Geometric convergence is observed for smooth sources at all times and
some nonsmooth sources at late times when there is local equilibrium. Also,
accurate solutions are achieved for step sources when the solution is not
smooth.


  In this paper, we study discrete-time quantum walks on one-dimensional
lattices. We find that the coherent dynamics depends on the initial states and
coin parameters. For infinite size of lattice, we derive an explicit expression
for the return probability, which shows scaling behavior $P(0,t)\sim t^{-1}$
and does not depends on the initial states of the walk. In the long-time limit,
the probability distribution shows various patterns, depending on the initial
states, coin parameters and the lattice size. The average mixing time
$M_{\epsilon}$ closes to the limiting probability in linear $N$ (size of the
lattice) for large values of thresholds $\epsilon$. Finally, we introduce
another kind of quantum walk on infinite or even-numbered size of lattices, and
show that the walk is equivalent to the traditional quantum walk with
symmetrical initial state and coin parameter.


  Here we present a new method of estimating global variations in outdoor
PM$_{2.5}$ concentrations using satellite images combined with ground-level
measurements and deep convolutional neural networks. Specifically, new deep
learning models were trained over the global PM$_{2.5}$ concentration range
($<$1-436 $\mu$g/m$^3$) using a large database of satellite images paired with
ground level PM$_{2.5}$ measurements available from the World Health
Organization. Final model selection was based on a systematic evaluation of
well-known architectures for the convolutional base including InceptionV3,
Xception, and VGG16. The Xception architecture performed best and the final
global model had a root mean square error (RMSE) value of 13.01 $\mu$g/m$^3$
(R$^2$=0.75) in the disjoint test set. The predictive performance of our new
global model (called IMAGE-PM$_{2.5}$) is similar to the current
state-of-the-art model used in the Global Burden of Disease study but relies
only on satellite images as input. As a result, the IMAGE-PM$_{2.5}$ model
offers a fast, cost-effective means of estimating global variations in
long-term average PM$_{2.5}$ concentrations and may be particularly useful for
regions without ground monitoring data or detailed emissions inventories. The
IMAGE-PM$_{2.5}$ model can be used as a stand-alone method of global exposure
estimation or incorporated into more complex hierarchical model structures.


  We have analysed the X-ray emission from a sample of close WR+O binaries
using data from the public Chandra and XMM-Newton archives. Global spectral
fits show that two-temperature plasma is needed to match the X-ray emission
from these objects as the hot component (kT > 2 keV) is an important ingredient
of the spectral models. In close WR+O binaries, X-rays likely originate in
colliding stellar wind (CSW) shocks driven by the massive winds of the binary
components. CSW shocks in these objects are expected to be radiative due to the
high density of the plasma in the interaction region. Opposite to this, our
analysis shows that the CSW shocks in the sample of close WR+O binaries are
adiabatic. This is possible only if the mass-loss rates of the stellar
components in the binary are at least one order of magnitude smaller than the
values currently accepted. The most likely explanation for the X-ray properties
of close WR+O binaries could be that their winds are two-component flows. The
more massive component (dense clumps) play role for the optical/UV emission
from these objects, while the smooth rarefied component is a key factor for
their X-ray emission.


  Recent work has explored using the stabilizer formalism to classically
simulate quantum circuits containing a few non-Clifford gates. The
computational cost of such methods is directly related to the notion of
stabilizer rank, which for a pure state $\psi$ is defined to be the smallest
integer $\chi$ such that $\psi$ is a superposition of $\chi$ stabilizer states.
Here we develop a comprehensive mathematical theory of the stabilizer rank and
the related approximate stabilizer rank. We also present a suite of classical
simulation algorithms with broader applicability and significantly improved
performance over the previous state-of-the-art. A new feature is the capability
to simulate circuits composed of Clifford gates and arbitrary diagonal gates,
extending the reach of a previous algorithm specialized to the Clifford+T gate
set. We implemented the new simulation methods and used them to simulate
quantum algorithms with 40-50 qubits and over 60 non-Clifford gates, without
resorting to high-performance computers. We report a simulation of the Quantum
Approximate Optimization Algorithm in which we process superpositions of
$\chi\sim10^6$ stabilizer states and sample from the full n-bit output
distribution, improving on previous simulations which used $\sim 10^3$
stabilizer states and sampled only from single-qubit marginals. We also
simulated instances of the Hidden Shift algorithm with circuits including up to
64 T gates or 16 CCZ gates; these simulations showcase the performance gains
available by optimizing the decomposition of a circuit's non-Clifford
components.


  Near-solar metallicity (and low-redshift) Pair-Instability Supernova (PISN)
candidates challenge stellar evolution models. Indeed, at such a metallicity,
even an initially very massive star generally loses so much mass by stellar
winds that it will avoid the electron-positron pair-creation instability. We
use recent results showing that a magnetic field at the surface of a massive
star can significantly reduce its effective mass-loss rate to compute magnetic
models of very massive stars (VMSs) at solar metallicity and explore the
possibility that such stars end as PISNe. We implement the quenching of the
mass loss produced by a surface dipolar magnetic field into the Geneva stellar
evolution code and compute new stellar models with an initial mass of
$200\,M_\odot$ at solar metallicity, with and without rotation. It considerably
reduces the total amount of mass lost by the star during its life. For the
non-rotating model, the total (CO-core) mass of the models is $72.8\,M_\odot$
($70.1\,M_\odot$) at the onset of the electron-positron pair-creation
instability. For the rotating model, we obtain $65.6\,M_\odot$
($62.4\,M\odot$). In both cases, a significant fraction of the internal mass
lies in the region where pair instability occurs in the $\log(T)-\log(\rho)$
plane. The interaction of the reduced mass loss with the magnetic field
efficiently brakes the surface of the rotating model, producing a strong shear
and hence a very efficient mixing that makes the star evolve nearly
homogeneously. The core characteristics of our models indicate that solar
metallicity models of magnetic VMSs may evolve to PISNe (and pulsation PISNe).


  Let $(G,H)$ be a symmetric pair over a $p$-adic field and $\pi$ a discrete
series representation of $G$. In this paper, for some type of symmetric pairs
$(G,H)$, we show that local periods in $Hom_H(\pi,\mathbb{C})$ can be
constructed by integrating the matrix coefficients of $\pi$ over $H$.


  Energy system models for long-term planning are widely used to explore the
future electricity system. Typically, to represent the future electricity
demand in these models, historical demand profiles are used directly or scaled
up linearly. Although the volume change for the electricity demand is
considered, the potential change of the demand pattern is ignored. Meanwhile,
the future electricity demand pattern is highly uncertain due to various
factors, including climate change, e-mobility, electric heating, and electric
cooling. We use a techno-economic cost optimization model to investigate a
stylized case and assess the effects on system cost and electricity supply mix
of assuming different demand patterns for the models. Our results show that
differences in diurnal demand patterns affect the system cost by less than 3%.
Similarly, demand profiles with a flat seasonal variation or a winter peak
result in only minor changes in system cost, as compared to the present demand
profile. Demand profiles with a summer peak may display a system cost increase
of up to 8%, whereas the electricity supply mix may differ by a factor of two.
A more detailed case study is conducted for Europe and the results are
consistent with the findings from the stylized case.


  Solution-grown single crystals of Fe2OBO3 were characterized by specific
heat, Mossbauer spectroscopy, and x-ray diffraction. A peak in the specific
heat at 340 K indicates the onset of charge order. Evidence for a doubling of
the unit cell at low temperature is presented. Combining structural refinement
of diffraction data and Mossbauer spectra, domains with diagonal charge order
are established. Bond-valence-sum analysis indicates integer valence states of
the Fe ions in the charge ordered phase, suggesting Fe2OBO3 is the clearest
example of ionic charge order so far.


  We highlight a general theory to engineer arbitrary Hermitian tight-binding
lattice models in electrical LC circuits, where the lattice sites are replaced
by the electrical nodes, connected to its neighbors and to the ground by
capacitors and inductors. In particular, by supplementing each node with $n$
subnodes, where the phases of the current and voltage are the $n$ distinct
roots of \emph{unity}, one can in principle realize arbitrary hopping amplitude
between the sites or nodes via the \emph{shift capacitor coupling} between
them. This general principle is then implemented to construct a plethora of
topological models in electrical circuits, \emph{topolectric circuits}, where
the robust zero-energy topological boundary modes manifest through a large
boundary impedance, when the circuit is tuned to the resonance frequency. The
simplicity of our circuit constructions is based on the fact that the existence
of the boundary modes relies only on the Clifford algebra of the corresponding
Hermitian matrices entering the Hamiltonian and not on their particular
representation. This in turn enables us to implement a wide class of
topological models through rather simple topolectric circuits with nodes
consisting of only two subnodes. We anchor these outcomes from the numerical
computation of the on-resonance impedance in circuit realizations of
first-order ($m=1$), such as Chern and quantum spin Hall insulators, and
second- ($m=2$) and third- ($m=3$) order topological insulators in different
dimensions, featuring sharp localization on boundaries of codimensionality
$d_c=m$. Finally, we subscribe to the \emph{stacked topolectric circuit}
construction to engineer three-dimensional Weyl, nodal-loop, quadrupolar Dirac
and Weyl semimetals, respectively displaying surface and hinge localized
impedance.


  Single-molecule atomic force spectroscopy probes elastic properties of titin,
ubiquitin and other relevant proteins. We explain bioprotein folding dynamics
under both length- and force-clamp by modeling polyprotein modules as particles
in a bistable potential, weakly connected by harmonic spring linkers.
Multistability of equilibrium extensions provides the characteristic sawtooth
force-extension curve. We show that abrupt or stepwise unfolding and refolding
under force-clamp conditions involve transitions through virtual states (which
are quasi-stationary domain configurations) modified by thermal noise. These
predictions agree with experimental observations.


  We measured both the differential cross section ($\sigma_{p,p^\prime}$
$=d^2\sigma/d\Omega dE_{x}$) and the $\gamma$-ray emission probability
($R_\gamma(E_x)$ $=\sigma_{p,p^\prime\gamma}$/$\sigma_{p,p^\prime}$) from the
giant resonances excited by $\rm^{12}C$(\textit{p,p}$^\prime$) reaction at 392
MeV and 0$^\circ$, using a magnetic spectrometer and an array of NaI(Tl)
counters. The absolute value of $R_\gamma(E_x)$ was calibrated by using the
well-known $\gamma$-ray emission probability from $\rm^{12}C^* ( 15.11$ MeV, $
1^+$, $T=1$) and $\rm^{16}O^* ( 6.9$ MeV, $2^+$, $T=0$) states within 5\%
uncertainty. We found that $R_\gamma(E_x)$ starts from zero at $E_x=16$ MeV,
increases to a maximum of 53.3$\pm$0.4$\pm$3.9\% at $E_x=27$ MeV and then
decreases. We also compared the measured values of $R_\gamma(E_x)$ with
statistical model calculation based on the Hauser-Feshbach formalism in the
energy region $E_x=$ 16-32 MeV and discussed the features of $\gamma$-ray
emission probability quantitatively.


  Networks are versatile representations of the interactions between entities
in complex systems. Cycles on such networks represent feedback processes which
play a central role in system dynamics. In this work, we introduce a measure of
the importance of any individual cycle, as the fraction of the total
information flow of the network passing through the cycle. This measure is
computationally cheap, numerically well-conditioned, induces a centrality
measure on arbitrary subgraphs and reduces to the eigenvector centrality on
vertices. We demonstrate that this measure accurately reflects the impact of
events on strategic ensembles of economic sectors, notably in the US economy.
As a second example, we show that in the protein-interaction network of the
plant Arabidopsis thaliana, a model based on cycle-centrality better accounts
for pathogen activity than the state-of-art one. This translates into
pathogen-targeted-proteins being concentrated in a small number of triads with
high cycle-centrality. Algorithms for computing the centrality of cycles and
subgraphs are available for download.


  In this paper, we study the control properties of a new class of stochastic
ensemble systems that consists of families of random variables. These random
variables provide an increasingly good approximation of an unknown discrete,
linear-time invariant (DLTI) system, and can be obtained by a standard,
data-driven procedure. Our first result relates the reachability properties of
the stochastic ensemble system with that of the limiting DLTI system. We then
provide a method to combine the control inputs obtained from the stochastic
ensemble systems to compute a control input for the DLTI system. Later, we deal
with a particular kind of stochastic ensemble system generated from realizing
Bernoulli random variables. For this, we characterize the variance of the
computed state and control. We also do the same for a situation where the data
is updated sequentially in a streaming fashion. We illustrate the results
numerically in various simulation examples.


  This paper considers online convex optimization (OCO) with stochastic
constraints, which generalizes Zinkevich's OCO over a known simple fixed set by
introducing multiple stochastic functional constraints that are i.i.d.
generated at each round and are disclosed to the decision maker only after the
decision is made. This formulation arises naturally when decisions are
restricted by stochastic environments or deterministic environments with noisy
observations. It also includes many important problems as special cases, such
as OCO with long term constraints, stochastic constrained convex optimization,
and deterministic constrained convex optimization. To solve this problem, this
paper proposes a new algorithm that achieves $O(\sqrt{T})$ expected regret and
constraint violations and $O(\sqrt{T}\log(T))$ high probability regret and
constraint violations. Experiments on a real-world data center scheduling
problem further verify the performance of the new algorithm.


  The family of quads of interrelated functions holomorphic on the universal
cover of the complex plane without zero (for brevity, pqrs-functions),
revealing a number of remarkable properties, is introduced. In particular,
under certain conditions the transformations of the argument $z$ of
pqrs-functions represented by lifts of the replacements $ z \leftarrow -1/z $ $
z \leftarrow -z $, and $ z \leftarrow 1/z $ are equivalent to linear
transformations with known coefficients. Pqrs-functions arise in a natural way
in constructing of certain linear operators acting as automorphisms on the
space of solutions to the special double confluent Heun equation (sDCHE).
Earlier such symmetries were known to exist only in the case of integer value
of one of the constant parameters when the predecessors of pqrs-functions
appear as polynomials. In the present work, leaning on the generalized notion
of pqrs-functions, discrete symmetries of the space of solutions to sDCHE are
extended to the general case, apart from some natural exceptions.


  In the bottom-up approach of emergent gravity we attempt to find symplectic
gauge fields emerging from Euclidean Schwarzschild instanton, which is studied
as electromagnetism defined on the symplectic space $(M,\omega)$. Geometrical
engineering with the emergent metric sets up the Seiberg Witten map between
commutative and non-commutative gauge fields, preparing the ground for the
evaluation of topological invariants in terms of the underlying gauge theory
quantities.


  In this paper, we study the atomic structure of Puiseux monoids generated by
monotone sequences. To understand this atomic structure, it is often useful to
know whether the monoid has a bounded generating set. We provide necessary and
sufficient conditions for the atomicity and boundedness to be transferred from
a monotone Puiseux monoid to all its submonoids. Finally, we present two
special subfamilies of monotone Puiseux monoids and fully classify their atomic
structure.


  We quantitatively relate the Patterson-Sullivant currents and generic
stretching factors for free group automorphisms to the asymmetric Lipschitz
metric on Outer space and to Guirardel's intersection number.


  Based on the metastable electron-pair energy band in a two-dimensional (2D)
periodic potential obtained previously by Hai and Castelano [J. Phys.: Condens.
Matter 26, 115502 (2014)], we present in this work a Hamiltonian of many
electrons consisting of single electrons and electron pairs in the 2D system.
The electron-pair states are metastable of energies higher than those of the
single-electron states at low electron density. We assume two different
scenarios for the single-electron band. When it is considered as the lowest
conduction band of a crystal, we compare the obtained Hamiltonian with the
phenomenological model Hamiltonian of a boson-fermion mixture proposed by
Friedberg and Lee [Phys. Rev. B 40, 6745 (1989)]. Single-electron-electron-pair
and electron-pair-electron-pair interaction terms appear in our Hamiltonian and
the interaction potentials can be determined from the electron-electron Coulomb
interactions. When we consider the single-electron band as the highest valence
band of a crystal, we show that holes in this valence band are important for
stabilization of the electron-pair states in the system.


  In this talk, we will review the signatures of dark matter in two inert
triplet models. For the first model with the hypercharge Y=0, the dark matter
mass around 5.5 TeV is favored by both the WMAP data and the direct detection.
In contrast, for the second model of Y=2, it is excluded by the direct
detection experiments although dark matter with its mass around 2.8 TeV is
allowed by WMAP.


  In case of a threat in a public space, the crowd in it should be moved to a
shelter or evacuated without delays. Risk management and evacuation planning in
public spaces should also take into account uncertainties in the traffic
patterns of crowd flow. One way to account for the uncertainties is to make use
of safety staff, or guides, that lead the crowd out of the building according
to an evacuation plan. Nevertheless, solving the minimum time evacuation plan
is a computationally demanding problem. In this paper, we model the evacuating
crowd and guides as a multi-agent system with the social force model. To
represent uncertainty, we construct probabilistic scenarios. The evacuation
plan should work well both on average and also for the worst-performing
scenarios. Thus, we formulate the problem as a bi-objective scenario
optimization problem, where the mean and conditional value-at-risk (CVaR) of
the evacuation time are objectives. A solution procedure combining numerical
simulation and genetic algorithm is presented. We apply it to the evacuation of
a fictional passenger terminal. In the mean-optimal solution, guides are
assigned to lead the crowd to the nearest exits, whereas in the CVaR-optimal
solution the focus is on solving the physical congestion occurring in the
worst-case scenario. With one guide positioned behind each agent group near
each exit, a plan that minimizes both objectives is obtained.


  A diagonalizable non-Hermitian Hamiltonian having a real spectrum may be used
to define a unitary quantum system, if one modifies the inner product of the
Hilbert space properly. We give a comprehensive and essentially self-contained
review of the basic ideas and techniques responsible for the recent
developments in this subject. We provide a critical assessment of the role of
the geometry of the Hilbert space in conventional quantum mechanics to reveal
the basic physical principle motivating our study. We then offer a survey of
the necessary mathematical tools and elaborate on a number of relevant issues
of fundamental importance. In particular, we discuss the role of the antilinear
symmetries such as PT, the true meaning and significance of the charge
operators C and the CPT-inner products, the nature of the physical observables,
the equivalent description of such models using ordinary Hermitian quantum
mechanics, the pertaining duality between local-non-Hermitian versus
nonlocal-Hermitian descriptions of their dynamics, the corresponding classical
systems, the pseudo-Hermitian canonical quantization scheme, various methods of
calculating the (pseudo-) metric operators, subtleties of dealing with
time-dependent quasi-Hermitian Hamiltonians and the path-integral formulation
of the theory, and the structure of the state space and its ramifications for
the quantum Brachistochrone problem. We also explore some concrete physical
applications of the abstract concepts and tools that have been developed in the
course of this investigation. These include applications in nuclear physics,
condensed matter physics, relativistic quantum mechanics and quantum field
theory, quantum cosmology, electromagnetic wave propagation, open quantum
systems, magnetohydrodynamics, quantum chaos, and biophysics.


  The $N=2$ minimal superconformal model can be twisted yielding an example of
topological conformal field theory. In this article we investigate a Lie
theoretic extension of this process.


  A surface with an involution can be viewed as a $C_2$-space where $C_2$ is
the cyclic group of order two. Using the classification of $C_2$-surfaces given
by Dugger, we compute the $RO(C_2)$-graded Bredon cohomology of all
$C_2$-surfaces in constant $\mathbb{Z}/2$ coefficients as modules over the
cohomology of a point. We show the cohomology depends only on three numerical
invariants in the nonfree case, and only on two numerical invariants in the
free case.


  The light echo phenomenon that accompanied the 2002 eruption of V838 Mon
allows one to study the properties of the diffuse dusty matter in the vicinity
of the object. We are aiming at obtaining estimates of the optical thickness of
the circumstellar matter in front of V838 Mon, as well as optical properties of
dust grains in the echoing medium. In particular, we are interested in studying
whether the echoing medium can be responsible for the observed faintness of the
B-type companion of V838 Mon when compared to three B-type stars that are seen
in the vicinty of V838 Mon and are believed to be at the same distance as V838
Mon. We used the V838 Mon light echo images obtained by the Hubble Space
Telescope (HST) in different filters and epochs. From the images we derived the
total brightness of the echo and its surface brightness. The results of the
measurements were compared to model light echoes. The present study allowed us
to estimate the optical thickness of the matter in front of the object and the
mean cosine value of the scattering angle of dust grains in three HST filters.
The optical thickness of the echoing matter is not sufficient to explain the
observed difference in brightness between the B-type companion of V838 Mon and
the other three B-type stars observed in the vicinity of V838 Mon. Implications
of this result are discussed. Our estimate of the mass of the diffuse matter
seen in the light echo shows that the matter cannot have resulted form a past
mass loss activity of V838 Mon. We probably observe remnants of an interstellar
cloud from which V838 Mon and other members of the observed cluster were
formed.


  The temporal communication patterns of human individuals are known to be
inhomogeneous or bursty, which is reflected as the heavy tail behavior in the
inter-event time distribution. As the cause of such bursty behavior two main
mechanisms have been suggested: a) Inhomogeneities due to the circadian and
weekly activity patterns and b) inhomogeneities rooted in human task execution
behavior. Here we investigate the roles of these mechanisms by developing and
then applying systematic de-seasoning methods to remove the circadian and
weekly patterns from the time-series of mobile phone communication events of
individuals. We find that the heavy tails in the inter-event time distributions
remain robustly with respect to this procedure, which clearly indicates that
the human task execution based mechanism is a possible cause for the remaining
burstiness in temporal mobile phone communication patterns.


  We demonstrate that with two small modifications, the popular dielectric
continuum model is capable of predicting, with high accuracy, ion solvation
thermodynamics in numerous polar solvents, and ion solvation free energies in
water--co-solvent mixtures. The first modification involves perturbing the
macroscopic dielectric-flux interface condition at the solute--solvent
interface with a nonlinear function of the local electric field, giving what we
have called a solvation-layer interface condition (SLIC). The second
modification is a simple treatment of the microscopic interface potential
(static potential). We show that the resulting model exhibits high accuracy
without the need for fitting solute atom radii in a state-dependent fashion.
Compared to experimental results in nine water--co-solvent mixtures, SLIC
predicts transfer free energies to within 2.5 kJ/mol. The co-solvents include
both protic and aprotic species, as well as biologically relevant denaturants
such as urea and dimethylformamide. Furthermore, our results indicate that the
interface potential is essential to reproduce entropies and heat capacities.
The present work, together with previous studies of SLIC illustrating its
accuracy for biomolecules in water, indicates it as a promising dielectric
continuum model for accurate predictions of molecular solvation in a wide range
of conditions.


  We study the K'-theory of a CM Henselian local ring R of finite
Cohen-Macaulay type. We first describe a long exact sequence involving the
groups $K_i'(R)$ and the K-groups of certain other rings, including the
Auslander algebra. By examining the terms and maps in the sequence, we obtain
information about K'(R).


  A Spectral Correlator is the main component of the real time signal
processing for a Radio Telescope array. The correlation of signals received at
each element with every other element of the array is a classic case of an
application requiring a complete graph connectivity between its data sources,
as well as a very large number of simple operations to carry out the
correlation. Datarates can be extremely large in order to achieve high
sensitivities required for the detection of weak celestial signals. Hence,
correlators are prime targets for HPC implementations. In this paper, we
present the design and implementation of a massively parallel software spectral
Correlator for a 44 element array. The correlator handles ~735 MB/s of incoming
data from the 44 spatially distributed sources, and concurrently sustains a
computational load of ~100 Gflops. We first describe how we partition the large
incoming data stream into grouped datasets suited for transport over high speed
serial networks, as well as ideal for processing on commodity multicore
processors. An OpenMP based software correlator optimized for operation on
multicore SMP systems and implemented on a set of Harpertown class dual
processor machines is then presented.


  Minimizing the Higgs potential is an essential task in any model involving
Higgs bosons. Exact minimization methods proposed in the literature are based
on the polynomial form of the potential. These methods will in general no
longer work if loop contributions to the potential are taken into account. We
present a method to keep the tree level global minimum unchanged in passing to
the effective potential. We illustrate the method for the case of the Minimal
Supersymmetric Model (MSSM).


  Primordial black holes may owe their origin to the small-scale enhancement of
the comoving curvature perturbation generated during inflation. Their mass
fraction at formation is markedly sensitive to possible non-Gaussianities in
such large, but rare fluctuations. We discuss a path-integral formulation which
provides the exact mass fraction of primordial black holes at formation in the
presence of non-Gaussianity. Through a couple of classes of models, one based
on single-field inflation and the other on spectator fields, we show that
restricting to a Gaussian statistics may lead to severe inaccuracies in the
estimate of the mass fraction as well as on the clustering properties of the
primordial black holes.


  The theoretical investigation of charge (and spin) transport at nanometer
length scales requires the use of advanced and powerful techniques able to deal
with the dynamical properties of the relevant physical systems, to explicitly
include out-of-equilibrium situations typical for electrical/heat transport as
well as to take into account interaction effects in a systematic way.
Equilibrium Green function techniques and their extension to non-equilibrium
situations via the Keldysh formalism build one of the pillars of current
state-of-the-art approaches to quantum transport which have been implemented in
both model Hamiltonian formulations and first-principle methodologies. We offer
a tutorial overview of the applications of Green functions to deal with some
fundamental aspects of charge transport at the nanoscale, mainly focusing on
applications to model Hamiltonian formulations.


  We study Giant Molecular Cloud (GMC) environments surrounding 10 Infrared
Dark Clouds (IRDCs), using $^{13}$CO(1-0) emission from the Galactic Ring
Survey. We measure physical properties of these IRDCs/GMCs on a range of scales
extending to radii, R, of 30 pc. By comparing different methods for defining
cloud boundaries and for deriving mass surface densities and velocity
dispersions, we settle on a preferred "CE,$\tau$,G" method of "Connected
Extraction" in position-velocity space plus Gaussian fitting to
opacity-corrected line profiles for velocity dispersion and mass estimation. We
examine how cloud definition affects measurements of the magnitude and
direction of line-of-sight velocity gradients and velocity dispersions,
including associated dependencies on size scale. CE,$\tau$,G-defined GMCs show
velocity dispersion versus size relations $\sigma\propto{s}^{1/2}$, which are
consistent with the large-scale gradients being caused by turbulence. However,
IRDCs have velocity dispersions that are moderately enhanced above those
predicted by this scaling relation. We examine the dynamical state of the
clouds finding mean virial parameters $\bar{\alpha}_{\rm{vir}}\simeq 1.0$ for
GMCs and 1.6 for IRDCs, broadly consistent with models of magnetized virialized
pressure-confined polytropic clouds, but potentially indicating that IRDCs have
more disturbed kinematics. CE,$\tau$,G-defined clouds exhibit a tight
correlation of $\sigma/R^{1/2}\propto\Sigma^n$, with $n\simeq0.7$ for GMCs and
1.3 for IRDCs (c.f., a value of 0.5 expected for a population of virialized
clouds). We conclude that while GMCs show evidence for virialization over a
range of scales, IRDCs may be moderately super virial. Alternatively, IRDCs
could be virialized but have systematically different $^{13}$CO gas phase
abundances, i.e., due to freeze-out, affecting mass estimations.


  Three sets of ladder operators in spheroconal coordinates and their
respective actions on Lam\'e spheroconal harmonic polynomials are presented in
this article. The polynomials are common eigenfunctions of the square of the
angular momentum operator and of the asymmetry distribution Hamiltonian for the
rotations of asymmetric molecules, in the body-fixed frame with principal axes.
The first set of operators for Lam\'e polynomials of a given species and a
fixed value of the square of the angular momentum raise and lower and lower and
raise in complementary ways the quantum numbers $n_1$ and $n_2$ counting the
respective nodal elliptical cones. The second set of operators consisting of
the cartesian components $\hat L_x$, $\hat L_y$, $\hat L_z$ of the angular
momentum connect pairs of the four species of polynomials of a chosen kind and
angular momentum. The third set of operators, the cartesian components $\hat
p_x$, $\hat p_y$, $\hat p_z$ of the linear momentum, connect pairs of the
polynomials differing in one unit in their angular momentum and in their
parities. Relationships among spheroconal harmonics at the levels of the three
sets of operators are illustrated.


  In this paper, we construct operator fractional L\'evy motion (ofLm), a broad
class of non-Gaussian stochastic processes that are covariance operator
self-similar, have wide-sense stationary increments and display infinitely
divisible marginal distributions. The ofLm class generalizes the univariate
fractional L\'evy motion as well as the multivariate operator fractional
Brownian motion (ofBm). The ofLm class can be divided into two types, namely,
moving average (maofLm) and real harmonizable (rhofLm), both of which share the
covariance structure of ofBm under assumptions. We show that maofLm and rhofLm
admit stochastic integral representations in the time and Fourier domains, and
establish their distinct small- and large-scale limiting behavior. We
characterize time reversibility for ofLm through parametric conditions related
to its L\'evy measure, starting from a framework for the uniqueness of finite
second moment, multivariate stochastic integral representations. In particular,
we show that, under non-Gaussianity, the parametric conditions for time
reversibility are generally more restrictive than those for the Gaussian case
(ofBm).


  Complexity and uncertainty associated with commodity resource valuation and
extraction requires stochastic control methods suitable for high dimensional
states. Recent progress in duality and trajectory-wise techniques has
introduced a variety of fresh ideas to this field with surprising results. This
paper presents a first application of this promising development to commodity
extraction problems. We introduce efficient algorithms for obtaining
approximate solutions along with a diagnostic technique, which provides a
quantitative measure for solution performance in terms of the distance between
the approximate and the optimal control policy.


  The past several years have witnessed the success of transformer-based
models, and their scale and application scenarios continue to grow
aggressively. The current landscape of transformer models is increasingly
diverse: the model size varies drastically with the largest being of
hundred-billion parameters; the model characteristics differ due to the
sparsity introduced by the Mixture-of-Experts; the target application scenarios
can be latency-critical or throughput-oriented; the deployment hardware could
be single- or multi-GPU systems with different types of memory and storage,
etc. With such increasing diversity and the fast-evolving pace of transformer
models, designing a highly performant and efficient inference system is
extremely challenging. In this paper, we present DeepSpeed Inference, a
comprehensive system solution for transformer model inference to address the
above-mentioned challenges. DeepSpeed Inference consists of (1) a multi-GPU
inference solution to minimize latency while maximizing the throughput of both
dense and sparse transformer models when they fit in aggregate GPU memory, and
(2) a heterogeneous inference solution that leverages CPU and NVMe memory in
addition to the GPU memory and compute to enable high inference throughput with
large models which do not fit in aggregate GPU memory. DeepSpeed Inference
reduces latency by up to 7.3X over the state-of-the-art for latency-oriented
scenarios and increases throughput by over 1.5x for throughput-oriented
scenarios. Moreover, it enables trillion parameter scale inference under
real-time latency constraints by leveraging hundreds of GPUs, an unprecedented
scale for inference. It can inference 25x larger models than with GPU-only
solutions, while delivering a high throughput of 84 TFLOPS (over $50\%$ of
A6000 peak).


  Some new inequalities for the norm and the numerical radius of composite
operators generated by a pair of operators are given.


  We present a detailed study of the 2016 eruption of nova V407 Lupi
(ASASSN-16kt), including optical, near-infrared, X-ray, and ultraviolet data
from SALT, SMARTS, SOAR, Chandra, Swift, and XMM-Newton. Timing analysis of the
multiwavelength light-curves shows that, from 168 days post-eruption and for
the duration of the X-ray supersoft source phase, two periods at 565 s and 3.57
h are detected. We suggest that these are the rotational period of the white
dwarf and the orbital period of the binary, respectively, and that the system
is likely to be an intermediate polar. The optical light-curve decline was very
fast ($t_2 \leq$ 2.9 d), suggesting that the white dwarf is likely massive
($\gtrsim 1.25$ M$_{\odot}$). The optical spectra obtained during the X-ray
supersoft source phase exhibit narrow, complex, and moving emission lines of He
II, also characteristics of magnetic cataclysmic variables. The optical and
X-ray data show evidence for accretion resumption while the X-ray supersoft
source is still on, possibly extending its duration.


  The chit-chat-based conversational recommendation systems (CRS) provide item
recommendations to users through natural language interactions. To better
understand user's intentions, external knowledge graphs (KG) have been
introduced into chit-chat-based CRS. However, existing chit-chat-based CRS
usually generate repetitive item recommendations, and they cannot properly
infuse knowledge from KG into CRS to generate informative responses. To remedy
these issues, we first reformulate the conversational recommendation task to
highlight that the recommended items should be new and possibly interested by
users. Then, we propose the Knowledge-Enriched Conversational Recommendation
System (KECRS). Specifically, we develop the Bag-of-Entity (BOE) loss and the
infusion loss to better integrate KG with CRS for generating more diverse and
informative responses. BOE loss provides an additional supervision signal to
guide CRS to learn from both human-written utterances and KG. Infusion loss
bridges the gap between the word embeddings and entity embeddings by minimizing
distances of the same words in these two embeddings. Moreover, we facilitate
our study by constructing a high-quality KG, \ie The Movie Domain Knowledge
Graph (TMDKG). Experimental results on a large-scale dataset demonstrate that
KECRS outperforms state-of-the-art chit-chat-based CRS, in terms of both
recommendation accuracy and response generation quality.


  Spin torques, that is, effects of conduction electrons on magnetization
dynamics, are calculated microscopically in the first order in spatial gradient
and time derivative of magnetization. Special attention is paid to the
so-called \beta-term and the Gilbert damping, \alpha, in the presence of
electrons' spin-relaxation processes, which are modeled by quenched magnetic
impurities. Two types of forces that the electric/spin current exerts on
magnetization are identified based on a general formula relating the force to
the torque.


  We show that the macroscopic current and charge can be formulated as a
Quantum Mechanical zero mode problem. We find that the current is given by the
velocity operator of a particle restricted to move around a circle. As an
explicit example we investigate a Luttinger liquid of length $L$ which is
perturbed by a time dependent impurity. Using the statistical mechanics of zero
modes we computed the non-equilibrium current. In particular we show that in
the low temperature limit, $L_T/L>1$, the zero mode method introduced here
becomes essential for computing the current.


  We study prospects of dark-matter direct-detection searches for probing
non-relativistic effective theory for WIMP-baryon scattering. We simulate a
large set of noisy recoil-energy spectra for different scattering scenarios
(beyond the standard momentum-independent contact interaction), for Generation
2 and futuristic experiments. We analyze these simulations and quantify the
probability of successfully identifying the operator governing the scattering,
if a WIMP signal is observed. We find that the success rate depends on a
combination of factors: the WIMP mass, the mediator mass, the type of
interaction, and the experimental energy window. For example, for a 20 GeV
WIMP, Generation 2 is only likely to identify the right operator if the
interaction is Coulomb-like, and is unlikely to do so in any other case. For a
WIMP with a mass of 200 GeV or higher, success is almost guaranteed. We also
find that, regardless of the scattering model and the WIMP parameters, a single
Generation 2 experiment is unlikely to successfully discern the momentum
dependence of the underlying operator on its own, but prospects improve
drastically when experiments with different target materials and energy windows
are analyzed jointly. Furthermore, we examine the quality of parameter
estimation and degeneracies in the multi-dimensional parameter space of the
effective theory. We find in particular that the resulting WIMP mass estimates
can be severely biased if data are analyzed assuming the standard
(momentum-independent) operator while the actual operator has
momentum-dependence. Finally, we evaluate the ultimate reach of direct
detection, finding that the prospects for successful operator selection prior
to reaching the irreducible backgrounds are excellent, if the signal is just
below the current limits, but slim if Generation 2 does not report WIMP
detection.