  In this paper we discuss how to extract information about physics beyond the
Standard Model (SM) from searches for a light SM Higgs at Tevatron Run II and
CERN LHC. We demonstrate that new (pseudo)scalar states predicted in both
supersymmetric and dynamical models can have enhanced visibility in standard
Higgs search channels, making them potentially discoverable at Tevatron Run II
and CERN LHC. We discuss the likely sizes of the enhancements in the various
search channels for each model and identify the model features having the
largest influence on the degree of enhancement. We compare the key signals for
the non-standard scalars across models and also with expectations in the SM, to
show how one could start to identify which state has actually been found. In
particular, we suggest the likely mass reach of the Higgs search in p pbar/pp
to H to tau^+tau^- for each kind of non-standard scalar state and we
demonstrate that p pbar/pp to H to gamma gamma may cleanly distinguish the
scalars of supersymmetric models from those of dynamical models.


  The Cryogenic Apparatus for Precision Tests of Argon Interactions with
Neutrinos (CAPTAIN) program is designed to make measurements of scientific
importance to long-baseline neutrino physics and physics topics that will be
explored by large underground detectors. The experiment employs two liquid
Argon time projection chambers (LArTPCs), a primary detector with a mass of
approximately 10 ton that will be deployed at different facilities for physics
measurements and a two ton prototype detector for configuration testing. The
physics programs for CAPTAIN include measuring neutron interactions at the Los
Alamos Neutron Science Center, measuring neutrino interactions in the
high-energy regime (1.5-5 GeV) at Fermilab NuMI beam, and measuring neutrino
interactions in the low-energy regime (<50 MeV) at stopped pion sources for
supernova neutrino studies. The prototype detector (Mini-CAPTAIN) has been
commissioned and the first UV laser track has been seen in its TPC. This paper
gives an overview of the CAPTAIN program and reports the status of the
commissioning. The up-to-date detector design and running plans are also
described.


  The Belle II physics potential for hadron spectroscopy, inputs to g-2,
fragmentation functions, proton form factors, measurements of $\alpha_s$ and
hadronic $\tau$ decays is outlined.


  Within the framework of the kinetic energy driven d-wave superconductivity,
the electronic structure of the electron doped cuprate superconductors is
studied. It is shown that although there is an electron-hole asymmetry in the
phase diagram, the electronic structure of the electron-doped cuprates in the
superconducting-state is similar to that in the hole-doped case. With
increasing the electron doping, the spectral weight in the $(\pi,0)$ point
increases, while the position of the superconducting quasiparticle peak is
shifted towards the Fermi energy. In analogy to the hole-doped case, the
superconducting quasiparticles around the $(\pi,0)$ point disperse very weakly
with momentum.


  We report the discovery of PSR J1847-0130, a radio pulsar with a 6.7-s spin
period, in the Parkes multibeam survey of the Galactic plane. The slowdown rate
for the pulsar, 1.3x10^{-12} s/s, is high and implies a surface dipole magnetic
field strength of 9.4x10^{13} G. This inferred dipolar magnetic field strength
is the highest by far among all known radio pulsars and over twice the
``quantum critical field'' above which some models predict radio emission
should not occur. The inferred dipolar magnetic field strength and period of
this pulsar are in the same range as those of the anomalous X-ray pulsars,
which have been identified as being "magnetars" whose luminous X-ray emission
is powered by their large magnetic fields. We have examined archival ASCA data
and place an upper limit on the X-ray luminosity of J1847-0130 which is lower
than the luminosities of all but one AXP. The properties of this pulsar prove
that inferred dipolar magnetic field strength and period cannot alone be
responsible for the unusual high-energy properties of the magnetars and creates
new challenges for understanding the possible relationship between these two
manifestations of young neutron stars.


  Artificial Intelligence (AI) is making a profound impact in almost every
domain. A vital enabler of its great success is the availability of abundant
and high-quality data for building machine learning models. Recently, the role
of data in AI has been significantly magnified, giving rise to the emerging
concept of data-centric AI. The attention of researchers and practitioners has
gradually shifted from advancing model design to enhancing the quality and
quantity of the data. In this survey, we discuss the necessity of data-centric
AI, followed by a holistic view of three general data-centric goals (training
data development, inference data development, and data maintenance) and the
representative methods. We also organize the existing literature from
automation and collaboration perspectives, discuss the challenges, and tabulate
the benchmarks for various tasks. We believe this is the first comprehensive
survey that provides a global view of a spectrum of tasks across various stages
of the data lifecycle. We hope it can help the readers efficiently grasp a
broad picture of this field, and equip them with the techniques and further
research ideas to systematically engineer data for building AI systems. A
companion list of data-centric AI resources will be regularly updated on
https://github.com/daochenzha/data-centric-AI


  In this paper, one can explicitly see the process of implementing the robust
residual generator on digital domain, especially on FPGA. Firstly, the baseline
model is developed in double precision floating point format. To develop the
baseline model, key parameters such as SNR and detection window length are
selected in the identification stage. (Please refer to the uploaded paper
because this box doesn't accept more typing beyond this point)


  Some considerations showing that renormalizable theories with consistent
perturbative theries can not be nonperturbatively finite (in terms of bare
parameters) are provided. Accordingly any fundamental unified theory has to be
either non renormalizable or order by order finite.


  In 1983, Banchoff and Kuhnel constructed a minimal triangulation of $\CP^2$
with 9 vertices. $\CP^3$ was first triangulated by Bagchi and Datta in 2012
with 18 vertices. Known lower bound on number of vertices of a triangulation of
$\CP^n$ is $1 + \frac{(n + 1)^2}{2}$ for $n \geq 3$. We give explicit
construction of some triangulations of complex projective space $\CP^n$ with
$\frac{4^{n+1}-1}{3}$ vertices for all $n$. No explicit triangulation of
$\CP^n$ is known for $n \geq 4$.


  A constant age population of blue galaxies, postulated in the model of
Gronwall & Koo (1995), seems to provide an attractive explanation of the excess
of very blue galaxies in the deep galaxy counts. Such a population may be
generated by a set of galaxies with cycling star formation rates, or at the
other extreme, be maintained by the continual formation of new galaxies which
fade after they reach the age specified in the Gronwall and Koo model. For both
of these hypotheses, we have calculated the luminosity functions including the
respective selection criteria, the redshift distributions, and the number
counts in the B_J and K bands. We find a substantial excess in the number of
galaxies at low redshift (0 < z < 0.05) over that observed in the CFH redshift
survey (Lilly et al. 1995) and at the faint end of the Las Campanas luminosity
function (Lin et al. 1996). Passive or mild evolution fails to account for the
deep galaxy counts because of the implications for low redshift determinations
of the I-selected redshift distribution and the r-selected luminosity function
in samples where the faded counterparts of the star-forming galaxies would be
detectable.


  Purpose: Neurovascular MRI suffers from a rapid drop in B1+ into the neck
when using transmit head coils at 7T. One solution to improving B1+ magnitude
in the major feeding arteries in the neck is to use custom RF shims on parallel
transmit (pTx) head coils. However, calculating such shims requires robust
multi-channel B1+ maps in both the head and the neck, which is challenging due
to low RF penetration into the neck, limited dynamic range of multi-channel B1+
mapping techniques, and B0 sensitivity. We therefore sought a robust
large-dynamic-range pTx field mapping protocol, and tested whether RF shimming
can improve carotid artery B1+ in practice.
  Methods: A pipeline is presented that combines B1+ mapping data acquired
using circularly polarized (CP-) and CP2-mode RF shims at multiple voltages.
The pipeline was evaluated by comparing the predicted and measured B1+ for
multiple random transmit shims, and by assessing the ability of RF shimming to
increase the B1+ in the carotid arteries.
  Results: The proposed method achieved good agreement between predicted and
measured B1+ in both the head and the neck. The B1+ magnitude in the carotid
arteries can be increased by 42% using tailored RF shims or by 37% using
universal RF shims, while also improving the RF homogeneity compared to CP
mode.
  Conclusion: B1+ in the neck can be increased using RF shims calculated from
multi-channel B1+ maps in both the head and the neck. This can be achieved
using universal phase-only RF shims, facilitating easy implementation in
existing sequences.


  The Double Pulsar system PSR J0737-3039A/B has proven to be an excellent
laboratory for high precision tests of general relativity. With additional
years of timing measurements and new telescopes like the Square Kilometre Array
(SKA), the precision of these tests will increase and new effects like the
Lense-Thirring precession of the orbit will become measurable. Here, we discuss
the prospects of measuring the Lense-Thirring effect and thereby constraining
the equations of state at supra-nuclear densities in neutron stars using the
Double Pulsar.


  How can the semantic interpretation of a formal symbol system be made
intrinsic to the system, rather than just parasitic on the meanings in our
heads? How can the meanings of the meaningless symbol tokens, manipulated
solely on the basis of their (arbitrary) shapes, be grounded in anything but
other meaningless symbols? The problem is analogous to trying to learn Chinese
from a Chinese/Chinese dictionary alone. A candidate solution is sketched:
Symbolic representations must be grounded bottom-up in nonsymbolic
representations of two kinds: (1) "iconic representations," which are analogs
of the proximal sensory projections of distal objects and events, and (2)
"categorical representations," which are learned and innate feature-detectors
that pick out the invariant features of object and event categories from their
sensory projections. Elementary symbols are the names of these object and event
categories, assigned on the basis of their (nonsymbolic) categorical
representations. Higher-order (3) "symbolic representations," grounded in these
elementary symbols, consist of symbol strings describing category membership
relations (e.g., "An X is a Y that is Z").


  A well-known problem in Malliavin calculus concerns the relation between the
determinant of the Malliavin matrix of a random vector and the determinant of
its covariance matrix. We give an explicit relation between these two
determinants for couples of random vectors of multiple integrals. In
particular, if the multiple integrals are of the same order and this order is
at most 4, we prove that two random variables in the same Wiener chaos either
admit a joint density, either are proportional and that the result is not true
for random variables in Wiener chaoses of different orders.


  With the advent of faster internet services and growth of multimedia content,
we observe a massive growth in the number of online videos. The users generate
these video contents at an unprecedented rate, owing to the use of smart-phones
and other hand-held video capturing devices. This creates immense potential for
the advertising and marketing agencies to create personalized content for the
users. In this paper, we attempt to assist the video editors to generate
augmented video content, by proposing candidate spaces in video frames. We
propose and release a large-scale dataset of outdoor scenes, along with
manually annotated maps for candidate spaces. We also benchmark several
deep-learning based semantic segmentation algorithms on this proposed dataset.


  Augmented Reality (AR) and Mobile Augmented Reality (MAR) applications have
gained much research and industry attention these days. The mobile nature of
MAR applications limits users' interaction capabilities such as inputs, and
haptic feedbacks. This survey reviews current research issues in the area of
human computer interaction for MAR and haptic devices. The survey first
presents human sensing capabilities and their applicability in AR applications.
We classify haptic devices into two groups according to the triggered sense:
cutaneous/tactile: touch, active surfaces, and mid-air, kinesthetic:
manipulandum, grasp, and exoskeleton. Due to the mobile capabilities of MAR
applications, we mainly focus our study on wearable haptic devices for each
category and their AR possibilities. To conclude, we discuss the future paths
that haptic feedbacks should follow for MAR applications and their challenges.


  We extend the operator preconditioning framework [R. Hiptmair, Comput. Math.
with Appl. 52 (2006), pp.~699--706] to Petrov-Galerkin methods while accounting
for parameter-dependent perturbations of both variational forms and their
preconditioners, as occurs when performing numerical approximations. By
considering different perturbation parameters for the original form and its
preconditioner, our bi-parametric abstract setting leads to robust and
controlled schemes. For Hilbert spaces, we derive exhaustive linear and
super-linear convergence estimates for iterative solvers, such as
$h$-independent convergence bounds, when preconditioning with low-accuracy or,
equivalently, with highly compressed approximations.


  Multilayer networks are a useful way to capture and model multiple, binary or
weighted relationships among a fixed group of objects. While community
detection has proven to be a useful exploratory technique for the analysis of
single-layer networks, the development of community detection methods for
multilayer networks is still in its infancy. We propose and investigate a
procedure, called Multilayer Extraction, that identifies densely connected
vertex-layer sets in multilayer networks. Multilayer Extraction makes use of a
significance based score that quantifies the connectivity of an observed
vertex-layer set through comparison with a fixed degree random graph model.
Multilayer Extraction directly handles networks with heterogeneous layers where
community structure may be different from layer to layer. The procedure can
capture overlapping communities, as well as background vertex-layer pairs that
do not belong to any community. We establish consistency of the vertex-layer
set optimizer of our proposed multilayer score under the multilayer stochastic
block model. We investigate the performance of Multilayer Extraction on three
applications and a test bed of simulations. Our theoretical and numerical
evaluations suggest that Multilayer Extraction is an effective exploratory tool
for analyzing complex multilayer networks. Publicly available R software for
Multilayer Extraction is available at
https://github.com/jdwilson4/MultilayerExtraction.


  The optimal learning rate for adaptive gradient methods applied to
{\lambda}-strongly convex functions relies on the parameters {\lambda} and
learning rate {\eta}. In this paper, we adapt a universal algorithm along the
lines of Metagrad, to get rid of this dependence on {\lambda} and {\eta}. The
main idea is to concurrently run multiple experts and combine their predictions
to a master algorithm. This master enjoys O(d log T) regret bounds.


  We propose a novel distributed monetary system called Hearsay that tolerates
both Byzantine and rational behavior without the need for agents to reach
consensus on executed transactions. Recent work [5, 10, 15] has shown that
distributed monetary systems do not require consensus and can operate using a
broadcast primitive with weaker guarantees, such as reliable broadcast.
However, these protocols assume that some number of agents may be Byzantine and
the remaining agents are perfectly correct. For the application of a monetary
system in which the agents are real people with economic interests, the
assumption that agents are perfectly correct may be too strong. We expand upon
this line of thought by weakening the assumption of correctness and instead
adopting a fault tolerance model which allows up to $t < \frac{N}{3}$ agents to
be Byzantine and the remaining agents to be rational. A rational agent is one
which will deviate from the protocol if it is in their own best interest. Under
this fault tolerance model, Hearsay implements a monetary system in which all
rational agents achieve agreement on executed transactions. Moreover, Hearsay
requires only a single broadcast per transaction. In order to incentivize
rational agents to behave correctly in Hearsay, agents are rewarded with
transaction fees for participation in the protocol and punished for noticeable
deviations from the protocol. Additionally, Hearsay uses a novel broadcast
primitive called Rational Reliable Broadcast to ensure that agents can
broadcast messages under Hearsay's fault tolerance model. Rational Reliable
Broadcast achieves equivalent guarantees to Byzantine Reliable Broadcast [7]
but can tolerate the presence of rational agents. To show this, we prove that
following the Rational Reliable Broadcast protocol constitutes a Nash
equilibrium between rational agents and may therefore be of independent
interest.


  We study the dynamic power optimization problem in data centers. We formulate
and solve the following offline problem: in which slot which server has to be
assigned to which job; and in which slot which server has to be switched ON or
OFF so that the total power is optimal for some time horizon. We show that the
offline problem is a new version of generalized assignment problem including
new constraints issuing from deadline characteristics of jobs and difference of
activation energy of servers. We propose an online algorithm that solves the
problem heuristically and compare it to randomized routing.


  This is an evolving document describing the meta-theory, the implementation,
and the instantiations of Gillian, a multi-language symbolic analysis platform.


  We show that charge inversion, i.e. interfacial charges attracting
counterions in excess of their own nominal charge, is a general effect that
takes place in most charged systems next to aqueous solutions with multivalent
ions and identify three different electrostatic origins for this effect 1)
counterion-counterion correlations, 2) correlations between counterions and
interfacial charges and 3) complexation. We briefly describe the first two
regimes and provide a detailed characterization of the complexation regime from
united atom molecular dynamics simulation of a phospholipid domain in contact
with an aqueous solution. We examine the expected conditions where each regime
should apply and describe a representative experimental example to illustrate
each case. We point out that our results provide a characterization of ionic
distributions irrespectively of whether charge inversion takes place and show
that processes such as proton release and transfer are also linked to ionic
correlations. We conclude with a discussion of further experimental and
theoretical implications.


  We consider a fully distributed constrained convex optimization problem over
a multi-agent (no central coordinator) network. We propose an asynchronous
gossip-based random projection (GRP) algorithm that solves the distributed
problem using only local communications and computations. We analyze the
convergence properties of the algorithm for an uncoordinated diminishing
stepsize and a constant stepsize. For a diminishing stepsize, we prove that the
iterates of all agents converge to the same optimal point with probability 1.
For a constant stepsize, we establish an error bound on the expected distance
from the iterates of the algorithm to the optimal point. We also provide
simulation results on a distributed robust model predictive control problem.


  Recent years have seen the concept of global symmetry extended to
non-invertible (or categorical) symmetries, for which composition of symmetry
generators is not necessarily invertible. Such non-invertible symmetries lead
to a generalization of the standard Landau paradigm. In this work we
substantiate this framework by providing a (1+1)d lattice model, whose gapped
phases and phase transitions can only be explained by symmetry breaking of
non-invertible symmetries.


  Stars in globular clusters are generally believed to have all formed at the
same time, early in the Galaxy's history. 'Blue stragglers' are stars massive
enough that they should have evolved into white dwarfs long ago. Two possible
mechanisms have been proposed for their formation: mass transfer between binary
companions and stellar mergers resulting from direct collisions between two
stars. Recently, the binary explanation was claimed to be dominant. Here we
report that there are two distinct parallel sequences of blue stragglers in
M30. This globular cluster is thought to have undergone 'core collapse', during
which both the collision rate and the mass transfer activity in binary systems
would have been enhanced. We suggest that the two observed sequences arise from
the cluster core collapse, with the bluer population arising from direct
stellar collisions and the redder one arising from the evolution of close
binaries that are probably still experiencing an active phase of mass transfer.


  This paper addresses the problem of averaging numbers across a wireless
network from an important, but largely neglected, viewpoint: bandwidth/energy
efficiency. We show that existing distributed averaging schemes have several
drawbacks and are inefficient, producing networked dynamical systems that
evolve with wasteful communications. Motivated by this, we develop Controlled
Hopwise Averaging (CHA), a distributed asynchronous algorithm that attempts to
"make the most" out of each iteration by fully exploiting the broadcast nature
of wireless medium and enabling control of when to initiate an iteration. We
show that CHA admits a common quadratic Lyapunov function for analysis, derive
bounds on its exponential convergence rate, and show that they outperform the
convergence rate of Pairwise Averaging for some common graphs. We also
introduce a new way to apply Lyapunov stability theory, using the Lyapunov
function to perform greedy, decentralized, feedback iteration control. Finally,
through extensive simulation on random geometric graphs, we show that CHA is
substantially more efficient than several existing schemes, requiring far fewer
transmissions to complete an averaging task.


  In the Landau problem on the two-dimensional plane, magnetic translation of a
quantum wave can be induced by an in-plane electric field. The geometric phase
accompanying such magnetic translation around a closed path differs from the
topological phase of Aharonov and Bohm in two essential aspects: The wave is in
direct contact with the magnetic flux and the geometric phase has an opposite
sign from the Aharonov-Bohm phase. We show that magnetic translation on the
two-dimensional cylinder implemented by the Schr\"odinger time evolution truly
leads to the Aharonov-Bohm effect. The magnetic field normal to the cylinder's
surface is given by a line of magnetic monopoles which can be simulated in cold
atom experiments. We propose an extension of the Aharonov-Bohm experiment which
can demonstrate the mutually counteracting effect between the local magnetic
translation geometric phase and the topological phase of Aharonov and Bohm.


  Sparse Blind Source Separation (BSS) has become a well established tool for a
wide range of applications - for instance, in astrophysics and remote sensing.
Classical sparse BSS methods, such as the Proximal Alternating Linearized
Minimization (PALM) algorithm, nevertheless often suffer from a difficult
hyperparameter choice, which undermines their results. To bypass this pitfall,
we propose in this work to build on the thriving field of algorithm
unfolding/unrolling. Unrolling PALM enables to leverage the data-driven
knowledge stemming from realistic simulations or ground-truth data by learning
both PALM hyperparameters and variables. In contrast to most existing unrolled
algorithms, which assume a fixed known dictionary during the training and
testing phases, this article further emphasizes on the ability to deal with
variable mixing matrices (a.k.a. dictionaries). The proposed Learned PALM
(LPALM) algorithm thus enables to perform semi-blind source separation, which
is key to increase the generalization of the learnt model in real-world
applications. We illustrate the relevance of LPALM in astrophysical
multispectral imaging: the algorithm not only needs up to $10^4-10^5$ times
fewer iterations than PALM, but also improves the separation quality, while
avoiding the cumbersome hyperparameter and initialization choice of PALM. We
further show that LPALM outperforms other unrolled source separation methods in
the semi-blind setting.


  Random backpropagation (RBP) is a variant of the backpropagation algorithm
for training neural networks, where the transpose of the forward matrices are
replaced by fixed random matrices in the calculation of the weight updates. It
is remarkable both because of its effectiveness, in spite of using random
matrices to communicate error information, and because it completely removes
the taxing requirement of maintaining symmetric weights in a physical neural
system. To better understand random backpropagation, we first connect it to the
notions of local learning and learning channels. Through this connection, we
derive several alternatives to RBP, including skipped RBP (SRPB), adaptive RBP
(ARBP), sparse RBP, and their combinations (e.g. ASRBP) and analyze their
computational complexity. We then study their behavior through simulations
using the MNIST and CIFAR-10 bechnmark datasets. These simulations show that
most of these variants work robustly, almost as well as backpropagation, and
that multiplication by the derivatives of the activation functions is
important. As a follow-up, we study also the low-end of the number of bits
required to communicate error information over the learning channel. We then
provide partial intuitive explanations for some of the remarkable properties of
RBP and its variations. Finally, we prove several mathematical results,
including the convergence to fixed points of linear chains of arbitrary length,
the convergence to fixed points of linear autoencoders with decorrelated data,
the long-term existence of solutions for linear systems with a single hidden
layer and convergence in special cases, and the convergence to fixed points of
non-linear chains, when the derivative of the activation functions is included.


  Under the assumption of finite energy, positive solutions to the critical
p-Laplace equation in $\mathbb{R}^n$ for $1< p<n$ have been classified
  completely by moving plane method. In this paper, the author provide a new
approach to obtain the same classification results for $\frac{n+1}{3}\leq p<n$,
  without any further assumptions.


  Remote sensing data has been widely used for various Earth Observation (EO)
missions such as land use and cover classification, weather forecasting,
agricultural management, and environmental monitoring. Most existing remote
sensing data-based models are based on supervised learning that requires large
and representative human-labelled data for model training, which is costly and
time-consuming. Recently, self-supervised learning (SSL) enables the models to
learn a representation from orders of magnitude more unlabelled data. This
representation has been proven to boost the performance of downstream tasks and
has potential for remote sensing applications. The success of SSL is heavily
dependent on a pre-designed pretext task, which introduces an inductive bias
into the model from a large amount of unlabelled data. Since remote sensing
imagery has rich spectral information beyond the standard RGB colour space, the
pretext tasks established in computer vision based on RGB images may not be
straightforward to be extended to the multi/hyperspectral domain. To address
this challenge, this work has designed a novel SSL framework that is capable of
learning representation from both spectra-spatial information of unlabelled
data. The framework contains two novel pretext tasks for object-based and
pixel-based remote sensing data analysis methods, respectively. Through two
typical downstream tasks evaluation (a multi-label land cover classification
task on Sentienl-2 multispectral datasets and a ground soil parameter retrieval
task on hyperspectral datasets), the results demonstrate that the
representation obtained through the proposed SSL achieved a significant
improvement in model performance.


  Resistance thermometry is a time-tested method for taking temperature
measurements. In recent years fundamental limits to resistance-based approaches
spurred considerable interest in developing photonic temperature sensors as a
viable alternative. In this study we demonstrate that our photonic thermometer,
which consists of a silicon waveguide integrated with a Bragg grating, can be
used to measure temperature changes over the range from 5 C to 160 C with a
combined expanded uncertainty [k = 2 ; 95% confidence level] of 1.25 degree C.
The computational modeling of the sensor predicts the resonance wavelength and
effective refractive index within 4% of the measured value.


  Objects composed of lattice defects exist within a one-dimensional
tight-binding model whose electron reflection coefficient in the low-energy
case is equal to zero. Localized states are absent as well. The effective mass
concept explains this not as some kind of reflectionless potential but as
homogeneous medium, in which effective object size collapses. Without making
effective mass approximations a new type of resonance is observed, in which the
reflection coefficient becomes zero at a certain energy.


  Models of inflation with super-Planckian excursion seem well in agreement
with the recent observations of B- mode polarization in the cosmic microwave
background (CMB) radiation by the BICEP2 data. In this note, we highlight the
challenges faced by such models from ultraviolet (UV) completion. In
particular, we will discus radiative corrections to the inflaton Lagrangian and
to the gravitational sector. We will emphasize why we would require an UV
complete theory of gravity to tackle some of the issues for the super-Planckian
excursion. In particular, we will highlight how higher derivative terms in the
inflaton and gravity sectors cause problems from non-locality and ghosts, if
considered order by order, and thus prompt us to take into account infinite
series of such terms. We will also stress how the presence of a scale of new
physics below the Planck scale would make some of the UV related problems more
compelling and invalidate some of the remedies that have been proposed in the
literature. Finally, we will briefly speculate on possible ways of curing some
of the challenges.


  We investigate Markovian lifts of stochastic Volterra equations (SVEs) with
completely monotone kernels and general coefficients within a class of weighted
Sobolev spaces. Our primary focus is developing a comprehensive solution theory
for a class of non-local stochastic evolution equations (SEEs) encompassing
these Markovian lifts. This enables us to provide conditions for the existence
of invariant measures for the lifted processes and the corresponding SVE.
Another key contribution is an Ito-type formula for the stochastic Volterra
equations under consideration.


  The rising availability of large volume data, along with increasing computing
power, has enabled a wide application of statistical Machine Learning (ML)
algorithms in the domains of Cyber-Physical Systems (CPS), Internet of Things
(IoT) and Smart Building Networks (SBN). This paper proposes a learning-based
framework for sequentially applying the data-driven statistical methods to
predict indoor temperature and yields an algorithm for controlling building
heating system accordingly. This framework consists of a two-stage modelling
effort: in the first stage, an univariate time series model (AR) was employed
to predict ambient conditions; together with other control variables, they
served as the input features for a second stage modelling where an multivariate
ML model (XGBoost) was deployed. The models were trained with real world data
from building sensor network measurements, and used to predict future
temperature trajectories. Experimental results demonstrate the effectiveness of
the modelling approach and control algorithm, and reveal the promising
potential of the mixed data-driven approach in smart building applications. By
making wise use of IoT sensory data and ML algorithms, this work contributes to
efficient energy management and sustainability in smart buildings.


  The electronic structure and magnetism of Sr$_2$CeIrO$_6$, an Ir-based double
perovskite system has been investigated using first-principles calculations. We
found that a strong spin-orbit coupling dictate the electronic and magnetic
properties of this system. A small value of $U$ along with SOC could open up a
gap at the Fermi level, offering the possibility of novel J$_{eff}$ = 1/2 Mott
quantum state. Our calculations reveal that the magnetic ground state is
antiferromagnetic in agreement with the magnetization data and provide the
value of spin and orbital moment for this system which is in agreement with the
other isostructural Ir-based compound.


  Here we study the single-particle, electronic transport and optical
properties of a gapped system described by a simple two-band Hamiltonian with
inverted valence bands. We analyze its properties in the three-dimensional (3D)
and the two-dimensional (2D) case. The insulating phase changes into a metallic
phase when the band gap is set to zero. The metallic phase in the 3D case is
characterized by a nodal surface. This nodal surface is equivalent to a nodal
ring in two dimensions. Within a simple theoretical framework, we calculate the
density of state, the total and effective charge carrier concentration, the
Hall concentration and the Hall coefficient, for both 2D and 3D cases. The main
result is that the three concentrations always differ from one another in the
present model. These concentrations can then be used to resolve the nature of
the electronic ground state. Similarly, the optical conductivity is calculated
and discussed for the insulating phase. We show that there are no optical
excitations in the metallic phase. Finally, we compare the calculated optical
conductivity with the rule-of-thumb derivation using the joint density of
states.


  The problem of image restoration in cryo-EM entails correcting for the
effects of the Contrast Transfer Function (CTF) and noise. Popular methods for
image restoration include `phase flipping', which corrects only for the Fourier
phases but not amplitudes, and Wiener filtering, which requires the spectral
signal to noise ratio. We propose a new image restoration method which we call
`Covariance Wiener Filtering' (CWF). In CWF, the covariance matrix of the
projection images is used within the classical Wiener filtering framework for
solving the image restoration deconvolution problem. Our estimation procedure
for the covariance matrix is new and successfully corrects for the CTF. We
demonstrate the efficacy of CWF by applying it to restore both simulated and
experimental cryo-EM images. Results with experimental datasets demonstrate
that CWF provides a good way to evaluate the particle images and to see what
the dataset contains even without 2D classification and averaging.


  Slow waves and tunneling waves can meet at the cutoff wavelengths and/or
transmission band edges of optical and quantum mechanical waveguides. The
experimental investigation of this phenomenon, previously performed using
various optical microstructures, is challenged by fabrication imperfections and
material losses. Here, we demonstrate this phenomenon in situ for whispering
gallery modes slowly propagating along a standard optical fiber, which
possesses the record uniformity and exceptionally small transmission losses.
Slow axial propagation dramatically increases the longitudinal wavelength of
light and allows us to measure nanosecond-long tunneling times along tunable
potential barriers having the width of hundreds of microns. This demonstration
paves a simple and versatile way to investigate and employ the interplaying
slow and tunneling light.


  Identifying features that leak information about sensitive attributes is a
key challenge in the design of information obfuscation mechanisms. In this
paper, we propose a framework to identify information-leaking features via
information density estimation. Here, features whose information densities
exceed a pre-defined threshold are deemed information-leaking features. Once
these features are identified, we sequentially pass them through a targeted
obfuscation mechanism with a provable leakage guarantee in terms of
$\mathsf{E}_\gamma$-divergence. The core of this mechanism relies on a
data-driven estimate of the trimmed information density for which we propose a
novel estimator, named the trimmed information density estimator (TIDE). We
then use TIDE to implement our mechanism on three real-world datasets. Our
approach can be used as a data-driven pipeline for designing obfuscation
mechanisms targeting specific features.


  We investigate finite temperature effects in O'Raifeartaigh models with
global symmetries which exhibit supersymmetry breaking at a meta-stable vacuum
accompanied by $U(1)_R$ breaking. The pseudo moduli field is stabilized at
one-loop order at zero temperature within some coupling parameter region. We
analyze the behavior of the parameter space according to non-zero temperatures
and find that the parameter region which allows $U(1)_R$ breaking is
considerably extended at sufficiently low temperature, even though it shrinks
down at high temperature as expected. We also discuss the thermal history of
the meta-stable supersymmetry breaking vacuum.


  Image Compression has become an absolute necessity in today's day and age.
With the advent of the Internet era, compressing files to share among other
users is quintessential. Several efforts have been made to reduce file sizes
while still maintain image quality in order to transmit files even on limited
bandwidth connections. This paper discusses the need for Discrete Cosine
Transform or DCT in the compression of images in Joint Photographic Experts
Group or JPEG file format. Via an intensive literature study, this paper first
introduces DCT and JPEG Compression. The section preceding it discusses how
JPEG compression is implemented by DCT. The last section concludes with further
real world applications of DCT in image processing.


  We present the first detailed chemical abundances for three giant stars which
are candidate members of the Canis Major overdensity, obtained by using
FLAMES-UVES at VLT. The stars, in the background of the open cluster NGC 2477,
have radial velocities compatible with a membership to this structure. However,
due to Galactic disc contamination, radial velocity by itself is unable to
firmly establish membership. The metallicities span the range -0.5 < [Fe/H] <
+0.1. Assuming that at least one of the three stars is indeed a member of CMa
implies that this structure has undergone a high level of chemical processing,
comparable to that of the Galactic disc. The most metal-rich star of the
sample, EIS 6631, displays several abundance ratios which are remarkably
different from those of Galactic stars: [alpha/Fe] ~-0.2, [Cu/Fe] ~+0.25,
[La/Fe]~+0.6, [Ce/Fe]~+0.8 and [Nd/Fe]~+0.6. These ratios make it likely that
this star was formed in an external galaxy.


  The fundamental relationship between the mesoscopic structure of neuronal
circuits and organismic functions they subserve is one of the major challenges
in contemporary neuroscience. Formation of structurally connected modules of
neurons enacts the conversion from single-cell firing to large-scale behaviour
of an organism, highlighting the importance of their accurate profiling in the
data. While connectomes are typically characterized by significant sparsity of
neuronal connections, recent advances in network theory and machine learning
have revealed fundamental limitations of traditionally used community detection
approaches in cases where the network is sparse. Here we studied the optimal
community structure in the structural connectome of C.elegans, for which we
exploited a non-conventional approach that is based on non-backtracking random
walks, virtually eliminating the sparsity issue. In full agreement with the
previous asymptotic results, we demonstrated that non-backtracking walks
resolve the ground truth annotation into clusters on stochastic block models
(SBM) with the size and density of the connectome better than the spectral
methods related to simple random walks. Based on the cluster detectability
threshold, we determined that the optimal number of modules in a recently
mapped connectome of C.elegans is 10, which precisely corresponds to the number
of isolated eigenvalues in the spectrum of the non-backtracking flow matrix.
Broadly, our work provides a robust network-based framework to reveal
mesoscopic structures in sparse connectomic datasets, paving way to further
investigation of connectome mechanisms for different functions.


  We explore the construction of nonsubjective prior distributions in Bayesian
statistics via a posterior predictive relative entropy regret criterion. We
carry out a minimax analysis based on a derived asymptotic predictive loss
function and show that this approach to prior construction has a number of
attractive features. The approach here differs from previous work that uses
either prior or posterior relative entropy regret in that we consider
predictive performance in relation to alternative nondegenerate prior
distributions. The theory is illustrated with an analysis of some specific
examples.


  Collaborative (federated) learning enables multiple parties to train a model
without sharing their private data, but through repeated sharing of the
parameters of their local models. Despite its advantages, this approach has
many known privacy and security weaknesses and performance overhead, in
addition to being limited only to models with homogeneous architectures. Shared
parameters leak a significant amount of information about the local (and
supposedly private) datasets. Besides, federated learning is severely
vulnerable to poisoning attacks, where some participants can adversarially
influence the aggregate parameters. Large models, with high dimensional
parameter vectors, are in particular highly susceptible to privacy and security
attacks: curse of dimensionality in federated learning. We argue that sharing
parameters is the most naive way of information exchange in collaborative
learning, as they open all the internal state of the model to inference
attacks, and maximize the model's malleability by stealthy poisoning attacks.
We propose Cronus, a robust collaborative machine learning framework. The
simple yet effective idea behind designing Cronus is to control, unify, and
significantly reduce the dimensions of the exchanged information between
parties, through robust knowledge transfer between their black-box local
models. We evaluate all existing federated learning algorithms against
poisoning attacks, and we show that Cronus is the only secure method, due to
its tight robustness guarantee. Treating local models as black-box, reduces the
information leakage through models, and enables us using existing
privacy-preserving algorithms that mitigate the risk of information leakage
through the model's output (predictions). Cronus also has a significantly lower
sample complexity, compared to federated learning, which does not bind its
security to the number of participants.


  We consider the early stages of cosmic hydrogen or helium reionization, when
ionizing sources were still rare. We show that Poisson fluctuations in the
galaxy distribution substantially affected the early bubble size distribution,
although galaxy clustering was also an essential factor even at the earliest
times. We find that even at high redshifts, a significant fraction of the
ionized volume resided in bubbles containing multiple sources, regardless of
the ionizing efficiency of sources or of the reionization redshift. In
particular, for helium reionization by quasars, one-source bubbles last
dominated (i.e., contained 90% of the ionized volume) at some redshift above
z=7.3, and hydrogen reionization by stars achieved this milestone at z>23. For
the early generations of atomic-cooling halos or molecular-hydrogen-cooling
halos, one-source ionized regions dominated the ionized volume only at z>31 and
z>48, respectively. To arrive at these results we develop a statistical model
for the effect of density correlations and discrete sources on reionization and
solve it with a Monte Carlo method.


  Stochastic gradient descent (SGD) has taken the stage as the primary
workhorse for large-scale machine learning. It is often used with its adaptive
variants such as AdaGrad, Adam, and AMSGrad. This paper proposes an adaptive
stochastic gradient descent method for distributed machine learning, which can
be viewed as the communication-adaptive counterpart of the celebrated Adam
method - justifying its name CADA. The key components of CADA are a set of new
rules tailored for adaptive stochastic gradients that can be implemented to
save communication upload. The new algorithms adaptively reuse the stale Adam
gradients, thus saving communication, and still have convergence rates
comparable to original Adam. In numerical experiments, CADA achieves impressive
empirical performance in terms of total communication round reduction.


  We propose Text2Motion, a language-based planning framework enabling robots
to solve sequential manipulation tasks that require long-horizon reasoning.
Given a natural language instruction, our framework constructs both a task- and
motion-level plan that is verified to reach inferred symbolic goals.
Text2Motion uses feasibility heuristics encoded in Q-functions of a library of
skills to guide task planning with Large Language Models. Whereas previous
language-based planners only consider the feasibility of individual skills,
Text2Motion actively resolves geometric dependencies spanning skill sequences
by performing geometric feasibility planning during its search. We evaluate our
method on a suite of problems that require long-horizon reasoning,
interpretation of abstract goals, and handling of partial affordance
perception. Our experiments show that Text2Motion can solve these challenging
problems with a success rate of 82%, while prior state-of-the-art
language-based planning methods only achieve 13%. Text2Motion thus provides
promising generalization characteristics to semantically diverse sequential
manipulation tasks with geometric dependencies between skills.


  We study motion in the field of two fixed centres described by a family of
Einstein-dilaton-Maxwell theories. Transitions between regular and chaotic
motion are observed as the dilaton coupling is varied.


  A solution to the long-standing problem of identifying the conformal field
theory governing the transition between quantized Hall plateaus of a disordered
noninteracting 2d electron gas, is proposed. The theory is a nonlinear sigma
model with a Wess-Zumino-Novikov-Witten term, and fields taking values in a
Riemannian symmetric superspace based on H^3 x S^3. Essentially the same
conformal field theory appeared in very recent work on string propagation in
AdS_3 backgrounds. We explain how the proposed theory manages to obey a number
of tight constraints, two of which are constancy of the partition function and
noncriticality of the local density of states. An unexpected feature is the
existence of a truly marginal deformation, restricting the extent to which
universality can hold in critical quantum Hall systems. The marginal coupling
is fixed by matching the short-distance singularity of the conductance between
two interior contacts to the classical conductivity sigma_xx = 1/2 of the
Chalker-Coddington network model. For this value, perturbation theory predicts
a critical exponent 2/pi for the typical point-contact conductance, in
agreement with numerical simulations. The irrational exponent is tolerated by
the fact that the symmetry algebra of the field theory is Virasoro but not
affine Lie algebraic.


  This is a (very subjective) survey paper for nonspecialists covering group
actions on Gromov hyperbolic spaces. The first section is about hyperbolic
groups themselves, while the rest of the paper focuses on mapping class groups
and $Out(F_n)$, and the way to understand their large scale geometry using
their actions on various hyperbolic spaces constructed using projection
complexes. This understanding for $Out(F_n)$ significantly lags behind that of
mapping class groups and the paper ends with a few open questions.


  Energy storage devices represent environmentally friendly candidates to cope
with volatile renewable energy generation. Motivated by the increase in
privately owned storage systems, this paper studies the problem of real-time
control of a storage unit co-located with a renewable energy generator and an
inelastic load. Unlike many approaches in the literature, no distributional
assumptions are being made on the renewable energy generation or the real-time
prices. Building on the deep Q-networks algorithm, a reinforcement learning
approach utilizing a neural network is devised where the storage unit
operational constraints are respected. The neural network approximates the
action-value function which dictates what action (charging, discharging, etc.)
to take. Simulations indicate that near-optimal performance can be attained
with the proposed learning-based control policy for the storage units.


  ChPT and the $1/N_c$ expansion provide systematic frameworks for the strong
interactions at low energy. A combined framework of both expansions has been
developed and applied for baryons with three light-quark-flavors. The small
scale expansion of the combined approach is identified as the $\xi$-expansion,
in which the power counting of the expansions is linked according to
$O(p)=O(1/N_c)=O(\xi)$. The physical baryon masses as well as lattice QCD
baryon masses for different quark mass masses are analyzed to $O(\xi^3)$ in
that framework. $\sigma$ terms are addressed using the Feynman Hellmann
theorem. For the nucleon, a useful connection between the deviation of the
Gell-Mann-Okubo relation and the $\sigma$ term $\sigma_{8N}$ associated with
the scalar density $\bar u u+\bar d d-2\bar s s$ is identified. In particular,
the deviation from the tree level relation $\sigma_{8N}=\frac 13(2
m_N-m_\Sigma-m_\Xi)$, which gives rise to the so called $\sigma$-term puzzle,
is studied in the $\xi$-expansion. A large correction non-analytic in $\xi$
results for that relation, making plausible the resolution of the puzzle.
Issues with the determination of the strangeness $\sigma$ terms are discussed,
emphasizing the need for lattice calculations at smaller $m_s$ for better
understanding the range of validity of the effective theory. The analysis
presented here leads to $\sigma_{\pi N}=69(10)$~MeV and $\sigma_{\pi
\Delta}=60(10)$~MeV.


  In this paper, we analyze a possible formalization of the deformed special
relativity as a five-dimensional theory. This is not the first attempt to do
so, but we feel that either these previous treatments are too arbitrary in the
choice of the new enlarged space, or they lack a satisfactory physical
interpretation. In this work, we propose an algorithm which fixes the shape of
the enlarged space. Afterwards, we focus our attention on the consequences of
our formalism, proposing a physical interpretation.


  Realistic simulations of detailed, biophysics-based, multi-scale models
require very high resolution and, thus, large-scale compute facilities.
Existing simulation environments, especially for biomedical applications, are
designed to allow for a high flexibility and generality in model development.
Flexibility and model development, however, are often a limiting factor for
large-scale simulations. Therefore, new models are typically tested and run on
small-scale compute facilities. By using a detailed biophysics-based,
chemo-electromechanical skeletal muscle model and the international open-source
software library OpenCMISS as an example, we present an approach to upgrade an
existing muscle simulation framework from a moderately parallel version towards
a massively parallel one that scales both in terms of problem size and in terms
of the number of parallel processes. For this purpose, we investigate different
modeling, algorithmic and implementational aspects. We present improvements
addressing both numerical and parallel scalability. In addition, our approach
includes a novel visualization environment, which is based on the MegaMol
environment capable of handling large amounts of simulated data. It offers a
platform for fast visualization prototyping, distributed rendering, and
advanced visualization techniques. We present results of a variety of scaling
studies at the Tier-1 supercomputer HazelHen at the High Performance Computing
Center Stuttgart (HLRS). We improve the overall runtime by a factor of up to
2.6 and achieved good scalability on up to 768 cores, where the previous
implementation used only 4 cores.


  In 1981, Erd\H{o}s and Simonovits conjectured that for any bipartite graph
$H$ we have $\mathrm{ex}(n,H)=O(n^{3/2})$ if and only if $H$ is $2$-degenerate.
Later, Erd\H{o}s offered 250 dollars for a proof and 500 dollars for a
counterexample. In this paper, we disprove the conjecture by finding, for any
$\varepsilon>0$, a $3$-regular bipartite graph $H$ with
$\mathrm{ex}(n,H)=O(n^{4/3+\varepsilon})$.


  Let $\Omega$ be a bounded domain with $C^\infty$ boundary in an
$n$-dimensional $C^\infty$ Riemannian manifold, and let $\varrho$ be a
non-negative bounded function defined on $\partial \Omega$. It is well-known
that for the biharmonic equation $\Delta^2 u=0$ in $\Omega$ with the
0-Dirichlet boundary condition, there exists an infinite set $\{u_k\}$ of
biharmonic functions in $\Omega$ with positive eigenvalues $\{\lambda_k\}$
satisfying $\Delta u_k+ \lambda_k \varrho \frac{\partial u_k}{\partial \nu}=0$
on the boundary $\partial \Omega$. In this paper, we give the Weyl-type
asymptotic formula with a sharp remainder estimate for the counting function of
the biharmonic Steklov eigenvalues $\lambda_k$.


  We report a metal-insulator like transition in single crystalline 3D
topological insulator Bi2Te3 at a temperature of 230K in presence of an
external magnetic field applied normal to the surface. This transition becomes
more prominent at larger magnetic field strength with the residual resistance
value increasing linearly with the magnetic field. At low temperature, the
magnetic field dependence of the magnetoresistance shows a transition from
logarithmic to linear behavior and the onset magnetic field value for this
transition decreases with increasing temperature. The logarithmic
magnetoresistance indicates the weak anti-localization of the surface Dirac
electrons while the high temperature behavior originates from the bulk carriers
due to intrinsic impurities. At even higher temperatures beyond~230 K, a
completely classical Lorentz model type quadratic behavior of the
magnetoresistance is observed. We also show that the experimentally observed
anomalies at ~230K in the magneto-transport properties do not originate from
any stacking fault in Bi2Te3.


  Element diffusion is a key physical process that substantially impacts the
superficial abundances, internal structure, pulsation properties, and evolution
of white dwarfs. We study the effect of Coulomb separation of ions in the
cooling times of evolving white dwarfs, their chemical profiles, the
Brunt-V\"ais\"al\"a (buoyancy) frequency, and the pulsational periods at the ZZ
Ceti instability strip. We follow the full evolution of white-dwarf models
derived from their progenitor history on the basis of a time-dependent element
diffusion scheme that incorporates the effect of gravitational settling of ions
due to Coulomb interactions at high densities. We find that Coulomb
sedimentation profoundly alters the chemical profiles of ultra-massive ($M_*> 1
M_{sun}$) white dwarfs along their evolution, preventing helium from diffusing
inward toward the core, and thus leading to much narrower chemical transition
zones. As a result, significant changes in the $g$-mode pulsation periods as
high as $15 \%$ are expected for ultra-massive ZZ Ceti stars. This should be
taken into account in detailed asteroseismological analyses of such stars. For
less-massive white dwarfs, the impact of Coulomb separation is much less
noticeable, inflicting period changes in ZZ Ceti stars that are below the
period changes that result from uncertainties in progenitor evolution, albeit
larger than typical uncertainties of observed periods.


  We use a recently developed quench protection heater modeling tool for an
analysis of heater delays in superconducting high-field Nb3Sn accelerator
magnets. The results suggest that the calculated delays are consistent with
experimental data, and show how the heater delay depends on the main heater
design parameters.


  In this paper, we establish blow-up results for the semilinear wave equation
in generalized Einstein-de Sitter spacetime with nonlinearity of derivative
type. Our approach is based on the integral representation formula for the
solution to the corresponding linear problem in the one-dimensional case, that
we will determine through Yagdjian's Integral Transform approach. As upper
bound for the exponent of the nonlinear term, we discover a Glassey-type
exponent which depends both on the space dimension and on the Lorentzian metric
in the generalized Einstein-de Sitter spacetime.


  Modern machine learning focuses on highly expressive models that are able to
fit or interpolate the data completely, resulting in zero training loss. For
such models, we show that the stochastic gradients of common loss functions
satisfy a strong growth condition. Under this condition, we prove that constant
step-size stochastic gradient descent (SGD) with Nesterov acceleration matches
the convergence rate of the deterministic accelerated method for both convex
and strongly-convex functions. We also show that this condition implies that
SGD can find a first-order stationary point as efficiently as full gradient
descent in non-convex settings. Under interpolation, we further show that all
smooth loss functions with a finite-sum structure satisfy a weaker growth
condition. Given this weaker condition, we prove that SGD with a constant
step-size attains the deterministic convergence rate in both the
strongly-convex and convex settings. Under additional assumptions, the above
results enable us to prove an O(1/k^2) mistake bound for k iterations of a
stochastic perceptron algorithm using the squared-hinge loss. Finally, we
validate our theoretical findings with experiments on synthetic and real
datasets.


  We investigate decay modes of spin-1 heavy vector bosons ($V'$) from the
viewpoint of perturbative unitarity in a model-independent manner. Perturbative
unitarity requires some relations among couplings. The relations are called
unitarity sum rules. We derive the unitarity sum rules from processes that
contain two fermions and two gauge bosons. We find the relations between $V'$
couplings to the SM fermions $(f)$ and $V'$ couplings to the SM gauge bosons
($V$). Using the coupling relations, we calculate partial decay widths for $V'$
decays into $VV$ and $ff$. We show that Br($W' \to WZ) \lesssim$ 2$\%$ in the
system that contains $V'$ and CP-even scalars as well as the SM particles. This
result is independent of the number of the CP-even scalars. We also show that
contributions of CP-odd scalars help to make Br($W' \to WZ$) larger than Br($W'
\to ff$) as long as the CP-odd scalars couple to both the SM fermions and the
SM gauge bosons. The existence of the CP-odd scalar couplings is a useful
guideline to construct models that predict Br($W' \to WZ) \gtrsim$ 2$\%$. Our
analysis relies only on the perturbative unitarity of $f\bar{f} \to WW'$.
Therefore our result can be applied to various models.


  In this work, we show the effect of the non-minimal coupling $\xi \phi^2 R$
on the inflationary parameters by considering the single-field inflation and
present the inflationary predictions of the appealing potential for the
particle physics viewpoint: Natural Inflation, an axion-like inflaton which has
a cosine-type periodic potential and the inflaton naturally emerges as a
pseudo-Nambu-Goldstone boson with a spontaneously broken global symmetry. We
present the inflationary predictions for this potential, $n_s$, $r$, and
$\alpha=\mathrm{d}n_s/\mathrm{d}\ln k$. In addition, we assume standard thermal
history after inflation, and using this, for considered potential, we show
compatible regions for the $n_s$, $r$ within the recent BICEP/Keck results.


  Reading dirty license plates accurately in moving vehicles is challenging for
automatic license plate recognition systems. Moreover, license plates are often
intentionally tampered with a malicious intent to avoid police apprehension.
Usually, such groups and individuals know how to fool the existing recognition
systems by making minor unnoticeable plate changes. Designing and developing
deep learning methods resilient to such real-world 'attack' practices remains
an active research problem. As a solution, this work develops a resilient
method to recognize license plate characters. Extracting 1057 character images
from 160 Nepalese vehicles, as the first step, we trained several standard deep
convolutional neural networks to obtain 99.5% character classification
accuracy. On adversarial images generated to simulate malicious tampering,
however, our model's accuracy dropped to 25%. Next, we enriched our dataset by
generating and adding geometrically masked images, retrained our models, and
investigated the models' predictions. The proposed approach of training with
generated adversarial images helped our adversarial attack-aware license plate
character recognition (AA-LPCR) model achieves an accuracy of 99.7%. This
near-perfect accuracy demonstrates that the proposed idea of random geometric
masking is highly effective for improving the accuracy of license plate
recognition models. Furthermore, by performing interpretability studies to
understand why our models work, we identify and highlight attack-prone regions
in the input character images. In sum, although Nepal's embossed license plate
detection systems are vulnerable to malicious attacks, our findings suggest
that these systems can be upgraded to close to 100% resilience.


  The concern of this paper is the Cauchy problem for the Prandtl equation.
This problem is known to be well-posed for analytic data, or for data with
monotonicity properties. We prove here that it is linearly ill-posed in Sobolev
type spaces. The key of the analysis is the construction, at high tangential
frequencies, of unstable quasimodes for the linearization around solutions with
non-degenerate critical points. Interestingly, the strong instability is due to
vicosity, which is coherent with well-posedness results obtained for the
inviscid version of the equation. A numerical study of this instability is also
provided.


  This paper is concerned with the global wellposedness of the
Euler-Poisson-alignment (EPA) system. This system arises from collective
dynamics, and features two types of nonlocal interactions: the repulsive
electric force and the alignment force. It is known that the repulsive electric
force generates oscillatory solutions, which is difficult to be controlled by
the nonlocal alignment force using conventional comparison principles. We
construct \emph{invariant regions} such that the solution trajectories cannot
exit, and therefore obtain global wellposedness for subcritical initial data
that lie in the invariant regions. Supercritical regions of initial data are
also derived which leads to finite-time singularity formations. To handle the
oscillation and the nonlocality, we introduce a new way to construct invariant
regions piece by piece in the phase plane of a reformulation of the EPA system.
Our result is extended to the case when the alignment force is weakly singular.
The singularity leads to the loss of a priori bounds crucial in our analysis.
With the help of improved estimates on the nonlocal quantities, we design
non-trivial invariant regions that guarantee global wellposedness of the EPA
system with weakly singular alignment interactions.


  A two-dimensional electron gas (2DEG) in two-valley semiconductors has two
discrete degrees of freedom given by the spin and valley quantum numbers. We
analyze the zero-temperature magnetic instabilities of two-valley
semiconductors with SOI, in-plane magnetic field, and electron-electron
interaction. The interplay of an applied in-plane magnetic field and the SOI
results in non-collinear spin quantization in different valleys. Together with
the exchange intervalley interaction this results in a rich phase diagram
containing four non-trivial magnetic phases. The negative non-analytic cubic
correction to the free energy, which is always present in an interacting 2DEG,
is responsible for first order phase transitions. Here, we show that non-zero
ground state values of the order parameters can cut this cubic non-analyticity
and drive certain magnetic phase transitions second order. We also find two
tri-critical points at zero temperature which together with the line of second
order phase transitions constitute the quantum critical sector of the phase
diagram. The phase transitions can be tuned externally by electrostatic gates
or by the in-plane magnetic field.


  Fish-eye cameras are becoming increasingly popular in computer vision, but
their use for 3D measurement is limited partly due to the lack of an accurate,
efficient and user-friendly calibration procedure. For such a purpose, we
propose a method to calibrate the intrinsic and extrinsic parameters (including
radial distortion parameters) of two/multiple fish-eye cameras simultaneously
by using a wand under general motions. Thanks to the generic camera model used,
the proposed calibration method is also suitable for two/multiple conventional
cameras and mixed cameras (e.g. two conventional cameras and a fish-eye
camera). Simulation and real experiments demonstrate the effectiveness of the
proposed method. Moreover, we develop the camera calibration toolbox, which is
available online.


  Nowadays, many of the images captured are `observed' by machines only and not
by humans, e.g., in autonomous systems. High-level machine vision models, such
as object recognition or semantic segmentation, assume images are transformed
into some canonical image space by the camera \ans{Image Signal Processor
(ISP)}. However, the camera ISP is optimized for producing visually pleasing
images for human observers and not for machines. Therefore, one may spare the
ISP compute time and apply vision models directly to RAW images. Yet, it has
been shown that training such models directly on RAW images results in a
performance drop. To mitigate this drop, we use a RAW and RGB image pairs
dataset, which can be easily acquired with no human labeling. We then train a
model that is applied directly to the RAW data by using knowledge distillation
such that the model predictions for RAW images will be aligned with the
predictions of an off-the-shelf pre-trained model for processed RGB images. Our
experiments show that our performance on RAW images for object classification
and semantic segmentation is significantly better than models trained on
labeled RAW images. It also reasonably matches the predictions of a pre-trained
model on processed RGB images, while saving the ISP compute overhead.


  Variational autoencoders learn unsupervised data representations, but these
models frequently converge to minima that fail to preserve meaningful semantic
information. For example, variational autoencoders with autoregressive decoders
often collapse into autodecoders, where they learn to ignore the encoder input.
In this work, we demonstrate that adding an auxiliary decoder to regularize the
latent space can prevent this collapse, but successful auxiliary decoding tasks
are domain dependent. Auxiliary decoders can increase the amount of semantic
information encoded in the latent space and visible in the reconstructions. The
semantic information in the variational autoencoder's representation is only
weakly correlated with its rate, distortion, or evidence lower bound. Compared
to other popular strategies that modify the training objective, our
regularization of the latent space generally increased the semantic information
content.


  We present methods employed in Coordinate, a prototype service that supports
collaboration and communication by learning predictive models that provide
forecasts of users s AND availability.We describe how data IS collected about
USER activity AND proximity FROM multiple devices, IN addition TO analysis OF
the content OF users, the time of day, and day of week. We review applications
of presence forecasting embedded in the Priorities application and then present
details of the Coordinate service that was informed by the earlier efforts.


  We develop and analyze a new `relax-and-split' (RS) approach for compositions
of separable nonconvex nonsmooth functions with linear maps. RS uses a
relaxation technique together with partial minimization, and brings classic
techniques including direct factorization, matrix decompositions, and fast
iterative methods to bear on nonsmooth nonconvex problems. We also extend the
approach to trimmed nonconvex-composite formulations; the resulting Trimmed RS
(TRS) can fit models while detecting outliers in the data.
  We then test RS and TRS on a diverse set of applications: (1) phase
retrieval, (2) stochastic shortest path problems, (3) semi-supervised
classification, and (4) new clustering approaches. RS/TRS can be applied to
models with very weak functional assumptions, are easy to implement,
competitive with existing methods, and enable a new level of modeling
formulations to be put forward to address emerging challenges in the
mathematical sciences.


  Kawauchi proved that every strongly negative amphichiral knot $K \subset S^3$
bounds a smoothly embedded disk in some rational homology ball $V_K$, whose
construction a priori depends on $K$. We show that $V_K$ is independent of $K$
up to diffeomorphism. Thus, a single 4-manifold, along with connected sums
thereof, accounts for all known examples of knots that are rationally slice but
not slice.


  We investigate an open $XYZ$ spin $1/2$ chain driven out of equilibrium by
boundary reservoirs targeting different spin orientations, aligned along the
principal axes of anisotropy. We show that by tuning local magnetic fields,
applied to spins at sites near the boundaries, one can change any
nonequilibrium steady state to a fully uncorrelated Gibbsian state at infinite
temperature. This phenomenon occurs for strong boundary coupling and on a
critical manifold in the space of the fields amplitudes. The structure of this
manifold depends on the anisotropy degree of the model and on the parity of the
chain size.


  A rotating black hole threaded by an infinitely long cosmic string is studied
in the framework of the Abelian Higgs model. We show that contrary to a common
belief, in the presence of rotation the backreaction of the string does not
induce a simple conical deficit. This leads to new distinct features of the
Kerr--string system such as modified ISCO or shifted ergosphere, though these
effects are likely outside the range of observational precision. For an
extremal rotating black hole, the system exhibits a first-order phase
transition for the gravitational Meissner effect: whereas the horizon of large
black holes is pierced by the vortex, small black holes exhibit a flux-expelled
solution, with the gauge and scalar field remaining identically in their false
vacuum state on the event horizon. A brief review prepared for the MG14
Proceedings.


  We study a weighted $\frac{N}{2}$ biharmonic equation involving a positive
continuous potential in $\overline{B}$. The non-linearity is assumed to have
critical exponential growth in view of logarithmic weighted Adams' type
inequalities in the unit ball of $\mathbb{R}^{N}$. It is proved that there is a
nontrivial weak solution to this problem by the mountain Pass Theorem. We avoid
the loss of compactness by proving a concentration compactness result and by a
suitable asymptotic condition.


  We construct a world-line representation for the fermionic one-loop effective
action with axial and also vector, scalar, and pseudo-scalar couplings. We use
this expression to compute a few selected scattering amplitudes. These allow us
to verify that our method yields the same results as standard field theory. In
particular, we are able to reproduce the chiral anomaly. Our starting point is
the second-order formulation for the Dirac fermion. We translate the second
order expressions into a world-line action.


  Barriers to women's education and employment in Europe and the U.S. in the
nineteenth century made it unlikely that any women would be among the few
physicists whose ideas are taught in high school and college courses. This
paper explores the social settings in which three influential physicists
worked--James Clerk Maxwell, Robert Millikan, and Albert Einstein--to better
understand the limited opportunities available to women. By acknowledging and
explaining why there weren't more women among these founding physicists,
instructors may help students understand the barriers that still exist and feel
more empowered to overcome them and pursue physics as a career.


  We analyze string-theoretic large-field inflation in the regime of
spontaneously-broken supergravity with conventional moduli stabilization by
fluxes and non-perturbative effects. The main ingredient is a shift-symmetric
Kahler potential, supplemented by flux-induced shift symmetry breaking in the
superpotential. The central technical observation is that all these features
are present for D7-brane position moduli in Type IIB orientifolds, allowing for
a realization of the axion monodromy proposal in a controlled string theory
compactification. On the one hand, in the large complex structure regime the
D7-brane position moduli inherit a shift symmetry from their mirror-dual Type
IIA Wilson lines. On the other hand, the Type IIB flux superpotential
generically breaks this shift symmetry and allows, by appealing to the large
flux discretuum, to tune the relevant coefficients to be small. The
shift-symmetric direction in D7-brane moduli space can then play the role of
the inflaton: While the D7-brane circles a certain trajectory on the Calabi-Yau
many times, the corresponding F-term energy density grows only very slowly,
thanks to the above-mentioned tuning of the flux. Thus, the large-field
inflationary trajectory can be realized in a regime where Kahler, complex
structure and other brane moduli are stabilized in a conventional manner, as we
demonstrate using the example of the Large Volume Scenario.


  We report studies of the wetting behavior of Ne on very weakly attractive
surfaces, carried out with the Grand Canonical Monte Carlo method. The Ne-Ne
interaction was taken to be of Lennard-Jones form, while the Ne-surface
interaction was derived from an ab initio calculation of Chizmeshya et al.
Nonwetting behavior was found for Li, Rb, and Cs in the temperature regime
explored (i.e., T < 42 K). Drying behavior was manifested in a depleted fluid
density near the Cs surface. In contrast, for the case of Mg (a more attractive
potential) a prewetting transition was found near T= 28 K. This temperature was
found to shift slightly when a corrugated potential was used instead of a
uniform potential. The isotherm shape and the density profiles did not differ
qualitatively between these cases.


  Linking the properties of galaxies to the assembly history of their dark
matter haloes is a central aim of galaxy evolution theory. This paper
introduces a dimensionless parameter $s\in[0,1]$, the "tree entropy", to
parametrise the geometry of a halo's entire mass assembly hierarchy, building
on a generalisation of Shannon's information entropy. By construction, the
minimum entropy ($s=0$) corresponds to smoothly assembled haloes without any
mergers. In contrast, the highest entropy ($s=1$) represents haloes grown
purely by equal-mass binary mergers. Using simulated merger trees extracted
from the cosmological $N$-body simulation SURFS, we compute the natural
distribution of $s$, a skewed bell curve peaking near $s=0.4$. This
distribution exhibits weak dependences on halo mass $M$ and redshift $z$, which
can be reduced to a single dependence on the relative peak height $\delta_{\rm
c}/\sigma(M,z)$ in the matter perturbation field. By exploring the correlations
between $s$ and global galaxy properties generated by the SHARK semi-analytic
model, we find that $s$ contains a significant amount of information on the
morphology of galaxies $-$ in fact more information than the spin,
concentration and assembly time of the halo. Therefore, the tree entropy
provides an information-rich link between galaxies and their dark matter
haloes.


  Impact factors (and similar measures such as the Scimago Journal Rankings)
suffer from two problems: (i) citation behavior varies among fields of science
and therefore leads to systematic differences, and (ii) there are no statistics
to inform us whether differences are significant. The recently introduced SNIP
indicator of Scopus tries to remedy the first of these two problems, but a
number of normalization decisions are involved which makes it impossible to
test for significance. Using fractional counting of citations-based on the
assumption that impact is proportionate to the number of references in the
citing documents-citations can be contextualized at the paper level and
aggregated impacts of sets can be tested for their significance. It can be
shown that the weighted impact of Annals of Mathematics (0.247) is not so much
lower than that of Molecular Cell (0.386) despite a five-fold difference
between their impact factors (2.793 and 13.156, respectively).


  We analyze the potential of Compact Linear Colliders based on the \gamma-e
collisions to search for the radion in the Randall-Sundrum model, where
compactification radius of the extra dimension is stabilized by the radion,
which is a scalar field lighter than the graviton Kaluza-Klein states. The
radion production in the high energy \gamma-e colliders with the polarization
of the electron beams are calculated in detail. Numerical evaluation shows that
if the radion mass is not too heavy with the mass order of GeV then the
reaction can give observable cross section in future colliders at the high
degree of polarization.


  Let $M$ be the space of real $n\times m$ matrices which can be identified
with the Euclidean space $R^{nm}$. We introduce continuous wavelet transforms
on $M$ with a multivalued scaling parameter represented by a positive definite
symmetric matrix. These transforms agree with the polar decomposition on $M$
and coincide with classical ones in the rank-one case $m=1$. We prove an analog
of Calderon's reproducing formula for $L^2$-functions and obtain explicit
inversion formulas for the Riesz potentials and Radon transforms on $M$. We
also introduce continuous ridgelet transforms associated to matrix planes in
$M$. An inversion formula for these transforms follows from that for the Radon
transform.


  To learn the optimal similarity function between probe and gallery images in
Person re-identification, effective deep metric learning methods have been
extensively explored to obtain discriminative feature embedding. However,
existing metric loss like triplet loss and its variants always emphasize
pair-wise relations but ignore the distribution context in feature space,
leading to inconsistency and sub-optimal. In fact, the similarity of one pair
not only decides the match of this pair, but also has potential impacts on
other sample pairs. In this paper, we propose a novel Distribution Context
Aware (DCA) loss based on triplet loss to combine both numerical similarity and
relation similarity in feature space for better clustering. Extensive
experiments on three benchmarks including Market-1501, DukeMTMC-reID and
MSMT17, evidence the favorable performance of our method against the
corresponding baseline and other state-of-the-art methods.


  Segmentation of objects in a video is challenging due to the nuances such as
motion blurring, parallax, occlusions, changes in illumination, etc. Instead of
addressing these nuances separately, we focus on building a generalizable
solution that avoids overfitting to the individual intricacies. Such a solution
would also help us save enormous resources involved in human annotation of
video corpora. To solve Video Object Segmentation (VOS) in an unsupervised
setting, we propose a new pipeline (FODVid) based on the idea of guiding
segmentation outputs using flow-guided graph-cut and temporal consistency.
Basically, we design a segmentation model incorporating intra-frame appearance
and flow similarities, and inter-frame temporal continuation of the objects
under consideration. We perform an extensive experimental analysis of our
straightforward methodology on the standard DAVIS16 video benchmark. Though
simple, our approach produces results comparable (within a range of ~2 mIoU) to
the existing top approaches in unsupervised VOS. The simplicity and
effectiveness of our technique opens up new avenues for research in the video
domain.


  Expanding the Landau gauge gluon and ghost two-point functions in a power
series we investigate their infrared behavior. The corresponding powers are
constrained through the ghost Dyson-Schwinger equation by exploiting
multiplicative renormalizability. Without recourse to any specific truncation
we demonstrate that the infrared powers of the gluon and ghost propagators are
uniquely related to each other. Constraints for these powers are derived, and
the resulting infrared enhancement of the ghost propagator signals that the
Kugo-Ojima confinement criterion is fulfilled in Landau gauge Yang-Mills
theory.


  We analyze the probability that a random m-dimensional linear subspace of R^n
both intersects a regular closed convex cone C\subseteq R^n and lies within
distance \alpha of an m-dimensional subspace not intersecting C (except at the
origin). The result is expressed in terms of the spherical intrinsic volumes of
the cone C. This allows us to perform an average analysis of the Grassmann
condition number \C(A) for the homogeneous convex feasibility problem \exists
x\in C\setminus 0 : Ax=0. The Grassmann condition number is a geometric version
of Renegar's condition number, that we have introduced recently in [SIOPT
22(3):1029-1041, 2012]. We thus give the first average analysis of convex
programming that is not restricted to linear programming. In particular, we
prove that if the entries of A\in R^{m\times n} are chosen i.i.d. standard
normal, then for any regular cone C, we have E[ln\C(A)]<1.5 ln(n)+1.5. The
proofs rely on various techniques from Riemannian geometry applied to Grassmann
manifolds.


  We present calculations showing how electron rest mass influences entropy
flow, neutrino decoupling, and Big Bang Nucleosynthesis (BBN) in the early
universe. To elucidate this physics and especially the sensitivity of BBN and
related epochs to electron mass, we consider a parameter space of rest mass
values larger and smaller than the accepted vacuum value. Electromagnetic
equilibrium, coupled with the high entropy of the early universe, guarantees
that significant numbers of electron-positron pairs are present, and dominate
over the number of ionization electrons to temperatures much lower than the
vacuum electron rest mass. Scattering between the electrons-positrons and the
neutrinos largely controls the flow of entropy from the plasma into the
neutrino seas. Moreover, the number density of electron-positron-pair targets
can be exponentially sensitive to the effective in-medium electron mass. This
entropy flow influences the phasing of scale factor and temperature, the
charged current weak-interaction-determined neutron-to-proton ratio, and the
spectral distortions in the relic neutrino energy spectra. Our calculations
show the sensitivity of the physics of this epoch to three separate effects:
finite electron mass, finite-temperature quantum electrodynamic (QED) effects
on the plasma equation of state, and Boltzmann neutrino energy transport. The
ratio of neutrino to plasma component energy scales manifests in Cosmic
Microwave Background (CMB) observables, namely the baryon density and the
radiation energy density, along with the primordial helium and deuterium
abundances. Our results demonstrate how the treatment of in-medium electron
mass (i.e., QED effects) could translate into an important source of
uncertainty in extracting neutrino and beyond-standard-model physics limits
from future high-precision CMB data.


  Energy efficiency and reliability have long been crucial factors for ensuring
cost-effective and safe missions in autonomous systems computers. With the
rapid evolution of industries such as space robotics and advanced air mobility,
the demand for these low size, weight, and power (SWaP) computers has grown
significantly. This study focuses on introducing an estimation framework based
on spike coding theories and spiking neural networks (SNN), leveraging the
efficiency and scalability of neuromorphic computers. Therefore, we propose an
SNN-based Kalman filter (KF), a fundamental and widely adopted optimal strategy
for well-defined linear systems. Furthermore, based on the modified sliding
innovation filter (MSIF) we present a robust strategy called SNN-MSIF. Notably,
the weight matrices of the networks are designed according to the system model,
eliminating the need for learning. To evaluate the effectiveness of the
proposed strategies, we compare them to their algorithmic counterparts, namely
the KF and the MSIF, using Monte Carlo simulations. Additionally, we assess the
robustness of SNN-MSIF by comparing it to SNN-KF in the presence of modeling
uncertainties and neuron loss. Our results demonstrate the applicability of the
proposed methods and highlight the superior performance of SNN-MSIF in terms of
accuracy and robustness. Furthermore, the spiking pattern observed from the
networks serves as evidence of the energy efficiency achieved by the proposed
methods, as they exhibited an impressive reduction of approximately 97 percent
in emitted spikes compared to possible spikes.


  In this paper, we consider the real rank zero $\mathrm{C}^*$-algebras which
can be written as an inductive limit of the Elliott-Thomsen building blocks and
prove a decomposition result for the connecting homomorphisms; this technique
will be used in the classification theorem.


  Using Cu-$L_3$ edge resonant inelastic x-ray scattering (RIXS) we measured
the dispersion and damping of spin excitations (magnons and paramagnons) in the
high-$T_\mathrm{c}$ superconductor (Bi,Pb)$_{2}$(Sr,La)$_{2}$CuO$_{6+\delta}$
(Bi2201), for a large doping range across the phase diagram ($0.03\lesssim
p\lesssim0.21$). Selected measurements with full polarization analysis
unambiguously demonstrate the spin-flip character of these excitations, even in
the overdoped sample. We find that the undamped frequencies increase slightly
with doping for all accessible momenta, while the damping grows rapidly, faster
in the (0,0)$\rightarrow$(0.5,0.5) nodal direction than in the
(0,0)$\rightarrow$(0.5,0) antinodal direction. We compare the experimental
results to numerically exact determinant quantum Monte Carlo (DQMC)
calculations that provide the spin dynamical structure factor
$S(\textbf{Q},\omega)$ of the three-band Hubbard model. The theory reproduces
well the momentum and doping dependence of the dispersions and spectral weights
of magnetic excitations. These results provide compelling evidence that
paramagnons, although increasingly damped, persist across the superconducting
dome of the cuprate phase diagram; this implies that long range
antiferromagnetic correlations are quickly washed away, while short range
magnetic interactions are little affected by doping.


  The 71{\deg} stripe domain patterns of epitaxial BiFeO3 thin films are
frequently being explored to achieve new functional properties, dissimilar from
the BiFeO3 bulk properties. We show that in-plane switching and out-of-plane
switching of these domains behave very differently. In the in-plane
configuration the domains are very stable, whereas in the out-of-plane
configuration the domains change their size and patterns, depending on the
applied switching voltage frequency. This paper has been published in Phys.
Status Solidi B (http://dx.doi.org/10.1002/pssb.201248329)


  We discuss some general aspects of acoustic black holes. We begin by
describing the associated formalism with which acoustic black holes are
established, then we show how to model arbitrary geometries by using a de Laval
nozzle. It is argued that even though the Hawking temperature of these black
holes is too low to be detected, acoustic black holes have interesting
classical properties, some of which are outlined here, that should be explored.


  Scientists have built a variety of covert channels for secretive information
transmission with CPU cache and main memory. In this paper, we turn to a lower
level in the memory hierarchy, i.e., persistent storage. Most programs store
intermediate or eventual results in the form of files and some of them call
fsync to synchronously persist a file with storage device for orderly
persistence. Our quantitative study shows that one program would undergo
significantly longer response time for fsync call if the other program is
concurrently calling fsync, although they do not share any data. We further
find that, concurrent fsync calls contend at multiple levels of storage stack
due to sharing software structures (e.g., Ext4's journal) and hardware
resources (e.g., disk's I/O dispatch queue).
  We accordingly build a covert channel named Sync+Sync. Sync+Sync delivers a
transmission bandwidth of 20,000 bits per second at an error rate of about
0.40% with an ordinary solid-state drive. Sync+Sync can be conducted in
cross-disk partition, cross-file system, cross-container, cross-virtual
machine, and even cross-disk drive fashions, without sharing data between
programs. Next, we launch side-channel attacks with Sync+Sync and manage to
precisely detect operations of a victim database (e.g., insert/update and
B-Tree node split). We also leverage Sync+Sync to distinguish applications and
websites with high accuracy by detecting and analyzing their fsync frequencies
and flushed data volumes. These attacks are useful to support further
fine-grained information leakage.


  Brundan and Kleshchev showed that some parts of the representation theory of
the affine Hecke-Clifford superalgebras and its finite-dimensional "cyclotomic"
quotients are controlled by the Lie theory of type $A^{(2)}_{2l}$ when the
quantum parameter $q$ is a primitive $(2l+1)$-th root of unity. We show in this
paper that similar theorems hold when $q$ is a primitive $4l$-th root of unity
by replacing the Lie theory of type $A^{(2)}_{2l}$ with that of type
$D^{(2)}_{l}$.


  In this paper, we first establish two versions of Landau-Bloch type theorem
for $(K,K')$-elliptic harmonic mappings with a bounded minimum distortion.
Next, we provide several coefficient estimates and a conjecture for
$(K,K')$-elliptic harmonic mappings. Then, we establish three new versions of
Landau-Bloch type theorem for sense-preserving harmonic mappings. Finally, we
establish two sharp versions of Landau-Bloch type theorem for certain harmonic
mappings. These results are sharp in some given cases and improve the related
results of different authors.


  The optimal constants in a class of exponential type inequalities for the
Ornstein-Uhlenbeck operator in the Gauss space are detected. The existence of
extremal functions in the relevant inequalities is also established. Our
results disclose analogies and dissimilarities in comparison with Adams'
inequality for the Laplace operator, a companion of our inequalities in the
Euclidean space.


  Due to the size and nature of intrusion detection datasets, intrusion
detection systems (IDS) typically take high computational complexity to examine
features of data and identify intrusive patterns. Data preprocessing techniques
such as feature selection can be used to reduce such complexity by eliminating
irrelevant and redundant features in the dataset. The objective of this study
is to analyze the efficiency and effectiveness of some feature selection
approaches namely, wrapper-based and filter-based modeling approaches. To
achieve that, a hybrid of feature selection algorithm in combination with
wrapper and filter selection processes is designed. We propose a wrapper-based
hybrid intrusion detection modeling with a decision tree algorithm to guide the
selection process. Five machine learning algorithms are used on the wrapper and
filter-based feature selection methods to build IDS models using the UNSW-NB15
dataset. The three filter-based methods namely, information gain, gain ratio,
and relief are used for comparison to determine the efficiency and
effectiveness of the proposed approach. Furthermore, a fair comparison with
other state-of-the-art intrusion detection approaches is also performed. The
experimental results show that our approach is quite effective in comparison to
state-of-the-art works, however, it takes high computational time in comparison
to the filter-based methods whilst achieves similar results. Our work also
revealed unobserved issues about the conformity of the UNSW-NB15 dataset.


  The interaction energy and minimum energy structure for different geometries
of the benzene dimer has been calculated using the recently developed nonlocal
correlation energy functional for calculating dispersion interactions. The
comparison of this straightforward and relatively quick density functional
based method with recent calculations can elucidate how the former, quicker
method might be exploited in larger more complicated biological, organic,
aromatic, and even infinite systems such as molecules physisorbed on surfaces,
and van der Waals crystals.


  DNA has immense potential as an emerging data storage medium. The principle
of DNA storage is the conversion and flow of digital information between binary
code stream, quaternary base, and actual DNA fragments. This process will
inevitably introduce errors, posing challenges to accurate data recovery.
Sequence reconstruction consists of inferring the DNA reference from a cluster
of erroneous copies. A common assumption in existing methods is that all the
strands within a cluster are noisy copies originating from the same reference,
thereby contributing equally to the reconstruction. However, this is not always
valid considering the existence of contaminated sequences caused, for example,
by DNA fragmentation and rearrangement during the DNA storage process.This
paper proposed a robust multi-read reconstruction model using DNN, which is
resilient to contaminated clusters with outlier sequences, as well as to noisy
reads with IDS errors. The effectiveness and robustness of the method are
validated on three next-generation sequencing datasets, where a series of
comparative experiments are performed by simulating varying contamination
levels that occurring during the process of DNA storage.


  Let Sp($k$) denote the number of the $\lfloor k/2\rfloor$-element subsets of
a finite $k$-element set. We prove that the least size of a generating subset
of the Boolean lattice with $n$ atoms (or, equivalently, the powerset lattice
of an $n$-element set) is the least number $k$ such that $n\leq$ Sp($k$). Based
on this fact and our 2021 protocol based on equivalence lattices, we outline a
cryptographic protocol for exchanging session keys, that is, frequently
changing secondary keys. In the present paper, which belongs mainly to lattice
theory, we do not elaborate and prove those details of this protocol that
modern cryptology would require to guarantee security; the security of the
protocol relies on heuristic considerations. However, as a first step, we prove
that if an eavesdropper could break every instance of an easier protocol in
polynomial time, then P would equal NP. As a byproduct, it turns out that in
each nontrivial finite lattice that has a prime filter, in particular, in each
nontrivial finite Boolean lattice, the solvability of systems of equations with
constant-free left sides but constant right sides is an NP-complete problem.


  The effect of uniaxial tensile strain on spin coherence in n-type GaAs
epilayers is probed using time-resolved Kerr rotation, photoluminescence, and
optically-detected nuclear magnetic resonance spectroscopies. The bandgap,
electron spin lifetime, electron g-factor, and nuclear quadrupole splitting are
simultaneously imaged over millimeter scale areas of the epilayers for
continuously varying values of strain. All-optical nuclear magnetic resonance
techniques allow access to the strain induced nuclear quadrupolar resonance
splitting in field regimes not easily addressable using conventional
optically-detected nuclear magnetic resonance.