Abstract:
  The estimation of the intrinsic dimension of a dataset is a fundamental step
in most dimensionality reduction techniques. This article illustrates
intRinsic, an R package that implements novel state-of-the-art likelihood-based
estimators of the intrinsic dimension of a dataset. In detail, the methods
included in this package are the TWO-NN, Gride, and Hidalgo models. To allow
these novel estimators to be easily accessible, the package contains a few
high-level, intuitive functions that rely on a broader set of efficient,
low-level routines. intRinsic encompasses models that fall into two categories:
homogeneous and heterogeneous intrinsic dimension estimators. The first
category contains the TWO-NN and Gride models. The functions dedicated to these
two methods carry out inference under both the frequentist and Bayesian
frameworks. In the second category we find Hidalgo, a Bayesian mixture model,
for which an efficient Gibbs sampler is implemented. After discussing the
theoretical background, we demonstrate the performance of the models on
simulated datasets. This way, we can assess the results by comparing them with
the ground truth. Then, we employ the package to study the intrinsic dimension
of the Alon dataset, obtained from a famous microarray experiment. We show how
the estimation of homogeneous and heterogeneous intrinsic dimensions allows us
to gain valuable insights about the topological structure of a dataset.


Abstract:
  The rate and pathways of relaxation of a magnetic medium to its equilibrium
following excitation with intense and short laser pulses are the key
ingredients of ultrafast optical control of spins. Here we study experimentally
the evolution of the magnetization and magnetic anisotropy of thin films of a
ferromagnetic metal galfenol (Fe$_{0.81}$Ga$_{0.19}$) resulting from excitation
with a femtosecond laser pulse. From the temporal evolution of the hysteresis
loops we deduce that the magnetization $M_S$ and magnetic anisotropy parameters
$K$ recover within a nanosecond, and the ratio between $K$ and $M_S$ satisfies
the thermal equilibrium's power law in the whole time range spanning from a few
picoseconds to 3 nanoseconds. We further use the experimentally obtained
relaxation times of $M_S$ and $K$ to analyze the laser-induced precession and
demonstrate how they contribute to its frequency evolution at the nanosecond
timescale.


Abstract:
  Magnons and phonons are two fundamental neutral excitations of magnetically
ordered materials which can significantly dominate the low-energy thermal
properties. In this work we study the interplay of magnons and phonons in
honeycomb and Kagome lattices. When the mirror reflection with respect to the
magnetic ordering direction is broken, the symmetry-allowed in-plane
Dzyaloshinskii-Moriya (DM) interaction will couple the magnons to the phonons
and the magnon-polaron states are formed. Besides, both lattice structures also
allow for an out-of-plane DM interaction rendering the uncoupled magnons to be
topological. Our aim is to study the interplay of such topological magnons with
phonons. We show that the hybridization between magnons and phonons can
significantly redistribute the Berry curvature among the bands. Especially, we
found that the topological magnon band becomes trivial while the hybridized
states at lower energy acquire Berry curvature strongly peaked near the avoided
crossings. As such the thermal Hall conductivity of topological magnons shows
significant changes due to coupling to the phonons.


Abstract:
  Strapdown inertial navigation research involves the parameterization and
computation of the attitude, velocity and position of a rigid body in a chosen
reference frame. The community has long devoted to finding the most concise and
efficient representation for the strapdown inertial navigation system (INS).
The current work is motivated by simplifying the existing dual quaternion
representation of the kinematic model. This paper proposes a compact and
elegant representation of the body's attitude, velocity and position, with the
aid of a devised trident quaternion tool in which the position is accounted for
by adding a second imaginary part to the dual quaternion. Eventually, the
kinematics of strapdown INS are cohesively unified in one concise differential
equation, which bears the same form as the classical attitude quaternion
equation. In addition, the computation of this trident quaternion-based
kinematic equation is implemented with the recently proposed functional
iterative integration approach. Numerical results verify the analysis and show
that incorporating the new representation into the functional iterative
integration scheme achieves high inertial navigation computation accuracy as
well.


Abstract:
  Comoving pairs, even at the separations of $\mathcal{O}(10^6)\,$AU, are a
predicted reservoir of conatal stars. We present detailed chemical abundances
of 62 stars in 31 comoving pairs with separations of $10^2 - 10^7\,$AU and 3D
velocity differences $< 2 \mathrm{\ km \ s^{-1}}$. This sample includes both
bound comoving pairs/wide binaries and unbound comoving pairs. Observations
were taken using the MIKE spectrograph on the Magellan/Clay Telescope at high
resolution ($\mathrm{R} \sim 45,000$) with a typical signal-to-noise ratio of
150 per pixel. With these spectra, we measure surface abundances for 24
elements, including Li, C, Na, Mg, Al, Si, Ca, Sc, Ti, V, Cr, Mn, Fe, Co, Ni,
Cu, Zn, Sr, Y, Zr, Ba, La, Nd, Eu. Taking iron as the representative element,
our sample of wide binaries is chemically homogeneous at the level of $0.05$
dex, which agrees with prior studies on wide binaries. Importantly, even
systems at separations $2\times10^5-10^7\,$AU are homogeneous to $0.09$ dex, as
opposed to the random pairs which have a dispersion of $0.23\,$dex. Assuming a
mixture model of the wide binaries and random pairs, we find that $73 \pm 22\%$
of the comoving pairs at separations $2\times10^5-10^7\,$AU are conatal. Our
results imply that a much larger parameter space of phase space may be used to
find conatal stars, to study M-dwarfs, star cluster evolution, exoplanets,
chemical tagging, and beyond.


Abstract:
  Flux-integrated semi-exclusive differential and integral cross sections for
quasi-elastic neutrino charged-current scattering on argon are analyzed. We
calculate these cross sections using the relativistic distorted-wave impulse
approximation and compare with recent MicroBooNE data. We found that the
measured cross sections can be described well within the experimental
uncertainties with value of the nucleon axial mass $1 < M_A < 1.2$ GeV. The
contribution of the exclusive channel $\boldmath{(\nu_{\mu}, \mu p)}$ to the
flux-integrated inclusive cross sections is about 50\%.


Abstract:
  Chromospheric umbral oscillations produce periodic brightenings in the core
of some spectral lines, known as umbral flashes. They are also accompanied by
fluctuations in velocity, temperature, and, according to several recent works,
magnetic field. In this study, we aim to ascertain the accuracy of the magnetic
field determined from inversions of the Ca II 8542 \AA\ line. We have developed
numerical simulations of wave propagation in a sunspot umbra. Synthetic Stokes
profiles emerging from the simulated atmosphere were computed and then inverted
using the NICOLE code. The atmospheres inferred from the inversions have been
compared with the original parameters from the simulations. Our results show
that the inferred chromospheric fluctuations in velocity and temperature match
the known oscillations from the numerical simulation. In contrast, the vertical
magnetic field obtained from the inversions exhibits an oscillatory pattern
with a $\sim$300 G peak-to-peak amplitude which is absent in the simulation. We
have assessed the error in the inferred parameters by performing numerous
inversions with slightly different configurations of the same Stokes profiles.
We find that when the atmosphere is approximately at rest, the inversion tends
to favor solutions that underestimate the vertical magnetic field strength. On
the contrary, during umbral flashes, the values inferred from most of the
inversions are concentrated at stronger fields than those from the simulation.
Our analysis provides a quantification of the errors associated with the
inversions of the Ca II 8542 \AA\ line and suggests caution with the
interpretation of the inferred magnetic field fluctuations.


Abstract:
  Unrestricted mutation of shared state is a source of many well-known
problems. The predominant safe solutions are pure functional programming, which
bans mutation outright, and flow sensitive type systems, which depend on
sophisticated typing rules. Mutable value semantics is a third approach that
bans sharing instead of mutation, thereby supporting part-wise in-place
mutation and local reasoning, while maintaining a simple type system. In the
purest form of mutable value semantics, references are second-class: they are
only created implicitly, at function boundaries, and cannot be stored in
variables or object fields. Hence, variables can never share mutable state.
  Because references are often regarded as an indispensable tool to write
efficient programs, it is legitimate to wonder whether such a discipline can
compete other approaches. As a basis for answering that question, we
demonstrate how a language featuring mutable value semantics can be compiled to
efficient native code. This approach relies on stack allocation for static
garbage collection and leverages runtime knowledge to sidestep unnecessary
copies.


Abstract:
  We prove the existence of multiple noise-induced transitions in the
Lasota-Mackey map, which is a class of one dimensional random dynamical system
with additive noise. The result is achieved by the help of rigorous computer
assisted estimates. We first approximate the stationary distribution of the
random dynamical system and then compute certified error intervals for the
Lyapunov exponent. We find that the sign of the Lyapunov exponent changes at
least three times when increasing the noise amplitude. We also show numerical
evidence that the standard non-rigorous numerical approximation by finite-time
Lyapunov exponent is valid with our model for a sufficiently large number of
iterations. Our method is expected to work for a broad class of nonlinear
stochastic phenomena.


Abstract:
  The results of a long-term multiwavelength study of the powerful flat
spectrum radio quasar 3C 454.3 using {\it Fermi}-LAT and Swift XRT/UVOT data
are reported. In the $\gamma$-ray band, {\it Fermi}-LAT observations show
several major flares when the source flux was $>10^{-5}\:{\rm
photon\:cm^{-2}\:s^{-1}}$; the peak $\gamma$-ray flux above $141.6$ MeV,
$(9.22\pm1.96)\times10^{-5}\:{\rm photon\:cm^{-2}\:s^{-1}}$ observed on MJD
55519.33, corresponds to $2.15\times10^{50}\:{\rm erg\:s^{-1}}$ isotropic
$\gamma$-ray luminosity. The analysis of Swift XRT and UVOT data revealed a
flux increase, although with smaller amplitudes, also in the X-ray and
optical/UV bands. The X-ray emission of 3C 454.3 is with a hard spectral index
of $\Gamma_{\rm X}=1.16-1.75$, and the flux in the flaring states increased up
to $(1.80\pm0.18)\times10^{-10}{\rm erg\:cm^{-2}\: s^{-1}}$. Through combining
the analyzed data, it was possible to assemble 362 high-quality and
quasi-simultaneous spectral energy distributions of 3C 454.3 in 2008-2018 which
all were modeled within a one-zone leptonic scenario assuming the emission
region is within the broad line region, involving synchrotron, synchrotron
self-Compton and external Compton mechanisms. Such an extensive modeling is the
key for constraining the underlying emission mechanisms in the 3C 454.3 jet and
allows to derive the physical parameters of the jet and investigate their
evolution in time. The modeling suggests that during the flares, along with the
variation of emitting electron parameters, the Doppler boosting factor
increased substantially implying that the emission in these periods has most
likely originated in a faster moving region.


Abstract:
  This article argues that low latency, high bandwidth, device proliferation,
sustainable digital infrastructure, and data privacy and sovereignty continue
to motivate the need for edge computing research even though its initial
concepts were formulated more than a decade ago.


Abstract:
  With the promulgation of data protection laws (e.g., GDPR in 2018), privacy
preservation has become a general agreement in applications where cross-domain
sensitive data are utilized. Out of many privacy-preserving techniques,
federated learning (FL) has received much attention as a bridge for secure data
connection and cooperation. Although FL's research works have surged, some
classical data modeling methods are not well accommodated in FL. In this paper,
we propose the first masking-based federated singular vector decomposition
method, called FedSVD. FedSVD protects the raw data through a singular value
invariance mask, which can be further removed from the SVD results. Compared
with prior privacy-preserving SVD solutions, FedSVD has lossless results, high
confidentiality, and excellent scalability. We provide privacy proof showing
that FedSVD has guaranteed data confidentiality. Empirical experiments on
real-life datasets and synthetic data have verified the effectiveness of our
method. The reconstruction error of FedSVD is around 0.000001% of the raw data,
validating the lossless property of FedSVD. The scalability of FedSVD is nearly
the same as the standalone SVD algorithm. Hence, FedSVD can bring privacy
protection almost without sacrificing any computation time or communication
overhead.


Abstract:
  Unmanned Aerial Vehicles (UAVs), as a recently emerging technology, enabled a
new breed of unprecedented applications in different domains. This technology's
ongoing trend is departing from large remotely-controlled drones to networks of
small autonomous drones to collectively complete intricate tasks time and
cost-effectively. An important challenge is developing efficient sensing,
communication, and control algorithms that can accommodate the requirements of
highly dynamic UAV networks with heterogeneous mobility levels. Recently, the
use of Artificial Intelligence (AI) in learning-based networking has gained
momentum to harness the learning power of cognizant nodes to make more
intelligent networking decisions by integrating computational intelligence into
UAV networks. An important example of this trend is developing learning-powered
routing protocols, where machine learning methods are used to model and predict
topology evolution, channel status, traffic mobility, and environmental factors
for enhanced routing.
  This paper reviews AI-enabled routing protocols designed primarily for aerial
networks, including topology-predictive and self-adaptive learning-based
routing algorithms, with an emphasis on accommodating highly-dynamic network
topology. To this end, we justify the importance and adaptation of AI into UAV
network communications. We also address, with an AI emphasis, the closely
related topics of mobility and networking models for UAV networks, simulation
tools and public datasets, and relations to UAV swarming, which serve to choose
the right algorithm for each scenario. We conclude by presenting future trends,
and the remaining challenges in AI-based UAV networking, for different aspects
of routing, connectivity, topology control, security and privacy, energy
efficiency, and spectrum sharing.


Abstract:
  Superconducting radio-frequency (SRF) niobium cavities are the modern means
of particle acceleration and an enabling technology for record coherence
superconducting quantum systems and ultra-sensitive searches for new physics.
Here, we report a systematic effect in Nb cavities indicative of improved
superconducting properties - an anomalous decrease (dip) in the resonant
frequency at temperatures just below the critical temperature $T_\mathrm{c}$.
The frequency dip magnitude correlates with cavity quality factor, near-surface
impurity distribution, and $T_\mathrm{c}$. It is also a precursor of the
peculiar decrease in the BCS surface impedance with increasing RF current. A
first demonstration of the coherence peak in the AC conductivity in Nb SRF
cavities is also presented and found to correlate with a large frequency dip.


Abstract:
  Recent discovery of spin-orbit torques (SOTs) within magnetic single-layers
has attracted attention in the field of spintronics. However, it has remained
elusive as to how to understand and how to tune the SOTs. Here, utilizing the
single layers of chemically disordered Fe$_x$Pt$_{1-x}$, we unveil the
mechanism of the "unexpected" bulk SOTs by studying their dependence on the
introduction of a controlled vertical composition gradient and on temperature.
We find that the bulk damping like SOT arises from an imbalanced internal spin
current that is transversely polarized and independent of the magnetization
orientation. The torque can be strong only in the presence of a vertical
composition gradient and the SOT efficiency per electric field is insensitive
to temperature but changes sign upon reversal of the orientation of the
composition gradient, which are in analogue to behaviors of the strain. From
these characteristics we conclude that the imbalanced internal spin current
originates from a bulk spin Hall effect and that the associated inversion
asymmetry that allows for a non-zero net torque is most likely a strain
non-uniformity induced by the composition gradient. The fieldlike SOT is a
relatively small bulk effect compared to the dampinglike SOT. This work points
to the possibility of developing low-power single-layer SOT devices by strain
engineering.


Abstract:
  In this paper we consider distributed adaptive stabilization for uncertain
multivariable linear systems with a time-varying diagonal matrix gain. We show
that uncertain multivariable linear systems are stabilizable by diagonal matrix
high gains if the system matrix is an H-matrix with positive diagonal entries.
Based on matrix measure and stability theory for diagonally dominant systems,
we consider two classes of uncertain linear systems, and derive a threshold
condition to ensure their exponential stability by a monotonically increasing
diagonal gain matrix. When each individual gain function in the matrix gain is
updated by state-dependent functions using only local state information, the
boundedness and convergence of both system states and adaptive matrix gains are
guaranteed. We apply the adaptive distributed stabilization approach to
adaptive synchronization control for large-scale complex networks consisting of
nonlinear node dynamics and time-varying coupling weights. A unified framework
for adaptive synchronization is proposed that includes several general design
approaches for adaptive coupling weights to guarantee network synchronization.


Abstract:
  Buildings rarely perform as designed/simulated and and there are numerous
tangible benefits if this gap is reconciled. A new scientific yet pragmatic
methodology - called Enhanced Parameter Estimation (EPE) - is proposed that
allows physically relevant parameter estimation rather than a blind force-fit
to energy use data. It calibrates a rapidly and inexpensively created
simulation model of the building in two stages: (a) building shell calibration
with the HVAC system is replaced by an by an ideal system that meets the loads.
EPE identifies a small number of high-level heat flows in the energy balance,
calculates them with specifically tailored individual driving functions,
introduces physically significant parameters to best accomplish energy balance,
and, estimates the parameters and their uncertainty bounds. Calibration is thus
done with corrective heat flows without any arbitrary tuning of input
parameters, (b) HVAC system calibration with the building shell replaced by a
box with only process loads; as many parameters as the data allows are
estimated. Calibration accuracy is enhanced by machine learning of the residual
errors. The EPE methodology is demonstrated through: a synthetic building and
one an actual 75,000 Sq.Ft. building in Pennsylvania. A subsequent paper will
provide further details and applications.


Abstract:
  In this article, we improve our main results from \emph{Chow groups and
$L$-derivatives of automorphic motives for unitary groups} in two direction:
First, we allow ramified places in the CM extension $E/F$ at which we consider
representations that are spherical with respect to a certain special maximal
compact subgroup, by formulating and proving an analogue of the Kudla--Rapoport
conjecture for exotic smooth Rapoport--Zink spaces. Second, we lift the
restriction on the components at split places of the automorphic
representation, by proving a more general vanishing result on certain
cohomology of integral models of unitary Shimura varieties with Drinfeld level
structures.


Abstract:
  Cache side-channel attacks lead to severe security threats to the settings
that a CPU is shared across users, e.g., in the cloud. The existing attacks
rely on sensing the micro-architectural state changes made by victims, and this
assumption can be invalidated by combining spatial (\eg, Intel CAT) and
temporal isolation (\eg, time protection). In this work, we advance the state
of cache side-channel attacks by showing stateless cache side-channel attacks
that cannot be defeated by both spatial and temporal isolation.
  This side-channel exploits the timing difference resulted from interconnect
congestion. Specifically, to complete cache transactions, for Intel CPUs, cache
lines would travel across cores via the CPU mesh interconnect. Nonetheless, the
mesh links are shared by all cores, and cache isolation does not segregate the
traffic. An attacker can generate interconnect traffic to contend with the
victim's on a mesh link, hoping that extra delay will be measured. With the
variant delays, the attacker can deduce the memory access pattern of a victim
program, and infer its sensitive data. Based on this idea, we implement Volcano
and test it against the existing RSA implementations of JDK. We found the RSA
private key used by a victim process can be partially recovered. In the end, we
propose a few directions for defense and call for the attention of the security
community.


Abstract:
  Transport is called nonreciprocal when not only the sign, but also the
absolute value of the current, depends on the polarity of the applied voltage.
It requires simultaneously broken inversion and time-reversal symmetries, e.g.,
by the interplay of spin-orbit coupling and magnetic field. So far, observation
of nonreciprocity was always tied to resistivity, and dissipationless
nonreciprocal circuit elements were elusive. Here, we engineer fully
superconducting nonreciprocal devices based on highly-transparent Josephson
junctions fabricated on InAs quantum wells. We demonstrate supercurrent
rectification far below the transition temperature. By measuring Josephson
inductance, we can link nonreciprocal supercurrent to the asymmetry of the
current-phase relation, and directly derive the supercurrent magnetochiral
anisotropy coefficient for the first time. A semi-quantitative model well
explains the main features of our experimental data. Nonreciprocal Josephson
junctions have the potential to become for superconducting circuits what
$pn$-junctions are for traditional electronics, opening the way to novel
nondissipative circuit elements.


Abstract:
  We propose a framework for reinforcement learning (RL) in fine time
discretization and a learning algorithm in this framework. One of the main
goals of RL is to provide a way for physical machines to learn optimal behavior
instead of being programmed. However, the machines are usually controlled in
fine time discretization. The most common RL methods apply independent random
elements to each action, which is not suitable in that setting. It is not
feasible because it causes the controlled system to jerk, and does not ensure
sufficient exploration since a single action is not long enough to create a
significant experience that could be translated into policy improvement. In the
RL framework introduced in this paper, policies are considered that produce
actions based on states and random elements autocorrelated in subsequent time
instants. The RL algorithm introduced here approximately optimizes such a
policy. The efficiency of this algorithm is verified against three other RL
methods (PPO, SAC, ACER) in four simulated learning control problems (Ant,
HalfCheetah, Hopper, and Walker2D) in diverse time discretization. The
algorithm introduced here outperforms the competitors in most cases considered.


Abstract:
  Kiukas, Lahti and Ylinen asked the following general question. When is a
positive operator measure projection valued? A version of this question
formulated in terms of operator moments was posed in a recent paper of the
present authors. Let $T$ be a selfadjoint operator and $F$ be a Borel
semispectral measure on the real line with compact support. For which positive
integers $p< q$ do the equalities $T^k =\int_{\mathbb{R}} x^k F(dx)$, $k=p, q$,
imply that $F$ is a spectral measure? In the present paper, we completely solve
the second problem. The answer is affirmative if $p$ is odd and $q$ is even,
and negative otherwise. The case $(p,q)=(1,2)$ closely related to intrinsic
noise operator was solved by several authors including Kruszy\'{n}ski and de
Muynck as well as Kiukas, Lahti and Ylinen. The counterpart of the second
problem concerning the multiplicativity of unital positive linear maps on
$C^*$-algebras is also solved.


Abstract:
  Thompson sampling is a popular algorithm for solving multi-armed bandit
problems, and has been applied in a wide range of applications, from website
design to portfolio optimization. In such applications, however, the number of
choices (or arms) $N$ can be large, and the data needed to make adaptive
decisions require expensive experimentation. One is then faced with the
constraint of experimenting on only a small subset of $K \ll N$ arms within
each time period, which poses a problem for traditional Thompson sampling. We
propose a new Thompson Sampling under Experimental Constraints (TSEC) method,
which addresses this so-called "arm budget constraint". TSEC makes use of a
Bayesian interaction model with effect hierarchy priors, to model correlations
between rewards on different arms. This fitted model is then integrated within
Thompson sampling, to jointly identify a good subset of arms for
experimentation and to allocate resources over these arms. We demonstrate the
effectiveness of TSEC in two problems with arm budget constraints. The first is
a simulated website optimization study, where TSEC shows noticeable
improvements over industry benchmarks. The second is a portfolio optimization
application on industry-based exchange-traded funds, where TSEC provides more
consistent and greater wealth accumulation over standard investment strategies.


Abstract:
  We report on a theoretical model for image formation in full-field optical
coherence tomography (FFOCT). Because the spatial incoherence of the
illumination acts as a virtual confocal pinhole in FFOCT, its imaging
performance is equivalent to a scanning time-gated coherent confocal
microscope. In agreement with optical experiments enabling a precise control of
aberrations, FFOCT is shown to have nearly twice the resolution of standard
imaging at moderate aberration level. Beyond a rigorous study on the
sensitivity of FFOCT with respect to aberrations, this theoretical model paves
the way towards an optimized design of adaptive optics and \rev{computational
tools} for high-resolution and deep imaging of biological tissues.


Abstract:
  It is a long-lasting task to understand heat conduction phenomena beyond
Fourier. Besides the low-temperature experiments on extremely pure crystals, it
has turned out recently that heterogeneous materials with macro-scale size can
also show thermal effects that cannot be modelled by the Fourier equation. This
is called over-diffusive propagation, different from low-temperature
observations, and is found in numerous samples made from metal foam, rocks, and
composites. The measured temperature history is indeed similar to what
Fourier's law predicts but the usual evaluation cannot provide reliable thermal
parameters. This paper is a report on our experiments on several rock types,
each type having multiple samples with different thicknesses. We show that
size-dependent thermal behaviour can occur for both Fourier and non-Fourier
situations. Moreover, based on the present experimental data, we find an
empirical relation between the Fourier and non-Fourier parameters, which may be
helpful in later experiments to develop a more robust and reliable evaluation
procedure.


Abstract:
  We consider a two-player zero-sum deterministic differential game where each
player uses both continuous and impulse controls in infinite-time horizon. We
assume that the impulses supposed to be of general term and the costs depend on
the state of the system. We use the dynamic programming principle and viscosity
solutions approach to show existence and uniqueness of a solution for the
Hamilton-Jacobi-Bellman-Isaacs (HJBI) partial differential equations (PDEs) of
the game. We prove under Isaacs condition that the upper and lower value
functions coincide.


Abstract:
  This paper addresses the problem of combining Byzantine resilience with
privacy in machine learning (ML). Specifically, we study if a distributed
implementation of the renowned Stochastic Gradient Descent (SGD) learning
algorithm is feasible with both differential privacy (DP) and
$(\alpha,f)$-Byzantine resilience. To the best of our knowledge, this is the
first work to tackle this problem from a theoretical point of view. A key
finding of our analyses is that the classical approaches to these two
(seemingly) orthogonal issues are incompatible. More precisely, we show that a
direct composition of these techniques makes the guarantees of the resulting
SGD algorithm depend unfavourably upon the number of parameters of the ML
model, making the training of large models practically infeasible. We validate
our theoretical results through numerical experiments on publicly-available
datasets; showing that it is impractical to ensure DP and Byzantine resilience
simultaneously.


Abstract:
  Image captioning has conventionally relied on reference-based automatic
evaluations, where machine captions are compared against captions written by
humans. This is in contrast to the reference-free manner in which humans assess
caption quality.
  In this paper, we report the surprising empirical finding that CLIP (Radford
et al., 2021), a cross-modal model pretrained on 400M image+caption pairs from
the web, can be used for robust automatic evaluation of image captioning
without the need for references. Experiments spanning several corpora
demonstrate that our new reference-free metric, CLIPScore, achieves the highest
correlation with human judgements, outperforming existing reference-based
metrics like CIDEr and SPICE. Information gain experiments demonstrate that
CLIPScore, with its tight focus on image-text compatibility, is complementary
to existing reference-based metrics that emphasize text-text similarities.
Thus, we also present a reference-augmented version, RefCLIPScore, which
achieves even higher correlation. Beyond literal description tasks, several
case studies reveal domains where CLIPScore performs well (clip-art images,
alt-text rating), but also where it is relatively weaker in comparison to
reference-based metrics, e.g., news captions that require richer contextual
knowledge.


Abstract:
  We establish a sharp bilinear estimate for the Klein--Gordon propagator in
the spirit of recent work of Beltran--Vega. Our approach is inspired by work in
the setting of the wave equation due to Bez, Jeavons and Ozawa. As a
consequence of our main bilinear estimate, we deduce several sharp estimates of
null-form type and recover some sharp Strichartz estimates found by Quilodr\'an
and Jeavons.


Abstract:
  Modelling the dynamics of urban venues is a challenging task as it is
multifaceted in nature. Demand is a function of many complex and nonlinear
features such as neighborhood composition, real-time events, and seasonality.
Recent advances in Graph Convolutional Networks (GCNs) have had promising
results as they build a graphical representation of a system and harness the
potential of deep learning architectures. However, there has been limited work
using GCNs in a temporal setting to model dynamic dependencies of the network.
Further, within the context of urban environments, there has been no prior work
using dynamic GCNs to support venue demand analysis and prediction. In this
paper, we propose a novel deep learning framework which aims to better model
the popularity and growth of urban venues. Using a longitudinal dataset from
location technology platform Foursquare, we model individual venues and venue
types across London and Paris. First, representing cities as connected networks
of venues, we quantify their structure and note a strong community structure in
these retail networks, an observation that highlights the interplay of
cooperative and competitive forces that emerge in local ecosystems of retail
businesses. Next, we present our deep learning architecture which integrates
both spatial and topological features into a temporal model which predicts the
demand of a venue at the subsequent time-step. Our experiments demonstrate that
our model can learn spatio-temporal trends of venue demand and consistently
outperform baseline models. Relative to state-of-the-art deep learning models,
our model reduces the RSME by ~ 28% in London and ~ 13% in Paris. Our approach
highlights the power of complex network measures and GCNs in building
prediction models for urban environments. The model could have numerous
applications within the retail sector to better model venue demand and growth.


Abstract:
  Non-uniform message quantization techniques such as
reconstruction-computation-quantization (RCQ) improve error-correction
performance and decrease hardware complexity of low-density parity-check (LDPC)
decoders that use a flooding schedule. Layered MinSum RCQ (L-msRCQ) enables
message quantization to be utilized for layered decoders and irregular LDPC
codes. We investigate field-programmable gate array (FPGA) implementations of
L-msRCQ decoders. Three design methods for message quantization are presented,
which we name the Lookup, Broadcast, and Dribble methods. The decoding
performance and hardware complexity of these schemes are compared to a layered
offset MinSum (OMS) decoder. Simulation results on a (16384, 8192)
protograph-based raptor-like (PBRL) LDPC code show that a 4-bit L-msRCQ decoder
using the Broadcast method can achieve a 0.03 dB improvement in
error-correction performance while using 12% fewer registers than the OMS
decoder. A Broadcast-based 3-bit L-msRCQ decoder uses 15% fewer lookup tables,
18% fewer registers, and 13% fewer routed nets than the OMS decoder, but
results in a 0.09 dB loss in performance.


Abstract:
  We examine a description of available cross section data for symmetric space
star (SST) configurations in the neutron-deuteron (nd) and proton-deuteron (pd)
breakup reaction using numerically exact solutions of the three-nucleon (3N)
Faddeev equation based on two- and three-nucleon (semi)phenomenological and
chiral forces. The predicted SST cross sections are very stable with respect to
the underlying dynamics for incoming nucleon laboratory energies below $\approx
25$ MeV. We discuss possible origins of the surprising discrepancies between
theory and data found in low-energy nd and pd SST breakup measurements.


Abstract:
  SrTiO$_3$ exhibits superconductivity for carrier densities $10^{19}-10^{21}$
cm$^{-3}$. Across this range, the Fermi level traverses a number of vibrational
modes in the system, making it ideal for studying dilute superconductivity. We
use high-resolution planar-tunneling spectroscopy to probe chemically-doped
SrTiO$_3$ across the superconducting dome. The over-doped superconducting
boundary aligns, with surprising precision, to the Fermi energy crossing the
Debye energy. Superconductivity emerges with decreasing density, maintaining
throughout the Bardeen-Cooper-Schrieffer (BCS) gap to transition-temperature
ratio, despite being in the anti-adiabatic regime. At lowest superconducting
densities, the lone remaining adiabatic phonon van Hove singularity is the soft
transverse-optic mode, associated with the ferroelectric instability. We
suggest a scenario for pairing mediated by this mode in the presence of
spin-orbit coupling, which naturally accounts for the superconducting dome and
BCS ratio.


Abstract:
  In today's context, deploying data-driven services like recommendation on
edge devices instead of cloud servers becomes increasingly attractive due to
privacy and network latency concerns. A common practice in building compact
on-device recommender systems is to compress their embeddings which are
normally the cause of excessive parameterization. However, despite the vast
variety of devices and their associated memory constraints, existing
memory-efficient recommender systems are only specialized for a fixed memory
budget in every design and training life cycle, where a new model has to be
retrained to obtain the optimal performance while adapting to a smaller/larger
memory budget. In this paper, we present a novel lightweight recommendation
paradigm that allows a well-trained recommender to be customized for arbitrary
device-specific memory constraints without retraining. The core idea is to
compose elastic embeddings for each item, where an elastic embedding is the
concatenation of a set of embedding blocks that are carefully chosen by an
automated search function. Correspondingly, we propose an innovative approach,
namely recommendation with universally learned elastic embeddings (RULE). To
ensure the expressiveness of all candidate embedding blocks, RULE enforces a
diversity-driven regularization when learning different embedding blocks. Then,
a performance estimator-based evolutionary search function is designed,
allowing for efficient specialization of elastic embeddings under any memory
constraint for on-device recommendation. Extensive experiments on real-world
datasets reveal the superior performance of RULE under tight memory budgets.


Abstract:
  Emerging interests have been brought to recognize previously unseen objects
given very few training examples, known as few-shot object detection (FSOD).
Recent researches demonstrate that good feature embedding is the key to reach
favorable few-shot learning performance. We observe object proposals with
different Intersection-of-Union (IoU) scores are analogous to the intra-image
augmentation used in contrastive approaches. And we exploit this analogy and
incorporate supervised contrastive learning to achieve more robust objects
representations in FSOD. We present Few-Shot object detection via Contrastive
proposals Encoding (FSCE), a simple yet effective approach to learning
contrastive-aware object proposal encodings that facilitate the classification
of detected objects. We notice the degradation of average precision (AP) for
rare objects mainly comes from misclassifying novel instances as confusable
classes. And we ease the misclassification issues by promoting instance level
intra-class compactness and inter-class variance via our contrastive proposal
encoding loss (CPE loss). Our design outperforms current state-of-the-art works
in any shot and all data splits, with up to +8.8% on standard benchmark PASCAL
VOC and +2.7% on challenging COCO benchmark. Code is available at: https:
//github.com/MegviiDetection/FSCE


Abstract:
  We propose a simple approach to implement a tunable, high power and narrow
linewidth laser source based on a series of highly coherent tones from an
electro-optic frequency comb and a set of 3 DFB slave lasers. We experimentally
demonstrate approximately 1.25 THz (10 nm) of tuning within the C-Band centered
at 192.9 THz (1555 nm). The output power is approximately 100 mW (20 dBm), with
a side band suppression ratio greater than 55 dB, and a linewidth below 400 Hz
across the full range of tunability. This approach is scalable and may be
extended to cover a significantly broader optical spectral range.


Abstract:
  Time-reparametrization invariance in general relativistic space-time does not
allow us to single out a time in quantum mechanics in a mechanical way of
measurement. Motivated by this problem, we examine this gauge invariance in the
ground state of the quasi-stationary coarse-grained state of a long-range
interacting closed system of identical or identified, macroscopic, and
spatiotemporally inhomogeneous Bose-Einstein condensates in the thermodynamic
and Newtonian limits. As a result, we find that it is a theoretical
counterexample of this gauge invariance, except for proper-time translational
invariance, at a coarse-grained level.


Abstract:
  The H^2-regularity of variational solutions to a two-dimensional transmission
problem with geometric constraint is investigated, in particular when part of
the interface becomes part of the outer boundary of the domain due to the
saturation of the geometric constraint. In such a situation, the domain
includes some non-Lipschitz subdomains with cusp points, but it is shown that
this feature does not lead to a regularity breakdown. Moreover, continuous
dependence of the solutions with respect to the domain is established.


Abstract:
  We study particle production and unitarity violation caused by a curved
target space right after inflation. We use the inflaton field value instead of
cosmic time as the time variable, and derive a semiclassical formula for the
spectrum of produced particles. We then derive a simple condition for unitarity
violation during preheating, which we confirm by our semiclassical method and
numerical solution. This condition depends not only on the target space
curvature but also on the height of the inflaton potential at the end of
inflation. This condition tells us, for instance, that unitarity is violated in
running kinetic inflation and Higgs inflation, while unitarity is conserved in
$\alpha$-attractor inflation and Higgs-Palatini inflation.


Abstract:
  A minimal absent word of a sequence x, is a sequence yt hat is not a factorof
x, but all of its proper factors are factors of x as well. The set of minimal
absent words uniquely defines the sequence itself. In recent times minimal
absent words have been used in order to compare sequences. In fact, to do this,
one can compare the sets of their minimal absent words. Chairungasee and
Crochemorein [2] define a distance between pairs of sequences x and y, where
the symmetric difference of the sets of minimal absent words of x and y is
involved. Here, weconsider a different distance, introduced in [1], based on a
specific subset of such symmetric difference that, in our opinion, better
capture the different features ofthe considered sequences. We show the result
of some experiments where the distance is tested on a dataset of genetic
sequences by 11 living species, in order to compare the new distance with the
ones existing in literature.


Abstract:
  The Semi-Digital Hadronic Calorimeter (SDHCAL) is proposed to equip the
future ILC detector. A technological prototype of the SDHCAL developed within
the CALICE collaboration has been extensively tested in test beams. We review
here the prototype performances in terms of hadronic shower reconstruction from
the most recent analyses test beam data.


Abstract:
  We propose a worldsheet formula for tree-level correlation functions
describing a scalar field with arbitrary mass and quartic self-interaction in
de Sitter space, which is a simple model for inflationary cosmology. The
correlation functions are located on the future boundary of the spacetime and
are Fourier-transformed to momentum space. Our formula is supported on
mass-deformed scattering equations involving conformal generators in momentum
space and reduces to the CHY formula for $\phi^4$ amplitudes in the flat space
limit. Using the global residue theorem, we verify that it reproduces the
Witten diagram expansion at four and six points, and sketch the extension to
$n$ points.


Abstract:
  Generative adversarial networks (GANs) are one of the greatest advances in AI
in recent years. With their ability to directly learn the probability
distribution of data, and then sample synthetic realistic data. Many
applications have emerged, using GANs to solve classical problems in machine
learning, such as data augmentation, class unbalance problems, and fair
representation learning. In this paper, we analyze and highlight fairness
concerns of GANs model. In this regard, we show empirically that GANs models
may inherently prefer certain groups during the training process and therefore
they're not able to homogeneously generate data from different groups during
the testing phase. Furthermore, we propose solutions to solve this issue by
conditioning the GAN model towards samples' group or using ensemble method
(boosting) to allow the GAN model to leverage distributed structure of data
during the training phase and generate groups at equal rate during the testing
phase.


Abstract:
  The classification of time-series data is pivotal for streaming data and
comes with many challenges. Although the amount of publicly available datasets
increases rapidly, deep neural models are only exploited in a few areas.
Traditional methods are still used very often compared to deep neural models.
These methods get preferred in safety-critical, financial, or medical fields
because of their interpretable results. However, their performance and
scale-ability are limited, and finding suitable explanations for time-series
classification tasks is challenging due to the concepts hidden in the numerical
time-series data. Visualizing complete time-series results in a cognitive
overload concerning our perception and leads to confusion. Therefore, we
believe that patch-wise processing of the data results in a more interpretable
representation. We propose a novel hybrid approach that utilizes deep neural
networks and traditional machine learning algorithms to introduce an
interpretable and scale-able time-series classification approach. Our method
first performs a fine-grained classification for the patches followed by sample
level classification.


Abstract:
  In growing active matter systems, a large collection of engineered or living
autonomous units metabolize free energy and create order at different length
scales as they proliferate and migrate collectively. One such example is
bacterial biofilms, which are surface-attached aggregates of bacterial cells
embedded in an extracellular matrix. However, how bacterial growth coordinates
with cell-surface interactions to create distinctive, long-range order in
biofilms remains elusive. Here we report a collective cell reorientation
cascade in growing Vibrio cholerae biofilms, leading to a differentially
ordered, spatiotemporally coupled core-rim structure reminiscent of a blooming
aster. Cell verticalization in the core generates differential growth that
drives radial alignment of the cells in the rim, while the radially aligned rim
in turn generates compressive stresses that expand the verticalized core. Such
self-patterning disappears in adhesion-less mutants but can be restored through
opto-manipulation of growth. Agent-based simulations and two-phase active
nematic modeling reveal the strong interdependence of the driving forces for
the differential ordering. Our findings provide insight into the collective
cell patterning in bacterial communities and engineering of phenotypes and
functions of living active matter.


Abstract:
  In vehicle-to-everything (V2X) communications, reliability is one of the most
important performance metrics in safety-critical applications such as advanced
driving, remote driving, and vehicle platooning. In this paper, the link
reliability of unicast concurrent transmission in mode 1 (centralized mode) of
5G New Radio based V2X (NR-V2X) is analyzed. The closed-form expression of link
reliability for concurrent unicast transmission is firstly derived for a
highway scenario under a given interference distance distribution. On this
basis, according to the macroscopic configuration of the system, a method to
control the number of concurrent transmission nodes is proposed, including the
communication range, message packet size, and the number of lanes, etc. The
results indicate that the proposed method can maximize the system load on the
premise of satisfying the link reliability requirements.


Abstract:
  We present optical VLT/MUSE integral field spectroscopy data of the merging
galaxy NGC 1487. We use fitting techniques to study the ionized gas emission of
this merger and its main morphological and kinematical properties. We measured
flat and sometimes inverted oxygen abundance gradients in the subsystems
composing NGC 1487, explained by metal mixing processes common in merging
galaxies. We also measured widespread star-forming bursts, indicating that
photoionisation by stars is the primary ionization source of the galaxy. The
kinematic map revealed a rotating pattern in the gas in the northern tail of
the system, suggesting that the galaxy may be in the process of rebuilding a
disc. The gas located in the central region has larger velocity dispersion
($\sigma\approx 50$ km s$^{-1}$) than the remaining regions, indicating
kinematic heating, possibly owing to the ongoing interaction. Similar trends
were, however, not observed in the stellar velocity-dispersion map, indicating
that the galaxy has not yet achieved equilibrium, and the nebular and stellar
components are still kinematically decoupled. Based on all our measurements and
findings, and specially on the mass estimates, metallicity gradients and
velocity fields of the system, we propose that NGC 1487 is the result of an
ongoing merger event involving smallish dwarf galaxies within a group, in a
pre-merger phase, resulting in a relic with mass and physical parameters
similar to a dwarf galaxy. Thus, we may be witnessing the formation of a dwarf
galaxy by merging of smaller clumps at z=0.


Abstract:
  Current dense symmetric eigenvalue (EIG) and singular value decomposition
(SVD) implementations may suffer from the lack of concurrency during the
tridiagonal and bidiagonal reductions, respectively. This performance
bottleneck is typical for the two-sided transformations due to the Level-2 BLAS
memory-bound calls. Therefore, the current state-of-the-art EIG and SVD
implementations may achieve only a small fraction of the system's sustained
peak performance. The QR-based Dynamically Weighted Halley (QDWH) algorithm may
be used as a pre-processing step toward the EIG and SVD solvers, while
mitigating the aforementioned bottleneck. QDWH-EIG and QDWH-SVD expose more
parallelism, while relying on compute-bound matrix operations. Both run closer
to the sustained peak performance of the system, but at the expense of
performing more FLOPS than the standard EIG and SVD algorithms. In this paper,
we introduce a new QDWH-based solver for computing the partial spectrum for EIG
(QDWHpartial-EIG) and SVD (QDWHpartial-SVD) problems. By optimizing the
rational function underlying the algorithms only in the desired part of the
spectrum, QDWHpartial-EIG and QDWHpartial-SVD algorithms efficiently compute a
fraction (say 1-20%) of the corresponding spectrum. We develop high-performance
implementations of QDWHpartial-EIG and QDWHpartial-SVD on distributed-memory
anymore systems and demonstrate their numerical robustness. Experimental
results using up to 36K MPI processes show performance speedups for
QDWHpartial-SVD up to 6X and 2X against PDGESVD from ScaLAPACK and KSVD,
respectively. QDWHpartial-EIG outperforms PDSYEVD from ScaLAPACK up to 3.5X but
remains slower compared to ELPA. QDWHpartial-EIG achieves, however, a better
occupancy of the underlying hardware by extracting higher sustained peak
performance than ELPA, which is critical moving forward with accelerator-based
supercomputers.


Abstract:
  We investigate attosecond time delays in the emission of photoelectrons using
a hierarchy of models of the $CO_2$ molecule including the strong field
approximation, Coulomb-scattering, short-range parts of the molecular
potential, Hartree and Hartree-Fock descriptions. In addition, we present an
{\it ab initio} calculation based on quantum-chemical structure in combination
with strong-field techniques, which fully includes multi-electron exchange and
correlation. Every single of these model constituents is found to modify delays
on the scale of 10 as or more, with exchange and correlation having the most
pronounced effect.


Abstract:
  We study the notion of indistinguishability obfuscation for null quantum
circuits (quantum null-iO). We present a construction assuming: - The quantum
hardness of learning with errors (LWE). - Post-quantum indistinguishability
obfuscation for classical circuits. - A notion of ''dual-mode'' classical
verification of quantum computation (CVQC).
  We give evidence that our notion of dual-mode CVQC exists by proposing a
scheme that is secure assuming LWE in the quantum random oracle model (QROM).
  Then we show how quantum null-iO enables a series of new cryptographic
primitives that, prior to our work, were unknown to exist even making heuristic
assumptions. Among others, we obtain the first witness encryption scheme for
QMA, the first publicly verifiable non-interactive zero-knowledge (NIZK) scheme
for QMA, and the first attribute-based encryption (ABE) scheme for BQP.


Abstract:
  One salient feature of cooperative formation tracking is its distributed
nature that relies on localized control and information sharing over a sparse
communication network. That is, a distributed control manner could be prone to
malicious attacks and unreliable communication that deteriorate the formation
tracking performance or even destabilize the whole multi-agent system. This
paper studies a safe and reliable time-varying output formation tracking
problem of linear multi-agent systems, where an attacker adversely injects any
unbounded time-varying signals (false data injection (FDI) attacks), while an
interruption of communication channels between the agents is caused by an
unreliable network. Both characteristics improve the practical relevance of the
problem to be addressed, which poses some technical challenges to the
distributed algorithm design and stability analysis. To mitigate the adverse
effect, a novel resilient distributed control architecture is established to
guarantee time-varying output formation tracking exponentially. The key
features of the proposed framework are threefold: 1) an observer-based
identifier is integrated to compensate for adverse effects; 2) a reliable
distributed algorithm is proposed to deal with time-varying topologies caused
by unreliable communication; and 3) in contrast to the existing remedies that
deal with attacks as bounded disturbances/faults with known knowledge, we
propose resilience strategies to handle unknown and unbounded attacks for
exponential convergence of dynamic formation tracking errors, whereas most of
existing results achieve uniformly ultimately boundedness (UUB) results.
Numerical simulations are given to show the effectiveness of the proposed
design.


Abstract:
  Linear embedding transformation has been shown to be effective for zero-shot
cross-lingual transfer tasks and achieve surprisingly promising results.
However, cross-lingual embedding space mapping is usually studied in static
word-level embeddings, where a space transformation is derived by aligning
representations of translation pairs that are referred from dictionaries. We
move further from this line and investigate a contextual embedding alignment
approach which is sense-level and dictionary-free. To enhance the quality of
the mapping, we also provide a deep view of properties of contextual
embeddings, i.e., anisotropy problem and its solution. Experiments on zero-shot
dependency parsing through the concept-shared space built by our embedding
transformation substantially outperform state-of-the-art methods using
multilingual embeddings.


Abstract:
  This paper provides an $H_2$ optimal scheme for reducing diffusively coupled
second-order systems evolving over undirected networks. The aim is to find a
reduced-order model that not only approximates the input-output mapping of the
original system but also preserves crucial structures, such as the second-order
form, asymptotically stability, and diffusive couplings. To this end, an $H_2$
optimal approach based on a convex relaxation is implemented to reduce the
dimension, yielding a lower order asymptotically stable approximation of the
original second-order network system. Then, a novel graph reconstruction
approach is employed to convert the obtained model to a reduced system that is
interpretable as an undirected diffusively coupled network. Finally, the
effectiveness of the proposed method is illustrated via a large-scale networked
mass-spring-damper system.


Abstract:
  The radio, optical, and $\gamma$-ray light curves of the blazar S5 1803+784,
from the beginning of the {\it Fermi} Large Area Telescope (LAT) mission in
August 2008 until December 2018, are presented. The aim of this work is to look
for correlations among different wavelengths useful for further theoretical
studies. We analyzed all the data collected by {\it Fermi} LAT for this source,
taking into account the presence of nearby sources, and we collected optical
data from our own observations and public archive data to build the most
complete optical and $\gamma$-ray light curve possible. Several $\gamma$-ray
flares ($\mathrm{F>2.3~10^{-7} ph(E>0.1 GeV)~cm^{-2}~s^{-1}}$) with optical
coverage were detected, all but one with corresponding optical enhancement; we
also found two optical flares without a $\gamma$-ray counterpart. We obtained
two {\it Swift} Target of Opportunity observations during the strong flare of
2015. Radio observations performed with VLBA and EVN through our proposals in
the years 2016-2020 were analyzed to search for morphological changes after the
major flares. The optical/$\gamma$-ray flux ratio at the flare peak varied for
each flare. Very minor optical V-I color changes were detected during the
flares. The X-ray spectrum was well fitted by a power law with photon spectral
index $\alpha$=1.5, nearly independent of the flux level: no clear correlation
with the optical or the $\gamma$-ray emission was found. The $\gamma$-ray
spectral shape was well fitted by a power law with average photon index
$\alpha$= 2.2. These findings support an Inverse Compton origin for the
high-energy emission of the source, nearly co-spatial with the optically
emitting region. The radio maps showed two new components originating from the
core and moving outwards, with ejection epochs compatible with the dates of the
two largest $\gamma$-ray flares.


Abstract:
  Deep convolutional networks have attracted great attention in image
restoration and enhancement. Generally, restoration quality has been improved
by building more and more convolutional block. However, these methods mostly
learn a specific model to handle all images and ignore difficulty diversity. In
other words, an area in the image with high frequency tend to lose more
information during compressing while an area with low frequency tends to lose
less. In this article, we adrress the efficiency issue in image SR by
incorporating a patch-wise rolling network(PRN) to content-adaptively recover
images according to difficulty levels. In contrast to existing studies that
ignore difficulty diversity, we adopt different stage of a neural network to
perform image restoration. In addition, we propose a rolling strategy that
utilizes the parameters of each stage more flexible. Extensive experiments
demonstrate that our model not only shows a significant acceleration but also
maintain state-of-the-art performance.


Abstract:
  We give an efficient algorithm for learning a binary function in a given
class C of bounded VC dimension, with training data distributed according to P
and test data according to Q, where P and Q may be arbitrary distributions over
X. This is the generic form of what is called covariate shift, which is
impossible in general as arbitrary P and Q may not even overlap. However,
recently guarantees were given in a model called PQ-learning (Goldwasser et
al., 2020) where the learner has: (a) access to unlabeled test examples from Q
(in addition to labeled samples from P, i.e., semi-supervised learning); and
(b) the option to reject any example and abstain from classifying it (i.e.,
selective classification). The algorithm of Goldwasser et al. (2020) requires
an (agnostic) noise tolerant learner for C. The present work gives a
polynomial-time PQ-learning algorithm that uses an oracle to a "reliable"
learner for C, where reliable learning (Kalai et al., 2012) is a model of
learning with one-sided noise. Furthermore, our reduction is optimal in the
sense that we show the equivalence of reliable and PQ learning.


Abstract:
  Drilling and Extraction Automated System (DREAMS) is a fully automated
prototype-drilling rig that can drill, extract water and assess subsurface
density profiles from simulated lunar and Martian subsurface ice. DREAMS system
is developed by the Texas A&M drilling automation team and composed of four
main components: 1- tensegrity rig structure, 2- drilling system, 3- water
extracting and heating system, and 4- electronic hardware, controls, and
machine algorithm. The vertical and rotational movements are controlled by
using an Acme rod, stepper, and rotary motor. DREAMS is a unique system and
different from other systems presented before in the NASA Rascal-Al competition
because 1- It uses the tensegrity structure concept to decrease the system
weight, improve mobility, and easier installation in space. 2- It cuts rock
layers by using a short bit length connected to drill pipes. This drilling
methodology is expected to drill hundreds and thousands of meters below the
moon and Martian surfaces without any anticipated problems (not only 1 m.). 3-
Drilling, heating, and extraction systems are integrated into one system that
can work simultaneously or individually to save time and cost.


Abstract:
  We consider the problem of forecasting multiple values of the future of a
vector time series, using some past values. This problem, and related ones such
as one-step-ahead prediction, have a very long history, and there are a number
of well-known methods for it, including vector auto-regressive models,
state-space methods, multi-task regression, and others. Our focus is on low
rank forecasters, which break forecasting up into two steps: estimating a
vector that can be interpreted as a latent state, given the past, and then
estimating the future values of the time series, given the latent state
estimate. We introduce the concept of forecast consistency, which means that
the estimates of the same value made at different times are consistent. We
formulate the forecasting problem in general form, and focus on linear
forecasters, for which we propose a formulation that can be solved via convex
optimization. We describe a number of extensions and variations, including
nonlinear forecasters, data weighting, the inclusion of auxiliary data, and
additional objective terms. We illustrate our methods with several examples.


Abstract:
  Deep image inpainting aims to restore damaged or missing regions in an image
with realistic contents. While having a wide range of applications such as
object removal and image recovery, deep inpainting techniques also have the
risk of being manipulated for image forgery. A promising countermeasure against
such forgeries is deep inpainting detection, which aims to locate the inpainted
regions in an image. In this paper, we make the first attempt towards universal
detection of deep inpainting, where the detection network can generalize well
when detecting different deep inpainting methods. To this end, we first propose
a novel data generation approach to generate a universal training dataset,
which imitates the noise discrepancies exist in real versus inpainted image
contents to train universal detectors. We then design a Noise-Image
Cross-fusion Network (NIX-Net) to effectively exploit the discriminative
information contained in both the images and their noise patterns. We
empirically show, on multiple benchmark datasets, that our approach outperforms
existing detection methods by a large margin and generalize well to unseen deep
inpainting techniques. Our universal training dataset can also significantly
boost the generalizability of existing detection methods.


Abstract:
  The recently established formalism of a worldline quantum field theory, which
describes the classical scattering of massive bodies in Einstein gravity, is
generalized up to quadratic order in spin -- for a pair of Kerr black holes
revealing a hidden ${\mathcal N}=2$ supersymmetry. The far-field time-domain
waveform of the gravitational waves produced in such a spinning encounter is
computed at leading order in the post-Minkowskian (weak field, but generic
velocity) expansion, and exhibits this supersymmetry. From the waveform we
extract the leading-order total radiated angular momentum in a generic
reference frame, and the total radiated energy in the center-of-mass frame to
leading order in a low-velocity approximation.


Abstract:
  We develop a uniform coalgebraic approach to Thomason and J\'{o}nsson-Tarski
type dualities for various classes of neighborhood frames and neighborhood
algebras. In the first part of the paper we construct an endofunctor on the
category of complete and atomic Boolean algebras that is dual to the double
powerset functor on $\mathsf{Set}$. This allows us to show that Thomason
duality for neighborhood frames can be viewed as an algebra-coalgebra duality.
We generalize this approach to any class of algebras for an endofunctor
presented by one-step axioms in the language of infinitary modal logic. As a
consequence, we obtain a uniform approach to dualities for various classes of
neighborhood frames, including monotone neighborhood frames, pretopological
spaces, and topological spaces.
  In the second part of the paper we develop a coalgebraic approach to
J\'{o}nsson-Tarski duality for neighborhood algebras and descriptive
neighborhood frames. We introduce an analogue of the Vietoris endofunctor on
the category of Stone spaces and show that descriptive neighborhood frames are
isomorphic to coalgebras for this endofunctor. This allows us to obtain a
coalgebraic proof of the duality between descriptive neighborhood frames and
neighborhood algebras. Using one-step axioms in the language of finitary modal
logic, we restrict this duality to other classes of neighborhood algebras
studied in the literature, including monotone modal algebras and contingency
algebras.
  We conclude the paper by connecting the two types of dualities via canonical
extensions, and discuss when these extensions are functorial.


Abstract:
  The main topic of this paper is a brief overview of the field of Artificial
Intelligence. The core of this paper is a practical implementation of an
algorithm for object detection and tracking. The ability to detect and track
fast-moving objects is crucial for various applications of Artificial
Intelligence like autonomous driving, ball tracking in sports, robotics or
object counting. As part of this paper the Fully Convolutional Neural Network
"CueNet" was developed. It detects and tracks the cueball on a labyrinth game
robustly and reliably. While CueNet V1 has a single input image, the approach
with CueNet V2 was to take three consecutive 240 x 180-pixel images as an input
and transform them into a probability heatmap for the cueball's location. The
network was tested with a separate video that contained all sorts of
distractions to test its robustness. When confronted with our testing data,
CueNet V1 predicted the correct cueball location in 99.6% of all frames, while
CueNet V2 had 99.8% accuracy.


Abstract:
  Novelty detection is the task of recognizing samples that do not belong to
the distribution of the target class. During training, the novelty class is
absent, preventing the use of traditional classification approaches. Deep
autoencoders have been widely used as a base of many unsupervised novelty
detection methods. In particular, context autoencoders have been successful in
the novelty detection task because of the more effective representations they
learn by reconstructing original images from randomly masked images. However, a
significant drawback of context autoencoders is that random masking fails to
consistently cover important structures of the input image, leading to
suboptimal representations - especially for the novelty detection task. In this
paper, to optimize input masking, we have designed a framework consisting of
two competing networks, a Mask Module and a Reconstructor. The Mask Module is a
convolutional autoencoder that learns to generate optimal masks that cover the
most important parts of images. Alternatively, the Reconstructor is a
convolutional encoder-decoder that aims to reconstruct unperturbed images from
masked images. The networks are trained in an adversarial manner in which the
Mask Module generates masks that are applied to images given to the
Reconstructor. In this way, the Mask Module seeks to maximize the
reconstruction error that the Reconstructor is minimizing. When applied to
novelty detection, the proposed approach learns semantically richer
representations compared to context autoencoders and enhances novelty detection
at test time through more optimal masking. Novelty detection experiments on the
MNIST and CIFAR-10 image datasets demonstrate the proposed approach's
superiority over cutting-edge methods. In a further experiment on the UCSD
video dataset for novelty detection, the proposed approach achieves
state-of-the-art results.


Abstract:
  We consider a meal delivery service fulfilling dynamic customer requests
given a set of couriers over the course of a day. A courier's duty is to
pick-up an order from a restaurant and deliver it to a customer. We model this
service as a Markov decision process and use deep reinforcement learning as the
solution approach. We experiment with the resulting policies on synthetic and
real-world datasets and compare those with the baseline policies. We also
examine the courier utilization for different numbers of couriers. In our
analysis, we specifically focus on the impact of the limited available
resources in the meal delivery problem. Furthermore, we investigate the effect
of intelligent order rejection and re-positioning of the couriers. Our
numerical experiments show that, by incorporating the geographical locations of
the restaurants, customers, and the depot, our model significantly improves the
overall service quality as characterized by the expected total reward and the
delivery times. Our results present valuable insights on both the courier
assignment process and the optimal number of couriers for different order
frequencies on a given day. The proposed model also shows a robust performance
under a variety of scenarios for real-world implementation.


Abstract:
  Let $(\pi_{\mathbf{z}},V_{\mathbf{z}})$ be an unramified principal series
representation of a reductive group over a nonarchimedean local field,
parametrized by an element $\mathbf{z}$ of the maximal torus in the Langlands
dual group. If $v$ is an element of the Weyl group $W$, then the standard
intertwining integral $\mathcal{A}_v$ maps $V_{\mathbf{z}}$ to
$V_{v\mathbf{z}}$. Letting $\psi^{\mathbf{z}}_w$ with $w\in W$ be a suitable
basis of the Iwahori fixed vectors in $V_{\mathbf{z}}$, and
$\widehat\psi^{\mathbf{z}}_w$ a basis of the contragredient representation, we
define $\sigma(u,v,w)$ (for $u,v,w\in W$) to be $\langle
\mathcal{A}_v\psi_u^{\mathbf{z}},\widehat\psi^{v\mathbf{z}}_w\rangle$. This is
an interesting function and we initiate its study. We show that given $u$ and
$w$, there is a minimal $v$ such that $\sigma(u,v,w)\neq 0$. Denoting this $v$
as $v_\hbox{min}=v_\hbox{min}(u,w)$, we will prove that
$\sigma(u,v_\hbox{min},w)$ is a polynomial of the cardinality $q$ of the
residue field. Indeed if $v>v_\hbox{min}$, then $\sigma(u,v,w)$ is a rational
function of $\mathbf{z}$ and $q$, whose denominator we describe. But if
$v=v_\hbox{min}$, the dependence on $\mathbf{z}$ disappears. We will express
$\sigma(u,v_\hbox{min},w)$ as the Poincar\'e polynomial of a Bruhat interval.
The proof leads to fairly intricate considerations of the Bruhat order.
  Thus our results require us to prove some facts that may be of independent
interest, relating the Bruhat order $\leqslant$ and the weak Bruhat order
$\leqslant_R$. For example we will prove (for finite Coxeter groups) the
following "mixed meet" property. If $u, w$ are elements of $W$, then there
exists a unique element $m \in W$ that is maximal with respect to the condition
that $m \leqslant_R u$ and $m \leqslant w$. Thus if $z \leqslant_R u$ and $z
\leqslant w$, then $x \leqslant m$. The value $v_\hbox{min}$ is $m^{-1}u$.


Abstract:
  Performative distribution shift captures the setting where the choice of
which ML model is deployed changes the data distribution. For example, a bank
which uses the number of open credit lines to determine a customer's risk of
default on a loan may induce customers to open more credit lines in order to
improve their chances of being approved. Because of the interactions between
the model and data distribution, finding the optimal model parameters is
challenging. Works in this area have focused on finding stable points, which
can be far from optimal. Here we introduce performative gradient descent
(PerfGD), which is the first algorithm which provably converges to the
performatively optimal point. PerfGD explicitly captures how changes in the
model affects the data distribution and is simple to use. We support our
findings with theory and experiments.


Abstract:
  We investigate robust linear consensus over networks under
capacity-constrained communication. The capacity of each edge is encoded as an
upper bound on the number of state variables that can be communicated
instantaneously. When the edge capacities are small compared to the
dimensionality of the state vectors, it is not possible to instantaneously
communicate full state information over every edge. We investigate how robust
consensus (small steady state variance of the states) can be achieved within a
linear time-invariant setting by optimally assigning edges to state-dimensions.
We show that if a finite steady state variance of the states can be achieved,
then both the minimum cut capacity and the total capacity of the network should
be sufficiently large. Optimal and approximate solutions are provided for some
special classes of graphs. We also consider the related problem of optimally
allocating additional capacity on a feasible initial solution. We show that
this problem corresponds to the maximization of a submodular function subject
to a matroid constraint, which can be approximated via a greedy algorithm.


Abstract:
  We propose an exact model of anyon ground states including higher Landau
levels, and use it to obtain fractionally quantized Hall states at filling
fractions $\nu=p/(p(m-1)+1)$ with $m$ odd, from integer Hall states at $\nu=p$
through adiabatic localization of magnetic flux. For appropriately chosen
two-body potential interactions, the energy gap remains intact during the
process. The construction hence establishes the existence of incompressible
states at these fillings.


Abstract:
  The velocity of dislocations is derived analytically to incorporate and
predict the intriguing effects induced by the preferential solute segregation
and Cottrell atmospheres in both two-dimensional and three-dimensional binary
systems of various crystalline symmetries. The corresponding mesoscopic
description of defect dynamics is constructed through the amplitude formulation
of the phase-field crystal model which has been shown to accurately capture
elasticity and plasticity in a wide variety of systems. Modifications of the
Peach-Koehler force as a result of solute concentration variations and
compositional stresses are presented, leading to interesting new predictions of
defect motion due to effects of Cottrell atmospheres. These include the
deflection of dislocation glide paths, the variation of climb speed and
direction, and the change or prevention of defect annihilation, all of which
play an important role in determining the fundamental behaviors of complex
defect network and dynamics. The analytic results are verified by numerical
simulations.


Abstract:
  We investigate the tail asymptotics of the response time distribution for the
cancel-on-start (c.o.s.) and cancel-on-completion (c.o.c.) variants of
redundancy-$d$ scheduling and the fork-join model with heavy-tailed job sizes.
We present bounds, which only differ in the pre-factor, for the tail
probability of the response time in the case of the first-come first-served
(FCFS) discipline. For the c.o.s. variant we restrict ourselves to
redundancy-$d$ scheduling, which is a special case of the fork-join model. In
particular, for regularly varying job sizes with tail index $-\nu$ the tail
index of the response time for the c.o.s. variant of redundancy-$d$ equals
$-\min\{d_{\mathrm{cap}}(\nu-1),\nu\}$, where $d_{\mathrm{cap}} =
\min\{d,N-k\}$, $N$ is the number of servers and $k$ is the integer part of the
load. This result indicates that for $d_{\mathrm{cap}} < \frac{\nu}{\nu-1}$ the
waiting time component is dominant, whereas for $d_{\mathrm{cap}} >
\frac{\nu}{\nu-1}$ the job size component is dominant. Thus, having $d = \lceil
\min\{\frac{\nu}{\nu-1},N-k\} \rceil$ replicas is sufficient to achieve the
optimal asymptotic tail behavior of the response time. For the c.o.c. variant
of the fork-join($n_{\mathrm{F}},n_{\mathrm{J}}$) model the tail index of the
response time, under some assumptions on the load, equals $1-\nu$ and
$1-(n_{\mathrm{F}}+1-n_{\mathrm{J}})\nu$, for identical and i.i.d. replicas,
respectively; here the waiting time component is always dominant.


Abstract:
  We study cosmic evolution based on the fixed points in the dynamical analysis
of the Degenerate Higher-Order Scalar-Tensor (DHOST) theories. We consider the
DHOST theories in which the propagation speed of gravitational waves is equal
to the speed of light, the tensor perturbations do not decay to dark energy
perturbations, and the scaling solutions exist. The scaling fixed point
associated with late time acceleration of universe can be either stable or
saddle depending on the parameters of the theory. For some ranges of the
parameters, the accelerated scaling point and the field dominated point can be
simultaneously stable. Cosmic evolution will reach the accelerated scaling
point if the time derivative of the scalar field in the theory is positive
during the matter domination. If the time derivative of the scalar field is
negative during the matter domination, the background universe will evolve
towards the field dominated point. The density parameter of the matter can be
larger than unity before reaching the scaling fixed point if the deviation from
the Einstein theory of gravity is too large and the initial conditions for the
dynamical variables during the matter domination are significantly different
from the accelerated scaling point. The stabilities of $\phi$MDE fixed point
are similar to the coupled dark energy models. In our consideration, the
universe can only evolve from the $\phi$MDE to the field dominated point.


Abstract:
  We study a bottom-up holographic description of the QCD colour
superconducting phase in the presence of higher derivative corrections. We
expand this holographic model in the context of Gauss-Bonnet (GB) gravity. The
Cooper pair condensate has been investigated in the deconfinement phase for
different values of the GB coupling parameter $\lambda_{G B}$, we observe a
change in the value of the critical chemical potential $\mu_c$ in comparison to
Einstein gravity. We find that $\mu_c$ grows as $\lambda_{G B}$ increases. We
add four fermion interactions and show that in the presence of these
corrections the main interesting features of the model are still present and
that the intrinsic attractive interaction can not be switched off. This study
suggests to find GB corrections to equation of state of holographic QCD matter.


Abstract:
  This paper proposes a new concept in which a digital twin derived from a
digital product description will automatically perform assembly planning and
orchestrate the production resources in a manufacturing cell. Thus the
manufacturing cell has generic services with minimal assumptions about what
kind of product will be assembled, while the digital product description is
designed collaboratively between the designer at an OEM and automated services
at potential manufacturers. This has several advantages. Firstly, the resulting
versatile manufacturing facility can handle a broad variety of products with
minimal or no reconfiguration effort, so it can cost-effectively offer its
services to a large number of OEMs. Secondly, a solution is presented to the
problem of performing concurrent product design and assembly planning over the
organizational boundary. Thirdly, the product design at the OEM is not
constrained to the capabilities of specific manufacturing facilities. The
concept is presented in general terms in UML and an implementation is provided
in a 3D simulation environment using Automation Markup Language for digital
product descriptions. Finally, two case studies are presented and applications
in a real industrial context are discussed.


Abstract:
  We present a practical analysis of the fermion sign problem in fermionic path
integral Monte Carlo (PIMC) simulations in the grand-canonical ensemble (GCE).
As a representative model system, we consider electrons in a $2D$ harmonic
trap. We find that the sign problem in the GCE is even more severe than in the
canonical ensemble at the same conditions, which, in general, makes the latter
the preferred option. Despite these difficulties, we show that fermionic PIMC
simulations in the GCE are still feasible in many cases, which potentially
gives access to important quantities like the compressiblity or the Matsubara
Greens function. This has important implications for contemporary fields of
research such as warm dense matter, ultracold atoms, and electrons in quantum
dots.


Abstract:
  We present a general approach to obtain effective field theories for
topological crystalline insulators whose low-energy theories are described by
massive Dirac fermions. We show that these phases are characterized by the
responses to spatially dependent mass parameters with interfaces. These mass
interfaces implement the dimensional reduction procedure such that the state of
interest is smoothly deformed into a topological crystal, which serves as a
representative state of a phase in the general classification. Effective field
theories are obtained by integrating out the massive Dirac fermions, and
various quantized topological terms are uncovered. Our approach can be
generalized to other crystalline symmetry protected topological phases and
provides a general strategy to derive effective field theories for such
crystalline topological phases.


Abstract:
  We investigate the creation of scalar particles inside a region delimited by
a bubble which is expanding with non-zero acceleration. The bubble is modelled
as a thin shell and plays the role of a moving boundary, thus influencing the
fluctuations of the test scalar field inside it. Bubbles expanding in Minkowski
spacetime as well as those dividing two de Sitter spacetimes are explored in a
unified way. Our results for the Bogoliubov coefficient $\beta_k$ in the
adiabatic approximation show that in all cases the creation of scalar particles
decreases with the mass, and is much more significant in the case of nonzero
curvature. They also show that the dynamics of the bubble and its size are
relevant for particle creation, but in the dS-dS case the combination of both
effects leads to a behaviour different from that of Minkowski space-time, due
to the presence of a length scale (the Hubble radius of the internal geometry).


Abstract:
  Deep neural networks have emerged as very successful tools for image
restoration and reconstruction tasks. These networks are often trained
end-to-end to directly reconstruct an image from a noisy or corrupted
measurement of that image. To achieve state-of-the-art performance, training on
large and diverse sets of images is considered critical. However, it is often
difficult and/or expensive to collect large amounts of training images.
Inspired by the success of Data Augmentation (DA) for classification problems,
in this paper, we propose a pipeline for data augmentation for accelerated MRI
reconstruction and study its effectiveness at reducing the required training
data in a variety of settings. Our DA pipeline, MRAugment, is specifically
designed to utilize the invariances present in medical imaging measurements as
naive DA strategies that neglect the physics of the problem fail. Through
extensive studies on multiple datasets we demonstrate that in the low-data
regime DA prevents overfitting and can match or even surpass the state of the
art while using significantly fewer training data, whereas in the high-data
regime it has diminishing returns. Furthermore, our findings show that DA can
improve the robustness of the model against various shifts in the test
distribution.


Abstract:
  Quantum circuits that are classically simulatable tell us when quantum
computation becomes less powerful than or equivalent to classical computation.
Such classically simulatable circuits are of importance because they illustrate
what makes universal quantum computation different from classical computers. In
this work, we propose a novel family of classically simulatable circuits by
making use of dual-unitary quantum circuits (DUQCs), which have been recently
investigated as exactly solvable models of non-equilibrium physics, and we
characterize their computational power. Specifically, we investigate the
computational complexity of the problem of calculating local expectation values
and the sampling problem of one-dimensional DUQCs, and we generalize them to
two spatial dimensions. We reveal that a local expectation value of a DUQC is
classically simulatable at an early time, which is linear in a system length.
In contrast, in a late time, they can perform universal quantum computation,
and the problem becomes a BQP-complete problem. Moreover, classical simulation
of sampling from a DUQC turns out to be hard.


Abstract:
  In this work, we consider a binary classification problem and cast it into a
binary hypothesis testing framework, where the observations can be perturbed by
an adversary. To improve the adversarial robustness of a classifier, we include
an abstain option, where the classifier abstains from making a decision when it
has low confidence about the prediction. We propose metrics to quantify the
nominal performance of a classifier with an abstain option and its robustness
against adversarial perturbations. We show that there exist a tradeoff between
the two metrics regardless of what method is used to choose the abstain region.
Our results imply that the robustness of a classifier with an abstain option
can only be improved at the expense of its nominal performance. Further, we
provide necessary conditions to design the abstain region for a 1- dimensional
binary classification problem. We validate our theoretical results on the MNIST
dataset, where we numerically show that the tradeoff between performance and
robustness also exist for the general multi-class classification problems.


Abstract:
  Off-policy evaluation learns a target policy's value with a historical
dataset generated by a different behavior policy. In addition to a point
estimate, many applications would benefit significantly from having a
confidence interval (CI) that quantifies the uncertainty of the point estimate.
In this paper, we propose a novel deeply-debiasing procedure to construct an
efficient, robust, and flexible CI on a target policy's value. Our method is
justified by theoretical results and numerical experiments. A Python
implementation of the proposed procedure is available at
https://github.com/RunzheStat/D2OPE.


Abstract:
  The successful amalgamation of cryptocurrency and consumer Internet of Things
(IoT) devices can pave the way for novel applications in machine-to-machine
economy. However, the lack of scalability and heavy resource requirements of
initial blockchain designs hinders the integration as they prioritized
decentralization and security. Numerous solutions have been proposed since the
emergence of Bitcoin to achieve this goal. However, none of them seem to
dominate and thus it is unclear how consumer devices will be adopting these
approaches. Therefore, in this paper, we critically review the existing
integration approaches and cryptocurrency designs that strive to enable
micro-payments among consumer devices. We identify and discuss solutions under
three main categories; direct integration, payment channel network and new
cryptocurrency design. The first approach utilizes a full node to interact with
the payment system. Offline channel payment is suggested as a second layer
solution to solve the scalability issue and enable instant payment with low
fee. New designs converge to semi-centralized scheme and focuson lightweight
consensus protocol that does not require highcomputation power which might mean
loosening the initial designchoices in favor of scalability. We evaluate the
pros and cons ofeach of these approaches and then point out future
researchchallenges. Our goal is to help researchers and practitioners tobetter
focus their efforts to facilitate micro-payment adoptions.


Abstract:
  We show that it is possible to have arbitrarily long sequences of Alices and
Bobs so every (Alice, Bob) pair violates a Bell inequality. We propose an
experiment to observe this effect with two Alices and two Bobs.


Abstract:
  One of the main goal of large-scale structure surveys is to test the
consistency of General Relativity at cosmological scales. In the $\Lambda$CDM
model of cosmology, the relations between the fields describing the geometry
and the content of our Universe are uniquely determined. In particular, the two
gravitational potentials -- that describe the spatial and temporal fluctuations
in the geometry -- are equal. Whereas large classes of dark energy models
preserve this equality, theories of modified gravity generally create a
difference between the potentials, known as anisotropic stress. Even though
measuring this anisotropic stress is one of the key goals of large-scale
structure surveys, there are currently no methods able to measure it directly.
Current methods all rely on measurements of galaxy peculiar velocities (through
redshift-space distortions), from which the time component of the metric is
inferred, assuming that dark matter follows geodesics. If this is not the case,
all the proposed tests fail to measure the anisotropic stress. In this letter,
we propose a novel test which directly measures anisotropic stress, without
relying on any assumption about the unknown dark matter. Our method uses
relativistic effects in the galaxy number counts to provide a direct
measurement of the time component of the metric. By comparing this with lensing
observations our test provides a direct measurement of the anisotropic stress.


Abstract:
  Intensification and poleward expansion of upwelling favourable winds have
been predicted as a response to anthropogenic global climate change and have
recently been documented in most Eastern Boundary Upwelling Ecosystems of the
world. To identify how these processes are impacting nearshore oceanographic
habitats and, especially, long term trends of primary productivity in the
Humboldt Upwelling Ecosystem (HUE), we analysed time series of sea level
pressure, wind stress, sea surface and atmospheric surface temperatures, and
Chlorophyll-a, as a proxy for primary productivity, along 26{\deg} - 36{\deg}
S. We show that climate induced trends in primary productivity are highly
heterogeneous across the region. On the one hand, the well documented poleward
migration of the South Pacific Anticyclone (SPA) has led to decreased spring
upwelling winds in the region between ca. 30{\deg} and 34{\deg} S, and to their
intensification to the south. Decreased winds have produced slight increases in
sea surface temperature and a pronounced and meridionally extensive decrease in
surface Chlorophyll-a in this region of central Chile. To the north of 30{\deg}
S, significant increases in upwelling winds, decreased SST, and enhanced
Chlorophyll-a concentration are observed in the nearshore. We show that this
increased in upwelling driven coastal productivity is probably produced by the
increased land-sea pressure gradients (Bakun's effect) that have occurred over
the past two decades north of 30{\deg} S. Thus, climate drivers along the HUE
are inducing contrasting trends in oceanographic conditions and primary
productivity, which can have far-reaching consequences for coastal pelagic and
benthic ecosystems and lead to geographic displacements of the major fisheries.


Abstract:
  Extensions of a set partition obtained by imposing bounds on the size of the
parts and the coloring of some of the elements are examined. Combinatorial
properties and the generating functions of some counting sequences associated
with these partitions are established. Connections with Riordan arrays are
presented.


Abstract:
  Finding the largest cardinality feasible subset of an infeasible set of
linear constraints is the Maximum Feasible Subsystem problem (MAX FS). Solving
this problem is crucial in a wide range of applications such as machine
learning and compressive sensing. Although MAX FS is NP-hard, useful heuristic
algorithms exist, but these can be slow for large problems. We extend the
existing heuristics for the case of dense constraint matrices to greatly
increase their speed while preserving or improving solution quality. We test
the extended algorithms on two applications that have dense constraint
matrices: binary classification, and sparse recovery in compressive sensing. In
both cases, speed is greatly increased with no loss of accuracy.


Abstract:
  Simulations of high energy density physics are expensive in terms of
computational resources. In particular, the computation of opacities of plasmas
in the non-local thermal equilibrium (NLTE) regime can consume as much as 90\%
of the total computational time of radiation hydrodynamics simulations for high
energy density physics applications. Previous work has demonstrated that a
combination of fully-connected autoencoders and a deep jointly-informed neural
network (DJINN) can successfully replace the standard NLTE calculations for the
opacity of krypton. This work expands this idea to combining multiple elements
into a single surrogate model with the focus here being on the autoencoder.


Abstract:
  We consider the minimal seesaw model, the Standard Model extended by two
right-handed neutrinos, for explaining the neutrino masses and mixing angles
measured in oscillation experiments. When one of right-handed neutrinos is
lighter than the electroweak scale, it can give a sizable contribution to
neutrinoless double beta ($0\nu \beta \beta$) decay. We show that the detection
of the $0 \nu \beta \beta$ decay by future experiments gives a significant
implication to the search for such light right-handed neutrino.


Abstract:
  In the Stochastic Thermodynamics theory, heat is a random variable with a
probability distribution associated. Studies in the distribution of heat are
mostly in the overdamped regime. Here we solve the heat distribution in the
underdamped regime for three different cases: the free particle, the linear
potential, and the harmonic potential. The results are exact and generalize
known results in the literature.


Abstract:
  For safety of autonomous driving, vehicles need to be able to drive under
various lighting, weather, and visibility conditions in different environments.
These external and environmental factors, along with internal factors
associated with sensors, can pose significant challenges to perceptual data
processing, hence affecting the decision-making and control of the vehicle. In
this work, we address this critical issue by introducing a framework for
analyzing robustness of the learning algorithm w.r.t varying quality in the
image input for autonomous driving. Using the results of sensitivity analysis,
we further propose an algorithm to improve the overall performance of the task
of "learning to steer". The results show that our approach is able to enhance
the learning outcomes up to 48%. A comparative study drawn between our approach
and other related techniques, such as data augmentation and adversarial
training, confirms the effectiveness of our algorithm as a way to improve the
robustness and generalization of neural network training for autonomous
driving.