Abstract:
  News recommendation is important for online news services. Precise user
interest modeling is critical for personalized news recommendation. Existing
news recommendation methods usually rely on the implicit feedback of users like
news clicks to model user interest. However, news click may not necessarily
reflect user interests because users may click a news due to the attraction of
its title but feel disappointed at its content. The dwell time of news reading
is an important clue for user interest modeling, since short reading dwell time
usually indicates low and even negative interest. Thus, incorporating the
negative feedback inferred from the dwell time of news reading can improve the
quality of user modeling. In this paper, we propose a neural news
recommendation approach which can incorporate the implicit negative user
feedback. We propose to distinguish positive and negative news clicks according
to their reading dwell time, and respectively learn user representations from
positive and negative news clicks via a combination of Transformer and additive
attention network. In addition, we propose to compute a positive click score
and a negative click score based on the relevance between candidate news
representations and the user representations learned from the positive and
negative news clicks. The final click score is a combination of positive and
negative click scores. Besides, we propose an interactive news modeling method
to consider the relatedness between title and body in news modeling. Extensive
experiments on real-world dataset validate that our approach can achieve more
accurate user interest modeling for news recommendation.


Abstract:
  The hunt for exotic quantum phase transitions described by emergent
fractionalized degrees of freedom coupled to gauge fields requires a precise
determination of the fixed point structure from the field theoretical side, and
an extreme sensitivity to weak first-order transitions from the numerical side.
Addressing the latter, we revive the classic definition of the order parameter
in the limit of a vanishing external field at the transition. We demonstrate
that this widely understood, yet so far unused approach provides a diagnostic
test for first-order versus continuous behavior that is distinctly more
sensitive than current methods. We first apply it to the family of $Q$-state
Potts models, where the nature of the transition is continuous for $Q\leq4$ and
turns (weakly) first order for $Q>4$, using an infinite system matrix product
state implementation. We then employ this new approach to address the unsettled
question of deconfined quantum criticality in the $S=1/2$ N\'eel to valence
bond solid transition in two dimensions, focusing on the square lattice $J$-$Q$
model. Our quantum Monte Carlo simulations reveal that both order parameters
remain finite at the transition, directly confirming a first-order scenario
with wide reaching implications in condensed matter and quantum field theory.


Abstract:
  Pancreatic ductal adenocarcinoma (PDAC) is the third most common cause of
cancer death in the United States. Predicting tumors like PDACs (including both
classification and segmentation) from medical images by deep learning is
becoming a growing trend, but usually a large number of annotated data are
required for training, which is very labor-intensive and time-consuming. In
this paper, we consider a partially supervised setting, where cheap image-level
annotations are provided for all the training data, and the costly per-voxel
annotations are only available for a subset of them. We propose an Inductive
Attention Guidance Network (IAG-Net) to jointly learn a global image-level
classifier for normal/PDAC classification and a local voxel-level classifier
for semi-supervised PDAC segmentation. We instantiate both the global and the
local classifiers by multiple instance learning (MIL), where the attention
guidance, indicating roughly where the PDAC regions are, is the key to bridging
them: For global MIL based normal/PDAC classification, attention serves as a
weight for each instance (voxel) during MIL pooling, which eliminates the
distraction from the background; For local MIL based semi-supervised PDAC
segmentation, the attention guidance is inductive, which not only provides
bag-level pseudo-labels to training data without per-voxel annotations for MIL
training, but also acts as a proxy of an instance-level classifier.
Experimental results show that our IAG-Net boosts PDAC segmentation accuracy by
more than 5% compared with the state-of-the-arts.


Abstract:
  The Bluetooth Special Interest Group (Bluetooth SIG) introduced a new feature
for highly accurate localization called the Direction Finding in the Bluetooth
Core Specification 5.1. Since this new localization feature is relatively new,
despite the significant interest of industry and academia in the accurate
positioning of Bluetooth devices/tags, there are only a handful of experimental
studies conducted to evaluate the performance of the new technology.
Furthermore, these experimental works are constrained to only indoor
environments or performed with hardware emulation of Bluetooth 5.1 via
Universal Software Radio Peripherals (USRPs). In this paper, we perform an
experimental study on the positioning accuracy of the direction finding using
COTS Bluetooth 5.1 devices in booth indoor and outdoor environments to provide
insights on the performance gap under these different experimental settings.
Our results demonstrate that the average angular error in an outdoor
environment is 0.28 degrees, significantly improving the angular error measured
in an indoor environment by 73%. It is also demonstrated that the average
positioning accuracy measured in an outdoor environment is 22cm which is 39.7%
smaller than that measured in an indoor environment.


Abstract:
  Silicon-substituted hydroxyapatite (SiHA) macroporous scaffolds have been
prepared by robocasting. In order to optimize their bone regeneration
properties, we have manufactured these scaffolds presenting different
microstructures: nanocrystalline and crystalline. Moreover, their surfaces have
been decorated with vascular endothelial growth factor (VEGF) to evaluate the
potential coupling between vascularization and bone regeneration. In vitro cell
culture tests evidence that nanocrystalline SiHA hinders pre-osteblast
proliferation, whereas the presence of VEGF enhances the biological functions
of both endothelial cells and pre-osteoblasts. The bone regeneration capability
has been evaluated using an osteoporotic sheep model. In vivo observations
strongly correlate with in vitro cell culture tests. Those scaffolds made of
nanocrystalline SiHA were colonized by fibrous tissue, promoted inflammatory
response and fostered osteoclast recruitment. These observations discard
nanocystalline SiHA as a suitable material for bone regeneration purposes. On
the contrary, those scaffolds made of crystalline SiHA and decorated with VEGF
exhibited bone regeneration properties, with high ossification degree, thicker
trabeculae and higher presence of osteoblasts and blood vessels. Considering
these results, macroporous scaffolds made of SiHA and decorated with VEGF are
suitable bone grafts for regeneration purposes, even in adverse pathological
scenarios such as osteoporosis.


Abstract:
  The narrow-line region (NLR) consists of gas clouds ionized by the strong
radiation from the active galactic nucleus (AGN), distributed in the spatial
scale of AGN host galaxies. The strong emission lines from the NLR are useful
to diagnose physical and chemical properties of the interstellar medium in AGN
host galaxies. However, the origin of the NLR is unclear; the gas clouds in
NLRs may be originally in the host and photoionized by the AGN radiation, or
they may be transferred from the nucleus with AGN-driven outflows. For studying
the origin of the NLR, we systematically investigate the gas density and
velocity dispersion of NLR gas clouds using a large spectroscopic data set
taken from the Sloan Digital Sky Survey. The [S II] emission-line flux ratio
and [O III] velocity width of 9,571 type-2 Seyfert galaxies and 110,041
star-forming galaxies suggest that the gas density and velocity dispersion of
NLR clouds in Seyfert galaxies (ne ~ 194 cm-3 and sigma([O III]) ~ 147 km s-1)
are systematically larger than those of clouds in H II regions of star-forming
galaxies (ne ~ 29 cm-3 and sigma([O III]) ~ 58 km s-1). Interestingly, the
electron density and velocity dispersion of NLR gas clouds are larger for
Seyfert galaxies with a higher [O III]/Hbeta flux ratio, i.e., with a more
active AGN. We also investigate the spatially-resolved kinematics of ionized
gas clouds using the Mapping Nearby Galaxies at the Apache Point Observatory
(MaNGA) survey data for 90 Seyfert galaxies and 801 star-forming galaxies. We
find that the velocity dispersion of NLR gas clouds in Seyfert galaxies is
larger than that in star-forming galaxies at a fixed stellar mass, at both
central and off-central regions. These results suggest that gas clouds in NLRs
come from the nucleus, probably through AGN outflows.


Abstract:
  In this work, we investigate the mean-field limit of a model consisting in $m
\geq 1 $ tracer particles, coupled to an interacting boson field. We assume the
mass of the tracer particles and the expected number of bosons to be of the
same order of magnitude $N \geq 1 $ and we investigate the $N\rightarrow \infty
$ limit. In particular, we show that the limiting system can be effectively
described by a pair of variables $( \textbf{X}_t ,\varphi_t ) \in
\mathbb{R}^{3m} \times H^1(\mathbb{R}^3)$ that solve a mean-field equation. Our
methods are based on proving estimates for the number of bosonic particles in a
suitable \textit{fluctuation state} $\Omega_{N,t }$. The main diffculty of the
problem comes from the fact that the interaction with the tracer particles can
create or destroy bosons for states close to the vacuum.


Abstract:
  In 1869, Jordan proved that the set $\mathcal{T}$ of all finite group that
can be represented as the automorphism group of a tree is containing the
trivial group and it is closed under taken direct product of groups of lower
order in $\mathcal{T}$ and wreath product of a member in $\mathcal{T}$ and the
symmetric group on $n$ symbols. The aim of this paper is to continue this work
and another works by Klav$\acute{\rm i}$k and Zeman in 2017 to present a class
$\mathcal{S}$ of finite groups for which the automorphism group of each
bicyclic graph is a member of $\mathcal{S}$ and this class is minimal with this
property.


Abstract:
  We consider here an elliptic coupled system describing the dynamics of liquid
crystals flows. This system is posed on the whole n-dimensional space. We
introduce first the notion of very weak solutions for this system. Then, within
the fairly general framework of the Morrey spaces, we derive some sufficient
conditions on the very weak solutions which improve their regularity. As a
bi-product, we also prove a new regularity criterium for the time-independing
Navier-Stokes equations.


Abstract:
  Let $S$ be a set of $n$ sites, each associated with a point in $\mathbb{R}^2$
and a radius $r_s$ and let $\mathcal{D}(S)$ be the disk graph on $S$. We
consider the problem of designing data structures that maintain the
connectivity structure of $\mathcal{D}(S)$ while allowing the insertion and
deletion of sites. For unit disk graphs we describe a data structure that has
$O(\log^2n)$ amortized update time and $O((\log n)/(\log\log n))$ amortized
query time.
  For disk graphs where the ratio $\Psi$ between the largest and smallest
radius is bounded, we consider the decremental and the incremental case
separately, in addition to the fully dynamic case. In the fully dynamic case we
achieve amortized $O(\Psi \lambda_6(\log n) \log^{9}n)$ update time and $O(\log
n)$ query time, where $\lambda_s(n)$ is the maximum length of a
Davenport-Schinzel sequence of order $s$ on $n$ symbols. This improves the
update time of the currently best known data structure by a factor of $\Psi$ at
the cost of an additional $O(\log \log n)$ factor in the query time. In the
incremental case we manage to achieve a logarithmic dependency on $\Psi$ with a
data structure with $O(\alpha(n))$ query and $O(\log\Psi \lambda_6(\log n)
\log^{9}n)$ update time.
  For the decremental setting we first develop a new dynamic data structure
that allows us to maintain two sets $B$ and $P$ of disks, such than at a
deletion of a disk from $B$ we can efficiently report all disks in $P$ that no
longer intersect any disk of $B$. Having this data structure at hand, we get
decremental data structures with an amortized query time of $O((\log n)/(\log
\log n))$ supporting $m$ deletions in $O((n\log^{5}n + m \log^{9}n)
\lambda_6(\log n) + n\log\Psi\log^4n)$ overall time for bounded radius ratio
$\Psi$ and $O(( n\log^{6} n + m \log^{10}n) \lambda_6(\log n))$ for general
disk graphs.


Abstract:
  Accidents that vehicles cross median and enter opposite lane happen
frequently, and the existing median barrier has weak anti-collision strength. A
new combined median barrier (NCMB) consisted of W-beam guardrail and concrete
structure was proposed to decrease deformation and enhance anti-collision
strength in this paper. However, there were some uncertainties in the initial
design of the NCMB. If the uncertainties were not considered in the design
process, the optimization objectives were especially sensitive to the small
fluctuation of the variables, and it might result in design failure. For this
purpose, the acceleration and deflection were taken as objectives; post
thickness, W-beam thickness and post spacing were chosen as design variables;
the velocity, mass of vehicle and the yield stress of barrier components were
taken as noise factors, a multi-objective robust optimization is carried out
for the NCMB based on Taguchi and grey relational analysis (GRA). The results
indicate that the acceleration and deflection after optimization are reduced by
47.3% and 76.7% respectively; Signal-to-noise ratio (SNR) of objectives after
optimization are increased, it greatly enhances the robustness of the NCMB. The
results demonstrate that the effectiveness of the methodology that based on
Taguchi method and grey relational analysis.


Abstract:
  We perform a numerical analysis of mid-infrared photoluminescence emitted by
praseodymium (III) doped chalcogenide selenide glass pumped at near-infrared
wavelengths. The results obtained show that an effective inversion of level
populations can be achieved using both 1480 nm and 1595 nm laser diodes. The
rate of the spontaneous emission achieved when pumping at 1480 nm and 1595 nm
is comparable to this achieved using the standard pumping wavelength of 2040
nm.


Abstract:
  The theory of magnetoresistance of the edge state of a two-dimensional
topological insulator is developed. The magnetic field violates the
time-reversal invariance. Magnetoresistance arises due to the energy gap opened
by a magnetic field parallel to the sample surface. The combined action of
impurities and the magnetic field causes backscattering of edge electrons.
Although impurities are necessary for scattering, sufficiently strong
interaction with impurities leads to the suppression of backscattering.


Abstract:
  The H.E.S.S. Galactic Plane Survey (HGPS) revealed 78 TeV sources among which
47 are not clearly associated with a known object. We present a multiwavelength
approach to constrain the origin of the emission from unidentified HGPS
sources. We present a generic pipeline that explores a large database of
multiwavelength archival data toward any region in the Galactic plane. Along
with a visual inspection of the retrieved multiwavelength observations to
search for faint and uncataloged counterparts, we derive a radio spectral index
that helps disentangle thermal from nonthermal emission and a mean magnetic
field through X-ray and TeV data in case of a leptonic scenario. We also search
for a spectral connection between the GeV and the TeV regimes with the
Fermi-LAT cataloged sources that may be associated with the unidentified HGPS
source. We complete the association procedure with catalogs of known objects
and with the source catalogs from instruments whose data are retrieved. The
method is applied on two unidentified sources, namely HESS J1427$-$608 and HESS
J1458$-$608, for which the multiwavelength constraints favor the pulsar wind
nebula (PWN) scenario. We model their broadband nonthermal spectra in a
leptonic scenario with a magnetic field $B \lesssim 10$ $\mu$G, which is
consistent with that obtained from ancient PWNe. We place both sources within
the context of the TeV PWN population to estimate the spin-down power and the
characteristic age of the putative pulsar. We also shed light on two possibly
significant $\gamma$-ray excesses in the HGPS: the first is located in the
south of the unidentified source HESS J1632$-$478 and the second is spatially
coincident with the synchrotron-emitting supernova remnant G28.6$-$0.1. The
multiwavelength counterparts found toward both $\gamma$-ray excesses make these
promising candidates for being new very-high energy $\gamma$-ray sources.


Abstract:
  Human volumetric capture is a long-standing topic in computer vision and
computer graphics. Although high-quality results can be achieved using
sophisticated off-line systems, real-time human volumetric capture of complex
scenarios, especially using light-weight setups, remains challenging. In this
paper, we propose a human volumetric capture method that combines temporal
volumetric fusion and deep implicit functions. To achieve high-quality and
temporal-continuous reconstruction, we propose dynamic sliding fusion to fuse
neighboring depth observations together with topology consistency. Moreover,
for detailed and complete surface generation, we propose detail-preserving deep
implicit functions for RGBD input which can not only preserve the geometric
details on the depth inputs but also generate more plausible texturing results.
Results and experiments show that our method outperforms existing methods in
terms of view sparsity, generalization capacity, reconstruction quality, and
run-time efficiency.


Abstract:
  The goal of this paper is to test solids4Foam, the fluid-structure
interaction (FSI) toolbox developed for foam-extend (a branch of OpenFOAM), and
assess its flexibility in handling more complex flows. For this purpose, we
consider the interaction of an incompressible fluid described by a Leray model
with a hyperelastic structure modeled as a Saint Venant-Kirchhoff material. We
focus on a strongly coupled, partitioned fluid-structure interaction (FSI)
solver in a finite volume environment, combined with an arbitrary
Lagrangian-Eulerian approach to deal with the motion of the fluid domain. For
the implementation of the Leray model, which features a nonlinear differential
low-pass filter, we adopt a three-step algorithm called Evolve-Filter-Relax. We
validate our approach against numerical data available in the literature for
the 3D cross flow past a cantilever beam at Reynolds number 100 and 400.


Abstract:
  We analyze the electromagnetic field near a plane interface between a
conductive and a dielectric media, under conditions supporting surface
plasmon-polariton (SPP) propagation. The conductive medium is described by the
hydrodynamic electron-gas model that enables a consistent analysis of the
field-induced variations of the electron density and velocity at the interface
and its nearest vicinity. The distributions of electromagnetic dynamical
characteristics: energy, energy flow, spin and momentum are calculated
analytically and illustrated numerically, employing silver-vacuum interface as
an example. A set of the "field" and material contributions to the energy, spin
and momentum are explicitly identified and classified with respect to their
physical origins and properties, and the orbital (canonical) and spin
(Belinfante) momentum constituents are separately examined. In this context, a
procedure for the spin-orbital momentum decomposition in the presence of free
charges is proposed and substantiated. The microscopic results agree with the
known phenomenological data but additionally show specific nanoscale structures
in the near-interface behavior of the SPP energy and momentum, which can be
deliberately created, controlled and used in nanotechnology applications.


Abstract:
  In this paper, a new $P_{2}-P_{1}$ finite element pair is proposed for
incompressible fluid. For this pair, the discrete inf-sup condition and the
discrete Korn's inequality hold on general triangulations. It yields exactly
divergence-free velocity approximations when applied to models of
incompressible flows. The robust capacity of the pair for incompressible flows
are verified theoretically and numerically.


Abstract:
  In this paper, we study PAC learnability and certification of predictions
under instance-targeted poisoning attacks, where the adversary who knows the
test instance may change a fraction of the training set with the goal of
fooling the learner at the test instance. Our first contribution is to
formalize the problem in various settings and to explicitly model subtle
aspects such as the proper or improper nature of the learning, learner's
randomness, and whether (or not) adversary's attack can depend on it. Our main
result shows that when the budget of the adversary scales sublinearly with the
sample complexity, (improper) PAC learnability and certification are
achievable; in contrast, when the adversary's budget grows linearly with the
sample complexity, the adversary can potentially drive up the expected 0-1 loss
to one. We also study distribution-specific PAC learning in the same attack
model and show that proper learning with certification is possible for learning
half spaces under natural distributions. Finally, we empirically study the
robustness of K nearest neighbour, logistic regression, multi-layer perceptron,
and convolutional neural network on real data sets against targeted-poisoning
attacks. Our experimental results show that many models, especially
state-of-the-art neural networks, are indeed vulnerable to these strong
attacks. Interestingly, we observe that methods with high standard accuracy
might be more vulnerable to instance-targeted poisoning attacks.


Abstract:
  We apply reinforcement learning to video compressive sensing to adapt the
compression ratio. Specifically, video snapshot compressive imaging (SCI),
which captures high-speed video using a low-speed camera is considered in this
work, in which multiple (B) video frames can be reconstructed from a snapshot
measurement. One research gap in previous studies is how to adapt B in the
video SCI system for different scenes. In this paper, we fill this gap
utilizing reinforcement learning (RL). An RL model, as well as various
convolutional neural networks for reconstruction, are learned to achieve
adaptive sensing of video SCI systems. Furthermore, the performance of an
object detection network using directly the video SCI measurements without
reconstruction is also used to perform RL-based adaptive video compressive
sensing. Our proposed adaptive SCI method can thus be implemented in low cost
and real time. Our work takes the technology one step further towards real
applications of video SCI.


Abstract:
  In the modern search for life elsewhere in the Universe, we are broadly
looking for the following: the planets similar to Earth - physical indicators
of habitability, and the manifestation of life - the biological signatures. A
biosignature is a measured parameter that has a high probability of being
caused by the living organisms, either atmospheric gas species or some surface
features. Therefore, the focus of a search is on a product or phenomena
produced by the living systems, mostly by microorganisms as these are the most
abundant on our planet like, say, methane. However, we may need to distinguish
the terms `biosignature' and `bioindicator'. A biosignature is what living
organisms produce - a bioproduct, while a bioindicator may be anything
necessary for life as we know it, such as water or a rocky planet. Oxygen in
this case is a double biomarker; first, it is a byproduct of oxygenic
photosynthesis and, second, it is a signature of a complex life, because
complex highly organized life requires high levels of oxygen. It is possible
that there are other such bioindicators. For example, in the atmospheric
compositions of terrestrial planets in our Solar System (including Titan),
argon is one of the major constituents, moreover it was recently acknowledged
to be a `biologically' active gas, exhibiting organprotective and
neuroprotective properties, especially under hypoxic conditions. Here we
propose that argon in the atmosphere of a rocky planet is a bioindicator of a
highly organized life, provided that the planet is already deemed potentially
habitable: with water, atmosphere, and of a certain age allowing for the
complex life to evolve. We also delineate its possible detection methods.


Abstract:
  Let $\Omega \subset \mathbb{R}^d$ be a set with finite Lebesgue measure such
that, for a fixed radius $r>0$, the Lebesgue measure of $\Omega \cap B_r (x)$
is equal to a positive constant when $x$ varies in the essential boundary of
$\Omega$. We prove that $\Omega$ is a ball (or a finite union of equal balls)
provided it satisfies a nondegeneracy condition, which holds in particular for
any set of diameter larger than $r$ which is either open and connected, or of
finite perimeter and indecomposable. The proof requires reinventing each step
of the moving planes method by Alexandrov in the framework of measurable sets.


Abstract:
  In this paper we introduce and study a type of Cayley graph -- subnormal
Cayley graph. We prove that a subnormal 2-arc transitive Cayley graph is a
normal Cayley graph or a normal cover of a complete bipartite graph
$K_{p^d,p^d}$ with $p$ prime. Then we obtain a generic method for constructing
half-symmetric (namely edge transitive but not arc transitive) Cayley graphs.


Abstract:
  A novel approach to simulate simple protein-ligand systems at large time- and
length-scales is to couple Markov state models (MSMs) of molecular kinetics
with particle-based reaction-diffusion (RD) simulations, MSM/RD. Currently,
MSM/RD lacks a mathematical framework to derive coupling schemes; is limited to
isotropic ligands in a single conformational state, and is lacking a
multi-particle extensions. In this work, we address these needs by developing a
general MSM/RD framework by coarse-graining molecular dynamics into hybrid
switching diffusion processes. Given enough data to parametrize the model, it
is capable of modeling protein-protein interactions over large time- and
length-scales, and it can be extended to handle multiple molecules. We derive
the MSM/RD framework, and we implement and verify it for two protein-protein
benchmark systems and one multiparticle implementation to model the formation
of pentameric ring molecules. To enable reproducibility, we have published our
code in the MSM/RD software package.


Abstract:
  We show that any conformal vector field on a compact lcK manifold is Killing
with respect to the Gauduchon metric. Furthermore, we prove that any conformal
vector field on a compact lcK manifold whose K\"ahler cover is neither flat,
nor hyperk\"ahler, is holomorphic.


Abstract:
  Importance: An artificial intelligence (AI)-based model to predict COVID-19
likelihood from chest x-ray (CXR) findings can serve as an important adjunct to
accelerate immediate clinical decision making and improve clinical decision
making. Despite significant efforts, many limitations and biases exist in
previously developed AI diagnostic models for COVID-19. Utilizing a large set
of local and international CXR images, we developed an AI model with high
performance on temporal and external validation.
  Conclusions and Relevance: AI-based diagnostic tools may serve as an adjunct,
but not replacement, for clinical decision support of COVID-19 diagnosis, which
largely hinges on exposure history, signs, and symptoms. While AI-based tools
have not yet reached full diagnostic potential in COVID-19, they may still
offer valuable information to clinicians taken into consideration along with
clinical signs and symptoms.


Abstract:
  User engagement is crucial to the long-term success of a mobile app. Several
metrics, such as dwell time, have been used for measuring user engagement.
However, how to effectively predict user engagement in the context of mobile
apps is still an open research question. For example, do the mobile usage
contexts (e.g.,~time of day) in which users access mobile apps impact their
dwell time? Answers to such questions could help mobile operating system and
publishers to optimize advertising and service placement. In this paper, we
first conduct an empirical study for assessing how user characteristics,
temporal features, and the short/long-term contexts contribute to gains in
predicting users' app dwell time on the population level. The comprehensive
analysis is conducted on large app usage logs collected through a mobile
advertising company. The dataset covers more than 12K anonymous users and 1.3
million log events. Based on the analysis, we further investigate a novel
mobile app engagement prediction problem -- can we predict simultaneously what
app the user will use next and how long he/she will stay on that app? We
propose several strategies for this joint prediction problem and demonstrate
that our model can improve the performance significantly when compared with the
state-of-the-art baselines. Our work can help mobile system developers in
designing a better and more engagement-aware mobile app user experience.


Abstract:
  We propose to examine the predictability and the complexity characteristics
of the Standard&Poor500 dynamics behaviors in a coarse-grained way using the
symbolic dynamics method and under the prism of the Information theory through
the concept of entropy and uncertainty. We believe that experimental
measurement of entropy as a way of examining the complexity of a system is more
relevant than more common tests of universality in the transition to chaos
because it does not make any prior prejudices on the underlying causes
associated with the system dynamics, whether deterministic or stochastic. We
regard the studied economic time series as being complex and propose to express
it in terms of the amount of information this last is producing on different
time scales and according to various scaling parameters.


Abstract:
  MnBi$_2$Te$_4$ (MBT) materials are promising antiferromagnetic topological
insulators where field driven ferromagnetism is predicted to cause a transition
between axion insulator and Weyl semimetallic states. However, the presence of
antiferromagnetic coupling between Mn/Bi antisite defects and the main Mn layer
can reduce the low-field magnetization, and it has been shown that such defects
are more prevalent in the structurally identical trivial magnetic insulator
MnSb$_2$Te$_4$ (MST). We use high-field magnetization measurements to show that
the magnetization of MBT and MST occur in stages and full saturation requires
fields of~$\sim$~60 Tesla. As a consequence, the low-field magnetization
plateau state in MBT, where many determinations of quantum anomalous Hall state
are studied, actually consists of ferrimagnetic septuple blocks containing both
a uniform and staggered magnetization component.


Abstract:
  Model deficiency that results from incomplete training data is a form of
structural blindness that leads to costly errors, oftentimes with high
confidence. During the training of classification tasks, underrepresented
class-conditional distributions that a given hypothesis space can recognize
results in a mismatch between the model and the target space. To mitigate the
consequences of this discrepancy, we propose Random Test Sampling and
Cross-Validation (RTSCV) as a general algorithmic framework that aims to
perform a post-training model rectification at deployment time in a supervised
way. RTSCV extracts unknown unknowns (u.u.s), i.e., examples from the
class-conditional distributions that a classifier is oblivious to, and works in
combination with a diverse family of modern prediction models. RTSCV augments
the training set with a sample of the test set (or deployment data) and uses
this redefined class layout to discover u.u.s via cross-validation, without
relying on active learning or budgeted queries to an oracle. We contribute a
theoretical analysis that establishes performance guarantees based on the
design bases of modern classifiers. Our experimental evaluation demonstrates
RTSCV's effectiveness, using 7 benchmark tabular and computer vision datasets,
by reducing a performance gap as large as 41% from the respective
pre-rectification models. Last we show that RTSCV consistently outperforms
state-of-the-art approaches.


Abstract:
  Decentralized conflict resolution for autonomous vehicles is needed in many
places where a centralized method is not feasible, e.g., parking lots, rural
roads, merge lanes, etc. However, existing methods generally do not fully
utilize optimization in decentralized conflict resolution. We propose a
decentralized conflict resolution method for autonomous vehicles based on a
novel extension to the Alternating Directions Method of Multipliers (ADMM),
called Online Adaptive ADMM (OA-ADMM), and on Model Predictive Control (MPC).
OA-ADMM is tailored to online systems, where fast and adaptive real-time
optimization is crucial, and allows the use of safety information about the
physical system to improve safety in real-time control. We prove convergence in
the static case and give requirements for online convergence. Combining OA-ADMM
and MPC allows for robust decentralized motion planning and control that
seamlessly integrates decentralized conflict resolution. The effectiveness of
our proposed method is shown through simulations in CARLA, an open-source
vehicle simulator, resulting in a reduction of 47.93% in mean added delay
compared with the next best method.


Abstract:
  The dynamics of suspensions plays a crucial role on the evolution of
geophysical systems such as lava lakes, magma chambers and magma oceans. During
their cooling and solidification, these magmatic bodies involve convective
viscous fluids and dispersed solid crystals that can form either a cumulate or
a floating lid by sedimentation. We study such systems based on internal
heating convection experiments in high Prandtl fluids bearing plastic beads. We
aim to determine the conditions required to produce a floating lid or a
sedimented deposit. We show that although the sign of particles buoyancy is the
key parameter, it is not sufficient to predict the particles fate. To
complement the model we introduce the Shields formalism and couple it with
scaling laws describing convection. We propose a generalised Shields number
that enables a self-consistent description of the fate of particles in the
system, especially the possibility to segregate from the convective bulk. We
provide a quantification of the partition of the mass of particles in the
different potential reservoirs (bulk suspension, floating lid, settled
cumulate) through reconciling the suspension stability framework with the
Shields formalism. We illustrate the geophysical implications of the model by
revisiting the problem of the stability of flotation crusts on solidifying
rocky bodies.


Abstract:
  In this paper we introduce refined canonical stable Grothendieck polynomials
and their duals with two infinite sequences of parameters. These polynomials
unify several generalizations of Grothendieck polynomials including canonical
stable Grothendieck polynomials due to Yeliussizov, refined Grothendieck
polynomials due to Chan and Pflueger, and refined dual Grothendieck polynomials
due to Galashin, Liu, and Grinberg. We give Jacobi--Trudi-type formulas,
combinatorial models, Schur expansions, Schur positivity, and dualities of
these polynomials. We also consider flagged versions of Grothendieck
polynomials and their duals with skew shapes.


Abstract:
  We investigate the massive Schwinger model in $d = 1 + 1$ dimensions using
bosonization and the non-perturbative functional renormalization group. In
agreement with previous studies we find that the phase transition, driven by a
change of the ratio $m/e$ between the mass and the charge of the fermions,
belongs to the two-dimensional Ising universality class. The temperature and
vacuum angle dependence of various physical quantities (chiral density,
electric field, entropy density) are also determined and agree with results
obtained from density matrix renormalization group studies. Screening of
fractional charges and deconfinement occur only at infinite temperature.


Abstract:
  In the past decade, contextual bandit and reinforcement learning algorithms
have been successfully used in various interactive learning systems such as
online advertising, recommender systems, and dynamic pricing. However, they
have yet to be widely adopted in high-stakes application domains, such as
healthcare. One reason may be that existing approaches assume that the
underlying mechanisms are static in the sense that they do not change over
different environments. In many real world systems, however, the mechanisms are
subject to shifts across environments which may invalidate the static
environment assumption. In this paper, we tackle the problem of environmental
shifts under the framework of offline contextual bandits. We view the
environmental shift problem through the lens of causality and propose
multi-environment contextual bandits that allow for changes in the underlying
mechanisms. We adopt the concept of invariance from the causality literature
and introduce the notion of policy invariance. We argue that policy invariance
is only relevant if unobserved confounders are present and show that, in that
case, an optimal invariant policy is guaranteed to generalize across
environments under suitable assumptions. Our results may be a first step
towards solving the environmental shift problem. They also establish concrete
connections among causality, invariance and contextual bandits.


Abstract:
  Given a prime power $q$ and $n \gg 1$, we prove that every integer in a large
subinterval of the Hasse--Weil interval $[(\sqrt{q}-1)^{2n},(\sqrt{q}+1)^{2n}]$
is $#A(\mathbb{F}_q)$ for some geometrically simple ordinary principally
polarized abelian variety $A$ of dimension $n$ over $\mathbb{F}_q$. As a
consequence, we generalize a result of Howe and Kedlaya for $\mathbb{F}_2$ to
show that for each prime power $q$, every sufficiently large positive integer
is realizable, i.e., $#A(\mathbb{F}_q)$ for some abelian variety $A$ over
$\mathbb{F}_q$. Our result also improves upon the best known constructions of
sequences of simple abelian varieties with point counts towards the extremes of
the Hasse--Weil interval. A separate argument determines, for fixed $n$, the
largest subinterval of the Hasse--Weil interval consisting of realizable
integers, asymptotically as $q \to \infty$; this gives an asymptotically
optimal improvement of a 1998 theorem of DiPippo and Howe. Our methods are
effective: We prove that if $q \le 5$, then every positive integer is
realizable, and for arbitrary $q$, every positive integer $\ge q^{3 \sqrt{q}
\log q}$ is realizable.


Abstract:
  We present a formal language for specifying qualitative preferences over
temporal goals and a preference-based planning method in stochastic systems.
Using automata-theoretic modeling, the proposed specification allows us to
express preferences over different sets of outcomes, where each outcome
describes a set of temporal sequences of subgoals. We define the value of
preference satisfaction given a stochastic process over possible outcomes and
develop an algorithm for time-constrained probabilistic planning in labeled
Markov decision processes where an agent aims to maximally satisfy its
preference formula within a pre-defined finite time duration. We present
experimental results using a stochastic gridworld example and discuss possible
extensions of the proposed preference model.


Abstract:
  The skip-gram (SG) model learns word representation by predicting the words
surrounding a center word from unstructured text data. However, not all words
in the context window contribute to the meaning of the center word. For
example, less relevant words could be in the context window, hindering the SG
model from learning a better quality representation. In this paper, we propose
an enhanced version of the SG that leverages context information to produce
word representation. The proposed model, Contextual Skip-gram, is designed to
predict contextual words with both the center words and the context
information. This simple idea helps to reduce the impact of irrelevant words on
the training process, thus enhancing the final performance


Abstract:
  We propose quadratic residual networks (QRes) as a new type of
parameter-efficient neural network architecture, by adding a quadratic residual
term to the weighted sum of inputs before applying activation functions. With
sufficiently high functional capacity (or expressive power), we show that it is
especially powerful for solving forward and inverse physics problems involving
partial differential equations (PDEs). Using tools from algebraic geometry, we
theoretically demonstrate that, in contrast to plain neural networks, QRes
shows better parameter efficiency in terms of network width and depth thanks to
higher non-linearity in every neuron. Finally, we empirically show that QRes
shows faster convergence speed in terms of number of training epochs especially
in learning complex patterns.


Abstract:
  We consider a class of random banded Hessenberg matrices with independent
entries having identical distributions along diagonals. The distributions may
be different for entries belonging to different diagonals. For a sequence of
$n\times n$ matrices in the class considered, we investigate the asymptotic
behavior of their empirical spectral distribution as $n$ tends to infinity.


Abstract:
  The polar covalent bond between a single Au atom terminating the apex of an
atomic force microscope tip and a C atom of graphene on SiC(0001) is exposed to
an external electric field. For one field orientation the Au-C bond is strong
enough to sustain the mechanical load of partially detached graphene, whilst
for the opposite orientation the bond breaks easily. Calculations based on
density functional theory and nonequilibrium Green's function methods support
the experimental observations by unveiling bond forces that reflect the polar
character of the bond. Field-induced charge transfer between the atomic
orbitals modifies the polarity of the different electronegative reaction
partners and the Au-C bond strength.


Abstract:
  Models for question answering, dialogue agents, and summarization often
interpret the meaning of a sentence in a rich context and use that meaning in a
new context. Taking excerpts of text can be problematic, as key pieces may not
be explicit in a local window. We isolate and define the problem of sentence
decontextualization: taking a sentence together with its context and rewriting
it to be interpretable out of context, while preserving its meaning. We
describe an annotation procedure, collect data on the Wikipedia corpus, and use
the data to train models to automatically decontextualize sentences. We present
preliminary studies that show the value of sentence decontextualization in a
user facing task, and as preprocessing for systems that perform document
understanding. We argue that decontextualization is an important subtask in
many downstream applications, and that the definitions and resources provided
can benefit tasks that operate on sentences that occur in a richer context.


Abstract:
  Detecting objects of interest in images was always a compelling task to
automate. In recent years this task was more and more explored using deep
learning techniques, mostly using region-based convolutional networks. In this
project we propose an alternative semantic segmentation technique making use of
Generative Adversarial Networks. We consider semantic segmentation to be a
domain transfer problem. Thus, we train a feed forward network (FFNN) to
receive as input a seed real image and generate as output its segmentation
mask.


Abstract:
  In stochastic optimisation, the large number of scenarios required to
faithfully represent the underlying uncertainty is often a barrier to finding
efficient numerical solutions. This motivates the scenario reduction problem:
by find a smaller subset of scenarios, reduce the numerical complexity while
keeping the error at an acceptable level.
  In this paper we propose a novel and computationally efficient methodology to
tackle the scenario reduction problem when the error to be minimised is the
implementation error, i.e. the error incurred by implementing the solution of
the reduced problem in the original problem. Specifically, we develop a
problem-driven scenario clustering method that produces a partition of the
scenario set. Each cluster contains a representative scenario that best
reflects the conditional objective values in each cluster of the partition to
be identified.
  We demonstrate the efficiency of our method by applying it to two challenging
stochastic combinatorial optimization problems: the two-stage stochastic
network design problem and the two-stage facility location problem. When
compared to alternative clustering methods and Monte Carlo sampling, our method
is shown to clearly outperform all other methods.


Abstract:
  Recent studies on unsupervised object detection based on spatial attention
have achieved promising results. Models, such as AIR and SPAIR, output "what"
and "where" latent variables that represent the attributes and locations of
objects in a scene, respectively. Most of the previous studies concentrate on
the "where" localization performance; however, we claim that acquiring "what"
object attributes is also essential for representation learning. This paper
presents a framework, GMAIR, for unsupervised object detection. It incorporates
spatial attention and a Gaussian mixture in a unified deep generative model.
GMAIR can locate objects in a scene and simultaneously cluster them without
supervision. Furthermore, we analyze the "what" latent variables and clustering
process. Finally, we evaluate our model on MultiMNIST and Fruit2D datasets and
show that GMAIR achieves competitive results on localization and clustering
compared to state-of-the-art methods.


Abstract:
  Baryogenesis in the context of the compact $SU\left( 3\right)_C\otimes
SU\left( 4\right)_{L}\otimes U\left( 1\right)_X$ model is investigated. Using
the finite temperature effective potential approach together with unitarity,
stability and no ghost masses constraints, the existence of a strong first
order electroweak phase transition (EWPT) was shown and checked numerically
during all steps of the spontaneous breakdown of the gauge symmetry of the
model. Higgs masses regions fulfilling the EWPT criteria are also discussed.
Moreover, and as a byproduct of our study, the B-violation via sphaleron was
also emphasized.


Abstract:
  Einstein-Podolsky-Rosen steering describes a quantum correlation in addition
to entanglement and Bell nonlocality. However, conceptually different from
entanglement and Bell nonlocality, quantum steering has an asymmetric
definition. Motivated by the asymmetric definition of quantum steering, we
study the steerability of two-interacting qubits, which have asymmetric energy
levels, coupled with asymmetric environments. The asymmetric (nonequilibrium)
environments are two environments with different temperatures or chemical
potentials. The Bloch-Redfield equation is applied to study the dynamics of two
qubits and its long-time behavior. In our study, the steady-state steerability
is determined by an experimentally friendly steering criteria, which
demonstrates steering through the entanglement detection. Our results show that
the steady states of two asymmetric qubits have the advantage for one direction
of steering, compared to the symmetric setup. We also provide analytical
results on the minimal coupling strength between the two qubits in order to be
steerable. The asymmetric steerability is collectively determined by the nature
of the two qubits and the influence from equilibrium or nonequilibrium
environments. Nonequilibrium environments with the cost of nonzero entropy
production can enhance the steerability in one direction. We also show the
strict hierarchy of entanglement, steering and Bell nonlocality of the
nonequilibrium steady states, which shows a richer structure of steering than
entanglement and Bell nonlocality.


Abstract:
  We discuss the evolution from BCS to Bose superconductivity versus carrier
density in gated two-dimensional s-wave superconductors. We show that the
transition from the normal to the superconducting state is controlled by the
Berezinskii-Kosterlitz-Thouless vortex-antivortex pairing mechanism, and that
the evolution from high carrier density (BCS pairing) to low carrier density
(Bose pairing) is just a crossover in s-wave systems. We compare our results to
recent experiments on the superconductor LixZrNCl, a lithium intercalated
layered nitride, and obtain very good quantitative agreement at low and
intermediate densities.


Abstract:
  In this article, we consider numerical schemes for polynomial diffusions on
the unit ball $\mathscr{B}^{d}$, which are solutions of stochastic differential
equations with a diffusion coefficient of the form $\sqrt{1-|x|^{2}}$. We
introduce a projection scheme on the unit ball $\mathscr{B}^{d}$ based on a
backward Euler--Maruyama scheme and provide the $L^{2}$-rate of convergence.
The main idea to consider the numerical scheme is the transformation argument
introduced by Swart [29] for proving the pathwise uniqueness for some
stochastic differential equation with a non-Lipschitz diffusion coefficient.


Abstract:
  We show that for any permutation $w$ that avoids a certain set of 13 patterns
of lengths 5 and 6, the Schubert polynomial $\mathfrak S_w$ can be expressed as
the determinant of a matrix of elementary symmetric polynomials in a manner
similar to the Jacobi-Trudi identity. For such $w$, this determinantal formula
is equivalent to a (signed) subtraction-free expansion of $\mathfrak S_w$ in
the basis of standard elementary monomials.


Abstract:
  A set $E$ in a Banach space $X$ is compactivorous if for every compact set
$K$ in $X$ there is a nonempty, (relatively) open subset of $K$ which can be
translated into $E$. In a separable Banach space, this is a sufficient
condition which guarantees the Haar nonnegligibility of Borel subsets. We give
some characterisations of this property in both separable and nonseparable
Banach spaces and prove an extension of the main theorem to countable products
of locally compact Polish groups.


Abstract:
  We study a problem of an online retailer who observes the unit sales of a
product, and dynamically changes the retail price, in order to maximize the
expected revenue. Assuming the demand of the product is price sensitive, we are
interested in the optimal pricing policy when future demand is uncertain. We
build a system to investigate the relationship between retail price and demand
and estimate the demand function. The system predicts demand and revenue at a
given retail price. We formulate a revenue maximization problem over a discrete
finite time horizon with discrete retail price. The optimal pricing policy is
solved based on the predicted demand and revenue values. With computational
experiments, we investigate the effect of optimal pricing policy to inventory
management.


Abstract:
  We propose a new hyper-reduction method for a recently introduced nonlinear
model reduction framework based on dynamically transformed basis functions and
especially well-suited for advection-dominated systems. Furthermore, we discuss
applying this new method to a wildland fire model whose dynamics feature
traveling combustion waves and local ignition and is thus challenging for
classical model reduction schemes based on linear subspaces. The new
hyper-reduction framework allows us to construct parameter-dependent
reduced-order models (ROMs) with efficient offline/online decomposition. The
numerical experiments demonstrate that the ROMs obtained by the novel method
outperform those obtained by a classical approach using the proper orthogonal
decomposition and the discrete empirical interpolation method in terms of run
time and accuracy.


Abstract:
  Methods are described for the solution of linear inference problems subject
to deterministic constraints. The approach builds on work by Backus (1970a,b,c)
and Parker (1977), but a range useful advances are suggested to address both
conceptual and practical issues. The theory is motivated by, and illustrated
with, the estimation of a finite number of a function's spherical harmonic
coefficients from a finite set of its point values. Numerical examples are
included to demonstrate that the methods can be efficiently applied to
realistic problems.


Abstract:
  We propose a unified framework that extends the inference methods for
classical hidden Markov models to continuous settings, where both the hidden
states and observations occur in continuous time. Two different settings are
analyzed: hidden jump process with a finite state space, and hidden diffusion
process with a continuous state space. For each setting, we first estimate the
hidden states given the observations and model parameters, showing that the
posterior distribution of the hidden states can be described by differential
equations in continuous time. We then consider the estimation of unknown model
parameters, deriving the continuous-time formulas for the
expectation-maximization algorithm. We also propose a Monte Carlo method based
on the continuous formulation, sampling the posterior distribution of the
hidden states and updating the parameter estimation.


Abstract:
  This paper studies regret minimization in multi-armed bandits, a classical
online learning problem. To develop more statistically-efficient algorithms, we
propose to use the assumption of a random-effect model. In this model, the mean
rewards of arms are drawn independently from an unknown distribution, whose
parameters we estimate. We provide an estimator of the arm means in this model
and also analyze its uncertainty. Based on these results, we design a UCB
algorithm, which we call ReUCB. We analyze ReUCB and prove a Bayes regret bound
on its $n$-round regret, which matches an existing lower bound. Our experiments
show that ReUCB can outperform Thompson sampling in various scenarios, without
assuming that the prior distribution of arm means is known.


Abstract:
  The mergers of double helium white dwarfs are believed to form isolated
helium-rich hot subdwarfs. Observation shows that the helium-rich hot subdwarfs
can be divided into two subgroups based on whether the surface is carbon-rich
or carbon-normal. But it is not clear whether this distribution directly comes
from binary evolution. We adopt the binary population synthesis (BPS) to obtain
the population of single helium-rich hot subdwarfs according to the channel of
double helium white dwarfs merger. We find that the merger channel can
represent the two subgroups in the $T_{\rm{eff}}-\log g$ plane related to
different masses of progenitor helium white dwarfs. For $Z$ = 0.02, the birth
rates and local density of helium-rich hot subdwarf stars by the mergers of two
helium white dwarfs is $\sim 4.82 \times 10^{-3}$ $\rm yr^{-1}$ and $\sim$
290.0 $\rm kpc^{-3}$ at 13.7 Gyr in our Galaxy, respectively. The proportion of
carbon-rich and carbon-normal helium-rich hot subdwarfs are 32$\%$ and 68$\%$,
respectively.


Abstract:
  Meta-learning has been sufficiently validated to be beneficial for
low-resource neural machine translation (NMT). However, we find that
meta-trained NMT fails to improve the translation performance of the domain
unseen at the meta-training stage. In this paper, we aim to alleviate this
issue by proposing a novel meta-curriculum learning for domain adaptation in
NMT. During meta-training, the NMT first learns the similar curricula from each
domain to avoid falling into a bad local optimum early, and finally learns the
curricula of individualities to improve the model robustness for learning
domain-specific knowledge. Experimental results on 10 different low-resource
domains show that meta-curriculum learning can improve the translation
performance of both familiar and unfamiliar domains. All the codes and data are
freely available at https://github.com/NLP2CT/Meta-Curriculum.


Abstract:
  Spectral imaging is an umbrella term for energy-resolved x-ray imaging in
medicine. The technique makes use of the energy dependence of x-ray attenuation
to either increase the contrast-to-noise ratio, or to provide quantitative
image data and reduce image artefacts by so-called material decomposition.
Spectral imaging is not new, but has gained interest in recent years because of
rapidly increasing availability of spectral and dual-energy CT and the dawn of
energy-resolved photon-counting detectors. This review examines the current
technological status of spectral and dual-energy imaging and a number of
practical applications of the technology.


Abstract:
  This document presents implementations of fundamental convolutional neural
network (CNN) layers on the Manticore cluster-based many-core architecture and
discusses their characteristics and trade-offs.


Abstract:
  Based on the Gale-Ryser theorem for the existence of suitable
$(0,1)$-matrices for different partitions of a natural number, we revisit the
classical result of G. G. Lorentz regarding the characterization of a plane
measurable set, in terms of its cross sections, and extend it to general
measure spaces.


Abstract:
  Modern visual-inertial navigation systems (VINS) are faced with a critical
challenge in real-world deployment: they need to operate reliably and robustly
in highly dynamic environments. Current best solutions merely filter dynamic
objects as outliers based on the semantics of the object category. Such an
approach does not scale as it requires semantic classifiers to encompass all
possibly-moving object classes; this is hard to define, let alone deploy. On
the other hand, many real-world environments exhibit strong structural
regularities in the form of planes such as walls and ground surfaces, which are
also crucially static. We present RP-VIO, a monocular visual-inertial odometry
system that leverages the simple geometry of these planes for improved
robustness and accuracy in challenging dynamic environments. Since existing
datasets have a limited number of dynamic elements, we also present a
highly-dynamic, photorealistic synthetic dataset for a more effective
evaluation of the capabilities of modern VINS systems. We evaluate our approach
on this dataset, and three diverse sequences from standard datasets including
two real-world dynamic sequences and show a significant improvement in
robustness and accuracy over a state-of-the-art monocular visual-inertial
odometry system. We also show in simulation an improvement over a simple
dynamic-features masking approach. Our code and dataset are publicly available.


Abstract:
  Estimation of parameters in differential equation models can be achieved by
applying learning algorithms to quantitative time-series data. However,
sometimes it is only possible to measure qualitative changes of a system in
response to a controlled condition. In dynamical systems theory, such change
points are known as bifurcations and lie on a function of the controlled
condition called the bifurcation diagram. In this work, we propose a
gradient-based approach for inferring the parameters of differential equations
that produce a user-specified bifurcation diagram. The cost function contains
an error term that is minimal when the model bifurcations match the specified
targets and a bifurcation measure which has gradients that push optimisers
towards bifurcating parameter regimes. The gradients can be computed without
the need to differentiate through the operations of the solver that was used to
compute the diagram. We demonstrate parameter inference with minimal models
which explore the space of saddle-node and pitchfork diagrams and the genetic
toggle switch from synthetic biology. Furthermore, the cost landscape allows us
to organise models in terms of topological and geometric equivalence.


Abstract:
  The ability to accurately extract low-amplitude voltage signals is crucial in
several fields, ranging from single-use diagnostics and medical technology to
robotics and the Internet of Things. The organic electrochemical transistor,
which features large transconductance values at low operation voltages, is
ideal for monitoring small signals. Its large transconductance translates small
gate voltage variations into significant changes in the drain current. However,
a current-to-voltage conversion is further needed to allow proper data
acquisition and signal processing. Low power consumption, high amplification,
and manufacturability on flexible and low-cost carriers are also crucial and
highly anticipated for targeted applications. Here, we report low-power and
high-gain flexible circuits based on printed complementary organic
electrochemical transistors (OECTs). We leverage the low threshold voltage of
both p-type and n-type enhancement-mode OECTs to develop complementary voltage
amplifiers that can sense voltages as low as 100 $\mu$V, with gains of 30.4 dB
and at a power consumption < 2.7 $\mu$W (single-stage amplifier). At the
optimal operating conditions, the voltage gain normalized to power consumption
reaches 169 dB/$\mu$W, which is > 50 times larger than state-of-the-art
OECT-based amplifiers. In a two-stage configuration, the complementary voltage
amplifiers reach a DC voltage gain of 193 V/V, which is the highest among
emerging CMOS-like technologies operating at supply voltages below 1 volt. Our
findings demonstrate that flexible complementary circuits based on printed
OECTs define a power-efficient platform for sensing and amplifying
low-amplitude voltage signals in several emerging beyond-silicon applications.


Abstract:
  Photothermal spectroscopy and microscopy provides a route to measure the
spectral and spatial properties of individual nanoscopic absorbers, independent
from scattering, extinction, and emission. The approach relies upon use of two
light sources, one that resonantly excites and heats the target and its
surrounding environment and a second off-resonant probe that scatters from the
resulting volume of thermally modified refractive index. Over the past twenty
years, considerable effort has been extended to apply photothermal methods to
detect, spatially resolve, and perform absorption spectroscopy on single
non-emissive molecules and other absorbers like plasmonic nanoparticles at room
temperature conditions. Companion theoretical models have been developed to
interpret these experimental advances, yet it is not clear how they are related
to each other nor how the effects of lock-in detection modify the theory. For
larger target systems that host their own intrinsic scattering resonances as
well as for background media that do not instantaneously thermalize with the
absorbing target, additional dependencies arise that are yet to be explored
theoretically. The aim of this Perspective is to overview the theory of
photothermal spectroscopy and microscopy and present a unifying theoretical
approach that recovers past models in certain limits while explicitly including
the effects of target scattering resonances, thermal and optical retardation,
and lock-in detection. Focus is made on plasmonic particles to interpret the
photothermal signal, yet all results are applicable equally to individual
molecules or nanoparticle absorbers. Consequently, we expect this review to
provide a useful foundation for the understanding of photothermal measurements
independent of target identity.


Abstract:
  Contamination by polarized foregrounds is one of the biggest challenges for
future polarized cosmic microwave background (CMB) surveys and the potential
detection of primordial $B$-modes. Future experiments, such as Simons
Observatory (SO) and CMB-S4, will aim at very deep observations in relatively
small ($f_{\rm sky} \sim 0.1$) areas of the sky. In this work, we investigate
the forecasted performance, as a function of the survey field location on the
sky, for regions over the full sky, balancing between polarized foreground
avoidance and foreground component separation modeling needs. To do this, we
simulate observations by an SO-like experiment and measure the error bar on the
detection of the tensor-to-scalar ratio, $\sigma(r)$, with a pipeline that
includes a parametric component separation method, the Correlated Component
Analysis, and the use of the Fisher information matrix. We forecast the
performance over 192 survey areas covering the full sky and also for optimized
low-foreground regions. We find that modeling the spectral energy distribution
of foregrounds is the most important factor, and any mismatch will result in
residuals and bias in the primordial $B$-modes. At these noise levels,
$\sigma(r)$ is not especially sensitive to the level of foreground
contamination, provided the survey targets the least-contaminated regions of
the sky close to the Galactic poles.


Abstract:
  In this paper, we propose a new Sound Event Classification (SEC) method which
is inspired in recent works for out-of-distribution detection. In our method,
we analyse all the activations of a generic CNN in order to produce feature
representations using Gram Matrices. The similarity metrics are evaluated
considering all possible classes, and the final prediction is defined as the
class that minimizes the deviation with respect to the features seeing during
training. The proposed approach can be applied to any CNN and our experimental
evaluation of four different architectures on two datasets demonstrated that
our method consistently improves the baseline models.


Abstract:
  A 10\% difference in the scale for the Hubble parameter constitutes a clear
problem for cosmology. Here, considering angular distribution of Type Ia
supernovae (SN) within the Pantheon compilation and working within flat
$\Lambda$CDM cosmology, we observe that the correlation between higher $H_0$
and the CMB dipole direction \cite{Krishnan:2021dyb}, can be extended from
strongly lensed quasars to SN. Concretely, we record a $\sim 1$ km/s/Mpc
variation in $H_0$ at antipodal points on the sky within the Pantheon sample,
which is evident in the Low $z$ subsample ($z \lesssim 0.075$) and gets
enhanced by higher redshift SN. Our work raises the possibility that we may be
at the precision required to probe anisotropic Hubble expansions, while
providing a concrete prediction for future inferences of $H_0$.


Abstract:
  The problem of efficiently generating random samples from high-dimensional
and non-log-concave posterior measures arising from nonlinear regression
problems is considered. Extending investigations from arXiv:2009.05298, local
and global stability properties of the model are identified under which such
posterior distributions can be approximated in Wasserstein distance by suitable
log-concave measures. This allows the use of fast gradient based sampling
algorithms, for which convergence guarantees are established that scale
polynomially in all relevant quantities (assuming `warm' initialisation). The
scope of the general theory is illustrated in a non-linear inverse problem from
integral geometry for which new stability results are derived.


Abstract:
  In data analysis problems where we are not able to rely on distributional
assumptions, what types of inference guarantees can still be obtained? Many
popular methods, such as holdout methods, cross-validation methods, and
conformal prediction, are able to provide distribution-free guarantees for
predictive inference, but the problem of providing inference for the underlying
regression function (for example, inference on the conditional mean
$\mathbb{E}[Y|X]$) is more challenging. In the setting where the features $X$
are continuously distributed, recent work has established that any confidence
interval for $\mathbb{E}[Y|X]$ must have non-vanishing width, even as sample
size tends to infinity. At the other extreme, if $X$ takes only a small number
of possible values, then inference on $\mathbb{E}[Y|X]$ is trivial to achieve.
In this work, we study the problem in settings in between these two extremes.
We find that there are several distinct regimes in between the finite setting
and the continuous setting, where vanishing-width confidence intervals are
achievable if and only if the effective support size of the distribution of $X$
is smaller than the square of the sample size.


Abstract:
  The device used in this work detects the objects over the surface of the
water using two thermal cameras which aid the users to detect and avoid the
objects in scenarios where the human eyes cannot (night, fog, etc.). To avoid
the obstacle collision autonomously, it is required to track the objects in
real-time and assign a specific identity to each object to determine its
dynamics (trajectory, velocity, etc.) for making estimated collision
predictions. In the following work, a Machine Learning (ML) approach for
Computer Vision (CV) called Convolutional Neural Network (CNN) was used using
TensorFlow as the high-level programming environment in Python. To validate the
algorithm a test set was generated using an annotation tool that was created
during the work for proper evaluation. Once validated, the algorithm was
deployed on the platform and tested with the sequence generated by the test
boat.


Abstract:
  Utilizing review information to enhance recommendation, the de facto
review-involved recommender systems, have received increasing interests over
the past few years. Thereinto, one advanced branch is to extract salient
aspects from textual reviews (i.e., the item attributes that users express) and
combine them with the matrix factorization technique. However, existing
approaches all ignore the fact that semantically different reviews often
include opposite aspect information. In particular, positive reviews usually
express aspects that users prefer, while negative ones describe aspects that
users reject. As a result, it may mislead the recommender systems into making
incorrect decisions pertaining to user preference modeling. Towards this end,
in this paper, we propose a Review Polarity-wise Recommender model, dubbed as
RPR, to discriminately treat reviews with different polarities. To be specific,
in this model, positive and negative reviews are separately gathered and
utilized to model the user-preferred and user-rejected aspects, respectively.
Besides, in order to overcome the imbalance problem of semantically different
reviews, we also develop an aspect-aware importance weighting approach to align
the aspect importance for these two kinds of reviews. Extensive experiments
conducted on eight benchmark datasets have demonstrated the superiority of our
model as compared to a series of state-of-the-art review-involved baselines.
Moreover, our method can provide certain explanations to the real-world rating
prediction scenarios.


Abstract:
  Asymptotic stationarity and regularity conditions turned out to be quite
useful to study the qualitative properties of numerical solution methods for
standard nonlinear and complementarity-constrained programs. In this paper, we
first extend these notions to nonlinear optimization problems with nonsmooth
but Lipschitzian data functions in order to find reasonable notions of
asymptotic stationarity and regularity in terms of Clarke's and Mordukhovich's
subdifferential construction. Particularly, we compare the associated novel
asymptotic constraint qualifications with already existing ones. The second
part of the paper presents two applications of the obtained theory. On the one
hand, we specify our findings for complementarity-constrained optimization
problems and recover recent results from the literature which demonstrates the
power of the approach. Furthermore, we hint at potential extensions to or- and
vanishing-constrained optimization. On the other hand, we demonstrate the
usefulness of asymptotic regularity in the context of bilevel optimization.
More precisely, we justify a well-known stationarity system for affinely
constrained bilevel optimization problems in a novel way. Afterwards, we
suggest a solution algorithm for this class of bilevel optimization problems
which combines a penalty method with ideas from DC-programming. After a brief
convergence analysis, we present results of some numerical experiments.


Abstract:
  The anticorrelation between quantum efficiency (QE) and electron spin
polarization (ESP) from a p-doped GaAs activated to negative electron affinity
(NEA) is studied in detail using an ensemble Monte Carlo approach. The
photoabsorption, momentum and spin relaxation during transport, and tunnelling
of electrons through the surface potential barrier are modeled to identify
fundamental mechanisms, which limit the efficiency of GaAs spin-polarized
electron sources. In particular, we study the response of QE and ESP to various
parameters such as the photoexcitation energy, doping density, and electron
affinity level. Our modeling results for various transport and emission
characteristics are in a good agreement with available experimental data. Our
findings show that the behaviour of both QE and ESP at room temperature can be
fully explained by the bulk relaxation mechanisms and the time which electrons
spend in the material before being emitted.


Abstract:
  We call an investment strategy survival, if an agent who uses it maintains a
non-vanishing share of market wealth over the infinite time horizon. In a
discrete-time multi-agent model with endogenous asset prices determined through
a short-run equilibrium of supply and demand, we show that a survival strategy
can be constructed as follows: an agent should assume that only their actions
determine the prices and use a growth optimal (log-optimal) strategy with
respect to these prices, disregarding the actual prices. Then any survival
strategy turns out to be close to this strategy asymptotically. The main
results are obtained under the assumption that the assets are short-lived.


Abstract:
  Machine learning (ML) currently exerts an outsized influence on the world,
increasingly affecting communities and institutional practices. It is therefore
critical that we question vague conceptions of the field as value-neutral or
universally beneficial, and investigate what specific values the field is
advancing. In this paper, we present a rigorous examination of the values of
the field by quantitatively and qualitatively analyzing 100 highly cited ML
papers published at premier ML conferences, ICML and NeurIPS. We annotate key
features of papers which reveal their values: how they justify their choice of
project, which aspects they uplift, their consideration of potential negative
consequences, and their institutional affiliations and funding sources. We find
that societal needs are typically very loosely connected to the choice of
project, if mentioned at all, and that consideration of negative consequences
is extremely rare. We identify 67 values that are uplifted in machine learning
research, and, of these, we find that papers most frequently justify and assess
themselves based on performance, generalization, efficiency, researcher
understanding, novelty, and building on previous work. We present extensive
textual evidence and analysis of how these values are operationalized. Notably,
we find that each of these top values is currently being defined and applied
with assumptions and implications generally supporting the centralization of
power. Finally, we find increasingly close ties between these highly cited
papers and tech companies and elite universities.


Abstract:
  One strategy for adversarially training a robust model is to maximize its
certified radius -- the neighborhood around a given training sample for which
the model's prediction remains unchanged. The scheme typically involves
analyzing a "smoothed" classifier where one estimates the prediction
corresponding to Gaussian samples in the neighborhood of each sample in the
mini-batch, accomplished in practice by Monte Carlo sampling. In this paper, we
investigate the hypothesis that this sampling bottleneck can potentially be
mitigated by identifying ways to directly propagate the covariance matrix of
the smoothed distribution through the network. To this end, we find that other
than certain adjustments to the network, propagating the covariances must also
be accompanied by additional accounting that keeps track of how the
distributional moments transform and interact at each stage in the network. We
show how satisfying these criteria yields an algorithm for maximizing the
certified radius on datasets including Cifar-10, ImageNet, and Places365 while
offering runtime savings on networks with moderate depth, with a small
compromise in overall accuracy. We describe the details of the key
modifications that enable practical use. Via various experiments, we evaluate
when our simplifications are sensible, and what the key benefits and
limitations are.


Abstract:
  Climate models project an uncertainty range of possible warming scenarios
from 1.5 to 5 degree Celsius global temperature increase until 2100, according
to the CMIP6 model ensemble. Climate risk management and infrastructure
adaptation requires the accurate quantification of the uncertainties at the
local level. Ensembles of high-resolution climate models could accurately
quantify the uncertainties, but most physics-based climate models are
computationally too expensive to run as ensemble. Recent works in
physics-informed neural networks (PINNs) have combined deep learning and the
physical sciences to learn up to 15k faster copies of climate submodels.
However, the application of PINNs in climate modeling has so far been mostly
limited to deterministic models. We leverage a novel method that combines
polynomial chaos expansion (PCE), a classic technique for uncertainty
propagation, with PINNs. The PCE-PINNs learn a fast surrogate model that is
demonstrated for uncertainty propagation of known parameter uncertainties. We
showcase the effectiveness in ocean modeling by using the local
advection-diffusion equation.


Abstract:
  M. Kruskal showed that each nearly-periodic dynamical system admits a formal
$U(1)$ symmetry, generated by the so-called roto-rate. We prove that such
systems also admit nearly-invariant manifolds of each order, near which rapid
oscillations are suppressed. We study the nonlinear normal stability of these
slow manifolds for nearly-periodic Hamiltonian systems on barely symplectic
manifolds -- manifolds equipped with closed, non-degenerate $2$-forms that may
be degenerate to leading order. In particular, we establish a sufficient
condition for long-term normal stability based on second derivatives of the
well-known adiabatic invariant. We use these results to investigate the problem
of embedding guiding center dynamics of a magnetized charged particle as a slow
manifold in a nearly-periodic system. We prove that one previous embedding, and
two new embeddings enjoy long-term normal stability, and thereby strengthen the
theoretical justification for these models.


Abstract:
  The emerging paradigm of the Social Internet of Things (SIoT) has transformed
the traditional notion of the Internet of Things (IoT) into a social network of
billions of interconnected smart objects by integrating social networking
facets into the same. In SIoT, objects can establish social relationships in an
autonomous manner and interact with the other objects in the network based on
their social behaviour. A fundamental problem that needs attention is
establishing of these relationships in a reliable and trusted way, i.e.,
establishing trustworthy relationships and building trust amongst objects. In
addition, it is also indispensable to ascertain and predict an object's
behaviour in the SIoT network over a period of time. Accordingly, in this
paper, we have proposed an efficient time-aware machine learning-driven trust
evaluation model to address this particular issue. The envisaged model
deliberates social relationships in terms of friendship and community-interest,
and further takes into consideration the working relationships and
cooperativeness (object-object interactions) as trust parameters to quantify
the trustworthiness of an object. Subsequently, in contrast to the traditional
weighted sum heuristics, a machine learning-driven aggregation scheme is
delineated to synthesize these trust parameters to ascertain a single trust
score. The experimental results demonstrate that the proposed model can
efficiently segregates the trustworthy and untrustworthy objects within a
network, and further provides the insight on how the trust of an object varies
with time along with depicting the effect of each trust parameter on a trust
score.


Abstract:
  Exploring the possible consequences of spatial reciprocity on the evolution
of cooperation is an intensively studied research avenue. Related works assumed
a certain interaction graph of competing players and studied how particular
topologies may influence the dynamical behavior. In this paper we apply a
numerically more demanding off-lattice population approach which could be
potentially relevant especially in microbiological environments. As expected,
results are conceptually similar to those which were obtained for lattice-type
interaction graphs, but some spectacular differences can also be revealed. On
one hand, in off-lattice populations spatial reciprocity may work more
efficiently than for a lattice-based system. On the other hand, competing
strategies may separate from each other in the continuous space concept, which
gives a chance for cooperators to survive even at relatively high temptation
values. Furthermore, the lack of strict neighborhood results in soft borders
between competing patches which jeopardizes the long term stability of
homogeneous domains. We survey the major social dilemma games based on pair
interactions of players and reveal all analogies and differences compared to
on-lattice simulations.


Abstract:
  The hierarchical overlap graph (HOG) is a graph that encodes overlaps from a
given set P of n strings, as the overlap graph does. A best known algorithm
constructs HOG in O(||P|| log n) time and O(||P||) space, where ||P|| is the
sum of lengths of the strings in P. In this paper we present a new algorithm to
construct HOG in O(||P||) time and space. Hence, the construction time and
space of HOG are better than those of the overlap graph, which are O(||P|| +
n^2).


Abstract:
  Of all the characteristics of a violin, those that concern its shape are
probably the most important ones, as the violin maker has complete control over
them. Contemporary violin making, however, is still based more on tradition
than understanding, and a definitive scientific study of the specific relations
that exist between shape and vibrational properties is yet to come and sorely
missed. In this article, using standard statistical learning tools, we show
that the modal frequencies of violin tops can, in fact, be predicted from
geometric parameters, and that artificial intelligence can be successfully
applied to traditional violin making. We also study how modal frequencies vary
with the thicknesses of the plate (a process often referred to as {\em plate
tuning}) and discuss the complexity of this dependency. Finally, we propose a
predictive tool for plate tuning, which takes into account material and
geometric parameters.


Abstract:
  The popularity of the online media-driven social network relation is proven
in today's digital era. The many challenges that these emergence has created
include a huge growing network of social relations, and the large amount of
data which is continuously been generated via the different platform of social
networking sites, viz. Facebook, Twitter, LinkedIn, Instagram, etc. These data
are Personally Identifiable Information (PII) of the users which are also
publicly available for some platform, and others allow with some restricted
permission to download it for research purposes. The users' accessible data
help in providing with better recommendation services to users, however, the
PII can be used to embezzle the users and cause severe detriment to them.
Hence, it is crucial to maintain the users' privacy while providing their PII
accessible for various services. Therefore, it is a burning issue to come up
with an approach that can help the users in getting better recommendation
services without their privacy being harmed. In this paper, a framework is
suggested for the same. Further, how data through Twitter API can be crawled
and used has been extensively discussed. In addition to this, various security
and legal perspectives regarding PII while crawling the data is highlighted. We
believe the presented approach in this paper can serve as a benchmark for
future research in the field of data privacy.


Abstract:
  LiteBIRD, the Lite (Light) satellite for the study of B-mode polarization and
Inflation from cosmic background Radiation Detection, is a space mission for
primordial cosmology and fundamental physics. JAXA selected LiteBIRD in May
2019 as a strategic large-class (L-class) mission, with its expected launch in
the late 2020s using JAXA's H3 rocket. LiteBIRD plans to map the cosmic
microwave background (CMB) polarization over the full sky with unprecedented
precision. Its main scientific objective is to carry out a definitive search
for the signal from cosmic inflation, either making a discovery or ruling out
well-motivated inflationary models. The measurements of LiteBIRD will also
provide us with an insight into the quantum nature of gravity and other new
physics beyond the standard models of particle physics and cosmology. To this
end, LiteBIRD will perform full-sky surveys for three years at the Sun-Earth
Lagrangian point L2 for 15 frequency bands between 34 and 448 GHz with three
telescopes, to achieve a total sensitivity of 2.16 micro K-arcmin with a
typical angular resolution of 0.5 deg. at 100GHz. We provide an overview of the
LiteBIRD project, including scientific objectives, mission requirements,
top-level system requirements, operation concept, and expected scientific
outcomes.


Abstract:
  Motivated by questions originating from the study of a class of shallow
student-teacher neural networks, methods are developed for the analysis of
spurious minima in classes of gradient equivariant dynamics related to neural
nets. In the symmetric case, methods depend on the generic equivariant
bifurcation theory of irreducible representations of the symmetric group on $n$
symbols, $S_n$; in particular, the standard representation of $S_n$. It is
shown that spurious minima do not arise from spontaneous symmetry breaking but
rather through a complex deformation of the landscape geometry that can be
encoded by a generic $S_n$-equivariant bifurcation. We describe minimal models
for forced symmetry breaking that give a lower bound on the dynamic complexity
involved in the creation of spurious minima when there is no symmetry. Results
on generic bifurcation when there are quadratic equivariants are also proved;
this work extends and clarifies results of Ihrig & Golubitsky and Chossat,
Lauterback & Melbourne on the instability of solutions when there are quadratic
equivariants.


Abstract:
  Cryo-electron microscopy (cryo-EM) has revolutionized experimental protein
structure determination. Despite advances in high resolution reconstruction, a
majority of cryo-EM experiments provide either a single state of the studied
macromolecule, or a relatively small number of its conformations. This reduces
the effectiveness of the technique for proteins with flexible regions, which
are known to play a key role in protein function. Recent methods for capturing
conformational heterogeneity in cryo-EM data model it in volume space, making
recovery of continuous atomic structures challenging. Here we present a fully
deep-learning-based approach using variational auto-encoders (VAEs) to recover
a continuous distribution of atomic protein structures and poses directly from
picked particle images and demonstrate its efficacy on realistic simulated
data. We hope that methods built on this work will allow incorporation of
stronger prior information about protein structure and enable better
understanding of non-rigid protein structures.


Abstract:
  We provide a generalization of pseudo-Frobenius numbers of numerical
semigroups to the context of the simplicial affine semigroups. In this way, we
characterize the Cohen-Macaulay type of the simplicial affine semigroup ring
$\mathbb{K}[S]$.
  We define the type of $S$, $\operatorname{type}$, in terms of some Ap\'ery
sets of $S$ and show that it coincides with the Cohen-Macaulay type of the
semigroup ring, when $\mathbb{K}[S]$ is Cohen-Macaulay. If $\mathbb{K}[S]$ is a
$d$-dimensional Cohen-Macaulay ring of embedding dimension at most $d+2$, then
$\operatorname{type}\leq 2$. Otherwise, $\operatorname{type}$ might be
arbitrary large and it has no upper bound in terms of the embedding dimension.
Finally, we present a generating set for the conductor of $S$ as an ideal of
its normalization.


Abstract:
  We develop the protocol for structural restoring of multi-quantum coherence
matrices of the multi-qubit quantum state transferred from the sender to the
receiver along a spin-1/2 chain. We also propose a protocol for constructing
such 0-order coherence matrix that can be perfectly transferred in this
process. The restoring protocol is based on the specially constructed unitary
transformation of the extended receiver.{This transformation for a given length
parameters of the chain is universally optimal in the sense that ones
constructed it can be applied to optimally restore any higher-order coherence
matrices.


Abstract:
  This paper presents an overview of the emerging area of collaborative
intelligence (CI). Our goal is to raise awareness in the signal processing
community of the challenges and opportunities in this area of growing
importance, where key developments are expected to come from signal processing
and related disciplines. The paper surveys the current state of the art in CI,
with special emphasis on signal processing-related challenges in feature
compression, error resilience, privacy, and system-level design.


Abstract:
  We gather and illustrate some functions that we wrote in the Magma computer
algebra system for curves of genus $2$ and $3$. In genus $3$, we furnish
functions both for non-hyperelliptic and for hyperelliptic curves. A fair bit
of the functionality in the latter case extends to hyperelliptic curves of
arbitrary genus.


Abstract:
  In this Letter we present strong arguments in favour of thoroughly revising
the negative mass cosmology (NMC), which has been proposed as a simple
alternative explanation of dark energy and dark matter effects, within the
framework of General Relativity. We show that there are various physical
predictions in this model which require fine-tuning in order to make them
compatible with current cosmological surveys. In this way, the original
motivation of the NMC model becomes obscured due to the imposition of
fine-tuned unknown variables. We conclude that a more rigorous theoretical
treatment is needed in order to make the NMC a viable cosmological model.


Abstract:
  We show a simple reduction which demonstrates the cryptographic hardness of
learning a single periodic neuron over isotropic Gaussian distributions in the
presence of noise. More precisely, our reduction shows that any polynomial-time
algorithm (not necessarily gradient-based) for learning such functions under
small noise implies a polynomial-time quantum algorithm for solving worst-case
lattice problems, whose hardness form the foundation of lattice-based
cryptography. Our core hard family of functions, which are well-approximated by
one-layer neural networks, take the general form of a univariate periodic
function applied to an affine projection of the data. These functions have
appeared in previous seminal works which demonstrate their hardness against
gradient-based (Shamir'18), and Statistical Query (SQ) algorithms (Song et
al.'17). We show that if (polynomially) small noise is added to the labels, the
intractability of learning these functions applies to all polynomial-time
algorithms, beyond gradient-based and SQ algorithms, under the aforementioned
cryptographic assumptions. Moreover, we demonstrate the necessity of noise in
the hardness result by designing a polynomial-time algorithm for learning
certain families of such functions under exponentially small adversarial noise.
Our proposed algorithm is not a gradient-based or an SQ algorithm, but is
rather based on the celebrated Lenstra-Lenstra-Lov\'asz (LLL) lattice basis
reduction algorithm. Furthermore, in the absence of noise, this algorithm can
be directly applied to solve CLWE detection (Bruna et al.'21) and phase
retrieval with an optimal sample complexity of $d+1$ samples. In the former
case, this improves upon the quadratic-in-$d$ sample complexity required in
(Bruna et al.'21).


Abstract:
  In recent years we have witnessed a boom in Internet of Things (IoT) device
deployments, which has resulted in big data and demand for low-latency
communication. This shift in the demand for infrastructure is also enabling
real-time decision making using artificial intelligence for IoT applications.
Artificial Intelligence of Things (AIoT) is the combination of Artificial
Intelligence (AI) technologies and the IoT infrastructure to provide robust and
efficient operations and decision making. Edge computing is emerging to enable
AIoT applications. Edge computing enables generating insights and making
decisions at or near the data source, reducing the amount of data sent to the
cloud or a central repository. In this paper, we propose a framework for
facilitating machine learning at the edge for AIoT applications, to enable
continuous delivery, deployment, and monitoring of machine learning models at
the edge (Edge MLOps). The contribution is an architecture that includes
services, tools, and methods for delivering fleet analytics at scale. We
present a preliminary validation of the framework by performing experiments
with IoT devices on a university campus's rooms. For the machine learning
experiments, we forecast multivariate time series for predicting air quality in
the respective rooms by using the models deployed in respective edge devices.
By these experiments, we validate the proposed fleet analytics framework for
efficiency and robustness.


Abstract:
  Reiher, R\"odl and Schacht [J. London Math. Soc. 97 (2018), 77--97] showed
that the uniform Tur\'an density of every $3$-uniform hypergraph is either $0$
or at least $1/27$, and asked whether there exist $3$-uniform hypergraphs with
uniform Tur\'an density equal or arbitrarily close to $1/27$. We construct
$3$-uniform hypergraphs with uniform Tur\'an density equal to $1/27$.


Abstract:
  This paper explores self-supervised learning of amodal 3D feature
representations from RGB and RGB-D posed images and videos, agnostic to object
and scene semantic content, and evaluates the resulting scene representations
in the downstream tasks of visual correspondence, object tracking, and object
detection. The model infers a latent3D representation of the scene in the form
of 3D feature points, where each continuous world 3D point is mapped to its
corresponding feature vector. The model is trained for contrastive view
prediction by rendering 3D feature clouds in queried viewpoints and matching
against the 3D feature point cloud predicted from the query view. Notably, the
representation can be queried for any 3D location, even if it is not visible
from the input view. Our model brings together three powerful ideas of recent
exciting research work: 3D feature grids as a neural bottleneck for view
prediction, implicit functions for handling resolution limitations of 3D grids,
and contrastive learning for unsupervised training of feature representations.
We show the resulting 3D visual feature representations effectively scale
across objects and scenes, imagine information occluded or missing from the
input viewpoints, track objects over time, align semantically related objects
in 3D, and improve 3D object detection. We outperform many existing
state-of-the-art methods for 3D feature learning and view prediction, which are
either limited by 3D grid spatial resolution, do not attempt to build amodal 3D
representations, or do not handle combinatorial scene variability due to their
non-convolutional bottlenecks.


Abstract:
  We consider remote state estimation of multiple discrete-time linear
time-invariant (LTI) systems over multiple wireless time-varying communication
channels. Each system state is measured by a sensor, and the measurements from
sensors are sent to a remote estimator over the shared wireless channels in a
scheduled manner. We answer the following open problem: what is the fundamental
requirement on the multi-sensor-multi-channel system to guarantee the existence
of a sensor scheduling policy that can stabilize the remote estimation system?
To tackle the problem, we propose a novel policy construction method, and
develop a new analytical approach by applying the asymptotic theory of spectral
radii of products of non-negative matrices. A necessary and sufficient
stability condition is derived in terms of the LTI system parameters and the
channel statistics, which is more effective than existing sufficient conditions
available in the literature. Explicit scheduling policies with stability
guarantees are presented as well. We further extend the analytical framework to
cover remote estimation with four alternative network setups and obtain
corresponding necessary and sufficient stability conditions.


Abstract:
  We consider the mixed problem on the exterior of the unit ball in
$\mathbb{R}^{n}$, $n\ge2$, for a defocusing Schr\"{o}dinger equation with a
power nonlinearity $|u|^{p-1}u$, with zero boundary data. Assuming that the
initial data are non radial, sufficiently small perturbations of \emph{large}
radial initial data, we prove that for all powers $p>n+6$ the solution exists
for all times, its Sobolev norms do not inflate, and the solution is unique in
the energy class.