title:
Channel Modelling and Error Performance Investigation for Reading Lights
  Based In-flight LiFi

abstract:
  The new generation of communication technologies are constantly being pushed
to meet a diverse range of user requirements such as high data rate, low power
consumption, very low latency, very high reliability and broad availability. To
address all these demands, 5G radio access technologies have been extended into
a wide range of new services. However, there are still only a limited number of
applications for RF based wireless communications inside aircraft cabins that
comply with the 5G vision. Potential interference and safety issues in on-board
wireless communications pose significant deployment challenges. By transforming
each reading light into an optical wireless AP, LiFi, could provide seamless
on-board connectivity in dense cabin environments without RF interference.
Furthermore, the utilization of available reading lights allows for a
relatively simple, cost-effective deployment with the high energy and spectral
efficiency. To successfully implement the aeronautical cabin LiFi applications,
comprehensive optical channel characterization is required. In this paper, we
propose a novel MCRT channel modelling technique to capture the details of
in-flight LiFi links. Accordingly, a realistic channel simulator, which takes
the cabin models, interior elements and measurement based optical source,
receiver, surface material characteristics into account is developed. The
effect of the operation wavelength, cabin model accuracy and user terminal
mobility on the optical channel conditions is also investigated. As a final
step, the on-board DCO-OFDM performance is evaluated by using obtained
in-flight LiFi channels. Numerical results show that the location of a mobile
terminal and accurate aircraft cabin modelling yield as much as 12 and 2 dB
performance difference, respectively.


title:
FrAUG: A Frame Rate Based Data Augmentation Method for Depression
  Detection from Speech Signals

abstract:
  In this paper, a data augmentation method is proposed for depression
detection from speech signals. Samples for data augmentation were created by
changing the frame-width and the frame-shift parameters during the feature
extraction process. Unlike other data augmentation methods (such as VTLP, pitch
perturbation, or speed perturbation), the proposed method does not explicitly
change acoustic parameters but rather the time-frequency resolution of
frame-level features. The proposed method was evaluated using two different
datasets, models, and input acoustic features. For the DAIC-WOZ (English)
dataset when using the DepAudioNet model and mel-Spectrograms as input, the
proposed method resulted in an improvement of 5.97% (validation) and 25.13%
(test) when compared to the baseline. The improvements for the CONVERGE
(Mandarin) dataset when using the x-vector embeddings with CNN as the backend
and MFCCs as input features were 9.32% (validation) and 12.99% (test). Baseline
systems do not incorporate any data augmentation. Further, the proposed method
outperformed commonly used data-augmentation methods such as noise
augmentation, VTLP, Speed, and Pitch Perturbation. All improvements were
statistically significant.


title: Improved Upper Bounds for Finding Tarski Fixed Points

abstract:
  We study the query complexity of finding a Tarski fixed point over the
$k$-dimensional grid $\{1,\ldots,n\}^k$. Improving on the previous best upper
bound of $\smash{O(\log^{2\lceil k/3\rceil} n)}$ [FPS20], we give a new
algorithm with query complexity $\smash{O(\log^{\lceil (k+1)/2\rceil} n)}$.
This is based on a novel decomposition theorem about a weaker variant of the
Tarski fixed point problem, where the input consists of a monotone function
$f:[n]^k\rightarrow [n]^k$ and a monotone sign function $b:[n]^k\rightarrow
\{-1,0,1\}$ and the goal is to find an $x\in [n]^k$ that satisfies $either$
$f(x)\preceq x$ and $b(x)\le 0$ $or$ $f(x)\succeq x$ and $b(x)\ge 0$.


title:
Operator identities on Lie algebras, rewriting systems and
  Gr\"obner-Shirshov bases

abstract:
  Motivated by the pivotal role played by linear operators, many years ago Rota
proposed to determine algebraic operator identities satisfied by linear
operators on associative algebras, later called Rota's program on algebraic
operators. Recent progresses on this program have been achieved in the contexts
of operated algebra, rewriting systems and Groebner-Shirshov bases. These
developments also suggest that Rota's insight can be applied to determine
operator identities on Lie algebras, and thus to put the various linear
operators on Lie algebras in a uniform perspective.
  This paper carries out this approach, utilizing operated polynomial Lie
algebras spanned by non-associative Lyndon-Shirshov bracketed words. The Lie
algebra analog of Rota's program was formulated in terms convergent rewriting
systems and equivalently in terms of Groebner-Shirshov bases. This Lie algebra
analog is shown to be compatible with Rota's program for associative algebras.
As applications, a classification of differential type operators and
Rota-Baxter operators are presented.


title: Collapsing Maps and Quasi-Isometries

abstract:
  We introduce a generalization of the b-metric we call a (b,c)-metric. We show
that if $X$ is a $(b,c)$-metric space and $\psi: X \longrightarrow Y$ is a
quasi-isometry then $Y$ is $(b,c)$-metrizable. We also define a particular kind
of collapsing map that can be applied to an arbitrary $(b,c)$-metric space. We
define a distance function on the image of this collapsing map and with this
prove that the collapsing map is a quasi-isometry.


title: On zeros of multilinear polynomials

abstract:
  We consider multivariable polynomials over a fixed number field, linear in
some of the variables. For a system of such polynomials satisfying certain
technical conditions we prove the existence of search bounds for simultaneous
zeros with respect to height. For a single such polynomial, we prove the
existence of search bounds with respect to height for zeros lying outside of a
prescribed algebraic set. We also obtain search bounds in the case of
homogeneous multilinear polynomials, which are related to a so-called "sparse"
version of Siegel's lemma. Among the tools we develop are height inequalities
that are of some independent interest.


title: Group-based Cryptography in the Quantum Era

abstract:
  In this expository article we present an overview of the current
state-of-the-art in post-quantum group-based cryptography. We describe several
families of groups that have been proposed as platforms, with special emphasis
in polycyclic groups and graph groups, dealing in particular with their
algorithmic properties and cryptographic applications. We then, describe some
applications of combinatorial algebra in fully homomorphic encryption. In the
end we discussing several open problems in this direction.


title:
Multi-channel experimental and theoretical constraints for the
  $^{116}$Cd($^{20}$Ne,$^{20}$F)$^{116}$In charge exchange reaction at 306 MeV

abstract:
  Charge exchange (CE) reactions offer a major opportunity to excite nuclear
isovector modes, providing clues about the nuclear interaction in the medium.
Moreover, double charge exchange (DCE) reactions are proving to be a tempting
tool to access nuclear transition matrix elements (NME) related to double
beta-decay processes. Through a multi-channel experimental analysis and a
consistent theoretical approach of the $^{116}$Cd($^{20}$Ne,$^{20}$F)$^{116}$In
single charge exchange (SCE) reaction at 306 MeV, we aim at disentangling from
the experimental cross section the contribution of the competing mechanisms,
associated with second or higher order sequential transfer and inelastic
processes. We measured excitation energy spectra and absolute cross sections
for elastic + inelastic, one-proton transfer and SCE channels, using the MAGNEX
large acceptance magnetic spectrometer to detect the ejectiles. For the first
two channels, we also extracted the experimental cross section angular
distributions. The experimental data are compared with theoretical predictions
obtained by performing two-step distorted wave Born approximation and coupled
reaction channel calculations. We employ spectroscopic amplitudes for
single-particle transitions derived within a large-scale shell model approach
and different optical potentials for modeling the initial and the final state
interactions. The present study significantly mitigates the possible model
dependence existing in the description of these complex reaction mechanisms,
thanks to the reproduction of several channels at once. In particular, our work
demonstrates that the two-step transfer mechanisms produce a non negligible
contribution to the total cross section of the
$^{116}$Cd($^{20}$Ne,$^{20}$F)$^{116}$In reaction channel, although a relevant
fraction is still missing, being ascribable to the direct SCE mechanism, which
is not addressed here.


title:
Almost Sure Uniform Convergence of Stochastic Processes in the Dual of a
  Nuclear Space

abstract:
  Let $\Phi$ be a nuclear space and let $\Phi'$ denote its strong dual. In this
paper we introduce sufficient conditions for the almost surely uniform
convergence on bounded intervals of time for a sequence of $\Phi'$-valued
processes having continuous (respectively c\`{a}dl\`{a}g) paths. The main
result is formulated first in the general setting of cylindrical processes but
later specialized to other situations of interest. In particular, we establish
conditions for the convergence to occur in a Hilbert space continuously
embedded in $\Phi'$. Furthermore, in the context of the dual of an
ultrabornological nuclear space (like spaces of smooth functions and
distributions) we also include applications to the convergence of a series of
independent c\`{a}dl\`{a}g process and to the convergence of solutions to
linear evolution equations driven by L\'{e}vy noise.


title:
Boosting Barely Robust Learners: A New Perspective on Adversarial
  Robustness

abstract:
  We present an oracle-efficient algorithm for boosting the adversarial
robustness of barely robust learners. Barely robust learning algorithms learn
predictors that are adversarially robust only on a small fraction $\beta \ll 1$
of the data distribution. Our proposed notion of barely robust learning
requires robustness with respect to a "larger" perturbation set; which we show
is necessary for strongly robust learning, and that weaker relaxations are not
sufficient for strongly robust learning. Our results reveal a qualitative and
quantitative equivalence between two seemingly unrelated problems: strongly
robust learning and barely robust learning.


title: The Three Gap Theorem and Periodic Functions

abstract:
  The Three Gap Theorem, also known as the Steinhaus Conjecture, is a classical
result on the combinatorics of the fractional part function, and has since been
generalized in many ways. In this paper, we pose a new problem related to these
results: for which other periodic functions does an analogue of the Three Gap
Theorem hold? We prove analogous results for certain classes of
piecewise-linear periodic functions and demonstrate the existence of functions
for which no bound exists on the number of gap lengths.


title: Deep Signatures -- Learning Invariants of Planar Curves

abstract:
  We propose a learning paradigm for numerical approximation of differential
invariants of planar curves. Deep neural-networks' (DNNs) universal
approximation properties are utilized to estimate geometric measures. The
proposed framework is shown to be a preferable alternative to axiomatic
constructions. Specifically, we show that DNNs can learn to overcome
instabilities and sampling artifacts and produce numerically-stable signatures
for curves subject to a given group of transformations in the plane. We compare
the proposed schemes to alternative state-of-the-art axiomatic constructions of
group invariant arc-lengths and curvatures.


abstract:
  NASA's next flagship mission, the Nancy Grace Roman Space Telescope, is a
2.4-meter observatory set to launch no later than May 2027. Roman features two
instruments: the Wide Field Imager and the Coronagraph Instrument. Roman's
Coronagraph is a Technology Demonstration that will push the current
capabilities of direct imaging to smaller contrast ratios ($\sim$10$^{-9}$) and
inner-working angles (3~$\lambda$/D). In order to achieve this high precision,
Roman Coronagraph data must be calibrated to remove as many potential sources
of error as possible. Here we present a detailed overview of the Nancy Grace
Roman Space Telescope Coronagraph Instrument Observation Calibration Plan
including identifying potential sources of error and how they will be mitigated
via on-sky calibrations.


title: Compute Trends Across Three Eras of Machine Learning

abstract:
  Compute, data, and algorithmic advances are the three fundamental factors
that guide the progress of modern Machine Learning (ML). In this paper we study
trends in the most readily quantified factor - compute. We show that before
2010 training compute grew in line with Moore's law, doubling roughly every 20
months. Since the advent of Deep Learning in the early 2010s, the scaling of
training compute has accelerated, doubling approximately every 6 months. In
late 2015, a new trend emerged as firms developed large-scale ML models with 10
to 100-fold larger requirements in training compute. Based on these
observations we split the history of compute in ML into three eras: the Pre
Deep Learning Era, the Deep Learning Era and the Large-Scale Era. Overall, our
work highlights the fast-growing compute requirements for training advanced ML
systems.


title:
Bispectrality and biorthogonality of the rational functions of $q$-Hahn
  type

abstract:
  We introduce families of rational functions that are biorthogonal with
respect to the $q$-hypergeometric distribution. A triplet of $q$-difference
operators $X$, $Y$, $Z$ is shown to play a role analogous to the pair of
bispectral operators of orthogonal polynomials. The recurrence relation and
difference equation take the form of generalized eigenvalue problems involving
the three operators. The algebra generated by $X$, $Y$, $Z$ is akin to the
algebras of Askey--Wilson type in the case of orthogonal polynomials. The
actions of these operators in three different basis are presented. Connections
with Wilson's ${}_{10}\phi_9$ biorthogonal rational functions are also
discussed.


title: Wave focusing by submerged islands and gravitational analogues

abstract:
  We study water waves propagating over a smooth obstacle in a fluid of varying
depth, motivated by the observation that submerged islands in the ocean act as
effective lenses that increase the amplitude and destructive power of tsunami
waves near focal points. We show that islands of substantial height (compared
to the water depth) lead to strong focusing in their immediate vicinity, and
generate caustics of either cusp or butterfly type. We highlight similarities
and differences with focusing of (high-frequency) gravitational waves by a
neutron star. In the linear regime, the comparison is made precise through an
effective-spacetime description of the island-fluid system. This description is
then put to practical use: we identify caustics by solving the Raychaudhuri
equation (a transport equation) along rays of the effective metric. Next, the
island-fluid scattering processes are examined in detail (i.e.~deflection
angle, phase shifts, scattering amplitudes) using numerical simulations and
analytical techniques, including the eikonal approximation and its
generalisation in the form of the Gaussian beam approximation. We show that the
techniques capture the key features of the simulations. Finally, we extend the
eikonal approximation to the dispersive regime, demonstrating that the
essential features are robust in dispersive settings. This paves the way for
future exploration in a controlled laboratory set-up.


title:
Energy landscape transformation of Ising problem with invariant
  eigenvalues for quantum annealing

abstract:
  Quantum annealing tends to be more difficult as the energy landscape of the
problem becomes complicated with many local minima. We have found a
transformation for changing the energy landscape that swaps the eigenvalues and
paired states without changing the eigenvalues of the instance at all. The
transformation is basically a partial recombination of the two-spin interaction
coefficient Jij and the longitudinal magnetic field interaction coefficient hi.
The Hamming distance corresponding to a barrier between the states changes by
the transformation, which in turn affects the ground state convergence. In the
quantum annealing simulation results of a small number of spin instances, the
annealing time was shortened by several orders of magnitude by applying the
transformation. In addition, we also obtained a result using a D-Wave quantum
annealer, which also showed a big improvement in the ground state convergence.


title:
Benign Overfitting without Linearity: Neural Network Classifiers Trained
  by Gradient Descent for Noisy Linear Data

abstract:
  Benign overfitting, the phenomenon where interpolating models generalize well
in the presence of noisy data, was first observed in neural network models
trained with gradient descent. To better understand this empirical observation,
we consider the generalization error of two-layer neural networks trained to
interpolation by gradient descent on the logistic loss following random
initialization. We assume the data comes from well-separated class-conditional
log-concave distributions and allow for a constant fraction of the training
labels to be corrupted by an adversary. We show that in this setting, neural
networks exhibit benign overfitting: they can be driven to zero training error,
perfectly fitting any noisy training labels, and simultaneously achieve test
error close to the Bayes-optimal error. In contrast to previous work on benign
overfitting that require linear or kernel-based predictors, our analysis holds
in a setting where both the model and learning dynamics are fundamentally
nonlinear.


title:
Improving Image-recognition Edge Caches with a Generative Adversarial
  Network

abstract:
  Image recognition is an essential task in several mobile applications. For
instance, a smartphone can process a landmark photo to gather more information
about its location. If the device does not have enough computational resources
available, it offloads the processing task to a cloud infrastructure. Although
this approach solves resource shortages, it introduces a communication delay.
Image-recognition caches on the Internet's edge can mitigate this problem.
These caches run on servers close to mobile devices and stores information
about previously recognized images. If the server receives a request with a
photo stored in its cache, it replies to the device, avoiding cloud offloading.
The main challenge for this cache is to verify if the received image matches a
stored one. Furthermore, for outdoor photos, it is difficult to compare them if
one was taken in the daytime and the other at nighttime. In that case, the
cache might wrongly infer that they refer to different places, offloading the
processing to the cloud. This work shows that a well-known generative
adversarial network, called ToDayGAN, can solve this problem by generating
daytime images using nighttime ones. We can thus use this translation to
populate a cache with synthetic photos that can help image matching. We show
that our solution reduces cloud offloading and, therefore, the application's
latency.


title: Detecting out-of-context objects using contextual cues

abstract:
  This paper presents an approach to detect out-of-context (OOC) objects in an
image. Given an image with a set of objects, our goal is to determine if an
object is inconsistent with the scene context and detect the OOC object with a
bounding box. In this work, we consider commonly explored contextual relations
such as co-occurrence relations, the relative size of an object with respect to
other objects, and the position of the object in the scene. We posit that
contextual cues are useful to determine object labels for in-context objects
and inconsistent context cues are detrimental to determining object labels for
out-of-context objects. To realize this hypothesis, we propose a graph
contextual reasoning network (GCRN) to detect OOC objects. GCRN consists of two
separate graphs to predict object labels based on the contextual cues in the
image: 1) a representation graph to learn object features based on the
neighboring objects and 2) a context graph to explicitly capture contextual
cues from the neighboring objects. GCRN explicitly captures the contextual cues
to improve the detection of in-context objects and identify objects that
violate contextual relations. In order to evaluate our approach, we create a
large-scale dataset by adding OOC object instances to the COCO images. We also
evaluate on recent OCD benchmark. Our results show that GCRN outperforms
competitive baselines in detecting OOC objects and correctly detecting
in-context objects.


title:
A Power Series Approximation in Symmetry Projected Coupled Cluster
  Theory

abstract:
  Projected Hartree-Fock theory provides an accurate description of many kinds
of strong correlations but does not properly describe weakly-correlated
systems. On the other hand, single-reference methods such as configuration
interaction or coupled cluster theory can handle weakly-correlated problems but
cannot properly account for strong correlations. Ideally, we would like to
combine these approaches in a symmetry-projected coupled cluster approach, but
this is far from straightforward. In this work, we provide an alternative
formulation to identify the so-called disentangled cluster operators which
arise when we combine these two methodological strands. Our formulation shows
promising results for model systems and small molecules.


title:
Metadata-Induced Contrastive Learning for Zero-Shot Multi-Label Text
  Classification

abstract:
  Large-scale multi-label text classification (LMTC) aims to associate a
document with its relevant labels from a large candidate set. Most existing
LMTC approaches rely on massive human-annotated training data, which are often
costly to obtain and suffer from a long-tailed label distribution (i.e., many
labels occur only a few times in the training set). In this paper, we study
LMTC under the zero-shot setting, which does not require any annotated
documents with labels and only relies on label surface names and descriptions.
To train a classifier that calculates the similarity score between a document
and a label, we propose a novel metadata-induced contrastive learning (MICoL)
method. Different from previous text-based contrastive learning techniques,
MICoL exploits document metadata (e.g., authors, venues, and references of
research papers), which are widely available on the Web, to derive similar
document-document pairs. Experimental results on two large-scale datasets show
that: (1) MICoL significantly outperforms strong zero-shot text classification
and contrastive learning baselines; (2) MICoL is on par with the
state-of-the-art supervised metadata-aware LMTC method trained on 10K-200K
labeled documents; and (3) MICoL tends to predict more infrequent labels than
supervised methods, thus alleviates the deteriorated performance on long-tailed
labels.


title:
Increasing FPGA Accelerators Memory Bandwidth with a Burst-Friendly
  Memory Layout

abstract:
  Offloading compute-intensive kernels to hardware accelerators relies on the
large degree of parallelism offered by these platforms. However, the effective
bandwidth of the memory interface often causes a bottleneck, hindering the
accelerator's effective performance. Techniques enabling data reuse, such as
tiling, lower the pressure on memory traffic but still often leave the
accelerators I/O-bound. A further increase in effective bandwidth is possible
by using burst rather than element-wise accesses, provided the data is
contiguous in memory.
  In this paper, we propose a memory allocation technique, and provide a
proof-of-concept source-to-source compiler pass, that enables such burst
transfers by modifying the data layout in external memory. We assess how this
technique pushes up the memory throughput, leaving room for exploiting
additional parallelism, for a minimal logic overhead.


title:
Representation of the gravitational potential of a level ellipsoid by a
  simple layer

abstract:
  A closed-form expression is obtained for the density of a simple layer,
equipotential to an oblate level ellipsoid of revolution in an outer space. The
potential of any level spheroid of positive mass with the inward direction of
attracting force on its surface can be represented in this way. A family of
density functions defined on the whole volume of a level ellipsoid of
revolution is found. Several density examples are considered.

categories: physics.class-ph astro-ph.EP astro-ph.SR gr-qc physics.geo-ph


title: POT-flavored estimator of Pickands dependence function

abstract:
  This work proposes an estimator with both Peak-Over-Threshold and
Block-Maxima flavors, uses it to estimate the Pickands dependence function of
bivariate time series, and illustrates how it brings down the asymptotic bias
and the overall mean squared error.


title: Constrained Vector-like quark model with perturbative unitarity

abstract:
  We propose a Vector-like quark (VLQ) model constrained by the requirement of
perturbative unitarity. In this scenario, the neutral and charged couplings of
the new heavy VLQs to SM quarks and bosons are strictly related. We derive the
corresponding sum rules and display the definite structure of the couplings. We
analyse, qualitatively, the phenomenological consequences of this model in
terms of expected width, production mechanism and decay channels. We show that
the upcoming LHC run 3 could have sensitivity to such particles but the search
strategy should be adapted to cover wide resonances decaying mainly in the
charged channel T(B) -> Wq.


title: Saturable time-varying mirror based on an ENZ material

abstract:
  We report a switchable time-varying mirror, composed of an ITO-Au stack,
which can be efficiently modulated in time with over a ten-fold increase in
reflectivity, with a change of 0.6. Upon interacting with the time-varying
mirror, the frequency content of a reflected pulse is extended to 31 THz. This
originates from the shortening of the response time of the mirror beyond
saturation, as confirmed by a time-varying model and by further four-wave
mixing experiments. A temporal response unbounded by the pump bandwidth opens
new avenues for spectral manipulation from time-varying systems with impact for
communication networks, optical switching and computing.


title: Pseudo Polynomial-Time Top-k Algorithms for d-DNNF Circuits

abstract:
  We are interested in computing $k$ most preferred models of a given d-DNNF
circuit $C$, where the preference relation is based on an algebraic structure
called a monotone, totally ordered, semigroup $(K, \otimes, <)$. In our
setting, every literal in $C$ has a value in $K$ and the value of an assignment
is an element of $K$ obtained by aggregating using $\otimes$ the values of the
corresponding literals. We present an algorithm that computes $k$ models of $C$
among those having the largest values w.r.t. $<$, and show that this algorithm
runs in time polynomial in $k$ and in the size of $C$. We also present a pseudo
polynomial-time algorithm for deriving the top-$k$ values that can be reached,
provided that an additional (but not very demanding) requirement on the
semigroup is satisfied. Under the same assumption, we present a pseudo
polynomial-time algorithm that transforms $C$ into a d-DNNF circuit $C'$
satisfied exactly by the models of $C$ having a value among the top-$k$ ones.
Finally, focusing on the semigroup $(\mathbb{N}, +, <)$, we compare on a large
number of instances the performances of our compilation-based algorithm for
computing $k$ top solutions with those of an algorithm tackling the same
problem, but based on a partial weighted MaxSAT solver.


title:
Weber number and the outcome of binary collisions between quantum
  droplets

abstract:
  A theoretical analysis of binary collisions of quantum droplets under
feasible experimental conditions is reported. Droplets formed from degenerate
dilute Bose gases made up from binary mixtures of ultra cold atoms are
considered. Reliable expressions for the surface tension of the droplets are
introduced based on a study of low energy excitations of their ground state
within the random phase approximation. Their relevance is evaluated considering
an estimation of the expected excitation energy having in mind the Thouless
variational theorem. The surface tension expressions allow calculating the
Weber number of the droplets involved in the collisions. Several regimes on the
outcomes of the binary frontal collisions that range from the coalescence of
the quantum droplets to their disintegration into smaller droplets are
identified. Atoms losses of the droplets derived from self-evaporation and
three-body scattering are quantified for both homo- and hetero-nuclear
mixtures. Their control is mandatory for the observation of some interesting
effects arising from droplets collisions.


title: Automatic Curriculum Generation for Learning Adaptation in Networking

abstract:
  As deep reinforcement learning (RL) showcases its strengths in networking and
systems, its pitfalls also come to the public's attention--when trained to
handle a wide range of network workloads and previously unseen deployment
environments, RL policies often manifest suboptimal performance and poor
generalizability.
  To tackle these problems, we present Genet, a new training framework for
learning better RL-based network adaptation algorithms. Genet is built on the
concept of curriculum learning, which has proved effective against similar
issues in other domains where RL is extensively employed. At a high level,
curriculum learning gradually presents more difficult environments to the
training, rather than choosing them randomly, so that the current RL model can
make meaningful progress in training. However, applying curriculum learning in
networking is challenging because it remains unknown how to measure the
"difficulty" of a network environment.
  Instead of relying on handcrafted heuristics to determine the environment's
difficulty level, our insight is to utilize traditional rule-based (non-RL)
baselines: If the current RL model performs significantly worse in a network
environment than the baselines, then the model's potential to improve when
further trained in this environment is substantial. Therefore, Genet
automatically searches for the environments where the current model falls
significantly behind a traditional baseline scheme and iteratively promotes
these environments as the training progresses. Through evaluating Genet on
three use cases--adaptive video streaming, congestion control, and load
balancing, we show that Genet produces RL policies which outperform both
regularly trained RL policies and traditional baselines in each context, not
only under synthetic workloads but also in real environments.


title:
Domain-Invariant Proposals based on a Balanced Domain Classifier for
  Object Detection

abstract:
  Object recognition from images means to automatically find object(s) of
interest and to return their category and location information. Benefiting from
research on deep learning, like convolutional neural networks~(CNNs) and
generative adversarial networks, the performance in this field has been
improved significantly, especially when training and test data are drawn from
similar distributions. However, mismatching distributions, i.e., domain shifts,
lead to a significant performance drop. In this paper, we build
domain-invariant detectors by learning domain classifiers via adversarial
training. Based on the previous works that align image and instance level
features, we mitigate the domain shift further by introducing a domain
adaptation component at the region level within Faster \mbox{R-CNN}. We embed a
domain classification network in the region proposal network~(RPN) using
adversarial learning. The RPN can now generate accurate region proposals in
different domains by effectively aligning the features between them. To
mitigate the unstable convergence during the adversarial learning, we introduce
a balanced domain classifier as well as a network learning rate adjustment
strategy. We conduct comprehensive experiments using four standard datasets.
The results demonstrate the effectiveness and robustness of our object
detection approach in domain shift scenarios.


title:
Broadband polarization insensitivity and high detection efficiency in
  high-fill-factor superconducting microwire single-photon detectors

abstract:
  Single-photon detection via absorption in current-biased nanoscale
superconducting structures has become a preferred technology in quantum optics
and related fields. Single-mode fiber packaged devices have seen new records
set in detection efficiency, timing jitter, recovery times, and largest
sustainable count rates. The popular approaches to decreasing polarization
sensitivity have thus far been limited to introduction of geometrically
symmetric nanowire meanders, such as spirals and fractals, in the active area.
The constraints on bending radii, and by extension, fill factors, in such
designs limits their maximum efficiency. The discovery of single-photon
sensitivity in micrometer-scale superconducting wires enables novel meander
patterns with no effective upper limit on fill factor. This work demonstrates
simultaneous low-polarization sensitivity ($1.02\pm 0.008$) and high detection
efficiency ($> 91.8\%$ with $67\%$ confidence at $2\times10^5$ counts per
second) across a $40$ nm bandwidth centered at 1550 nm in 0.51 $\mu\text{m}$
wide microwire devices made of silicon-rich tungsten silicide, with a $0.91$
fill factor in the active area. These devices boasted efficiencies of
$96.5-96.9\% \pm 0.5\%$ at $1\times10^5$ counts per second for 1550 nm light.


title:
Semi-supervised New Event Type Induction and Description via Contrastive
  Loss-Enforced Batch Attention

abstract:
  Most event extraction methods have traditionally relied on an annotated set
of event types. However, creating event ontologies and annotating supervised
training data are expensive and time-consuming. Previous work has proposed
semi-supervised approaches which leverage seen (annotated) types to learn how
to automatically discover new event types. State-of-the-art methods, both
semi-supervised or fully unsupervised, use a form of reconstruction loss on
specific tokens in a context. In contrast, we present a novel approach to
semi-supervised new event type induction using a masked contrastive loss, which
learns similarities between event mentions by enforcing an attention mechanism
over the data minibatch. We further disentangle the discovered clusters by
approximating the underlying manifolds in the data, which allows us to increase
normalized mutual information and Fowlkes-Mallows scores by over 20% absolute.
Building on these clustering results, we extend our approach to two new tasks:
predicting the type name of the discovered clusters and linking them to
FrameNet frames.


title:
The interdisciplinary quantum information classroom: Themes from a
  survey of quantum information science instructors

abstract:
  Interdisciplinary introduction to quantum information science (QIS) courses
are proliferating at universities across the US, but the experiences of
instructors in these courses have remained largely unexplored in the
discipline-based education research (DBER) communities. Here, we address this
gap by reporting on the findings of a survey of instructors teaching
introduction to QIS courses at institutions across the US, primarily at the
undergraduate or hybrid undergraduate/graduate level, as well as follow-up
focus interviews with six individual instructors. Key themes from this analysis
include challenges and opportunities associated with the diversity of
instructor and student backgrounds, student difficulties with the mathematical
formalism (especially though not exclusively with linear algebra), and the need
for better textbooks and curricular materials.


title:
Droplet dissolution driven by emerging thermal gradients and Marangoni
  flow

abstract:
  The lifetime $\tau$ of an isothermal and purely diffusively dissolving
droplet in a host liquid scales as $\tau \sim R_0^2$ with its initial radius
$R_0$ [Langmuir, Phys. Rev. 12, 368 (1919)]. For a droplet dissolving due to
natural convection driven by density differences, its lifetime scales as
$\tau\sim R_0^{5/4}$ [Dietrich et al., J. Fluid Mech. 794, 45 (2016)]. In this
paper we experimentally find and theoretically derive yet another droplet
dissolution behavior, resulting in $\tau \sim R_0^4$. It occurs when the
dissolution dynamics is controlled by local heating of the liquid, leading to a
modified solubility and a thermal Marangoni flow around the droplet. The
thermal gradient is achieved by plasmonic heating of a gold nanoparticle
decorated sample surface, on which a sessile water droplet immersed in
water-saturated 1-butanol solution is sitting. The resulting off-wall thermal
Marangoni flow and the temperature dependence of the solubility determine the
droplet dissolution rate, resulting in a shrinkage $R(t) \sim (\tau -t )^{1/4}$
of the droplet radius and thus in $\tau \sim R_0^{4}$.


title: Games of Artificial Intelligence: A Continuous-Time Approach

abstract:
  This paper studies the strategic interaction of algorithms in economic games.
We analyze games where learning algorithms play against each other while
searching for the best strategy. We first establish a fluid approximation
technique that enables us to characterize the learning outcomes in continuous
time. This tool allows to identify the equilibria of games played by Artificial
Intelligence algorithms and perform comparative statics analysis. Thus, our
results bridge a gap between traditional learning theory and applied models,
allowing quantitative analysis of traditionally experimental systems. We
describe the outcomes of a social dilemma, and we provide analytical guidance
for the design of pricing algorithms in a Bertrand game. We uncover a new
phenomenon, the coordination bias, which explains how algorithms may fail to
learn dominant strategies.


title: Artificial Intelligence and Auction Design

abstract:
  Motivated by online advertising auctions, we study auction design in repeated
auctions played by simple Artificial Intelligence algorithms (Q-learning). We
find that first-price auctions with no additional feedback lead to
tacit-collusive outcomes (bids lower than values), while second-price auctions
do not. We show that the difference is driven by the incentive in first-price
auctions to outbid opponents by just one bid increment. This facilitates
re-coordination on low bids after a phase of experimentation. We also show that
providing information about lowest bid to win, as introduced by Google at the
time of switch to first-price auctions, increases competitiveness of auctions.


title:
Magnetic field-tunable valley-contrasting pseudomagnetic confinement in
  graphene

abstract:
  Introducing quantum confinement has uncovered a rich set of interesting
quantum phenomena and allows one to directly probe the physics of confined
(quasi-)particles. In most experiments, however, electrostatic potential is the
only available method to generate the quantum confinement in a continuous
system. Here, we demonstrated experimentally that inhomogeneous pseudomagnetic
fields in strained graphene can introduce exotic quantum confinement of
massless Dirac fermions. The pseudomagnetic fields have opposite directions in
the two distinct valleys of graphene. By tuning real magnetic field, the total
effective magnetic fields in the two valleys are imbalanced. Then, we realized
valley-contrasting spatial confinement, which lifts the valley degeneracy and
results in field-tunable valley-polarized confined states in graphene. Our
results provide a new avenue to manipulate the valley degree of freedom.


title:
Bias on Tensor-to-Scalar Ratio Inference With Estimated Covariance
  Matrices

abstract:
  We investigate simulation-based bandpower covariance matrices commonly used
in cosmological parameter inferences such as the estimation of the
tensor-to-scalar ratio~$r$. We find that upper limits on $r$ can be biased low.
The underestimation of the upper limit is most severe when the number of
simulation realizations is similar to the number of observables. Convergence of
the covariance-matrix estimation can require a number of simulations an order
of magnitude larger than the number of observables. This is found to be caused
by an additional scatter in the posterior probability of $r$ due to Monte Carlo
noise in the estimated bandpower covariance matrix, in particular, by spurious
non-zero off-diagonal elements. We show that matrix conditioning can be a
viable mitigation strategy in the case that legitimate covariance assumptions
can be made.


title:
Chaos, Phase Transitions and Curvature Invariants of (rotating, warped,
  massive) BTZ Black Holes

abstract:
  Combining several results from the previous works of the author, three main
subtleties will be clarified here. First, similar to the rotating BTZ black
holes, we show how for the warped BTZ black holes, the consideration of the
effective temperatures could solve the seemingly unsaturation issue of the
chaos bound. Second, comparing the Hawking-Page phase diagrams of BTZ and
warped BTZ black holes in the topologically massive gravity and new massive
gravity theories, we show how the characteristics of the action would specify
the behaviors of the chaos modes, and there we emphasize the importance of
using the local and "physical" thermodynamical ensembles for studying the
boundary chaos modes in warped CFTs and also the connections with the bulk
reconstruction. Third, we propose that the boundary modular scrambling modes
which saturate the modular chaos bound are related to the curvature invariants
in the geometrical side.


title:
Historical Context and Outlook of Quantum Hall Research for the
  Redefined SI

abstract:
  To fully appreciate the impacts that the discovery of the quantum Hall effect
had on electrical metrology, it may benefit the reader to cultivate a general
understanding of the phenomenon. Two-dimensional electron systems can exhibit
many interesting quantum phenomena. The QHE may be exhibited by a 2D electron
system when placed under a strong magnetic field perpendicular to the plane of
the system. These conditions allow for Landau quantization, or the
discretization of available energies of the electrons affected by the magnetic
field. These energy values, determined by solving the Schr\"odinger equation,
are known as Landau levels. During the course of a QHE measurement, one defines
the Hall resistance Rxy as the measured voltage, perpendicular to the direction
of the applied current, divided by that same current. The characteristic
longitudinal resistivity \r{ho}xx goes to zero as Rxy approaches a quantized
value (nominally called a plateau).


title: Marking the Graphene Era in Disseminating the Redefined SI

abstract:
  The history of quantum Hall standards stretches several decades and mostly
begins with the use of GaAs given that 2D electron systems exhibit interesting
quantum phenomena. At the end 2000s, research in 2D materials like graphene
became prevalent. The QHE was observed and quickly became accessible to
metrologists. QHR devices were becoming graphene-based, with fabrications
performed by chemical vapor deposition (CVD), epitaxial growth, and the
exfoliation of graphite. Given the many methods of available graphene
synthesis, efforts to find an optimal synthesis method for metrological
purposes were underway. Exfoliated graphene was widely known to exhibit the
highest mobilities due to its pristine crystallinity. It was a primary initial
candidate as far as metrological testing was concerned.


title: Open-set Adversarial Defense with Clean-Adversarial Mutual Learning

abstract:
  Open-set recognition and adversarial defense study two key aspects of deep
learning that are vital for real-world deployment. The objective of open-set
recognition is to identify samples from open-set classes during testing, while
adversarial defense aims to robustify the network against images perturbed by
imperceptible adversarial noise. This paper demonstrates that open-set
recognition systems are vulnerable to adversarial samples. Furthermore, this
paper shows that adversarial defense mechanisms trained on known classes are
unable to generalize well to open-set samples. Motivated by these observations,
we emphasize the necessity of an Open-Set Adversarial Defense (OSAD) mechanism.
This paper proposes an Open-Set Defense Network with Clean-Adversarial Mutual
Learning (OSDN-CAML) as a solution to the OSAD problem. The proposed network
designs an encoder with dual-attentive feature-denoising layers coupled with a
classifier to learn a noise-free latent feature representation, which
adaptively removes adversarial noise guided by channel and spatial-wise
attentive filters. Several techniques are exploited to learn a noise-free and
informative latent feature space with the aim of improving the performance of
adversarial defense and open-set recognition. First, we incorporate a decoder
to ensure that clean images can be well reconstructed from the obtained latent
features. Then, self-supervision is used to ensure that the latent features are
informative enough to carry out an auxiliary task. Finally, to exploit more
complementary knowledge from clean image classification to facilitate feature
denoising and search for a more generalized local minimum for open-set
recognition, we further propose clean-adversarial mutual learning, where a peer
network (classifying clean images) is further introduced to mutually learn with
the classifier (classifying adversarial images).


title: Expansion of Graphene-Based Device Technology for Resistance Metrology

abstract:
  The field of Quantum Hall metrology had a strong start with the implemntation
of GaAs-based devices, given that 2D materials systems provided access to
interesting quantum phenomena, including the infrastructure associated with
making relevant measurements. With the technology laid out, further
improvements in both infrastructure and standards were achieved in the previous
two decades as EG-based quantized Hall resistance (QHR) devices became
established as national standards. Since the metrology community has reached
some understanding that a comparison against GaAs-based QHR devices had been
accomplished, the next steps became clearer as far as how the EG-based QHR with
a single Hall bar could be further developed. Since the early 90s, it has been
of modest interest that QHR devices have a means of interconnecting several
single Hall bar elements and has since been a subject of research. NMIs are now
presently at a juncture where consideration must be granted beyond just
simplicity of operation. A natural direction for resistance standards would be
to increase the total accessible parameter space. This means using EG-based QHR
devices to output more than the single value at the $\nu = 2$ plateau (about
12.9 k$\Omega$). A first natural question is whether one may use the $\nu = 6$
plateau or $\nu = 10$ plateau, and though some work has been done with these
Landau levels in graphene, they simply do not offer the same level of precision
as the $\nu = 2$ plateau.


title: Random primes in arithmetic progressions

abstract:
  We describe a straightforward method to generate a random prime q such that
the multiplicative group GF(q)* also has a random large prime-order subgroup.
The described algorithm also yields this order p as well as a p'th primitive
root of unity. The methods here are efficient asymptotically, but due to large
constants may not be very useful in practical settings.


title: Fixed Points and Continuity of Semihypergroup Actions

abstract:
  In a couple of previous papers, we initiated a systematic study of
semihypergroups and had a thorough discussion on certain analytic and algebraic
aspects associated to this class of objects. In this article, we introduce and
examine (separately) continuous actions on the category of semihypergroups. In
particular, we discuss the continuity properties of such actions and explore
the equivalence relations between different fixed-point properties of certain
actions and the existence of left-invariant mean(s) on the space of almost
periodic functions on a semihypergroup.


title: Confident AI

abstract:
  In this paper, we propose "Confident AI" as a means to designing Artificial
Intelligence (AI) and Machine Learning (ML) systems with both algorithm and
user confidence in model predictions and reported results. The 4 basic tenets
of Confident AI are Repeatability, Believability, Sufficiency, and
Adaptability. Each of the tenets is used to explore fundamental issues in
current AI/ML systems and together provide an overall approach to Confident AI.


title:
Detecting network communities via greedy expanding based on local
  superiority index

abstract:
  Community detection is a significant and challenging task in network science.
Nowadays, plenty of attention has been paid on local methods for community
detection. Greedy expanding is a popular and efficient class of local
algorithms, which typically starts from some selected central nodes and expands
those nodes to obtain provisional communities by optimizing a certain quality
function. In this paper, we propose a novel index, called local superiority
index (LSI), to identify central nodes. In the process of expansion, we apply
the fitness function to estimate the quality of provisional communities and
ensure that all provisional communities must be weak communities. Evaluation
based on the normalized mutual information suggests: (1) LSI is superior to the
global maximal degree index and the local maximal degree index on most
considered networks; (2) The greedy algorithm based on LSI is better than the
classical fast algorithm on most considered networks.


title: Formalization of a Stochastic Approximation Theorem

abstract:
  Stochastic approximation algorithms are iterative procedures which are used
to approximate a target value in an environment where the target is unknown and
direct observations are corrupted by noise. These algorithms are useful, for
instance, for root-finding and function minimization when the target function
or model is not directly known. Originally introduced in a 1951 paper by
Robbins and Monro, the field of Stochastic approximation has grown enormously
and has come to influence application domains from adaptive signal processing
to artificial intelligence. As an example, the Stochastic Gradient Descent
algorithm which is ubiquitous in various subdomains of Machine Learning is
based on stochastic approximation theory. In this paper, we give a formal proof
(in the Coq proof assistant) of a general convergence theorem due to Aryeh
Dvoretzky, which implies the convergence of important classical methods such as
the Robbins-Monro and the Kiefer-Wolfowitz algorithms. In the process, we build
a comprehensive Coq library of measure-theoretic probability theory and
stochastic processes.


title: Structure-aware Visualization Retrieval

abstract:
  With the wide usage of data visualizations, a huge number of Scalable Vector
Graphic (SVG)-based visualizations have been created and shared online.
Accordingly, there has been an increasing interest in exploring how to retrieve
perceptually similar visualizations from a large corpus, since it can benefit
various downstream applications such as visualization recommendation. Existing
methods mainly focus on the visual appearance of visualizations by regarding
them as bitmap images. However, the structural information intrinsically
existing in SVG-based visualizations is ignored. Such structural information
can delineate the spatial and hierarchical relationship among visual elements,
and characterize visualizations thoroughly from a new perspective. This paper
presents a structure-aware method to advance the performance of visualization
retrieval by collectively considering both the visual and structural
information. We extensively evaluated our approach through quantitative
comparisons, a user study and case studies. The results demonstrate the
effectiveness of our approach and its advantages over existing methods.


title: Audio-Visual Fusion Layers for Event Type Aware Video Recognition

abstract:
  Human brain is continuously inundated with the multisensory information and
their complex interactions coming from the outside world at any given moment.
Such information is automatically analyzed by binding or segregating in our
brain. While this task might seem effortless for human brains, it is extremely
challenging to build a machine that can perform similar tasks since complex
interactions cannot be dealt with single type of integration but requires more
sophisticated approaches. In this paper, we propose a new model to address the
multisensory integration problem with individual event-specific layers in a
multi-task learning scheme. Unlike previous works where single type of fusion
is used, we design event-specific layers to deal with different audio-visual
relationship tasks, enabling different ways of audio-visual formation.
Experimental results show that our event-specific layers can discover unique
properties of the audio-visual relationships in the videos. Moreover, although
our network is formulated with single labels, it can output additional true
multi-labels to represent the given videos. We demonstrate that our proposed
framework also exposes the modality bias of the video data category-wise and
dataset-wise manner in popular benchmark datasets.


title:
High-throughput discovery of chemical structure-polarity relationships
  combining automation and machine learning techniques

abstract:
  As an essential attribute of organic compounds, polarity has a profound
influence on many molecular properties such as solubility and phase transition
temperature. Thin layer chromatography (TLC) represents a commonly used
technique for polarity measurement. However, current TLC analysis presents
several problems, including the need for a large number of attempts to obtain
suitable conditions, as well as irreproducibility due to non-standardization.
Herein, we describe an automated experiment system for TLC analysis. This
system is designed to conduct TLC analysis automatically, facilitating
high-throughput experimentation by collecting large experimental data under
standardized conditions. Using these datasets, machine learning (ML) methods
are employed to construct surrogate models correlating organic compounds'
structures and their polarity using retardation factor (Rf). The trained ML
models are able to predict the Rf value curve of organic compounds with high
accuracy. Furthermore, the constitutive relationship between the compound and
its polarity can also be discovered through these modeling methods, and the
underlying mechanism is rationalized through adsorption theories. The trained
ML models not only reduce the need for empirical optimization currently
required for TLC analysis, but also provide general guidelines for the
selection of conditions, making TLC an easily accessible tool for the broad
scientific community.


title: Private Adaptive Optimization with Side Information

abstract:
  Adaptive optimization methods have become the default solvers for many
machine learning tasks. Unfortunately, the benefits of adaptivity may degrade
when training with differential privacy, as the noise added to ensure privacy
reduces the effectiveness of the adaptive preconditioner. To this end, we
propose AdaDPS, a general framework that uses non-sensitive side information to
precondition the gradients, allowing the effective use of adaptive methods in
private settings. We formally show AdaDPS reduces the amount of noise needed to
achieve similar privacy guarantees, thereby improving optimization performance.
Empirically, we leverage simple and readily available side information to
explore the performance of AdaDPS in practice, comparing to strong baselines in
both centralized and federated settings. Our results show that AdaDPS improves
accuracy by 7.7% (absolute) on average -- yielding state-of-the-art
privacy-utility trade-offs on large-scale text and image benchmarks.


title:
Awe Versus Aww: The Effectiveness of Two Kinds of Positive Emotional
  Stimulation on Stress Reduction for Online Content Moderators

abstract:
  When people have the freedom to create and post content on the internet,
particularly anonymously, they do not always respect the rules and regulations
of the websites on which they post, leaving other unsuspecting users vulnerable
to sexism, racism, threats, and other unacceptable content in their daily
cyberspace diet. However, content moderators witness the worst of humanity on a
daily basis in place of the average netizen. This takes its toll on moderators,
causing stress, fatigue, and emotional distress akin to the symptomology of
post-traumatic stress disorder (PTSD). The goal of the present study was to
explore whether adding positive stimuli to breaktimes-images of baby animals or
beautiful, aweinspiring landscapes-could help reduce the negative side-effects
of being a content moderator. To test this, we had over 300 experienced content
moderators read and decide whether 200 fake text-based social media posts were
acceptable or not for public consumption. Although we set out to test positive
emotional stimulation, however, we actually found that it is the cumulative
nature of the negative emotions that likely negates most of the effects of the
intervention: the longer the person had practiced content moderation, the
stronger their negative experience. Connections to compassion fatigue and how
best to spend work breaks as a content moderator are discussed.


title:
Capacity Maximization Pattern Design for Reconfigurable MIMO Antenna
  Array

abstract:
  Multi-functional and reconfigurable multiple-input multiple-output (MR-MIMO)
can provide performance gains over traditional MIMO by introducing additional
degrees of freedom. In this paper, we focus on the capacity maximization
pattern design for MR-MIMO systems. Firstly, we introduce the matrix
representation of MR-MIMO, based on which a pattern design problem is
formulated. To further reveal the effect of the radiation pattern on the
wireless channel, we consider pattern design for both the single-pattern case
where the optimized radiation pattern is the same for all the antenna elements,
and the multi-pattern case where different antenna elements can adopt different
radiation patterns. For the single-pattern case, we show that the pattern
design is equivalent to a redistribution of power among all scattering paths,
and an eigenvalue optimization based solution is obtained. For the
multi-pattern case, we propose a sequential optimization framework with
manifold optimization and eigenvalue decomposition to obtain near-optimal
solutions. Numerical results validate the superiority of MR-MIMO systems over
traditional MIMO in terms of capacity, and also show the effectiveness of the
proposed solutions.


title: Mahler/Zeta Correspondence

abstract:
  The Mahler measure was introduced by Mahler in the study of number theory. It
is known that the Mahler measure appears in different areas of mathematics and
physics. On the other hand, we have been investigated a new class of zeta
functions for various kinds of walks including quantum walks by a series of our
previous work on "Zeta Correspondence". The quantum walk is a quantum
counterpart of the random walk. In this paper, we present a new relation
between the Mahler measure and our zeta function for quantum walks. Firstly we
consider this relation in the case of one-dimensional quantum walks. Afterwards
we deal with higher-dimensional quantum walks. For comparison with the case of
the quantum walk, we also treat the case of higher-dimensional random walks.
Our results bridge between the Mahler measure and the zeta function via quantum
walks for the first time.

categories: quant-ph math-ph math.CO math.MP math.NT math.PR


title: Development of Beams Optics Code LEADS-v5

abstract:
  To calculate nonlinear transport of space charge dominated beam in 6D phase
spaces, a computer code package LEADS-v5 (Linear and Electrostatic Accelerator
Dynamics Simulations) has been developed. The codes calculate particle motions
in the beam transport systems consisting of electrostatic and magnetic focusing
lenses, ion analyzers, multipoles and RF accelerating structures. The nonlinear
forces of external electric/magnetic fields are analyzed by the Lie algebraic
method, and the space charge forces are obtained by the particle in cell (PIC)
scheme. In the codes, Uniform and Gaussian particle distributions can be chosen
to generate randomly the particle initial coordinates. The optimization
procedures are provided to make the beam optics designs reasonable and fast.
Graphically displays of calculated results are provided.


title:
Tuning the electronic states and superconductivity in alkali fulleride
  films

abstract:
  The successful preparation of superconducting alkali fulleride
(A$_x$C$_{60}$, A = K, Rb, Cs) films using state-of-the-art molecular beam
epitaxy overcomes the disadvantages of the air-sensitivity and phase separation
in bulk A$_x$C$_{60}$, enabling for the first time a direct investigation of
the superconductivity in alkali fullerides on the molecular scale. In this
paper, we briefly review recent cryogenic scanning tunneling microscopy results
of the structural, electronic, and superconducting properties of the fcc
A$_x$C$_{60}$ films grown on graphitized SiC substrates. Robust $s$-wave
superconductivity is revealed against the pseudogap, electronic correlation,
non-magnetic impurities, and merohedral disorder. By controlling the alkali
metal species, film thickness, and electron doping, we systematically tune the
C$_{60}^{x-}$ orientational orderings and superconductivity in A$_x$C$_{60}$
films and then complete a unified phase diagram of superconducting gap size vs
electronic correlation and doping. These investigations are conclusive and
elucidated that the $s$-wave superconductivity retains in alkali fullerides
despite of the electronic correlation and presence of pseudogap.


title:
Global tangentially analytical solutions of the 3D axially symmetric
  Prandtl equations

abstract:
  In this paper, we will prove the global existence of solutions to the three
dimensional axially symmetric Prandtl boundary layer equations with small
initial data, which lies in $H^1$ Sobolev space with respect to the normal
variable and is analytical with respect to the tangential variables. Proof of
the main result relies on the construction of a tangentially weighted analytic
energy functional, which acts on a specially designed good unknown. The
constructed energy functional can find its two dimensional parallel in
Ignatova-Vicol [2016ARMA] where no tangential weight is introduced and the
specially good unknown is set to control the lower bound of the analytical
radius, whose two dimensional similarity can be traced to Paicu-Zhang
[2021ARMA]. Our result is an improvement of that in Ignatova-Vicol [2016ARMA]
from the almost global existence to the global existence and an extension of
that in Paicu-Zhang [2021ARMA] from the two dimensional case to the three
dimensional axially symmetric case.


title:
Electrostatic control of quasiparticle poisoning in a hybrid
  semiconductor-superconductor island

abstract:
  The performance of superconducting devices is often degraded by the
uncontrolled appearance and disappearance of quasiparticles, a process known as
poisoning. We demonstrate electrostatic control of quasiparticle poisoning in
the form of single-charge tunneling across a fixed barrier onto a Coulomb
island in an InAs/Al hybrid nanowire. High-bandwidth charge sensing was used to
monitor charge occupancy of the island across Coulomb blockade peaks, where
tunneling rates were maximal, and Coulomb valleys, where tunneling was absent.
Electrostatic gates changed on-peak tunneling rates by two orders of magnitude
for a barrier with fixed normal-state resistance, which we attribute to gate
dependence of the size and softness of the induced superconducting gap on the
island, corroborated by separate density-of-states measurements. Temperature
and magnetic field dependence of tunneling rates are also investigated.


title:
Improving Contextual Coherence in Variational Personalized and
  Empathetic Dialogue Agents

abstract:
  In recent years, latent variable models, such as the Conditional Variational
Auto Encoder (CVAE), have been applied to both personalized and empathetic
dialogue generation. Prior work have largely focused on generating diverse
dialogue responses that exhibit persona consistency and empathy. However, when
it comes to the contextual coherence of the generated responses, there is still
room for improvement. Hence, to improve the contextual coherence, we propose a
novel Uncertainty Aware CVAE (UA-CVAE) framework. The UA-CVAE framework
involves approximating and incorporating the aleatoric uncertainty during
response generation. We apply our framework to both personalized and empathetic
dialogue generation. Empirical results show that our framework significantly
improves the contextual coherence of the generated response. Additionally, we
introduce a novel automatic metric for measuring contextual coherence, which
was found to correlate positively with human judgement.


title:
Low-light Image Enhancement by Retinex Based Algorithm Unrolling and
  Adjustment

abstract:
  Motivated by their recent advances, deep learning techniques have been widely
applied to low-light image enhancement (LIE) problem. Among which, Retinex
theory based ones, mostly following a decomposition-adjustment pipeline, have
taken an important place due to its physical interpretation and promising
performance. However, current investigations on Retinex based deep learning are
still not sufficient, ignoring many useful experiences from traditional
methods. Besides, the adjustment step is either performed with simple image
processing techniques, or by complicated networks, both of which are
unsatisfactory in practice. To address these issues, we propose a new deep
learning framework for the LIE problem. The proposed framework contains a
decomposition network inspired by algorithm unrolling, and adjustment networks
considering both global brightness and local brightness sensitivity. By virtue
of algorithm unrolling, both implicit priors learned from data and explicit
priors borrowed from traditional methods can be embedded in the network,
facilitate to better decomposition. Meanwhile, the consideration of global and
local brightness can guide designing simple yet effective network modules for
adjustment. Besides, to avoid manually parameter tuning, we also propose a
self-supervised fine-tuning strategy, which can always guarantee a promising
performance. Experiments on a series of typical LIE datasets demonstrated the
effectiveness of the proposed method, both quantitatively and visually, as
compared with existing methods.


title:
Model-independent reconstruction of the cosmological scale factor as a
  function of lookback time

abstract:
  We present a model-independent method of reconstructing scale factor against
lookback time from the Observational Hubble parameter Data (OHD). The
reconstruction method is independent of dynamical models and is only based on
the Friedmann-Robertson-Walker metric. We also calculate the propagation of
error in the reconstruction process. The reconstruction data errors mainly come
from trapezoidal rule approximation and the uncertainty from OHD. Furthermore,
the model discrimination ability of original OHD and reconstructed a-t data is
discussed under a dimensionless standard method. a-t data can present the
differences between cosmology models more clearly than H-z data by comparing
their coefficients of variations. Finally, we add fifty simulated H(z) data to
estimate the influence of future observation. More Hubble measurements in the
future will help constrain cosmological parameters more accurately.


title:
Parity switching in a full-shell superconductor-semiconductor nanowire
  qubit

abstract:
  The rate of charge-parity switching in a full-shell
superconductor-semiconductor nanowire qubit is measured by directly monitoring
the dispersive shift of a readout resonator. At zero magnetic field, the
measured switching time scale $T_P$ is on the order of 100 ms. Two-tone
spectroscopy data post-selected on charge-parity is demonstrated. With
increasing temperature or magnetic field, TP is at first constant, then
exponentially suppressed, consistent with a model that includes both
non-equilibrium and thermally activated quasiparticles. As TP is suppressed,
qubit lifetime T1 also decreases. The long $T_P\sim 0.1$ s at zero field is
promising for future development of qubits based on hybrid nanowires.


title: Robust Deep Semi-Supervised Learning: A Brief Introduction

abstract:
  Semi-supervised learning (SSL) is the branch of machine learning that aims to
improve learning performance by leveraging unlabeled data when labels are
insufficient. Recently, SSL with deep models has proven to be successful on
standard benchmark tasks. However, they are still vulnerable to various
robustness threats in real-world applications as these benchmarks provide
perfect unlabeled data, while in realistic scenarios, unlabeled data could be
corrupted. Many researchers have pointed out that after exploiting corrupted
unlabeled data, SSL suffers severe performance degradation problems. Thus,
there is an urgent need to develop SSL algorithms that could work robustly with
corrupted unlabeled data. To fully understand robust SSL, we conduct a survey
study. We first clarify a formal definition of robust SSL from the perspective
of machine learning. Then, we classify the robustness threats into three
categories: i) distribution corruption, i.e., unlabeled data distribution is
mismatched with labeled data; ii) feature corruption, i.e., the features of
unlabeled examples are adversarially attacked; and iii) label corruption, i.e.,
the label distribution of unlabeled data is imbalanced. Under this unified
taxonomy, we provide a thorough review and discussion of recent works that
focus on these issues. Finally, we propose possible promising directions within
robust SSL to provide insights for future research.


title:
High Spatial and Temporal Resolution NIR-IIb Gastrointestinal Imaging in
  Mice

abstract:
  Conventional biomedical imaging modalities, including endoscopy, X-rays, and
magnetic resonance, are invasive and cannot provide sufficient spatial and
temporal resolutions for regular imaging of gastrointestinal (GI) tract to
guide prognosis and therapy of GI diseases. Here we report a non-invasive
method for optical imaging of GI tract. It is based on a new type of
lanthanide-doped nanocrystal with near-infrared (NIR) excitation at 980 nm and
second NIR window (NIR-IIb) (1500~1700 nm) fluorescence emission at around 1530
nm. The rational design and controlled synthesis of nanocrystals with high
brightness have led to an absolute quantum yield (QY) up to 48.6%. Further
benefitting from the minimized scattering through the NIR-IIb window, we
enhanced the spatial resolution by 3 times compared with the other NIR-IIa
(1000~1500 nm) contract agents for GI tract imaging. The approach also led to a
high temporal resolution of 8 frames per second, so that the moment of mice
intestinal peristalsis happened in one minute can be captured. Furthermore,
with a light-sheet imaging system, we demonstrated a three-dimensional (3D)
imaging of the stereoscopic structure of the GI tract. Moreover, we
successfully translate these advances to diagnose inflammatory bowel disease
(IBD) in a pre-clinical model of mice colitis.


title:
Real-time Monte Carlo Denoising with Weight Sharing Kernel Prediction
  Network

abstract:
  Real-time Monte Carlo denoising aims at removing severe noise under low
samples per pixel (spp) in a strict time budget. Recently, kernel-prediction
methods use a neural network to predict each pixel's filtering kernel and have
shown a great potential to remove Monte Carlo noise. However, the heavy
computation overhead blocks these methods from real-time applications. This
paper expands the kernel-prediction method and proposes a novel approach to
denoise very low spp (e.g., 1-spp) Monte Carlo path traced images at real-time
frame rates. Instead of using the neural network to directly predict the kernel
map, i.e., the complete weights of each per-pixel filtering kernel, we predict
an encoding of the kernel map, followed by a high-efficiency decoder with
unfolding operations for a high-quality reconstruction of the filtering
kernels. The kernel map encoding yields a compact single-channel representation
of the kernel map, which can significantly reduce the kernel-prediction
network's throughput. In addition, we adopt a scalable kernel fusion module to
improve denoising quality. The proposed approach preserves kernel prediction
methods' denoising quality while roughly halving its denoising time for 1-spp
noisy inputs. In addition, compared with the recent neural bilateral grid-based
real-time denoiser, our approach benefits from the high parallelism of
kernel-based reconstruction and produces better denoising results at equal
time.


title: A new conformal heat flow of harmonic maps

abstract:
  We introduce and study a conformal heat flow of harmonic maps defined by an
evolution equation for a pair consisting of a map and a conformal factor of
metric on the two-dimensional domain. This flow is designed to postpone finite
time singularity but does not get rid of possibility of bubble forming. We show
that a strong solution exists for short time.


title:
On the Impacts of Phase Shifting Design and Eavesdropping Uncertainty on
  Secrecy Metrics of RIS-aided Systems

abstract:
  This paper investigates the secrecy outage probability (SOP), the lower bound
of SOP, and the probability of non-zero secrecy capacity (PNZ) of
reconfigurable intelligent surface (RIS)-assisted systems from an
information-theoretic perspective. In particular, we consider the impacts of
eavesdroppers' location uncertainty and the phase adjustment uncertainty,
namely imperfect coherent phase shifting and discrete phase shifting on RIS.
More specifically, analytical and simulation results are presented to show that
(i) the SOP gain due to the increase of the RIS reflecting elements number
gradually decreases; and (ii) both phase shifting designs demonstrate the same
PNZ secrecy performance, in other words, the random discrete phase shifting
outperforms the imperfect coherent phase shifting design with reduced
complexity.


title: From a single-system game to robust violations of the Bell's inequality

abstract:
  Recently, Fan et al. [Mod. Phys. Lett. A 36, 2150223 (2021)], presented a
generalized Clauser-Horne-Shimony-Holt (CHSH) inequality, to identify $N$-qubit
Greenberger-Horne-Zeilinger (GHZ) states. They showed an interesting phenomenon
that the maximal violation of the generalized CHSH inequality is robust under
some specific noises. In this work, we map the inequality to the CHSH game, and
consequently to the CHSH* game in a single-qubit system. This mapping provides
an explanation for the robust violations in $N$-qubit systems. Namely, the
robust violations, resulting from the degeneracy of the generalized CHSH
operators corresponds to the symmetry of the maximally entangled two-qubit
states and the identity transformation in the single-qubit game. This
explanation enables us to exactly demonstrate that the degeneracy is $2^{N-2}$.


title:
Scaling properties of exclusive vector meson production cross section
  from gluon saturation

abstract:
  It is already known from phenomenological studies that in exclusive
deep-inelastic scattering off nuclei there appears to be a scaling behavior of
vector meson production cross section in both nuclear mass number, $A$, and
photon virtuality, $Q^{2}$, which is strongly modified due to gluon saturation
effects. In this work we continue those studies in a realistic setup based upon
using the Monte Carlo event generator Sar{\em t}re. We make quantitative
predictions for the kinematics of the Electron-Ion Collider, focusing on this
$A$ and $Q^{2}$ scaling picture, along with establishing a small region of
squared momentum transfer, $t$, where there are signs of this scaling that may
potentially be observed at the EIC. Our results are represented as pseudo-data
of vector meson production diffractive cross section and/or their ratios, which
are obtained by parsing data collected by the event generator through smearing
functions, emulating the proposed detector resolutions for the future EIC.


title:
Detecting False Alarms from Automatic Static Analysis Tools: How Far are
  We?

abstract:
  Automatic static analysis tools (ASATs), such as Findbugs, have a high false
alarm rate. The large number of false alarms produced poses a barrier to
adoption. Researchers have proposed the use of machine learning to prune false
alarms and present only actionable warnings to developers. The state-of-the-art
study has identified a set of "Golden Features" based on metrics computed over
the characteristics and history of the file, code, and warning. Recent studies
show that machine learning using these features is extremely effective and that
they achieve almost perfect performance.
  We perform a detailed analysis to better understand the strong performance of
the "Golden Features". We found that several studies used an experimental
procedure that results in data leakage and data duplication, which are subtle
issues with significant implications. Firstly, the ground-truth labels have
leaked into features that measure the proportion of actionable warnings in a
given context. Secondly, many warnings in the testing dataset appear in the
training dataset. Next, we demonstrate limitations in the warning oracle that
determines the ground-truth labels, a heuristic comparing warnings in a given
revision to a reference revision in the future. We show the choice of reference
revision influences the warning distribution. Moreover, the heuristic produces
labels that do not agree with human oracles. Hence, the strong performance of
these techniques previously seen is overoptimistic of their true performance if
adopted in practice. Our results convey several lessons and provide guidelines
for evaluating false alarm detectors.


title: Uncalibrated Models Can Improve Human-AI Collaboration

abstract:
  In many practical applications of AI, an AI model is used as a decision aid
for human users. The AI provides advice that a human (sometimes) incorporates
into their decision-making process. The AI advice is often presented with some
measure of "confidence" that the human can use to calibrate how much they
depend on or trust the advice. In this paper, we demonstrate that presenting AI
models as more confident than they actually are, even when the original AI is
well-calibrated, can improve human-AI performance (measured as the accuracy and
confidence of the human's final prediction after seeing the AI advice). We
first learn a model for how humans incorporate AI advice using data from
thousands of human interactions. This enables us to explicitly estimate how to
transform the AI's prediction confidence, making the AI uncalibrated, in order
to improve the final human prediction. We empirically validate our results
across four different tasks -- dealing with images, text and tabular data --
involving hundreds of human participants. We further support our findings with
simulation analysis. Our findings suggest the importance of and a framework for
jointly optimizing the human-AI system as opposed to the standard paradigm of
optimizing the AI model alone.


title: scpi: Uncertainty Quantification for Synthetic Control Estimators

abstract:
  The synthetic control method offers a way to estimate the effect of an
aggregate intervention using weighted averages of untreated units to
approximate the counterfactual outcome that the treated unit(s) would have
experienced in the absence of the intervention. This method is useful for
program evaluation and causal inference in observational studies. We introduce
the software package \texttt{scpi} for estimation and inference using synthetic
controls, implemented in \texttt{Python}, \texttt{R}, and \texttt{Stata}. For
point estimation or prediction of treatment effects, the package offers an
array of (possibly penalized) approaches leveraging the latest optimization
methods. For uncertainty quantification, the package offers the prediction
interval methods introduced by Cattaneo, Feng and Titiunik (2021) and Cattaneo,
Feng, Palomba and Titiunik (2022). The discussion contains numerical
illustrations and a comparison with other synthetic control software.


title:
Entangled two-photon absorption detection through a Hong-Ou-Mandel
  interferometer

abstract:
  Recently, different experimental methods to investigate the entangled
two-photon absorption (ETPA) process in a variety of materials have been
reported. The present work reports on a different approach on which the ETPA
process is detected based on the changes induced on the Hong-Ou-Mandel (HOM)
interferogram of two photons produced by a spontaneous parametric down
conversion (SPDC) Type-II process. Using an organic solution of the laser dye
Rhodamine B as a model, it is demonstrated that ETPA at 800nm can be monitored
by the changes in the temporal-width and visibility of the HOM interferogram
produced by the photons at a beam splitter after interacting with the sample.
Additionally, we present a detail model in which the sample is modeled as a
filtering function which fullfills the energy conservation conditions required
by ETPA, allowing to explain the experimental observations with excellent
agreement. We believe that this work represents a new perspective to study the
ETPA process, by using an ultra-sensitive quantum interference technique and a
detailed mathematical model of the process.


title:
Outstanding thermoelectric performance predicted for out-of-plane
  p-doped GeSe

abstract:
  The record-breaking thermoelectric performance of tin selenide (SnSe) has
motivated the investigation of analogue compounds with the same structure. A
promising candidate that emerged recently is germanium selenide (GeSe). Here,
using extensive first-principles calculations of the hole-phonon and
hole-impurity scattering, we investigate the thermoelectric transport
properties of the orthorhombic phase of p-doped GeSe. We predict outstanding
thermoelectric performance for GeSe over a broad range of temperatures due to
its high Seebeck coefficients, extremely low Lorenz numbers, ultralow total
thermal conductivity, and relatively large band gap. In particular, the
out-of-plane direction in GeSe presents equivalent or even higher performance
than SnSe for temperatures above 500 K. By extending the analysis to 900 K, we
obtained an ultrahigh value for the thermoelectric figure of merit (zT = 3.2)
at the optimal hole density of 4x10^19 cm^-3. Our work provides strong
motivation for continued experimental work focusing on improving the GeSe
doping efficiency in order to achieve this optimal hole density.


title:
Distance Laplacian eigenvalues of graphs and chromatic and independence
  number

abstract:
  For a connected graph $G$ of order $n$, let $Diag(Tr)$ be the diagonal matrix
of vertex transmissions and $D(G)$ be the distance matrix of $G$. The distance
Laplacian matrix of $G$ is defined as $D^L(G)=Diag(Tr)-D(G)$ and the
eigenvalues of $D^{L}(G)$ are called the distance Laplacian eigenvalues of $G$.
Let $\partial_{1}^{L}(G)\geq \partial_{2}^{L}(G)\geq \dots \geq
\partial_{n}^{L}(G)$ be the distance Laplacian eigenvalues of $G$. Given an
interval $I$, let $m_{D^{L} (G)} I$ (or simply $m_{D^{L} } I$) be the number of
distance Laplacian eigenvalues of $G$ which lie in the interval $I$. For a
prescribed interval $I$, we determine $m_{D^{L} }I$ in terms of independence
number $\alpha(G)$, chromatic number $\chi$, number of pendant vertices and
diameter $d$ of the graph $G$. In particular, we prove that $m_{D^{L}(G)
}[n,n+2)\leq \chi-1$, ~$m_{D^{L}(G) }[n,n+\alpha(G))\leq n-\alpha(G)$ and we
show that the inequalities are sharp. We also show that $m_{D^{L} (G )}\bigg(
n,n+\left\lceil\frac{n}{\chi}\right\rceil\bigg)\leq n-
\left\lceil\frac{n}{\chi}\right\rceil-C_{\overline{G}}+1 $, where
$C_{\overline{G}}$ is the number of components in $\overline{G}$, and discuss
some cases where the bound is best possible. In addition, we prove that
$m_{D^{L} (G )}[n,n+p)\leq n-p$, where $p\geq 1$ is the number of pendant
vertices. Also, we characterize graphs of diameter $d\leq 2$ which satisfy
$m_{D^{L}(G) } (2n-1,2n )= \alpha(G)-1=\frac{n}{2}-1$. At the end, we propose
some problems of interest.


title: RSINet: Inpainting Remotely Sensed Images Using Triple GAN Framework

abstract:
  We tackle the problem of image inpainting in the remote sensing domain.
Remote sensing images possess high resolution and geographical variations, that
render the conventional inpainting methods less effective. This further entails
the requirement of models with high complexity to sufficiently capture the
spectral, spatial and textural nuances within an image, emerging from its high
spatial variability. To this end, we propose a novel inpainting method that
individually focuses on each aspect of an image such as edges, colour and
texture using a task specific GAN. Moreover, each individual GAN also
incorporates the attention mechanism that explicitly extracts the spectral and
spatial features. To ensure consistent gradient flow, the model uses residual
learning paradigm, thus simultaneously working with high and low level
features. We evaluate our model, alongwith previous state of the art models, on
the two well known remote sensing datasets, Open Cities AI and Earth on Canvas,
and achieve competitive performance.


title:
Tight Approximation Algorithms for Two Dimensional Guillotine Strip
  Packing

abstract:
  In the Strip Packing problem (SP), we are given a vertical half-strip
$[0,W]\times[0,\infty)$ and a set of $n$ axis-aligned rectangles of width at
most $W$. The goal is to find a non-overlapping packing of all rectangles into
the strip such that the height of the packing is minimized. A well-studied and
frequently used practical constraint is to allow only those packings that are
guillotine separable, i.e., every rectangle in the packing can be obtained by
recursively applying a sequence of edge-to-edge axis-parallel cuts (guillotine
cuts) that do not intersect any item of the solution. In this paper, we study
approximation algorithms for the Guillotine Strip Packing problem (GSP), i.e.,
the Strip Packing problem where we require additionally that the packing needs
to be guillotine separable. This problem generalizes the classical Bin Packing
problem and also makespan minimization on identical machines, and thus it is
already strongly NP-hard. Moreover, due to a reduction from the Partition
problem, it is NP-hard to obtain a polynomial-time
$(3/2-\varepsilon)$-approximation algorithm for GSP for any $\varepsilon>0$
(exactly as Strip Packing). We provide a matching polynomial time
$(3/2+\varepsilon)$-approximation algorithm for GSP. Furthermore, we present a
pseudo-polynomial time $(1+\varepsilon)$-approximation algorithm for GSP. This
is surprising as it is NP-hard to obtain a $(5/4-\varepsilon)$-approximation
algorithm for (general) Strip Packing in pseudo-polynomial time. Thus, our
results essentially settle the approximability of GSP for both the polynomial
and the pseudo-polynomial settings.


title: Distributed D-core Decomposition over Large Directed Graphs

abstract:
  Given a directed graph $G$ and integers $k$ and $l$, a D-core is the maximal
subgraph $H \subseteq G$ such that for every vertex of $H$, its in-degree and
out-degree are no smaller than $k$ and $l$, respectively. For a directed graph
$G$, the problem of D-core decomposition aims to compute the non-empty D-cores
for all possible values of $k$ and $l$. In the literature, several
\emph{peeling-based} algorithms have been proposed to handle D-core
decomposition. However, the peeling-based algorithms that work in a sequential
fashion and require global graph information during processing are mainly
designed for \emph{centralized} settings, which cannot handle large-scale
graphs efficiently in distributed settings. Motivated by this, we study the
\emph{distributed} D-core decomposition problem in this paper. We start by
defining a concept called \emph{anchored coreness}, based on which we propose a
new H-index-based algorithm for distributed D-core decomposition. Furthermore,
we devise a novel concept, namely \emph{skyline coreness}, and show that the
D-core decomposition problem is equivalent to the computation of skyline
corenesses for all vertices. We design an efficient D-index to compute the
skyline corenesses distributedly. We implement the proposed algorithms under
both vertex-centric and block-centric distributed graph processing frameworks.
Moreover, we theoretically analyze the algorithm and message complexities.
Extensive experiments on large real-world graphs with billions of edges
demonstrate the efficiency of the proposed algorithms in terms of both the
running time and communication overhead.


title: Generators and the (Accelerated) Future

abstract:
  With the High Luminosity LHC coming online in the near future, event
generators will need to provide very large event samples to match the
experimental precision. Currently, the estimated cost to generate these events
exceeds the computing budget of the LHC experiments. To address these issues,
the computing efficiency of event generators need to be improved. Many
different approaches are being taken to achieve this goal. I will cover the
ongoing work on implementing event generators on the GPUs, machine learning the
matrix element, machine learning the phase space, and minimizing the number of
negative weight events.


title: Soliton Microcombs in Integrated Chalcogenide Microresonators

abstract:
  Photonic integrated microcombs have enabled advanced applications in optical
communication, microwave synthesis, and optical metrology, which in nature
unveil an optical dissipative soliton pattern under cavity-enhanced nonlinear
processes. The most decisive factor of microcombs lies in the photonic material
platforms, where materials with high nonlinearity and in capacity of
high-quality chip integration are highly demanded. In this work, we present a
home-developed chalcogenide glasses-Ge25Sb10S65 (GeSbS) for the nonlinear
photonic integration and for the dissipative soliton microcomb generation.
Compared with the current integrated nonlinear platforms, the GeSbS features
wider transparency from the visible to 11 um region, stronger nonlinearity, and
lower thermo-refractive coefficient, and is CMOS compatible in fabrication. In
this platform, we achieve chip-integrated optical microresonators with a
quality (Q) factor above 2 x 10^6, and carry out lithographically controlled
dispersion engineering. In particular, we demonstrate that both a bright
soliton-based microcomb and a dark-pulsed comb are generated in a single
microresonator, in its separated fundamental polarized mode families under
different dispersion regimes. The overall pumping power is on the ten-milliwatt
level, determined by both the high Q-factor and the high material nonlinearity
of the microresonator. Our results may contribute to the field of nonlinear
photonics with an alternative material platform for highly compact and
high-intensity nonlinear interactions, while on the application aspect,
contribute to the development of soliton microcombs at low operation power,
which is potentially required for monolithically integrated optical frequency
combs.


title: Wav2Vec2.0 on the Edge: Performance Evaluation

abstract:
  Wav2Vec2.0 is a state-of-the-art model which learns speech representations
through unlabeled speech data, aka, self supervised learning. The pretrained
model is then fine tuned on small amounts of labeled data to use it for
speech-to-text and machine translation tasks. Wav2Vec 2.0 is a transformative
solution for low resource languages as it is mainly developed using unlabeled
audio data. Getting large amounts of labeled data is resource intensive and
especially challenging to do for low resource languages such as Swahilli,
Tatar, etc. Furthermore, Wav2Vec2.0 word-error-rate(WER) matches or surpasses
the very recent supervised learning algorithms while using 100x less labeled
data. Given its importance and enormous potential in enabling speech based
tasks on world's 7000 languages, it is key to evaluate the accuracy, latency
and efficiency of this model on low resource and low power edge devices and
investigate the feasibility of using it in such devices for private, secure and
reliable speech based tasks. On-device speech tasks preclude sending audio data
to the server hence inherently providing privacy, reduced latency and enhanced
reliability. In this paper, Wav2Vec2.0 model's accuracy and latency has been
evaluated on Raspberry Pi along with the KenLM language model for speech
recognition tasks. How to tune certain parameters to achieve desired level of
WER rate and latency while meeting the CPU, memory and energy budgets of the
product has been discussed.


title:
Physics-Guided Problem Decomposition for Scaling Deep Learning of
  High-dimensional Eigen-Solvers: The Case of Schr\"{o}dinger's Equation

abstract:
  Given their ability to effectively learn non-linear mappings and perform fast
inference, deep neural networks (NNs) have been proposed as a viable
alternative to traditional simulation-driven approaches for solving
high-dimensional eigenvalue equations (HDEs), which are the foundation for many
scientific applications. Unfortunately, for the learned models in these
scientific applications to achieve generalization, a large, diverse, and
preferably annotated dataset is typically needed and is computationally
expensive to obtain. Furthermore, the learned models tend to be memory- and
compute-intensive primarily due to the size of the output layer. While
generalization, especially extrapolation, with scarce data has been attempted
by imposing physical constraints in the form of physics loss, the problem of
model scalability has remained.
  In this paper, we alleviate the compute bottleneck in the output layer by
using physics knowledge to decompose the complex regression task of predicting
the high-dimensional eigenvectors into multiple simpler sub-tasks, each of
which are learned by a simple "expert" network. We call the resulting
architecture of specialized experts Physics-Guided Mixture-of-Experts (PG-MoE).
We demonstrate the efficacy of such physics-guided problem decomposition for
the case of the Schr\"{o}dinger's Equation in Quantum Mechanics. Our proposed
PG-MoE model predicts the ground-state solution, i.e., the eigenvector that
corresponds to the smallest possible eigenvalue. The model is 150x smaller than
the network trained to learn the complex task while being competitive in
generalization. To improve the generalization of the PG-MoE, we also employ a
physics-guided loss function based on variational energy, which by quantum
mechanics principles is minimized iff the output is the ground-state solution.


title:
Wildfires and Climate Change: What does social media tell us on linkages
  and public understanding? (Preliminary White Paper, February 11, 2022)

abstract:
  Wildfires are increasing in frequency and size across the western U.S., with
some of the deadliest fires in recorded history occurring in the last few
years. The public, as well as elected officials, use social media to convey
opinions and knowledge on topics that are impacting their communities. We
utilize the platform Twitter to assess connections of wildfire to climate
change during recent events and evaluate the differences in knowledge between
the public and their government officials. Results show some linkages of
wildfire cause and effect, although this relationship was not large (only 5%)
and was even lower at the governmental level (2%), suggesting that a broader
number of the public and government did not relate climate change to recent
extreme wildfires.


title: Coupling Online-Offline Learning for Multi-distributional Data Streams

abstract:
  The distributions of real-life data streams are usually nonstationary, where
one exciting setting is that a stream can be decomposed into several offline
intervals with a fixed time horizon but different distributions and an
out-of-distribution online interval. We call such data multi-distributional
data streams, on which learning an on-the-fly expert for unseen samples with a
desirable generalization is demanding yet highly challenging owing to the
multi-distributional streaming nature, particularly when initially limited data
is available for the online interval. To address these challenges, this work
introduces a novel optimization method named coupling online-offline learning
(CO$_2$) with theoretical guarantees about the knowledge transfer, the regret,
and the generalization error. CO$_2$ extracts knowledge by training an offline
expert for each offline interval and update an online expert by an
off-the-shelf online optimization method in the online interval. CO$_2$ outputs
a hypothesis for each sample by adaptively coupling both the offline experts
and the underlying online expert through an expert-tracking strategy to adapt
to the dynamic environment. To study the generalization performance of the
output hypothesis, we propose a general theory to analyze its excess risk bound
related to the loss function properties, the hypothesis class, the data
distribution, and the regret.