title:
On the observability of gravitational source rotation in neutrino flavor
  oscillations

abstract:
  We study neutrino oscillations in a rotating spacetime under the weak gravity
limit for the neutrino trajectories constrained in the equatorial plane. Using
the asymptotic form of the Kerr metric, we show that the gravitational source's
rotation non-trivially modifies the neutrino phase. Further, we find that the
flavor transition probability deviates more prominently from the Schwarzschild
spacetime results when neutrinos are produced and detected on the same side of
the gravitational source, i.e., neutrino propagates only towards or away from
the gravitational source. For the neutrino weak gravity lensing in the
equatorial plane, we find that the effects of gravitational object spin on the
neutrino phase are subdominant compared with the Schwarzschild spacetime
neutrino phase. However, the rotational effect can be comparable with the
gravitational mass effect in the neutrino lensing if the naked singularity
condition that bounds the Kerr rotation is violated. Furthermore, we find that
the neutrino phase for the non-radial neutrino emission depends on the relative
angular momentum direction of the neutrino and the gravitational source. For an
asymptotic observer, we find that the neutrino phase, in the leading orders of
gravitational spin corrections, differs from the Schwarzschild spacetime due to
the difference in the proper spatial distance of the neutrino trajectory that
connects the neutrino source and the detector. In contrast, the higher-order
terms, other than those arriving from the proper spatial distance, which lead
to the deviation from the Schwarzschild spacetime neutrino phase, are
significantly suppressed. Further, we discuss neutrino gravitational
decoherence. We find that decoherence length is also sensitive to the relative
direction of the neutrino angular momentum and the gravitational source
rotation. Finally, we demonstrate results using numerical examples.


title: R2-D2: Roman and Rubin -- From Data to Discovery

abstract:
  The NASA Nancy Grace Roman Space Telescope (Roman) and the Vera C. Rubin
Observatory Legacy Survey of Space and Time (Rubin), will transform our view of
the wide-field sky, with similar sensitivities, but complementary in
wavelength, spatial resolution, and time domain coverage. Here we present
findings from the AURA Roman+Rubin Synergy Working group, charged by the STScI
and NOIRLab Directors to identify frontier science questions in General
Astrophysics, beyond the well-covered areas of Dark Energy and Cosmology, that
can be uniquely addressed with Roman and Rubin synergies in observing strategy,
data products and archiving, joint analysis, and community engagement. This
analysis was conducted with input from the community in the form of brief (1-2
paragraph) "science pitches" (see Appendix), and testimony from "outside
experts" (included as co-authors). We identify a rich and broad landscape of
potential discoveries catalyzed by the combination of exceptional quality and
quantity of Roman and Rubin data, and summarize implementation requirements
that would facilitate this bounty of additional science with coordination of
survey fields, joint coverage of the Galactic plane, bulge, and ecliptic,
expansion of General Investigator and Target of Opportunity observing modes,
co-location of Roman and Rubin data, and timely distribution of data, transient
alerts, catalogs, value-added joint analysis products, and simulations to the
broad astronomical community.

categories: astro-ph.IM astro-ph.EP astro-ph.GA astro-ph.HE astro-ph.SR


title:
Oolong: Investigating What Makes Crosslingual Transfer Hard with
  Controlled Studies

abstract:
  Little is known about what makes cross-lingual transfer hard, since factors
like tokenization, morphology, and syntax all change at once between languages.
To disentangle the impact of these factors, we propose a set of controlled
transfer studies: we systematically transform GLUE tasks to alter different
factors one at a time, then measure the resulting drops in a pretrained model's
downstream performance. In contrast to prior work suggesting little effect from
syntax on knowledge transfer, we find significant impacts from syntactic shifts
(3-6% drop), though models quickly adapt with continued pretraining on a small
dataset. However, we find that by far the most impactful factor for
crosslingual transfer is the challenge of aligning the new embeddings with the
existing transformer layers (18% drop), with little additional effect from
switching tokenizers (<2% drop) or word morphologies (<2% drop). Moreover,
continued pretraining with a small dataset is not very effective at closing
this gap - suggesting that new directions are needed for solving this problem.


title: Gauge dependence of the quark gap equation

abstract:
  We study the gauge dependence of the quark propagator in QCD by solving the
gap equation with a nonperturbative quark-gluon vertex derived from
Slavnov-Taylor identities and gauge covariance. To this end, we employ gluon
propagators in renormalizable $R_\xi$ gauges from lattice-QCD. We observe a net
linear increase proportional to the gauge-fixing parameter $\xi$ of the mass
function in the infrared domain, whereas the wave renormalization decreases
with $\xi$. Within the error estimates stemming from the lattice Green
functions, the quark condensate as a function of $\xi$ is consistent with a
constant value. Our results depict for the first time the functional dependence
of the quark propagator on the gauge parameter and therefore numerically
express the content of the Landau-Khalatnikov-Fradkin transformations in QCD,
that is gauge covariance.


title: The Hubble PanCET program: Emission spectrum of hot Jupiter HAT-P-41b

abstract:
  We present the most complete emission spectrum for inflated hot Jupiter
HAT-P-41b combining new HST WFC/G141 spectrum from the Hubble Panchromatic
Comparative Exoplanet Treasury (PanCET) program with archival Spitzer eclipse
observations. We found a near blackbody-like emission spectrum which is best
fitted with an isothermal temperature-pressure (TP) profile that agrees well
with the dayside heat redistribution scenario assuming zero Bond albedo. The
non-inverted TP profile is consistent with the non-detection of NUV/optical
absorbers in the transit spectra. We do not find any evidence for significant
H$^-$ opacity nor a metal-rich atmosphere. HAT-P-41b is an ideal target that
sits in the transitioning parameter space between hot and ultra-hot Jupiters,
and future JWST observations will help us to better constrain the thermal
structure and chemical composition.


title:
Magnetic-field Induced Topological Transitions and Thermal Conductivity
  in a Generalized Kitaev Model

abstract:
  Recent experiments on Kitaev spin liquid candidate materials reported
non-monotonic behavior of thermal conductivity as a function of magnetic field,
which lead to conflicting interpretations of its origin. Motivated by this
development, we study the magnetic field dependence of thermal conductivity of
a generalized Kitaev model, which allows the phase transitions between
different flux sectors as a function of the magnetic field. The thermal
conductivity due to Majorana fermions shows dip-bump structures as the magnetic
field increases, which is caused by either the transitions between different
flux sectors of Kitaev spin liquids or the topological transitions that change
the Majorana Chern number within the same flux sector. It is shown that the
change of Chern number is closely related to the four-Majorana-fermion
interaction induced by the magnetic field. The non-monotonic behavior in
thermal conductivity emerges at finite temperature, and it becomes weaker when
temperature decreases towards zero. Our model provides a generic mechanism for
the Kitaev spin liquids to develop non-monotonic magnetic-field dependence of
thermal conductivity while the detailed comparison to realistic materials
remains an open question for future investigation.


title: AutoIP: A United Framework to Integrate Physics into Gaussian Processes

abstract:
  Physics modeling is critical for modern science and engineering applications.
From data science perspective, physics knowledge -- often expressed as
differential equations -- is valuable in that it is highly complementary to
data, and can potentially help overcome data sparsity, noise, inaccuracy, etc.
In this work, we propose a simple yet powerful framework that can integrate all
kinds of differential equations into Gaussian processes (GPs) to enhance
prediction accuracy and uncertainty quantification. These equations can be
linear, nonlinear, temporal, time-spatial, complete, incomplete with unknown
source terms, etc. Specifically, based on kernel differentiation, we construct
a GP prior to jointly sample the values of the target function,
equation-related derivatives, and latent source functions from a multivariate
Gaussian distribution. The sampled values are fed to two likelihoods -- one is
to fit the observations and the other to conform to the equation. We use the
whitening trick to evade the strong dependency between the sampled function
values and kernel parameters, and develop a stochastic variational learning
algorithm. Our method shows improvement upon vanilla GPs in both simulation and
several real-world applications, even using rough, incomplete equations.


title: New limits on leptophilic ALPs and Majorons from ArgoNeuT

abstract:
  Axion-like-particles are among the most studied extensions of the Standard
Model. In this letter we study the bounds that the ArgoNeuT experiment can put
on the parameter space of two specific scenarios: leptophilic
axion-like-particles and Majorons. We find that such bounds are currently the
most constraining ones in the (0.2 - 1.7) GeV mass range.


title:
Property (T) in random quotients of hyperbolic groups at densities above
  1/3

abstract:
  We study random quotients of a fixed non-elementary hyperbolic group in the
Gromov density model. Let $G=\langle S\;\vert\; T\rangle $ be a finite
presentation of a non-elementary hyperbolic group, and let $Ann_{l,\omega }(G)$
be the set of elements of norm between $l-\omega(l)$ and $l$ in $G$. A random
quotient at density $d$ and length $\omega$-near $l$ is defined by killing a
uniformly randomly chosen set of $\vert S_{l}(G)\vert ^{d}$ words in
$Ann_{l,\omega (l)}(G)$, where $\omega (l) =o_{l}(l)$. We prove that for any
d>1/3, such a quotient has Property (T) with probability tending to $1$ as $l$
tends to infinity. This result answers a question of Gromov--Ollivier and
strengthens a theorem of \.{Z}uk (c.f Kotowski--Kotowski).


title: Physics solutions for machine learning privacy leaks

abstract:
  Machine learning systems are becoming more and more ubiquitous in
increasingly complex areas, including cutting-edge scientific research. The
opposite is also true: the interest in better understanding the inner workings
of machine learning systems motivates their analysis under the lens of
different scientific disciplines. Physics is particularly successful in this,
due to its ability to describe complex dynamical systems. While explanations of
phenomena in machine learning based on physics are increasingly present,
examples of direct application of notions akin to physics in order to improve
machine learning systems are more scarce. Here we provide one such application
in the problem of developing algorithms that preserve the privacy of the
manipulated data, which is especially important in tasks such as the processing
of medical records. We develop well-defined conditions to guarantee robustness
to specific types of privacy leaks, and rigorously prove that such conditions
are satisfied by tensor-network architectures. These are inspired by the
efficient representation of quantum many-body systems, and have shown to
compete and even surpass traditional machine learning architectures in certain
cases. Given the growing expertise in training tensor-network architectures,
these results imply that one may not have to be forced to make a choice between
accuracy in prediction and ensuring the privacy of the information processed.

categories: cs.CR cond-mat.stat-mech cs.AI cs.LG quant-ph


title: Electron-phonon coupling in copper intercalated Bi$_{2}$Se$_{3}$

abstract:
  We report charge and heat transport studies in copper-intercalated
topological insulator Bi$_2$Se$_3$ hybrid devices. Measured conductivity shows
impact of quantum corrections, electron-electron and electron-phonon
interactions. Our shot noise measurements reveal that heat flux displays a
crossover between $T^2$ and $T^4$ with the increase of temperature. The results
can be explained by a model of inelastic electron scattering on disorder,
increasing the role of transverse acoustic phonons in the electron-phonon
coupling process.


title:
Monte Carlo simulations of multiple populations in globular clusters:
  constraints on the initial size of the second generation from binary stars

abstract:
  We present the result of a survey of Monte Carlo simulations of globular
clusters hosting two generations of stars including a large (f_b=50%) fraction
of primordial binaries in both populations. The dynamical evolution of the two
stellar populations is followed for a Hubble time taking into account the
effect of the tidal field, two-body relaxation, stellar evolution and
three/four-bodies interactions. The fraction of surviving binaries, once
accounted for the observational bias and uncertainties, is compared with the
available radial velocity time-series performed in real globular clusters, and
it is used to constrain the initial spatial concentration of the second
generation. The fraction of second generation binaries appears to depend only
on the ratio between the total cluster mass and the initial size of the second
generation which determines the average velocity dispersion across the extent
of this stellar population. In spite of the various uncertainties, we find that
the observed fraction can be obtained only assuming a strong initial
concentration of the second generation (r_h,S~ 0.1 (M/10^6 M_s) pc). The
evolution of the first generation binary fraction is more sensitive to the
tidal field strength (with a non negligible effect of the cluster orbital
eccentricity) since the tidal field has a direct impact on the first generation
structural properties.


title:
Evolving-to-Learn Reinforcement Learning Tasks with Spiking Neural
  Networks

abstract:
  Inspired by the natural nervous system, synaptic plasticity rules are applied
to train spiking neural networks with local information, making them suitable
for online learning on neuromorphic hardware. However, when such rules are
implemented to learn different new tasks, they usually require a significant
amount of work on task-dependent fine-tuning. This paper aims to make this
process easier by employing an evolutionary algorithm that evolves suitable
synaptic plasticity rules for the task at hand. More specifically, we provide a
set of various local signals, a set of mathematical operators, and a global
reward signal, after which a Cartesian genetic programming process finds an
optimal learning rule from these components. Using this approach, we find
learning rules that successfully solve an XOR and cart-pole task, and discover
new learning rules that outperform the baseline rules from literature.


title: The oriented chromatic number of random graphs of bounded degree

abstract:
  The chromatic number of the random graph $\mathcal{G}(n,p)$ has long been
studied and has inspired several landmark results. In the case where $p = d/n$,
Achlioptas and Naor showed the chromatic number is asymptotically concentrated
at $k_d$ or $k_d+1$, where $k_d$ is the smallest integer such that $d <
2k_d\log k_d$. Kemkes et al. later proved the same result holds for
$\mathcal{G}(n,d)$, the random $d$-regular graph. We consider the oriented
chromatic number of the directed models $\vec{\mathcal{G}}(n,p)$ and
$\vec{\mathcal{G}}(n,d)$, improving the best known upper bound from $O(d^2
2^d)$ to $O(\sqrt{e}^d)$.


title:
The space of Hardy-weights for quasilinear equations: Maz'ya-type
  characterization and sufficient conditions for existence of minimizers

abstract:
  Let $p \in (1,\infty)$ and $\Omega \subset \mathbb{R}^N$ be a domain. Let $
A: =(a_{ij}) \in L^{\infty}_{\text{loc}}(\Omega; \mathbb{R}^{N\times N})$ be a
symmetric and locally uniformly positive definite matrix. Set $|\xi|_A^2:=
\displaystyle \sum_{i,j=1}^N a_{ij}(x) \xi_i \xi_j$, $\xi \in \mathbb{R}^N$,
and let $V$ be a given potential in a certain local Morrey space. We assume
that the energy functional
  $$Q_{p,A,V}(\phi):=\displaystyle \int_{\Omega} [|\nabla \phi|_A^p +
V|\phi|^p] {\rm dx} $$ is nonnegative in $W^{1,p}(\Omega)\cap C_c(\Omega)$.
  We introduce a generalized notion of $Q_{p,A,V}$-capacity and characterize
the space of all Hardy-weights for the functional $Q_{p,A,V}$, extending
Maz'ya's well known characterization of the space of Hardy-weights for the
$p$-Laplacian. In addition, we provide various sufficient conditions on the
potential $V$ and the Hardy-weight $g$ such that the best constant of the
corresponding variational problem is attained in an appropriate Beppo-Levi
space.


title:
Bounding threshold dimension: realizing graphic Boolean functions as the
  AND of majority functions

abstract:
  A graph $G$ on $n$ vertices is a \emph{threshold graph} if there exist real
numbers $a_1,a_2, \ldots, a_n$ and $b$ such that the zero-one solutions of the
linear inequality $\sum \limits_{i=1}^n a_i x_i \leq b$ are the characteristic
vectors of the cliques of $G$. Introduced in [Chv{\'a}tal and Hammer, Annals of
Discrete Mathematics, 1977], the \emph{threshold dimension} of a graph $G$ is
the minimum number of threshold graphs whose intersection yields $G$. Given a
graph $G$ on $n$ vertices, in line with Chv{\'a}tal and Hammer, $f_G\colon
\{0,1\}^n \rightarrow \{0,1\}$ is the Boolean function that has the property
that $f_G(x) = 1$ if and only if $x$ is the characteristic vector of a clique
in $G$. A Boolean function $f$ for which there exists a graph $G$ such that
$f=f_G$ is called a \emph{graphic} Boolean function. It follows that for a
graph $G$, threshold dimension of $G$ is precisely the minimum number of
\emph{majority} functions whose AND (or conjunction) realizes the graphic
Boolean function $f_G$. The fact that there exist Boolean functions which can
be realized as the AND of only exponentially many majority functions motivates
us to study threshold dimension of graphs. We give tight or nearly tight upper
bounds for the threshold dimension of a graph in terms of its treewidth,
maximum degree, degeneracy, number of vertices, size of a minimum vertex cover,
etc. We also study threshold dimension of random graphs and graphs with high
girth.


title:
Towards Better Meta-Initialization with Task Augmentation for
  Kindergarten-aged Speech Recognition

abstract:
  Children's automatic speech recognition (ASR) is always difficult due to, in
part, the data scarcity problem, especially for kindergarten-aged kids. When
data are scarce, the model might overfit to the training data, and hence good
starting points for training are essential. Recently, meta-learning was
proposed to learn model initialization (MI) for ASR tasks of different
languages. This method leads to good performance when the model is adapted to
an unseen language. However, MI is vulnerable to overfitting on training tasks
(learner overfitting). It is also unknown whether MI generalizes to other
low-resource tasks. In this paper, we validate the effectiveness of MI in
children's ASR and attempt to alleviate the problem of learner overfitting. To
achieve model-agnostic meta-learning (MAML), we regard children's speech at
each age as a different task. In terms of learner overfitting, we propose a
task-level augmentation method by simulating new ages using frequency warping
techniques. Detailed experiments are conducted to show the impact of task
augmentation on each age for kindergarten-aged speech. As a result, our
approach achieves a relative word error rate (WER) improvement of 51% over the
baseline system with no augmentation or initialization.


title:
Measurement of the Drell-Yan forward-backward asymmetry at high dilepton
  masses in proton-proton collisions at $\sqrt{s}$ = 13 TeV

abstract:
  A measurement of the forward-backward asymmetry of pairs of oppositely
charged leptons (dimuons and dielectrons) produced by the Drell-\-Yan process
in proton-proton collisions is presented. The data sample corresponds to an
integrated luminosity of 138 fb$^{-1}$ collected with the CMS detector at the
LHC at a center-of-mass energy of 13 TeV. The asymmetry is measured as a
function of lepton pair mass for masses larger than 170\GeV and compared with
standard model predictions. An inclusive measurement across both channels and
the full mass range yields an asymmetry of 0.599 $\pm$ 0.005 (stat) $\pm$ 0.007
(syst). As a test of lepton flavor universality, the difference between the
dimuon and dielectron asymmetries is measured as well. No statistically
significant deviations from standard model predictions are observed. The
measurements are used to set limits on the presence of additional gauge bosons.
For a Z' in the sequential standard model, a lower mass limit of 4.4 TeV is set
at 95% confidence level.


title: Cutting Some Slack for SGD with Adaptive Polyak Stepsizes

abstract:
  Tuning the step size of stochastic gradient descent is tedious and error
prone. This has motivated the development of methods that automatically adapt
the step size using readily available information. In this paper, we consider
the family of SPS (Stochastic gradient with a Polyak Stepsize) adaptive
methods. These are methods that make use of gradient and loss value at the
sampled points to adaptively adjust the step size. We first show that SPS and
its recent variants can all be seen as extensions of the Passive-Aggressive
methods applied to nonlinear problems. We use this insight to develop new
variants of the SPS method that are better suited to nonlinear models. Our new
variants are based on introducing a slack variable into the interpolation
equations. This single slack variable tracks the loss function across
iterations and is used in setting a stable step size. We provide extensive
numerical results supporting our new methods and a convergence theory.


title: A Dynamic Fast Gaussian Transform

abstract:
  The Fast Gaussian Transform (FGT) enables subquadratic-time multiplication of
an $n\times n$ Gaussian kernel matrix $\mathsf{K}_{i,j}= \exp ( - \| x_i - x_j
\|_2^2 ) $ with an arbitrary vector $h \in \mathbb{R}^n$, where $x_1,\dots, x_n
\in \mathbb{R}^d$ are a set of fixed source points. This kernel plays a central
role in machine learning and random feature maps. Nevertheless, in most modern
ML and data analysis applications, datasets are dynamically changing, and
recomputing the FGT from scratch in (kernel-based) algorithms, incurs a major
computational overhead ($\gtrsim n$ time for a single source update $\in
\mathbb{R}^d$). These applications motivate the development of a dynamic FGT
algorithm, which maintains a dynamic set of sources under kernel-density
estimation (KDE) queries in sublinear time, while retaining Mat-Vec
multiplication accuracy and speed.
  Our main result is an efficient dynamic FGT algorithm, supporting the
following operations in $\log^{O(d)}(n/\epsilon)$ time: (1) Adding or deleting
a source point, and (2) Estimating the "kernel-density" of a query point with
respect to sources with $\epsilon$ additive accuracy. The core of the algorithm
is a dynamic data structure for maintaining the "interaction rank" between
source and target boxes, which we decouple into finite truncation of Taylor
series and Hermite expansions.


title:
Spectroscopy Signatures of Electron Correlations in a Trilayer
  Graphene/hBN Moir\'e Superlattice

abstract:
  ABC-stacked trilayer graphene/hBN moir\'e superlattice (TLG/hBN) has emerged
as a playground for correlated electron physics. We report spectroscopy
measurements of dual-gated TLG/hBN using Fourier transformed infrared
photocurrent spectroscopy. We observed a strong optical transition between
moir\'e mini-bands that narrows continuously as a bandgap is opened by gating,
indicating a reduction of the single particle bandwidth. At half-filling of the
valence flat band, a broad absorption peak emerges at ~18 meV, indicating
direct optical excitation across an emerging Mott gap. Similar photocurrent
spectra are observed in two other correlated insulating states at quarter- and
half-filling of the first conduction band. Our findings provide key parameters
of the Hubbard model for the understanding of electron correlation in TLG/hBN.


title:
The evolution of turbulent galactic discs: gravitational instability,
  feedback and accretion

abstract:
  We study the driving of turbulence in star-froming disc galaxies of different
masses at different epochs, using an analytic "bathtub" model. The disc of gas
and stars is assumed to be in marginal Toomre instability. The appropriate
supersonic turbulence is assumed to be sustained via an energy balance between
its rapid dissipation and three simultaneous energy sources. These are (a)
stellar feedback, (b) inward transport due to instability-driven torques, and
(c) clumpy accretion via streams. The transport rate is computed either via
clump encounters within the disc or via viscous torques, with similar results.
To achieve the energy balance, the disc self-regulates the corresponding
parameter, either the mass fraction in clumps or the viscous torque parameter.
In this version of the model, the efficiency by which the stream kinetic energy
is converted into turbulence is a free input parameter, $\xi_a$. We find that
the contributions of the three energy sources are in the same ball park to
within a factor of $\sim\!2$ in all discs at all times. In haloes that evolve
to a mass $\leq 10^{12}\,{\rm M_\odot}$ by $z=0$ ($\leq 10^{11.5}\,{\rm
M_\odot}$ at $z\!\sim\!2$), feedback is the main driver throughout their
lifetimes. Above this mass, the main driver is either transport or accretion
for very low or very high values of $\xi_a$, respectively. For an assumed
$\xi_a(t)$ that declines in time mimicking stream clumpiness, galaxies in halos
with present-day mass $>\!10^{12}$ ${\rm M_\odot}$ make a transition from
accretion to transport dominance at intermediate redshifts, $z\! \sim\!3$, when
their mass was $\geq\!10^{11.5}\,{\rm M_\odot}$. The predicted relation between
star-formation rate and gas velocity dispersion is consistent with
observations.


title: A Classical Bulk-Boundary Correspondence

abstract:
  In this article, we use the language of $\mathbb{P}_0$-factorization algebras
to articulate a classical bulk-boundary correspondence between 1) the
observables of a Poisson Batalin-Vilkovisky (BV) theory on a manifold $N$ and
2) the observables of the associated universal bulk-boundary system on $N\times
\mathbb{R}_{\geq 0}$. The archetypal such example is the Poisson BV theory on
$\mathbb{R}$ encoding the algebra of functions on a Poisson manifold, whose
associated bulk-boundary system on the upper half-plane is the Poisson sigma
model. In this way, we obtain a generalization and partial justification of the
basic insight that led Kontsevich to his deformation quantization of Poisson
manifolds. The proof of these results relies significantly on the operadic
homotopy theory of $\mathbb{P}_0$-algebras.


title: Queue-Aware STAR-RIS Assisted NOMA Communication Systems

abstract:
  In this paper, the queue-aware simultaneously transmitting and reflecting
reconfigurable intelligent surfaces (STAR-RIS) assisted non-orthogonal multiple
access (NOMA) communication system is investigated to ensure the system
stability, where the long-term stability-oriented problem is reformulated to
maximize the per-slot queue-weighted sum rate (QWSR) of users based on the
Lyapunov drift theory. By jointly optimizing the NOMA decoding order, the
active beamforming coefficients at the BS, and the passive transmission and
reflection coefficients at the STAR-RIS, three STAR-RIS operating protocols are
considered, namely energy splitting (ES), mode switching (MS), and time
switching (TS). For ES, the blocked coordinate descent and the successive
convex approximation methods are invoked to handle the highly-coupled and
non-convex problem. For MS, the proposed algorithm is further extended to a
penalty-based two-loop algorithm to solve the binary amplitude constrained
problem. For TS, the formulated problem is decomposed into two subproblems,
each of which can be solved in a similar manner to ES. Simulation results show
that: i) our proposed STAR-RIS assisted NOMA communication achieves better
performance than the conventional schemes; ii) the reformulated QWSR
maximization problem confirms the system stability; and iii) TS achieves
superior performance with respect to both the QWSR and the average queue
length.


title:
Trade-offs between Group Fairness Metrics in Societal Resource
  Allocation

abstract:
  We consider social resource allocations that deliver an array of scarce
supports to a diverse population. Such allocations pervade social service
delivery, such as provision of homeless services, assignment of refugees to
cities, among others. At issue is whether allocations are fair across
sociodemographic groups and intersectional identities. Our paper shows that
necessary trade-offs exist for fairness in the context of scarcity; many
reasonable definitions of equitable outcomes cannot hold simultaneously except
under stringent conditions. For example, defining fairness in terms of
improvement over a baseline inherently conflicts with defining fairness in
terms of loss compared with the best possible outcome. Moreover, we demonstrate
that the fairness trade-offs stem from heterogeneity across groups in
intervention responses. Administrative records on homeless service delivery
offer a real-world example. Building on prior work, we measure utilities for
each household as the probability of reentry into homeless services if given
three homeless services. Heterogeneity in utility distributions (conditional on
received services) for several sociodemographic groups (e.g. single women with
children versus without children) generates divergence across fairness metrics.
We argue that such heterogeneity, and thus, fairness trade-offs pervade many
social policy contexts.


title:
Robustness test of the spacegroupMining model for determining space
  groups from atomic pair distribution function data

abstract:
  Machine learning models based on convolutional neural networks have been used
for predicting space groups of crystal structures from their atomic pair
distribution function (PDF). However, the PDFs used to train the model are
calculated using a fixed set of parameters that reflect specific experimental
conditions, and the accuracy of the model when given PDFs generated with
different choices of these parameters is unknown. In this paper, we report that
the results of the top-1 accuracy and top-6 accuracy are robust when applied to
PDFs of different choices of experimental parameters $r_\text{max}$,
$Q_\text{max}$, $Q_\text{damp}$ and atomic displacement parameters.


title:
Automatically Mitigating Vulnerabilities in x86 Binary Programs via
  Partially Recompilable Decompilation

abstract:
  When vulnerabilities are discovered after software is deployed, source code
is often unavailable, and binary patching may be required to mitigate the
vulnerability. However, manually patching binaries is time-consuming, requires
significant expertise, and does not scale to the rate at which new
vulnerabilities are discovered. To address these problems, we introduce
Partially Recompilable Decompilation (PRD), which extracts and decompiles
suspect binary functions to source where they can be patched or analyzed,
applies transformations to enable recompilation of these functions (partial
recompilation), then employs binary rewriting techniques to create a patched
binary. Although decompilation and recompilation do not universally apply,
PRD's fault localization identifies a function subset that is small enough to
admit decompilation and large enough to address many vulnerabilities. Our
approach succeeds because decompilation is limited to a few functions and
lifting facilitates analysis and repair.
  To demonstrate the scalability of PRD, we evaluate it in the context of a
fully automated end-to-end scenario that relies on source-level Automated
Program Repair (APR) methods to mitigate the vulnerabilities. We also evaluate
PRD in the context of human-generated source-level repairs. In the end-to-end
experiment, PRD produced test-equivalent binaries in 84% of cases; and the
patched binaries incur no significant run-time overhead. When combined with APR
tools and evaluated on the DARPA Cyber Grand Challenge (CGC) benchmarks, PRD
achieved similar success rates as the winning CGC entries, while presenting
repairs as source-level patches which can be reviewed by humans; In some cases,
PRD finds higher-quality mitigations than those produced by top CGC teams. We
also demonstrate that PRD successfully extends to real-world binaries and
binaries that are produced from languages other than C.


title:
Time Efficient Training of Progressive Generative Adversarial Network
  using Depthwise Separable Convolution and Super Resolution Generative
  Adversarial Network

abstract:
  Generative Adversarial Networks have been employed successfully to generate
high-resolution augmented images of size 1024^2. Although the augmented images
generated are unprecedented, the training time of the model is exceptionally
high. Conventional GAN requires training of both Discriminator as well as the
Generator. In Progressive GAN, which is the current state-of-the-art GAN for
image augmentation, instead of training the GAN all at once, a new concept of
progressing growing of Discriminator and Generator simultaneously, was
proposed. Although the lower stages such as 4x4 and 8x8 train rather quickly,
the later stages consume a tremendous amount of time which could take days to
finish the model training. In our paper, we propose a novel pipeline that
combines Progressive GAN with slight modifications and Super Resolution GAN.
Super Resolution GAN up samples low-resolution images to high-resolution images
which can prove to be a useful resource to reduce the training time
exponentially.


title: Squeezed Ground States in a Spin-1 Bose-Einstein Condensate

abstract:
  We generate spin squeezed ground states in an atomic spin-1 Bose-Einstein
condensate tuned nearthe quantum critical point between the polar and
ferromagnetic quantum phases of the interactingspin ensemble. In contrast to
typical non-equilibrium methods for preparing atomic squeezed statesby
quenching through a quantum phase transition, squeezed ground states are
time-stationary andremain squeezed for the lifetime of the condensate. A
squeezed ground state with a metrologicalimprovement up to 6-8 dB and a
constant squeezing angle maintained over 2 s is demonstrated.


title:
Living and perceiving a crisis: how the pandemic influenced Americans'
  preferences and beliefs

abstract:
  Crises can cause important societal changes by shifting citizens' preferences
and beliefs, but how such change happens remains an open question. Following a
representative sample of Americans in a longitudinal multi-wave survey
throughout 2020, we find that citizens reduced trust in public institutions and
became more supportive of government spending after being directly impacted by
the crisis, such as when they lost a sizeable portion of their income or knew
someone hospitalized with the virus. These shifts occurred very rapidly,
sometimes in a matter of weeks, and persisted over time. We also record an
increase in the partisan gap on the same outcomes, which can be largely
explained by misperceptions about the crisis inflated by the consumption of
partisan leaning news. In an experiment, we expose respondents to the same
source of information and find that it successfully recalibrates perceptions,
with persistent effects. We complement our analysis by employing machine
learning to estimate heterogeneous treatment effects, and show that our
findings are robust to several specifications and estimation strategies. In
sum, both lived experiences and media inflated misperceptions can alter
citizens' beliefs rapidly during a crisis.


title:
Basic Elements for Simulations of Standard Model Physics with Quantum
  Annealers: Multigrid and Clock States

abstract:
  We explore the potential of D-Wave's quantum annealers for computing some of
the basic components required for quantum simulations of Standard Model
physics. By implementing a basic multigrid (including "zooming") and
specializing Feynman-clock algorithms, D-Wave's Advantage is used to study
harmonic and anharmonic oscillators relevant for lattice scalar field theories
and effective field theories, the time evolution of a single plaquette of SU(3)
Yang-Mills lattice gauge field theory, and the dynamics of flavor entanglement
in four neutrino systems.


title:
Kinetic-scale current sheets in near-Sun solar wind: properties,
  scale-dependent features and reconnection onset

abstract:
  We present statistical analysis of 11,200 proton kinetic-scale current sheets
(CS) observed by Parker Solar Probe during 10 days around the first perihelion.
The CS thickness $\lambda$ is in the range from a few to 200 km with the
typical value around 30 km, while current densities are in the range from 0.1
to 10\;$\mu {\rm A/m^2}$ with the typical value around 0.7\;$\mu {\rm A/m^2}$.
These CSs are resolved thanks to magnetic field measurements at 73--290
Samples/s resolution. In terms of proton inertial length $\lambda_{p}$, the CS
thickness $\lambda$ is in the range from about $0.1$ to $10\lambda_{p}$ with
the typical value around 2$\lambda_{p}$. The magnetic field magnitude does not
substantially vary across the CSs and, accordingly, the current density is
dominated by the magnetic field-aligned component. The CSs are typically
asymmetric with statistically different magnetic field magnitudes at the CS
boundaries. The current density is larger for smaller-scale CSs, $J_0\approx
0.15 \cdot (\lambda/100\;{\rm km})^{-0.76}$ $\mu {\rm A/m^2}$, but does not
statistically exceed the Alfv\'en current density $J_A$ corresponding to the
ion-electron drift of local Alfv\'{e}n speed. The CSs exhibit remarkable
scale-dependent current density and magnetic shear angles, $J_0/J_{A}\approx
0.17\cdot (\lambda/\lambda_{p})^{-0.67}$ and $\Delta \theta\approx
21^{\circ}\cdot (\lambda/\lambda_{p})^{0.32}$. Based on these observations and
comparison to recent studies at 1 AU, we conclude that proton kinetic-scale CSs
in the near-Sun solar wind are produced by turbulence cascade and they are
automatically in the parameter range, where reconnection is not suppressed by
the diamagnetic mechanism, due to their geometry dictated by turbulence
cascade.


title:
Differentially-Private Publication of Origin-Destination Matrices with
  Intermediate Stops

abstract:
  Conventional origin-destination (OD) matrices record the count of trips
between pairs of start and end locations, and have been extensively used in
transportation, traffic planning, etc. More recently, due to use case scenarios
such as COVID-19 pandemic spread modeling, it is increasingly important to also
record intermediate points along an individual's path, rather than only the
trip start and end points. This can be achieved by using a multi-dimensional
frequency matrix over a data space partitioning at the desired level of
granularity. However, serious privacy constraints occur when releasing OD
matrix data, and especially when adding multiple intermediate points, which
makes individual trajectories more distinguishable to an attacker. To address
this threat, we propose a technique for privacy-preserving publication of
multi-dimensional OD matrices that achieves differential privacy (DP), the
de-facto standard in private data release. We propose a family of approaches
that factor in important data properties such as data density and homogeneity
in order to build OD matrices that provide provable protection guarantees while
preserving query accuracy. Extensive experiments on real and synthetic datasets
show that the proposed approaches clearly outperform existing state-of-the-art.


title: Physics Opportunities with PROSPECT-II

abstract:
  The PROSPECT experiment has substantially addressed the original 'Reactor
Antineutrino Anomaly' by performing a high-resolution spectrum measurement from
an enriched compact reactor core and a reactor model-independent sterile
neutrino oscillation search based on the unique spectral distortions the
existence of eV$^2$-scale sterile neutrinos would impart. But as the field has
evolved, the current short-baseline (SBL) landscape supports many complex
phenomenological interpretations, establishing a need for complementary
experimental approaches to resolve the situation.
  While the global suite of SBL reactor experiments, including PROSPECT, have
probed much of the sterile neutrino parameter space, there remains a large
region above 1 eV$^2$ that remains unaddressed. Recent results from BEST
confirm the Gallium Anomaly, increasing its significance to $\sim 5\sigma$,
with sterile neutrinos providing a possible explanation of this anomaly.
Separately, the MicroBooNE exclusion of electron-like signatures causing the
MiniBooNE low-energy excess does not eliminate the possibility of sterile
neutrinos as an explanation. Focusing specifically on the future use of
reactors as a neutrino source for beyond-the-standard-model physics and
applications, higher-precision spectral measurements still have a role to play.
  These recent results have created a confusing landscape which requires new
data to disentangle the seemingly contradictory measurements. To directly probe
$\overline{\nu}_{e}$ disappearance from high $\Delta m^2$ sterile neutrinos,
the PROSPECT collaboration proposes to build an upgraded and improved detector,
PROSPECT-II. It features an evolutionary detector design which can be
constructed and deployed within one year and have impactful physics with as
little as one calendar year of data.


title:
On some high-dimensional limits of matricial stochastic processes seen
  from a quantum probability perspective

abstract:
  We generalize the result of block-wise convergence of the Brownian motion on
the unitary group $U(nm)$ towards a quantum L\'evy process on the unitary dual
group $U\langle n\rangle$ when $m\rightarrow\infty$, obtained by the author in
a previous paper, by showing that the Brownian motions on the orthogonal group
$O(nm)$ and the symplectic group $Sp(nm)$ also converge block-wise to this same
quantum L\'evy process.


title: Derived categories of families of Fano threefolds

abstract:
  We construct $S$-linear semiorthogonal decompositions of derived categories
of smooth Fano threefold fibrations $X/S$ with relative Picard rank $1$ and
rational geometric fibers and discuss how the structure of components of these
decompositions is related to rationality properties of $X/S$.


title: Flexible multivariate spatio-temporal Hawkes process models of terrorism

abstract:
  We develop flexible multivariate spatio-temporal Hawkes process models to
analyze patterns of terrorism. Previous applications of point process methods
to political violence data mainly utilize temporal Hawkes process models,
neglecting spatial variation in these attack patterns. This limits what can be
learned from these models as any effective counter-terrorism strategy requires
knowledge on both when and where attacks are likely to occur. Even the existing
work on spatio-temporal Hawkes processes imposes restrictions on the triggering
function that are not well-suited for terrorism data. Therefore, we generalize
the structure of the spatio-temporal triggering function considerably, allowing
for nonseprability, nonstatitionarity, and cross-triggering (i.e., across the
groups). To demonstrate the utility of our models, we analyze two samples of
real-world terrorism data: Afghanistan (2002-2013) for univariate analysis and
Nigeria (2009-2017) for bivariate analysis. Jointly, these two studies
demonstrate that our models outperform standard Hawkes process models, besting
widely-used alternatives in overall model fit and revealing spatio-temporal
patterns that are, by construction, masked in these models (e.g., increasing
dispersion in cross-triggering over time).


title: Multiple multi-sample testing under arbitrary covariance dependency

abstract:
  Modern high-throughput biomedical devices routinely produce data on a large
scale, and the analysis of high-dimensional datasets has become commonplace in
biomedical studies. However, given thousands or tens of thousands of measured
variables in these datasets, extracting meaningful features poses a challenge.
In this article, we propose a procedure to evaluate the strength of the
associations between a nominal (categorical) response variable and multiple
features simultaneously. Specifically, we propose a framework of large-scale
multiple testing under arbitrary correlation dependency among test statistics.
First, marginal multinomial regressions are performed for each feature
individually. Second, we use an approach of multiple marginal models for each
baseline-category pair to establish asymptotic joint normality of the stacked
vector of the marginal multinomial regression coefficients. Third, we estimate
the (limiting) covariance matrix between the estimated coefficients from all
marginal models. Finally, our approach approximates the realized false
discovery proportion of a thresholding procedure for the marginal p-values, for
each baseline-category pair. The proposed approach offers a sensible trade-off
between the expected numbers of true and false rejections. Furthermore, we
demonstrate a practical application of the method on hyperspectral imaging
data. This dataset is obtained by a matrix-assisted laser desorption/ionization
(MALDI) instrument. MALDI demonstrates tremendous potential for clinical
diagnosis, particularly for cancer research. In our application, the nominal
response categories represent cancer subtypes.


title: Bayesian Deep Learning for Graphs

abstract:
  The adaptive processing of structured data is a long-standing research topic
in machine learning that investigates how to automatically learn a mapping from
a structured input to outputs of various nature. Recently, there has been an
increasing interest in the adaptive processing of graphs, which led to the
development of different neural network-based methodologies. In this thesis, we
take a different route and develop a Bayesian Deep Learning framework for graph
learning. The dissertation begins with a review of the principles over which
most of the methods in the field are built, followed by a study on graph
classification reproducibility issues. We then proceed to bridge the basic
ideas of deep learning for graphs with the Bayesian world, by building our deep
architectures in an incremental fashion. This framework allows us to consider
graphs with discrete and continuous edge features, producing unsupervised
embeddings rich enough to reach the state of the art on several classification
tasks. Our approach is also amenable to a Bayesian nonparametric extension that
automatizes the choice of almost all model's hyper-parameters. Two real-world
applications demonstrate the efficacy of deep learning for graphs. The first
concerns the prediction of information-theoretic quantities for molecular
simulations with supervised neural models. After that, we exploit our Bayesian
models to solve a malware-classification task while being robust to
intra-procedural code obfuscation techniques. We conclude the dissertation with
an attempt to blend the best of the neural and Bayesian worlds together. The
resulting hybrid model is able to predict multimodal distributions conditioned
on input graphs, with the consequent ability to model stochasticity and
uncertainty better than most works. Overall, we aim to provide a Bayesian
perspective into the articulated research field of deep learning for graphs.


title:
openFEAT: Improving Speaker Identification by Open-set Few-shot
  Embedding Adaptation with Transformer

abstract:
  Household speaker identification with few enrollment utterances is an
important yet challenging problem, especially when household members share
similar voice characteristics and room acoustics. A common embedding space
learned from a large number of speakers is not universally applicable for the
optimal identification of every speaker in a household. In this work, we first
formulate household speaker identification as a few-shot open-set recognition
task and then propose a novel embedding adaptation framework to adapt speaker
representations from the given universal embedding space to a
household-specific embedding space using a set-to-set function, yielding better
household speaker identification performance. With our algorithm, Open-set
Few-shot Embedding Adaptation with Transformer (openFEAT), we observe that the
speaker identification equal error rate (IEER) on simulated households with 2
to 7 hard-to-discriminate speakers is reduced by 23% to 31% relative.


title:
DoCoGen: Domain Counterfactual Generation for Low Resource Domain
  Adaptation

abstract:
  Natural language processing (NLP) algorithms have become very successful, but
they still struggle when applied to out-of-distribution examples. In this paper
we propose a controllable generation approach in order to deal with this domain
adaptation (DA) challenge. Given an input text example, our DoCoGen algorithm
generates a domain-counterfactual textual example (D-con) - that is similar to
the original in all aspects, including the task label, but its domain is
changed to a desired one. Importantly, DoCoGen is trained using only unlabeled
examples from multiple domains - no NLP task labels or parallel pairs of
textual examples and their domain-counterfactuals are required. We show that
DoCoGen can generate coherent counterfactuals consisting of multiple sentences.
We use the D-cons generated by DoCoGen to augment a sentiment classifier and a
multi-label intent classifier in 20 and 78 DA setups, respectively, where
source-domain labeled data is scarce. Our model outperforms strong baselines
and improves the accuracy of a state-of-the-art unsupervised DA algorithm.


title:
Atomic-scale imaging of emergent order at a magnetic-field-induced
  Lifshitz transition

abstract:
  The phenomenology and radical changes seen in materials properties traversing
a quantum phase transition has captivated condensed matter research over past
decades. Strong electronic correlations lead to novel electronic ground states,
including magnetic order, nematicity and unconventional superconductivity. To
provide a microscopic model for these requires detailed knowledge of the
electronic structure in the vicinity of the Fermi energy, promising a complete
understanding of the physics of the quantum critical point. Here, we
demonstrate such a measurement at the surface of Sr$_3$Ru$_2$O$_7$. Our results
show that even in zero field the electronic structure is strongly $C_2$
symmetric and that a magnetic-field drives both a Lifshitz transition and
induces a charge-stripe order. We track the changes of the electronic structure
as a function of field via quasi-particle interference imaging at ultra-low
temperatures. Our results provide a complete microscopic picture of the
field-induced changes of the electronic structure across the Lifshitz
transition.


title:
Bayesian Model Averaging of Chain Event Graphs for Robust Explanatory
  Modelling

abstract:
  Chain Event Graphs (CEGs) are a widely applicable class of probabilistic
graphical models that are able to represent context-specific independence
statements and asymmetric unfoldings of events in an easily interpretable way.
Existing model selection literature on CEGs has focused on obtaining the
maximum a posteriori (MAP) CEG. However, MAP selection is well-known to ignore
model uncertainty. Here, we explore the use of model averaging over this class.
We demonstrate that such methods express model uncertainty and lead to more
robust inference. Because the space of possible CEGs is huge, scoring models
exhaustively for model averaging in all but small problems is prohibitive.
However we show that a bespoke class of hybrid forward sampling and greedy
search algorithms can successfully and intelligently traverse this space of
candidate models. By applying a simple version of our search method to two
known case studies, we can illustrate the efficacy of such methods compared to
more standard MAP modelling. We also demonstrate how its outputs systematically
inform those component hypotheses that are most robustly supported the data and
high-scoring alternative models to the MAP model.


title:
Equilibration and "Thermalization'' in the Adapted Caldeira-Leggett
  model

abstract:
  I explore the processes of equilibration exhibited by the Adapted
Caldeira-Leggett (ACL) model, a small unitary "toy model" developed for
numerical studies of quantum decoherence between an SHO and an environment. I
demonstrate how dephasing allows equilibration to occur in a wide variety of
situations. While the finite model size and other "unphysical" aspects prevent
the notions of temperature and thermalization from being generally applicable,
certain primitive aspects of thermalization can be realized for particular
parameter values. I link the observed behaviors to intrinsic properties of the
global energy eigenstates, and argue that the phenomena I observe contain
elements which might be key ingredients that lead to ergodic behavior in larger
more realistic systems. The motivations for this work range from curiosity
about phenomena observed in earlier calculations with the ACL model, to much
larger questions related to the nature of equilibrium, thermalization and the
emergence of physical laws.


title: An Automorphism group of a rational surface: Not too big not too small

abstract:
  This article concerns the realization problem of subgroups of Coxeter groups.
We construct a subgroup $G$ of the Coxeter group $W_{15}$ such that $G$ is
realized as automorphism groups of a rational surface $X$ and $G \cong Aut(X)^*
\cong D_3 \times \mathbb{Z}$. We also show that there is an element $\omega$ in
$W_{14}$ is not realizable.


title:
Symmetric Mass Generation in the 1+1 Dimensional Chiral Fermion 3-4-5-0
  Model

abstract:
  Lattice regularization of chiral fermions has been a long-standing problem in
physics. In this work, we present the density matrix renormalization group
(DMRG) simulation of the 3-4-5-0 model of (1+1)D chiral fermions with an
anomaly-free chiral U(1) symmetry, which contains two left-moving and two
right-moving fermions carrying U(1) charges 3,4 and 5,0, respectively.
Following the Wang-Wen chiral fermion model, we realize the chiral fermions and
their mirror partners on the opposite boundaries of a thin strip of (2+1)D
lattice model of multi-layer Chern insulator, whose finite-width implies the
quantum system is effectively (1+1)D. By introducing carefully designed two
sets of six-fermion local interactions to the mirror sector only, we
demonstrate that the mirror fermions can be gapped out by the interaction
beyond a critical strength without breaking the chiral U(1) symmetry, via the
symmetric mass generation (SMG) mechanism. We show that the interaction-driven
gapping transition is in the Berezinskii-Kosterlitz-Thouless (BKT) universality
class. We determine the evolution of Luttinger parameters before the
transition, which confirms that the transition happens exactly at the point
when the interaction term becomes marginal. As the mirror sector is gapped
after the transition, we check that the fermions in the light chiral fermion
sector remain gapless, which provides the desired lattice regularization of
chiral fermions.


title:
Constraints on the Lambda-neutron interaction from charge-symmetry
  breaking of A=4 hypernuclei

abstract:
  We include the leading charge symmetry breaking contributions into the
hyperon-nucleon interactions derived within chiral effective field theory up to
next-to-leading order. Two low energy constants are determined using the
experimentally known differences of Lambda separation energies of 4-Lambda-He
and 4-Lambda-H. This allows one to predict the Lambda-neutron scattering
lengths for the first time based on data. Various sources of uncertainty are
discussed.


title: PRNU Emphasis: a Generalization of the Multiplicative Model

abstract:
  The photoresponse non-uniformity (PRNU) is a camera-specific pattern, widely
adopted to solve multimedia forensics problems such as device identification or
forgery detection. The theoretical analysis of this fingerprint customarily
relies on a multiplicative model for the denoising residuals. This setup
assumes that the nonlinear mapping from the scene irradiance to the
preprocessed luminance, that is, the composition of the Camera Response
Function (CRF) with the optical and digital preprocessing pipelines, is a gamma
correction. Yet, this assumption seldom holds in practice. In this letter, we
improve the multiplicative model by including the influence of this nonlinear
mapping on the denoising residuals. We also propose a method to estimate this
effect. Results evidence that the response of typical cameras deviates from a
gamma correction. Experimental device identification with our model increases
the TPR by a $4.93\, \%$ on average for a fixed FPR of $0.01$.


title:
Physics Informed RNN-DCT Networks for Time-Dependent Partial
  Differential Equations

abstract:
  Physics-informed neural networks allow models to be trained by physical laws
described by general nonlinear partial differential equations. However,
traditional architectures struggle to solve more challenging time-dependent
problems due to their architectural nature. In this work, we present a novel
physics-informed framework for solving time-dependent partial differential
equations. Using only the governing differential equations and problem initial
and boundary conditions, we generate a latent representation of the problem's
spatio-temporal dynamics. Our model utilizes discrete cosine transforms to
encode spatial frequencies and recurrent neural networks to process the time
evolution. This efficiently and flexibly produces a compressed representation
which is used for additional conditioning of physics-informed models. We show
experimental results on the Taylor-Green vortex solution to the Navier-Stokes
equations. Our proposed model achieves state-of-the-art performance on the
Taylor-Green vortex relative to other physics-informed baseline models.


title: UnifiedQA-v2: Stronger Generalization via Broader Cross-Format Training

abstract:
  We present UnifiedQA-v2, a QA model built with the same process as UnifiedQA,
except that it utilizes more supervision -- roughly 3x the number of datasets
used for UnifiedQA. This generally leads to better in-domain and cross-domain
results.


title: Stability of the microcanonical ensemble in Euclidean Quantum Gravity

abstract:
  This work resolves a longstanding tension between the physically-expected
stability of the microcanonical ensemble for gravitating systems and the fact
that the known negative mode of the asymptotically flat Schwarzschild black
hole decays too rapidly at infinity to affect the ADM energy boundary term at
infinity. The key to our study is that we fix an appropriate {\it off-shell}
notion of energy, which we obtain by constructing the microcanonical partition
function as an integral transform of the canonical partition function. After
applying the rule-of-thumb for Wick rotations from our recent companion paper
to deal with the conformal mode problem of Euclidean gravity, we find a
positive definite action for linear perturbations about any Euclidean
Schwarzchild (-AdS) black hole. Most of our work is done in a cavity with
reflecting boundary conditions, but the cavity wall can be removed by taking an
appropriate limit.


title:
RescueNet: A High Resolution UAV Semantic Segmentation Benchmark Dataset
  for Natural Disaster Damage Assessment

abstract:
  Due to climate change, we can observe a recent surge of natural disasters all
around the world. These disasters are causing disastrous impact on both nature
and human lives. Economic losses are getting greater due to the hurricanes.
Quick and prompt response of the rescue teams are crucial in saving human lives
and reducing economic cost. Deep learning based computer vision techniques can
help in scene understanding, and help rescue teams with precise damage
assessment. Semantic segmentation, an active research area in computer vision,
can put labels to each pixel of an image, and therefore can be a valuable
arsenal in the effort of reducing the impacts of hurricanes. Unfortunately,
available datasets for natural disaster damage assessment lack detailed
annotation of the affected areas, and therefore do not support the deep
learning models in total damage assessment. To this end, we introduce the
RescueNet, a high resolution post disaster dataset, for semantic segmentation
to assess damages after natural disasters. The RescueNet consists of post
disaster images collected after Hurricane Michael. The data is collected using
Unmanned Aerial Vehicles (UAVs) from several areas impacted by the hurricane.
The uniqueness of the RescueNet comes from the fact that this dataset provides
high resolution post-disaster images and comprehensive annotation of each
image. While most of the existing dataset offer annotation of only part of the
scene, like building, road, or river, RescueNet provides pixel level annotation
of all the classes including building, road, pool, tree, debris, and so on. We
further analyze the usefulness of the dataset by implementing state-of-the-art
segmentation models on the RescueNet. The experiments demonstrate that our
dataset can be valuable in further improvement of the existing methodologies
for natural disaster damage assessment.


title: StyleCLIPDraw: Coupling Content and Style in Text-to-Drawing Translation

abstract:
  Generating images that fit a given text description using machine learning
has improved greatly with the release of technologies such as the CLIP
image-text encoder model; however, current methods lack artistic control of the
style of image to be generated. We present an approach for generating styled
drawings for a given text description where a user can specify a desired
drawing style using a sample image. Inspired by a theory in art that style and
content are generally inseparable during the creative process, we propose a
coupled approach, known here as StyleCLIPDraw, whereby the drawing is generated
by optimizing for style and content simultaneously throughout the process as
opposed to applying style transfer after creating content in a sequence. Based
on human evaluation, the styles of images generated by StyleCLIPDraw are
strongly preferred to those by the sequential approach. Although the quality of
content generation degrades for certain styles, overall considering both
content \textit{and} style, StyleCLIPDraw is found far more preferred,
indicating the importance of style, look, and feel of machine generated images
to people as well as indicating that style is coupled in the drawing process
itself. Our code (https://github.com/pschaldenbrand/StyleCLIPDraw), a
demonstration (https://replicate.com/pschaldenbrand/style-clip-draw), and style
evaluation data
(https://www.kaggle.com/pittsburghskeet/drawings-with-style-evaluation-styleclipdraw)
are publicly available.


title:
Estimators of Entropy and Information via Inference in Probabilistic
  Models

abstract:
  Estimating information-theoretic quantities such as entropy and mutual
information is central to many problems in statistics and machine learning, but
challenging in high dimensions. This paper presents estimators of entropy via
inference (EEVI), which deliver upper and lower bounds on many information
quantities for arbitrary variables in a probabilistic generative model. These
estimators use importance sampling with proposal distribution families that
include amortized variational inference and sequential Monte Carlo, which can
be tailored to the target model and used to squeeze true information values
with high accuracy. We present several theoretical properties of EEVI and
demonstrate scalability and efficacy on two problems from the medical domain:
(i) in an expert system for diagnosing liver disorders, we rank medical tests
according to how informative they are about latent diseases, given a pattern of
observed symptoms and patient attributes; and (ii) in a differential equation
model of carbohydrate metabolism, we find optimal times to take blood glucose
measurements that maximize information about a diabetic patient's insulin
sensitivity, given their meal and medication schedule.


title: Orthonormal Matrix Codebook Design for Adaptive Transform Coding

abstract:
  A novel algorithm for designing optimized orthonormal transform-matrix
codebooks for adaptive transform coding of a non-stationary vector process is
proposed. This algorithm relies on a block-wise stationary model of a
non-stationary process and finds a codebook of transform-matrices by minimizing
the end-to-end mean square error of transform coding averaged over the
distribution of stationary blocks of vectors. The algorithm, which belongs to
the class of block-coordinate descent algorithms, solves an intermediate
minimization problem involving matrix-orthonormality constraints in a
computationally efficient manner by mapping the problem from the Euclidean
space to the Stiefel manifold. As such, the algorithm can be broadly applied to
any adaptive transform coding problem. Preliminary results obtained with
inter-prediction residuals in an H265 video codec are presented to demonstrate
the advantage of optimized adaptive transform codes over non-adaptive codes
based on the standard DCT.


title: Alternative Metrics to Select Motors for Quasi-Direct Drive Actuators

abstract:
  Robotic systems for legged locomotion -- including legged robots,
exoskeletons, and prosthetics -- require actuators with low inertia and high
output torque. Traditionally, motors have been selected for these applications
by maximizing the motor gap radius. We present alternative metrics for motor
selection that are invariant to transmission ratio. The proposed metrics reward
minimizing the motor inertia while maximizing the torque and motor constants
without special consideration for gap radius, providing a better balance of
properties for legged locomotion applications. We rigorously characterize the
T-Motor RI50 and demonstrate the use of the metrics by comparing the RI50 to
the widely-used T-Motor U8 as a case study.


title: Bredon motivic cohomology of the complex numbers

abstract:
  Over the complex numbers, we compute the $C_2$-equivariant Bredon motivic
cohomology ring with $\mathbb{Z}/2$ coefficients. By rigidity, this extends
Suslin's calculation of the motivic cohomology ring of algebraically closed
fields of characteristic zero to the $C_2$-equivariant motivic setting.


title: Smooth Linearization of Nonautonomous Coupled Systems

abstract:
  In a joint work with Palmer we have formulated sufficient conditions under
which there exist continuous and invertible transformations of the form
$H_n(x,y)$ taking solutions of a coupled system \begin{equation*} x_{n+1}
=A_nx_n+f_n(x_n, y_n), \quad y_{n+1}=g_n( y_n), \end{equation*} onto the
solutions of the associated partially linearized uncoupled system
\begin{equation*} x_{n+1} =A_nx_n, \quad y_{n+1}=g_n( y_n). \end{equation*} In
the present work we go one step further and provide conditions under which
$H_n$ and $H_n^{-1}$ are smooth in one of the variables $x$ and $y$.
  We emphasise that our conditions are of a general form and do not involve any
kind of dichotomy, nonresonance or spectral gap assumptions for the linear part
which are present on most of the related works.


title: Instantaneous Physiological Estimation using Video Transformers

abstract:
  Video-based physiological signal estimation has been limited primarily to
predicting episodic scores in windowed intervals. While these intermittent
values are useful, they provide an incomplete picture of patients'
physiological status and may lead to late detection of critical conditions. We
propose a video Transformer for estimating instantaneous heart rate and
respiration rate from face videos. Physiological signals are typically
confounded by alignment errors in space and time. To overcome this, we
formulated the loss in the frequency domain. We evaluated the method on the
large scale Vision-for-Vitals (V4V) benchmark. It outperformed both shallow and
deep learning based methods for instantaneous respiration rate estimation. In
the case of heart-rate estimation, it achieved an instantaneous-MAE of 13.0
beats-per-minute.


title:
On Monocular Depth Estimation and Uncertainty Quantification using
  Classification Approaches for Regression

abstract:
  Monocular depth is important in many tasks, such as 3D reconstruction and
autonomous driving. Deep learning based models achieve state-of-the-art
performance in this field. A set of novel approaches for estimating monocular
depth consists of transforming the regression task into a classification one.
However, there is a lack of detailed descriptions and comparisons for
Classification Approaches for Regression (CAR) in the community and no in-depth
exploration of their potential for uncertainty estimation. To this end, this
paper will introduce a taxonomy and summary of CAR approaches, a new
uncertainty estimation solution for CAR, and a set of experiments on depth
accuracy and uncertainty quantification for CAR-based models on KITTI dataset.
The experiments reflect the differences in the portability of various CAR
methods on two backbones. Meanwhile, the newly proposed method for uncertainty
estimation can outperform the ensembling method with only one forward
propagation.


title: Conductivity reconstruction from power density data in limited view

abstract:
  In Acousto-Electric tomography, the objective is to extract information about
the interior electrical conductivity in a physical body from knowledge of the
interior power density data generated from prescribed boundary conditions for
the governing elliptic partial differential equation. In this note, we consider
the problem when the controlled boundary conditions are applied only on a small
subset of the full boundary. We demonstrate using the unique continuation
principle that the Runge approximation property is valid also for this special
case of limited view data. As a consequence, we guarantee the existence of
finitely many boundary conditions such that the corresponding solutions locally
satisfy a non-vanishing gradient condition. This condition is essential for
conductivity reconstruction from power density data. In addition, we adapt an
existing reconstruction method intended for the full data situation to our
setting. We implement the method numerically and investigate the opportunities
and shortcomings when reconstructing from two fixed boundary conditions.


title: Aggregation as an antipredator strategy in the rock-paper-scissors model

abstract:
  We study a nonhierarchical tritrophic system, whose predator-prey
interactions are described by the rock-paper-scissors game rules. In our
stochastic simulations, individuals may move strategically towards the
direction with more conspecifics to form clumps instead of moving aimlessly on
the lattice. Considering that the conditioning to move gregariously depends on
the organism's physical and cognitive abilities, we introduce a maximum
distance an individual can perceive the environment and a minimum conditioning
level to perform the gregarious movement. We investigate the pattern formation
and compute the average size of the single-species spatial domains emerging
from the grouping behaviour. The results reveal that the defence tactic reduces
the predation risk significantly, being more profitable if individuals perceive
further distances, thus creating bigger groups. Our outcomes show that the
species with more conditioned organisms dominate the cyclic spatial game,
controlling most of the territory. On the other hand, the species with fewer
individuals ready to perform aggregation strategy gives its predator the chance
to fill the more significant fraction of the grid. The spatial interactions
assumed in our numerical experiments constitute a data set that may help
biologists and data scientists understand how local interactions influence
ecosystem dynamics.


title:
The parabolic and near-parabolic renormalization for a class of
  polynomial maps and its applications

abstract:
  For a class of polynomial maps of one variable with a parabolic fixed points
and degrees bigger than $21$, the parabolic renormalization is introduced based
on Fatou coordinates and horn maps, and a type of maps which are invariant
under the parabolic renormalization is also given. For the small perturbation
of these kinds of maps, the near-parabolic renormalization is also introduced
based on the first return maps defined on the fundamental regions. As an
application, we show the existence of non-renormalizable polynomial maps with
degrees bigger than $21$ such that the Julia sets have positive Lebesgue
measure and Cremer fixed points, this provides a positive answer for the
classical Fatou conjecture (the existence of Julia set with positive area) with
degrees bigger than $21$.


title: Learning POD of Complex Dynamics Using Heavy-ball Neural ODEs

abstract:
  Proper orthogonal decomposition (POD) allows reduced-order modeling of
complex dynamical systems at a substantial level, while maintaining a high
degree of accuracy in modeling the underlying dynamical systems. Advances in
machine learning algorithms enable learning POD-based dynamics from data and
making accurate and fast predictions of dynamical systems. In this paper, we
leverage the recently proposed heavy-ball neural ODEs (HBNODEs) [Xia et al.
NeurIPS, 2021] for learning data-driven reduced-order models (ROMs) in the POD
context, in particular, for learning dynamics of time-varying coefficients
generated by the POD analysis on training snapshots generated from solving full
order models. HBNODE enjoys several practical advantages for learning POD-based
ROMs with theoretical guarantees, including 1) HBNODE can learn long-term
dependencies effectively from sequential observations and 2) HBNODE is
computationally efficient in both training and testing. We compare HBNODE with
other popular ROMs on several complex dynamical systems, including the von
K\'{a}rm\'{a}n Street flow, the Kurganov-Petrova-Popov equation, and the
one-dimensional Euler equations for fluids modeling.


title:
A Globally Convergent LP and SOCP-based algorithm for Semidefinite
  Programming

abstract:
  Semidefinite programs (SDP) are one of the most versatile frameworks in
numerical optimization, serving as generalizations of many conic programs and
as relaxations of NP-hard combinatorial problems. Their main drawback is their
computational and memory complexity, which sets a practical limit to the size
of problems solvable by off-the-shelf SDP solvers. To circumvent this fact,
many algorithms have been proposed to exploit the structure of particular
problems and increase the scalability of SDPs for those problem instances.
Progress has been less steep, however, for general-case SDPs. In this paper,
motivated by earlier results by Ahmadi and Hall, we show that a general SDP can
be solved to $\epsilon$-optimality, in polynomial time, by performing a
sequence of less computationally demanding Linear or Second Order Cone
programs. In addition, we provide a bound on the number of iterations required
to achieve $\epsilon$-optimality. These results are illustrated using random
SDPs and well-known problems from the SDPLib dataset.


title: Highly-Efficient Binary Neural Networks for Visual Place Recognition

abstract:
  VPR is a fundamental task for autonomous navigation as it enables a robot to
localize itself in the workspace when a known location is detected. Although
accuracy is an essential requirement for a VPR technique, computational and
energy efficiency are not less important for real-world applications. CNN-based
techniques archive state-of-the-art VPR performance but are computationally
intensive and energy demanding. Binary neural networks (BNN) have been recently
proposed to address VPR efficiently. Although a typical BNN is an order of
magnitude more efficient than a CNN, its processing time and energy usage can
be further improved. In a typical BNN, the first convolution is not completely
binarized for the sake of accuracy. Consequently, the first layer is the
slowest network stage, requiring a large share of the entire computational
effort. This paper presents a class of BNNs for VPR that combines depthwise
separable factorization and binarization to replace the first convolutional
layer to improve computational and energy efficiency. Our best model achieves
state-of-the-art VPR performance while spending considerably less time and
energy to process an image than a BNN using a non-binary convolution as a first
stage.


title:
A novel four-field mixed variational approach to Kirchhoff rods
  implemented with finite element exterior calculus

abstract:
  A four-field mixed variational principle is proposed for large deformation
analysis of Kirchhoff rods with a $C^0$ mixed FE approximations. The core idea
behind the approach is to introduce a one-parameter family of points (the
centerline) and a separate one-parameter family of orthonormal frames (the
Cartan moving frame) that are specified independently. The curvature and
torsion of the curve are related to the relative rotation of neighboring
frames. The relationship between the frame and the centerline is then enforced
at the solution step using a Lagrange multiplier (which plays the role of
section force). Well known frames like the Frenet-Serret are defined only using
the centerline, which demands higher-order smoothness for the centerline
approximation. Decoupling the frame from the position vector of the base curve
leads to a description of torsion and curvature that is independent of the
position information, thus allowing for simpler interpolations. This approach
is then cast in the language of exterior calculus. In this framework, the
strain energy may be interpreted as a differential form leading to the natural
force-displacement (velocity) pairing. The four-field mixed variational
principle we propose has frame, differential forms, and position vector as
input arguments. While the position vector is interpolated linearly, the frames
are interpolated as piecewise geodesics on the rotation group. Similarly,
consistent interpolation schemes are also adopted to obtain finite dimensional
approximations for other differential forms aswell. Using these discrete
approximations, a discrete mixed variational principle is laid out which is
then numerically extremized. The discrete approximation is then applied to
benchmark problems, our numerical studies reveal an impressive performance of
the proposed method without numerical instabilities or locking.


title:
Calder\'on Preconditioners for the TD-EFIE discretized with Convolution
  Quadratures

abstract:
  This work focuses on the preconditioning and DC stabilization of the time
domain electric field integral equation discretized in time with the
convolution quadrature method. The standard formulation of the equation suffers
from severe ill-conditioning for large time steps and refined meshes, in
addition to DC instabilities plaguing standard solutions for late time steps.
This work addresses all these issues by preconditioning the TD-EFIE operator
matrices with a Calder\'on approach. Numerical results will corroborate the
theory, showing the practical relevance of the proposed advancements.


title:
Deep Learning to advance the Eigenspace Perturbation Method for
  Turbulence Model Uncertainty Quantification

abstract:
  The Reynolds Averaged Navier Stokes (RANS) models are the most common form of
model in turbulence simulations. They are used to calculate Reynolds stress
tensor and give robust results for engineering flows. But RANS model
predictions have large error and uncertainty. In past, there has been some work
towards using data-driven methods to increase their accuracy. In this work we
outline a machine learning approach to aid the use of the Eigenspace
Perturbation Method to predict the uncertainty in the turbulence model
prediction. We use a trained neural network to predict the discrepancy in the
shape of the RANS predicted Reynolds stress ellipsoid. We apply the model to a
number of turbulent flows and demonstrate how the approach correctly identifies
the regions in which modeling errors occur when compared to direct numerical
simulation (DNS), large eddy simulation (LES) or experimental results from
previous works.


title: Learning to Combine Instructions in LLVM Compiler

abstract:
  Instruction combiner (IC) is a critical compiler optimization pass, which
replaces a sequence of instructions with an equivalent and optimized
instruction sequence at basic block level. There can be thousands of
instruction-combining patterns which need to be frequently updated as new
coding idioms/applications and novel hardware evolve over time. This results in
frequent updates to the IC optimization pass thereby incurring considerable
human effort and high software maintenance costs. To mitigate these challenges
associated with the traditional IC, we design and implement a Neural
Instruction Combiner (NIC) and demonstrate its feasibility by integrating it
into the standard LLVM compiler optimization pipeline. NIC leverages neural
sequence-to-sequence (Seq2Seq) models for generating optimized encoded IR
sequence from the unoptimized encoded IR sequence. To the best of our
knowledge, ours is the first work demonstrating the feasibility of a neural
instruction combiner built into a full-fledged compiler pipeline. Given the
novelty of this task, we built a new dataset for training our NIC neural model.
We show that NIC achieves exact match results percentage of 72% for optimized
sequences as compared to traditional IC and neural machine translation metric
Bleu precision score of 0.94, demonstrating its feasibility in a production
compiler pipeline.


title: Fast Matching Pursuit with Multi-Gabor Dictionaries

abstract:
  Finding the best K-sparse approximation of a signal in a redundant dictionary
is an NP-hard problem. Suboptimal greedy matching pursuit (MP) algorithms are
generally used for this task. In this work, we present an acceleration
technique and an implementation of the matching pursuit algorithm acting on a
multi-Gabor dictionary, i.e., a concatenation of several Gabor-type
time-frequency dictionaries, each of which consisting of translations and
modulations of a possibly different window and time and frequency shift
parameters. The technique is based on pre-computing and thresholding inner
products between atoms and on updating the residual directly in the coefficient
domain, i.e., without the round-trip to the signal domain. Since the proposed
acceleration technique involves an approximate update step, we provide
theoretical and experimental results illustrating the convergence of the
resulting algorithm. The implementation is written in C (compatible with C99
and C++11) and we also provide Matlab and GNU Octave interfaces. For some
settings, the implementation is up to 70 times faster than the standard
Matching Pursuit Toolkit (MPTK).

categories: math.NA cs.IT cs.MS cs.NA eess.SP math.IT


title:
Parallel Type Decomposition Scheme for Quasi-Linear Abstract Hyperbolic
  Equation

abstract:
  Cauchy problem for an abstract hyperbolic equation with the Lipschitz
continuous operator is considered in the Hilbert space. The operator
corresponding to the elliptic part of the equation is a sum of operators
$A_{1},\,A_{2},\,\ldots,\,A_{m}$. Each addend is a self-adjoint and positive
definite operator. A parallel type decomposition scheme for an approximate
solution of the stated problem is constructed. The main idea of the scheme is
that on each local interval classic difference problems are solved in parallel
(independently from each other) respectively with the operators
$A_{1},\,A_{2},\,\ldots,\,A_{m}$. The weighted average of the received
solutions is announced as an approximate solution at the right end of the local
interval. Convergence of the proposed scheme is proved and the approximate
solution error is estimated, as well as the error of the difference analogue
for the first-order derivative for the case when the initial problem data
satisfy the natural sufficient conditions for solution existence.


title:
Fine-Grained Prediction of Political Leaning on Social Media with
  Unsupervised Deep Learning

abstract:
  Predicting the political leaning of social media users is an increasingly
popular task, given its usefulness for electoral forecasts, opinion dynamics
models and for studying the political dimension of polarization and
disinformation. Here, we propose a novel unsupervised technique for learning
fine-grained political leaning from the textual content of social media posts.
Our technique leverages a deep neural network for learning latent political
ideologies in a representation learning task. Then, users are projected in a
low-dimensional ideology space where they are subsequently clustered. The
political leaning of a user is automatically derived from the cluster to which
the user is assigned. We evaluated our technique in two challenging
classification tasks and we compared it to baselines and other state-of-the-art
approaches. Our technique obtains the best results among all unsupervised
techniques, with micro F1 = 0.426 in the 8-class task and micro F1 = 0.772 in
the 3-class task. Other than being interesting on their own, our results also
pave the way for the development of new and better unsupervised approaches for
the detection of fine-grained political leaning.


title: Multimode capacity of atomic-frequency comb quantum memories

abstract:
  Ensemble-based quantum memories are key to developing multiplexed quantum
repeaters, able to overcome the intrinsic rate limitation imposed by finite
communication times over long distances. Rare-earth ion doped crystals are main
candidates for highly multimode quantum memories, where time, frequency and
spatial multiplexing can be exploited to store multiple modes. In this context
the atomic frequency comb (AFC) quantum memory provides large temporal
multimode capacity, which can readily be combined with multiplexing in
frequency and space. In this article, we derive theoretical formulas for
quantifying the temporal multimode capacity of AFC-based memories, for both
optical memories with fixed storage time and spin-wave memories with longer
storage times and on-demand read out. The temporal multimode capacity is
expressed in key memory parameters, such as AFC bandwidth, fixed-delay storage
time, memory efficiency, and control field Rabi frequency. Current experiments
in europium- and praseodymium-doped Y$_2$SiO$_5$ are analyzed within this
theoretical framework, and prospects for higher temporal capacity in these
materials are considered. In addition we consider the possibility of spectral
and spatial multiplexing to further increase the mode capacity, with examples
given for both rare earh ions.


title: TwistSLAM: Constrained SLAM in Dynamic Environment

abstract:
  Moving objects are present in most scenes of our life. However they can be
very problematic for classical SLAM algorithms that assume the scene to be
rigid. This assumption limits the applicability of those algorithms as they are
unable to accurately estimate the camera pose and world structure in many
scenarios. Some SLAM systems have been proposed to detect and mask out dynamic
objects, making the static scene assumption valid. However this information can
allow the system to track objects within the scene, while tracking the camera,
which can be crucial for some applications. In this paper we present TwistSLAM
a semantic, dynamic, stereo SLAM system that can track dynamic objects in the
scene. Our algorithm creates clusters of points according to their semantic
class. It uses the static parts of the environment to robustly localize the
camera and tracks the remaining objects. We propose a new formulation for the
tracking and the bundle adjustment to take in account the characteristics of
mechanical joints between clusters to constrain and improve their pose
estimation. We evaluate our approach on several sequences from a public dataset
and show that we improve camera and object tracking compared to state of the
art.


title: A Collision-Free MPC for Whole-Body Dynamic Locomotion and Manipulation

abstract:
  In this paper, we present a real-time whole-body planner for collision-free
legged mobile manipulation. We enforce both self-collision and
environment-collision avoidance as soft constraints within a Model Predictive
Control (MPC) scheme that solves a multi-contact optimal control problem. By
penalizing the signed distances among a set of representative primitive
collision bodies, the robot is able to safely execute a variety of dynamic
maneuvers while preventing any self-collisions. Moreover, collision-free
navigation and manipulation in both static and dynamic environments are made
viable through efficient queries of distances and their gradients via a
euclidean signed distance field. We demonstrate through a comparative study
that our approach only slightly increases the computational complexity of the
MPC planning. Finally, we validate the effectiveness of our framework through a
set of hardware experiments involving dynamic mobile manipulation tasks with
potential collisions, such as locomotion balancing with the swinging arm,
weight throwing, and autonomous door opening.


title: Limits and colimits of synthetic $\infty$-categories

abstract:
  We develop the theory of limits and colimits in $\infty$-categories within
the synthetic framework of simplicial homotopy type theory developed by Riehl
and Shulman. We also show that in this setting, the limit of a family of spaces
can be computed as a dependant product.


title:
Provable Stochastic Optimization for Global Contrastive Learning: Small
  Batch Does Not Harm Performance

abstract:
  In this paper, we study contrastive learning from an optimization
perspective, aiming to analyze and address a fundamental issue of existing
contrastive learning methods that either rely on a large batch size or a large
dictionary. We consider a global objective for contrastive learning, which
contrasts each positive pair with all negative pairs for an anchor point. From
the optimization perspective, we explain why existing methods such as SimCLR
requires a large batch size in order to achieve a satisfactory result. In order
to remove such requirement, we propose a memory-efficient Stochastic
Optimization algorithm for solving the Global objective of Contrastive Learning
of Representations, named SogCLR. We show that its optimization error is
negligible under a reasonable condition after a sufficient number of iterations
or is diminishing for a slightly different global contrastive objective.
Empirically, we demonstrate that on ImageNet with a batch size 256, SogCLR
achieves a performance of 69.4% for top-1 linear evaluation accuracy using
ResNet-50, which is on par with SimCLR (69.3%) with a large batch size 8,192.
We also attempt to show that the proposed optimization technique is generic and
can be applied to solving other contrastive losses, e.g., two-way contrastive
losses for bimodal contrastive learning.


title:
Kibble-Zurek Mechanism for Nonequilibrium Phase Transitions in Driven
  Systems with Quenched Disorder

abstract:
  We numerically study the density of topological defects for a two-dimensional
assembly of particles driven over quenched disorder as a function of quench
rate through the nonequilibrium phase transition from a plastic disordered
flowing state to a moving anisotropic crystal. A dynamical ordering transition
of this type occurs for vortices in type-II superconductors, colloids, and
other particle-like systems in the presence of random disorder. We find that on
the ordered side of the transition, the density of topological defects $\rho_d$
scales as a power law, $\rho_d \propto 1/t_{q}^\beta$, where $t_{q}$ is the
time duration of the quench across the transition. This type of scaling is
predicted in the Kibble-Zurek mechanism for varied quench rates across a
continuous phase transition. We show that scaling with the same exponent holds
for varied strengths of quenched disorder. The value of the exponent can be
connected to the directed percolation universality class. Our results suggest
that the Kibble-Zurek mechanism can be applied to general nonequilibrium phase
transitions.


title:
Direct evidence of magnetic reconnection onset via the tearing
  instability

abstract:
  Magnetic reconnection is a sporadic process responsible for energy release in
space and laboratory plasmas. It is believed that the tearing mode instability
may be responsible for the onset of reconnection in the magnetotail. However,
due to its elusive nature, there is an absence of in-situ observations of the
tearing instability prior to magnetic reconnection in our nearest natural
plasma laboratory. Using neural network outlier detection methods in
conjunction with Cluster spacecraft data, we find unique electron pitch angle
distributions that are consistent with simulation predictions of the tearing
instability and the subsequent evolution of plasma electrons and reconnection.
We confirm that the events identified via our neural network outlier method are
well above the tearing stability threshold based on the criterion detailed in
this paper. We find signatures of magnetic reconnection minutes after the
majority of tearing observations. Our analysis of the tearing instability
provides new insights into the fundamental understanding of the mechanism
responsible for reconnection, a process that is ubiquitous in different
astrophysical plasma regimes across the universe and in laboratory experiments
on Earth.


title: Scales and Locomotion: Non-Reversible Longitudinal Drag

abstract:
  Locomotion requires that an animal or robot be able to move itself forward
farther than it moves backward in each gait cycle (formally, that it be able to
break the symmetry of its interactions with the world). Previous work has
established that a difference between lateral and longitudinal drag provides
sufficient conditions for locomotion to be possible. The geometric mechanics
community has used this principle to build a geometric framework for describing
the effectiveness and efficiency of undulatory locomotion. Researchers in
biology and robotics have observed that structures such as snake scales
additionally provide a difference between forward and backward longitudinal
drag. As yet, however, the impact of scales on the geometric features relevant
to locomotion effectiveness and efficiency have not yet been explored. We
present a geometric model for a single-joint undulating system with scales and
identify the features needed to understand its motion. Mathematically, the
scales can be treated as inducing a "Finsler metric" on the configuration
space, and this paper lays the groundwork for further research into application
of such Finsler metrics to robotic locomotion.


title: HeRo 2.0: A Low-Cost Robot for Swarm Robotics Research

abstract:
  The current state of electronic component miniaturization coupled with the
increasing efficiency in hardware and software allow the development of smaller
and compact robotic systems. The convenience of using these small, simple, yet
capable robots has gathered the research community's attention towards
practical applications of swarm robotics. This paper presents the design of a
novel platform for swarm robotics applications that is low cost, easy to
assemble using off-the-shelf components, and deeply integrated with the most
used robotic framework available today: ROS (Robot Operating System). The
robotic platform is entirely open, composed of a 3D printed body and
open-source software. We describe its architecture, present its main features,
and evaluate its functionalities executing experiments using a couple of
robots. Results demonstrate that the proposed mobile robot is very effective
given its small size and reduced cost, being suitable for swarm robotics
research and education.


title: Radiopurity studies of a rock sample from the Aut region

abstract:
  Efforts are underway to set up an underground laboratory in India for rare
event studies like double beta decay, dark matter, etc. For such experiments,
mitigation of radiation background is of paramount importance and understanding
ambient background at the site, originating from the rock, is one of the
crucial factors. With this motivation, the radiopurity studies of a rock sample
from the potential laboratory site in the Aut tunnel of Himachal Pradesh
(India) have been carried out using the TIFR low background experimental setup
(TiLES). The concentration of $^{40}$K in Aut rock is observed to be lower by a
factor of ~1000 as compared to the samples from BWH (Bodi West Hill), Tamil
Nadu (India), current designated site for India-based Neutrino Observatory. The
natural radioactive trace impurity $^{232}$Th is lower in the Aut rock, while
$^{238}$U is somewhat higher than the BWH rock. Overall, the ambient gamma ray
background at Aut is expected to be lower than the BWH, while ambient neutron
background is expected to be similar. Further, to assess the neutron-induced
long lived activity, fast neutron activation studies have been carried out on
the both Aut and BWH rock samples at the Pelletron Linac Facility, Mumbai.


title:
Imaging Scatterometer for Observing Changes to Optical Coatings During
  Air Annealing

abstract:
  Air annealing generally reduces absorption, scattering, and mechanical loss
in amorphous coatings up to temperatures where damage occurs. Our instrument
uses an industrial oven with viewports to observe coating scatter and damage
during annealing.