title:
Extreme mass-ratio inspirals as probes of scalar fields: eccentric
  equatorial orbits around Kerr black holes

abstract:
  We study binary systems in which a stellar mass compact object spirals into a
massive black hole, known as extreme mass ratio inspirals, in scenarios with a
new fundamental scalar field. Earlier work has shown that, in most interesting
such scenarios and to leading order in the mass ratio, the massive black holes
can be adequately approximated by the Kerr metric and the imprint of the scalar
field on the waveform is fully controlled by the scalar charge of the stellar
mass object. Here we use this drastic simplification in the inspiral modelling
and consider eccentric equatorial orbits. We study how the scalar charge
affects the orbital evolution for different eccentricities and different values
of the black hole spin. We then determine how changes in the orbital evolution
get imprinted on the waveform and assess LISA's capability to detect or
constrain the scalar charge.


title: Nonlinear Bosonization of Fermi Surfaces: The Method of Coadjoint Orbits

abstract:
  We develop a new method for bosonizing the Fermi surface based on the
formalism of the coadjoint orbits. This allows one to parametrize the Fermi
surface by a bosonic field that depends on the spacetime coordinates and on the
position on the Fermi surface. The Wess-Zumino-Witten term in the effective
action, governing the adiabatic phase acquired when the Fermi surface changes
its shape, is completely fixed. As an effective field theory the action also
involves a Hamiltonian which contains, beside the kinetic energy and the Landau
interaction, terms with arbitrary number of derivatives and fields. We show
that the resulting local effective field theory captures both linear and
nonlinear effects in Landau's Fermi liquid theory. The approach can be extended
to incorporate spin degrees of freedom and the charge-2 fields corresponding to
the BCS order parameter.


title: Computational methods for adapted optimal transport

abstract:
  Adapted optimal transport (AOT) problems are optimal transport problems for
distributions of a time series where couplings are constrained to have a
temporal causal structure. In this paper, we develop computational tools for
solving AOT problems numerically. First, we show that AOT problems are stable
with respect to perturbations in the marginals and thus arbitrary AOT problems
can be approximated by sequences of linear programs. We further study entropic
methods to solve AOT problems. We show that any entropically regularized AOT
problem converges to the corresponding unregularized problem if the
regularization parameter goes to zero. The proof is based on a novel method -
even in the non-adapted case - to easily obtain smooth approximations of a
given coupling with fixed marginals. Finally, we show tractability of the
adapted version of Sinkhorn's algorithm. We give explicit solutions for the
occurring projections and prove that the procedure converges to the optimizer
of the entropic AOT problem.


title:
Resource-Efficient Invariant Networks: Exponential Gains by Unrolled
  Optimization

abstract:
  Achieving invariance to nuisance transformations is a fundamental challenge
in the construction of robust and reliable vision systems. Existing approaches
to invariance scale exponentially with the dimension of the family of
transformations, making them unable to cope with natural variabilities in
visual data such as changes in pose and perspective. We identify a common
limitation of these approaches--they rely on sampling to traverse the
high-dimensional space of transformations--and propose a new computational
primitive for building invariant networks based instead on optimization, which
in many scenarios provides a provably more efficient method for
high-dimensional exploration than sampling. We provide empirical and
theoretical corroboration of the efficiency gains and soundness of our proposed
method, and demonstrate its utility in constructing an efficient invariant
network for a simple hierarchical object detection task when combined with
unrolled optimization. Code for our networks and experiments is available at
https://github.com/sdbuch/refine.


title: Stellar Masses of Clumps in Gas-rich, Turbulent Disk Galaxies

abstract:
  In this paper we use HST/WFC3 observations of 6 galaxies from the DYNAMO
survey, combined with stellar population modelling of the SED, to determine the
stellar masses of DYNAMO clumps. The DYNAMO sample has been shown to have
properties similar to $z\approx1.5$ turbulent, clumpy disks. DYNAMO sample
clump masses offer a useful comparison for studies of $z>1$ in that the
galaxies have the same properties, yet the observational biases are
significantly different. Using DYNAMO we can more easily probe rest-frame
near-IR wavelengths and also probe finer spatial scales. We find that the
stellar mass of DYNAMO clumps is typically $10^{7}-10^8 \mathrm{M}_\odot$. We
employ a technique that makes non-parametric corrections in removal of light
from nearby clumps, and carries out a locally determined disk subtraction. The
process of disk subtraction is the dominant effect, and can alter clump masses
at the 0.3~dex level. Using these masses, we investigate the stellar mass
function of clumps in DYNAMO galaxies. DYNAMO stellar mass functions follow a
declining power law with slope $\alpha \approx -1.4$, which is slightly
shallower than, but similar to what is observed in $z>1$ lensed galaxies. We
compare DYNAMO clump masses to results of simulations. The masses and
galactocentric position of clumps in DYNAMO galaxies are more similar to
long-lived clumps in simulations. Similar to recent DYNAMO results on the
stellar population gradients, these results are consistent with simulations
that do not employ strong "early" radiative feedback prescriptions.


title:
Sentence-Select: Large-Scale Language Model Data Selection for Rare-Word
  Speech Recognition

abstract:
  Language model fusion helps smart assistants recognize words which are rare
in acoustic data but abundant in text-only corpora (typed search logs).
However, such corpora have properties that hinder downstream performance,
including being (1) too large, (2) beset with domain-mismatched content, and
(3) heavy-headed rather than heavy-tailed (excessively many duplicate search
queries such as "weather"). We show that three simple strategies for selecting
language modeling data can dramatically improve rare-word recognition without
harming overall performance. First, to address the heavy-headedness, we
downsample the data according to a soft log function, which tunably reduces
high frequency (head) sentences. Second, to encourage rare-word exposure, we
explicitly filter for words rare in the acoustic data. Finally, we tackle
domain-mismatch via perplexity-based contrastive selection, filtering for
examples matched to the target domain. We down-select a large corpus of web
search queries by a factor of 53x and achieve better LM perplexities than
without down-selection. When shallow-fused with a state-of-the-art, production
speech engine, our LM achieves WER reductions of up to 24% relative on
rare-word sentences (without changing overall WER) compared to a baseline LM
trained on the raw corpus. These gains are further validated through favorable
side-by-side evaluations on live voice search traffic.


title:
Extremely expensive likelihoods: A variational-Bayes solution for
  precision cosmology

abstract:
  We present a variational-Bayes solution to compute non-Gaussian posteriors
from extremely expensive likelihoods. Our approach is an alternative for
parameter inference when MCMC sampling is numerically prohibitive or
conceptually unfeasible. For example, when either the likelihood or the
theoretical model cannot be evaluated at arbitrary parameter values, but only
previously selected values, then traditional MCMC sampling is impossible,
whereas our variational-Bayes solution still succeeds in estimating the full
posterior. In cosmology, this occurs e.g. when the parametric model is based on
costly simulations that were run for previously selected input parameters. We
demonstrate our posterior construction on the KiDS-450 weak lensing analysis,
where we reconstruct the original KiDS MCMC posterior at 0.6% of its former
numerical cost. The reduction in numerical cost implies that systematic effects
which formerly exhausted the numerical budget could now be included.


title: New Ideas in Baryogenesis: A Snowmass White Paper

abstract:
  The Standard Model of Particle Physics cannot explain the observed baryon
asymmetry of the Universe. This observation is a clear sign of new physics
beyond the Standard Model. There have been many recent theoretical developments
to address this question. Critically, many new physics models that generate the
baryon asymmetry have a wide range of repercussions for many areas of
theoretical and experimental particle physics. This white paper provides an
overview of such recent theoretical developments with an emphasis on
experimental testability.


title:
Vector cnoidal and solitary plasmon polariton waves in a planar
  waveguide

abstract:
  The paper considers the dynamics of nonlinear surface plasmon polariton waves
in a planar plasmon waveguide, which is a heterostructure of non-magnetic
metallic and dielectric layers. The obtained in the work nonlinear equations
and their analytical solutions describe the vector cnoidal and solitary plasmon
polariton waves excited by single electromagnetic pulse at the waveguide.
Nonlinear plasmon polariton waves arise under the influence of the Kerr
nonlinearity of metal and the saturation of nonlinearity at the
heterostructure. The period and profile of envelope of the excited nonlinear
surface plasmon polariton wave vary depending on the conditions of excitation
and the power of exciting electromagnetic pulse.


title: Learning to control from expert demonstrations

abstract:
  In this paper, we revisit the problem of learning a stabilizing controller
from a finite number of demonstrations by an expert. By first focusing on
feedback linearizable systems, we show how to combine expert demonstrations
into a stabilizing controller, provided that demonstrations are sufficiently
long and there are at least $n+1$ of them, where $n$ is the number of states of
the system being controlled. When we have more than $n+1$ demonstrations, we
discuss how to optimally choose the best $n+1$ demonstrations to construct the
stabilizing controller. We then extend these results to a class of systems that
can be embedded into a higher-dimensional system containing a chain of
integrators. The feasibility of the proposed algorithm is demonstrated by
applying it on a CrazyFlie 2.0 quadrotor.


title: Local Complete Intersections and Weierstrass Points

abstract:
  We provide a simple proof that the moduli space of complete integral
Gorenstein curves with prescribed symmetric Weierstrass semigroup $\mathrm{S}$
is a projective space whenever the affine monomial curve associated to
$\mathrm{S}$ is a local complete intersection and $\mathrm{S}$ is realizable.
In addition, we also prove the same statement by assuming that the affine
monomial curve is a complete intersection but not requiring that $\mathrm{S}$
is realizable.


title: Exchange spin waves in thin films with gradient composition

abstract:
  We report investigation of ferromagnetic resonance phenomenon in
ferromagnetic thin films with essentially non-uniform composition. Epitaxial
Pd-Fe thin film with linear distribution of Fe content across the thickness is
used as the model material. Anomalous perpendicular standing spin waves are
observed and quantified using the collective dynamic equation. Numerical
analysis yields the exchange stiffness constant for diluted Pd-Fe alloy
$D=2A/\mu_0M_s=15$~T$\cdot$nm$^2$ and the ratio of the effective magnetization
to the saturation magnetization $M_{eff}/M_s=1.16$. It is demonstrated that,
overall, engineering of thin films with non-uniform composition across the
thickness can be used for high-frequency or low-field magnonic operations using
exchange spin waves.


title: COMMAND: Certifiable Open Measurable Mandates

abstract:
  Security mandates today are often in the form of checklists and are generally
inflexible and slow to adapt to changing threats. This paper introduces an
alternate approach called open mandates, which mandate that vendors must
dedicate some amount of resources (e.g. system speed, energy, design cost,
etc.) towards security but unlike checklist security does not prescribe
specific controls that must be implemented. The goal of open mandates is to
provide flexibility to vendors in implementing security controls that they see
fit while requiring all vendors to commit to a certain level of security. In
this paper, we first demonstrate the usefulness of open security mandates: for
instance, we show that mandating 10% of resources towards security reduces
defenders losses by 8% and forestalls attackers by 10%. We then show how open
mandates can be implemented in practice. Specifically, we solve the problem of
identifying a system's overhead due to security, a key problem towards making
such an open mandate enforceable in practice. As examples we demonstrate our
open mandate system -- COMMAND -- for two contemporary software hardening
techniques and show that our methodology can predict security overheads to a
very high degree of accuracy (<1% mean and median error) with low resource
requirements. We also present experiments that quantify, in terms of dollars,
how much end users value the performance lost to security, which help determine
the costs of such a program. Taken together -- the usefulness of mandates,
their enforceability, and their quantifiable costs -- make the case for an
alternate resource-based mandate.


title:
Shfl-BW: Accelerating Deep Neural Network Inference with Tensor-Core
  Aware Weight Pruning

abstract:
  Weight pruning in deep neural networks (DNNs) can reduce storage and
computation cost, but struggles to bring practical speedup to the model
inference time. Tensor-cores can significantly boost the throughput of GPUs on
dense computation, but exploiting tensor-cores for sparse DNNs is very
challenging. Compared to existing CUDA-cores, tensor-cores require higher data
reuse and matrix-shaped instruction granularity, both difficult to yield from
sparse DNN kernels. Existing pruning approaches fail to balance the demands of
accuracy and efficiency: random sparsity preserves the model quality well but
prohibits tensor-core acceleration, while highly-structured block-wise sparsity
can exploit tensor-cores but suffers from severe accuracy loss.
  In this work, we propose a novel sparse pattern, Shuffled Block-wise sparsity
(Shfl-BW), designed to efficiently utilize tensor-cores while minimizing the
constraints on the weight structure. Our insight is that row- and column-wise
permutation provides abundant flexibility for the weight structure, while
introduces negligible overheads using our GPU kernel designs. We optimize the
GPU kernels for Shfl-BW in linear and convolution layers. Evaluations show that
our techniques can achieve the state-of-the-art speed-accuracy trade-offs on
GPUs. For example, with small accuracy loss, we can accelerate the
computation-intensive layers of Transformer by 1.81, 4.18 and 1.90 times on
NVIDIA V100, T4 and A100 GPUs respectively at 75% sparsity.


title: Asymmetric Duffing oscillator: jump manifold and border set

abstract:
  We study the jump phenomenon present in the forced asymmetric Duffing
oscillator using the known steady-state asymptotic solution. The major result
is the computation of the jump manifold, which encodes global information about
all possible jumps.


title:
Application of neural-network hybrid models in estimating the infection
  functions of nonlinear epidemic models

abstract:
  Hybrid neural network models combine the advantages of a neural network's
fitting functionality with differential equation models to reflect actual
physical processes and are widely used in analyzing time-series data. Most
related studies have focused on linear hybrid models, but only a few have
examined nonlinear problems. In this work, we use a hybrid nonlinear epidemic
neural network as the entry point to study its power in predicting the correct
infection function of an epidemic model. To achieve this goal, we combine the
bifurcation theory of the nonlinear differential model with the mean-squared
error loss and design a novel loss function to ensure model trainability.
Furthermore, we find the unique existence conditions supporting ordinary
differential equations to estimate the correct infection function. Using the
Runge Kutta method, we perform numerical experiments on our proposed model and
verify its soundness. We also apply it to real COVID-19 data to accurately
discover the change law of its infectivity.


title: Quantum and Classical Algorithms for Bounded Distance Decoding

abstract:
  In this paper, we provide a comprehensive overview of a recent debate over
the quantum versus classical solvability of bounded distance decoding (BDD).
Specifically, we review the work of Eldar and Hallgren [EH22], [Hal21]
demonstrating a quantum algorithm solving $\lambda_1 2^{-\Omega(\sqrt{k \log
q})}$-BDD in polynomial time for lattices of periodicity $q$, finite group rank
$k$, and shortest lattice vector length $\lambda_1$. Subsequently, we prove the
results of [DvW21a], [DvW21b] with far greater detail and elaboration than in
the original work. Namely, we show that there exists a deterministic, classical
algorithm achieving the same result.


title:
Noncommutative $D=5$ Chern-Simons Gravity: Kaluza-Klein Reduction and
  Chiral Gravitational Anomaly

abstract:
  Actions for noncommutative (NC) gauge field theories can be expanded
perturbatively in powers of the noncommutativity parameter $\theta$ using the
Seiberg-Witten map between ordinary classical fields and their NC counterparts.
The leading order term represents classical ($\theta=0$) action while
higher-order terms give us $\theta$-dependent NC corrections that ought to
capture some aspects of quantum gravity. Building on previous work of Aschieri
and Castellani on NC Chern-Simons (CS) gauge and gravity theories, showing that
non-trivial $\theta$-dependence exists only for spacetime dimensions $D\geq 5$,
we investigate a correlated effect of these extra spatial dimensions and
noncommutativity on four-dimensional physics, up to first-order in $\theta$.
Assuming that one spatial dimension is compactified into a circle, we apply the
Kaluza-Klein reduction procedure on the NC $D=5$ CS theory for the conformal
gauge group $SO(4,2)$, to obtain an effective, $\theta$-dependent
four-dimensional theory of gravity that has Einstein-Hilbert gravity with
negative cosmological constant as its commutative limit. We derive field
equations for this modified theory of gravity and study the effect of NC
interactions on some classical geometries, such as the AdS-Schwarzschild black
hole. We find that this NC background spacetime gives rise to chiral
gravitational anomaly due to the nonvanishing $\theta$-dependent Pontryagin
density.


title: LSTM-driven Forecast of CO2 Injection in Porous Media

abstract:
  The ability to simulate the partial differential equations (PDE's) that
govern multi-phase flow in porous media is essential for different applications
such as geologic sequestration of CO2, groundwater flow monitoring and
hydrocarbon recovery from geologic formations [1]. These multi-phase flow
problems can be simulated by solving the governing PDE's numerically, using
various discretization schemes such as finite elements, finite volumes,
spectral methods, etc. More recently, the application of Machine Learning (ML)
to approximate the solutions to PDE's has been a very active research area.
However, most researchers have focused on the performance of their models
within the time-space domain in which the models were trained. In this work, we
apply ML techniques to approximate PDE solutions and focus on the forecasting
problem outside of the training domain. To this end, we use two different ML
architectures - the feed forward neural (FFN) network and the long short-term
memory (LSTM)-based neural network, to predict the PDE solutions in future
times based on the knowledge of the solutions in the past. The results of our
methodology are presented on two example PDE's - namely a form of PDE that
models the underground injection of CO2 and its hyperbolic limit which is a
common benchmark case. In both cases, the LSTM architecture shows a huge
potential to predict the solution behavior at future times based on prior data


title:
A proof of P != NP (New symmetric encryption algorithm against any
  linear attacks and differential attacks)

abstract:
  P vs NP problem is the most important unresolved problem in the field of
computational complexity. Its impact has penetrated into all aspects of
algorithm design, especially in the field of cryptography. The security of
cryptographic algorithms based on short keys depends on whether P is equal to
NP. In fact, Shannon[1] strictly proved that the one-time-pad system meets
unconditional security, but because the one-time-pad system requires the length
of key to be at least the length of plaintext, how to transfer the key is a
troublesome problem that restricts the use of the one-time-pad system in
practice. Cryptography algorithms used in practice are all based on short key,
and the security of the short key mechanism is ultimately based on "one-way"
assumption, that is, it is assumed that a one-way function exists. In fact, the
existence of one-way function can directly lead to the important conclusion P
!= NP. In this paper, we originally constructed a short-key block cipher
algorithm. The core feature of this algorithm is that for any block, when a
plaintext-ciphertext pair is known, any key in the key space can satisfy the
plaintext-ciphertext pair, that is, for each block, the plaintext-ciphertext
pair and the key are independence, and the independence between blocks is also
easy to construct. This feature is completely different from all existing
short-key cipher algorithms. Based on the above feature, we construct a problem
and theoretically prove that the problem satisfies the properties of one-way
functions, thereby solving the problem of the existence of one-way functions,
that is, directly proving that P != NP.


title:
AdS black brane coupled to non-abelian logarithmic gauge theory and
  color DC conductivity

abstract:
  In this paper, we consider Einstein-Hilbert gravity in the presence of
cosmological constant and non-abelian nonlinear electromagnetic field of
logarithmic type which is minimally coupled to gravity. First, black brane
solution of this model is introduced and then color non-abelian DC conductivity
is calculated for this solution by using AdS/CFT duality. Our result recovers
the Yang-Mills model in $\lambda \to 0$ limit.


title: $3$-anti-circulant digraphs are $\alpha$-diperfect and BE-diperfect

abstract:
  Let $D$ be a digraph. A subset $S$ of $V(D)$ is a stable set if every pair of
vertices in $S$ is non-adjacent in $D$. A collection of disjoint paths
$\mathcal{P}$ of $D$ is a path partition of $V(D)$, if every vertex in $V(D)$
is exactly on a path of $\mathcal{P}$. We say that a stable set $S$ and a path
partition $\mathcal{P}$ are orthogonal if each path of $P$ contains exactly one
vertex of $S$. A digraph $D$ satisfies the $\alpha$-property if for every
maximum stable set $S$ of $D$, there exists a path partition $\mathcal{P}$ such
that $S$ and $\mathcal{P}$ are orthogonal. A digraph $D$ is $\alpha$-diperfect
if every induced subdigraph of $D$ satisfies the $\alpha$-property. In 1982,
Claude Berge proposed a characterization for $\alpha$-diperfect digraphs in
terms of forbidden anti-directed odd cycles. In 2018, Sambinelli, Silva and Lee
proposed a similar conjecture. A digraph $D$ satisfies the Begin-End-property
or BE-property if for every maximum stable set $S$ of $D$, there exists a path
partition $\mathcal{P}$ such that (i) $S$ and $\mathcal{P}$ are orthogonal and
(ii) for each path $P \in \mathcal{P}$, either the start or the end of $P$
belongs to $S$. A digraph $D$ is BE-diperfect if every induced subdigraph of
$D$ satisfies the BE-property. Sambinelli, Silva and Lee proposed a
characterization for BE-diperfect digraphs in terms of forbidden blocking odd
cycles. In this paper, we verified both conjectures for $3$-anti-circulant
digraphs. We also present some structural results for $\alpha$-diperfect and
BE-diperfect digraphs.


title:
Power-of-Two Quantization for Low Bitwidth and Hardware Compliant Neural
  Networks

abstract:
  Deploying Deep Neural Networks in low-power embedded devices for real
time-constrained applications requires optimization of memory and computational
complexity of the networks, usually by quantizing the weights. Most of the
existing works employ linear quantization which causes considerable degradation
in accuracy for weight bit widths lower than 8. Since the distribution of
weights is usually non-uniform (with most weights concentrated around zero),
other methods, such as logarithmic quantization, are more suitable as they are
able to preserve the shape of the weight distribution more precise. Moreover,
using base-2 logarithmic representation allows optimizing the multiplication by
replacing it with bit shifting. In this paper, we explore non-linear
quantization techniques for exploiting lower bit precision and identify
favorable hardware implementation options. We developed the Quantization Aware
Training (QAT) algorithm that allowed training of low bit width Power-of-Two
(PoT) networks and achieved accuracies on par with state-of-the-art floating
point models for different tasks. We explored PoT weight encoding techniques
and investigated hardware designs of MAC units for three different quantization
schemes - uniform, PoT and Additive-PoT (APoT) - to show the increased
efficiency when using the proposed approach. Eventually, the experiments showed
that for low bit width precision, non-uniform quantization performs better than
uniform, and at the same time, PoT quantization vastly reduces the
computational complexity of the neural network.


title:
Transfer Learning as an Essential Tool for Digital Twins in Renewable
  Energy Systems

abstract:
  Transfer learning (TL), the next frontier in machine learning (ML), has
gained much popularity in recent years, due to the various challenges faced in
ML, like the requirement of vast amounts of training data, expensive and
time-consuming labelling processes for data samples, and long training duration
for models. TL is useful in tackling these problems, as it focuses on
transferring knowledge from previously solved tasks to new tasks. Digital twins
and other intelligent systems need to utilise TL to use the previously gained
knowledge and solve new tasks in a more self-reliant way, and to incrementally
increase their knowledge base. Therefore, in this article, the critical
challenges in power forecasting and anomaly detection in the context of
renewable energy systems are identified, and a potential TL framework to meet
these challenges is proposed. This article also proposes a feature embedding
approach to handle the missing sensors data. The proposed TL methods help to
make a system more autonomous in the context of organic computing.


title: A First-Order Numerical Algorithm without Matrix Operations

abstract:
  This paper offers a matrix-free first-order numerical method to solve
large-scale conic optimization problems. Solving systems of linear equations
pose the most computationally challenging part in both first-order and
second-order numerical algorithms. Existing direct and indirect methods are
either computationally expensive or compromise on solution accuracy.
Alternatively, we propose an easy-to-compute decomposition method to solve
sparse linear systems that arise in conic optimization problems. Its iterations
are tractable, highly parallelizable, with closed-form solutions. This
algorithm can be easily implemented on distributed platforms, such as graphics
processing units, with orders-of-magnitude time improvement. The performance of
the proposed solver is demonstrated on large-scale conic optimization problems
and is compared with the state-of-the-art first-order solvers.


title: Dynamic Instance Domain Adaptation

abstract:
  Most existing studies on unsupervised domain adaptation (UDA) assume that
each domain's training samples come with domain labels (e.g., painting, photo).
Samples from each domain are assumed to follow the same distribution and the
domain labels are exploited to learn domain-invariant features via feature
alignment. However, such an assumption often does not hold true -- there often
exist numerous finer-grained domains (e.g., dozens of modern painting styles
have been developed, each differing dramatically from those of the classic
styles). Therefore, forcing feature distribution alignment across each
artificially-defined and coarse-grained domain can be ineffective. In this
paper, we address both single-source and multi-source UDA from a completely
different perspective, which is to view each instance as a fine domain. Feature
alignment across domains is thus redundant. Instead, we propose to perform
dynamic instance domain adaptation (DIDA). Concretely, a dynamic neural network
with adaptive convolutional kernels is developed to generate instance-adaptive
residuals to adapt domain-agnostic deep features to each individual instance.
This enables a shared classifier to be applied to both source and target domain
data without relying on any domain annotation. Further, instead of imposing
intricate feature alignment losses, we adopt a simple semi-supervised learning
paradigm using only a cross-entropy loss for both labeled source and pseudo
labeled target data. Our model, dubbed DIDA-Net, achieves state-of-the-art
performance on several commonly used single-source and multi-source UDA
datasets including Digits, Office-Home, DomainNet, Digit-Five, and PACS.


title:
Self-consistency in $GW\Gamma$ formalism leading to
  quasiparticle-quasiparticle couplings

abstract:
  Within many-body perturbation theory, Hedin's formalism offers a systematic
way to iteratively compute the self-energy $\Sigma$ of any interacting system,
provided one can evaluate the interaction vertex $\Gamma$ exactly. This is
however impossible in general, for it involves the functional derivative of
$\Sigma$ with respect to the Green's function. Here, we analyze the structure
of this derivative, splitting it into four contributions and outlining the type
of quasiparticle interactions that each of them generate. Moreover we show how,
in the implementation of self-consistency, the action of these contributions
can be classified into two: a quantitative renormalization of previously
included interaction terms, and the inclusion of qualitatively novel
interaction terms through successive functional derivatives of $\Gamma$ itself,
neglected until now. Implementing this latter type of self-consistency can
extend the validity of Hedin's equations towards the high interaction limit, as
we show in the example of the Hubbard dimer. Our analysis also provides a
unifying perspective on the perturbation theory landscape, showing how the
T-matrix approach is completely contained in Hedin's formalism.


title:
Orthogonality, density and shift-invariance in the Hardy space approach
  to the RH

abstract:
  A Hardy space approach to the Nyman-Beurling and B\'aez-Duarte criterion for
the Riemann Hypothesis (RH) was introduced in [16]. It states that the RH holds
true if and only if the linear manifold $\mathcal{N}$ generated by a sequence
of functions $(h_k)_{k\geq 2}$ is dense in the Hardy space $H^2$. In this
article we ask the following three questions: $\mathbf{A.}$ How small is the
orthogonal complement of $\mathcal{N}$? $\mathbf{B.}$ Is $\mathcal{N}$ dense in
$H^2$ under a weaker topology? $\mathbf{C.}$ Is the closure of $\mathcal{N}$ a
shift-invariant subspace of $H^2$? We use tools from local Dirichlet spaces,
the de Branges-Rovnyak spaces and the Smirnov class to address these questions.
In particular we prove that $\mathbf{C}$ is equivalent to the RH.


title:
A PDE-based Adaptive Kernel Method for Solving Optimal Filtering
  Problems

abstract:
  In this paper, we introduce an adaptive kernel method for solving the optimal
filtering problem. The computational framework that we adopt is the Bayesian
filter, in which we recursively generate an optimal estimate for the state of a
target stochastic dynamical system based on partial noisy observational data.
The mathematical model that we use to formulate the propagation of the state
dynamics is the Fokker-Planck equation, and we introduce an operator
decomposition method to efficiently solve the Fokker-Planck equation. An
adaptive kernel method is introduced to adaptively construct Gaussian kernels
to approximate the probability distribution of the target state. Bayesian
inference is applied to incorporate the observational data into the state model
simulation. Numerical experiments have been carried out to validate the
performance of our kernel method.


title: A Neural Programming Language for the Reservoir Computer

abstract:
  From logical reasoning to mental simulation, biological and artificial neural
systems possess an incredible capacity for computation. Such neural computers
offer a fundamentally novel computing paradigm by representing data
continuously and processing information in a natively parallel and distributed
manner. To harness this computation, prior work has developed extensive
training techniques to understand existing neural networks. However, the lack
of a concrete and low-level programming language for neural networks precludes
us from taking full advantage of a neural computing framework. Here, we provide
such a programming language using reservoir computing -- a simple recurrent
neural network -- and close the gap between how we conceptualize and implement
neural computers and silicon computers. By decomposing the reservoir's internal
representation and dynamics into a symbolic basis of its inputs, we define a
low-level neural machine code that we use to program the reservoir to solve
complex equations and store chaotic dynamical systems as random access memory
(dRAM). Using this representation, we provide a fully distributed neural
implementation of software virtualization and logical circuits, and even
program a playable game of pong inside of a reservoir computer. Taken together,
we define a concrete, practical, and fully generalizable implementation of
neural computation.


title:
Probing phonon dynamics with multi-dimensional high harmonic carrier
  envelope phase spectroscopy

abstract:
  We explore pump-probe high harmonic generation (HHG) from monolayer
hexagonal-Boron-Nitride, where a terahertz pump excites coherent optical
phonons that are subsequently probed by an intense infrared pulse that drives
HHG. We find, through state-of-the-art ab-initio calculations, that the
structure of the emission spectrum is attenuated by the presence of coherent
phonons, and is no longer comprised of discrete harmonic orders, but rather of
a continuous emission in the plateau region. The HHG yield strongly oscillates
as a function of the pump-probe delay, corresponding to ultrafast changes in
the lattice such as bond compression or stretching. We further show that in the
regime where the excited phonon period and the pulse duration are of the same
order of magnitude, the HHG process becomes sensitive to the
carrier-envelope-phase (CEP) of the driving field, even though the pulse
duration is so long that no such sensitivity is observed in the absence of
coherent phonons. The degree of CEP sensitivity vs. pump-probe delay is shown
to be a highly selective measure for instantaneous structural changes in the
lattice, providing a new approach for ultrafast multi-dimensional
HHG-spectroscopy. Remarkably, the obtained temporal resolution for phonon
dynamics is ~1 femtosecond, which is much shorter than the probe pulse duration
because of the inherent sub-cycle contrast mechanism. Our work paves the way
towards novel routes of probing phonons and ultrafast material structural
changes and provides a mechanism for controlling the high harmonic response.


title:
Multiplicity of solutions to the multiphasic Allen-Cahn-Hilliard system
  with a small volume constraint on closed parallelizable manifolds

abstract:
  We prove the existence of multiple solutions to the Allen--Cahn--Hilliard
(ACH) vectorial equation involving a multi-well (multiphasic) potential with a
small volume constraint on a closed parallelizable Riemannian manifold. More
precisely, we find a lower bound for the number of solutions depending on
topological invariants of the underlying manifold. The phase transition
potential is considered to have a finite set of global minima, where it also
vanishes, and a subcritical growth at infinity. Our strategy is to employ the
Lusternik--Schnirelmann and infinite-dimensional Morse theories for the
vectorial energy functional. To this end, we exploit that the associated ACH
energy $\Gamma$-converges to the vectorial perimeter for clusters, which
combined with some deep theorems from isoperimetric theory yields the suitable
setup to apply the photography method. Along the way, the lack of a closed
analytic expression for the Euclidean multi-isoperimetric function for clusters
imposes a delicate issue. Furthermore, using a transversality theorem, we also
show the genericity of the set of metrics for which solutions to our geometric
system are nondegenerate.


title:
Electrons with Planckian scattering obey standard orbital motion in a
  magnetic field

abstract:
  In various "strange" metals, electrons undergo Planckian dissipation, a
strong and anomalous scattering that grows linearly with temperature, in
contrast to the quadratic temperature dependence expected from the standard
theory of metals. In some cuprates and pnictides, a linear dependence of the
resistivity on magnetic field has also been considered anomalous - possibly an
additional facet of Planckian dissipation. Here we show that the resistivity of
the cuprate strange metals Nd-LSCO and LSCO is quantitatively consistent with
the standard Boltzmann theory of electron motion in a magnetic field, in all
aspects - field strength, field direction, temperature, and disorder level. The
linear field dependence is found to be simply the consequence of scattering
rate anisotropy. We conclude that Planckian dissipation is anomalous in its
temperature dependence but not in its field dependence. The scattering rate in
these cuprates does not depend on field, which means their Planckian
dissipation is robust against fields up to at least 85 T.


title: On the origin of close massive binaries in the M17 star-forming region

abstract:
  Spectroscopic multiplicity surveys of O stars in young clusters and OB
associations have revealed that a large portion ($\sim$ 70%) of these massive
stars (M$_{i}$ $\gt$ 15 $M_{\odot}$) belong to close and short-period binaries
(physical separation d $\lt$few au). Despite the recent and significant
progress, the formation mechanisms leading to such close massive multiple
systems remain to be elucidated. As a result, young massive close binaries (or
higher-order multiple systems) are unique laboratories to figure out the
pairing mechanism of high-mass stars. We present the first VLTI/GRAVITY
observations of six young O stars in the M17 star-forming region ($\lesssim$ 1
Myr) and two additional foreground stars. From the interferometric model
fitting of visibility amplitudes and closure phases, we search for companions
and measure their positions and flux ratios. Combining the resulting magnitude
difference with atmosphere models and evolutionary tracks, we further constrain
the masses of the individual components. All of the six high-mass stars are in
multiple systems, leading to a multiplicity fraction (MF) of 100%, yielding a
68% confidence interval of 94-100%. We detect a total number of 9 companions
with separations up to 120 au. Including previously identified spectroscopic
companions, the companion fraction of the young O stars in our sample reaches
2.3$\pm$0.6. The derived masses span a wide range from 2.5 to 50 $M_{\odot}$,
with a great tendency towards high-mass companions. While based on a modest
sample, our results clearly indicate that the origin of the high degree of
multiplicity is rooted in their star formation mechanism. No clear evidence for
one of the competing concepts of massive star formation (core accretion or
competitive accretion) could be found. However, our results are compatible with
migration as a scenario for the formation of close massive binaries.


title: Human-GDPR Interaction: Practical Experiences of Accessing Personal Data

abstract:
  In our data-centric world, most services rely on collecting and using
personal data. The EU's General Data Protection Regulation (GDPR) aims to
enhance individuals' control over their data, but its practical impact is not
well understood. We present a 10-participant study, where each participant
filed 4-5 data access requests. Through interviews accompanying these requests
and discussions scrutinising returned data, it appears that GDPR falls short of
its goals due to non-compliance and low-quality responses. Participants found
their hopes to understand providers' data practices or harness their own data
unmet. This causes increased distrust without any subjective improvement in
power, although more transparent providers do earn greater trust. We propose
designing more effective, data-inclusive and open policies and data access
systems to improve both customer relations and individual agency, and also that
wider public use of GDPR rights could help with delivering accountability and
motivating providers to improve data practices.


title:
Electronic correlations and universal long-range scaling in kagome
  metals

abstract:
  We investigate the real-space profile of effective Coulomb interactions in
correlated kagome materials. By particularizing to KV$_3$Sb$_5$,
Co$_3$Sn$_2$S$_2$, FeSn, and Ni$_3$In, we analyze representative cases that
exhibit a large span of correlation-mediated phenomena, and contrast them to
prototypical prevoskite transition metal oxides. From our constrained random
phase approximation studies we find that the on-site interaction strength in
kagome metals not only depend on the screening processes at high energy, but
also on the low-energy hybriziation profile of the electronic density of
states. Our results indicate that rescaled by the onsite interaction amplitude,
all kagome metals exhibit a universal long-range Coulomb behaviour.


title:
Comment on "Deformations, relaxation and broken symmetries in liquids,
  solids and glasses: a unified topological field theory"

abstract:
  We discuss a field-theoretical approach to liquids, solids and glasses,
published recently [Phys.Rev.E {\bf105}, 034108 (2022)], which aims to describe
these materials in a common quantum formalism. We argue that such quantum
formalism is not applicable to classical liquids, and the results presented,
which rely heavily on the concept of phase relaxation borrowed from quantum
fluids, contradict the known hydrodynamic theory of classical liquids. In
particular, the authors miss the important particle-number conservation law and
the density fluctuations as hydrodynamic slow variable. Instead, the authors
invoke Goldstone bosons as elementary hydrodynamic excitations. We point out
that in a classical liquid there are no broken continuous symmetries and
consequently no Goldstone bosons. The authors claim that the Goldstone bosons
would be responsible for the existence of sound in liquids, instead of
resulting from combined particle-number and momentum conservation, a fact well
documented in fluid-mechanics textbooks.


title: Two Applications of Nilpotent Higgsing in Class-S

abstract:
  We introduce a class of Higgs-branch RG flows in theories of class-S, which
flow between $d=4$ $\mathcal{N}=2$ SCFTs of the same ADE type. We discuss two
applications of this class of RG flows: 1) determining the current-algebra
levels in SCFTs where they were previously unknown - a program we carry out for
the class-S theories of type $E_6$ and $E_7$ - and 2) constructing a multitude
of examples of pairs of $\mathcal{N}=2$ SCFTs whose "conventional invariants"
coincide. We disprove the conjecture of [1] that the global form of the flavour
symmetry group is a reliable diagnostic for determining when two such theories
are isomorphic.


title:
Toward Development of an Improved Friction Correlation for the Near-Wall
  Region of Pebble Bed Systems

abstract:
  The development of nuclear reactors that utilize pebble fuel has drastically
increased the demand for improving the capabilities to simulate the packed beds
found in these reactors. The complex flow fields found in a pebble bed make
computational fluid dynamics (CFD) simulations time consuming and costly.
Intermediate fidelity porous media models, however, are capable of
approximating these flow fields in a much more computationally efficient
manner. These models require the use of closures to capture the effects of
complex flow phenomena without modeling them explicitly. This research employs
data obtained from high-fidelity CFD simulations of a pebble bed to improve the
drag closures used in porous media models in the near-wall region of the bed.
Specifically, NekRS, a GPU-enabled spectral element CFD code, was used to
simulate a bed of 1,568 pebbles at multiple Reynolds numbers. The case was
divided into five concentric subdomains to extract radial profiles of the
average porosity, velocity, and wall shear in each subdomain. A model
consistent with the high-fidelity model was created in Idaho National
Laboratory's Pronghorn porous media code and the KTA correlation was chosen as
the drag closure of comparison. It was found that the KTA correlation
overestimates the velocity in the near-wall region. An investigation of the
drag coefficients between the two codes revealed that the KTA correlation
underestimated the form factor in the outermost region while overestimating it
in the inner four regions. This analysis in this work has revealed the
underlying inaccuracy in the near-wall region of the KTA correlation and has
set up the process for using high-fidelity simulation to predict more accurate
drag coefficients a priori, rather than with a manual velocity-matching
approach. This process will allow for the development of an improved drag
closure for use in porous media models.


title: Three-loop QCD corrections to the electroweak boson masses

abstract:
  I find the three-loop corrections at leading order in QCD to the physical
masses of the Higgs, W, and Z bosons in the Standard Model. The results are
obtained as functions of the $\overline{\rm{MS}}$ Lagrangian parameters only,
using the tadpole-free scheme for the vacuum expectation value. The dependences
of the computed masses on the renormalization scale are found to be smaller
than present experimental uncertainties in each case. These results are now
included in the SMDR (Standard Model in Dimensional Regularization) computer
code.


title: In-Place Rotation for Enhancing Snake-like Robot Mobility

abstract:
  Gaits engineered for snake-like robots to rotate in-place instrumentally fill
a gap in the set of locomotive gaits that have traditionally prioritized
translation. This paper designs a Turn-in-Place gait and demonstrates the
ability of a shape-centric modeling framework to capture the gait's locomotive
properties. Shape modeling for turning involves a time-varying continuous body
curve described by a standing wave. Presumed viscous robot-ground frictional
interactions lead to body dynamics conditioned on the time-varying shape model.
The dynamic equations describing the Turn-in-Place gait are validated by an
articulated snake-like robot using a physics-based simulator and a physical
robot. The results affirm the shape-centric modeling framework's capacity to
model a variety of snake-like robot gaits with fundamentally different
body-ground contact patterns. As an applied demonstration, example locomotion
scenarios partner the shape-centric Turn-in-Place gait with a Rectilinear gait
for maneuvering through constrained environments based on a multi-modal
locomotive planning strategy. Unified shape-centric modeling facilitates
trajectory planning and tracking for a snake-like robot to successfully
negotiate non-trivial obstacle configurations.


title: Hydrodynamics, spin currents and torsion

abstract:
  We construct the canonical constitutive relations for a fluid description of
a system with a spin current, valid in an arbitrary number of dimensions in the
absence of parity breaking or time reversal breaking terms. Our study
encompasses the hydrostatic partition function, the entropy current, Kubo
formula, conformal invariance, and the effect of charge. At some stages of the
computation we turn on a background torsion tensor which naturally couples to
the spin current.


title:
Do Small Code Changes Merge Faster? A Multi-Language Empirical
  Investigation

abstract:
  Code velocity, or the speed with which code changes are integrated into a
production environment, plays a crucial role in Continuous Integration and
Continuous Deployment. Many studies report factors influencing code velocity.
However, solutions to increase code velocity are unclear. Meanwhile, the
industry continues to issue guidelines on "ideal" code change size, believing
it increases code velocity despite lacking evidence validating the practice.
Surprisingly, this fundamental question has not been studied to date. This
study investigates the practicality of improving code velocity by optimizing
pull request size and composition (ratio of insertions, deletions, and
modifications). We start with a hypothesis that a moderate correlation exists
between pull request size and time-to-merge. We selected 100 most popular,
actively developed projects from 10 programming languages on GitHub. We
analyzed our dataset of 845,316 pull requests by size, composition, and context
to explore its relationship to time-to-merge - a proxy to measure code
velocity. Our study shows that pull request size and composition do not relate
to time-to-merge. Regardless of the contextual factors that can influence pull
request size or composition (e.g., programming language), the observation
holds. Pull request data from two other platforms: Gerrit and Phabricator
(401,790 code reviews) confirms the lack of relationship. This negative result
as in "... eliminate useless hypotheses ..." challenges a widespread belief by
showing that small code changes do not merge faster to increase code velocity.


title: Adaptive Trajectory Prediction via Transferable GNN

abstract:
  Pedestrian trajectory prediction is an essential component in a wide range of
AI applications such as autonomous driving and robotics. Existing methods
usually assume the training and testing motions follow the same pattern while
ignoring the potential distribution differences (e.g., shopping mall and
street). This issue results in inevitable performance decrease. To address this
issue, we propose a novel Transferable Graph Neural Network (T-GNN) framework,
which jointly conducts trajectory prediction as well as domain alignment in a
unified framework. Specifically, a domain-invariant GNN is proposed to explore
the structural motion knowledge where the domain-specific knowledge is reduced.
Moreover, an attention-based adaptive knowledge learning module is further
proposed to explore fine-grained individual-level feature representations for
knowledge transfer. By this way, disparities across different trajectory
domains will be better alleviated. More challenging while practical trajectory
prediction experiments are designed, and the experimental results verify the
superior performance of our proposed model. To the best of our knowledge, our
work is the pioneer which fills the gap in benchmarks and techniques for
practical pedestrian trajectory prediction across different domains.


title: Scalar warm inflation in holographic cosmology

abstract:
  We consider warm inflation in the context of holographic cosmology. The weak
and the strong dissipative regime are analysed in the slow-roll approximation
and within what is known as intermediate inflation. For an appropriate choice
of the equation of state, the intermediate inflation is not only an exact
solution in general relativity, but also in the holographic setup considered in
this paper. Within this approach several dissipative and physically relevant
functions are considered. We constrain our model using the latest Planck data.
We conclude that three of the models analysed are consistent with Planck data
for some ranges of the model parameters. However, one of them is ruled out by
the observations.


title:
Mining Code Review Data to Understand Waiting Times Between Acceptance
  and Merging: An Empirical Analysis

abstract:
  Increasing code velocity (or the speed with which code changes are reviewed
and merged) is integral to speeding up development and contributes to the work
satisfaction of engineers. While factors affecting code change acceptance have
been investigated in the past, solutions to decrease the code review lifetime
are less understood. This study investigates the code review process to
quantify delays and investigate opportunities to potentially increase code
velocity. We study the temporal characteristics of half a million code reviews
hosted on Gerrit and Phabricator, starting from the first response, to a
decision to accept or reject the changes, and until the changes are merged into
a target branch. We identified two types of time delays: (a) the wait time from
the proposal of code changes until first response, and (b) the wait time
between acceptance and merging. Our study indicates that reducing the time
between acceptance and merging has the potential to speed up Phabricator code
reviews by 29-63%. Small code changes and changes made by authors with a large
number of previously accepted code reviews have a higher chance of being
immediately accepted, without code review iterations. Our analysis suggests
that switching from manual to automatic merges can help increase code velocity.


title: Benchmark Planes for Higgs-to-Higgs Decays in the NMSSM

abstract:
  We present benchmark planes (or lines) with cross sections via gluon fusion
for the processes $H\to h+H_{S}$, resonant Higgs pair and triple Higgs
production, $A \to h+(A_S \to \gamma\gamma)$, $A \to Z+ h$ and $A \to Z+ H_{S}$
within the Next-to-Minimal Supersymmetric Standard Model. Moreover we propose
new searches for $H\to h+(H_{S} \to t\bar{t})$, $A \to Z+ (H_{S}\to h+h)$ and
$A \to Z+ (H_{S}\to t\bar{t})$ for which possible cross sections are given.
These allow the experimental collaborations to verify in the future which
search channels cover yet unexplored regions of the parameter space.
Expressions for the dominant contributions to trilinear Higgs couplings and
Higgs--Z couplings are discussed which allow to identify the dominant processes
contributing to a given final state.


title:
On the polytropic Bondi accretion in two-component galaxy models with a
  central massive BH

abstract:
  In many investigations involving accretion on a central point mass, ranging
from observational studies to cosmological simulations, including
semi-analytical modelling, the classical Bondi accretion theory is the standard
tool widely adopted. Previous works generalised the theory to include the
effects of the gravitational field of the galaxy hosting a central black hole,
and of electron scattering in the optically thin limit. Here we apply this
extended Bondi problem, in the general polytropic case, to a class of new
two-component galaxy models recently presented. In these models, a Jaffe
stellar density profile is embedded in a dark matter halo such that the total
density distribution follows a $r^{-3}$ profile at large radii; the stellar
dynamical quantities can be expressed in a fully analytical way. The
hydrodynamical properties of the flow are set by imposing that the gas
temperature at infinity is proportional to the virial temperature of the
stellar component. The isothermal and adiabatic (monoatomic) cases can be
solved analytically, in the other cases we explore the accretion solution
numerically. As non-adiabatic accretion inevitably leads to an exchange of heat
with the ambient, we also discuss some important thermodynamical properties of
the polytropic Bondi accretion, and provide the expressions needed to compute
the amount of heat exchanged with the environment, as a function of radius. The
results can be useful for the subgrid treatment of accretion in numerical
simulations, as well as for the interpretation of observational data.


title: Evaluating Proposed Fairness Models for Face Recognition Algorithms

abstract:
  The development of face recognition algorithms by academic and commercial
organizations is growing rapidly due to the onset of deep learning and the
widespread availability of training data. Though tests of face recognition
algorithm performance indicate yearly performance gains, error rates for many
of these systems differ based on the demographic composition of the test set.
These "demographic differentials" in algorithm performance can contribute to
unequal or unfair outcomes for certain groups of people, raising concerns with
increased worldwide adoption of face recognition systems. Consequently,
regulatory bodies in both the United States and Europe have proposed new rules
requiring audits of biometric systems for "discriminatory impacts" (European
Union Artificial Intelligence Act) and "fairness" (U.S. Federal Trade
Commission). However, no standard for measuring fairness in biometric systems
yet exists. This paper characterizes two proposed measures of face recognition
algorithm fairness (fairness measures) from scientists in the U.S. and Europe.
We find that both proposed methods are challenging to interpret when applied to
disaggregated face recognition error rates as they are commonly experienced in
practice. To address this, we propose a set of interpretability criteria,
termed the Functional Fairness Measure Criteria (FFMC), that outlines a set of
properties desirable in a face recognition algorithm fairness measure. We
further develop a new fairness measure, the Gini Aggregation Rate for Biometric
Equitability (GARBE), and show how, in conjunction with the Pareto
optimization, this measure can be used to select among alternative algorithms
based on the accuracy/fairness trade-space. Finally, we have open-sourced our
dataset of machine-readable, demographically disaggregated error rates. We
believe this is currently the largest open-source dataset of its kind.


title:
An 8 GeV Linac as the Booster Replacement in the Fermilab Power Upgrade:
  a Snowmass 2021 White Paper

abstract:
  Following the PIP-II 800 MeV Linac, Fermilab will need an accelerator that
extends from that linac to the MI injection energy of ~8 GeV, completing the
modernization of the Fermilab high-intensity accelerator complex. This will
maximize the beam available for neutrino production for the long baseline DUNE
experiment to greater than 2.5 MW and enable a next generation of intensity
frontier experiments. In this white paper, we propose an 8 GeV Linac for that
purpose. The Linac consists of an extension of the PIP-II Linac to 2.4 GeV
using PIP-II 650 MHz SRF cryomodules, followed by a 2.4-->8.0 GeV Linac
composed of 1300 MHz SRF cryomodules, based upon the LCLS-II cryomodules
developed at Fermilab. The 8 GeV Linac will incorporate recent improvements in
SRF technology. The research needed to implement this Linac is described.


title: Optical Flow Training under Limited Label Budget via Active Learning

abstract:
  Supervised training of optical flow predictors generally yields better
accuracy than unsupervised training. However, the improved performance comes at
an often high annotation cost. Semi-supervised training trades off accuracy
against annotation cost. We use a simple yet effective semi-supervised training
method to show that even a small fraction of labels can improve flow accuracy
by a significant margin over unsupervised training. In addition, we propose
active learning methods based on simple heuristics to further reduce the number
of labels required to achieve the same target accuracy. Our experiments on both
synthetic and real optical flow datasets show that our semi-supervised networks
generally need around 50% of the labels to achieve close to full-label
accuracy, and only around 20% with active learning on Sintel. We also analyze
and show insights on the factors that may influence our active learning
performance. Code will be made available soon.


title:
Origin of dielectric loss at the Nb/oxide interface: Evidence for atomic
  two-level systems

abstract:
  While several experiments claim that two-level system (TLS) defects in
amorphous oxides are responsible for energy relaxation in superconducting
resonators and qubits, only a few are able to provide quantitative explanation
of their data in terms of the conventional noninteracting TLS model. Here
several models of dielectric loss are considered to explain available
experimental data of photon loss in TESLA cavities. These cavities contain a
single amorphous niobium/niobium oxide interface in their interior, making them
convenient testbeds for analysis of TLS parameters. It is shown that the
noninteracting model does indeed yield the best fit to experimental data,
provided that a one-species model with sharp modulus of electric dipole moment
$p$ is assumed for electropolished cavities with a thin ($5$ nm) amorphous
oxide. In contrast, a two-species model with two different $p$'s is required to
explain data in anodized cavities with a thick $100$ nm oxide layer. The small
variation in TLS microscopic structure is indicative of "atomic TLSs" such as
Nb:O,H, realized by an atom (H) trapped in one of two locations near an O
interstitial.


title:
Two types of charge order in the superconducting kagome material
  CsV$_3$Sb$_5$

abstract:
  The kagome metals of the family $A$V$_3$Sb$_5$, featuring a unique structural
motif, harbor an array of intriguing phenomena such as chiral charge order and
superconductivity. CsV$_3$Sb$_5$ is of particular interest because it displays
a double superconducting dome in the region of the temperature-pressure phase
diagram where charge order is still present. However, the microscopic origin of
such an unusual behavior remains an unsolved issue. Here, to address it, we
combine high-pressure, low-temperature muon spin relaxation with
first-principles calculations. We observe a pressure-induced threefold
enhancement of the superfluid density, which also displays a double peak
feature, similar to the superconducting critical temperature. This leads to
three distinct regions in the phase diagram, each of which features distinct
slopes of the linear relation between superfluid density and the critical
temperature. These results are attributed to a possible evolution of the charge
order pattern from the superimposed tri-hexagonal Star-of-David phase at low
pressures (within the first dome) to the staggered tri-hexagonal phase at
intermediate pressures (between the first and second domes). Our findings
suggest a change in the nature of the charge ordered state across the phase
diagram of CsV$_3$Sb$_5$, with varying degrees of competition with
superconductivity.


title:
SynWoodScape: Synthetic Surround-view Fisheye Camera Dataset for
  Autonomous Driving

abstract:
  Surround-view cameras are a primary sensor for automated driving, used for
near field perception. It is one of the most commonly used sensors in
commercial vehicles. Four fisheye cameras with a 190{\deg} field of view cover
the 360{\deg} around the vehicle. Due to its high radial distortion, the
standard algorithms do not extend easily. Previously, we released the first
public fisheye surround-view dataset named WoodScape. In this work, we release
a synthetic version of the surround-view dataset, covering many of its
weaknesses and extending it. Firstly, it is not possible to obtain ground truth
for pixel-wise optical flow and depth. Secondly, WoodScape did not have all
four cameras simultaneously in order to sample diverse frames. However, this
means that multi-camera algorithms cannot be designed, which is enabled in the
new dataset. We implemented surround-view fisheye geometric projections in
CARLA Simulator matching WoodScape's configuration and created SynWoodScape. We
release 80k images from the synthetic dataset with annotations for 10+ tasks.
We also release the baseline code and supporting scripts.


title: On Linking Level Segments

abstract:
  An increasingly common area of study in procedural content generation is the
creation of level segments: short pieces that can be used to form larger
levels. Previous work has used basic concatenation to form these larger levels.
However, even if the segments themselves are completable and well-formed,
concatenation can fail to produce levels that are completable and can cause
broken in-game structures (e.g. malformed pipes in Mario). We show this with
three tile-based games: a side-scrolling platformer, a vertical platformer, and
a top-down roguelike. Additionally, we present a Markov chain and a tree search
algorithm that finds a link between two level segments, which uses filters to
ensure completability and unbroken in-game structures in the linked segments.
We further show that these links work well for multi-segment levels. We find
that this method reliably finds links between segments and is customizable to
meet a designer's needs.


title: Quantum Error Correction in SYK and Bulk Emergence

abstract:
  We analyze the error correcting properties of the Sachdev-Ye-Kitaev model,
with errors that correspond to erasures of subsets of fermions. We study the
limit where the number of fermions erased is large but small compared to the
total number of fermions. We compute the price of the quantum error correcting
code, defined as the number of physical qubits needed to reconstruct whether a
given operator has been acted upon the thermal state or not. By thinking about
reconstruction via quantum teleportation, we argue for a bound that relates the
price to the ordinary operator size in systems that display so-called detailed
size winding of Nezami et al. (2021). We then find that in SYK the price
roughly saturates this bound. Computing the price requires computing modular
flowed correlators with respect to the density matrix associated to a subset of
fermions. We offer an interpretation of these correlators as probing a quantum
extremal surface in the AdS dual of SYK. In the large $N$ limit, the operator
algebras associated to subsets of fermions in SYK satisfy half-sided modular
inclusion, which is indicative of an emergent Type III$_1$ von Neumann algebra.
We discuss the relationship between the emergent algebra of half-sided modular
inclusions and bulk symmetry generators.


title:
Route towards classical frustration and band flattening via optical
  lattice distortion

abstract:
  We propose and experimentally explore a method for realizing frustrated
lattice models using a Bose-Einstein condensate held in an optical square
lattice. A small lattice distortion opens up an energy gap such the lowest band
splits into two. Along the edge of the first Brillouin zone for both bands a
nearly flat energy-momentum dispersion is realized. For the excited band a
highly degenerate energy minimum arises. By loading ultracold atoms into the
excited band, a classically frustrated $XY$ model is formed, describing rotors
on a square lattice with competing nearest and next nearest tunnelling
couplings. Our experimental optical lattice provides a regime, where a fully
coherent Bose-Einstein condensate is observed, and a regime where frustration
is expected. If we adiabatically tune from the condensate regime to the regime
of frustration, the momentum spectra shows a complete loss of coherence. Upon
slowly tuning back to the condensate regime, coherence is largely restored.
Good agreement with model calculations is obtained.


title:
Resize Me! Exploring the User Experience of Embodied Realistic
  Modulatable Avatars for Body Image Intervention in Virtual Reality

abstract:
  Obesity is a serious disease that can affect both physical and psychological
well-being. Due to weight stigmatization, many affected individuals suffer from
body image disturbances whereby they perceive their body in a distorted way,
evaluate it negatively, or neglect it. Beyond established interventions such as
mirror exposure, recent advancements aim to complement body image treatments by
the embodiment of visually altered virtual bodies in virtual reality (VR). We
present a high-fidelity prototype of an advanced VR system that allows users to
embody a rapidly generated personalized, photorealistic avatar and to
realistically modulate its body weight in real-time within a carefully designed
virtual environment. In a formative multi-method approach, a total of 12
participants rated the general user experience (UX) of our system during body
scan and VR experience using semi-structured qualitative interviews and
multiple quantitative UX measures. By using body weight modification tasks, we
further compared three different interaction methods for real-time body weight
modification and measured our system's impact on the body image relevant
measures body awareness and body weight perception. From the feedback received,
demonstrating an already solid UX of our overall system and providing
constructive input for further improvement, we derived a set of design
guidelines to guide future development and evaluation processes of systems
supporting body image interventions.


title:
HealthPrompt: A Zero-shot Learning Paradigm for Clinical Natural
  Language Processing

abstract:
  Deep learning algorithms are dependent on the availability of large-scale
annotated clinical text datasets. The lack of such publicly available datasets
is the biggest bottleneck for the development of clinical Natural Language
Processing(NLP) systems. Zero-Shot Learning(ZSL) refers to the use of deep
learning models to classify instances from new classes of which no training
data have been seen before. Prompt-based learning is an emerging ZSL technique
where we define task-based templates for NLP tasks. We developed a novel
prompt-based clinical NLP framework called HealthPrompt and applied the
paradigm of prompt-based learning on clinical texts. In this technique, rather
than fine-tuning a Pre-trained Language Model(PLM), the task definitions are
tuned by defining a prompt template. We performed an in-depth analysis of
HealthPrompt on six different PLMs in a no-data setting. Our experiments prove
that prompts effectively capture the context of clinical texts and perform
remarkably well without any training data.


title: Pre-Calabi--Yau algebras and homotopy double Poisson gebras

abstract:
  We prove that the notion of a curved pre-Calabi--Yau algebra is equivalent to
the notion of a curved homotopy double Poisson gebra, thereby settling the
equivalence between the two ways to define derived noncommutative Poisson
structures. We actually prove that the respective differential graded Lie
algebras controlling both deformation theories are isomorphic.This allows us to
apply the recent developments of the properadic calculus in order to establish
the homotopical properties of curved pre-Calabi--Yau algebras:
infinity-morphisms, homotopy transfer theorem, formality, Koszul hierarchy, and
twisting procedure.

categories: math.QA math-ph math.CO math.CT math.MP math.RA


title:
Path integral framework for characterizing and controlling decoherence
  induced by non-stationary environments on a quantum probe

abstract:
  Reliable processing of quantum information is a milestone to achieve for the
deployment of quantum technologies. Uncontrolled, out-of-equilibrium sources of
decoherence need to be characterized in detail for designing the control of
quantum devices to mitigate the loss of quantum information. However, quantum
sensing of such environments is still a challenge due to their non-stationary
nature that in general can generate complex high-order correlations. We here
introduce a path integral framework to characterize non-stationary
environmental fluctuations by a quantum probe. We found the solution for the
decoherence decay of non-stationary, generalized Gaussian processes that induce
pure dephasing. This dephasing when expressed in a suitable basis, based on the
non-stationary noise eigenmodes, is defined by the overlap of a generalized
noise spectral density and a filter function that depends on the control
fields. This result thus extends the validity to out-of-equilibrium
environments, of the similar general expression for the dephasing of open
quantum systems coupled to stationary noises. We show physical insights for a
broad subclass of non-stationary noises that are local-in-time, in the sense
that the noise correlation functions contain memory based on constraints of the
derivatives of the fluctuating noise paths. Spectral and non-Markovian
properties are discussed together with implementations of the framework to
treat paradigmatic environments that are out-of-equilibrium, e.g. due to a
quench and a pulsed noise. We show that our results provide tools for probing
the spectral and time-correlation properties, and for mitigating decoherence
effects of out-of-equilibrium -- non-stationary -- environments.


title:
Bismuth vanadate layers alternated with nanoparticle-doped silicon
  dioxide layers for one-dimensional multilayer photonic crystals

abstract:
  Bismuth vanadate is one of most studied materials in the field of
photocatalysis due to its high photocatalytic activity. The modulation of its
optical properties can be engineered by fabricating photonic crystals in which
bismuth vanadate is included. In the present study, bismuth vanadate in the
monoclinic clinobisvanite structure has been included in a one-dimensional
photonic crystal. Bismuth vanadate layers are alternated with layers on
silicone dioxide doped with titanium dioxide nanoparticles and silver
nanoparticles. The optical properties of the crystal have been studied by
employing the transfer matrix method, taking into account all the refractive
index dispersions of the selected materials. The transmission spectra have been
studied as a function of the bismuth vanadate layers thickness and the filling
factor of titanium dioxide and silver dioxide in the silicon dioxide layers.


title:
A Robust Functional Partial Least Squares for
  Scalar-on-Multiple-Function Regression

abstract:
  The scalar-on-function regression model has become a popular analysis tool to
explore the relationship between a scalar response and multiple functional
predictors. Most of the existing approaches to estimate this model are based on
the least-squares estimator, which can be seriously affected by outliers in
empirical datasets. When outliers are present in the data, it is known that the
least-squares-based estimates may not be reliable. This paper proposes a robust
functional partial least squares method, allowing a robust estimate of the
regression coefficients in a scalar-on-multiple-function regression model. In
our method, the functional partial least squares components are computed via
the partial robust M-regression. The predictive performance of the proposed
method is evaluated using several Monte Carlo experiments and two chemometric
datasets: glucose concentration spectrometric data and sugar process data. The
results produced by the proposed method are compared favorably with some of the
classical functional or multivariate partial least squares and functional
principal component analysis methods.


title:
Combining dependent p-values resulting from multiple effect size
  homogeneity tests in meta-analysis for binary outcomes

abstract:
  Testing effect size homogeneity is an essential part when conducting a
meta-analysis. Comparative studies of effect size homogeneity tests in case of
binary outcomes are found in the literature, but no test has come out as an
absolute winner. A alternative approach would be to carry out multiple effect
size homogeneity tests on the same meta-analysis and combine the resulting
dependent p-values. In this article we applied the correlated Lancaster method
for dependent statistical tests. To investigate the proposed approach's
performance, we applied eight different effect size homogeneity tests on a case
study and on simulated datasets, and combined the resulting p-values. The
proposed method has similar performance to that of tests based on the score
function in the presence of a effect size when the number of studies is small,
but outperforms these tests as the number of studies increases. However, the
method's performance is sensitive to the correlation coefficient value assumed
between dependent tests, and only performs well when this value is high. More
research is needed to investigate the method's assumptions on correlation in
case of effect size homogeneity tests, and to study the method's performance in
meta-analysis of continuous outcomes.


title: Universal Regression with Adversarial Responses

abstract:
  We provide algorithms for regression with adversarial responses under large
classes of non-i.i.d. instance sequences, on general separable metric spaces,
with provably minimal assumptions. We also give characterizations of
learnability in this regression context. We consider universal consistency
which asks for strong consistency of a learner without restrictions on the
value responses. Our analysis shows that such objective is achievable for a
significantly larger class of instance sequences than stationary processes, and
unveils a fundamental dichotomy between value spaces: whether finite-horizon
mean-estimation is achievable or not. We further provide optimistically
universal learning rules, i.e., such that if they fail to achieve universal
consistency, any other algorithm will fail as well. For unbounded losses, we
propose a mild integrability condition under which there exist algorithms for
adversarial regression under large classes of non-i.i.d. instance sequences. In
addition, our analysis also provides a learning rule for mean-estimation in
general metric spaces that is consistent under adversarial responses without
any moment conditions on the sequence, a result of independent interest.


title: An Empirical Eye-Tracking Study of Feature Model Comprehension

abstract:
  Software Product Lines (SPLs) are families of related software systems which
are distinguished by the set of features each system provides. Feature Models
are the de facto standard for modelling the variability of SPLs because they
describe the features, their relations, and all the combinations of features
that constitute a SPL. Because of their key role, feature models are at the
core of many tasks in SPL engineering. Our work presents an empirical study on
the comprehension of feature models for the task of checking the validity of
configurations. Our study explored the relation between the number of features
and the number of cross-tree constraints with the accuracy of participants'
answers to validity checking questions, and used eye fixations for analyzing
the difficulty in interpreting fixated information and the amount of cognitive
processing of the different parts of the feature model stimuli. We found that
answer accuracy does not relate individually to the number of features or to
the number of cross-tree constrains of a feature model, but both factors do
show an interaction on accuracy. Additionally, our study identified differences
in feature models with cross-tree constraints in both number of fixations and
fixation time, but no differences in those models without cross-tree
constraints.


title:
Neutrino-Flux Variability, Nuclear-Decay Variability, and Their Apparent
  Relationship

abstract:
  Homestake, Gallex and GNO data reveal variability of the solar neutrino flux.
Kamiokande records for 1996-2001 reveal oscillations at 9.43 and 12.6
yr$^{-1}$, well within a range (6-16 yr$^{-1}$) that, according to
helioseismology, may be related to internal solar rotation. A nuclear-decay
experiment at Brookhaven National Laboratory (for 1982-86) reveals strong
oscillations at 11.2 and 13.2 yr$^{-1}$. Similar oscillations are found in
nuclear-decay measurements conducted by A. Parkhomov. By contrast, S. Pomme
points out that nuclear-decay experiments at standards laboratories tend not to
exhibit variability. The most extensive series of nuclear-decay measurements
comes from an experiment initiated by G. Steinitz at the Geological Survey of
Israel (2007-16), which recorded 340,000 radon-related measurements from each
of 3 gamma detectors and 3 environmental sensors. Analysis of a subset of
85,000 hourly gamma measurements reveals a number of oscillation frequencies
compatible with influences of internal solar rotation. There is no correlation
between the gamma and environmental measurements. The solar internal magnetic
field may lead to neutrino modulation by the RSFP (Resonant Spin-Flavor
Precession) mechanism. A triplet of oscillations (7.43, 8.43 and 9.43
yr$^{-1}$) may be attributed to an internal region (presumably the core) with a
sidereal rotation rate of 8.43 yr$^{-1}$ and a rotation axis roughly orthogonal
to that of the photosphere. This suggests that the Sun had its origin in more
than one stage of condensation of interplanetary material (one on top of
another), which could lead to present-day layers with different metallicities,
rotation rates and axes. The peak modulation occurs near local midnight in
early June, suggestive of a role of cosmic neutrinos. These neutrinos could
provide the mass attributed to dark matter for a neutrino mass of order 0.1 eV.


title:
Evaluation of the three-flavor quark-disconnected contribution to the
  muon anomalous magnetic moment from experimental data

abstract:
  We point out that the sum of the strange-quark-connected and full,
three-flavor quark-disconnected contributions to the leading-order hadronic
vacuum polarization contribution, $a_\mu^{\mathrm{LO,HVP}}$, to the anomalous
magnetic moment of the muon, is a physical observable, and provide a data-based
determination of this quantity in the isospin limit. The result,
$40.1(1.5)\times 10^{-10}$, or $38.7(2.0)\times 10^{-10}$, depending on which
data compilation is used, serves as a target of comparison for lattice
calculations of the same isospin-limit combination. Subtracting from this
result the average of lattice determinations of the strange-quark-connected
contribution, one obtains also an alternate determination of the isospin-limit
three-flavor disconnected contribution to $a_\mu^{\mathrm{LO,HVP}}$. The result
of this determination, $-13.3(1.5)\times 10^{-10}$, or $-14.6(2.0)\times
10^{-10}$, depending on which data compilation is used, agrees well and is
competitive with the most precise current lattice determination.


title:
On the influence of over-parameterization in manifold based surrogates
  and deep neural operators

abstract:
  Constructing accurate and generalizable approximators for complex
physico-chemical processes exhibiting highly non-smooth dynamics is
challenging. In this work, we propose new developments and perform comparisons
for two promising approaches: manifold-based polynomial chaos expansion (m-PCE)
and the deep neural operator (DeepONet), and we examine the effect of
over-parameterization on generalization. We demonstrate the performance of
these methods in terms of generalization accuracy by solving the 2D
time-dependent Brusselator reaction-diffusion system with uncertainty sources,
modeling an autocatalytic chemical reaction between two species. We first
propose an extension of the m-PCE by constructing a mapping between latent
spaces formed by two separate embeddings of input functions and output QoIs. To
enhance the accuracy of the DeepONet, we introduce weight self-adaptivity in
the loss function. We demonstrate that the performance of m-PCE and DeepONet is
comparable for cases of relatively smooth input-output mappings. However, when
highly non-smooth dynamics is considered, DeepONet shows higher accuracy. We
also find that for m-PCE, modest over-parameterization leads to better
generalization, both within and outside of distribution, whereas aggressive
over-parameterization leads to over-fitting. In contrast, an even highly
over-parameterized DeepONet leads to better generalization for both smooth and
non-smooth dynamics. Furthermore, we compare the performance of the above
models with another operator learning model, the Fourier Neural Operator, and
show that its over-parameterization also leads to better generalization. Our
studies show that m-PCE can provide very good accuracy at very low training
cost, whereas a highly over-parameterized DeepONet can provide better accuracy
and robustness to noise but at higher training cost. In both methods, the
inference cost is negligible.


title: Exoshuffle: Large-Scale Shuffle at the Application Level

abstract:
  Shuffle is a key primitive in large-scale data processing applications. The
difficulty of large-scale shuffle has inspired a myriad of implementations.
While these have greatly improved shuffle performance and reliability over
time, it comes at a cost: flexibility. First, each shuffle system is
essentially built from scratch, which is a significant developer effort.
Second, because each shuffle system is monolithic, they are not flexible to
supporting other applications, such as online aggregation of shuffle results.
  We show that shuffle can be implemented with high performance and reliability
on a general-purpose abstraction for distributed computing: distributed
futures. While previous systems have implemented shuffle on top of distributed
futures before, we are the first to identify and build the common components
necessary to support a large-scale shuffle. We show that it is possible to: (1)
express optimizations from previous shuffle systems in a few hundred lines of
purely application-level Python code, and (2) achieve interoperability with
other data processing applications without modifying the shuffle system. Thus,
we present Exoshuffle, an application-level shuffle system that outperforms
Spark and achieves 82% of theoretical performance on a 100TB sort on 100 nodes.


title: Sub-Riemannian Geodesics on $SL(2, \mathbb{R})$

abstract:
  We explicitly describe the length minimizing geodesics for a sub-Riemannian
structure of the elliptic type defined on $SL(2, \mathbb{R})$. Our method uses
a symmetry reduction which translates the problem into a Riemannian problem on
a two dimensional quotient space, on which projections of geodesics can be
easily visualized. As a byproduct, we obtain an alternative derivation of the
characterization of the cut-locus obtained in \cite{BoscaRossi}. We use
classification results for three dimensional right invariant sub-Riemannian
structures on Lie groups \cite{AGBD}, \cite{Biggs}, \cite{HB2} to identify
exactly automorphic structures on which our results apply.


title:
The Transitive Information Theory and its Application to Deep Generative
  Models

abstract:
  Paradoxically, a Variational Autoencoder (VAE) could be pushed in two
opposite directions, utilizing powerful decoder model for generating realistic
images but collapsing the learned representation, or increasing regularization
coefficient for disentangling representation but ultimately generating blurry
examples. Existing methods narrow the issues to the rate-distortion trade-off
between compression and reconstruction. We argue that a good reconstruction
model does learn high capacity latents that encode more details, however, its
use is hindered by two major issues: the prior is random noise which is
completely detached from the posterior and allow no controllability in the
generation; mean-field variational inference doesn't enforce hierarchy
structure which makes the task of recombining those units into plausible novel
output infeasible. As a result, we develop a system that learns a hierarchy of
disentangled representation together with a mechanism for recombining the
learned representation for generalization. This is achieved by introducing a
minimal amount of inductive bias to learn controllable prior for the VAE. The
idea is supported by here developed transitive information theory, that is, the
mutual information between two target variables could alternately be maximized
through the mutual information to the third variable, thus bypassing the
rate-distortion bottleneck in VAE design. In particular, we show that our
model, named SemafoVAE (inspired by the similar concept in computer science),
could generate high-quality examples in a controllable manner, perform smooth
traversals of the disentangled factors and intervention at a different level of
representation hierarchy.


title: Radar-based Respiratory Rate Monitoring in Standing Position

abstract:
  Estimating human vital signs in a contactless non-invasive method using radar
provides a convenient method in the medical field to conduct several health
checkups easily and quickly. In addition to monitoring while sitting and
sleeping, the standing position has aroused interest for both the industrial
and medical fields. However, it is more challenging due to the micro motions
induced by the body for balancing that may cause false respiratory rate
estimation. In this work, we focus on the measurement of the respiratory rate
of a standing person accurately with the capability of heavy breath detection
and estimation. Multiple estimation approaches are presented and compared,
including spectral estimation, deep-learning-based approaches, and adaptive
peak selection with Kalman filtering. The latest technique is showing the best
performance with an absolute error rate of 1.5 bpm, when compared to a Vernier
Go Direct\textsuperscript{\textregistered} respiration belt.


title:
Connecting sufficient conditions for domain adaptation: source-guided
  uncertainty, relaxed divergences and discrepancy localization

abstract:
  Recent advances in domain adaptation establish that requiring a low risk on
the source domain and equal feature marginals degrade the adaptation's
performance. At the same time, empirical evidence shows that incorporating an
unsupervised target domain term that pushes decision boundaries away from the
high-density regions, along with relaxed alignment, improves adaptation. In
this paper, we theoretically justify such observations via a new bound on the
target risk, and we connect two notions of relaxation for divergence, namely
$\beta-$relaxed divergences and localization. This connection allows us to
incorporate the source domain's categorical structure into the relaxation of
the considered divergence, provably resulting in a better handling of the label
shift case in particular.


title:
Polarization and orbital magnetization in Chern insulators: A
  microscopic perspective

abstract:
  We derive macroscopic expressions for the polarization and orbital
magnetization of a Chern insulator in its zero temperature ground state using a
previously developed formalism for treating microscopic polarization and
magnetization fields in extended media. In the limit of a topologically trivial
insulator, our results reduce to those of the "modern theories of polarization
and magnetization." In a Chern insulator, however, we find a generically
non-vanishing microscopic free current density, the macroscopic average of
which vanishes. Moreover, the expression that we obtain for the polarization is
qualitatively similar to that of the "modern theory," while the expressions for
the orbital magnetization fundamentally differ; the manner in which they differ
elucidates the distinct philosophies of these theoretical frameworks.


title: Snowmass Whitepaper: Physical Mathematics 2021

abstract:
  This is a Snowmass whitepaper on physical mathematics. It briefly summarizes
and highlights some of the key questions drawn from a much more extensive
essay, by the same authors, entitled, "A Panorama Of Physical Mathematics
2021."


title:
SAGE: Generating Symbolic Goals for Myopic Models in Deep Reinforcement
  Learning

abstract:
  Model-based reinforcement learning algorithms are typically more sample
efficient than their model-free counterparts, especially in sparse reward
problems. Unfortunately, many interesting domains are too complex to specify
the complete models required by traditional model-based approaches. Learning a
model takes a large number of environment samples, and may not capture critical
information if the environment is hard to explore. If we could specify an
incomplete model and allow the agent to learn how best to use it, we could take
advantage of our partial understanding of many domains. Existing hybrid
planning and learning systems which address this problem often impose highly
restrictive assumptions on the sorts of models which can be used, limiting
their applicability to a wide range of domains. In this work we propose SAGE,
an algorithm combining learning and planning to exploit a previously unusable
class of incomplete models. This combines the strengths of symbolic planning
and neural learning approaches in a novel way that outperforms competing
methods on variations of taxi world and Minecraft.


title:
Natural Gas Short-Term Operation Problem with Dynamics: A Rank
  Minimization Approach

abstract:
  Natural gas-fired generation units can hedge against the volatility in the
uncertain renewable generation, which may occur during very short periods. It
is crucial to utilize models capable of correctly capturing the natural gas
network dynamics induced by the volatile demand of gas-fired units. The
Weymouth equation is commonly implemented in literature to avoid dealing with
the mathematical complications of solving the original governing differential
equations of the natural gas dynamics. However, it is shown in this paper that
this approach is not reliable in the short-term operation problem. Here, the
merit of the non-convex transient model is compared with the simplified
Weymouth equation, and the drawbacks of employing the Weymouth equation are
illustrated. The results demonstrate how changes in the natural gas demand are
met by adjustment in the pressure within pipelines rather than the output of
natural gas suppliers. This work presents a convex relaxation scheme for the
original non-linear and non-convex natural gas flow equations with dynamics,
utilizing a rank minimization approach to ensure the tightness. The proposed
method renders a computationally efficient framework that can accurately solve
the non-convex non-linear gas operation problem and accurately capture its
dynamics. Also, the results suggest that the proposed model improves the
solution optimality and solution time compared to the original non-linear
non-convex model. Finally, the scalability of the proposed approach is verified
in the case study.


title:
NLX-GPT: A Model for Natural Language Explanations in Vision and
  Vision-Language Tasks

abstract:
  Natural language explanation (NLE) models aim at explaining the
decision-making process of a black box system via generating natural language
sentences which are human-friendly, high-level and fine-grained. Current NLE
models explain the decision-making process of a vision or vision-language model
(a.k.a., task model), e.g., a VQA model, via a language model (a.k.a.,
explanation model), e.g., GPT. Other than the additional memory resources and
inference time required by the task model, the task and explanation models are
completely independent, which disassociates the explanation from the reasoning
process made to predict the answer. We introduce NLX-GPT, a general, compact
and faithful language model that can simultaneously predict an answer and
explain it. We first conduct pre-training on large scale data of image-caption
pairs for general understanding of images, and then formulate the answer as a
text prediction task along with the explanation. Without region proposals nor a
task model, our resulting overall framework attains better evaluation scores,
contains much less parameters and is 15$\times$ faster than the current SoA
model. We then address the problem of evaluating the explanations which can be
in many times generic, data-biased and can come in several forms. We therefore
design 2 new evaluation measures: (1) explain-predict and (2) retrieval-based
attack, a self-evaluation framework that requires no labels. Code is at:
https://github.com/fawazsammani/nlxgpt.


title:
Givens Coordinate Descent Methods for Rotation Matrix Learning in
  Trainable Embedding Indexes

abstract:
  Product quantization (PQ) coupled with a space rotation, is widely used in
modern approximate nearest neighbor (ANN) search systems to significantly
compress the disk storage for embeddings and speed up the inner product
computation. Existing rotation learning methods, however, minimize quantization
distortion for fixed embeddings, which are not applicable to an end-to-end
training scenario where embeddings are updated constantly. In this paper, based
on geometric intuitions from Lie group theory, in particular the special
orthogonal group $SO(n)$, we propose a family of block Givens coordinate
descent algorithms to learn rotation matrix that are provably convergent on any
convex objectives. Compared to the state-of-the-art SVD method, the Givens
algorithms are much more parallelizable, reducing runtime by orders of
magnitude on modern GPUs, and converge more stably according to experimental
studies. They further improve upon vanilla product quantization significantly
in an end-to-end training scenario.