title: The Challenges of Continuous Self-Supervised Learning

abstract:
  Self-supervised learning (SSL) aims to eliminate one of the major bottlenecks
in representation learning - the need for human annotations. As a result, SSL
holds the promise to learn representations from data in-the-wild, i.e., without
the need for finite and static datasets. Instead, true SSL algorithms should be
able to exploit the continuous stream of data being generated on the internet
or by agents exploring their environments. But do traditional self-supervised
learning approaches work in this setup? In this work, we investigate this
question by conducting experiments on the continuous self-supervised learning
problem. While learning in the wild, we expect to see a continuous (infinite)
non-IID data stream that follows a non-stationary distribution of visual
concepts. The goal is to learn a representation that can be robust, adaptive
yet not forgetful of concepts seen in the past. We show that a direct
application of current methods to such continuous setup is 1) inefficient both
computationally and in the amount of data required, 2) leads to inferior
representations due to temporal correlations (non-IID data) in some sources of
streaming data and 3) exhibits signs of catastrophic forgetting when trained on
sources with non-stationary data distributions. We propose the use of replay
buffers as an approach to alleviate the issues of inefficiency and temporal
correlations. We further propose a novel method to enhance the replay buffer by
maintaining the least redundant samples. Minimum redundancy (MinRed) buffers
allow us to learn effective representations even in the most challenging
streaming scenarios composed of sequential visual data obtained from a single
embodied agent, and alleviates the problem of catastrophic forgetting when
learning from data with non-stationary semantic distributions.


title: Stimulated generation of deterministic platicon frequency microcombs

abstract:
  Dissipative Kerr soliton generation in chip-scale nonlinear resonators has
recently observed remarkable advances, spanning from massively-parallel
communications, self-referenced oscillators, to dual-comb spectroscopy. Often
working in the anomalous dispersion regime, unique driving protocols and
dispersion in these nonlinear resonators have been examined to achieve the
soliton and soliton-like temporal pulse shapes and coherent frequency comb
generation. The normal dispersion regime provides a complementary approach to
bridge the nonlinear dynamical studies, including the possibility of square
pulse formation with flat-top plateaus, or platicons. Here we report
observations of square pulse formation in chip-scale frequency combs, through
stimulated pumping at one free-spectral-range and in silicon nitride rings with
+55 fs2/mm normal group velocity dispersion. Tuning of the platicon frequency
comb via a varied sideband modulation frequency is examined in both spectral
and temporal measurements. Determined by second-harmonic auto-correlation and
cross-correlation, we observe bright square platicon pulse of 17 ps pulsewidth
on a 19 GHz flat frequency comb. With auxiliary-laser-assisted thermal
stabilization, we surpass the thermal bistable dragging and extend the
mode-locking access to narrower 2 ps platicon pulse states, supported by
nonlinear dynamical modeling and boundary limit discussions.


title: OJXPerf: Featherlight Object Replica Detection for Java Programs

abstract:
  Memory bloat is an important source of inefficiency in complex production
software, especially in software written in managed languages such as Java.
Prior approaches to this problem have focused on identifying objects that
outlive their life span. Few studies have, however, looked into whether and to
what extent myriad objects of the same type are identical. A quantitative
assessment of identical objects with code-level attribution can assist
developers in refactoring code to eliminate object bloat, and favor reuse of
existing object(s). The result is reduced memory pressure, reduced allocation
and garbage collection, enhanced data locality, and reduced re-computation, all
of which result in superior performance.
  We develop OJXPerf, a lightweight sampling-based profiler, which
probabilistically identifies identical objects. OJXPerf employs hardware
performance monitoring units (PMU) in conjunction with hardware debug registers
to sample and compare field values of different objects of the same type
allocated at the same calling context but potentially accessed at different
program points. The result is a lightweight measurement, a combination of
object allocation contexts and usage contexts ordered by duplication frequency.
This class of duplicated objects is relatively easier to optimize. OJXPerf
incurs 9% runtime and 6% memory overheads on average. We empirically show the
benefit of OJXPerf by using its profiles to instruct us to optimize a number of
Java programs, including well-known benchmarks and real-world applications. The
results show a noticeable reduction in memory usage (up to 11%) and a
significant speedup (up to 25%).


title:
Optimized Quantum Program Execution Ordering to Mitigate Errors in
  Simulations of Quantum Systems

abstract:
  Simulating the time evolution of a physical system at quantum mechanical
levels of detail -- known as Hamiltonian Simulation (HS) -- is an important and
interesting problem across physics and chemistry. For this task, algorithms
that run on quantum computers are known to be exponentially faster than
classical algorithms; in fact, this application motivated Feynman to propose
the construction of quantum computers. Nonetheless, there are challenges in
reaching this performance potential.
  Prior work has focused on compiling circuits (quantum programs) for HS with
the goal of maximizing either accuracy or gate cancellation. Our work proposes
a compilation strategy that simultaneously advances both goals. At a high
level, we use classical optimizations such as graph coloring and travelling
salesperson to order the execution of quantum programs. Specifically, we group
together mutually commuting terms in the Hamiltonian (a matrix characterizing
the quantum mechanical system) to improve the accuracy of the simulation. We
then rearrange the terms within each group to maximize gate cancellation in the
final quantum circuit. These optimizations work together to improve HS
performance and result in an average 40% reduction in circuit depth. This work
advances the frontier of HS which in turn can advance physical and chemical
modeling in both basic and applied sciences.


title:
Searches for New Particles, Dark Matter, and Gravitational Waves with
  SRF Cavities

abstract:
  This is a Snowmass white paper on the utility of existing and future
superconducting cavities to probe fundamental physics. Superconducting radio
frequency (SRF) cavity technology has seen tremendous progress in the past
decades, as a tool for accelerator science. With advances spear-headed by the
SQMS center at Fermilab, they are now being brought to the quantum regime
becoming a tool in quantum science thanks to the high degree of coherence. The
same high quality factor can be leveraged in the search for new physics,
including searches for new particles, dark matter, including the QCD axion, and
gravitational waves. We survey some of the physics opportunities and the
required directions of R&D. Given the already demonstrated integration of SRF
cavities in large accelerator systems, this R&D may enable larger scale
searches by dedicated experiments.


title:
Predicting Multi-Antenna Frequency-Selective Channels via Meta-Learned
  Linear Filters based on Long-Short Term Channel Decomposition

abstract:
  An efficient data-driven prediction strategy for multi-antenna
frequency-selective channels must operate based on a small number of pilot
symbols. This paper proposes novel channel prediction algorithms that address
this goal by integrating transfer and meta-learning with a reduced-rank
parametrization of the channel. The proposed methods optimize linear predictors
by utilizing data from previous frames, which are generally characterized by
distinct propagation characteristics, in order to enable fast training on the
time slots of the current frame. The proposed predictors rely on a novel
long-short-term decomposition (LSTD) of the linear prediction model that
leverages the disaggregation of the channel into long-term space-time
signatures and fading amplitudes. We first develop predictors for
single-antenna frequency-flat channels based on transfer/meta-learned quadratic
regularization. Then, we introduce transfer and meta-learning algorithms for
LSTD-based prediction models that build on equilibrium propagation (EP) and
alternating least squares (ALS). Numerical results under the 3GPP 5G standard
channel model demonstrate the impact of transfer and meta-learning on reducing
the number of pilots for channel prediction, as well as the merits of the
proposed LSTD parametrization.


title:
Growth of Pure and Intercalated ZrTe2, TiTe2 and HfTe2 Dichalcogenide
  Single Crystals by Isothermal Chemical Vapor Transport

abstract:
  We report on a modified chemical vapor transport (CVT) methodology for the
growth of pure and intercalated Zr, Ti, and Hf dichalcogenide single crystals,
e.g. ZrTe2, Gd0.05ZrTe2, HfTe2, and Cu0.05TiTe2. While the most common method
for CVT growth is carried out in quartz tubes subjected to a temperature
gradient between the charge and the growth location, the growth using this
isothermal-CVT (ICVT) method takes place isothermally in sealed quartz tubes
placed horizontally in box furnaces, using iodine (I2) as the transport agent.
The structure and composition of crystals were determined by means of X-ray
diffraction (XRD), scanning electron microscopy (SEM), and induced coupling
plasma (ICP). The crystals grown with this method can be large, and show
excellent crystallinity and homogeneity. Their morphology is plate-like, and
the larger dimensions can be as long as 15 mm.


title: Reducing Memory Requirements of Quantum Optimal Control

abstract:
  Quantum optimal control problems are typically solved by gradient-based
algorithms such as GRAPE, which suffer from exponential growth in storage with
increasing number of qubits and linear growth in memory requirements with
increasing number of time steps. These memory requirements are a barrier for
simulating large models or long time spans. We have created a nonstandard
automatic differentiation technique that can compute gradients needed by GRAPE
by exploiting the fact that the inverse of a unitary matrix is its conjugate
transpose. Our approach significantly reduces the memory requirements for
GRAPE, at the cost of a reasonable amount of recomputation. We present
benchmark results based on an implementation in JAX.


abstract:
  Let $G$ be a finite group, $p$ a prime, and $(K,\mathcal{O},F)$ a $p$-modular
system. We prove that the trivial source ring of $\mathcal{O} G$ is isomorphic
to the ring of {\em coherent} $G$-stable tuples $(\chi_P)$, where $\chi_P$ is a
virtual character of $K[N_G(P)/P]$, $P$ runs through all $p$-subgroups of $G$,
and the coherence condition is the equality of certain character values. We use
this result to describe the group of orthogonal units of the trivial source
ring as the product of the unit group of the Burnside ring of the fusion system
of $G$ with the group of coherent $G$-stable tuples $(\varphi_P)$ of
homomorphisms $N_G(P)/P\to F^\times$. The orthogonal unit group of the trivial
source ring of $\mathcal{O} G$ is of interest, since it embeds into the group
of $p$-permutation autoequivalences of $\mathcal{O} G$.


title: What to Hide from Your Students: Attention-Guided Masked Image Modeling

abstract:
  Transformers and masked language modeling are quickly being adopted and
explored in computer vision as vision transformers and masked image modeling
(MIM). In this work, we argue that image token masking is fundamentally
different from token masking in text, due to the amount and correlation of
tokens in an image. In particular, to generate a challenging pretext task for
MIM, we advocate a shift from random masking to informed masking. We develop
and exhibit this idea in the context of distillation-based MIM, where a teacher
transformer encoder generates an attention map, which we use to guide masking
for the student encoder. We thus introduce a novel masking strategy, called
attention-guided masking (AttMask), and we demonstrate its effectiveness over
random masking for dense distillation-based MIM as well as plain
distillation-based self-supervised learning on classification tokens. We
confirm that AttMask accelerates the learning process and improves the
performance on a variety of downstream tasks.


title: Towards Backwards-Compatible Data with Confounded Domain Adaptation

abstract:
  Most current domain adaptation methods address either covariate shift or
label shift, but are not applicable where they occur simultaneously and are
confounded with each other. Domain adaptation approaches which do account for
such confounding are designed to adapt covariates to optimally predict a
particular label whose shift is confounded with covariate shift. In this paper,
we instead seek to achieve general-purpose data backwards compatibility. This
would allow the adapted covariates to be used for a variety of downstream
problems, including on pre-existing prediction models and on data analytics
tasks. To do this we consider a special case of generalized label shift (GLS),
which we call confounded shift. We present a novel framework for this problem,
based on minimizing the expected divergence between the source and target
conditional distributions, conditioning on possible confounders. Within this
framework, we propose using the reverse Kullback-Leibler divergence,
demonstrating the use of parametric and nonparametric Gaussian estimators of
the conditional distribution. We also propose using the Maximum Mean
Discrepancy (MMD), introducing a dynamic strategy for choosing the kernel
bandwidth, which is applicable even outside the confounded shift setting.
Finally, we demonstrate our approach on synthetic and real datasets.


title: Out-of-Core Edge Partitioning at Linear Run-Time

abstract:
  Graph edge partitioning is an important preprocessing step to optimize
distributed computing jobs on graph-structured data. The edge set of a given
graph is split into $k$ equally-sized partitions, such that the replication of
vertices across partitions is minimized. Out-of-core edge partitioning
algorithms are able to tackle the problem with low memory overhead. Exsisting
out-of-core algorithms mainly work in a streaming manner and can be grouped
into two types. While \emph{stateless} streaming edge partitioning is fast and
yields low partitioning quality, stateful streaming edge partitioning yields
better quality, but is expensive, as it requires a scoring function to be
evaluated for every edge on every partition, leading to a time complexity of
$\mathcal{O}(|E|*k)$. In this paper, we propose 2PS-L, a novel out-of-core edge
partitioning algorithm that builds upon the stateful streaming model, but
achieves linear run-time (i.e., $\mathcal{O}(|E|)$). 2PS-L consists of two
phases. In the first phase, vertices are separated into clusters by a
lightweight streaming clustering algorithm. In the second phase, the graph is
re-streamed and vertex clustering from the first phase is exploited to reduce
the search space of graph partitioning to only two target partitions for every
edge. Our evaluations show that 2PS-L can achieve better partitioning quality
than existing stateful streaming edge partitioners while having a much lower
run-time. As a consequence, the total run-time of partitioning and subsequent
distributed graph processing can be significantly reduced.


title: Interference of Holon Strings in 2D Hubbard Model

abstract:
  The 2D Hubbard model with large repulsion is a central and yet unsolved
problem in condensed matter physics for decades. The challenge appears below
half filling, where the system is a doped antiferromagnet. In this regime, the
fermion excitations are nothing like those in a Fermi liquid, which carry both
spin and charge. Rather, they split up into holons and spinons, carrying charge
and spin separately. Moreover, the motion of a holon is believed to stir up the
underlying antiferromagnetic order, leaving behind it a string of "wrong"
spins. While direct observation of the holon string is difficult in electron
systems, it has become possible in cold atom experiments due to recent
experimental advances. Here, we point out the key feature of the holon strings,
i.e. its Marshall phase, can be observed through measurements of spin
correlations. Moreover, the interference of these strings leads to an
anisotropic holon propagation clearly distinguishable than those of spinless
fermions, as well as a large suppression of the magnetic order in the region
swept through by the strings, as if the system is driven towards a spin liquid.
We further illustrate the effect of the Marshall phase by showing the motion of
a holon in the so-called $\sigma tJ$-model where the Marshall phase is removed.


title: Spin and Orbital Metallic Magnetism in Rhombohedral Trilayer Graphene

abstract:
  We provide a complete theoretical interpretation of the metallic broken
spin/valley symmetry states recently discovered in ABC trilayer graphene (ABC)
perturbed by a large transverse displacement field. Our conclusions combine
insights from ABC trilayer graphene electronic structure models and mean field
theory, and are guided by precise magneto-oscillation Fermi-surface-area
measurements. We conclude that the physics of ABC trilayer graphene is shaped
by the principle of momentum-space condensation, which favors Fermi surface
reconstructions enabled by broken spin/valley flavor symmetries when the
single-particle bands imply thin annular Fermi seas. We find one large outer
Fermi surface enclosed majority-flavor states and one or more small inner
hole-like Fermi surfaces enclosed minority-flavor states that are primarily
responsible for nematic order. The smaller surfaces can rotate along a ring of
van-Hove singularities or reconstruct into multiple Fermi surfaces with little
cost in energy. We propose that the latter property is responsible for the
quantum oscillation frequency fractionalization seen experimentally in some
regions of the carrier-density/displacement-field phase diagram.


title:
Lessons Learned from Blockchain Applications of Trusted Execution
  Environments and Implications for Future Research

abstract:
  Modern computer systems tend to rely on large trusted computing bases (TCBs)
for operations. To address the TCB bloating problem, hardware vendors have
developed mechanisms to enable or facilitate the creation of a trusted
execution environment (TEE) in which critical software applications can execute
securely in an isolated environment. Even under the circumstance that a host OS
is compromised by an adversary, key security properties such as confidentiality
and integrity of the software inside the TEEs can be guaranteed. The promise of
integrity and security has driven developers to adopt it for use cases
involving access control, PKS, IoT among other things.
  Among these applications include blockchain-related use cases. The usage of
the TEEs doesn't come without its own implementation challenges and potential
pitfalls. In this paper, we examine the assumptions, security models, and
operational environments of the proposed TEE use cases of blockchain-based
applications. The exercise and analysis help the hardware TEE research
community to identify some open challenges and opportunities for research and
rethink the design of hardware TEEs in general.


title:
Robust Coordinate Ascent Variational Inference with Markov chain Monte
  Carlo simulations

abstract:
  Variational Inference (VI) is a method that approximates a
difficult-to-compute posterior density using better behaved distributional
families. VI is an alternative to the already well-studied Markov chain Monte
Carlo (MCMC) method of approximating densities. With each algorithm, there are
of course benefits and drawbacks; does there exist a combination of the two
that mitigates the flaws of both? We propose a method to combine Coordinate
Ascent Variational Inference (CAVI) with MCMC. This new methodology, termed
Hybrid CAVI, seeks to improve the sensitivity to initialization and convergence
problems of CAVI by proposing an initialization using method of moments
estimates obtained from a short MCMC burn-in period. Unlike CAVI, Hybrid CAVI
proves to also be effective when the posterior is not from a conditionally
conjugate exponential family.


title: Treatment Effect Estimation with Efficient Data Aggregation

abstract:
  Data aggregation, also known as meta analysis, is widely used to synthesize
knowledge on parameters shared in common (e.g., average treatment effect)
between multiple studies. We introduce in this paper an attractive data
aggregation protocol based on summary statistics from existing studies, to
inform the design of a (new) validation study and estimate shared parameters by
combining all the studies. In particular, we focus on the scenario where each
existing study relies on an $\ell_1$-regularized regression analysis to select
a parsimonious model from a set of high dimensional covariates. We derive an
estimator for the shared parameter by aggregating summary statistics from each
study through a novel technique called data carving. Our estimator (a) aims to
make the fullest possible use of data from the existing studies without the
bias from data re-use in model selection and parameter estimation, and (b)
provides the added benefit of individual data privacy, because full data from
these studies need not be shared for efficient estimation.


title: Entanglement duality in spin-spin interactions

abstract:
  We examine entanglement of thermal states for spin-12 dimers in external
magnetic fields. Entanglement transition in the temperature-magnetic field
plane demonstrates a duality in spin-spin interactions. This identifies dual
categories of symmetric and anti-symmetric dimers with each category classified
into toric entanglement classes. The entanglement transition line is preserved
from each toric entanglement class to its dual toric class. The toric
classification is an indication of topological nature of the entanglement.


title: Risk, Resilience and Reward: Impacts of Shifting to Digital Sex Work

abstract:
  Workers from a variety of industries rapidly shifted to remote work at the
onset of the COVID-19 pandemic. While existing work has examined the impact of
this shift on office workers, little work has examined how shifting from
in-person to online work affected workers in the informal labor sector. We
examine the impact of shifting from in-person to online-only work on a
particularly marginalized group of workers: sex workers. Through 34 qualitative
interviews with sex workers from seven countries in the Global North, we
examine how a shift to online-only sex work impacted: (1) working conditions,
(2) risks and protective behaviors, and (3) labor rewards. We find that online
work offers benefits to sex workers' financial and physical well-being.
However, online-only work introduces new and greater digital and mental health
risks as a result of the need to be publicly visible on more platforms and to
share more explicit content. From our findings we propose design and platform
governance suggestions for digital sex workers and for informal workers more
broadly, particularly those who create and sell digital content.


title:
Gradient-free temperature control over micrometric areas for
  thermoplasmonic applications in micro-and nano-devices

abstract:
  Control over surface temperature is of paramount importance in
optoelectronics, photocatalysis and biosensing applications, among others.
Thermoplasmonic approaches have demonstrated unrivalled performance for
controlling surface temperature, in terms of spatial resolution and thermal
amplitude. Most efforts have been done on optimizing the temperature gradient
and developing nanoscale temperature patterns. Alternatively, a temperature
gradient-free thermoplasmonic surface will enable further functionalities. For
example, a constant temperature area of a few square microns can be employed to
tune the gap of a 2D crystal or to control transition in a phase change
material without the need of local resistive heaters and external electronics.
In this work, we present a thermoplasmonic platform conceived for this purpose.
It consists of an array of gold nanoantennas on a 'silicon on insulator'
substrate, where the heat is generated by a laser beam focused on the array,
diffused over the whole silicon layer and confined in the vertical direction by
the insulator underneath. As an advantage to previous approaches, heat
diffusion allows the temperature control at a desired distance from the
excitation spot, thus showing the proposed platform as a candidate for hosting
and controlling the temperature during the characterization of light-sensitive
materials.


title: Adaptive Regularization of B-Spline Models for Scientific Data

abstract:
  B-spline models are a powerful way to represent scientific data sets with a
functional approximation. However, these models can suffer from spurious
oscillations when the data to be approximated are not uniformly distributed.
Model regularization (i.e., smoothing) has traditionally been used to minimize
these oscillations; unfortunately, it is sometimes impossible to sufficiently
remove unwanted artifacts without smoothing away key features of the data set.
In this article, we present a method of model regularization that preserves
significant features of a data set while minimizing artificial oscillations.
Our method varies the strength of a smoothing parameter throughout the domain
automatically, removing artifacts in poorly-constrained regions while leaving
other regions unchanged. The behavior of our method is validated on a
collection of two- and three-dimensional data sets produced by scientific
simulations.


title:
Complete Modified Logarithmic Sobolev inequality for sub-Laplacian on
  $SU(2)$

abstract:
  We prove that the canonical sub-Laplacian on $SU(2)$ admits a uniform
modified log-Sobolev inequality for all its matrix valued functions,
independent of the matrix dimension. This is the first example of sub-Laplacian
that a matrix valued modified log-Sobolev inequality has been obtained. As an
application, we obtain a dimension-free estimate for modified log-Sobolev
constants of the corresponding quantum Markov semigroups transferred from Lie
algebra representations.


title: Tests of Linear Hypotheses using Indirect Information

abstract:
  In multigroup data settings with small within-group sample sizes, standard
$F$-tests of group-specific linear hypotheses can have low power, particularly
if the within-group sample sizes are not large relative to the number of
explanatory variables. To remedy this situation, in this article we derive
alternative test statistics based on information-sharing across groups. Each
group-specific test has potentially much larger power than the standard
$F$-test, while still exactly maintaining a target type I error rate if the
hypothesis for the group is true. The proposed test for a given group uses a
statistic that has optimal marginal power under a prior distribution derived
from the data of the other groups. This statistic approaches the usual
$F$-statistic as the prior distribution becomes more diffuse, but approaches a
limiting "cone" test statistic as the prior distribution becomes extremely
concentrated. We compare the power and $p$-values of the cone test to that of
the $F$-test in some high-dimensional asymptotic scenarios. An analysis of
educational outcome data is provided, demonstrating empirically that the
proposed test is more powerful than the $F$-test.


title: Quantitative phase and absorption contrast imaging

abstract:
  Phase retrieval in its most general form is the problem of reconstructing a
complex valued function from phaseless information of some transform of that
function. This problem arises in various fields such as X-ray crystallography,
electron microscopy, coherent diffractive imaging, astronomy, speech
recognition, and quantum mechanics. The mathematical and computational analysis
of these problems has a long history and a variety of different algorithms has
been proposed in the literature. The performance of which usually depends on
the constraints imposed on the sought function and the number of measurements.
In this paper, we present an algorithm for coherent diffractive imaging with
phaseless measurements. The algorithm accounts for both coherent and incoherent
wave propagation and allows for reconstructing absorption as well as phase
images that quantify the attenuation and the refraction of the waves when they
go through an object. The algorithm requires coherent or partially coherent
illumination, and several detectors to record the intensity of the distorted
wave that passes through the object under inspection. To obtain enough
information for imaging, a series of masks are introduced between the source
and the object that create a diversity of illumination patterns.


title:
A practical guide to piecewise pseudospectral collocation for Floquet
  multipliers of delay equations in MATLAB

abstract:
  In recent years we provided numerical methods based on pseudospectral
collocation for computing the Floquet multipliers of different types of delay
equations, with the goal of studying the stability of their periodic solutions.
The latest work of the series concerns the extension of these methods to a
piecewise approach, in order to take the properties of numerically computed
solutions into account. In this chapter we describe the MATLAB implementation
of this method and provide practical usage examples.


title: Integer colorings with no rainbow $k$-term arithmetic progression

abstract:
  In this paper, we study the rainbow Erd\H{o}s-Rothschild problem with respect
to $k$-term arithmetic progressions. For a set of positive integers $S
\subseteq [n]$, an $r$-coloring of $S$ is \emph{rainbow $k$-AP-free} if it
contains no rainbow $k$-term arithmetic progression. Let $g_{r,k}(S)$ denote
the number of rainbow $k$-AP-free $r$-colorings of $S$. For sufficiently large
$n$ and fixed integers $r\ge k\ge 3$, we show that $g_{r,k}(S)<g_{r,k}([n])$
for any proper subset $S\subset [n]$. Further, we prove that $\lim_{n\to
\infty}g_{r,k}([n])/(k-1)^n= \binom{r}{k-1}$. Our result is asymptotically best
possible and implies that, almost all rainbow $k$-AP-free $r$-colorings of
$[n]$ use only $k-1$ colors.


title: An interactive music infilling interface for pop music composition

abstract:
  Artificial intelligence (AI) has been widely applied to music generation
topics such as continuation, melody/harmony generation, genre transfer and
music infilling application. Although with the burst interest to apply AI to
music, there are still few interfaces for the musicians to take advantage of
the latest progress of the AI technology. This makes those tools less valuable
in practice and harder to find its advantage/drawbacks without utilizing them
in the real scenario. This work builds a max patch for interactive music
infilling application with different levels of control, including track
density/polyphony/occupation rate and bar tonal tension control. The user can
select the melody/bass/harmony track as the infilling content up to 16 bars.
The infilling algorithm is based on the author's previous work, and the
interface sends/receives messages to the AI system hosted in the cloud. This
interface lowers the barrier of AI technology and can generate different
variations of the selected content. Those results can give several alternatives
to the musicians' composition, and the interactive process realizes the value
of the AI infilling system.


title:
Analysis of Hospital Bed Requirements Using Discrete Event Simulation
  and Mathematical Modeling

abstract:
  Using SimPy and Discrete Event Simulation we have observed the different
model responses of a system consisting of a hospital and people getting
sick/healing under different initial conditions.
  In our model, each independent person can get sick at an exponential rate.
The hospital's capacity is limited and accepts only a limited amount of sick
people. When the hospital gets full, people are sent home to heal. This is
simulated under different conditions and the simulation results have
demonstrated that the model reaches the steady-state in every scenario. These
cause the outputs of the simulation to be similar to each other even when the
simulation is run with different initial conditions. The results are compared
with the Machine Repair Problem and Erlang-Loss System and the model is
validated.


title:
Contextual Model Aggregation for Fast and Robust Federated Learning in
  Edge Computing

abstract:
  Federated learning is a prime candidate for distributed machine learning at
the network edge due to the low communication complexity and privacy protection
among other attractive properties. However, existing algorithms face issues
with slow convergence and/or robustness of performance due to the considerable
heterogeneity of data distribution, computation and communication capability at
the edge. In this work, we tackle both of these issues by focusing on the key
component of model aggregation in federated learning systems and studying
optimal algorithms to perform this task. Particularly, we propose a contextual
aggregation scheme that achieves the optimal context-dependent bound on loss
reduction in each round of optimization. The aforementioned context-dependent
bound is derived from the particular participating devices in that round and an
assumption on smoothness of the overall loss function. We show that this
aggregation leads to a definite reduction of loss function at every round.
Furthermore, we can integrate our aggregation with many existing algorithms to
obtain the contextual versions. Our experimental results demonstrate
significant improvements in convergence speed and robustness of the contextual
versions compared to the original algorithms. We also consider different
variants of the contextual aggregation and show robust performance even in the
most extreme settings.


title:
Dipolar Magnetic Interactions and A-type Antiferromagnetic Order in the
  Zintl Phase Insulator EuZn2P2

abstract:
  Zintl phases, containing strongly covalently bonded frameworks with separate
ionically bonded ions, have emerged as a critical materials family in which to
couple magnetism and strong spin-orbit coupling to drive diverse topological
phases of matter. Here we report the single-crystal synthesis, magnetic,
thermodynamic, transport, and theoretical properties of the Zintl compound
EuZn2P2 that crystallizes in the anti-La2O3 P-3m1 structure, containing
triangular layers of Eu2+ ions. In-plane resistivity measurements reveal
insulating behavior with an estimated bandgap of Eg=0.11eV. Comparing Eu
magnetic ordering temperatures across trigonal EuM2X2 (M=divalent metal,
X=pnictide) shows that EuZn2P2 exhibits the highest ordering temperature, with
variations in TN correlating with changes in expected dipolar interaction
strengths within and between layers and independent of the magnitude of
electrical conductivity. These results provide experimental validation of the
cytochemical intuition that the cation Eu2+ layers and the anionic (M2X2)2-
framework can be treated as electronically distinct subunits, enabling further
predictive materials design.


title: Correcting Attrition Bias using Changes-in-Changes

abstract:
  Attrition is a common and potentially important threat to internal validity
in treatment effect studies. We extend the changes-in-changes approach to
identify the average treatment effect for respondents and the entire study
population in the presence of attrition. Our method can be applied to
randomized experiments as well as difference-in-difference designs. A
simulation experiment points to the advantages of this approach relative to one
of the most commonly used approaches in the literature, inverse probability
weighting. Those advantages are further illustrated with an application to a
large-scale randomized experiment.


title: Black holes at cosmic dawn in the redshifted 21cm signal of HI

abstract:
  Indirect insights of Pop III stars and Black Holes (BHs) at Cosmic Dawn (CD)
may be imprinted as an absorption signal in the 21cm line of HI against the
CMB, when the Universe was less than 200 Myr old. To explain the additional
large amplitude of the 21cm HI absorption reported by EDGES there have been
proposed models based on an additional synchrotron Cosmic Radio Background
(CRB) from BH-jet sources that boost the HI absorption signal at CD. The recent
observations of radio loud supermassive BHs (SMBHs) in high-z quasars up to z=7
suggest the existence of a CRB from growing BHs at z > 15, of unknown
intensity. To match the onset of the EDGES signal a CRB of comparable intensity
to that of the CMB is required. Here we provide approximate calculations to
analyze this type of absorption signals, taking that of EDGES as an example.
Assuming a BH mass to radio luminosity ratio as observed in radio-loud SMBHs of
~10^9 solar masses in quasars at z = 6-7, we find that rapidly growing radio
luminous BHs of Intermediate Mass (IMBHs) in their way to become SMBHs, are the
only type of astrophysical radio sources of a CRB that could explain the
amplitude of the HI absorption reported by EDGES in the interval of z = 18-20.
At those redshifts the EDGES signal would imply that the global mass density of
IMBHs must be dominant over that of stars, more than 70% of the maximum Stellar
Mass Density (SMD) expected at those high redshifts. This suggests that those
IMBHs are formed before and grow faster than the bulk of stars, with no large
mass contribution from stellar-mass BH remnants of typical Pop III stars. The
highly redshifted signals from these IMBHs may be detected at long radio
wavelengths with ultrasensitive interferometers such as the SKA, in the
infrared with the JWST, and in the X-rays with future space missions.


title:
Accelerating Bayesian Optimization for Biological Sequence Design with
  Denoising Autoencoders

abstract:
  Bayesian optimization is a gold standard for query-efficient continuous
optimization. However, its adoption for drug and antibody sequence design has
been hindered by the discrete, high-dimensional nature of the decision
variables. We develop a new approach (LaMBO) which jointly trains a denoising
autoencoder with a discriminative multi-task Gaussian process head, enabling
gradient-based optimization of multi-objective acquisition functions in the
latent space of the autoencoder. These acquisition functions allow LaMBO to
balance the explore-exploit trade-off over multiple design rounds, and to
balance objective tradeoffs by optimizing sequences at many different points on
the Pareto frontier. We evaluate LaMBO on a small-molecule task based on the
ZINC dataset and introduce a new large-molecule task targeting fluorescent
proteins. In our experiments, LaMBO outperforms genetic optimizers and does not
require a large pretraining corpus, demonstrating that Bayesian optimization is
practical and effective for biological sequence design.


title:
A new method for measurement of the Michel parameters that describe the
  daughter muon polarization in the $\tau^- \to \mu^- \bar{\nu}_\mu\nu_\tau$
  decay

abstract:
  This paper provides a detailed description of the method for the first direct
measurement of all Michel parameters in the $\tau^- \to \mu^-
\bar{\nu}_\mu\nu_\tau$ decay related to the polarization of the daughter muon.
An application of the suggested method in the existing and future experiments
at $e^+e^-$ colliders is considered. We have performed a feasibility study for
the future Super Charm-Tau Factory and Belle II experiments. For the first one,
the sensitivity to the Michel parameters $\xi'$, $\xi''$, $\eta''$,
$\alpha'/A$, and $\beta'/A$ is estimated. For the latter, only one Michel
parameter, $\xi'$, for which the sensitivity is maximum, is considered.


title: Trying to bridge the gap between skyline and top-k queries

abstract:
  There are two most common paradigms that are used in order to identify
records of preference in a multi-objective settings, one relies on dominance,
like the skyline operator, the other instead, on a utility function defined
over the records' attributes, typically using top-k queries. Although they are
very popular, we have to take in account their main disadvantages, which bring
us to describe three hard requirements: personalization, controllable output
size, and flexibility in preference specification. In fact Skyline queries are
simple to specify but they are not equipped with any means to accommodate user
preferences or to control the cardinality of the result set. Ranking queries
adopt, instead, a specific scoring function to rank tuples, and can easily
control the output size, but it is difficult to specify correctly the weights
of this scoring function in order to give different importance to the
attributes. In this paper we describe three different approaches which try to
satisfy the three hard requirements mentioned above embracing the advantages
either of the Skyline queries or of the ranking queries. These approaches are
namely: Flexible Skyline, ORD-ORU and UTK.


title:
UMT: Unified Multi-modal Transformers for Joint Video Moment Retrieval
  and Highlight Detection

abstract:
  Finding relevant moments and highlights in videos according to natural
language queries is a natural and highly valuable common need in the current
video content explosion era. Nevertheless, jointly conducting moment retrieval
and highlight detection is an emerging research topic, even though its
component problems and some related tasks have already been studied for a
while. In this paper, we present the first unified framework, named Unified
Multi-modal Transformers (UMT), capable of realizing such joint optimization
while can also be easily degenerated for solving individual problems. As far as
we are aware, this is the first scheme to integrate multi-modal (visual-audio)
learning for either joint optimization or the individual moment retrieval task,
and tackles moment retrieval as a keypoint detection problem using a novel
query generator and query decoder. Extensive comparisons with existing methods
and ablation studies on QVHighlights, Charades-STA, YouTube Highlights, and
TVSum datasets demonstrate the effectiveness, superiority, and flexibility of
the proposed method under various settings. Source code and pre-trained models
are available at https://github.com/TencentARC/UMT.


title:
Sufficient Conditions for Robust Probabilistic Reach-Avoid-Stay
  Specifications using Stochastic Lyapunov-Barrier Functions

abstract:
  Stability and safety are crucial in safety-critical control of dynamical
systems. The reach-avoid-stay objectives for deterministic dynamical systems
can be effectively handled by formal methods as well as Lyapunov methods with
soundness and approximate completeness guarantees. However, for continuous-time
stochastic dynamical systems, probabilistic reach-avoid-stay problems are
viewed as challenging tasks. Motivated by the recent surge of applications in
characterizing safety-critical properties using Lyapunov-barrier functions, we
aim to provide a stochastic version for the probabilistic reach-avoid-stay
problems in consideration of robustness. To this end, we first establish a
connection between stochastic stability with safety constraints and
reach-avoid-stay specifications. We then prove that stochastic Lyapunov-barrier
functions provide sufficient conditions for the target objectives. We apply
Lyapunovbarrier conditions in control synthesis for reach-avoid-stay
specifications, and show its effectiveness in a case study.


title:
Seven Years of SN 2014C: a Multi-Wavelength Synthesis of an
  Extraordinary Supernova

abstract:
  SN 2014C was originally classified as a Type Ib supernova, but at phase
{\phi} = 127 d post-explosion strong H{\alpha} emission was observed. SN 2014C
has since been observed in radio, infrared, optical and X-ray bands. Here we
present new optical spectroscopic and photometric data spanning {\phi} = 947 -
2494 d post-explosion. We address the evolution of the broadened H{\alpha}
emission line, as well as broad [O III] emission and other lines. We also
conduct a parallel analysis of all publicly available multi-wavelength data.
From our spectra, we find a nearly constant H{\alpha} FWHM velocity width of
{\sim}2000 km/s that is significantly lower than that of other broadened atomic
transitions ({\sim}3000 - 7000 km/s) present in our spectra ([O I]
{\lambda}6300; [O III] {\lambda}{\lambda}4959,5007; He I {\lambda}7065; [Ca II]
{\lambda}{\lambda}7291,7324). The late radio data demand a fast forward shock
({\sim}10,000 km/s at {\phi} = 1700 d) in rarified matter that contrasts with
the modest velocity of the H{\alpha}. We propose that the infrared flux
originates from a toroidal-like structure of hydrogen surrounding the
progenitor system, while later emission at other wavelengths (radio, X-ray)
likely originates predominantly from the reverse shock in the ejecta and the
forward shock in the quasi-spherical progenitor He wind. We propose that the
H{\alpha} emission arises in the boundary layer between the ejecta and torus.
We also consider the possible roles of a pulsar and a binary companion.


title:
Is Fairness Only Metric Deep? Evaluating and Addressing Subgroup Gaps in
  Deep Metric Learning

abstract:
  Deep metric learning (DML) enables learning with less supervision through its
emphasis on the similarity structure of representations. There has been much
work on improving generalization of DML in settings like zero-shot retrieval,
but little is known about its implications for fairness. In this paper, we are
the first to evaluate state-of-the-art DML methods trained on imbalanced data,
and to show the negative impact these representations have on minority subgroup
performance when used for downstream tasks. In this work, we first define
fairness in DML through an analysis of three properties of the representation
space -- inter-class alignment, intra-class alignment, and uniformity -- and
propose finDML, the fairness in non-balanced DML benchmark to characterize
representation fairness. Utilizing finDML, we find bias in DML representations
to propagate to common downstream classification tasks. Surprisingly, this bias
is propagated even when training data in the downstream task is re-balanced. To
address this problem, we present Partial Attribute De-correlation (PARADE) to
de-correlate feature representations from sensitive attributes and reduce
performance gaps between subgroups in both embedding space and downstream
metrics.


title: Passive Haptic Rehearsal for Accelerated Piano Skill Acquisition

abstract:
  Passive haptic learning (PHL) uses vibrotactile stimulation to train piano
songs using repetition, even when the recipient of stimulation is focused on
other tasks. However, many of the benefits of playing piano cannot be acquired
without actively playing the instrument. In this position paper, we posit that
passive haptic rehearsal, where active piano practice is assisted by separate
sessions of passive stimulation, is of greater everyday use than solely PHL. We
propose a study to examine the effects of passive haptic rehearsal for
self-paced piano learners and consider how to incorporate passive rehearsal
into everyday practice.


title: Copula-based Modeling for IBNR Claim Loss Reserving

abstract:
  There are growing concerns for reserves estimation of incurred but not
reported (IBNR) claims in actuarial sciences. In this paper, we propose a
copula-based dependency model to capture the relationship between two main IBNR
reserve variables, i.e., the time between two successive occurrences and delay
time. A maximum likelihood estimation method is used to estimate the parameters
of the model. A simulation study is conducted to evaluate the validity of the
theoretical results. Moreover, the proposed method is applied to predict the
number of claims for the next years of a portfolio from a major automobile
insurer and is compared to the classical CL model forecasting.


title:
ThingTalk: An Extensible, Executable Representation Language for
  Task-Oriented Dialogues

abstract:
  Task-oriented conversational agents rely on semantic parsers to translate
natural language to formal representations. In this paper, we propose the
design and rationale of the ThingTalk formal representation, and how the design
improves the development of transactional task-oriented agents.
  ThingTalk is built on four core principles: (1) representing user requests
directly as executable statements, covering all the functionality of the agent,
(2) representing dialogues formally and succinctly to support accurate
contextual semantic parsing, (3) standardizing types and interfaces to maximize
reuse between agents, and (4) allowing multiple, independently-developed agents
to be composed in a single virtual assistant. ThingTalk is developed as part of
the Genie Framework that allows developers to quickly build transactional
agents given a database and APIs.
  We compare ThingTalk to existing representations: SMCalFlow, SGD, TreeDST.
Compared to the others, the ThingTalk design is both more general and more
cost-effective. Evaluated on the MultiWOZ benchmark, using ThingTalk and
associated tools yields a new state of the art accuracy of 79% turn-by-turn.


title:
Functional mimicry of Ruffini receptors with Fiber Bragg Gratings and
  Deep Neural Networks enables a bio-inspired large-area tactile sensitive skin

abstract:
  Collaborative robots are expected to physically interact with humans in daily
living and workplace, including industrial and healthcare settings. A related
key enabling technology is tactile sensing, which currently requires addressing
the outstanding scientific challenge to simultaneously detect contact location
and intensity by means of soft conformable artificial skins adapting over large
areas to the complex curved geometries of robot embodiments. In this work, the
development of a large-area sensitive soft skin with a curved geometry is
presented, allowing for robot total-body coverage through modular patches. The
biomimetic skin consists of a soft polymeric matrix, resembling a human
forearm, embedded with photonic Fiber Bragg Grating (FBG) transducers, which
partially mimics Ruffini mechanoreceptor functionality with diffuse,
overlapping receptive fields. A Convolutional Neural Network deep learning
algorithm and a multigrid Neuron Integration Process were implemented to decode
the FBG sensor outputs for inferring contact force magnitude and localization
through the skin surface. Results achieved 35 mN (IQR = 56 mN) and 3.2 mm (IQR
= 2.3 mm) median errors, for force and localization predictions, respectively.
Demonstrations with an anthropomorphic arm pave the way towards AI-based
integrated skins enabling safe human-robot cooperation via machine
intelligence.


title: Spectral Form Factor of a Quantum Spin Glass

abstract:
  It is widely expected that systems which fully thermalize are chaotic in the
sense of exhibiting random-matrix statistics of their energy level spacings,
whereas integrable systems exhibit Poissonian statistics. In this paper, we
investigate a third class: spin glasses. These systems are partially chaotic
but do not achieve full thermalization due to large free energy barriers. We
examine the level spacing statistics of a canonical infinite-range quantum spin
glass, the quantum $p$-spherical model, using an analytic path integral
approach. We find statistics consistent with a direct sum of independent random
matrices, and show that the number of such matrices is equal to the number of
distinct metastable configurations -- the exponential of the spin glass
"complexity" as obtained from the quantum Thouless-Anderson-Palmer equations.
We also consider the statistical properties of the complexity itself and
identify a set of contributions to the path integral which suggest a Poissonian
distribution for the number of metastable configurations. Our results show that
level spacing statistics can probe the ergodicity-breaking in quantum spin
glasses and provide a way to generalize the notion of spin glass complexity
beyond models with a semi-classical limit.

categories: cond-mat.stat-mech cond-mat.dis-nn cond-mat.str-el hep-th


title:
Thermally switchable polarization manipulation and diodelike
  transmission in scalable, resonator-free, mid-infrared metasurfaces with
  vanadium-dioxide grids

abstract:
  We conceptualized three-array scalable metasurfaces comprising only three
thin strip grids and numerically demonstrated their characteristics in the
mid-infrared spectral regime for switchable polarization manipulation and
related asymmetric diodelike transmission (AT). A few or all of the grids were
taken to be made of VO$_2$, a phase-change material. For each proposed
metasurface, two effective structures and, therefore, two different
functionalities exist, corresponding to the metallic and insulating phases of
VO$_2$. The achieved scenarios of functionality switching that depend on the
VO$_2$ phase are shown to significantly depend in the way in which VO$_2$ is
incorporated to the metasurface. Switchable bands of polarization manipulation
are up to 40 THz wide. The AT band can be modulated when Fabry--Perot
(anti-)resonances come into play.


title: SelfAPR: Self-supervised Program Repair with Test Execution Diagnostics

abstract:
  Neural program repair has achieved good results in a recent series of papers.
Yet, we observe that the related work fails to repair some bugs because of a
lack of knowledge about 1) the program being repaired, and 2) the actual fault
being repaired. In this paper, we solve both problems by changing the learning
paradigm from supervised training to self-supervised training in an approach
called SelfAPR. First, SelfAPR generates and constructs training samples by
perturbing a previous version of the program being repaired, enforcing the
neural model to capture project-specific knowledge. This is different from all
the existing work based on past commits. Second, SelfAPR extracts and encodes
test execution diagnostics into the input representation, steering the neural
model to fix the specific kind of fault. This is different from the existing
studies that only consider static source code in the input. We implement
SelfAPR and evaluate it in a systematic manner. We train SelfAPR with 253 411
training samples obtained by perturbing 17 open-source projects. We evaluate
SelfAPR on 818 bugs from Defects4J, SelfAPR correctly repairs 112 of them.


title: Effect of chirality imbalance on Hall transport of PrRhC$_2$

abstract:
  Much have been learned about the topological transport in real materials. We
investigate the interplay between magnetism and topology in the
magneto-transport of PrRhC$_2$. The four-fold degeneracy reduces to two-fold
followed by non-degenerate Weyl nodes (WNs) when the orientation of magnetic
quantization axis (MQA) is changed from easy axis to body-diagonal through
face-diagonal. This engenders chirality imbalance between positive and negative
chirality WNs around the Fermi energy. We observe significant enhancement in
the chiral anomaly mediated response such as planar Hall conductivity and
longitudinal magneto-conductivity, due to the emergence of chirality imbalance
upon orienting the MQA to body-diagonal. We further investigate the profiles of
anomalous Hall conductivities as a function of Fermi energy to explore the
effects of symmetries as well as chirality imbalance on Berry curvature.


title: Adaptive variational algorithms for quantum Gibbs state preparation

abstract:
  The preparation of Gibbs thermal states is an important task in quantum
computation with applications in quantum simulation, quantum optimization, and
quantum machine learning. However, many algorithms for preparing Gibbs states
rely on quantum subroutines which are difficult to implement on near-term
hardware. Here, we address this by (i) introducing an objective function that,
unlike the free energy, is easily measured, and (ii) using dynamically
generated, problem-tailored ans\"atze. This allows for arbitrarily accurate
Gibbs state preparation using low-depth circuits. To verify the effectiveness
of our approach, we numerically demonstrate that our algorithm can prepare
high-fidelity Gibbs states across a broad range of temperatures and for a
variety of Hamiltonians.


title:
Mokey: Enabling Narrow Fixed-Point Inference for Out-of-the-Box
  Floating-Point Transformer Models

abstract:
  Increasingly larger and better Transformer models keep advancing
state-of-the-art accuracy and capability for Natural Language Processing
applications. These models demand more computational power, storage, and
energy. Mokey reduces the footprint of state-of-the-art 32-bit or 16-bit
floating-point transformer models by quantizing all values to 4-bit indexes
into dictionaries of representative 16-bit fixed-point centroids. Mokey does
not need fine-tuning, an essential feature as often the training resources or
datasets are not available to many. Exploiting the range of values that
naturally occur in transformer models, Mokey selects centroid values to also
fit an exponential curve. This unique feature enables Mokey to replace the bulk
of the original multiply-accumulate operations with narrow 3b fixed-point
additions resulting in an area- and energy-efficient hardware accelerator
design. Over a set of state-of-the-art transformer models, the Mokey
accelerator delivers an order of magnitude improvements in energy efficiency
over a Tensor Cores-based accelerator while improving performance by at least
$4\times$ and as much as $15\times$ depending on the model and on-chip
buffering capacity. Optionally, Mokey can be used as a memory compression
assist for any other accelerator, transparently stashing wide floating-point or
fixed-point activations or weights into narrow 4-bit indexes. Mokey proves
superior to prior state-of-the-art quantization methods for Transformers.


title:
Asynchronous Reinforcement Learning for Real-Time Control of Physical
  Robots

abstract:
  An oft-ignored challenge of real-world reinforcement learning is that the
real world does not pause when agents make learning updates. As standard
simulated environments do not address this real-time aspect of learning, most
available implementations of RL algorithms process environment interactions and
learning updates sequentially. As a consequence, when such implementations are
deployed in the real world, they may make decisions based on significantly
delayed observations and not act responsively. Asynchronous learning has been
proposed to solve this issue, but no systematic comparison between sequential
and asynchronous reinforcement learning was conducted using real-world
environments. In this work, we set up two vision-based tasks with a robotic
arm, implement an asynchronous learning system that extends a previous
architecture, and compare sequential and asynchronous reinforcement learning
across different action cycle times, sensory data dimensions, and mini-batch
sizes. Our experiments show that when the time cost of learning updates
increases, the action cycle time in sequential implementation could grow
excessively long, while the asynchronous implementation can always maintain an
appropriate action cycle time. Consequently, when learning updates are
expensive, the performance of sequential learning diminishes and is
outperformed by asynchronous learning by a substantial margin. Our system
learns in real-time to reach and track visual targets from pixels within two
hours of experience and does so directly using real robots, learning completely
from scratch.


title: On the principle of Astrometric Gravitational Wave Antenna

abstract:
  The direct detection of gravitational waves by ground-based optical
interferometers has opened a new window in astronomy. Nevertheless, as these
detectors are a combination of two Michelson-Morley like baselines, their
sensitivity for determining the incident direction of a gravitational wave is
quite weak compared to current high-precision of space astrometry. We therefore
present a novel concept for a gravitational wave antenna in space that uses
close pairs of point-like sources as natural sensors to record and characterize
the very tiny variations in angular separations induced by a passing
gravitational wave, thus operating complementarily to linear arm detectors and
enabling to gain informations on the gravitational wave incoming direction.
Indeed, the proposed astrometric gravitational wave observable builds on
methods of relativistic astrometry that can substantially enhance the strength
of gravitational wave signals by exploiting to the fullest the telescope
optical resolution and, at same time, provide a powerful tool for identifying
the direction to the sources that originated the gravitational wave by
monitoring close pairs of stars. Furthermore, the use of two
local-line-of-sights in a differential formulation avoids issues related to
high order modeling of the local (Solar System) background geometry and the
need for accurate satellite ephemeris and attitude.


title:
Energy-Efficient VM Placement in PON-based Data Center Architectures
  with Cascaded AWGRs

abstract:
  Data centers based on Passive Optical Networks (PONs) can offer scalability,
low cost and high energy-efficiency. Application in data centers can use
Virtual Machines (VMs) to provide efficient utilization of the physical
resources. This paper investigates the impact of VM placement on the
energyefficiency in a PON-based data center architecture that utilizes cascaded
Arrayed Waveguide Grating Routers (AWGRs). In this paper, we develop a Mixed
Integer Linear Programming (MILP) optimization model to optimize the VM
placement in the proposed PON-based data center architecture. This optimization
aims to minimize the power consumption of the networking and computing by
placing the VMs and their demands in the optimum number of resources (i.e.,
servers and networking devices) in the data center. We first minimize the
processing power consumption only and then we minimize the processing and
networking power consumption. The results show that a reduction in the
networking power consumption by up to 75% is achieved when performing joint
minimization of processing and networking power consumption compared to
considering the minimization of the processing power consumption only.


title: Relation between the two geometric Satake equivalence via nearby cycle

abstract:
  Fargues and Scholze proved the geometric Satake equivalence over the
Fargues--Fontaine curve. On the other hand, Zhu proved the geometric Satake
equivalence using a Witt vector affine Grassmannian. In this paper, we explain
the relation between the two version of the geometric Satake equivalence via
nearby cycle.


title: A Generalized Method for the Darboux Transformation

abstract:
  A method is presented to obtain the change in the potential and in the
relevant wavefunction of a linear system of ordinary differential equations
containing a spectral parameter, when that linear system is perturbed and a
finite number of discrete eigenvalues are added to or removed from the
spectrum. Some explicit formulas are derived for those changes by introducing
certain fundamental linear integral equations for the corresponding unperturbed
and perturbed linear systems. This generalized method is applicable in a
unified manner on a wide class of linear systems. This is in contrast to the
standard method for a Darboux transformation, which is specific to the
particular linear system on which it applies. A comparison is provided in some
special cases between this generalized method and the standard method for the
Darboux transformation. In particular, when a bound state is added to the
discrete spectrum, some Darboux transformation formulas are presented for the
full-line Schr\"odinger equation, where those formulas resemble the Darboux
transformation formulas for the half-line Schr\"odinger equation. The theory
presented is illustrated with some explicit examples.


title: Discrete Approximation to Brownian Motion with Darning

abstract:
  Brownian motion with darning (BMD in abbreviation) is introduced and studied
in [4] and [5, Chapter 7]. Roughly speaking, BMD travels across the "darning
area" at infinite speed, while it behaves like a regular BM outside of this
area. In this paper we show that starting from a single point in its state
space, BMD is the weak limit of a family of continuous-time simple random walks
on square lattices with diminishing mesh sizes. From any vertex in their state
spaces, the approximating random walks jump to its nearest neighbors with equal
probability after an exponential holding time.


title: Sun dual theory for bi-continuous semigroups

abstract:
  The sun dual space corresponding to a strongly continuous semigroup is a
known concept when dealing with dual semigroups, which are in general only
weak$^*$-continuous. In this paper we develop a corresponding theory for
bi-continuous semigroups under mild assumptions on the involved locally convex
topologies. We also discuss sun reflexivity and Favard spaces in this context,
extending classical results by van Neerven.


title:
Cosmological evolution of a statistical system of degenerate scalar
  charged fermions with an asymmetric scalar doublet. II. One-component system
  of doubly charged fermions

abstract:
  Based on the previously formulated mathematical model of a statistical system
with scalar interaction of fermions, a cosmological model based on a
one-component statistical system of doubly scalar charged degenerate fermions
interacting with an asymmetric scalar doublet-canonical and phantom scalar
fields-is studied. The connection of the presented model with previously
studied models based on one-component and two-component fermion systems is
investigated. The asymptotic and limiting properties of the cosmological model
are investigated, it is shown that among all models there is a class of models
with a finite lifetime. The asymptotic behavior of models near the
corresponding singularities is investigated, a qualitative analysis of the
corresponding dynamical system is carried out, and numerical implementations of
such models are constructed. Based on numerical integration, it is shown that
in the presented model there can be transitions from a stable asymptotically
vacuum state with a zero canonical field and a constant phantom field
corresponding to the phase of cosmological compression to a symmetric state
corresponding to the expansion phase. The time interval of the transition
between phases is accompanied by oscillations of the canonical scalar field.


title: Nonlinear optical Hall effect of few-layered NbSe2

abstract:
  NbSe$_2$ is one of metallic two-dimensional (2D) transition-metal
dichalcogenide (TMDC) materials. Because of broken crystal inversion symmetry,
large spin splitting is induced by Ising-type spin-orbit coupling in
odd-number-layered NbSe$_2$, but absent for even-number-layered NbSe$_2$ with
the inversion symmetry. In this paper, we numerically calculate nonlinear
optical charge and spin Hall conductivities of few-layered NbSe$_2$ based on an
effective tight-binding model which includes $d_{z^2}$, $d_{x^2-y^2}$ and
$d_{xy}$ orbitals of Nb atom. We show that the nonlinear optical Hall
conductivity for second harmonic generation (SHG) process has nonvanishing
value in odd-number-layered NbSe$_2$. Also, we provide nonlinear optical
selection rule in few-layered NbSe$_2$ and their polarization dependences. In
further, for even-number-layered case, the nonlinear optical Hall currents can
be generated by applying electric fields which break inversion symmetry. We
also discuss that the nonlinear optical Hall effect is expected to occur in
TMDC materials in general. Thus, our results will serve to design potential
opt-spintronics devices based on 2D materials to generate the spin Hall current
by SHG.


title: Multidimensional Belief Quantification for Label-Efficient Meta-Learning

abstract:
  Optimization-based meta-learning offers a promising direction for few-shot
learning that is essential for many real-world computer vision applications.
However, learning from few samples introduces uncertainty, and quantifying
model confidence for few-shot predictions is essential for many critical
domains. Furthermore, few-shot tasks used in meta training are usually sampled
randomly from a task distribution for an iterative model update, leading to
high labeling costs and computational overhead in meta-training. We propose a
novel uncertainty-aware task selection model for label efficient meta-learning.
The proposed model formulates a multidimensional belief measure, which can
quantify the known uncertainty and lower bound the unknown uncertainty of any
given task. Our theoretical result establishes an important relationship
between the conflicting belief and the incorrect belief. The theoretical result
allows us to estimate the total uncertainty of a task, which provides a
principled criterion for task selection. A novel multi-query task formulation
is further developed to improve both the computational and labeling efficiency
of meta-learning. Experiments conducted over multiple real-world few-shot image
classification tasks demonstrate the effectiveness of the proposed model.


title: Periodic homogenization in the context of structured deformations

abstract:
  An energy for first-order structured deformations in the context of periodic
homogenization is obtained. This energy, defined in principle by relaxation of
an initial energy of integral type featuring contributions of bulk and
interfacial terms, is proved to possess an integral representation in terms of
relaxed bulk and interfacial energy densities. These energy densities, in turn,
are obtained via asymptotic cell formulae defined by suitably averaging, over
larger and larger cubes, the bulk and surface contributions of the initial
energy. The integral representation theorem, the main result of this paper, is
obtained by mixing blow-up techniques, typical in the context of structured
deformations, with the averaging process proper of the theory of
homogenization.


title:
The Scaling Relation between Galaxy Luminosity and WHIM Density from
  EAGLE Simulations with application to SDSS data

abstract:
  This paper presents an updated scaling relation between the optical
luminosity density (LD) of galaxies in the $r$ band and the density of the
warm-hot intergalactic medium (WHIM) in cosmic filaments, using the
high-resolution EAGLE simulations. We find a strong degree of correlation
between the WHIM density and the galaxy luminosity density, resulting in a
scaling relation between the two quantities that permits to predict the WHIM
density of filaments with a scatter of less than $\frac{1}{2}$ dex in a broad
range of smoothed filament luminosity densities. In order to estimate the
performance of the simulation-based calibration of the LD-WHIM density
relation, we applied it to a sample of low-redshift filaments detected with the
\emph{Bisous} method in the Legacy Survey SDSS~DR12 data. In the volume covered
by the SDSS data, our relation predicts a WHIM density amounting to
$31\pm7\pm12$ % (statistical errors followed by systematic) of cosmic baryon
density. This agrees, albeit within the large uncertainties, with the current
estimates of the cosmological missing baryon fraction, implying that our
LD-WHIM density relation may be a useful tool in the search for the missing
baryons. This method of analysis provides a new promising avenue to study the
physical properties of the missing baryons, using an observable that is
available for large volumes of the sky, complementary and independent from WHIM
searches with absorption-line systems in the FUV or X-rays.


title:
Large anomalous Hall effect in layered antiferromagnet
  Co$_{0.29}$TaS$_2$

abstract:
  We present a study on the magnetization, anomalous Hall effect (AHE) and
novel longitudinal resistivity in layered antiferromagnet Co$_{0.29}$TaS$_{2}$.
Of particular interests in Co$_{0.29}$TaS$_{2}$ are abundant magnetic
transitions, which show that the magnetic structures are tuned by temperature
or magnetic field. With decreasing temperature, Co$_{0.29}$TaS$_{2}$ undergoes
two transitions at T$_{t1}\sim$ 38.3 K and T$_{t2}\sim$ 24.3 K. Once the
magnetic field is applied, another transition T$_{t3}\sim$ 34.3 K appears
between 0.3 T and 5 T. At 2 K, an obvious ferromagnetic hysteresis loop within
H$_{t1}\sim\pm$ 6.9 T is observed, which decreases with increasing temperature
and eventually disappears at T$_{t2}$. Besides, Co$_{0.29}$TaS$_{2}$ displays
step-like behavior as another magnetic transition around H$_{t2}\sim\pm$ 4 T,
which exists until $\sim$ T$_{t1}$. These characteristic temperatures and
magnetic fields mark complex magnetic phase transitions in
Co$_{0.29}$TaS$_{2}$, which are also evidenced in transport results. Large AHE
dominates in the Hall resistivity with the conspicuous value of
R$_{s}$/R$_{0}\sim 10^{5}$, considering that the tiny net magnetization
(0.0094$\mu_{B}$/Co) alone would not lead to this value, thus the contribution
of Berry curvature is necessary. The longitudinal resistivity illustrates a
prominent irreversible behavior within H$_{t1}$. The abrupt change at H$_{t2}$
below T$_{t1}$, corresponding to the step-like magnetic transitions, is also
observed. Synergy between the magnetism and topological properties, both
playing a crucial role, may be the key factor of large AHE in antiferromagnet,
which also offers a new perspective in magnetic topological materials with the
platform of Co$_{0.29}$TaS$_{2}$.


title:
Quantum oscillations and weak anisotropic resistivity in the chiral
  Fermion semimetal PdGa

abstract:
  We perform a detailed analysis of the magnetotransport and de Haas-van Alphen
(dHvA) oscillations in crystal PdGa which is predicted to be a typical chiral
Fermion semimetal from CoSi family holding a large Chern number. The
unsaturated quadratic magnetoresistance (MR) and nonlinear Hall resistivity
indicate that PdGa is a multi-band system without electron-hole compensation.
Angle-dependent resistivity in PdGa shows weak anisotropy with twofold or
threefold symmetry when the magnetic field rotates within the (1$\bar{1}$0) or
(111) plane perpendicular to the current. Nine or three frequencies are
extracted after the fast Fourier-transform analysis (FFT) of the dHvA
oscillations with B//[001] or B//[011], respectively, which is confirmed to be
consistent with the Fermi surfaces (FSs) obtained from first-principles
calculations with spin-orbit coupling (SOC) considered.


title:
Deciphering Effects of Relativistic Kinematics, Dimensionality,
  Interactions and Short-Range Correlations on the Ratio of Quartic over
  Quadratic Nuclear Symmetry Energies

abstract:
  While ample evidence for the so-called empirical parabolic law of the
Equation of State (EOS) of isospin asymmetric nuclear matter (ANM) has been
obtained in many studies within both non-relativistic and relativistic nuclear
many-body theories using various interactions, it has been unclear if there is
any fundamental physics reason for the small quartic symmetry energy compared
to the quadratic one even as the ANM approaches pure neutron matter. Within
both relativistic and non-relativistic Free Fermi Gas (FFG) models in
coordinate spaces of arbitrary dimension $d$ with and without considering
Short-Range Correlations (SRC) as well as non-linear Relativistic Mean Field
(RMF) models, we study effects of relativistic kinematics, dimensionality,
interactions and SRC on the ratio $\Psi(\rho)$ of quartic over quadratic
symmetry energies in ANM EOSs. We found that the ratio $\Psi(\rho)$ in the FFG
model depends strongly on the dimension $d$. While it is very small already in
the normal 3D space, it could be even smaller in spaces with reduced dimensions
for sub-systems of particles in heavy-ion reactions and/or whole neutron stars
due to constraints, collectivities and/or symmetries. We also found that the
ratio $\Psi(\rho)$ could theoretically become very large only at the
ultra-relativistic limit far above the density reachable in neutron stars. On
the other hand, nuclear interaction directly and/or indirectly through
SRC-induced high-momentum nucleons affect significantly the density dependence
of $\Psi(\rho)$ compared to the relativistic FFG model prediction. The SRC
affects significantly not only the kinetic energy of symmetric nuclear matter
but also the ratio $\Psi(\rho)$ while the relativistic corrections are found
negligible. The results may help better understand the EOS of dense
neutron-rich matter.


title:
Efficient Exploration via First-Person Behavior Cloning Assisted
  Rapidly-Exploring Random Trees

abstract:
  Modern day computer games have extremely large state and action spaces. To
detect bugs in these games' models, human testers play the games repeatedly to
explore the game and find errors in the games. Such gameplay is exhaustive and
time consuming. Moreover, since robotics simulators depend on similar methods
of model specification and debugging, the problem of finding errors in the
model is of interest to the robotics community to ensure robot behaviors and
interactions are consistent in simulators. Previous methods have used
reinforcement learning arXiv:2103.13798 and search based methods (Chang, 2019,
(Chang, 2021) arXiv:1811.06962 including Rapidly-exploring Random Trees (RRT)
to explore a game's state-action space to find bugs. However, such search and
exploration based methods are not efficient at exploring the state-action space
without a pre-defined heuristic. In this work we attempt to combine a
human-tester's expertise in solving games, and the RRT's exhaustiveness to
search a game's state space efficiently with high coverage. This paper
introduces Cloning Assisted RRT (CA-RRT) to test a game through search. We
compare our methods to two existing baselines: 1) a weighted-RRT as described
by arXiv:1812.03125; 2) human demonstration seeded RRT as described by Chang
et. al. We find CA-RRT is applicable to more game maps and explores more game
states in fewer tree expansions/iterations when compared to the existing
baselines. In each test, CA-RRT reached more states on average in the same
number of iterations as weighted-RRT. In our tested environments, CA-RRT
reached the same number of states as weighted-RRT by more than 5000 fewer
iterations on average, almost a 50% reduction and applied to more scenarios
than. Moreover, as a consequence of our first person behavior cloning approach,
CA-RRT worked on unseen game maps than just seeding the RRT with human
demonstrated states.


title:
ZSM-based Management and Orchestration of 3GPP Network Slicing: An
  Architectural Framework and Deployment Options

abstract:
  Driven by closed-loop automation, the Zero-Touch Network and Services
Management (ZSM) framework offers invigorating features for the end-to-end
management and orchestration of a sliced network. Although ZSM is considered a
promising framework by 3GPP, there is a lack of concrete ZSM-based solutions
for the 3GPP Network Slicing management. This article presents an architectural
framework of a ZSM-based management system for 3GPP Network Slicing. The
proposed framework employs recursive ZSM management domains to meet the
specific management requirements of the standard 3GPP Network Slicing
framework. Two deployment options are considered for the presented framework.
The first option features the integration of the standard services of the 3GPP
Management System within ZSM. The other one considers ZSM as a complementary
system for the standard 3GPP Management System. From these deployment options,
some key questions are identified on ZSM's interoperability with the existing
3GPP Network Slicing systems. Finally, for each option, the architectural and
operational feasibility of the ZSM interoperation with 3GPP Network Slicing
systems is shown through an example use case.


abstract:
  Unit testing is an essential part of the software development process, which
helps to identify issues with source code in early stages of development and
prevent regressions. Machine learning has emerged as viable approach to help
software developers generate automated unit tests. However, generating reliable
unit test cases that are semantically correct and capable of catching software
bugs or unintended behavior via machine learning requires large, metadata-rich,
datasets. In this paper we present Methods2Test: A dataset of focal methods
mapped to test cases: a large, supervised dataset of test cases mapped to
corresponding methods under test (i.e., focal methods). This dataset contains
780,944 pairs of JUnit tests and focal methods, extracted from a total of
91,385 Java open source projects hosted on GitHub with licenses permitting
re-distribution. The main challenge behind the creation of the Methods2Test was
to establish a reliable mapping between a test case and the relevant focal
method. To this aim, we designed a set of heuristics, based on developers' best
practices in software testing, which identify the likely focal method for a
given test case. To facilitate further analysis, we store a rich set of
metadata for each method-test pair in JSON-formatted files. Additionally, we
extract textual corpus from the dataset at different context levels, which we
provide both in raw and tokenized forms, in order to enable researchers to
train and evaluate machine learning models for Automated Test Generation.
Methods2Test is publicly available at:
https://github.com/microsoft/methods2test


title: Kernel Robust Hypothesis Testing

abstract:
  The problem of robust hypothesis testing is studied, where under the null and
the alternative hypotheses, the data-generating distributions are assumed to be
in some uncertainty sets, and the goal is to design a test that performs well
under the worst-case distributions over the uncertainty sets. In this paper,
uncertainty sets are constructed in a data-driven manner using kernel method,
i.e., they are centered around empirical distributions of training samples from
the null and alternative hypotheses, respectively; and are constrained via the
distance between kernel mean embeddings of distributions in the reproducing
kernel Hilbert space, i.e., maximum mean discrepancy (MMD). The Bayesian
setting and the Neyman-Pearson setting are investigated. For the Bayesian
setting where the goal is to minimize the worst-case error probability, an
optimal test is firstly obtained when the alphabet is finite. When the alphabet
is infinite, a tractable approximation is proposed to quantify the worst-case
average error probability, and a kernel smoothing method is further applied to
design test that generalizes to unseen samples. A direct robust kernel test is
also proposed and proved to be exponentially consistent. For the Neyman-Pearson
setting, where the goal is to minimize the worst-case probability of miss
detection subject to a constraint on the worst-case probability of false alarm,
an efficient robust kernel test is proposed and is shown to be asymptotically
optimal. Numerical results are provided to demonstrate the performance of the
proposed robust tests.


title: Critical behavior in the Mn$_{5}$Ge$_{3}$ ferromagnet

abstract:
  High-Curie-temperature ferromagnets are promising candidates for designing
new spintronic devices. Here we have successfully synthesized a single-crystal
sample of the itinerant ferromagnet Mn$ _{5}$Ge$_{3}$ used flux method and its
critical properties were investigated by means of bulk dc-magnetization at the
boundary between the ferromagnetic (FM) and paramagnetic (PM) phase. Critical
exponents $ \beta=0.336 \pm 0.001 $ with a critical temperature $ T_{c}=300.29
\pm 0.01 $ K and $ \gamma=1.193 \pm 0.003 $ with $ T_{c} = 300.15 \pm 0.05 $ K
are obtained by the modified Arrott plot, whereas $ \delta = 4.61 \pm 0.03 $ is
deduced by a critical isotherm analysis at $ T_{c} = 300 $ K. The
self-consistency and reliability of these critical exponents are verified by
the Widom scaling law and the scaling equations. Further analysis reveals that
the spin coupling in Mn$ _{5}$Ge$_{3}$ exhibits three-dimensional Ising-like
behavior. The magnetic exchange is found to decay as $ J(r)\approx r^{-4.855} $
and the spin interactions are extended beyond the nearest neighbors, which may
be related to different set of Mn--Mn interactions with unequal magnitude of
exchange strengths. Additionally, the existence of noncollinear spin
configurations in Mn$ _{5} $Ge$ _{3} $ results in a small deviation of obtained
critical exponents from those for standard 3D-Ising model.


title: Estimating Viral Genetic Linkage Rates in the Presence of Missing Data

abstract:
  Although the interest in the the use of social and information networks has
grown, most inferences on networks assume the data collected represents the
complete. However, when ignoring missing data, even when missing completely at
random, this results in bias for estimators regarding inference network related
parameters. In this paper, we focus on constructing estimators for the
probability that a randomly selected node has node has at least one edge under
the assumption that nodes are missing completely at random along with their
corresponding edges. In addition, issues also arise in obtaining asymptotic
properties for such estimators, because linkage indicators across nodes are
correlated preventing the direct application of the Central Limit Theorem and
Law of Large Numbers. Using a subsampling approach, we present an improved
estimator for our parameter of interest that accommodates for missing data.
Utilizing the theory U-statistics, we derive consistency and asymptotic
normality of the proposed estimator. This approach decreases the bias in
estimating our parameter of interest. We illustrate our approach using the HIV
viral strains from a large cluster-randomized trial of a combination HIV
prevention intervention -- the Botswana Combination Prevention Project (BCPP).


title:
Learning Motion-Dependent Appearance for High-Fidelity Rendering of
  Dynamic Humans from a Single Camera

abstract:
  Appearance of dressed humans undergoes a complex geometric transformation
induced not only by the static pose but also by its dynamics, i.e., there
exists a number of cloth geometric configurations given a pose depending on the
way it has moved. Such appearance modeling conditioned on motion has been
largely neglected in existing human rendering methods, resulting in rendering
of physically implausible motion. A key challenge of learning the dynamics of
the appearance lies in the requirement of a prohibitively large amount of
observations. In this paper, we present a compact motion representation by
enforcing equivariance -- a representation is expected to be transformed in the
way that the pose is transformed. We model an equivariant encoder that can
generate the generalizable representation from the spatial and temporal
derivatives of the 3D body surface. This learned representation is decoded by a
compositional multi-task decoder that renders high fidelity time-varying
appearance. Our experiments show that our method can generate a temporally
coherent video of dynamic humans for unseen body poses and novel views given a
single view video.


title:
Classifying Cyber-Risky Clinical Notes by Employing Natural Language
  Processing

abstract:
  Clinical notes, which can be embedded into electronic medical records,
document patient care delivery and summarize interactions between healthcare
providers and patients. These clinical notes directly inform patient care and
can also indirectly inform research and quality/safety metrics, among other
indirect metrics. Recently, some states within the United States of America
require patients to have open access to their clinical notes to improve the
exchange of patient information for patient care. Thus, developing methods to
assess the cyber risks of clinical notes before sharing and exchanging data is
critical. While existing natural language processing techniques are geared to
de-identify clinical notes, to the best of our knowledge, few have focused on
classifying sensitive-information risk, which is a fundamental step toward
developing effective, widespread protection of patient health information. To
bridge this gap, this research investigates methods for identifying
security/privacy risks within clinical notes. The classification either can be
used upstream to identify areas within notes that likely contain sensitive
information or downstream to improve the identification of clinical notes that
have not been entirely de-identified. We develop several models using unigram
and word2vec features with different classifiers to categorize sentence risk.
Experiments on i2b2 de-identification dataset show that the SVM classifier
using word2vec features obtained a maximum F1-score of 0.792. Future research
involves articulation and differentiation of risk in terms of different global
regulatory requirements.


title: Norm attaining Lipschitz maps toward vectors

abstract:
  We extend the recent result of G. Godefroy which concerns the existence of
non-norm attaining Lipschitz maps in order to characterize the norm attainment
toward vectors for Lipschitz maps in the general setting of underlying space.
The main theorem of the present paper states that the existence of non-norm
attaining Lipschitz maps toward vectors on a large class of metric spaces is
characterized by the finite-dimensionality of range space. As an extension of
his counterexample, some denseness results on norm attaining Lipschitz maps
toward vectors are also shown.


title:
Spherical Autoregressive Models, With Application to Distributional and
  Compositional Time Series

abstract:
  We introduce a new class of autoregressive models for spherical time series,
where the dimension of the spheres on which the observations of the time series
are situated may be finite-dimensional or infinite-dimensional as in the case
of a general Hilbert sphere. Spherical time series arise in various settings.
We focus here on distributional and compositional time series. Applying a
square root transformation to the densities of the observations of a
distributional time series maps the distributional observations to the Hilbert
sphere, equipped with the Fisher-Rao metric. Likewise, applying a square root
transformation to the components of the observations of a compositional time
series maps the compositional observations to a finite-dimensional sphere,
equipped with the geodesic metric on spheres. The challenge in modeling such
time series lies in the intrinsic non-linearity of spheres and Hilbert spheres,
where conventional arithmetic operations such as addition or scalar
multiplication are no longer available. To address this difficulty, we consider
rotation operators to map observations on the sphere. Specifically, we
introduce a class of skew-symmetric operator such that the associated
exponential operators are rotation operators that for each given pair of points
on the sphere map one of the points to the other one. We exploit the fact that
the space of skew-symmetric operators is Hilbertian to develop autoregressive
modeling of geometric differences that correspond to rotations of spherical and
distributional time series. Motivating data for our methods include a time
series of yearly observations of bivariate distributions of the minimum/maximum
temperatures for a period of 120 days during each summer for the years
1990-2018 at Los Angeles (LAX) and John F. Kennedy (JFK) international
airports.


title:
A Simple Data-Driven Level Finding Method of Quantum Many-Body Systems
  based on Statistical Outlier Detection

abstract:
  We report a simple and pure data-driven method to find new energy levels of
quantum many-body systems only from observed line wavelengths. In our method,
all the possible combinations are computed from known energy levels and
wavelengths of unidentified lines. As each excited state exhibits many
transition lines to different lower levels, the true levels should be
reconstructed coincidentally from many level-line combinations, while the wrong
combinations distribute randomly. Such a coincidence can be easily detected
statistically. We demonstrate this statistical method by finding new levels for
various atomic and nuclear systems from unidentified line lists available
online.


title: Moduli of hybrid curves II: Tropical and hybrid Laplacians

abstract:
  The present paper is a sequel to our work on hybrid geometry of curves and
their moduli spaces. We introduce a notion of hybrid Laplacian, formulate a
hybrid Poisson equation, and give a mathematical meaning to the convergence
both of the Laplace operator and the solutions to the Poisson equation on
Riemann surfaces. As the main theorem of this paper, we then obtain a layered
description of the asymptotics of Arakelov Green functions on Riemann surfaces
close to the boundary of their moduli spaces. This is done in terms of a
suitable notion of hybrid Green functions.
  As a byproduct of our approach, we obtain other results of independent
interest. In particular, we introduce higher rank canonical compactifications
of fans and polyhedral spaces and use them to define the moduli space of higher
rank tropical curves. Moreover, we develop the first steps of a function theory
in higher rank non-Archimedean, hybrid, and tame analysis. Furthermore, we
establish the convergence of the Laplace operator on metric graphs toward the
tropical Laplace operator on limit tropical curves in the corresponding moduli
spaces, leading to new perspectives in operator theory on metric graphs.
  Our result on the Arakelov Green function is inspired by the works of several
authors, in particular those of Faltings, de Jong, Wentworth and Wolpert, and
solves a long-standing open problem arising from the Arakelov geometry of
Riemann surfaces. The hybrid layered behavior close to the boundary of moduli
spaces is expected to be a broad phenomenon and will be explored in our
forthcoming work.


title: Bellman Residual Orthogonalization for Offline Reinforcement Learning

abstract:
  We introduce a new reinforcement learning principle that approximates the
Bellman equations by enforcing their validity only along an user-defined space
of test functions. Focusing on applications to model-free offline RL with
function approximation, we exploit this principle to derive confidence
intervals for off-policy evaluation, as well as to optimize over policies
within a prescribed policy class. We prove an oracle inequality on our policy
optimization procedure in terms of a trade-off between the value and
uncertainty of an arbitrary comparator policy. Different choices of test
function spaces allow us to tackle different problems within a common
framework. We characterize the loss of efficiency in moving from on-policy to
off-policy data using our procedures, and establish connections to
concentrability coefficients studied in past work. We examine in depth the
implementation of our methods with linear function approximation, and provide
theoretical guarantees with polynomial-time implementations even when Bellman
closure does not hold.


title: Design of an Internet of Things System for Smart Hospitals

abstract:
  With the fast advancement of smart devices and Internet of Things (IoT)
technologies, certain established situations are opening up new avenues of
exploration. Particularly in the sphere of healthcare, the diverse and big
population, the complicated and professional data, and the stringent
environmental requirements for certain medical scenes and equipment all impose
exceptionally high standards on hospital administration. As a result, an
effective and secure Internet of things system is critical. This article
proposes an IoT system that might be used in hospitals for a variety of
purposes. This system collects data by LoRa, Wi-Fi, and other ways, uploads it
to a cloud platform for processing over a secure connection, and then feeds it
back to users in real-time via the user interface. This system enables precise
indoor localization through the use of UWB, ECG signal detection, environmental
monitoring, and data on people flow.


title: Evaluating Distributional Distortion in Neural Language Modeling

abstract:
  A fundamental characteristic of natural language is the high rate at which
speakers produce novel expressions. Because of this novelty, a heavy-tail of
rare events accounts for a significant amount of the total probability mass of
distributions in language (Baayen, 2001). Standard language modeling metrics
such as perplexity quantify the performance of language models (LM) in
aggregate. As a result, we have relatively little understanding of whether
neural LMs accurately estimate the probability of sequences in this heavy-tail
of rare events. To address this gap, we develop a controlled evaluation scheme
which uses generative models trained on natural data as artificial languages
from which we can exactly compute sequence probabilities. Training LMs on
generations from these artificial languages, we compare the sequence-level
probability estimates given by LMs to the true probabilities in the target
language. Our experiments reveal that LSTM and Transformer language models (i)
systematically underestimate the probability of sequences drawn from the target
language, and (ii) do so more severely for less-probable sequences.
Investigating where this probability mass went, (iii) we find that LMs tend to
overestimate the probability of ill formed (perturbed) sequences. In addition,
we find that this underestimation behaviour (iv) is weakened, but not
eliminated by greater amounts of training data, and (v) is exacerbated for
target distributions with lower entropy.


title: Random Matrix Time Series

abstract:
  In this paper, a time series model with coefficients that take values from
random matrix ensembles is proposed. Formal definitions, theoretical solutions,
and statistical properties are derived. Estimation and forecast methodologies
for random matrix time series are discussed with examples. Random matrix
differential equations and potential applications of the time series model are
suggested at the end.