title: From implicit learning to explicit representations

abstract:
  Using the reservoir computing framework, we demonstrate how a simple model
can solve an alternation task without an explicit working memory. To do so, a
simple bot equipped with sensors navigates inside a 8-shaped maze and turns
alternatively right and left at the same intersection in the maze. The analysis
of the model's internal activity reveals that the memory is actually encoded
inside the dynamics of the network. However, such dynamic working memory is not
accessible such as to bias the behavior into one of the two attractors (left
and right). To do so, external cues are fed to the bot such that it can follow
arbitrary sequences, instructed by the cue. This model highlights the idea that
procedural learning and its internal representation can be dissociated. If the
former allows to produce behavior, it is not sufficient to allow for an
explicit and fine-grained manipulation.


title:
Training-Free Robust Multimodal Learning via Sample-Wise Jacobian
  Regularization

abstract:
  Multimodal fusion emerges as an appealing technique to improve model
performances on many tasks. Nevertheless, the robustness of such fusion methods
is rarely involved in the present literature. In this paper, we propose a
training-free robust late-fusion method by exploiting conditional independence
assumption and Jacobian regularization. Our key is to minimize the Frobenius
norm of a Jacobian matrix, where the resulting optimization problem is relaxed
to a tractable Sylvester equation. Furthermore, we provide a theoretical error
bound of our method and some insights about the function of the extra modality.
Several numerical experiments on AV-MNIST, RAVDESS, and VGGsound demonstrate
the efficacy of our method under both adversarial attacks and random
corruptions.


title: Massive quarks in NLO dipole factorization for DIS: Transverse photon

abstract:
  We calculate the light cone wave functions for the QCD Fock components in a
transverse virtual photon necessary for applications at next-to-leading order
in the QCD coupling, including quark masses. We present a detailed calculation
of both the one loop wave function for the quark-antiquark Fock component and
the tree level wave function for the quark-antiquark-gluon Fock component. The
quark masses are renormalized in the pole mass scheme, satisfying constraints
from the requirement of Lorentz invariance. In particular the quark Pauli form
factor at NLO is recovered from the on-shell limit of the quark-antiquark Fock
component. We use our result to calculate the next-to-leading order correction
to the high energy deep inelastic scattering (DIS) transverse structure
function on a dense target in the dipole factorization framework. Together with
our earlier result for longitudinal photons, this completes the calculation of
the total deep inelastic scattering cross section in the dipole picture with
massive quarks at next-to-leading order, enabling a comparison with
experimental data.


title: Convex polytopes in restricted point sets in $\mathbb{R}^d$

abstract:
  For a finite point set $P \subset \mathbb{R}^d$, denote by $\text{diam}(P)$
the ratio of the largest to the smallest distances between pairs of points in
$P$. Let $c_{d, \alpha}(n)$ be the largest integer $c$ such that any $n$-point
set $P \subset \mathbb{R}^d$ in general position, satisfying $\text{diam}(P) <
\alpha\sqrt[d]{n}$, contains an $c$-point convex independent subset. We
determine the asymptotics of $c_{d, \alpha}(n)$ as $n \to \infty$ by showing
the existence of positive constants $\beta = \beta(d, \alpha)$ and $\gamma =
\gamma(d)$ such that $\beta n^{\frac{d-1}{d+1}} \le c_{d, \alpha}(n) \le \gamma
n^{\frac{d-1}{d+1}}$ for $\alpha\geq 2$.


title:
Discovering and forecasting extreme events via active learning in neural
  operators

abstract:
  Extreme events in society and nature, such as pandemic spikes or rogue waves,
can have catastrophic consequences. Characterizing extremes is difficult as
they occur rarely, arise from seemingly benign conditions, and belong to
complex and often unknown infinite-dimensional systems. Such challenges render
attempts at characterizing them as moot. We address each of these difficulties
by combining novel training schemes in Bayesian experimental design (BED) with
an ensemble of deep neural operators (DNOs). This model-agnostic framework
pairs a BED scheme that actively selects data for quantifying extreme events
with an ensemble of DNOs that approximate infinite-dimensional nonlinear
operators. We find that not only does this framework clearly beat Gaussian
processes (GPs) but that 1) shallow ensembles of just two members perform best;
2) extremes are uncovered regardless of the state of initial data (i.e. with or
without extremes); 3) our method eliminates "double-descent" phenomena; 4) the
use of batches of suboptimal acquisition points compared to step-by-step global
optima does not hinder BED performance; and 5) Monte Carlo acquisition
outperforms standard minimizers in high-dimensions. Together these conclusions
form the foundation of an AI-assisted experimental infrastructure that can
efficiently infer and pinpoint critical situations across many domains, from
physical to societal systems.


title:
Pareto-optimal clustering with the primal deterministic information
  bottleneck

abstract:
  At the heart of both lossy compression and clustering is a trade-off between
the fidelity and size of the learned representation. Our goal is to map out and
study the Pareto frontier that quantifies this trade-off. We focus on the
Deterministic Information Bottleneck (DIB) formulation of lossy compression,
which can be interpreted as a clustering problem. To this end, we introduce the
{\it primal} DIB problem, which we show results in a much richer frontier than
its previously studied dual counterpart. We present an algorithm for mapping
out the Pareto frontier of the primal DIB trade-off that is also applicable to
most other two-objective clustering problems. We study general properties of
the Pareto frontier, and give both analytic and numerical evidence for
logarithmic sparsity of the frontier in general. We provide evidence that our
algorithm has polynomial scaling despite the super-exponential search space;
and additionally propose a modification to the algorithm that can be used where
sampling noise is expected to be significant. Finally, we use our algorithm to
map the DIB frontier of three different tasks: compressing the English
alphabet, extracting informative color classes from natural images, and
compressing a group theory inspired dataset, revealing interesting features of
frontier, and demonstrating how the structure of the frontier can be used for
model selection with a focus on points previously hidden by the cloak of the
convex hull.


title:
Motion Correction via Locally Linear Embedding for Helical
  Photon-counting CT

abstract:
  X-ray photon-counting detector (PCD) offers low noise, high resolution, and
spectral characterization, representing a next generation of CT and enabling
new biomedical applications. It is well known that involuntary patient motion
may induce image artifacts with conventional CT scanning, and this problem
becomes more serious with PCD due to its high detector pitch and extended scan
time. Furthermore, PCD often comes with a substantial number of bad pixels,
making analytic image reconstruction challenging and ruling out
state-of-the-art motion correction methods that are based on analytical
reconstruction. In this paper, we extend our previous locally linear embedding
(LLE) cone-beam motion correction method to the helical scanning geometry,
which is especially desirable given the high cost of large-area PCD. In
addition to our adaption of LLE-based parametric searching to helical cone-beam
photon-counting CT geometry, we introduce an unreliable-volume mask to improve
the motion estimation accuracy and perform incremental updating on gradually
refined sampling grids for optimization of both accuracy and efficiency. Our
numerical results demonstrate that our method reduces the estimation errors
near the two longitudinal ends of the reconstructed volume and overall image
quality. The experimental results on clinical photon-counting scans of the
patient extremities show significant resolution improvement after motion
correction using our method, which reveals subtle fine structures previously
hidden under motion blurring and artifacts.


title: Text2LIVE: Text-Driven Layered Image and Video Editing

abstract:
  We present a method for zero-shot, text-driven appearance manipulation in
natural images and videos. Given an input image or video and a target text
prompt, our goal is to edit the appearance of existing objects (e.g., object's
texture) or augment the scene with visual effects (e.g., smoke, fire) in a
semantically meaningful manner. We train a generator using an internal dataset
of training examples, extracted from a single input (image or video and target
text prompt), while leveraging an external pre-trained CLIP model to establish
our losses. Rather than directly generating the edited output, our key idea is
to generate an edit layer (color+opacity) that is composited over the original
input. This allows us to constrain the generation process and maintain high
fidelity to the original input via novel text-driven losses that are applied
directly to the edit layer. Our method neither relies on a pre-trained
generator nor requires user-provided edit masks. We demonstrate localized,
semantic edits on high-resolution natural images and videos across a variety of
objects and scenes.


title: Towards End-to-end Unsupervised Speech Recognition

abstract:
  Unsupervised speech recognition has shown great potential to make Automatic
Speech Recognition (ASR) systems accessible to every language. However,
existing methods still heavily rely on hand-crafted pre-processing. Similar to
the trend of making supervised speech recognition end-to-end, we introduce
\wvu~which does away with all audio-side pre-processing and improves accuracy
through better architecture. In addition, we introduce an auxiliary
self-supervised objective that ties model predictions back to the input.
Experiments show that \wvu~improves unsupervised recognition results across
different languages while being conceptually simpler.


title: Distributed Robust Control for Systems with Structured Uncertainties

abstract:
  We present D-Phi iteration: an algorithm for distributed, localized, and
scalable robust control of systems with structured uncertainties. This
algorithm combines the System Level Synthesis (SLS) parametrization for
distributed control with stability criteria from L1, L-infinity, and nu robust
control. We show in simulation that this algorithm achieves near-optimal
nominal performance (within 12% of the LQR controller) while doubling or
tripling the stability margin (depending on the stability criterion) compared
to the LQR controller. To the best of our knowledge, this is the first
distributed and localized algorithm for structured robust control; furthermore,
algorithm complexity depends only on the size of local neighborhoods and is
independent of global system size. We additionally characterize the suitability
of different robustness criteria for distributed and localized computation, and
discuss open questions on the topic of distributed robust control.


title:
Leveraging Disentangled Representations to Improve Vision-Based
  Keystroke Inference Attacks Under Low Data

abstract:
  Keystroke inference attacks are a form of side-channel attacks in which an
attacker leverages various techniques to recover a user's keystrokes as she
inputs information into some display (e.g., while sending a text message or
entering her pin). Typically, these attacks leverage machine learning
approaches, but assessing the realism of the threat space has lagged behind the
pace of machine learning advancements, due in-part, to the challenges in
curating large real-life datasets. We aim to overcome the challenge of having
limited number of real data by introducing a video domain adaptation technique
that is able to leverage synthetic data through supervised disentangled
learning. Specifically, for a given domain, we decompose the observed data into
two factors of variation: Style and Content. Doing so provides four learned
representations: real-life style, synthetic style, real-life content and
synthetic content. Then, we combine them into feature representations from all
combinations of style-content pairings across domains, and train a model on
these combined representations to classify the content (i.e., labels) of a
given datapoint in the style of another domain. We evaluate our method on
real-life data using a variety of metrics to quantify the amount of information
an attacker is able to recover. We show that our method prevents our model from
overfitting to a small real-life training set, indicating that our method is an
effective form of data augmentation, thereby making keystroke inference attacks
more practical.


title: Efficient Pragmatic Program Synthesis with Informative Specifications

abstract:
  Providing examples is one of the most common way for end-users to interact
with program synthesizers. However, program synthesis systems assume that
examples consistent with the program are chosen at random, and do not exploit
the fact that users choose examples pragmatically. Prior work modeled program
synthesis as pragmatic communication, but required an inefficient enumeration
of the entire program space. In this paper, we show that it is possible to
build a program synthesizer that is both pragmatic and efficient by
approximating the joint distribution of programs with a product of independent
factors, and performing pragmatic inference on each factor separately. This
factored distribution approximates the exact joint distribution well when the
examples are given pragmatically, and is compatible with a basic neuro-symbolic
program synthesis algorithm. Surprisingly, we find that the synthesizer
assuming a factored approximation performs better than a synthesizer assuming
an exact joint distribution when evaluated on natural human inputs. This
suggests that humans may be assuming a factored distribution while
communicating programs.


title:
Towards Designing and Exploiting Generative Networks for Neutrino
  Physics Experiments using Liquid Argon Time Projection Chambers

abstract:
  In this paper, we show that a hybrid approach to generative modeling via
combining the decoder from an autoencoder together with an explicit generative
model for the latent space is a promising method for producing images of
particle trajectories in a liquid argon time projection chamber (LArTPC).
LArTPCs are a type of particle physics detector used by several current and
future experiments focused on studies of the neutrino. We implement a
Vector-Quantized Variational Autoencoder (VQ-VAE) and PixelCNN which produces
images with LArTPC-like features and introduce a method to evaluate the quality
of the images using a semantic segmentation that identifies important
physics-based features.


title:
Privacy-Preserving Federated Learning via System Immersion and Random
  Matrix Encryption

abstract:
  Federated learning (FL) has emerged as a privacy solution for collaborative
distributed learning where clients train AI models directly on their devices
instead of sharing their data with a centralized (potentially adversarial)
server. Although FL preserves local data privacy to some extent, it has been
shown that information about clients' data can still be inferred from model
updates. In recent years, various privacy-preserving schemes have been
developed to address this privacy leakage. However, they often provide privacy
at the expense of model performance or system efficiency and balancing these
tradeoffs is a crucial challenge when implementing FL schemes. In this
manuscript, we propose a Privacy-Preserving Federated Learning (PPFL) framework
built on the synergy of matrix encryption and system immersion tools from
control theory. The idea is to immerse the learning algorithm, a Stochastic
Gradient Decent (SGD), into a higher-dimensional system (the so-called target
system) and design the dynamics of the target system so that: the trajectories
of the original SGD are immersed/embedded in its trajectories, and it learns on
encrypted data (here we use random matrix encryption). Matrix encryption is
reformulated at the server as a random change of coordinates that maps original
parameters to a higher-dimensional parameter space and enforces that the target
SGD converges to an encrypted version of the original SGD optimal solution. The
server decrypts the aggregated model using the left inverse of the immersion
map. We show that our algorithm provides the same level of accuracy and
convergence rate as the standard FL with a negligible computation cost while
revealing no information about the clients' data.


title:
On the Sustainability of Lightweight Cryptography Based on PUFs
  Implemented on NAND Flash Memories Using Programming Disturbances

abstract:
  In this work, we examine the potential of Physical Unclonable Functions
(PUFs) that have been implemented on NAND Flash memories using programming
disturbances to act as sustainable primitives for the purposes of lightweight
cryptography. In particular, we investigate the ability of such PUFs to
tolerate temperature and voltage variations, and examine the current
shortcomings of existing NAND-Flash-memory PUFs that are based on programming
disturbances as well as how these could potentially be addressed in order to
provide more robust and more sustainable security solutions.


title:
Simulations of DNA-origami self-assembly reveal design-dependent
  nucleation barriers

abstract:
  Nucleation is the rate-determining step in the kinetics of many self-assembly
processes. However, the importance of nucleation in the kinetics of DNA-origami
self-assembly, which involves both the binding of staple strands and the
folding of the scaffold strand, is unclear. Here, using Monte Carlo simulations
of a lattice model of DNA origami, we find that some, but not all, designs can
have a nucleation barrier, and that this barrier disappears at lower
temperatures, rationalizing the success of isothermal assembly. We show that
the height of the nucleation barrier depends primarily on the co-axial stacking
of staples that are adjacent on the same helix, a parameter that can be
modified with staple design. Creating a nucleation barrier to DNA-origami
assembly could be useful in optimizing assembly times and yields, while
eliminating the barrier would be important for creating fast molecular sensors
that can fold or unfold without hysteresis in response to changes in the local
environment.


title:
User-Level Differential Privacy against Attribute Inference Attack of
  Speech Emotion Recognition in Federated Learning

abstract:
  Many existing privacy-enhanced speech emotion recognition (SER) frameworks
focus on perturbing the original speech data through adversarial training
within a centralized machine learning setup. However, this privacy protection
scheme can fail since the adversary can still access the perturbed data. In
recent years, distributed learning algorithms, especially federated learning
(FL), have gained popularity to protect privacy in machine learning
applications. While FL provides good intuition to safeguard privacy by keeping
the data on local devices, prior work has shown that privacy attacks, such as
attribute inference attacks, are achievable for SER systems trained using FL.
In this work, we propose to evaluate the user-level differential privacy (UDP)
in mitigating the privacy leaks of the SER system in FL. UDP provides
theoretical privacy guarantees with privacy parameters $\epsilon$ and $\delta$.
Our results show that the UDP can effectively decrease attribute information
leakage while keeping the utility of the SER system with the adversary
accessing one model update. However, the efficacy of the UDP suffers when the
FL system leaks more model updates to the adversary. We make the code publicly
available to reproduce the results in
https://github.com/usc-sail/fed-ser-leakage.


title:
Pulsating hydrogen-deficient white dwarfs and pre-white dwarfs observed
  with TESS -- IV. Discovery of two new GW Vir stars: TIC0403800675 and
  TIC1989122424

abstract:
  We present two new GW Vir-type pulsating white dwarf stars, TIC\,0403800675
(WD\,J115727.68-280349.64) and TIC\,1989122424 (WD J211738.38-552801.18)
discovered in the Transiting Exoplanet Survey Satellite (TESS) photometric
data. For both stars, the TESS light curves reveal the presence of oscillations
with periods in a narrow range between 400 and 410\,s, which are associated
with typical gravity ($g$)-modes. Follow-up ground-based spectroscopy shows
that both stars have similar effective temperature ($T_\mathrm{eff} = 110,000
\pm 10,000$\,K) and surface gravity ($\log g = 7.5 \pm 0.5$), but different
He/C composition (mass fractions): He\,=\,0.75 and C\,=\,0.25 for
TIC\,0403800675, and He\,=\,0.50 and C\,=\,0.50 for TIC\,1989122424. By
performing a fit to their spectral energy distributions, we found for both
stars radii and luminosities of $R=0.019\pm0.002\,R_\odot$ and
$\log(L/L_\odot)=1.68^{+0.15}_{-0.24}$, respectively. By employing evolutionary
tracks of PG~1159 stars, we find the masses of both stars to be $0.56\pm0.18
M_{\odot}$ from the $\log g$-$T_\mathrm{eff}$ diagram and $0.60^{+0.11}_{-0.09}
M_{\odot}$ from the Hertzsprung Russell diagram.


title:
Higher order moments dynamics for some multimode quantum master
  equations

abstract:
  We derive Heisenberg equations for arbitrary high order moments of creation
and annihilation operators in the case of the quantum master equation with a
multimode generator which is quadratic in creation and annihilation operators
and obtain their solutions. Based on them we also derive similar equations for
the case of the quantum master equation, which occur after averaging the
dynamics with a quadratic generator with respect to the classical Poisson
process. This allows us to show that dynamics of arbitrary finite-order moments
of creation and annihilation operators is fully defined by finite number of
linear differential equations in this case.


title: Global Rigidity of Triangulated Manifolds

abstract:
  We prove that if $G$ is the graph of a connected triangulated
$(d-1)$-manifold, for $d\geq 3$, then $G$ is generically globally rigid in
$\mathbb R^d$ if and only if it is $(d+1)$-connected and, if $d=3$, $G$ is not
planar. The special case $d=3$ resolves a conjecture of Connelly. Our results
actually apply to a much larger class of simplicial complexes, namely the
circuits of the simplicial matroid. We also give two significant applications
of our main theorems. We show that that the characterisation of pseudomanifolds
with extremal edge numbers given by the Lower Bound Theorem extends to circuits
of the simplicial matroid. We also prove the generic case of a conjecture of
Kalai concerning the reconstructability of a polytope from its space of
stresses. The proofs of our main results adapt earlier ideas of Fogelsanger and
Whiteley to the setting of global rigidity. In particular we resolve a special
case of Whiteley's vertex splitting conjecture for global rigidity.


title:
Recursive Restoration Refinement: A Fast Heuristic for Near-Optimal
  Restoration Prioritization in Power Systems

abstract:
  The prioritization of restoration actions after large power system outages
plays a key role in how quickly power can be restored. It has been shown that
fast and intuitive heuristics for restoration prioritization most often result
in low-quality restoration plans. Meanwhile, mathematical optimization tools
that find high-quality restoration plans are too slow to be applied to
restoration planning problems of practical interest. This work makes a
significant step in closing this quality vs compute time gap by proposing the
Recursive Restoration Refinement heuristic for power system restoration. This
heuristic is shown to produce near-optimal restoration plans up to 1,000 times
faster than other state-of-the-art solution methods on a range of test cases
with up to 500 buses and 700 damaged components. The potential impact of this
new heuristic is demonstrated by a preliminary analysis of the key features of
high-quality restoration plans. The recursive restoration refinement algorithm
and other methods explored in this work have been made available as part of the
open-source software package, PowerModelsRestoration, to support ongoing
research in power restoration algorithms.


title: Celestial Geometry

abstract:
  Celestial holography expresses $\mathcal{S}$-matrix elements as correlators
in a CFT living on the night sky. Poincar\'e invariance imposes additional
selection rules on the allowed positions of operators. As a consequence,
$n$-point correlators are only supported on certain patches of the celestial
sphere, depending on the labeling of each operator as incoming/outgoing. Here
we initiate a study of the celestial geometry, examining the kinematic support
of celestial amplitudes for different crossing channels. We give simple
geometric rules for determining this support. For $n\ge 5$, we can view these
channels as tiling together to form a covering of the celestial sphere. Our
analysis serves as a stepping off point to better understand the analyticity of
celestial correlators and illuminate the connection between the 4D kinematic
and 2D CFT notions of crossing symmetry.


title: Deep Graphic FBSDEs for Opinion Dynamics Stochastic Control

abstract:
  In this paper, we present a scalable deep learning approach to solve opinion
dynamics stochastic optimal control problems with mean field term coupling in
the dynamics and cost function. Our approach relies on the probabilistic
representation of the solution of the Hamilton-Jacobi-Bellman partial
differential equation. Grounded on the nonlinear version of the Feynman-Kac
lemma, the solutions of the Hamilton-Jacobi-Bellman partial differential
equation are linked to the solution of Forward-Backward Stochastic Differential
Equations. These equations can be solved numerically using a novel deep neural
network with architecture tailored to the problem in consideration. The
resulting algorithm is tested on a polarized opinion consensus experiment. The
large-scale (10K) agents experiment validates the scalability and
generalizability of our algorithm. The proposed framework opens up the
possibility for future applications on extremely large-scale problems.


title:
Co-optimization of power line shutoff and restoration for electric grids
  under high wildfire ignition risk

abstract:
  Electric power infrastructure has ignited several of the most destructive
wildfires in recent history. Preemptive power shut-offs are an effective tool
to mitigate the risk of ignitions from power lines, but at the same time can
cause widespread power outages. Electric utilities are thus faced with the
challenging trade-off of where and when to implement these shut-offs, as well
as how to most efficiently restore power once the wildfire risk is reduced.
This work proposes a mathematical optimization problem to help utilities make
these decisions. Our model co-optimizes the power shut-off (considering both
wildfire risk reduction and power outages) as well as the post-event inspection
and energization of lines. It is implemented as a rolling horizon optimization
problem that is resolved whenever new forecasts of load and wildfire risk
become available. We demonstrate our method on the IEEE RTS-GMLC test case
using real wildfire risk data US Geological Survey, and investigate the
sensitivity of the results to the forecast quality, decision horizon and system
restoration budget. The software implementation is available in the open source
software package PowerModelsWildfire.jl.


title: A robust scalar-on-function logistic regression for classification

abstract:
  Scalar-on-function logistic regression, where the response is a binary
outcome and the predictor consists of random curves, has become a general
framework to explore a linear relationship between the binary outcome and
functional predictor. Most of the methods used to estimate this model are based
on the least-squares type estimators. However, the least-squares estimator is
seriously hindered by outliers, leading to biased parameter estimates and an
increased probability of misclassification. This paper proposes a robust
partial least squares method to estimate the regression coefficient function in
the scalar-on-function logistic regression. The regression coefficient function
represented by functional partial least squares decomposition is estimated by a
weighted likelihood method, which downweighs the effect of outliers in the
response and predictor. The estimation and classification performance of the
proposed method is evaluated via a series of Monte Carlo experiments and a
strawberry puree data set. The results obtained from the proposed method are
compared favorably with existing methods.


title: Depth-Guided Sparse Structure-from-Motion for Movies and TV Shows

abstract:
  Existing approaches for Structure from Motion (SfM) produce impressive 3-D
reconstruction results especially when using imagery captured with large
parallax. However, to create engaging video-content in movies and TV shows, the
amount by which a camera can be moved while filming a particular shot is often
limited. The resulting small-motion parallax between video frames makes
standard geometry-based SfM approaches not as effective for movies and TV
shows. To address this challenge, we propose a simple yet effective approach
that uses single-frame depth-prior obtained from a pretrained network to
significantly improve geometry-based SfM for our small-parallax setting. To
this end, we first use the depth-estimates of the detected keypoints to
reconstruct the point cloud and camera-pose for initial two-view
reconstruction. We then perform depth-regularized optimization to register new
images and triangulate the new points during incremental reconstruction. To
comprehensively evaluate our approach, we introduce a new dataset (StudioSfM)
consisting of 130 shots with 21K frames from 15 studio-produced videos that are
manually annotated by a professional CG studio. We demonstrate that our
approach: (a) significantly improves the quality of 3-D reconstruction for our
small-parallax setting, (b) does not cause any degradation for data with
large-parallax, and (c) maintains the generalizability and scalability of
geometry-based sparse SfM. Our dataset can be obtained at
https://github.com/amazon-research/small-baseline-camera-tracking.


title:
Unraveling the stable cathode electrolyte interface in all solid-state
  thin-film battery operating at 5V

abstract:
  Spinel-type LiNi0.5Mn1.5O4 (LNMO) is one of the most promising 5 V-class
cathode materials for Li-ion batteries that can achieve high energy density and
low production costs. However, in liquid electrolyte cells, the high voltage
causes continuous cell degradation through the oxidative decomposition of
carbonate-based liquid electrolytes. In contrast, some solid-state electrolytes
have a wide electrochemical stability range and can withstand the required
oxidative potential. In this work, a thin-film battery consisting of a LNMO
cathode with a solid lithium phosphorus oxynitride (LiPON) electrolyte is
tested and their interface before and after cycling is characterized. With Li
metal as the anode, this system can deliver stable performance for 600 cycles
with an average Coulombic efficiency > 99%. Neutron depth profiling indicates a
slight overlithiated layer at the interface prior to cycling; a result that is
consistent with the excess charge capacity measured during the first cycle.
Cryogenic electron microscopy further reveals intimate contact between LNMO and
LiPON without noticeable structure and chemical composition evolution after
extended cycling, demonstrating the superior stability of LiPON against a high
voltage cathode. Consequently, we propose design guidelines for interface
engineering that could accelerate the commercialization of a high voltage cell
with solid or liquid electrolytes.


title: SILCC-Zoom: the dynamic balance in molecular cloud substructures

abstract:
  How molecular clouds fragment and create the dense structures which go on to
form stars is an open question. We investigate the relative importance of
different energy terms (kinetic, thermal, magnetic, and gravity - both
self-gravity and tidal forces) for the formation and evolution of molecular
clouds and their sub-structures based on the SILCC-Zoom simulations. These
simulations follow the self-consistent formation of cold molecular clouds down
to scales of 0.1 pc from the diffuse supernova-driven interstellar medium in a
stratified galactic disc. We study the time evolution of seven molecular clouds
(five with magnetic fields and two without) for 1.5-2 Myr. Using a dendrogram,
we identify hierarchical 3D sub-structures inside the clouds with the aim to
understand their dynamics and distinguish between the theories of
gravo-turbulent fragmentation and global hierarchical collapse. The virial
analysis shows that the dense gas is indeed dominated by the interplay of
gravity and turbulence, while magnetic fields and thermal pressure are only
important for fluffy, atomic structures. Over time, gravitationally bound
sub-structures emerge from a marginally bound medium (viral ratio $1 \leq
\alpha_{\rm vir}^{\rm vol} <2$) as a result of large-scale supernova-driven
inflows rather than global collapse. A detailed tidal analysis shows that the
tidal tensor is highly anisotropic. Yet the tidal forces are generally not
strong enough to disrupt either large-scale or dense sub-structures but cause
their deformation. By comparing tidal and crossing time scales, we find that
tidal forces do not seem to be the main driver of turbulence within the
molecular clouds.


title: Fluorescence profile of an NV centre in a nanodiamond

abstract:
  Nanodiamonds containing luminescent point defects are widely explored for
applications in quantum bio-sensing such as nanoscale magnetometry,
thermometry, and electrometry. A key challenge in the development of such
applications is a large variation in fluorescence properties observed between
particles, even when obtained from the same batch or nominally identical
fabrication processes. By theoretically modelling the emission of
nitrogen-vacancy colour centres in spherical nanoparticles, we are able to show
that the fluorescence spectrum varies with the exact position of the emitter
within the nanoparticle, with noticeable effects seen when the diamond radius,
$a$, is larger than around 110 nm, and significantly modified fluorescence
profiles found for larger particles when $a=200$ nm and $a=300$ nm, while
negligible effects below $a=100$ nm. These results show that the reproducible
geometry of point defect position within narrowly sized batch of diamond
crystals is necessary for controlling the emission properties. Our results are
useful for understanding the extent to which nanodiamonds can be optimised for
bio-sensing applications.


title:
In-Pocket 3D Graphs Enhance Ligand-Target Compatibility in Generative
  Small-Molecule Creation

abstract:
  Proteins in complex with small molecule ligands represent the core of
structure-based drug discovery. However, three-dimensional representations are
absent from most deep-learning-based generative models. We here present a
graph-based generative modeling technology that encodes explicit 3D
protein-ligand contacts within a relational graph architecture. The models
combine a conditional variational autoencoder that allows for activity-specific
molecule generation with putative contact generation that provides predictions
of molecular interactions within the target binding pocket. We show that
molecules generated with our 3D procedure are more compatible with the binding
pocket of the dopamine D2 receptor than those produced by a comparable
ligand-based 2D generative method, as measured by docking scores, expected
stereochemistry, and recoverability in commercial chemical databases. Predicted
protein-ligand contacts were found among highest-ranked docking poses with a
high recovery rate. This work shows how the structural context of a protein
target can be used to enhance molecule generation.


title: Moir\'e band structures of twisted phosphorene bilayers

abstract:
  We report on the theoretical electronic spectra of twisted phosphorene
bilayers exhibiting moir\'e patterns, as computed by means of a continuous
approximation to the moir\'e superlattice Hamiltonian. Our model is constructed
by interpolating between effective $\Gamma$-point conduction- and valence-band
Hamiltonians for the different stacking configurations approximately realized
across the moir\'e supercell, formulated on symmetry grounds. We predict the
realization of three distinct regimes for $\Gamma$-point electrons and holes at
different twist angle ranges: a Hubbard regime for small twist angles $\theta <
2^\circ$, where the electronic states form arrays of quantum-dot-like states,
one per moir\'e supercell. A Tomonaga-Luttinger regime at intermediate twist
angles $2^\circ < \theta \lesssim 10^\circ$, characterized by the appearance of
arrays of quasi-1D states. Finally, a ballistic regime at large twist angles
$\theta \gtrsim 10^\circ$, where the band-edge states are delocalized, with
dispersion anisotropies modulated by the twist angle. Our method correctly
reproduces recent results based on large-scale ab initio calculations at a much
lower computational cost, and with fewer restrictions on the twist angles
considered.


title: Inferring Rewards from Language in Context

abstract:
  In classic instruction following, language like "I'd like the JetBlue flight"
maps to actions (e.g., selecting that flight). However, language also conveys
information about a user's underlying reward function (e.g., a general
preference for JetBlue), which can allow a model to carry out desirable actions
in new contexts. We present a model that infers rewards from language
pragmatically: reasoning about how speakers choose utterances not only to
elicit desired actions, but also to reveal information about their preferences.
On a new interactive flight-booking task with natural language, our model more
accurately infers rewards and predicts optimal actions in unseen environments,
in comparison to past work that first maps language to actions (instruction
following) and then maps actions to rewards (inverse reinforcement learning).


title: Compositions and tensor products of linear maps between matrix algebras

abstract:
  We exhibit an identity which connects tensor products and compositions of
linear maps between matrix algebras through the Choi matrices. As applications
of this identity, we characterize mapping cones in terms of tensor products,
provide several equivalent statements to PPT (positive partial transpose)
square conjecture in terms of tensor products, and show that the Choi matrix
may be defined independent of coordinates to some extent. We also consider
various mapping cones arising from ampliation and factorization.


title:
On decay of the solutions for the dispersion generalized-Benjamin-Ono
  and Benjamin-Ono equations

abstract:
  We show that uniqueness results of the kind those obtained for KdV and
Schr\"odinger equations ([7], [28]), are not valid for the dispersion
generalized-Benjamin-Ono equation in the weighted Sobolev spaces
  $$H^s(\R)\cap L^2(x^{2r}dx),$$ for appropriated $s$ and $r$.
  In particular, we obtain that the uniqueness result proved for the dispersion
generalized-Benjamin-Ono equation ([13]), is not true for all pairs of
solutions $u_1\neq 0$ and $u_2\neq 0$. To achieve these results we employ the
techniques present in our recent work [6]. We also improve some Theorems
established for the dispersion generalized-Benjamin-Ono equation and for the
Benjamin-Ono equation ([13], [12]).


title: Widely Tunable Berry curvature in the Magnetic Semimetal Cr1+dTe2

abstract:
  Magnetic semimetals have increasingly emerged as lucrative platforms hosting
spin-based topological phenomena in real and momentum spaces. Of particular
interest is the emergence of Berry curvature, whose geometric origin,
accessibility from Hall transport experiments, and material tunability, bodes
well for new physics and practical devices. Cr1+dTe2, a self-intercalated
magnetic transition metal dichalcogenide, TMD, exhibits attractive natural
attributes relevant to such applications, including topological magnetism,
tunable electron filling, magnetic frustration etc. While recent studies have
explored real-space Berry curvature effects in this material, similar
considerations of momentum-space Berry curvature are lacking. Here, we
systematically investigate the electronic structure and transport properties of
epitaxial Cr1+dTe2 thin films over a wide range of doping, d between 0.33 and
0.71. Spectroscopic experiments reveal the presence of a characteristic
semi-metallic band region near the Brillouin Zone edge, which shows a rigid
band like energy shift as a function of d. Transport experiments show that the
intrinsic component of the anomalous Hall effect, AHE, is sizable, and
undergoes a sign flip across d. Finally, density functional theory calculations
establish a causal link between the observed doping evolution of the band
structure and AHE: the AHE sign flip is shown to emerge from the sign change of
the Berry curvature, as the semi-metallic band region crosses the Fermi energy.
Our findings underscore the increasing relevance of momentum-space Berry
curvature in magnetic TMDs and provide a unique platform for intertwining
topological physics in real and momentum spaces.


title: Maintaining Expander Decompositions via Sparse Cuts

abstract:
  In this article, we show that the algorithm of maintaining expander
decompositions in graphs undergoing edge deletions directly by removing sparse
cuts repeatedly can be made efficient.
  Formally, for an $m$-edge undirected graph $G$, we say a cut $(S, \bar{S})$
is $\phi$-sparse if $|E_G(S, \bar{S})| < \phi \cdot \min\{vol_G(S),
vol_G(\bar{S})\}$. A $\phi$-expander decomposition of $G$ is a partition of $V$
into sets $X_1, X_2, \ldots, X_k$ such that each cluster $G[X_i]$ contains no
$\phi$-sparse cut (meaning it is a $\phi$-expander) with $\tilde{O}(\phi m)$
edges crossing between clusters. A natural way to compute a $\phi$-expander
decomposition is to decompose clusters by $\phi$-sparse cuts until no such cut
is contained in any cluster. We show that even in graphs undergoing edge
deletions, a slight relaxation of this meta-algorithm can be implemented
efficiently with amortized update time $m^{o(1)}/\phi^2$.
  Our approach naturally extends to maintaining directed $\phi$-expander
decompositions and $\phi$-expander hierarchies and thus gives a unifying
framework while having simpler proofs than previous state-of-the-art work. In
all settings, our algorithm matches the run-times of previous algorithms up to
subpolynomial factors. Moreover, our algorithm provides stronger guarantees for
$\phi$-expander decompositions, for example, for graphs undergoing edge
deletions, our approach achieves the first sublinear $\phi m^{o(1)}$ recourse
bounds on the number of edges to become crossing between clusters.
  Our techniques also give by far the simplest, deterministic algorithms for
maintaining Strongly-Connected Components (SCCs) in directed graphs undergoing
edge deletions, and for maintaining connectivity in undirected fully-dynamic
graphs, both matching the current state-of-the-art run-times up to
subpolynomial factors.


title: Bikei Module Invariants of Unoriented Surface-Links

abstract:
  We extend our previous work from arXiv:1903.06863 on biquandle module
invariants of oriented surface-links to the case of unoriented surface-links
using bikei modules. The resulting infinite family of enhanced invariants
proves be effective at distinguishing unoriented and especially non-orientable
surface-links; in particular, we show that these invariants are more effective
than the bikei homset cardinality invariant alone at distinguishing
non-orientable surface-links. Moreover, as another application we note that our
previous biquandle modules which do not satisfy the bikei module axioms are
capable of distinguishing different choices of orientation for orientable
surface-links as well as classical and virtual links.


title:
Service resource allocation problem in the IoT driven personalized
  healthcare information platform

abstract:
  With real-time monitoring of the personalized healthcare condition, the IoT
wearables collect the health data and transfer it to the healthcare information
platform. The platform processes the data into healthcare recommendations and
then delivers them to the users. The IoT structures in the personalized
healthcare information service allows the users to engage in the loop in
servitization more convenient in the COVID-19 pandemic. However, the
uncertainty of the engagement behavior among the individual may result in
inefficient of the service resource allocation. This paper seeks an efficient
way to allocate the service resource by controlling the service capacity and
pushing the service to the active users automatically. In this study, we
propose a deep reinforcement learning method to solve the service resource
allocation problem based on the proximal policy optimization (PPO) algorithm.
Experimental results using the real world (open source) sport dataset reveal
that our proposed proximal policy optimization adapts well to the users'
changing behavior and with improved performance over fixed service resource
policies.


title:
Solving integer multi-objective optimization problems using TOPSIS,
  Differential Evolution and Tabu Search

abstract:
  This paper presents a method to solve non-linear integer multiobjective
optimization problems. First the problem is formulated using the Technique for
Order Preference by Similarity to Ideal Solution (TOPSIS). Next, the
Differential Evolution (DE) algorithm in its three versions (standard DE, DE
best and DEGL) are used as optimizer. Since the solutions found by the DE
algorithms are continuous, the Tabu Search (TS) algorithm is employed to find
integer solutions during the optimization process. Experimental results show
the effectiveness of the proposed method.


title: LADUMA: Discovery of a luminous OH megamaser at $z > 0.5$

abstract:
  In the local Universe, OH megamasers (OHMs) are detected almost exclusively
in infrared-luminous galaxies, with a prevalence that increases with IR
luminosity, suggesting that they trace gas-rich galaxy mergers. Given the
proximity of the rest frequencies of OH and the hyperfine transition of neutral
atomic hydrogen (HI), radio surveys to probe the cosmic evolution of HI in
galaxies also offer exciting prospects for exploiting OHMs to probe the cosmic
history of gas-rich mergers. Using observations for the Looking At the Distant
Universe with the MeerKAT Array (LADUMA) deep HI survey, we report the first
untargeted detection of an OHM at $z > 0.5$, LADUMA J033046.20$-$275518.1
(nicknamed "Nkalakatha"). The host system, WISEA J033046.26$-$275518.3, is an
infrared-luminous radio galaxy whose optical redshift $z \approx 0.52$ confirms
the MeerKAT emission line detection as OH at a redshift $z_{\rm OH} = 0.5225
\pm 0.0001$ rather than HI at lower redshift. The detected spectral line has
18.4$\sigma$ peak significance, a width of $459 \pm 59\,{\rm km\,s^{-1}}$, and
an integrated luminosity of $(6.31 \pm 0.18\,{\rm [statistical]}\,\pm
0.31\,{\rm [systematic]}) \times 10^3\,L_\odot$, placing it among the most
luminous OHMs known. The galaxy's far-infrared luminosity $L_{\rm FIR} = (1.576
\pm 0.013) \times 10^{12}\,L_\odot$ marks it as an ultra-luminous infrared
galaxy; its ratio of OH and infrared luminosities is similar to those for
lower-redshift OHMs. A comparison between optical and OH redshifts offers a
slight indication of an OH outflow. This detection represents the first step
towards a systematic exploitation of OHMs as a tracer of galaxy growth at high
redshifts.


title: Simple and Effective Unsupervised Speech Synthesis

abstract:
  We introduce the first unsupervised speech synthesis system based on a
simple, yet effective recipe. The framework leverages recent work in
unsupervised speech recognition as well as existing neural-based speech
synthesis. Using only unlabeled speech audio and unlabeled text as well as a
lexicon, our method enables speech synthesis without the need for a
human-labeled corpus. Experiments demonstrate the unsupervised system can
synthesize speech similar to a supervised counterpart in terms of naturalness
and intelligibility measured by human evaluation.


title:
Mars: Near-Optimal Throughput with Shallow Buffers in Reconfigurable
  Datacenter Networks

abstract:
  The performance of large-scale computing systems often critically depends on
high-performance communication networks. Dynamically reconfigurable topologies,
e.g., based on optical circuit switches, are emerging as an innovative new
technology to deal with the explosive growth of datacenter traffic.
Specifically, periodic reconfigurable datacenter networks (RDCNs) such as
RotorNet (SIGCOMM 2017), Opera (NSDI 2020) and Sirius (SIGCOMM 2020) have been
shown to provide high throughput, by emulating a complete graph through fast
periodic circuit switch scheduling.
  However, to achieve such a high throughput, existing reconfigurable network
designs pay a high price: in terms of potentially high delays, but also, as we
show as a first contribution in this paper, in terms of the high buffer
requirements. In particular, we show that under buffer constraints, emulating
the high-throughput complete-graph is infeasible at scale, and we uncover a
spectrum of unvisited and attractive alternative RDCNs, which emulate regular
graphs of lower node degree.
  We present Mars, a periodic reconfigurable topology which emulates a
$d$-regular graph with near-optimal throughput. In particular, we
systematically analyze how the degree $d$ can be optimized for throughput given
the available buffer and delay tolerance of the datacenter.


title:
Emphasis on the Minimization of False Negatives or False Positives in
  Binary Classification

abstract:
  The minimization of specific cases in binary classification, such as false
negatives or false positives, grows increasingly important as humans begin to
implement more machine learning into current products. While there are a few
methods to put a bias towards the reduction of specific cases, these methods
aren't very effective, hence their minimal use in models. To this end, a new
method is introduced to reduce the False Negatives or False positives without
drastically changing the overall performance or F1 score of the model. This
method involving the careful change to the real value of the input after
pre-training the model. Presenting the results of this method being applied on
various datasets, some being more complex than others. Through experimentation
on multiple model architectures on these datasets, the best model was found. In
all the models, an increase in the recall or precision, minimization of False
Negatives or False Positives respectively, was shown without a large drop in F1
score.


title:
Topological Data Analysis of Human Brain Networks Through Order
  Statistics

abstract:
  Understanding the topological characteristics of the brain network across a
population is central to understanding brain functions. The abstraction of
human connectome as a graph has been pivotal in gaining insights on the
topological features of the brain network. The development of group-level
statistical inference procedures in brain graphs while accounting for the
heterogeneity and randomness still remains a difficult task. In this study, we
develop a robust statistical framework based on persistent homology using the
order statistics for analyzing brain networks. The use of order statistics
greatly simplifies the computation of the persistent barcodes. We validate the
proposed methods using comprehensive simulation studies and subsequently apply
to the resting-state functional magnetic resonance images. We conclude a
statistically significant topological difference between the male and female
brain networks.


title: Prime Principal Right Ideal Rings

abstract:
  Let R be a commutative ring with unity $1\in R$. In this article, we
introduce the concept of prime principal right ideal rings (\textbf{PPRIR}), A
prime ideal P of R is said to be prime principal right ideal (\textbf{PPRI}) is
given by $P =\{ ar : r\in R\}$ for some element a. The ring R is said to be
prime principal right ideal ring (\textbf{PPRIR}) if every prime ideal of R is
a prime principal right ideal (\textbf{PPRI}). A prime principal right ideal
ring R is called a prime principal right ideal domain (\textbf{PPRID}) if R is
a domain. Several properties and characteristics of prime principal right ideal
ring (\textbf{PPRIR}).


title: Minimal rational curves and 1-flat irreducible G-structures

abstract:
  1-flat irreducible G-structures, equivalently, irreducible G-structures
admitting torsion-free affine connections, have been studied extensively in
differential geometry, especially in connection with the theory of affine
holonomy groups. We propose to study them in a setting in algebraic geometry,
where they arise from varieties of minimal rational tangents (VMRT) associated
to families of minimal rational curves on uniruled projective manifolds.
  We prove that such a structure is locally symmetric when the dimension of the
uniruled projective manifold is at least 5. By the classification result of
Merkulov and Schwachh\"ofer on irreducible affine holonomy, the problem is
reduced to the case when the VMRT at a general point of the uniruled projective
manifold is isomorphic to a subadjoint variety. In the latter situation, we
prove a stronger result that, without the assumption of 1-flatness, the
structure arising from VMRT is always locally flat. The proof employs the
method of Cartan connections. An interesting feature is that Cartan connections
are considered not for the G-structures themselves, but for certain geometric
structures on the spaces of minimal rational curves.


title: Prosodic Alignment for off-screen automatic dubbing

abstract:
  The goal of automatic dubbing is to perform speech-to-speech translation
while achieving audiovisual coherence. This entails isochrony, i.e.,
translating the original speech by also matching its prosodic structure into
phrases and pauses, especially when the speaker's mouth is visible. In previous
work, we introduced a prosodic alignment model to address isochrone or
on-screen dubbing. In this work, we extend the prosodic alignment model to also
address off-screen dubbing that requires less stringent synchronization
constraints. We conduct experiments on four dubbing directions - English to
French, Italian, German and Spanish - on a publicly available collection of TED
Talks and on publicly available YouTube videos. Empirical results show that
compared to our previous work the extended prosodic alignment model provides
significantly better subjective viewing experience on videos in which on-screen
and off-screen automatic dubbing is applied for sentences with speakers mouth
visible and not visible, respectively.


title: Improving Zero-Shot Event Extraction via Sentence Simplification

abstract:
  The success of sites such as ACLED and Our World in Data have demonstrated
the massive utility of extracting events in structured formats from large
volumes of textual data in the form of news, social media, blogs and discussion
forums. Event extraction can provide a window into ongoing geopolitical crises
and yield actionable intelligence. With the proliferation of large pretrained
language models, Machine Reading Comprehension (MRC) has emerged as a new
paradigm for event extraction in recent times. In this approach, event argument
extraction is framed as an extractive question-answering task. One of the key
advantages of the MRC-based approach is its ability to perform zero-shot
extraction. However, the problem of long-range dependencies, i.e., large
lexical distance between trigger and argument words and the difficulty of
processing syntactically complex sentences plague MRC-based approaches. In this
paper, we present a general approach to improve the performance of MRC-based
event extraction by performing unsupervised sentence simplification guided by
the MRC model itself. We evaluate our approach on the ICEWS geopolitical event
extraction dataset, with specific attention to `Actor' and `Target' argument
roles. We show how such context simplification can improve the performance of
MRC-based event extraction by more than 5% for actor extraction and more than
10% for target extraction.


title:
Critical Sets of Elliptic Equations with Rapidly Oscillating
  Coefficients in Two Dimensions

abstract:
  In this paper we continue the study of critical sets of solutions $u_\e$ of
second-order elliptic equations in divergence form with rapidly oscillating and
periodic coefficients. In \cite{Lin-Shen-3d}, by controling the "turning" of
approximate tangent planes, we show that the $(d-2)$-dimensional Hausdorff
measures of the critical sets are bounded uniformly with respect to the period
$\e$, provided that doubling indices for solutions are bounded. In this paper
we use a different approach, based on the reduction of the doubling indices of
$u_\e$, to study the two-dimensional case. The proof relies on the fact that
the critical set of a homogeneous harmonic polynomial of degree two or higher
in dimension two contains only one point.


title: Magnon mediated spin entanglement in the strong coupling regime

abstract:
  We present that two spin defects (SDs) can be entangled through a magnon
polariton mode, within the strong coupling regime. The magnonic modes are
provided by an antiferromagnetic (AF) MnF$_{2}$ layer and their dispersion is
characterized by the layer's thickness. The macroscopic quantum electrodynamics
theory is used to describe the light-matter interactions, where the Green's
functions are its core element. The individual SD relaxes by exciting the
magnon polariton modes, exhibiting high enhancement values of the Purcell
factor. When two SDs are considered, an oscillatory population exchange is
observed between them, a sign of strong light-matter coupling, where the
concurrence value is used to quantify the level of entanglement. The thinner AF
layers can potentially be used to promote interactions between multiple spins
through long range coupling, this is a desired feature to fabricate high demand
applications in the fields of quantum measurement and computation.


title: The spectrum consistency of fractional quantum Hall effect model

abstract:
  The spectrum consistency of the three fermion family $U(1)\times U(1)$
quantum electrodynamics in three space-time dimensions, proposed by Kaplan and
Sen [Phys. Rev. Lett. 124 (2020) 131601], is analyzed.

categories: hep-th cond-mat.mes-hall cond-mat.other cond-mat.str-el quant-ph


title: Schmidt Type Partitions

abstract:
  Recently, Andrews and Paule studied Schmidt type partitions using MacMahon's
Partition Analysis and obtained various interesting results. In this paper, we
focus on the combinatorics of Schmidt type partition theorems and characterize
them in a general and refined form. In addition, we also present some
overpartition analogues of Schmidt type partition theorems.


title:
Next-Generation Superconducting RF Technology based on Advanced Thin
  Film Technologies and Innovative Materials for Accelerator Enhanced
  Performance and Energy Reach

abstract:
  Superconducting RF is a key technology for future particle accelerators, now
relying on advanced surfaces beyond bulk Nb for a leap in performance and
efficiency. The SRF thin film strategy aims at transforming the current SRF
technology by using highly functional materials, addressing all the necessary
functions. The community is deploying efforts in three research thrusts to
develop next-generation thin-film based cavities. Nb on Cu cavities are
developed to perform as good as or better than bulk Nb at reduced cost and with
better thermal stability. Recent results showing improved accelerating field
and dramatically reduced Q slope show their potential for many applications.
The second research thrust is to develop cavities coated with materials that
can operate at higher temperatures or sustain higher fields. Proof of principle
has been established for the merit of Nb3Sn for SRF application. Research is
now needed to further exploit the material and reach its full potential with
novel deposition techniques. The third line of research is to push SRF
performance beyond the capabilities of the superconductors alone with
multilayered coatings. In parallel, developments are needed to provide quality
substrates, cooling schemes and cryomodule design tailored to thin film
cavities. Recent results in these three research thrusts suggest that SRF thin
film technologies are at the eve of a technological revolution. For them to
mature, active community support and sustained funding are needed to address
fundamental developments supporting material deposition techniques, surface and
RF research, technical challenges associated with scaling and
industrialization. With dedicated and sustained investment, next-generation
thin-film based cavities will become a reality with high performance and
efficiency, facilitating energy sustainable science while enabling higher
luminosity, and higher energy.


title:
Nearly Tight Spectral Sparsification of Directed Hypergraphs by a Simple
  Iterative Sampling Algorithm

abstract:
  Spectral hypergraph sparsification, which is an attempt to extend well-known
spectral graph sparsification to hypergraphs, has been extensively studied over
the past few years. For undirected hypergraphs, Kapralov, Krauthgamer, Tardos,
and Yoshida (2022) have recently obtained an algorithm for constructing an
$\varepsilon$-spectral sparsifier of optimal $O^*(n)$ size, where $O^*$
suppresses the $\varepsilon^{-1}$ and $\log n$ factors, while the optimal
sparsifier size has not been known for directed hypergraphs. In this paper, we
present the first algorithm for constructing an $\varepsilon$-spectral
sparsifier for a directed hypergraph with $O^*(n^2)$ hyperarcs. This improves
the previous bound by Kapralov, Krauthgamer, Tardos, and Yoshida (2021), and it
is optimal up to the $\varepsilon^{-1}$ and $\log n$ factors since there is a
lower bound of $\Omega(n^2)$ even for directed graphs. For general directed
hypergraphs, we show the first non-trivial lower bound of
$\Omega(n^2/\varepsilon)$.
  Our algorithm can be regarded as an extension of the spanner-based graph
sparsification by Koutis and Xu (2016). To exhibit the power of the
spanner-based approach, we also examine a natural extension of Koutis and Xu's
algorithm to undirected hypergraphs. We show that it outputs an
$\varepsilon$-spectral sparsifier of an undirected hypergraph with $O^*(nr^3)$
hyperedges, where $r$ is the maximum size of a hyperedge. Our analysis of the
undirected case is based on that of Bansal, Svensson, and Trevisan (2019), and
the bound matches that of the hypergraph sparsification algorithm by Bansal et
al. We further show that our algorithm inherits advantages of the spanner-based
sparsification in that it is fast, can be implemented in parallel, and can be
converted to be fault-tolerant.


title: IoT-Scan: Network Reconnaissance for the Internet of Things

abstract:
  Network reconnaissance is a core networking and security procedure aimed at
discovering devices and their properties. For IP-based networks, several
network reconnaissance tools are available, such as Nmap. For the Internet of
Things (IoT), there is currently no similar tool capable of discovering devices
across multiple protocols. In this paper, we present IoT-Scan, a universal IoT
network reconnaissance tool.
  IoT-Scan is based on software defined radio (SDR) technology, which allows
for a flexible software-based implementation of radio protocols. We present a
series of passive, active, multi-channel, and multi-protocol scanning
algorithms to speed up the discovery of devices with IoT-Scan. We benchmark the
passive scanning algorithms against a theoretical traffic model based on the
non-uniform coupon collector problem. We implement the scanning algorithms and
compare their performance for four popular IoT protocols: Zigbee, Bluetooth LE,
Z-Wave, and LoRa. Through extensive experiments with dozens of IoT devices, we
demonstrate that our implementation experiences minimal packet losses and
achieves performance near the theoretical benchmark. Using multi-protocol
scanning, we further demonstrate a reduction of 70\% in the discovery times of
Bluetooth and Zigbee devices in the 2.4\,GHz band and of LoRa and Z-Wave
devices in the 900\,MHz band, compared to sequential passive scanning. We make
our implementation and data available to the research community to allow
independent replication of our results and facilitate further development of
the tool.


title: Six New Compact Triply Eclipsing Triples Found With TESS

abstract:
  In this work we report the discovery and analysis of six new compact triply
eclipsing triple star systems found with the TESS mission: TICs 37743815,
42565581, 54060695, 178010808, 242132789, and 456194776. All of these exhibit
distinct third body eclipses where the inner eclipsing binary (EB) occults the
third (`tertiary') star, or vice versa. We utilized the TESS photometry,
archival photometric data, and available archival spectral energy distribution
curves (SED) to solve for the properties of all three stars, as well as many of
the orbital elements. We describe in detail our SED fits, search of the
archival data for the outer orbital period, and the final global photodynamical
analyses. From these analyses we find that all six systems are coplanar to
within $0^\circ$ - $5^\circ$, and are viewed nearly edge on (i.e., within a
couple of degrees). The outer orbital periods and eccentricities of the six
systems are {$P_{\rm out}$ (days), $e$}: {68.7, 0.36}, {123, 0.16}, {60.7,
0.01}, {69.0, 0.29}, {41.5, 0.01}, {93.9, 0.29}, respectively, in the order the
sources are listed above. The masses of all 12 EB stars were in the range of
0.7-1.8 M$_\odot$ and were situated near the main sequence. By contrast, the
masses and radii of the tertiary stars ranged from 1.5-2.3 M$_\odot$ and 2.9-12
R$_\odot$, respectively. We use this information to estimate the occurrence
rate of compact flat triple systems.


title:
Refinement of the convex shape model and tumbling spin state of (99942)
  Apophis using the 2020-2021 apparition data

abstract:
  Context. The close approach of the near-Earth asteroid (99942) Apophis to
Earth in 2029 will provide a unique opportunity to examine how the physical
properties of the asteroid could be changed due to the Earth's gravitational
perturbation. As a result, the Republic of Korea is planning a rendezvous
mission to Apophis. Aims. Our aim was to use photometric data from the
apparitions in 2020-2021 to refine the shape model and spin state of Apophis.
Methods. Using thirty-six 1 to 2-m class ground-based telescopes and the
Transiting Exoplanet Survey Satellite, we performed a photometric observation
campaign throughout the 2020-2021 apparition. The convex shape model and spin
state were refined using the light-curve inversion method. Results. According
to our best-fit model, Apophis is rotating in a short axis mode with rotation
and precession periods of 264.178 hours and 27.38547 hours, respectively. The
angular momentum vector orientation of Apophis was found as (275$^\circ$,
-85$^\circ$) in the ecliptic coordinate system. The ratio of the dynamic
moments of inertia of this asteroid was fitted to $I_a:I_b:I_c=0.64:0.97:1$,
which corresponds to an elongated prolate ellipsoid. These findings regarding
the spin state and shape model could be used to not only design the space
mission scenario but also investigate the impact of the Earth's tidal force
during close encounters.


title: Towards Better Test Coverage: Merging Unit Tests for Autonomous Systems

abstract:
  We present a framework for merging unit tests for autonomous systems.
Typically, it is intractable to test an autonomous system for every scenario in
its operating environment. The question of whether it is possible to design a
single test for multiple requirements of the system motivates this work. First,
we formally define three attributes of a test: a test specification that
characterizes behaviors observed in a test execution, a test environment, and a
test policy. Using the merge operator from contract-based design theory, we
provide a formalism to construct a merged test specification from two unit test
specifications. Temporal constraints on the merged test specification guarantee
that non-trivial satisfaction of both unit test specifications is necessary for
a successful merged test execution. We assume that the test environment remains
the same across the unit tests and the merged test. Given a test specification
and a test environment, we synthesize a test policy filter using a receding
horizon approach, and use the test policy filter to guide a search procedure
(e.g. Monte-Carlo Tree Search) to find a test policy that is guaranteed to
satisfy the test specification. This search procedure finds a test policy that
maximizes a pre-defined robustness metric for the test while the filter
guarantees a test policy for satisfying the test specification. We prove that
our algorithm is sound. Furthermore, the receding horizon approach to
synthesizing the filter ensures that our algorithm is scalable. Finally, we
show that merging unit tests is impactful for designing efficient test
campaigns to achieve similar levels of coverage in fewer test executions. We
illustrate our framework on two self-driving examples in a discrete-state
setting.


title:
Early life height and weight production functions with endogenous energy
  and protein inputs

abstract:
  We examine effects of protein and energy intakes on height and weight growth
for children between 6 and 24 months old in Guatemala and the Philippines.
Using instrumental variables to control for endogeneity and estimating multiple
specifications, we find that protein intake plays an important and positive
role in height and weight growth in the 6-24 month period. Energy from other
macronutrients, however, does not have a robust relation with these two
anthropometric measures. Our estimates indicate that in contexts with
substantial child undernutrition, increases in protein-rich food intake in the
first 24 months can have important growth effects, which previous studies
indicate are related significantly to a range of outcomes over the life cycle.


title:
Linear Response Theory and Fluctuation Dissipation Theorem for Systems
  with Absorbing States

abstract:
  The Fluctuation Dissipation Theorem (FDT) is one of the fundamental results
of statistical mechanics and is a powerful tool that connects the macroscopic
properties to microscopic dynamics. The FDT and the linear response theory are
mainly restricted to systems in the vicinity of stationary states. However,
frequently, physical systems do not conserve the total probability. In systems
with absorbing states, the net flux out of the system is positive, and the
total probability decays with time. In this case the stationary distribution is
trivially zero throughout and the tools provided by standard linear response
theory fail. Here we present a new FDT for decaying systems which connects the
response of observables conditioned on survival to conditional correlations
without perturbations. The results have been verified through simulations and
numerics in various important examples


title:
Exploring the cosmic 21-cm signal from the Epoch of Reionisation using
  the Wavelet Scattering Transform

abstract:
  Detecting the cosmic 21-cm signal during the Epoch of Reionisation and Cosmic
Dawn will reveal insights into the properties of the first galaxies and advance
cosmological parameter estimation. Until recently, the primary focus for
astrophysical parameter inference from the 21-cm signal centred on the power
spectrum (PS). However, the cosmic 21-cm signal is highly non-Gaussian
rendering the PS sub-optimal for characterising the cosmic signal. In this
work, we introduce a new technique to analyse the non-Gaussian information in
images of the 21-cm signal called the Wavelet Scattering Transform (WST). This
approach closely mirrors that of convolutional neural networks with the added
advantage of not requiring tuning or training of a neural network. Instead, it
compresses the 2D spatial information into a set of coefficients making it
easier to interpret while also providing a robust statistical description of
the non-Gaussian information contained in the cosmic 21-cm signal. First, we
explore the application of the WST to mock 21-cm images to gain valuable
physical insights by comparing to the known behaviour from the 21-cm PS. Then
we quantitatively explore the WST applied to the 21-cm signal by extracting
astrophysical parameter constraints using Fisher Matrices from a realistic 1000
hr mock observation with the Square Kilometre Array. We find that: (i) the WST
applied only to 2D images can outperform the 3D spherically averaged 21-cm PS,
(ii) the excision of foreground contaminated modes can degrade the constraining
power by a factor of ~1.5-2 with the WST and (iii) higher cadences between the
21-cm images can further improve the constraining power.


title: Stateful Greybox Fuzzing

abstract:
  Many bugs in protocol implementations may only manifest when the system is in
a particular "state". For instance, to trigger one of the bugs we found in an
RTSP implementation, the fuzzer must first send two different types of messages
to usher the protocol implementation from the INIT via the READY to the PLAY
state where the bug is exposed. Without knowledge of the protocol, it is
inherently difficult for a fuzzer to discover such stateful bugs. A key
challenge in fuzzing stateful systems, therefore, is to cover the state space
without an explicit specification of the protocol.
  So, how can we help our fuzzer navigate an unknown state space? In our
analysis of the Top-50 most widely used open-source protocol implementations,
we found that every implementation uses state variables that are assigned named
constants (such as INIT, READY) to represent the current state. In this work,
we propose to automatically identify such state variables and track the
sequence of values assigned to them during fuzzing to produce a "map" of the
explored state space. Our stateful greybox fuzzing approach uses this map to
focus on the most promising regions of the code and state space.
  Our experiments confirm that our stateful fuzzer discovers stateful bugs
twice as fast as the baseline greybox fuzzer that we extended. The state
sequence for an input is determined by the sequence of values assigned to the
state variables during its execution. Starting from the initial state, our
fuzzer exercises one order of magnitude more state sequences and covers the
same code two times faster than the baseline fuzzer. Several zero-day bugs in
prominent protocol implementations were found by our fuzzer, and 8 CVEs have
been assigned.


title: Quick Starting Dialog Systems with Paraphrase Generation

abstract:
  Acquiring training data to improve the robustness of dialog systems can be a
painstakingly long process. In this work, we propose a method to reduce the
cost and effort of creating new conversational agents by artificially
generating more data from existing examples, using paraphrase generation. Our
proposed approach can kick-start a dialog system with little human effort, and
brings its performance to a level satisfactory enough for allowing actual
interactions with real end-users. We experimented with two neural paraphrasing
approaches, namely Neural Machine Translation and a Transformer-based seq2seq
model. We present the results obtained with two datasets in English and in
French:~a crowd-sourced public intent classification dataset and our own
corporate dialog system dataset. We show that our proposed approach increased
the generalization capabilities of the intent classification model on both
datasets, reducing the effort required to initialize a new dialog system and
helping to deploy this technology at scale within an organization.


title:
Modeling Motion with Multi-Modal Features for Text-Based Video
  Segmentation

abstract:
  Text-based video segmentation aims to segment the target object in a video
based on a describing sentence. Incorporating motion information from optical
flow maps with appearance and linguistic modalities is crucial yet has been
largely ignored by previous work. In this paper, we design a method to fuse and
align appearance, motion, and linguistic features to achieve accurate
segmentation. Specifically, we propose a multi-modal video transformer, which
can fuse and aggregate multi-modal and temporal features between frames.
Furthermore, we design a language-guided feature fusion module to progressively
fuse appearance and motion features in each feature level with guidance from
linguistic features. Finally, a multi-modal alignment loss is proposed to
alleviate the semantic gap between features from different modalities.
Extensive experiments on A2D Sentences and J-HMDB Sentences verify the
performance and the generalization ability of our method compared to the
state-of-the-art methods.


title:
Style-Hallucinated Dual Consistency Learning for Domain Generalized
  Semantic Segmentation

abstract:
  In this paper, we study the task of synthetic-to-real domain generalized
semantic segmentation, which aims to learn a model that is robust to unseen
real-world scenes using only synthetic data. The large domain shift between
synthetic and real-world data, including the limited source environmental
variations and the large distribution gap between synthetic and real-world
data, significantly hinders the model performance on unseen real-world scenes.
In this work, we propose the Style-HAllucinated Dual consistEncy learning
(SHADE) framework to handle such domain shift. Specifically, SHADE is
constructed based on two consistency constraints, Style Consistency (SC) and
Retrospection Consistency (RC). SC enriches the source situations and
encourages the model to learn consistent representation across
style-diversified samples. RC leverages real-world knowledge to prevent the
model from overfitting to synthetic data and thus largely keeps the
representation consistent between the synthetic and real-world models.
Furthermore, we present a novel style hallucination module (SHM) to generate
style-diversified samples that are essential to consistency learning. SHM
selects basis styles from the source distribution, enabling the model to
dynamically generate diverse and realistic samples during training. Experiments
show that our SHADE yields significant improvement and outperforms
state-of-the-art methods by 5.07% and 8.35% on the average mIoU of three
real-world datasets on single- and multi-source settings respectively.


title: C3KG: A Chinese Commonsense Conversation Knowledge Graph

abstract:
  Existing commonsense knowledge bases often organize tuples in an isolated
manner, which is deficient for commonsense conversational models to plan the
next steps. To fill the gap, we curate a large-scale multi-turn human-written
conversation corpus, and create the first Chinese commonsense conversation
knowledge graph which incorporates both social commonsense knowledge and dialog
flow information. To show the potential of our graph, we develop a
graph-conversation matching approach, and benchmark two graph-grounded
conversational tasks.


title:
Continuous LWE is as Hard as LWE & Applications to Learning Gaussian
  Mixtures

abstract:
  We show direct and conceptually simple reductions between the classical
learning with errors (LWE) problem and its continuous analog, CLWE (Bruna,
Regev, Song and Tang, STOC 2021). This allows us to bring to bear the powerful
machinery of LWE-based cryptography to the applications of CLWE. For example,
we obtain the hardness of CLWE under the classical worst-case hardness of the
gap shortest vector problem. Previously, this was known only under quantum
worst-case hardness of lattice problems. More broadly, with our reductions
between the two problems, any future developments to LWE will also apply to
CLWE and its downstream applications.
  As a concrete application, we show an improved hardness result for density
estimation for mixtures of Gaussians. In this computational problem, given
sample access to a mixture of Gaussians, the goal is to output a function that
estimates the density function of the mixture. Under the (plausible and widely
believed) exponential hardness of the classical LWE problem, we show that
Gaussian mixture density estimation in $\mathbb{R}^n$ with roughly $\log n$
Gaussian components given $\mathsf{poly}(n)$ samples requires time
quasi-polynomial in $n$. Under the (conservative) polynomial hardness of LWE,
we show hardness of density estimation for $n^{\epsilon}$ Gaussians for any
constant $\epsilon > 0$, which improves on Bruna, Regev, Song and Tang (STOC
2021), who show hardness for at least $\sqrt{n}$ Gaussians under polynomial
(quantum) hardness assumptions.
  Our key technical tool is a reduction from classical LWE to LWE with
$k$-sparse secrets where the multiplicative increase in the noise is only
$O(\sqrt{k})$, independent of the ambient dimension $n$.


title: Ribbon Yetter--Drinfeld modules and tangle invariants

abstract:
  We define notions of pivotal and ribbon objects in a monoidal category. These
constructions give pivotal or ribbon monoidal categories from a monoidal
category which is not necessarily with duals. We apply this construction to the
braided monoidal category of Yetter--Drinfeld modules over a Hopf algebra. This
gives rise to the notion of ribbon Yetter--Drinfeld modules over a Hopf
algebra, which form ribbon categories. This gives an invariant of tangles.


title:
Quantum Approximate Counting for Markov Chains and Application to
  Collision Counting

abstract:
  In this paper we show how to generalize the quantum approximate counting
technique developed by Brassard, H{\o}yer and Tapp [ICALP 1998] to a more
general setting: estimating the number of marked states of a Markov chain (a
Markov chain can be seen as a random walk over a graph with weighted edges).
This makes it possible to construct quantum approximate counting algorithms
from quantum search algorithms based on the powerful "quantum walk based
search" framework established by Magniez, Nayak, Roland and Santha [SIAM
Journal on Computing 2011]. As an application, we apply this approach to the
quantum element distinctness algorithm by Ambainis [SIAM Journal on Computing
2007]: for two injective functions over a set of $N$ elements, we obtain a
quantum algorithm that estimates the number $m$ of collisions of the two
functions within relative error $\epsilon$ by making
$\tilde{O}\left(\frac{1}{\epsilon^{25/24}}\big(\frac{N}{\sqrt{m}}\big)^{2/3}\right)$
queries, which gives an improvement over the
$\Theta\big(\frac{1}{\epsilon}\frac{N}{\sqrt{m}}\big)$-query classical
algorithm based on random sampling when $m\ll N$.


title:
RODD: A Self-Supervised Approach for Robust Out-of-Distribution
  Detection

abstract:
  Recent studies have addressed the concern of detecting and rejecting the
out-of-distribution (OOD) samples as a major challenge in the safe deployment
of deep learning (DL) models. It is desired that the DL model should only be
confident about the in-distribution (ID) data which reinforces the driving
principle of the OOD detection. In this paper, we propose a simple yet
effective generalized OOD detection method independent of out-of-distribution
datasets. Our approach relies on self-supervised feature learning of the
training samples, where the embeddings lie on a compact low-dimensional space.
Motivated by the recent studies that show self-supervised adversarial
contrastive learning helps robustify the model, we empirically show that a
pre-trained model with self-supervised contrastive learning yields a better
model for uni-dimensional feature learning in the latent space. The method
proposed in this work referred to as RODD outperforms SOTA detection
performance on an extensive suite of benchmark datasets on OOD detection tasks.
On the CIFAR-100 benchmarks, RODD achieves a 26.97 $\%$ lower false-positive
rate (FPR@95) compared to SOTA methods.


title:
Unconventional ferromagnetism and spin-triplet superconductivity in the
  imbalanced Kagome-lattice Hubbard model

abstract:
  Unconventional ferromagnetism and superconductivity in the imbalanced
kagome-lattice Hubbard model are investigated by the mean-field theory and
determinant quantum Monte Carlo method. Due to the asymmetric band structure of
kagome lattice, the spin-$z$ ferromagnetic order intrinsically exists in the
system, which is first enhanced by the interaction, and then continuously
destructed after reaching a maximum at a moderate interaction strength. In
contrast, the $xy$-plane ferromagnetism develops only above a critical
interaction, which is estimated to be $U_c/t=3.65 \pm 0.05$ by finite-size
scaling. We further verify the nature of the above transverse magnetic
transition, and demonstrate it belongs to the three-dimensional $XY$
universality class. Finally, we study the superconducting property, and reveal
the possible superconducting state has a triplet $f$-wave pairing symmetry. Our
results uncover the exotic quantum states induced by the interactions on kagome
lattice, and provide important insights regarding the interplay between
electronic correlations and geometry frustrations.


title: Approximate quantum gates compiling with self-navigation algorithm

abstract:
  The compiling of quantum gates is crucial for the successful quantum
algorithm implementations. The environmental noise as well as the bandwidth of
control pulses pose a challenge to precise and fast qubit control, especially
in a weakly anharmonic system. In this work, we propose an algorithm to
approximately compile single-qubit gates with arbitrary accuracy. Evaluation
results show that the overall rotation distance generated by our algorithm is
significantly shorter than the commonly used $U3$ gate, then the gate time can
be effectively shortened. The requisite number of pulses and the runtime of
scheme design scale up as $\mathcal{O}[\mathrm{Log}(1/\epsilon)]$ with very
small prefactors, indicating low overhead costs. Moreover, we explore the
trade-off between effectiveness and cost, and find a balance point. In short,
our work opens a new avenue for efficient quantum algorithm implementations
with contemporary quantum technology.


title: An involution on set partitions

abstract:
  We present an involution on set partitions that interchanges two statistics
related to relative size of block entries and use it to establish an
equidistribution on objects counted by the Bessel numbers.


title: MixFormer: Mixing Features across Windows and Dimensions

abstract:
  While local-window self-attention performs notably in vision tasks, it
suffers from limited receptive field and weak modeling capability issues. This
is mainly because it performs self-attention within non-overlapped windows and
shares weights on the channel dimension. We propose MixFormer to find a
solution. First, we combine local-window self-attention with depth-wise
convolution in a parallel design, modeling cross-window connections to enlarge
the receptive fields. Second, we propose bi-directional interactions across
branches to provide complementary clues in the channel and spatial dimensions.
These two designs are integrated to achieve efficient feature mixing among
windows and dimensions. Our MixFormer provides competitive results on image
classification with EfficientNet and shows better results than RegNet and Swin
Transformer. Performance in downstream tasks outperforms its alternatives by
significant margins with less computational costs in 5 dense prediction tasks
on MS COCO, ADE20k, and LVIS. Code is available at
\url{https://github.com/PaddlePaddle/PaddleClas}.


title:
DouZero+: Improving DouDizhu AI by Opponent Modeling and Coach-guided
  Learning

abstract:
  Recent years have witnessed the great breakthrough of deep reinforcement
learning (DRL) in various perfect and imperfect information games. Among these
games, DouDizhu, a popular card game in China, is very challenging due to the
imperfect information, large state space, elements of collaboration and a
massive number of possible moves from turn to turn. Recently, a DouDizhu AI
system called DouZero has been proposed. Trained using traditional Monte Carlo
method with deep neural networks and self-play procedure without the
abstraction of human prior knowledge, DouZero has outperformed all the existing
DouDizhu AI programs. In this work, we propose to enhance DouZero by
introducing opponent modeling into DouZero. Besides, we propose a novel coach
network to further boost the performance of DouZero and accelerate its training
process. With the integration of the above two techniques into DouZero, our
DouDizhu AI system achieves better performance and ranks top in the Botzone
leaderboard among more than 400 AI agents, including DouZero.


title:
X-CAR: An Experimental Vehicle Platform for Connected Autonomy Research
  Powered by CARMA

abstract:
  Autonomous vehicles promise a future with a safer, cleaner, more efficient,
and more reliable transportation system. However, the current approach to
autonomy has focused on building small, disparate intelligences that are closed
off to the rest of the world. Vehicle connectivity has been proposed as a
solution, relying on a vision of the future where a mix of connected autonomous
and human-driven vehicles populate the road. Developed by the U.S. Department
of Transportation Federal Highway Administration as a reusable, extensible
platform for controlling connected autonomous vehicles, the CARMA Platform is
one of the technologies enabling this connected future. Nevertheless, the
adoption of the CARMA Platform has been slow, with a contributing factor being
the limited, expensive, and somewhat old vehicle configurations that are
officially supported. To alleviate this problem, we propose X-CAR (eXperimental
vehicle platform for Connected Autonomy Research). By implementing the CARMA
Platform on more affordable, high quality hardware, X-CAR aims to increase the
versatility of the CARMA Platform and facilitate its adoption for research and
development of connected driving automation.


title:
A Novel 3D Non-Stationary Channel Model for 6G Indoor Visible Light
  Communication Systems

abstract:
  The visible light communication (VLC) technology has attracted much attention
in the research of the sixth generation (6G) communication systems. In this
paper, a novel three dimensional (3D) space-time-frequency non-stationary
geometry-based stochastic model (GBSM) is proposed for indoor VLC channels. The
proposed VLC GBSM can capture unique indoor VLC channel characteristics such as
the space-time-frequency non-stationarity caused by large light-emitting diode
(LED) arrays in indoor scenarios, long travelling paths, and large bandwidths
of visible light waves, respectively. In addition, the proposed model can
support special radiation patterns of LEDs, 3D translational and rotational
motions of the optical receiver (Rx), and can be applied to angle diversity
receivers (ADRs). Key channel properties are simulated and analyzed, including
the space-time-frequency correlation function (STFCF), received power, root
mean square (RMS) delay spread, and path loss (PL). Simulation results verify
the space-time-frequency non-stationarity in indoor VLC channels. Finally, the
accuracy and practicality of the proposed model are validated by comparing the
simulation result of channel 3dB bandwidth with the existing measurement data.
The proposed channel model will play a supporting role in the design of future
6G VLC systems.


title: Engineered Tunable Decay Rate and Controllable Dissipative Dynamics

abstract:
  We investigate the steering dissipative dynamics of a two-level system
(qubit) by means of the modulation of an assisted tunneling degree of freedom
which is described by a quantum-oscillator spin-boson model. Our results reveal
that the decoherence rate of the qubit can be significantly suppressed and
simultaneously its quality factor is enhanced. Moreover, the modulated
dynamical susceptibility exhibits a multi-peak feature which is indicative of
the underlying structure and measurable in experiment. Our findings demonstrate
that the interplay between the combined degrees of freedom and the qubit is
crucial for reducing the dissipation of qubit and expanding the coherent regime
of quantum operation much large. The strategy might be used to fight against
deterioration of quantum coherence in quantum information processing.

categories: quant-ph cond-mat.stat-mech physics.atom-ph physics.chem-ph


title: Cram\'{e}r's moderate deviations for martingales with applications

abstract:
  Let $(\xi_i,\mathcal{F}_i)_{i\geq1}$ be a sequence of martingale differences.
Set $X_n=\sum_{i=1}^n \xi_i $ and $ \langle X \rangle_n=\sum_{i=1}^n
\mathbf{E}(\xi_i^2|\mathcal{F}_{i-1}).$ We prove Cram\'er's moderate deviation
expansions for $\displaystyle \mathbf{P}(X_n/\sqrt{\langle X\rangle_n} \geq x)$
and $\displaystyle \mathbf{P}(X_n/\sqrt{ \mathbf{E}X_n^2} \geq x)$ as
$n\to\infty.$ Our results extend the classical Cram\'{e}r result to the cases
of normalized martingales $X_n/\sqrt{\langle X\rangle_n}$ and standardized
martingales $X_n/\sqrt{ \mathbf{E}X_n^2}$, with martingale differences
satisfying the conditional Bernstein condition. Applications to elephant random
walks and autoregressive processes are also discussed.


title:
$\delta^{44/40}$Ca-$\delta^{88/86}$Sr multi-proxy constrains primary
  origin of Marinoan cap carbonates

abstract:
  The Neoproterozoic Earth experienced at least two global-scale glaciations
termed Snowball Earth events. 'Cap carbonates' were widely deposited after the
events, but controversy surrounds their origin. Here, we apply the novel
$\delta^{44/40}$Ca-$\delta^{88/86}$Sr multi-proxy to two Marinoan (ca. 635 Ma)
cap carbonate sequences from Namibia and show that the rocks archive primary
environmental signals deriving from a combination of seawater-glacial meltwater
mixing and kinetic isotope effects. In an outer platform section, dolostone
$\delta^{44/40}$Ca and $\delta^{88/86}$Sr values define a line predicted for
kinetic mass-dependent isotope fractionation. This dolostone mostly
precipitated from meltwater. Moreover, stratigraphically higher samples
exhibiting the fastest precipitation rates correlate with elevated 87Sr/86Sr
ratios, consistent with long-held expectations that a rapid deglacial
weathering pulse forced cap carbonate formation. An inner-platform dolostone
shows greater effects from water-mass mixing but still reveals that
precipitation rates increased up-section. Overlying limestones show the
greatest Ca and Sr contributions from seawater. Amplification of local coastal
processes during global ice sheet collapse offers a simple but sufficient
proposition to explain the Ca isotope heterogeneity of cap carbonates.
Detection of kinetic isotope effects in the rock record provides a basis for
developing the $\delta^{44/40}$Ca-$\delta^{88/86}$Sr multi-proxy as an
indicator of saturation state and $p$CO$_2$.


title:
Low phase noise THz generation from a fiber-referenced Kerr
  microresonator soliton comb

abstract:
  THz oscillators generated via frequency-multiplication of microwaves are
facing difficulty in achieving low phase noise. Photonics-based techniques, in
which optical two tones are translated to a THz wave through opto-electronic
conversion, are promising if the relative phase noise between the two tones is
well suppressed. Here, a THz ($\approx$ 560 GHz) wave with an unprecedented
phase noise is provided by a frequency-stabilized, dissipative Kerr
microresonator soliton comb. The repetition frequency of the comb is stabilized
to a long fiber in a two-wavelength delayed self-heterodyne interferometer,
significantly reducing the phase noise of the THz wave. A new measurement
technique to characterize the phase noise of the THz wave beyond the limit of a
frequency-multiplied microwave is also demonstrated, showing the superior phase
noise of the THz wave to any other THz oscillators (> 300 GHz).


title:
State Initialization of a Hot Spin Qubit in a Double Quantum Dot by
  Measurement-Based Quantum Feedback Control

abstract:
  A measurement-based quantum feedback protocol is developed for spin state
initialization in a gate-defined double quantum dot spin qubit coupled to a
superconducting resonator. The protocol improves qubit state initialization as
it is able to robustly prepare the spin in shorter time and reach a higher
fidelity, which can be pre-set. Being able to pre-set the fidelity aimed at is
a highly desired feature enabling qubit initialization to be more
deterministic. The protocol developed herein is also effective at high
temperatures, which is critical for the current efforts towards scaling up the
number of qubits in quantum computers.


title:
Modeling Temporal-Modal Entity Graph for Procedural Multimodal Machine
  Comprehension

abstract:
  Procedural Multimodal Documents (PMDs) organize textual instructions and
corresponding images step by step. Comprehending PMDs and inducing their
representations for the downstream reasoning tasks is designated as Procedural
MultiModal Machine Comprehension (M3C). In this study, we approach Procedural
M3C at a fine-grained level (compared with existing explorations at a document
or sentence level), that is, entity. With delicate consideration, we model
entity both in its temporal and cross-modal relation and propose a novel
Temporal-Modal Entity Graph (TMEG). Specifically, graph structure is formulated
to capture textual and visual entities and trace their temporal-modal
evolution. In addition, a graph aggregation module is introduced to conduct
graph encoding and reasoning. Comprehensive experiments across three Procedural
M3C tasks are conducted on a traditional dataset RecipeQA and our new dataset
CraftQA, which can better evaluate the generalization of TMEG.