title:
A Deep Learning Technique using a Sequence of Follow Up X-Rays for
  Disease classification

abstract:
  The ability to predict lung and heart based diseases using deep learning
techniques is central to many researchers, particularly in the medical field
around the world. In this paper, we present a unique outlook of a very familiar
problem of disease classification using X-rays. We present a hypothesis that
X-rays of patients included with the follow up history of their most recent
three chest X-ray images would perform better in disease classification in
comparison to one chest X-ray image input using an internal CNN to perform
feature extraction. We have discovered that our generic deep learning
architecture which we propose for solving this problem performs well with 3
input X ray images provided per sample for each patient. In this paper, we have
also established that without additional layers before the output
classification, the CNN models will improve the performance of predicting the
disease labels for each patient. We have provided our results in ROC curves and
AUROC scores. We define a fresh approach of collecting three X-ray images for
training deep learning models, which we have concluded has clearly improved the
performance of the models. We have shown that ResNet, in general, has a better
result than any other CNN model used in the feature extraction phase. With our
original approach to data pre-processing, image training, and pre-trained
models, we believe that the current research will assist many medical
institutions around the world, and this will improve the prediction of
patients' symptoms and diagnose them with more accurate cure.


title:
Does social distancing matter for infectious disease propagation? A SEIR
  model and Gompertz law based cellular automaton

abstract:
  In this paper, we present stochastic synchronous cellular automaton defined
on a square lattice. The automaton rules are based on SEIR model with
probabilistic parameters gathered from real-world data on human mortality and
SARS-CoV-2 disease characteristics. With computer simulations, we show the
influence of the neighbourhood radius on the number of infected and deceased
agents in the artificial population. The increase of the neighbourhood radius
favors the spread of the epidemic. However, for a large range of interactions
of exposed (who are both inward for and/or untested against) agents, even
isolation of infected agents cannot prevent successful disease propagation.
This supports aggressive testing against disease as one of the useful
strategies preventing large peaks of infection in SARS-CoV-2-like disease
spreading.


title: Fast (optical) Links

abstract:
  In this write-up we focus on fast data links that read out particle
detectors. This includes developments of ASICs, optical modules, identifying
passive components such as fiber and connector, and a link system design. A
review of the current status is provided. The goal of R\&D is to double (near
term) and quadruple (longer term) the present data transmission rate for
particle detectors, enabling a wide range of physics exploration and
measurements.


title: The Basics of Quantum Computing for Chemists

abstract:
  The rapid and successful strides in quantum chemistry in the past decades can
be largely credited to a conspicuous synergy between theoretical and
computational advancements. However, the architectural computer archetype that
enabled such a progress is approaching a state of more stagnant development.
One of the most promising technological avenues for the continuing progress of
quantum chemistry is the emerging quantum computing paradigm. This
revolutionary proposal comes with several challenges, which span a wide array
of disciplines. In chemistry, it implies, among other things, a need to
reformulate some of its long established cornerstones in order to adjust to the
operational demands and constraints of quantum computers. Due to its relatively
recent emergence, much of quantum computing may still seem fairly nebulous and
largely unknown to most chemists. It is in this context that here we review and
illustrate the basic aspects of quantum information and their relation to
quantum computing insofar as enabling simulations of quantum chemistry. We
consider some of the most relevant developments in light of these aspects and
discuss the current landscape when of relevance to quantum chemical simulations
in quantum computers.


abstract:
  CounterFactual (CF) visual explanations try to find images similar to the
query image that change the decision of a vision system to a specified outcome.
Existing methods either require inference-time optimization or joint training
with a generative adversarial model which makes them time-consuming and
difficult to use in practice. We propose a novel approach, Cycle-Consistent
Counterfactuals by Latent Transformations (C3LT), which learns a latent
transformation that automatically generates visual CFs by steering in the
latent space of generative models. Our method uses cycle consistency between
the query and CF latent representations which helps our training to find better
solutions. C3LT can be easily plugged into any state-of-the-art pretrained
generative network. This enables our method to generate high-quality and
interpretable CF images at high resolution such as those in ImageNet. In
addition to several established metrics for evaluating CF explanations, we
introduce a novel metric tailored to assess the quality of the generated CF
examples and validate the effectiveness of our method on an extensive set of
experiments.


title: DeepShadow: Neural Shape from Shadow

abstract:
  This paper presents DeepShadow, a one-shot method for recovering the depth
map and surface normals from photometric stereo shadow maps. Previous works
that try to recover the surface normals from photometric stereo images treat
cast shadows as a disturbance. We show that the self and cast shadows not only
do not disturb 3D reconstruction, but can be used alone, as a strong learning
signal, to recover the depth map and surface normals. We demonstrate that 3D
reconstruction from shadows can even outperform shape-from-shading in certain
cases. To the best of our knowledge, our method is the first to reconstruct 3D
shape-from-shadows using neural networks. The method does not require any
pre-training or expensive labeled data, and is optimized during inference time.


title:
Inverse design of broadband, strongly-coupled plexcitonic nonlinear
  metasurfaces

abstract:
  Hybrid photonic structures of plasmonic metasurfaces coupled to atomically
thin semiconductors have emerged as a versatile platform for strong
light-matter interaction, supporting both strong coupling and parametric
nonlinearities. However, designing optimized nonlinear hybrid metasurfaces is a
complex task, as the multiple parameters' contribution to the nonlinear
response is elusive. Here we present a simple yet powerful strategy for
maximizing the nonlinear response of the hybrid structures based on
evolutionary inverse design of the metasurface's near-field enhancement around
the excitonic frequency. We show that the strong coupling greatly enhances the
nonlinear signal, and that its magnitude is mainly determined by the Rabi
splitting, making it robust to geometrical variations of the metasurface.
Furthermore, the large Rabi splitting attained by these hybrid structures
enables broadband operation over the frequencies of the hybridized modes. Our
results constitute a significant step towards achieving flexible nonlinear
control, which can benefit applications in nonlinear frequency conversion,
all-optical switching, and phase-controlled nonlinear metasurfaces.


title: Triangular structures on flat Lie algebras

abstract:
  In this work we study a large class of Lie bialgebras arising from
noncommutative deformations of Lie groups endowed with left invariant
riamannian metric. We will refer to these structures as flat and metaflat
triangular Lie bialgebras. We give a complete classification of these
structures. We conclude with the dominant case where the two dual bialgebras
have the same structures which constitutes an important kind of symmetry.


title: Face Verification Bypass

abstract:
  Face verification systems aim to validate the claimed identity using feature
vectors and distance metrics. However, no attempt has been made to bypass such
a system using generated images that are constrained by the same feature
vectors. In this work, we train StarGAN v2 to generate diverse images based on
a human user, that have similar feature vectors yet qualitatively look
different. We then demonstrate a proof of concept on a custom face verification
system and verify our claims by demonstrating the same proof of concept in a
black box setting on dating applications that utilize similar face verification
systems.


title:
Leveraging Tactile Sensors for Low Latency Embedded Smart Hands for
  Prosthetic and Robotic Applications

abstract:
  Tactile sensing is a crucial perception mode for robots and human amputees in
need of controlling a prosthetic device. Today robotic and prosthetic systems
are still missing the important feature of accurate tactile sensing. This lack
is mainly due to the fact that the existing tactile technologies have limited
spatial and temporal resolution and are either expensive or not scalable. In
this paper, we present the design and the implementation of a hardware-software
embedded system called SmartHand. It is specifically designed to enable the
acquisition and the real-time processing of high-resolution tactile information
from a hand-shaped multi-sensor array for prosthetic and robotic applications.
During data collection, our system can deliver a high throughput of 100 frames
per second, which is 13.7x higher than previous related work. We collected a
new tactile dataset while interacting with daily-life objects during five
different sessions. We propose a compact yet accurate convolutional neural
network that requires one order of magnitude less memory and 15.6x fewer
computations compared to related work without degrading classification
accuracy. The top-1 and top-3 cross-validation accuracies are respectively
98.86% and 99.83%. We further analyze the inter-session variability and obtain
the best top-3 leave-one-out-validation accuracy of 77.84%. We deploy the
trained model on a high-performance ARM Cortex-M7 microcontroller achieving an
inference time of only 100 ms minimizing the response latency. The overall
measured power consumption is 505 mW. Finally, we fabricate a new control
sensor and perform additional experiments to provide analyses on sensor
degradation and slip detection. This work is a step forward in giving robotic
and prosthetic devices a sense of touch and demonstrates the practicality of a
smart embedded system empowered by tiny machine learning.


title: Is there a polaron signature in angle-resolved photoemission of CsPbBr3?

abstract:
  The formation of large polarons has been proposed as reason for the high
defect tolerance, low mobility, low charge carrier trapping and low
nonradiative recombination rates of lead halide perovskites. Recently, direct
evidence for large-polaron formation has been reported from a 50% effective
mass enhancement in angle-resolved photoemission of CsPbBr3 over theory for the
orthorhombic structure. We present in-depth band dispersion measurements of
CsPbBr3 and GW calculations which lead to almost identical effective masses at
the valence band maximum of 0.203+/-0.016 m0 in experiment and 0.217 m0 in
orthorhombic theory. We argue that the effective mass can be explained solely
on the basis of electron-electron correlation and large polaron formation
cannot be concluded from photoemission data.


title: User Driven Model Adjustment via Boolean Rule Explanations

abstract:
  AI solutions are heavily dependant on the quality and accuracy of the input
training data, however the training data may not always fully reflect the most
up-to-date policy landscape or may be missing business logic. The advances in
explainability have opened the possibility of allowing users to interact with
interpretable explanations of ML predictions in order to inject modifications
or constraints that more accurately reflect current realities of the system. In
this paper, we present a solution which leverages the predictive power of ML
models while allowing the user to specify modifications to decision boundaries.
Our interactive overlay approach achieves this goal without requiring model
retraining, making it appropriate for systems that need to apply instant
changes to their decision making. We demonstrate that user feedback rules can
be layered with the ML predictions to provide immediate changes which in turn
supports learning with less data.


title:
Semantic Motion Correction Via Iterative Nonlinear Optimization and
  Animation

abstract:
  Here, we present an end-to-end method to create 2D animation for a goalkeeper
attempting to block a penalty kick, and then correct that motion using an
iterative nonlinear optimization scheme. The input is a raw video that is fed
into pose and object detection networks to find the skeleton of the goalkeeper
and the ball. The output is a set of key frames of the skeleton associated with
the corrected motion so that if the goalkeeper missed the ball, the animation
will show then successfully deflecting it. Our method is robust enough correct
different kinds of mistakes the goalkeeper can make, such as not lunging far
enough or jumping to the incorrect side. Our method is also meant to be
semantically similar to the goalkeeper's original motion, which helps keep our
animation grounded with respect to actual human behavior.


title: Understanding Questions that Arise When Working with Business Documents

abstract:
  While digital assistants are increasingly used to help with various
productivity tasks, less attention has been paid to employing them in the
domain of business documents. To build an agent that can handle users'
information needs in this domain, we must first understand the types of
assistance that users desire when working on their documents. In this work, we
present results from two user studies that characterize the information needs
and queries of authors, reviewers, and readers of business documents. In the
first study, we used experience sampling to collect users' questions in-situ as
they were working with their documents, and in the second, we built a
human-in-the-loop document Q&A system which rendered assistance with a variety
of users' questions. Our results have implications for the design of document
assistants that complement AI with human intelligence including whether
particular skillsets or roles within the document are needed from human
respondents, as well as the challenges around such systems.


title:
Giotto e Galileu: novos olhares sobre o C\'eu e sobre o Livro da
  Natureza

abstract:
  In this work, an attempt is presented to understand the contribution of
Galileo Galilei in the field of the History of Ideas, from the moment he points
a spyglass to the sky. For that, it was previously necessary that Giotto
painting the sky in blue. We reconstruct this fascinating trajectory between
the 13th and 17th centuries in Europe, relating different practices and
knowledges, as well as discussing the scientific and philosophical thought of
that time. The main thesis of this essay is to show that Giotto, who painted
the sky blue in a culture that wanted it to be golden, followed a conceptual
path very similar to that followed by Galileo centuries later.


title:
A Comparison of Hamming Errors of Representative Variable Selection
  Methods

abstract:
  Lasso is a celebrated method for variable selection in linear models, but it
faces challenges when the variables are moderately or strongly correlated. This
motivates alternative approaches such as using a non-convex penalty, adding a
ridge regularization, or conducting a post-Lasso thresholding. In this paper,
we compare Lasso with 5 other methods: Elastic net, SCAD, forward selection,
thresholded Lasso, and forward backward selection. We measure their
performances theoretically by the expected Hamming error, assuming that the
regression coefficients are iid drawn from a two-point mixture and that the
Gram matrix is block-wise diagonal. By deriving the rates of convergence of
Hamming errors and the phase diagrams, we obtain useful conclusions about the
pros and cons of different methods.


title: Neurosymbolic hybrid approach to driver collision warning

abstract:
  There are two main algorithmic approaches to autonomous driving systems: (1)
An end-to-end system in which a single deep neural network learns to map
sensory input directly into appropriate warning and driving responses. (2) A
mediated hybrid recognition system in which a system is created by combining
independent modules that detect each semantic feature. While some researchers
believe that deep learning can solve any problem, others believe that a more
engineered and symbolic approach is needed to cope with complex environments
with less data. Deep learning alone has achieved state-of-the-art results in
many areas, from complex gameplay to predicting protein structures. In
particular, in image classification and recognition, deep learning models have
achieved accuracies as high as humans. But sometimes it can be very difficult
to debug if the deep learning model doesn't work. Deep learning models can be
vulnerable and are very sensitive to changes in data distribution.
Generalization can be problematic. It's usually hard to prove why it works or
doesn't. Deep learning models can also be vulnerable to adversarial attacks.
Here, we combine deep learning-based object recognition and tracking with an
adaptive neurosymbolic network agent, called the Non-Axiomatic Reasoning System
(NARS), that can adapt to its environment by building concepts based on
perceptual sequences. We achieved an improved intersection-over-union (IOU)
object recognition performance of 0.65 in the adaptive retraining model
compared to IOU 0.31 in the COCO data pre-trained model. We improved the object
detection limits using RADAR sensors in a simulated environment, and
demonstrated the weaving car detection capability by combining deep
learning-based object detection and tracking with a neurosymbolic model.


title: How to grow a flat leaf

abstract:
  Growing a flat lamina such as a leaf is almost impossible without some
feedback to stabilize long wavelength modes that are easy to trigger since they
are energetically cheap. Here we combine the physics of thin elastic plates
with feedback control theory to explore how a leaf can remain flat while
growing. We investigate both in-plane (metric) and out-of-plane (curvature)
growth variation and account for both local and nonlocal feedback laws. We show
that a linearized feedback theory that accounts for both spatially nonlocal and
temporally delayed effects suffices to suppress long wavelength fluctuations
effectively and explains recently observed statistical features of growth in
tobacco leaves. Our work provides a framework for understanding the regulation
of the shape of leaves and other laminar objects.


title:
CD-Net: Histopathology Representation Learning using Pyramidal
  Context-Detail Network

abstract:
  Extracting rich phenotype information, such as cell density and arrangement,
from whole slide histology images (WSIs), requires analysis of large field of
view, i.e more contexual information. This can be achieved through analyzing
the digital slides at lower resolution. A potential drawback is missing out on
details present at a higher resolution. To jointly leverage complementary
information from multiple resolutions, we present a novel transformer based
Pyramidal Context-Detail Network (CD-Net). CD-Net exploits the WSI pyramidal
structure through co-training of proposed Context and Detail Modules, which
operate on inputs from multiple resolutions. The residual connections between
the modules enable the joint training paradigm while learning self-supervised
representation for WSIs. The efficacy of CD-Net is demonstrated in classifying
Lung Adenocarcinoma from Squamous cell carcinoma.


title:
Rotor-Routing Induces the Only Consistent Sandpile Torsor Structure on
  Plane Graphs

abstract:
  A sandpile torsor algorithm is a map which associates each plane graph (i.e.
planar embedding) with a free transitive action of its sandpile group on its
spanning trees. We define a notion of consistency, which requires a torsor
algorithm to be preserved with respect to a certain class of contractions and
deletions. We then show that the rotor-routing sandpile torsor algorithm (which
is equivalent to the Bernardi algorithm) is consistent. Furthermore, we
demonstrate that there are only three other consistent algorithms, which all
have the same structure as rotor-routing. This proves a conjecture of Klivans.
We also define sandpile torsor algorithms on regular matroids and suggest a
notion of consistency in this context. We conjecture that the
Backman-Baker-Yuen algorithm is consistent, and that there are only three other
consistent sandpile torsor algorithms on regular matroids, all with the same
structure.


title: Photometric and Spectroscopic Analysis of the SX Phe Star BL Cam

abstract:
  In the present paper, we report the photometric and spectroscopic
observations obtained by the 1.88 m telescope at the Kottamia astronomical
observatory of the pulsating star BL Cam. Fourier analysis of the light curves
reveals that the fundamental mode has two harmonics. The O-C method is used to
establish the period changes. So far, the analysis has been very successful in
mapping the pulsation amplitude of the star across the instability strip. By
using the formalism of Eddington and Plakidis (1929), we found significant
results and strong indications of the evolutionary period change. A total of 55
new maximum light timings are reported. New values of (1/P) dP/dt are estimated
using the O-C diagram based on all newly obtained times of maximum light
combined with those taken from the literature, assuming the periods are
decreasing and changing smoothly. To compute the effective temperature and
surface gravity of the star, we performed model atmosphere analysis on its
spectra. The physical parameters of the star are calculated and compared with
the evolutionary models.


title: Word Discovery in Visually Grounded, Self-Supervised Speech Models

abstract:
  We present a method for visually-grounded spoken term discovery. After
training either a HuBERT or wav2vec2.0 model to associate spoken captions with
natural images, we show that powerful word segmentation and clustering
capability emerges within the model's self-attention heads. Our experiments
reveal that this ability is not present to nearly the same extent in the base
HuBERT and wav2vec2.0 models, suggesting that the visual grounding task is a
crucial component of the word discovery capability we observe. We also evaluate
our method on the Buckeye word segmentation and ZeroSpeech spoken term
discovery tasks, where we outperform all currently published methods on several
metrics.


title: Iterative, Deep Synthetic Aperture Sonar Image Segmentation

abstract:
  Synthetic aperture sonar (SAS) systems produce high-resolution images of the
seabed environment. Moreover, deep learning has demonstrated superior ability
in finding robust features for automating imagery analysis. However, the
success of deep learning is conditioned on having lots of labeled training
data, but obtaining generous pixel-level annotations of SAS imagery is often
practically infeasible. This challenge has thus far limited the adoption of
deep learning methods for SAS segmentation. Algorithms exist to segment SAS
imagery in an unsupervised manner, but they lack the benefit of
state-of-the-art learning methods and the results present significant room for
improvement. In view of the above, we propose a new iterative algorithm for
unsupervised SAS image segmentation combining superpixel formation, deep
learning, and traditional clustering methods. We call our method Iterative Deep
Unsupervised Segmentation (IDUS). IDUS is an unsupervised learning framework
that can be divided into four main steps: 1) A deep network estimates class
assignments. 2) Low-level image features from the deep network are clustered
into superpixels. 3) Superpixels are clustered into class assignments (which we
call pseudo-labels) using $k$-means. 4) Resulting pseudo-labels are used for
loss backpropagation of the deep network prediction. These four steps are
performed iteratively until convergence. A comparison of IDUS to current
state-of-the-art methods on a realistic benchmark dataset for SAS image
segmentation demonstrates the benefits of our proposal even as the IDUS incurs
a much lower computational burden during inference (actual labeling of a test
image). Finally, we also develop a semi-supervised (SS) extension of IDUS
called IDSS and demonstrate experimentally that it can further enhance
performance while outperforming supervised alternatives that exploit the same
labeled training imagery.


title:
Observing and braiding topological Majorana modes on programmable
  quantum simulators

abstract:
  Despite its great promise of fault tolerance, the simplest demonstration of
topological quantum computation remains elusive. Majorana modes are the
primitive building blocks and their experimental realization on various
platforms is yet to be confirmed. This work presents an experimental and
theoretical framework for the space-resolved detection and exchange of Majorana
modes on programmable (noisy) quantum hardware. We have implemented our
framework by performing a series of measurements on a driven Ising-type quantum
spin model with tunable interactions, which confirm the existence of the
topological Majorana modes and distinguishes them from trivial modes. Lastly,
we propose and demonstrate a novel technique for braiding the Majorana modes
which results in the correct statistics but decreases the magnitude of the
signal. The present work may be seen as the first reliable observation of
Majorana modes on existing quantum hardware.


title: Symmetric ordering and Weyl realizations for quantum Minkowski spaces

abstract:
  Symmetric ordering and Weyl realizations for non commutative quantum
Minkowski spaces are reviewed. Weyl realizations of Lie deformed spaces and
corresponding star products, as well as twist corresponding to Weyl realization
and coproduct of momenta are presented. Drinfeld twists understood in Hopf
algebroid sense are also discussed. A few examples of corresponding Weyl
realizations are given. We show that for the original Snyder space there exists
symmetric ordering, but no Weyl realization. Quadratic deformations of
Minkowski space are considered and it is demonstrated that symmetric ordering
is deformed and a generalized Weyl realization can be defined.


title:
Efficient and flexible causal mediation with time-varying mediators,
  treatments, and confounders

abstract:
  Interventional effects have been proposed as a solution to the
unidentifiability of natural (in)direct effects under mediator-outcome
confounders affected by the exposure. Such confounders are an intrinsic
characteristic of studies with time-varying exposures and mediators, yet the
generalization of the interventional effect framework to the time-varying case
has received little attention in the literature. We present an identification
result for interventional effects in a general longitudinal data structure that
allows flexibility in the specification of treatment-outcome,
treatment-mediator, and mediator-outcome relationships. Identification is
achieved under the standard no-unmeasured-confounders and positivity
assumptions. We also present a theoretical and computational study of the
properties of the identifying functional based on the efficient influence
function (EIF). We use the EIF to propose a sequential regression estimation
algorithm that yields doubly robust, $\sqrt{n}$-consistent, asymptotically
Gaussian, and efficient estimators under slow convergence rates for the
regression algorithms used. This allows the use of flexible machine learning
for regression while permitting uncertainty quantification through confidence
intervals and p-values. A free and open source \texttt{R} package implementing
our proposed estimators is made available on GitHub. We apply the proposed
estimator to an application from a comparative effectiveness trial of two
medications for opioid use disorder. In the application, we estimate the extent
to which differences between the two treatments' on subsequent risk of opioid
use is mediated by craving symptoms.


title: X-Pool: Cross-Modal Language-Video Attention for Text-Video Retrieval

abstract:
  In text-video retrieval, the objective is to learn a cross-modal similarity
function between a text and a video that ranks relevant text-video pairs higher
than irrelevant pairs. However, videos inherently express a much wider gamut of
information than texts. Instead, texts often capture sub-regions of entire
videos and are most semantically similar to certain frames within videos.
Therefore, for a given text, a retrieval model should focus on the text's most
semantically similar video sub-regions to make a more relevant comparison. Yet,
most existing works aggregate entire videos without directly considering text.
Common text-agnostic aggregations schemes include mean-pooling or
self-attention over the frames, but these are likely to encode misleading
visual information not described in the given text. To address this, we propose
a cross-modal attention model called X-Pool that reasons between a text and the
frames of a video. Our core mechanism is a scaled dot product attention for a
text to attend to its most semantically similar frames. We then generate an
aggregated video representation conditioned on the text's attention weights
over the frames. We evaluate our method on three benchmark datasets of MSR-VTT,
MSVD and LSMDC, achieving new state-of-the-art results by up to 12% in relative
improvement in Recall@1. Our findings thereby highlight the importance of joint
text-video reasoning to extract important visual cues according to text. Full
code and demo can be found at: https://layer6ai-labs.github.io/xpool/


title: The glass-forming ability of binary Lennard-Jones systems

abstract:
  The glass-forming ability (GFA) of alloys, colloidal dispersions, and other
particulate materials, as measured by the critical cooling rate $R_c$, can span
more than ten orders of magnitude. Even after numerous previous studies, the
physical features that control the GFA are still not well understood. For
example, it is well-known that mixtures are better glass-formers than
monodisperse systems and that particle size and cohesive energy differences
among constituents improve the GFA, but it is not currently known how particle
size differences couple to cohesive energy differences to determine the GFA. We
perform molecular dynamics simulations to determine the GFA of equimolar,
binary Lennard-Jones (LJ) mixtures versus the normalized cohesive energy
difference $\epsilon_\_$ and mixing energy $\bar \epsilon_{AB}$ between
particles A and B. We find several important results. First, the $\log_{10}
R_c$ contours in the $\bar \epsilon_{AB}$-$\epsilon_\_$ plane are ellipsoidal
for all diameter ratios, and thus $R_c$ is determined by the Mahalanobis
distance $d_M$ from a given point in the $\bar \epsilon_{AB}$-$\epsilon_\_$
plane to the center of the ellipsoidal contours. Second, LJ systems for which
the larger particles have larger cohesive energy are generally better glass
formers than those for which the larger particles have smaller cohesive energy.
Third, $d_M(\epsilon_\_,\bar \epsilon_{AB})$ is determined by the relative
Voronoi volume difference between particles and local chemical order $S_{AB}$,
which gives the average fraction of nearest-neighbor B particles surrounding an
A particle and vice-versa. In particular, the shifted Mahalanobis distance $d_M
- d^0_M$ versus the shifted chemical order $S_{AB}-S_{AB}^0$ collapses onto a
hyperbolic master curve for all diameter ratios.


title: Surface-induced decoherence and heating of charged particles

abstract:
  Levitating charged particles in ultra-high vacuum provides a preeminent
platform for quantum information processing, for quantum-enhanced force and
torque sensing, for probing physics beyond the standard model, and for
high-mass tests of the quantum superposition principle. Existing setups range
from single atomic ions, to ion chains and crystals, to charged molecules and
nanoparticles. Future technological applications of such quantum systems will
be crucially affected by fluctuating electric fields emanating from nearby
electrodes, which interact with the levitated particles' monopole and higher
charge moments. In this article, we provide a theoretical toolbox for
describing how the rotational and translational quantum dynamics of charged
nano- to microscale objects is affected by near metallic and dielectric
surfaces, as characterized by their macroscopic dielectric response. The
resulting quantum master equations describe the coherent surface-particle
interaction, due to image charges and Casimir-Polder potentials, as well as
surface-induced decoherence and heating, with the experimentally observed
frequency and distance scaling. We explicitly evaluate the master equations for
typical charge distributions and types of motion, thereby providing the tools
required for describing and mitigating surface-induced decoherence in a variety
of experiments with charged objects.


title: Learning Optical Flow, Depth, and Scene Flow without Real-World Labels

abstract:
  Self-supervised monocular depth estimation enables robots to learn 3D
perception from raw video streams. This scalable approach leverages projective
geometry and ego-motion to learn via view synthesis, assuming the world is
mostly static. Dynamic scenes, which are common in autonomous driving and
human-robot interaction, violate this assumption. Therefore, they require
modeling dynamic objects explicitly, for instance via estimating pixel-wise 3D
motion, i.e. scene flow. However, the simultaneous self-supervised learning of
depth and scene flow is ill-posed, as there are infinitely many combinations
that result in the same 3D point. In this paper we propose DRAFT, a new method
capable of jointly learning depth, optical flow, and scene flow by combining
synthetic data with geometric self-supervision. Building upon the RAFT
architecture, we learn optical flow as an intermediate task to bootstrap depth
and scene flow learning via triangulation. Our algorithm also leverages
temporal and geometric consistency losses across tasks to improve multi-task
learning. Our DRAFT architecture simultaneously establishes a new state of the
art in all three tasks in the self-supervised monocular setting on the standard
KITTI benchmark. Project page: https://sites.google.com/tri.global/draft.


title:
New pyramidal hybrid textural and deep features based automatic skin
  cancer classification model: Ensemble DarkNet and textural feature extractor

abstract:
  Background: Skin cancer is one of the widely seen cancer worldwide and
automatic classification of skin cancer can be benefited dermatology clinics
for an accurate diagnosis. Hence, a machine learning-based automatic skin
cancer detection model must be developed. Material and Method: This research
interests to overcome automatic skin cancer detection problem. A colored skin
cancer image dataset is used. This dataset contains 3297 images with two
classes. An automatic multilevel textural and deep features-based model is
presented. Multilevel fuse feature generation using discrete wavelet transform
(DWT), local phase quantization (LPQ), local binary pattern (LBP), pre-trained
DarkNet19, and DarkNet53 are utilized to generate features of the skin cancer
images, top 1000 features are selected threshold value-based neighborhood
component analysis (NCA). The chosen top 1000 features are classified using the
10-fold cross-validation technique. Results: To obtain results, ten-fold
cross-validation is used and 91.54% classification accuracy results are
obtained by using the recommended pyramidal hybrid feature generator and NCA
selector-based model. Further, various training and testing separation ratios
(90:10, 80:20, 70:30, 60:40, 50:50) are used and the maximum classification
rate is calculated as 95.74% using the 90:10 separation ratio. Conclusions: The
findings and accuracies calculated are denoted that this model can be used in
dermatology and pathology clinics to simplify the skin cancer detection process
and help physicians.


title:
Hydrodynamics for one-dimensional ASEP in contact with a class of
  reservoirs

abstract:
  We study the hydrodynamic behaviour of the asymmetric simple exclusion
process on the lattice of size $n$. In the bulk, the exclusion dynamics
performs rightward flux. At the boundaries, the dynamics is attached to
reservoirs. We investigate two types of reservoirs: (1) the reservoirs that are
weakened by $n^\theta$ for some $\theta<0$ and (2) the reservoirs that create
particles only at the right boundary and annihilate particles only at the left
boundary. We prove that the spatial density of particles, under the hyperbolic
time scale, evolves with the entropy solution to a scalar conservation law on
$[0,1]$ with boundary conditions. The boundary conditions are characterised by
the boundary traces at $x=0$ and $x=1$ which take values from $\{0,1\}$.


title:
Improved singing voice separation with chromagram-based pitch-aware
  remixing

abstract:
  Singing voice separation aims to separate music into vocals and accompaniment
components. One of the major constraints for the task is the limited amount of
training data with separated vocals. Data augmentation techniques such as
random source mixing have been shown to make better use of existing data and
mildly improve model performance. We propose a novel data augmentation
technique, chromagram-based pitch-aware remixing, where music segments with
high pitch alignment are mixed. By performing controlled experiments in both
supervised and semi-supervised settings, we demonstrate that training models
with pitch-aware remixing significantly improves the test signal-to-distortion
ratio (SDR)


title:
Guns, Zombies, and Steelhead Axes: cost-effective recommendations for
  surviving human societies

abstract:
  In pop culture, there are many strategies to curtail zombie apocalypses.
However, it remains unclear what routes we should take to eliminate zombies
effectively and affordably. So, we created a mathematical model to examine
interventions that armed adults with steelhead axes or provided enough
ammunition and a 9mm handgun to kill zombies in either a "single-tap" or
"double-tap". We investigate each case over two years under slow, moderate
speed, and fast zombies scenarios. We quantify health burden by zombies
averted, disability-adjusted life-years averted, and determine
cost-effectiveness using the incremental cost-effectiveness ratio. Our
predictions show the single=tap intervention is the best for stopping the
zombie apocalypse, as it would avert hundreds of millions of zombies and deaths
while also being the most cost-effective intervention. Altogether, this
suggests conserving ammunition and supplying ranged weapons would be an
effective use of limited resources in the event of a zombie uprising.


title: Matroid schemes and geometric posets

abstract:
  The intersection data of a hyperplane arrangement is described by a geometric
lattice, or equivalently a simple matroid. There is a rich interplay between
this combinatorial structure and the topology of the arrangement complement. In
this paper, we characterize the combinatorial structure underlying an abelian
arrangement (such as a toric or elliptic arrangement) by defining a class of
geometric posets and a generalization of matroids called matroid schemes. The
intersection data of an abelian arrangement is encoded in a geometric poset,
and we prove that a geometric poset is equivalent to a simple matroid scheme.
We lay foundations for the theory of matroid schemes, discussing rank, flats,
and independence. We also extend the definition of the Tutte polynomial to this
setting and prove that it satisfies a deletion-contraction recurrence.


title: Robust Speaker Recognition with Transformers Using wav2vec 2.0

abstract:
  Recent advances in unsupervised speech representation learning discover new
approaches and provide new state-of-the-art for diverse types of speech
processing tasks. This paper presents an investigation of using wav2vec 2.0
deep speech representations for the speaker recognition task. The proposed
fine-tuning procedure of wav2vec 2.0 with simple TDNN and statistic pooling
back-end using additive angular margin loss allows to obtain deep speaker
embedding extractor that is well-generalized across different domains. It is
concluded that Contrastive Predictive Coding pretraining scheme efficiently
utilizes the power of unlabeled data, and thus opens the door to powerful
transformer-based speaker recognition systems. The experimental results
obtained in this study demonstrate that fine-tuning can be done on relatively
small sets and a clean version of data. Using data augmentation during
fine-tuning provides additional performance gains in speaker verification. In
this study speaker recognition systems were analyzed on a wide range of
well-known verification protocols: VoxCeleb1 cleaned test set, NIST SRE 18
development set, NIST SRE 2016 and NIST SRE 2019 evaluation set, VOiCES
evaluation set, NIST 2021 SRE, and CTS challenges sets.


title: Exactness of limits and colimits in abelian categories revisited

abstract:
  Let $\Sigma$ be a small category and $\mathcal{A}$ be a $\Sigma$-co-complete
(resp. $\Sigma$-complete) abelian category. It is a well-known fact that the
category $\operatorname{Fun}(\Sigma,\mathcal{A})$ of functors of $\Sigma$ in
$\mathcal{A}$ is an abelian category, and that the functor
$\mathsf{colim}_\Sigma(-):\operatorname{Fun}(\Sigma,\mathcal{A})\rightarrow\mathcal{A}$
(resp.
$\mathsf{lim}_{\Sigma}(-):\operatorname{Fun}(\Sigma,\mathcal{A})\rightarrow\mathcal{A}$)
is left (resp. right) adjoint to
$\kappa^{\Sigma}:\mathcal{A}\rightarrow\operatorname{Fun}(\Sigma,\mathcal{A})$,
where $\kappa^{\Sigma}$ is the associated constant diagram functor. In this
paper we will show that the functor $\mathsf{colim}_\Sigma(-)$ (resp.
$\mathsf{lim}_{\Sigma}(-)$) is exact if and only if the pair of functors
$\left(\mathsf{colim}_\Sigma(-),\kappa^{\Sigma}\right)$ (resp.
$\left(\kappa^{\Sigma},\mathsf{lim}_{\Sigma}(-)\right)$) is Ext-adjoint. As an
application of our findings, we will give new proofs of known results on the
exactness of limits and colimits in abelian categories.


title:
Dissipation-preserving discretization of the Cahn--Hilliard equation
  with dynamic boundary conditions

abstract:
  This paper deals with time stepping schemes for the Cahn--Hilliard equation
with three different types of dynamic boundary conditions. The proposed schemes
of first and second order are mass-conservative and energy-dissipative and --
as they are based on a formulation as a coupled system of partial differential
equations -- allow different spatial discretizations in the bulk and on the
boundary. The latter enables refinements on the boundary without an adaptation
of the mesh in the interior of the domain. The resulting computational gain is
illustrated in numerical experiments.


title: Poincar\'e dualization and formal domination

abstract:
  We consider the question of whether formality of the domain of a non-zero
degree map of closed manifolds implies formality of the target. Though there
are various situations where this is indeed the case, we show the answer is
negative in general, with a counterexample given by a non-zero degree map from
a formal manifold to one that carries a non-vanishing quadruple Massey product.
This violates a general heuristic that the domain of a non-zero degree map
should be more complicated than its target. For the construction of the
counterexample we introduce a method to turn a cdga into one that satisfies
Poincar\'e duality, which is natural in certain situations.


title:
LogicInference: A New Dataset for Teaching Logical Inference to seq2seq
  Models

abstract:
  Machine learning models such as Transformers or LSTMs struggle with tasks
that are compositional in nature such as those involving reasoning/inference.
Although many datasets exist to evaluate compositional generalization, when it
comes to evaluating inference abilities, options are more limited. This paper
presents LogicInference, a new dataset to evaluate the ability of models to
perform logical inference. The dataset focuses on inference using propositional
logic and a small subset of first-order logic, represented both in semi-formal
logical notation, as well as in natural language. We also report initial
results using a collection of machine learning models to establish an initial
baseline in this dataset.


title:
Understanding out-of-distribution accuracies through quantifying
  difficulty of test samples

abstract:
  Existing works show that although modern neural networks achieve remarkable
generalization performance on the in-distribution (ID) dataset, the accuracy
drops significantly on the out-of-distribution (OOD) datasets
\cite{recht2018cifar, recht2019imagenet}. To understand why a variety of models
consistently make more mistakes in the OOD datasets, we propose a new metric to
quantify the difficulty of the test images (either ID or OOD) that depends on
the interaction of the training dataset and the model. In particular, we
introduce \textit{confusion score} as a label-free measure of image difficulty
which quantifies the amount of disagreement on a given test image based on the
class conditional probabilities estimated by an ensemble of trained models.
Using the confusion score, we investigate CIFAR-10 and its OOD derivatives.
Next, by partitioning test and OOD datasets via their confusion scores, we
predict the relationship between ID and OOD accuracies for various
architectures. This allows us to obtain an estimator of the OOD accuracy of a
given model only using ID test labels. Our observations indicate that the
biggest contribution to the accuracy drop comes from images with high confusion
scores. Upon further inspection, we report on the nature of the misclassified
images grouped by their confusion scores: \textit{(i)} images with high
confusion scores contain \textit{weak spurious correlations} that appear in
multiple classes in the training data and lack clear \textit{class-specific
features}, and \textit{(ii)} images with low confusion scores exhibit spurious
correlations that belong to another class, namely \textit{class-specific
spurious correlations}.


title: Federated Named Entity Recognition

abstract:
  We present an analysis of the performance of Federated Learning in a
paradigmatic natural-language processing task: Named-Entity Recognition (NER).
For our evaluation, we use the language-independent CoNLL-2003 dataset as our
benchmark dataset and a Bi-LSTM-CRF model as our benchmark NER model. We show
that federated training reaches almost the same performance as the centralized
model, though with some performance degradation as the learning environments
become more heterogeneous. We also show the convergence rate of federated
models for NER. Finally, we discuss existing challenges of Federated Learning
for NLP applications that can foster future research directions.


title: Rethinking Semantic Segmentation: A Prototype View

abstract:
  Prevalent semantic segmentation solutions, despite their different network
designs (FCN based or attention based) and mask decoding strategies (parametric
softmax based or pixel-query based), can be placed in one category, by
considering the softmax weights or query vectors as learnable class prototypes.
In light of this prototype view, this study uncovers several limitations of
such parametric segmentation regime, and proposes a nonparametric alternative
based on non-learnable prototypes. Instead of prior methods learning a single
weight/query vector for each class in a fully parametric manner, our model
represents each class as a set of non-learnable prototypes, relying solely on
the mean features of several training pixels within that class. The dense
prediction is thus achieved by nonparametric nearest prototype retrieving. This
allows our model to directly shape the pixel embedding space, by optimizing the
arrangement between embedded pixels and anchored prototypes. It is able to
handle arbitrary number of classes with a constant amount of learnable
parameters. We empirically show that, with FCN based and attention based
segmentation models (i.e., HRNet, Swin, SegFormer) and backbones (i.e., ResNet,
HRNet, Swin, MiT), our nonparametric framework yields compelling results over
several datasets (i.e., ADE20K, Cityscapes, COCO-Stuff), and performs well in
the large-vocabulary situation. We expect this work will provoke a rethink of
the current de facto semantic segmentation model design.


title:
Adversarial Motion Priors Make Good Substitutes for Complex Reward
  Functions

abstract:
  Training a high-dimensional simulated agent with an under-specified reward
function often leads the agent to learn physically infeasible strategies that
are ineffective when deployed in the real world. To mitigate these unnatural
behaviors, reinforcement learning practitioners often utilize complex reward
functions that encourage physically plausible behaviors. However, a tedious
labor-intensive tuning process is often required to create hand-designed
rewards which might not easily generalize across platforms and tasks. We
propose substituting complex reward functions with "style rewards" learned from
a dataset of motion capture demonstrations. A learned style reward can be
combined with an arbitrary task reward to train policies that perform tasks
using naturalistic strategies. These natural strategies can also facilitate
transfer to the real world. We build upon Adversarial Motion Priors -- an
approach from the computer graphics domain that encodes a style reward from a
dataset of reference motions -- to demonstrate that an adversarial approach to
training policies can produce behaviors that transfer to a real quadrupedal
robot without requiring complex reward functions. We also demonstrate that an
effective style reward can be learned from a few seconds of motion capture data
gathered from a German Shepherd and leads to energy-efficient locomotion
strategies with natural gait transitions.


title:
FedADMM: A Federated Primal-Dual Algorithm Allowing Partial
  Participation

abstract:
  Federated learning is a framework for distributed optimization that places
emphasis on communication efficiency. In particular, it follows a client-server
broadcast model and is particularly appealing because of its ability to
accommodate heterogeneity in client compute and storage resources, non-i.i.d.
data assumptions, and data privacy. Our contribution is to offer a new
federated learning algorithm, FedADMM, for solving non-convex composite
optimization problems with non-smooth regularizers. We prove converges of
FedADMM for the case when not all clients are able to participate in a given
communication round under a very general sampling model.


title:
In search of the ground state crystal structure of Ta$_2$O$_5$ from
  ab-initio and Monte Carlo simulations

abstract:
  Tantalum oxides (Ta$_2$O$_5$) are characterised by attractive physical and
chemical properties, such as high dielectric constants and anti-reflection
behaviour. Recently, Ta$_2$O$_5$ nanoparticles have also been proposed as
possible enhancers of the relative biological effectiveness in hadrontherapy
for cancer treatment. In principle, their electronic properties can be
accurately investigated from first-principles simulations. However, the
existence of several stable polymorphs of these oxides represents a major
difficulty in order to calculate and disentangle their respective spectral
features. To assess this problem, we use linear-response time-dependent density
functional to investigate the energy loss function $\mbox{Im}
(-1/\bar{\epsilon})$, which is a unique fingerprint of the material, in the
optical limit for various polymorphs. We show that the experimental reflection
energy loss signals can be rationalized and interpreted by assuming that the
$\gamma$-phase of Ta$_2$O$_5$ represents the underlying structural model. We
notice that both the inclusion of local field effects and spin-orbit coupling
are crucial to compute the energy loss functions of this material. Finally, to
further validate the $\gamma$-Ta$_2$O$_5$ polymorph as a model for experimental
tantalum oxide, we compute the reflection energy loss spectra using a Monte
Carlo approach, finding an excellent agreement with the experimental data.


title:
Investigation of Different Calibration Methods for Deep Speaker
  Embedding based Verification Systems

abstract:
  Deep speaker embedding extractors have already become new state-of-the-art
systems in the speaker verification field. However, the problem of verification
score calibration for such systems often remains out of focus. An irrelevant
score calibration leads to serious issues, especially in the case of unknown
acoustic conditions, even if we use a strong speaker verification system in
terms of threshold-free metrics. This paper presents an investigation over
several methods of score calibration: a classical approach based on the
logistic regression model; the recently presented magnitude estimation network
MagnetO that uses activations from the pooling layer of the trained deep
speaker extractor and generalization of such approach based on separate scale
and offset prediction neural networks. An additional focus of this research is
to estimate the impact of score normalization on the calibration performance of
the system. The obtained results demonstrate that there are no serious problems
if in-domain development data are used for calibration tuning. Otherwise, a
trade-off between good calibration performance and threshold-free system
quality arises. In most cases using adaptive s-norm helps to stabilize score
distributions and to improve system performance. Meanwhile, some experiments
demonstrate that novel approaches have their limits in score stabilization on
several datasets.


title:
Motion Planning for Agile Legged Locomotion using Failure Margin
  Constraints

abstract:
  The complex dynamics of agile robotic legged locomotion requires motion
planning to intelligently adjust footstep locations. Often, bipedal footstep
and motion planning use mathematically simple models such as the linear
inverted pendulum, instead of dynamically-rich models that do not have
closed-form solutions. We propose a real-time optimization method to plan for
dynamical models that do not have closed form solutions and experience
irrecoverable failure. Our method uses a data-driven approximation of the
step-to-step dynamics and of a failure margin function. This failure margin
function is an oriented distance function in state-action space where it
describes the signed distance to success or failure. The motion planning
problem is formed as a nonlinear program with constraints that enforce the
approximated forward dynamics and the validity of state-action pairs. For
illustration, this method is applied to create a planner for an actuated
spring-loaded inverted pendulum model. In an ablation study, the failure margin
constraints decreased the number of invalid solutions by between 24 and 47
percentage points across different objectives and horizon lengths. While we
demonstrate the method on a canonical model of locomotion, we also discuss how
this can be applied to data-driven models and full-order robot models.


title:
A Well-Composed Text is Half Done! Composition Sampling for Diverse
  Conditional Generation

abstract:
  We propose Composition Sampling, a simple but effective method to generate
diverse outputs for conditional generation of higher quality compared to
previous stochastic decoding strategies. It builds on recently proposed
plan-based neural generation models (Narayan et al, 2021) that are trained to
first create a composition of the output and then generate by conditioning on
it and the input. Our approach avoids text degeneration by first sampling a
composition in the form of an entity chain and then using beam search to
generate the best possible text grounded to this entity chain. Experiments on
summarization (CNN/DailyMail and XSum) and question generation (SQuAD), using
existing and newly proposed automatic metrics together with human-based
evaluation, demonstrate that Composition Sampling is currently the best
available decoding strategy for generating diverse meaningful outputs.


title: 27 Open Problems in Kolmogorov Complexity

abstract:
  The paper proposes open problems in classical Kolmogorov complexity. Each
problem is presented with background information and thus the article also
surveys some recent studies in the area.


title: The State of Fortran

abstract:
  A community of developers has formed to modernize the Fortran ecosystem. In
this article, we describe the high-level features of Fortran that continue to
make it a good choice for scientists and engineers in the 21st century. Ongoing
efforts include the development of a Fortran standard library and package
manager, the fostering of a friendly and welcoming online community, improved
compiler support, and language feature development. The lessons learned are
common across contemporary programming languages and help reduce the learning
curve and increase adoption of Fortran.


title: Multi-constrained topology optimization via the topological sensitivity

abstract:
  The objective of this paper is to introduce and demonstrate a robust method
for multi-constrained topology optimization. The method is derived by combining
the topological sensitivity with the classic augmented Lagrangian formulation.
The primary advantages of the proposed method are: (1) it rests on
well-established augmented Lagrangian formulation for constrained optimization,
(2) the augmented topological level-set can be derived systematically for an
arbitrary set of loads and constraints, and (3) the level-set can be updated
efficiently. The method is illustrated through numerical experiments.


title:
Domain Knowledge Driven Pseudo Labels for Interpretable Goal-Conditioned
  Interactive Trajectory Prediction

abstract:
  Motion forecasting in highly interactive scenarios is a challenging problem
in autonomous driving. In such scenarios, we need to accurately predict the
joint behavior of interacting agents to ensure the safe and efficient
navigation of autonomous vehicles. Recently, goal-conditioned methods have
gained increasing attention due to their advantage in performance and their
ability to capture the multimodality in trajectory distribution. In this work,
we study the joint trajectory prediction problem with the goal-conditioned
framework. In particular, we introduce a
conditional-variational-autoencoder-based (CVAE) model to explicitly encode
different interaction modes into the latent space. However, we discover that
the vanilla model suffers from posterior collapse and cannot induce an
informative latent space as desired. To address these issues, we propose a
novel approach to avoid KL vanishing and induce an interpretable interactive
latent space with pseudo labels. The pseudo labels allow us to incorporate
arbitrary domain knowledge on interaction. We motivate the proposed method
using an illustrative toy example. In addition, we validate our framework on
the Waymo Open Motion Dataset with both quantitative and qualitative
evaluations.


title:
Stefan problem with surface tension: global existence of physical
  solutions under radial symmetry

abstract:
  We consider the Stefan problem with surface tension, also known as the
Stefan-Gibbs-Thomson problem, in an ambient space of arbitrary dimension.
Assuming the radial symmetry of the initial data we introduce a novel
"probabilistic" notion of solution, which can accommodate the discontinuities
in time (of the radius) of the evolving aggregate. Our main result establishes
the global existence of a probabilistic solution satisfying the natural upper
bound on the sizes of the discontinuities. Moreover, we prove that the upper
bound is sharp in dimensions d>2, in the sense that none of the discontinuities
in the solution can be decreased in magnitude. The detailed analysis of the
discontinuities, via appropriate stochastic representations, differentiates
this work from the previous literature on weak solutions to the Stefan problem
with surface tension.


title:
Type IIA embeddings of $D=5$ minimal gauged supergravity via Non-Abelian
  T-duality

abstract:
  In this note, we construct explicit Type IIA uplifts of $D=5$ minimal gauged
supergravity, by T-dualising known Type IIB uplifts on $N_5 = S^5$, $T^{1,1}$
and $Y^{p,q}$ along their $SU(2)$ isometries. When the $D=5$ gauge field is set
to zero, our uplifts recover precisely the known non-Abelian T-duals of the
$AdS_5\times N_5$ solutions. As an application, we obtain new supersymmetric
$AdS_3\times\Sigma\times M_5$ solutions in Type IIA, where $\Sigma =
\mathbb{WCP}^1_{[n_-,n_+]}$ is a weighted projective space. Existing
holographic results of T-dualised AdS solutions suggest that our solutions
capture features of $d = 2$ SCFTs with $\mathcal{N}=(0, 2)$ supersymmetry.


title:
Multi-constrained 3D topology optimization via augmented topological
  level-set

abstract:
  The objective of this paper is to introduce and demonstrate a robust
methodology for solving multi-constrained 3D topology optimization problems.
The proposed methodology is a combination of the topological level-set
formulation, augmented Lagrangian algorithm, and assembly-free deflated finite
element analysis (FEA). The salient features of the proposed method include:
(1) it exploits the topological sensitivity fields that can be derived for a
variety of constraints, (2) it rests on well-established augmented Lagrangian
formulation to solve constrained problems, and (3) it overcomes the
computational challenges by employing assembly-free deflated FEA. The proposed
method is illustrated through several 3D numerical experiments.


title:
Rotation Distributions around the Kraft Break with TESS and Kepler: The
  Influences of Age, Metallicity, and Binarity

abstract:
  Stellar rotation is a complex function of mass, metallicity, and age and can
be altered by binarity. To understand the importance of these parameters in
main sequence stars, we have assembled a sample of observations that spans a
range of these parameters using a combination of observations from The
Transiting Exoplanet Survey Satellite (TESS) and the Kepler Space Telescope. We
find that while we can measure rotation periods and identify other classes of
stellar variability (e.g., pulsations) from TESS lightcurves, instrument
systematics prevent the detection of rotation signals longer than the TESS
orbital period of 13.7 days. Due to this detection limit, we also utilize
rotation periods constrained using rotational velocities measured by the APOGEE
spectroscopic survey and radii estimated using the Gaia mission for both TESS
and Kepler stars. From these rotation periods, we 1) find we can track
rotational evolution along discrete mass tracks as a function of stellar age,
2) find we are unable to recover trends between rotation and metallicity that
were observed by previous studies, and 3) note that our sample reveals that
wide binary companions do not affect rotation, while close binary companions
cause stars to exhibit more rapid rotation than single stars.


title:
Stress constrained thermo-elastic topology optimization with varying
  temperature fields via augmented topological sensitivity based level-set

abstract:
  Engineering structures must often be designed to resist thermally induced
stresses. Significant progress has been made on the design of such structures
through thermo-elastic topology optimization. However, a computationally
efficient framework to handle stress-constrained large-scale problems is
lacking. The main contribution of this paper is to address this limitation. In
particular, a unified topological-sensitivity (TS) based level-set approach is
presented in this paper for optimizing thermo-elastic structures subject to
non-uniform temperatures. The TS fields for various thermo-elastic objectives
are derived, and, to address multiple constraints, an augmented Lagrangian
method is developed to explore Pareto topologies. Numerical examples
demonstrate the capability of the proposed framework to solve large-scale
design problems. Comparison is made between pure elastic problems, and its
thermo-elastic counterpart, shedding light on the influence of thermo-elastic
coupling on optimized topologies.


title: LiDAR Snowfall Simulation for Robust 3D Object Detection

abstract:
  3D object detection is a central task for applications such as autonomous
driving, in which the system needs to localize and classify surrounding traffic
agents, even in the presence of adverse weather. In this paper, we address the
problem of LiDAR-based 3D object detection under snowfall. Due to the
difficulty of collecting and annotating training data in this setting, we
propose a physically based method to simulate the effect of snowfall on real
clear-weather LiDAR point clouds. Our method samples snow particles in 2D space
for each LiDAR line and uses the induced geometry to modify the measurement for
each LiDAR beam accordingly. Moreover, as snowfall often causes wetness on the
ground, we also simulate ground wetness on LiDAR point clouds. We use our
simulation to generate partially synthetic snowy LiDAR data and leverage these
data for training 3D object detection models that are robust to snowfall. We
conduct an extensive evaluation using several state-of-the-art 3D object
detection methods and show that our simulation consistently yields significant
performance gains on the real snowy STF dataset compared to clear-weather
baselines and competing simulation approaches, while not sacrificing
performance in clear weather. Our code is available at
www.github.com/SysCV/LiDAR_snow_sim.


title: Visual Odometry for RGB-D Cameras

abstract:
  Visual odometry is the process of estimating the position and orientation of
a camera by analyzing the images associated to it. This paper develops a quick
and accurate approach to visual odometry of a moving RGB-D camera navigating on
a static environment. The proposed algorithm uses SURF (Speeded Up Robust
Features) as feature extractor, RANSAC (Random Sample Consensus) to filter the
results and Minimum Mean Square to estimate the rigid transformation of six
parameters between successive video frames. Data from a Kinect camera were used
in the tests. The results show that this approach is feasible and promising,
surpassing in performance the algorithms ICP (Interactive Closest Point) and
SfM (Structure from Motion) in tests using a publicly available dataset.


title:
The...Tinderverse?: Opportunities and Challenges for User Safety in
  Extended Reality (XR) Dating Apps

abstract:
  Dating apps such as Tinder have announced plans for a dating metaverse: the
incorporation of XR technologies into the online dating process to augment
interactions between potential sexual partners across virtual and physical
worlds. While the dating metaverse is still in conceptual stages we can
forecast significant harms that it may expose daters to given prior research
into the frequency and severity of sexual harms facilitated by dating apps as
well as harms within social VR environments. In this workshop paper we envision
how XR could enrich virtual-to-physical interaction between potential sexual
partners and outline harms that it will likely perpetuate as well. We then
introduce our ongoing research to preempt such harms: a participatory design
study with sexual violence experts and demographics at disproportionate risk of
sexual violence to produce mitigative solutions to sexual violence perpetuated
by XR-enabled dating apps.


title: Tightly Seal Your Sensitive Pointers with PACTight

abstract:
  ARM is becoming more popular in desktops and data centers, opening a new
realm in terms of security attacks against ARM. ARM has released Pointer
Authentication, a new hardware security feature that is intended to ensure
pointer integrity with cryptographic primitives. In this paper, we utilize
Pointer Authentication (PA) to build a novel scheme to completely prevent any
misuse of security-sensitive pointers. We propose PACTight to tightly seal
these pointers. PACTight utilizes a strong and unique modifier that addresses
the current issues with the state-of-the-art PA defense mechanisms. We
implement four defenses based on the PACTight mechanism. Our security and
performance evaluation results show that PACTight defenses are more efficient
and secure. Using real PA instructions, we evaluated PACTight on 30 different
applications, including NGINX web server, with an average performance overhead
of 4.07% even when enforcing our strongest defense. PACTight demonstrates its
effectiveness and efficiency with real PA instructions on real hardware.


title:
Multiple Riemann wave solutions of the general form of quasilinear
  hyperbolic systems

abstract:
  The objective of this paper is to construct geometrically Riemann $k$-wave
solutions of the general form of first-order quasilinear hyperbolic systems of
partial differential equations. To this end, we adapt and combine elements of
two approaches to the construction of Riemann $k$-waves, namely the symmetry
reduction method and the generalized method of characteristics. We formulate a
geometrical setting for the general form of the $k$-wave problem and discuss in
detail the conditions for the existence of $k$-wave solutions. An auxiliary
result concerning the Frobenius theorem is established. We use it to obtain
formulae describing the $k$-wave solutions in closed form. Our theoretical
considerations are illustrated by examples of hydrodynamic type systems
including the Brownian motion equation.


title: Continuity of coordinate functionals of filter bases in Banach spaces

abstract:
  We prove that the coordinate functionals associated with filter bases in
Banach spaces are continuous as long as the underlying filter is analytic. This
removes the large-cardinal hypothesis from the result established by the two
last-named authors ([Bull. Lond. Math. Soc. 53 (2021)]) at the expense of
reducing the generality from projective to analytic. In particular, we obtain a
ZFC solution to Kadets' problem of continuity of coordinate functionals
associated with bases with respect to the filter of statistical convergence.
Even though the automatic continuity of coordinate functionals beyond the
projective class remains a mystery, we prove that a basis with respect to an
arbitrary filter that has continuous coordinate functionals is also a basis
with respect to a filter that is analytic.


title: Toward Deep Learning Based Access Control

abstract:
  A common trait of current access control approaches is the challenging need
to engineer abstract and intuitive access control models. This entails
designing access control information in the form of roles (RBAC), attributes
(ABAC), or relationships (ReBAC) as the case may be, and subsequently,
designing access control rules. This framework has its benefits but has
significant limitations in the context of modern systems that are dynamic,
complex, and large-scale, due to which it is difficult to maintain an accurate
access control state in the system for a human administrator. This paper
proposes Deep Learning Based Access Control (DLBAC) by leveraging significant
advances in deep learning technology as a potential solution to this problem.
We envision that DLBAC could complement and, in the long-term, has the
potential to even replace, classical access control models with a neural
network that reduces the burden of access control model engineering and
updates. Without loss of generality, we conduct a thorough investigation of a
candidate DLBAC model, called DLBAC_alpha, using both real-world and synthetic
datasets. We demonstrate the feasibility of the proposed approach by addressing
issues related to accuracy, generalization, and explainability. We also discuss
challenges and future research directions.


title: Text2Pos: Text-to-Point-Cloud Cross-Modal Localization

abstract:
  Natural language-based communication with mobile devices and home appliances
is becoming increasingly popular and has the potential to become natural for
communicating with mobile robots in the future. Towards this goal, we
investigate cross-modal text-to-point-cloud localization that will allow us to
specify, for example, a vehicle pick-up or goods delivery location. In
particular, we propose Text2Pos, a cross-modal localization module that learns
to align textual descriptions with localization cues in a coarse- to-fine
manner. Given a point cloud of the environment, Text2Pos locates a position
that is specified via a natural language-based description of the immediate
surroundings. To train Text2Pos and study its performance, we construct
KITTI360Pose, the first dataset for this task based on the recently introduced
KITTI360 dataset. Our experiments show that we can localize 65% of textual
queries within 15m distance to query locations for top-10 retrieved locations.
This is a starting point that we hope will spark future developments towards
language-based navigation.


title:
Network Performance Estimator with Applications to Route Selection for
  IoT Multimedia Applications

abstract:
  Estimating the performance of multimedia traffic is important in numerous
contexts, including routing and forwarding, QoS provisioning, and adaptive
video streaming. This paper proposes a network performance estimator which aims
at providing, in quasi real-time, network performance estimates for IoT
multimedia traffic in IEEE 802.11 multihop wireless networks. To our knowledge,
the proposed multimedia-aware performance estimator, or MAPE, is the first
deterministic simulation-based estimator that provides real-time per-flow
throughput, packet loss and delay estimates while considering inter-flow
interference and multi-rate flows, typical of multimedia traffic. Our
experimental results indicate that MAPE is able to provide network performance
estimates that can be used by IoT multimedia services, notably to inform
real-time route selection in IoT video transmission, at a fraction of the
execution time when compared to stochastic network simulators. When compared to
existing deterministic simulators, MAPE yields higher accuracy at comparable
execution times due to its ability to consider multi-rate flows.


title:
An Online Approach to Solve the Dynamic Vehicle Routing Problem with
  Stochastic Trip Requests for Paratransit Services

abstract:
  Many transit agencies operating paratransit and microtransit services have to
respond to trip requests that arrive in real-time, which entails solving hard
combinatorial and sequential decision-making problems under uncertainty. To
avoid decisions that lead to significant inefficiency in the long term,
vehicles should be allocated to requests by optimizing a non-myopic utility
function or by batching requests together and optimizing a myopic utility
function. While the former approach is typically offline, the latter can be
performed online. We point out two major issues with such approaches when
applied to paratransit services in practice. First, it is difficult to batch
paratransit requests together as they are temporally sparse. Second, the
environment in which transit agencies operate changes dynamically (e.g.,
traffic conditions), causing estimates that are learned offline to become
stale. To address these challenges, we propose a fully online approach to solve
the dynamic vehicle routing problem (DVRP) with time windows and stochastic
trip requests that is robust to changing environmental dynamics by
construction. We focus on scenarios where requests are relatively sparse - our
problem is motivated by applications to paratransit services. We formulate DVRP
as a Markov decision process and use Monte Carlo tree search to evaluate
actions for any given state. Accounting for stochastic requests while
optimizing a non-myopic utility function is computationally challenging;
indeed, the action space for such a problem is intractably large in practice.
To tackle the large action space, we leverage the structure of the problem to
design heuristics that can sample promising actions for the tree search. Our
experiments using real-world data from our partner agency show that the
proposed approach outperforms existing state-of-the-art approaches both in
terms of performance and robustness.


title: Programs Enabling Deep Technology Transfer from National Labs

abstract:
  To maximize the technology transfer potential, it is important to create an
ecosystem for the inventors to adapt the technologies developed for basic
science to successful commercial ventures. In this white paper we present a
brief overview of technology transfer programs at high energy physics (HEP)
laboratories with a focus on the programs at Fermilab and KEK, and identify
opportunities and recommendations for increasing partnerships and
commercialization at HEP-centric laboratories.


title:
A Study of Reinforcement Learning Algorithms for Aggregates of
  Minimalistic Robots

abstract:
  The aim of this paper is to study how to apply deep reinforcement learning
for the control of aggregates of minimalistic robots. We define aggregates as
groups of robots with a physical connection that compels them to form a
specified shape. In our case, the robots are pre-attached to an object that
must be collectively transported to a known location. Minimalism, in our
setting, stems from the barebone capabilities we assume: The robots can sense
the target location and the immediate obstacles, but lack the means to
communicate explicitly through, e.g., message-passing. In our setting,
communication is implicit, i.e., mediated by aggregated push-and-pull on the
object exerted by each robot. We analyze the ability to reach coordinated
behavior of four well-known algorithms for deep reinforcement learning (DQN,
DDQN, DDPG, and TD3). Our experiments include robot failures and different
types of environmental obstacles. We compare the performance of the best
control strategies found, highlighting strengths and weaknesses of each of the
considered training algorithms.


title: Perturbative higher-derivative terms in N=6 asymmetric orbifolds

abstract:
  We analyse the perturbative four-point amplitudes in the simplest string
theory examples of T-fold backgrounds, which enjoy N=6 supersymmetries in four
dimensions. There are two theories defined as asymmetric orbifolds of order 2
and 3, respectively. The perturbative spectrum and the one-loop four-point
amplitudes are shown to be invariant under an arithmetic symplectic group
defined over the Hurwitz (respectively Eisenstein) quaternions. The
supersymmetry constraints on the low energy effective couplings are determined
and we conjecture the U-duality group of the theory and the non-perturbative
completion of the six-derivative coupling as a rank one theta series. We
discuss the non-perturbative spectrum of BPS states in the light of our
analysis.


title: A dynamical approach to validated numerics

abstract:
  We describe a method, using periodic points and determinants, for giving
alternative expressions for dynamical quantities (including Lyapunov exponents
and Hausdorff dimension of invariant sets) associated to analytic hyperbolic
systems. This leads to validated numerical estimates on their values


title: LocalBins: Improving Depth Estimation by Learning Local Distributions

abstract:
  We propose a novel architecture for depth estimation from a single image. The
architecture itself is based on the popular encoder-decoder architecture that
is frequently used as a starting point for all dense regression tasks. We build
on AdaBins which estimates a global distribution of depth values for the input
image and evolve the architecture in two ways. First, instead of predicting
global depth distributions, we predict depth distributions of local
neighborhoods at every pixel. Second, instead of predicting depth distributions
only towards the end of the decoder, we involve all layers of the decoder. We
call this new architecture LocalBins. Our results demonstrate a clear
improvement over the state-of-the-art in all metrics on the NYU-Depth V2
dataset. Code and pretrained models will be made publicly available.


title: Centralizers of sections of a reductive group scheme

abstract:
  In this paper we prove a number of flatness results for centralizers of
sections of a reductive group scheme over a general base scheme. To this end,
we establish relative versions of the Jordan decomposition and establish the
existence of an integral Springer isomorphism. Using our results, we obtain a
canonical flattening stratification for the universal centralizer of a simply
connected semisimple group scheme over a base of good characteristic. We also
investigate the structure of centralizers of nilpotent sections over general
bases and establish some regularity properties of unipotent varieties over
fields of small characteristic.


title:
Detection of strong scattering close to the eclipse region of PSR
  B1957+20

abstract:
  We present the first measurement of pulse scattering close to the eclipse
region of PSR B1957+20, which is in a compact binary system with a low-mass
star. We measured pulse scattering time-scales up to 0.2 ms close to the
eclipse and showed that it scales with the dispersion measure (DM) excess
roughly as $\tau\propto\Delta{\rm DM}^{2}$. Our observations provide the first
evidence of strong scattering due to multi-path propagation effects in the
eclipsing material. We show that Kolmogorov turbulence in the eclipsing
material with an inner scale of $\sim100$ m and an outer scale of the size of
the eclipse region can naturally explain the observation. Our results show that
the eclipsing material in such systems can be highly turbulent and suggest that
scattering is one of the main eclipsing mechanisms at around 1.4 GHz.


title: Filler Word Detection and Classification: A Dataset and Benchmark

abstract:
  Filler words such as `uh' or `um' are sounds or words people use to signal
they are pausing to think. Finding and removing filler words from recordings is
a common and tedious task in media editing. Automatically detecting and
classifying filler words could greatly aid in this task, but few studies have
been published on this problem. A key reason is the absence of a dataset with
annotated filler words for training and evaluation. In this work, we present a
novel speech dataset, PodcastFillers, with 35K annotated filler words and 50K
annotations of other sounds that commonly occur in podcasts such as breaths,
laughter, and word repetitions. We propose a pipeline that leverages VAD and
ASR to detect filler candidates and a classifier to distinguish between filler
word types. We evaluate our proposed pipeline on PodcastFillers, compare to
several baselines, and present a detailed ablation study. In particular, we
evaluate the importance of using ASR and how it compares to a
transcription-free approach resembling keyword spotting. We show that our
pipeline obtains state-of-the-art results, and that leveraging ASR strongly
outperforms a keyword spotting approach. We make PodcastFillers publicly
available, and hope our work serves as a benchmark for future research.


title: The ALMA REBELS Survey: Dust Continuum Detections at z > 6.5

abstract:
  We report 18 dust continuum detections ($\geq 3.3\sigma$) at $\sim88{\rm \mu
m}$ and $158{\rm \mu m}$ out of 49 ultraviolet(UV)-bright galaxies ($M_{\rm UV}
< -21.3$ mag) at $z>6.5$, observed by the Cycle-7 ALMA Large Program, REBELS
and its pilot programs. This has more than tripled the number of dust continuum
detections known at $z>6.5$. Out of these 18 detections, 12 are reported for
the first time as part of REBELS. In addition, 15 of the dust continuum
detected galaxies also show a [CII]$_{\rm 158{\rm \mu m}}$ emission line,
providing us with accurate redshifts. We anticipate more line emission
detections from six targets (including three continuum detected targets) where
observations are still ongoing. The dust continuum detected sources in our
sample tend to have a redder UV spectral slope than the ones without a dust
continuum detection. We estimate that all of the sources have an infrared (IR)
luminosity ($L_{\rm IR}$) in a range of $3-8 \times 10^{11} L_\odot$, except
for one with $L_{\rm IR} = 1.5^{+0.8}_{-0.5} \times 10^{12}\,L_{\odot}$. Their
fraction of obscured star formation is significant at $\gtrsim 50\%$. Some of
the dust continuum detected galaxies show spatial offsets ($\sim 0.5-1.5''$)
between the rest-UV and far-IR emission peaks. These separations appear to have
an increasing trend against an indicator that suggests spatially decoupled
phases of obscured and unobscured star formation. REBELS offers the best
available statistical constraints on obscured star formation in UV-bright,
massive galaxies at $z > 6.5$.


title: Six proofs of the F\'ary--Milnor theorem

abstract: We sketch several proofs of F\'ary--Milnor theorem.


title: Particle Flow Calorimetry

abstract:
  The motivation for PF calorimetry is to experimentally measure the energy of
hadron jets with excellent resolution. In particle flow designs, sigma(E)/E <
5% should be possible for a range of jet energies from 50 GeV to 250 GeV,
important particularly for experiments at electron-positron colliders (ILC,
CLIC, FCCee, CEPC). The high granularity, which is essential for PF
calorimetry, can also be very beneficial for removal of background from pile-up
on an event-by-event basis making such calorimeters an attractive approach for
hadron collider experiments, for example the HGCAL under construction for CMS
at the CERN HL-LHC.


title:
On Generalized blob algebras: Vertical idempotent truncations and
  Gelfand-Tsetlin subalgebras

abstract:
  We study a family of idempotent truncations of the generalized blob algebra.
Particularly we study the structure of the Gelfand-Tsetlin subalgebras of these
algebras, obtaining optimal presentations in terms of generators and relations,
monomial basis and dimension formula.


title:
Improving Source Separation by Explicitly Modeling Dependencies Between
  Sources

abstract:
  We propose a new method for training a supervised source separation system
that aims to learn the interdependent relationships between all combinations of
sources in a mixture. Rather than independently estimating each source from a
mix, we reframe the source separation problem as an Orderless Neural
Autoregressive Density Estimator (NADE), and estimate each source from both the
mix and a random subset of the other sources. We adapt a standard source
separation architecture, Demucs, with additional inputs for each individual
source, in addition to the input mixture. We randomly mask these input sources
during training so that the network learns the conditional dependencies between
the sources. By pairing this training method with a block Gibbs sampling
procedure at inference time, we demonstrate that the network can iteratively
improve its separation performance by conditioning a source estimate on its
earlier source estimates. Experiments on two source separation datasets show
that training a Demucs model with an Orderless NADE approach and using Gibbs
sampling (up to 512 steps) at inference time strongly outperforms a Demucs
baseline that uses a standard regression loss and direct (one step) estimation
of sources.


title: A Straight Forward Path to a Path Integration of Einstein's Gravity

abstract:
  Path integration is a respected form of quantization that all theoretical
quantum physicists should welcome. This elaboration begins with simple examples
of three different versions of path integration. After an important
clarification of how gravity can be properly quantized, an appropriate path
integral, that also incorporates necessary constraint issues, becomes a proper
path integral for gravity that can effectively be obtained. How to evaluate
such path integrals is another aspect, but most likely best done by
computational efforts including Monte Carlo-like procedures.


title: On Bloch seminorm of finite Blaschke products in the unit disk

abstract:
  We prove that, for any finite Blaschke product $w=B(z)$ in the unit disk, the
corresponding Riemann surface over the $w$--plane contains a one-sheeted disk
of the radius $0.5$. Moreover, it contains a unit one-sheeted disk with a
radial slit. We apply this result to obtain a universal sharp lower estimate of
the Bloch seminorm for finite Blaschke products.


title: Towards End-to-End Unified Scene Text Detection and Layout Analysis

abstract:
  Scene text detection and document layout analysis have long been treated as
two separate tasks in different image domains. In this paper, we bring them
together and introduce the task of unified scene text detection and layout
analysis. The first hierarchical scene text dataset is introduced to enable
this novel research task. We also propose a novel method that is able to
simultaneously detect scene text and form text clusters in a unified way.
Comprehensive experiments show that our unified model achieves better
performance than multiple well-designed baseline methods. Additionally, this
model achieves state-of-the-art results on multiple scene text detection
datasets without the need of complex post-processing. Dataset and code:
https://github.com/google-research-datasets/hiertext.


title:
Human-AI Collaboration Enables More Empathic Conversations in Text-based
  Peer-to-Peer Mental Health Support

abstract:
  Advances in artificial intelligence (AI) are enabling systems that augment
and collaborate with humans to perform simple, mechanistic tasks like
scheduling meetings and grammar-checking text. However, such Human-AI
collaboration poses challenges for more complex, creative tasks, such as
carrying out empathic conversations, due to difficulties of AI systems in
understanding complex human emotions and the open-ended nature of these tasks.
Here, we focus on peer-to-peer mental health support, a setting in which
empathy is critical for success, and examine how AI can collaborate with humans
to facilitate peer empathy during textual, online supportive conversations. We
develop Hailey, an AI-in-the-loop agent that provides just-in-time feedback to
help participants who provide support (peer supporters) respond more
empathically to those seeking help (support seekers). We evaluate Hailey in a
non-clinical randomized controlled trial with real-world peer supporters on
TalkLife (N=300), a large online peer-to-peer support platform. We show that
our Human-AI collaboration approach leads to a 19.60% increase in
conversational empathy between peers overall. Furthermore, we find a larger
38.88% increase in empathy within the subsample of peer supporters who
self-identify as experiencing difficulty providing support. We systematically
analyze the Human-AI collaboration patterns and find that peer supporters are
able to use the AI feedback both directly and indirectly without becoming
overly reliant on AI while reporting improved self-efficacy post-feedback. Our
findings demonstrate the potential of feedback-driven, AI-in-the-loop writing
systems to empower humans in open-ended, social, creative tasks such as
empathic conversations.


title:
RoBoa: Construction and Evaluation of a Steerable Vine Robot for Search
  and Rescue Applications

abstract:
  RoBoa is a vine-like search and rescue robot that can explore narrow and
cluttered environments such as destroyed buildings. The robot assists rescue
teams in finding and communicating with trapped people. It employs the
principle of vine robots for locomotion, everting the tip of its tube to move
forward. Inside the tube, pneumatic actuators enable lateral movement. The head
carries sensors and is mounted outside at the tip of the tube. At the back, a
supply box contains the rolled up tube and provides pressurized air, power,
computation, as well as an interface for the user to interact with the system.
A decentralized control scheme was implemented that reduces the required number
of cables and takes care of the low-level control of the pneumatic actuators.
The design, characterization, and experimental evaluation of the system and its
crucial components is shown. The complete prototype is fully functional and was
evaluated in a realistic environment of a collapsed building where the
remote-controlled robot was able to repeatedly locate a trapped person after a
travel distance of about 10 m.


title: A proof of the $\frac{n!}{2}$ conjecture for hook shapes

abstract:
  A well-known representation-theoretic model for the transformed Macdonald
polynomial $\widetilde{H}_\mu(Z;t,q)$, where $\mu$ is an integer partition, is
given by the Garsia-Haiman module $\mathcal{H}_\mu$. We study the
$\frac{n!}{k}$ conjecture of Bergeron and Garsia, which concerns the behavior
of certain $k$-tuples of Garsia-Haiman modules under intersection. In the
special case that $\mu$ has hook shape, we use a basis for $\mathcal{H}_\mu$
due to Adin, Remmel, and Roichman to resolve the $\frac{n!}{2}$ conjecture by
constructing an explicit basis for the intersection of two Garsia-Haiman
modules.


title: Separate What You Describe: Language-Queried Audio Source Separation

abstract:
  In this paper, we introduce the task of language-queried audio source
separation (LASS), which aims to separate a target source from an audio mixture
based on a natural language query of the target source (e.g., "a man tells a
joke followed by people laughing"). A unique challenge in LASS is associated
with the complexity of natural language description and its relation with the
audio sources. To address this issue, we proposed LASS-Net, an end-to-end
neural network that is learned to jointly process acoustic and linguistic
information, and separate the target source that is consistent with the
language query from an audio mixture. We evaluate the performance of our
proposed system with a dataset created from the AudioCaps dataset. Experimental
results show that LASS-Net achieves considerable improvements over baseline
methods. Furthermore, we observe that LASS-Net achieves promising
generalization results when using diverse human-annotated descriptions as
queries, indicating its potential use in real-world scenarios. The separated
audio samples and source code are available at
https://liuxubo717.github.io/LASS-demopage.


title: Discrete Quantum Walks on the Symmetric Group

abstract:
  The theory of random walks on finite graphs is well developed with numerous
applications. In quantum walks, the propagation is governed by quantum
mechanical rules; generalizing random walks to the quantum setting. They have
been successfully applied in the development of quantum algorithms. In
particular, to solve problems that can be mapped to searching or property
testing on some specific graph. In this paper we investigate the discrete time
coined quantum walk (DTCQW) model using tools from non-commutative Fourier
analysis. Specifically, we are interested in characterizing the DTCQW on Cayley
graphs generated by the symmetric group ($\sym$) with appropriate generating
sets. The lack of commutativity makes it challenging to find an analytical
description of the limiting behavior with respect to the spectrum of the
walk-operator. We determine certain characteristics of these walks using a path
integral approach over the characters of $\sym$.


title: CMGAN: Conformer-based Metric GAN for Speech Enhancement

abstract:
  Recently, convolution-augmented transformer (Conformer) has achieved
promising performance in automatic speech recognition (ASR) and time-domain
speech enhancement (SE), as it can capture both local and global dependencies
in the speech signal. In this paper, we propose a conformer-based metric
generative adversarial network (CMGAN) for SE in the time-frequency (TF)
domain. In the generator, we utilize two-stage conformer blocks to aggregate
all magnitude and complex spectrogram information by modeling both time and
frequency dependencies. The estimation of magnitude and complex spectrogram is
decoupled in the decoder stage and then jointly incorporated to reconstruct the
enhanced speech. In addition, a metric discriminator is employed to further
improve the quality of the enhanced estimated speech by optimizing the
generator with respect to a corresponding evaluation score. Quantitative
analysis on Voice Bank+DEMAND dataset indicates the capability of CMGAN in
outperforming various previous models with a margin, i.e., PESQ of 3.41 and
SSNR of 11.10 dB.


title: A super-polynomial lower bound for learning nonparametric mixtures

abstract:
  We study the problem of learning nonparametric distributions in a finite
mixture, and establish a super-polynomial lower bound on the sample complexity
of learning the component distributions in such models. Namely, we are given
i.i.d. samples from $f$ where $$ f=\sum_{i=1}^k w_i f_i, \quad\sum_{i=1}^k
w_i=1, \quad w_i>0 $$ and we are interested in learning each component $f_i$.
Without any assumptions on $f_i$, this problem is ill-posed. In order to
identify the components $f_i$, we assume that each $f_i$ can be written as a
convolution of a Gaussian and a compactly supported density $\nu_i$ with
$\text{supp}(\nu_i)\cap \text{supp}(\nu_j)=\emptyset$. Our main result shows
that $\Omega((\frac{1}{\varepsilon})^{C\log\log \frac{1}{\varepsilon}})$
samples are required for estimating each $f_i$. The proof relies on a fast rate
for approximation with Gaussians, which may be of independent interest. This
result has important implications for the hardness of learning more general
nonparametric latent variable models that arise in machine learning
applications.