title:
A Computational Approach to Understand Mental Health from Reddit:
  Knowledge-aware Multitask Learning Framework

abstract:
  Analyzing gender is critical to study mental health (MH) support in CVD
(cardiovascular disease). The existing studies on using social media for
extracting MH symptoms consider symptom detection and tend to ignore user
context, disease, or gender. The current study aims to design and evaluate a
system to capture how MH symptoms associated with CVD are expressed differently
with the gender on social media. We observe that the reliable detection of MH
symptoms expressed by persons with heart disease in user posts is challenging
because of the co-existence of (dis)similar MH symptoms in one post and due to
variation in the description of symptoms based on gender. We collect a corpus
of $150k$ items (posts and comments) annotated using the subreddit labels and
transfer learning approaches. We propose GeM, a novel task-adaptive multi-task
learning approach to identify the MH symptoms in CVD patients based on gender.
Specifically, we adapt a knowledge-assisted RoBERTa based bi-encoder model to
capture CVD-related MH symptoms. Moreover, it enhances the reliability for
differentiating the gender language in MH symptoms when compared to the
state-of-art language models. Our model achieves high (statistically
significant) performance and predicts four labels of MH issues and two gender
labels, which outperforms RoBERTa, improving the recall by 2.14% on the symptom
identification task and by 2.55% on the gender identification task.


title:
Optimal Slicing of Virtualised Passive Optical Networks to Support Dense
  Deployment of Cloud-RAN and Multi-Access Edge Computing

abstract:
  The commercialization of Cloud-RAN, and Open-RAN in particular, is a key
factor to enable 5G cell densification, by providing lower cost and more agile
deployment of small cells. In addition, the adoption of MEC is important to
support ultra-low latency and high reliability required by mission-critical
applications, which constitute a milestone of the 5G and beyond vision of a
fully connected society. However, connecting antenna site, C-RAN processing and
MEC at low cost is challenging, as it requires high-capacity, low latency
connectivity delivered through a highly inter-connected topology. While PON is
being considered as a solution for providing low-cost connectivity to C-RAN,
they only allow data transmission from the endpoints (for example hosting RU at
the antenna site) towards a central node (e.g., the central office, hosting
computing equipment), thus cannot support traffic from RU towards MEC end nodes
that could host DU and possibly CU and network core. This led to research into
the evolution of PON architectures with the ability to provide direct
communications between endpoints, thus supporting mesh traffic patterns
required by MEC installations. In this context, virtualization plays a key role
in enabling efficient resource allocation (i.e. optical transmission capacity)
to endpoints, according to their communication patterns. In this article, we
address the challenge of dynamic allocation of virtual PON slices over mesh-PON
architectures to support C-RAN and MEC nodes. We make use of a mixed
analytical-iterative model to compute optimal virtual PON slice allocation,
with the objective of minimizing the use of MEC node resources, while meeting a
target latency threshold (100 $\mu s$ in our scenario). Our method is
particularly effective in reducing computation time, enabling virtual PON slice
allocation in timescales compatible with real-time or near real-time
operations.


title:
Lattice Dynamics, Mechanical Properties, Electronic Structure and
  Magnetic Properties of Equiatomic Quaternary Heusler Alloys CrTiCoZ (Z
  =Al,Si) using first principles calculations

abstract:
  First principles calculations are performed to investigate the
thermodynamical stability, dynamical, mechanical, electronic and magnetic
properties of CrTiCoZ (Z= Al/Si) novel quaternary Heusler alloys. Y-type III
atomic configuration is found to be the most stable structure for both
compounds. The melting temperatures of both compounds are as high as 2142 K and
2420 K for CrTiCoAl and CrTiCoSi, respectively. The electronic structure
calculations using GGA-PBE approach show a half metallic behavior of CrTiCoAl.
The spin-down channel exhibits a direct band gap of 0.15 eV, whereas the
spin-up channel is metallic making CrTiCoAl a half metallic ferromagnet with
100% spin polarization and an appreciable magnetic moment of -2 Bohr Magnetons.
The Curie temperature of CrTiCoAl is well above the room temperature (385K),
whereas that of CrTiCoSi is below the room temperature (203K).


title:
Super-resolution Airy disk microscopy of individual color centers in
  diamond

abstract:
  Super-resolution imaging techniques enable nanoscale microscopy in fields
such as physics, biology, and chemistry. We present a novel technique,
Super-resolution Airy disk Microscopy (SAM) that can be used in a standard
confocal microscope without any specialized optics. We demonstrate this
technique, in combination with ground state depletion, to image and control
nitrogen-vacancy (NV) centers in bulk diamond below the diffraction limit. A
greater than 14-fold improvement in resolution compared to the diffraction
limit is achieved, corresponding to a spatial resolution of 16.9(8) nm for a
1.3 NA microscope with 589 nm light. We make use of our enhanced spatial
resolution to control the spins states of individual NV centers separated from
each other by less than the diffraction limit, including pairs sharing the same
orientation that are indistinguishable with a conventional electron spin
resonance measurement.


title:
Practical tradeoffs between memory, compute, and performance in learned
  optimizers

abstract:
  Optimization plays a costly and crucial role in developing machine learning
systems. In learned optimizers, the few hyperparameters of commonly used
hand-designed optimizers, e.g. Adam or SGD, are replaced with flexible
parametric functions. The parameters of these functions are then optimized so
that the resulting learned optimizer minimizes a target loss on a chosen class
of models. Learned optimizers can both reduce the number of required training
steps and improve the final test loss. However, they can be expensive to train,
and once trained can be expensive to use due to computational and memory
overhead for the optimizer itself. In this work, we identify and quantify the
design features governing the memory, compute, and performance trade-offs for
many learned and hand-designed optimizers. We further leverage our analysis to
construct a learned optimizer that is both faster and more memory efficient
than previous work.


title:
Supervised and Unsupervised Machine Learning of Structural Phases of
  Polymers Adsorbed to Nanowires

abstract:
  We identify configurational phases and structural transitions in a polymer
nanotube composite by means of machine learning. We employ various unsupervised
dimensionality reduction methods, conventional neural networks, as well as the
confusion method, an unsupervised neural-network-based approach. We find neural
networks are able to reliably recognize all configurational phases that have
been found previously in experiment and simulation. Furthermore, we locate the
boundaries between configurational phases in a way that removes human intuition
or bias. This could be done before only by relying on preconceived, ad-hoc
order parameters.


title: Generating natural images with direct Patch Distributions Matching

abstract:
  Many traditional computer vision algorithms generate realistic images by
requiring that each patch in the generated image be similar to a patch in a
training image and vice versa. Recently, this classical approach has been
replaced by adversarial training with a patch discriminator. The adversarial
approach avoids the computational burden of finding nearest neighbors of
patches but often requires very long training times and may fail to match the
distribution of patches. In this paper we leverage the recently developed
Sliced Wasserstein Distance and develop an algorithm that explicitly and
efficiently minimizes the distance between patch distributions in two images.
Our method is conceptually simple, requires no training and can be implemented
in a few lines of codes. On a number of image generation tasks we show that our
results are often superior to single-image-GANs, require no training, and can
generate high quality images in a few seconds. Our implementation is available
at https://github.com/ariel415el/GPDM


title: Integrality Gaps for Random Integer Programs via Discrepancy

abstract:
  We give bounds on the additive gap between the value of a random integer
program $\max c^\mathsf{T} x, Ax \leq b, x \in \{0,1\}^n$ with $m$ constraints
and that of its linear programming relaxation for a range of distributions on
$(A,b,c)$. Dyer and Frieze (MOR '89) and Borst et al (IPCO '21) respectively
showed that for random packing and Gaussian IPs, where the entries of $A,c$ are
independently distributed according to either uniform $[0,1]$ or
$\mathcal{N}(0,1)$, that the integrality gap is bounded by $O_m(s \log^2 n /
n)$ with probability at least $1-1/n-e^{-\Omega_m(s)}$ for $s \geq 1$. In this
paper, we extend these results to the case where $A$ is discretely distributed
(e.g., entries $\{-1,0,1\}$), and where the columns of $A$ are logconcave
distributed. Second, we improve the success probability from constant, for
fixed $s$ and $m$, to $1-1/\mathrm{poly}(n)$. Using a connection between
integrality gaps and Branch-and-Bound due to Dey, Dubey and Molinaro (SODA
'21), our gap results imply that Branch-and-Bound is polynomial for these IPs.
  Our main technical contribution and the key for achieving the above results,
is a new discrepancy theoretic theorem which gives general conditions for when
a target $t$ is equal or very close to a $\{0,1\}$ combination of the columns
of a random matrix $A$. Compared to prior results, our theorem handles a much
wider range of distributions on $A$, both continuous and discrete, and achieves
success probability exponentially close to $1$ as opposed to constant. We prove
this lemma using a Fourier analytic approach, building on the work of Hoberg
and Rothvoss (SODA '19) and Franks and Saks (RSA '20) who studied similar
questions for $\{-1,1\}$ combinations.


title:
On the (Non-)Robustness of Two-Layer Neural Networks in Different
  Learning Regimes

abstract:
  Neural networks are known to be highly sensitive to adversarial examples.
These may arise due to different factors, such as random initialization, or
spurious correlations in the learning problem. To better understand these
factors, we provide a precise study of robustness and generalization in
different scenarios, from initialization to the end of training in different
regimes, as well as intermediate scenarios, where initialization still plays a
role due to "lazy" training. We consider over-parameterized networks in high
dimensions with quadratic targets and infinite samples. Our analysis allows us
to identify new trade-offs between generalization and robustness, whereby
robustness can only get worse when generalization improves, and vice versa. We
also show how linearized lazy training regimes can worsen robustness, due to
improperly scaled random initialization. Our theoretical results are
illustrated with numerical experiments.


title:
Disentangling kinetics from thermodynamics in heterogeneous colloidal
  systems

abstract:
  Nucleation and growth (N&G) - the emergence of a new phase within an
initially homogeneous one - is one of the most important physical phenomena by
which gas-liquid, liquid-liquid and solid-liquid phase separation takes place.
Accordingly, thermodynamics sets the asymptotic boundaries towards which the
system must evolve, while kinetics tries to cope with it by imposing the
transport rates at which phase separation is realized. In all heterogeneous
colloidal systems observed in nature, the composition, shape, structure and
ultimately physical properties result from the trade-off between thermodynamics
and kinetics. In this work we demonstrate, by carefully selecting colloidal
systems and controlling phase separation in microfluidic devices, that it
becomes possible to go beyond N&G, disentangling kinetics effects from
thermodynamics in composition, structure and physical properties of the final
system. Using amyloid fibril and cellulose nanocrystal filamentous colloids for
which the binodal curve defining the two-phase region in the phase diagram is
given by two separate vertical lines, we extrude a solution set at one
thermodynamic branch inside the other branch, realizing nematic or cholesteric
droplets where the composition is set by thermodynamics, while the structure
and morphology are defined by dynamic flow parameters. We demonstrate that
departing from the N&G paradigm unveils new physical phenomena, such as orders
of magnitude shorter timescales, a wider phase diagram and internal cholesteric
structures that are not observable via conventional LLPS. We also show that by
co-dispersing plasmonic gold nanoparticles within colloidal liquid crystalline
droplets, our approach enables on-demand fabrication of multicomponent
heterogeneous liquid crystals, enhancing their potential, and introducing
original fundamental and technological directions in multicomponent structured
fluids.


title: Nanoscale magnetism probed in a matter-wave interferometer

abstract:
  We explore a wide range of fundamental magnetic phenomena by measuring the
dephasing of matter-wave interference fringes upon application of a variable
magnetic gradient. The versatility of our interferometric Stern-Gerlach
technique enables us to study alkali atoms, organic radicals and fullerenes in
the same device, with magnetic moments ranging from a Bohr magneton to less
than a nuclear magneton. We find evidence for magnetization of a supersonic
beam of organic radicals and, most notably, observe a strong magnetic response
of a thermal C$_{60}$ beam consistent with high-temperature atom-like
deflection of rotational magnetic moments.


title: Length control of long cell protrusions: rulers, timers and transport

abstract:
  A living cell uses long tubular appendages for locomotion and sensory
purposes. Hence, assembling and maintaining a protrusion of correct length is
crucial for its survival and overall performance. Usually the protrusions lack
the machinery for the synthesis of building blocks and imports them from the
cell body. What are the unique features of the transport logistics which
facilitate the exchange of these building blocks between the cell and the
protrusion? What kind of `rulers' and `timers' does the cell use for
constructing its appendages of correct length on time? How do the multiple
appendages coordinate and communicate among themselves during different stages
of their existence? How frequently do the fluctuations drive the length of
these dynamic protrusions out of the acceptable bounds? These questions are
addressed from a broad perspective in this review which is organized in three
parts. In part-I the list of all known cell protrusions is followed by a
comprehensive list of the mechanisms of length control of cell protrusions
reported in the literature. We review not only the dynamics of the genesis of
the protrusions, but also their resorption and regrowth as well as regeneration
after amputation. As a case study in part-II, the specific cell protrusion that
has been discussed in detail is eukaryotic flagellum (also known as cilium);
this choice was dictated by the fact that flagellar length control mechanisms
have been studied most extensively over more than half a century in cells with
two or more flagella. Although limited in scope, brief discussions on a few
non-flagellar cell protrusions in part-III of this review is intended to
provide a glimpse of the uncharted territories and challenging frontiers of
research on subcellular length control phenomena that awaits vigorous
investigations.

categories: physics.bio-ph cond-mat.stat-mech nlin.AO q-bio.SC


title:
Detailed Chemical Abundances of Star Clusters in the Large Magellanic
  Cloud

abstract:
  We derive the first detailed chemical abundances of three star clusters in
the Large Magellanic Cloud (LMC), NGC1831 (436+/-22 Myr), NGC1856 (350+/-18
Myr) and [SL63]268 (1230+/-62 Myr) using integrated-light spectroscopic
observations obtained with the Magellan Echelle spectrograph on Magellan Baade
telescope. We derive [Fe/H], [Mg/Fe], [Ti/Fe], [Ca/Fe], [Ni/Fe], [Mn/Fe],
[Cr/Fe] and [Na/Fe] for the three clusters. Overall, our results match the LMC
abundances obtained in the literature as well as those predicted by detailed
chemical evolution models. For clusters NGC1831 and NGC1856, the [Mg/Fe] ratios
appear to be slightly depleted compared to [Ca/Fe] and [Ti/Fe]. This could be
hinting at the well-known Mg-Al abundance anti-correlation observed in several
Milky Way globular clusters. We note, however, that higher signal-to-noise
observations are needed to confirm such a scenario, particularly for NGC 1831.
We also find a slightly enhanced integrated-light [Na/Fe] ratio for cluster
[SL63]268 compared to those from the LMC field stars, possibly supporting a
scenario of intracluster abundance variations. We stress that detailed
abundance analysis of individual stars in these LMC clusters is required to
confirm the presence or absence of MSPs.


title:
An Optimal Transport Formulation of Bayes' Law for Nonlinear Filtering
  Algorithms

abstract:
  This paper presents a variational representation of the Bayes' law using
optimal transportation theory. The variational representation is in terms of
the optimal transportation between the joint distribution of the (state,
observation) and their independent coupling. By imposing certain structure on
the transport map, the solution to the variational problem is used to construct
a Brenier-type map that transports the prior distribution to the posterior
distribution for any value of the observation signal. The new formulation is
used to derive the optimal transport form of the Ensemble Kalman filter (EnKF)
for the discrete-time filtering problem and propose a novel extension of EnKF
to the non-Gaussian setting utilizing input convex neural networks. Finally,
the proposed methodology is used to derive the optimal transport form of the
feedback particle filler (FPF) in the continuous-time limit, which constitutes
its first variational construction without explicitly using the nonlinear
filtering equation or Bayes' law.


title: Galois Covers of Singular Curves in Positive Characteristics

abstract:
  We study the \'{e}tale fundamental groups of singular reduced connected
curves defined over an algebraically closed field of arbitrary prime
characteristic. It is shown that when the curve is projective, the \'{e}tale
fundamental group is a free product of the \'{e}tale fundamental group of its
normalization with a free finitely generated profinite group whose rank is well
determined. As a consequence of this result and the known results for the
smooth case, necessary conditions are given for a finite group to appear as a
quotient of the \'{e}tale fundamental group. Next, we provide similar results
for an affine integral curve $U$. We provide a complete group theoretic
classification on which finite groups occur as the Galois groups for Galois
\'{e}tale connected covers of $U$. In fact, when $U$ is a seminormal curve
embedded in a connected seminormal curve $X$ such that $X - U$ consists of
smooth points, the tame fundamental group $\pi_1^t(U \subset X)$ is shown to be
isomorphic to a free product of the tame fundamental group of the normalization
of $U$ with a free finitely generated profinite group whose rank is known. An
analogue of the Inertia Conjecture is also posed for certain singular curves.


title: Charge detection using a WSe$_2$ van der Waals heterostructure

abstract:
  Detecting single charging events in quantum devices is an important step
towards realizing practical quantum circuits for quantum information
processing. In this work, we demonstrate that van derWaals heterostructure
devices with gated nano-constrictions in monolayer WSe2 can be used as charge
detectors for nearby quantum dots. These results open the possibility of
implementing charge detection schemes based on 2D materials in complex quantum
circuits.


title:
Performance of long short-term memory artificial neural networks in
  nowcasting during the COVID-19 crisis

abstract:
  The COVID-19 pandemic has demonstrated the increasing need of policymakers
for timely estimates of macroeconomic variables. A prior UNCTAD research paper
examined the suitability of long short-term memory artificial neural networks
(LSTM) for performing economic nowcasting of this nature. Here, the LSTM's
performance during the COVID-19 pandemic is compared and contrasted with that
of the dynamic factor model (DFM), a commonly used methodology in the field.
Three separate variables, global merchandise export values and volumes and
global services exports, were nowcast with actual data vintages and performance
evaluated for the second, third, and fourth quarters of 2020 and the first and
second quarters of 2021. In terms of both mean absolute error and root mean
square error, the LSTM obtained better performance in two-thirds of
variable/quarter combinations, as well as displayed more gradual forecast
evolutions with more consistent narratives and smaller revisions. Additionally,
a methodology to introduce interpretability to LSTMs is introduced and made
available in the accompanying nowcast_lstm Python library, which is now also
available in R, MATLAB, and Julia.


title:
Nonstationary Nearest Neighbor Gaussian Process: hierarchical model
  architecture and MCMC sampling

abstract:
  Nonstationary spatial modeling presents several challenges including, but not
limited to, computational cost, the complexity and lack of interpretation of
multi-layered hierarchical models, and the challenges in model assessment and
selection. This manuscript develops a class of nonstationary Nearest Neighbor
Gaussian Process (NNGP) models. NNGPs are a good starting point to address the
problem of the computational cost because of their accuracy and affordability.
We study the behavior of NNGPs that use a nonstationary covariance function,
exploring their properties and the impact of ordering on the effective
covariance induced by NNGPs. To simplify spatial data analysis and model
selection, we introduce an interpretable hierarchical model architecture,
where, in particular, we make parameter interpretation and model selection
easier by integrating stationary range, nonstationary range with circular
parameters, and nonstationary range with elliptic parameters within a coherent
probabilistic structure. Given the NNGP approximation and the model framework,
we propose a MCMC implementation based on Hybrid Monte-Carlo and nested
interweaving of parametrizations. We carry out experiments on synthetic data
sets to explore model selection and parameter identifiability and assess
inferential improvements accrued from the nonstationary model. Finally, we use
those guidelines to analyze a data set of lead contamination in the United
States of America.


title: Neural manifold analysis of brain circuit dynamics in health and disease

abstract:
  Recent developments in experimental neuroscience make it possible to
simultaneously record the activity of thousands of neurons. However, the
development of analysis approaches for such large-scale neural recordings have
been slower than those applicable to single-cell experiments. One approach that
has gained recent popularity is neural manifold learning. This approach takes
advantage of the fact that often, even though neural datasets may be very high
dimensional, the dynamics of neural activity tends to traverse a much
lower-dimensional space. The topological structures formed by these
low-dimensional neural subspaces are referred to as neural manifolds, and may
potentially provide insight linking neural circuit dynamics with cognitive
function and behavioural performance. In this paper we review a number of
linear and non-linear approaches to neural manifold learning, by setting them
within a common mathematical framework, and comparing their advantages and
disadvantages with respect to their use for neural data analysis. We apply them
to a number of datasets from published literature, comparing the manifolds that
result from their application to hippocampal place cells, motor cortical
neurons during a reaching task, and prefrontal cortical neurons during a
multi-behaviour task. We find that in many circumstances linear algorithms
produce similar results to non-linear methods, although in particular in cases
where the behavioural complexity is greater, nonlinear methods tend to find
lower dimensional manifolds, at the possible expense of interpretability. We
demonstrate that these methods are applicable to the study of neurological
disorders through simulation of a mouse model of Alzheimers Disease, and
speculate that neural manifold analysis may help us to understand the
circuit-level consequences of molecular and cellular neuropathology.


title:
Condensed interior-point methods: porting reduced-space approaches on
  GPU hardware

abstract:
  The interior-point method (IPM) has become the workhorse method for nonlinear
programming. The performance of IPM is directly related to the linear solver
employed to factorize the Karush--Kuhn--Tucker (KKT) system at each iteration
of the algorithm. When solving large-scale nonlinear problems, state-of-the art
IPM solvers rely on efficient sparse linear solvers to solve the KKT system.
Instead, we propose a novel reduced-space IPM algorithm that condenses the KKT
system into a dense matrix whose size is proportional to the number of degrees
of freedom in the problem. Depending on where the reduction occurs we derive
two variants of the reduced-space method: linearize-then-reduce and
reduce-then-linearize. We adapt their workflow so that the vast majority of
computations are accelerated on GPUs. We provide extensive numerical results on
the optimal power flow problem, comparing our GPU-accelerated reduced space IPM
with Knitro and a hybrid full space IPM algorithm. By evaluating the
derivatives on the GPU and solving the KKT system on the CPU, the hybrid
solution is already significantly faster than the CPU-only solutions. The two
reduced-space algorithms go one step further by solving the KKT system entirely
on the GPU. As expected, the performance of the two reduction algorithms
depends intrinsically on the number of available degrees of freedom: their
performance is poor when the problem has many degrees of freedom, but the two
algorithms are up to 3 times faster than Knitro as soon as the relative number
of degrees of freedom becomes smaller.


title: Open-Vocabulary DETR with Conditional Matching

abstract:
  Open-vocabulary object detection, which is concerned with the problem of
detecting novel objects guided by natural language, has gained increasing
attention from the community. Ideally, we would like to extend an
open-vocabulary detector such that it can produce bounding box predictions
based on user inputs in form of either natural language or exemplar image. This
offers great flexibility and user experience for human-computer interaction. To
this end, we propose a novel open-vocabulary detector based on DETR -- hence
the name OV-DETR -- which, once trained, can detect any object given its class
name or an exemplar image. The biggest challenge of turning DETR into an
open-vocabulary detector is that it is impossible to calculate the
classification cost matrix of novel classes without access to their labeled
images. To overcome this challenge, we formulate the learning objective as a
binary matching one between input queries (class name or exemplar image) and
the corresponding objects, which learns useful correspondence to generalize to
unseen queries during testing. For training, we choose to condition the
Transformer decoder on the input embeddings obtained from a pre-trained
vision-language model like CLIP, in order to enable matching for both text and
image queries. With extensive experiments on LVIS and COCO datasets, we
demonstrate that our OV-DETR -- the first end-to-end Transformer-based
open-vocabulary detector -- achieves non-trivial improvements over current
state of the arts.


title: Co-evolving dynamic networks

abstract:
  We propose a general class of co-evolving tree network models driven by local
exploration where new vertices attach to the current network via randomly
sampling a vertex and then exploring the graph for a random number of steps in
the direction of the root, connecting to the terminal vertex. Specific choices
of the exploration step distribution lead to the well-studied affine
preferential attachment and uniform attachment models, as well as less well
understood dynamic network models with global attachment functionals such as
PageRank scores [Chebolu-Melsted (2008)]. We obtain local weak limits for such
networks and use them to derive asymptotics for the limiting empirical degree
and PageRank distribution. We also quantify asymptotics for the degree and
PageRank of fixed vertices, including the root, and the height of the network.
Two distinct regimes are seen to emerge, based on the expected exploration
distance of incoming vertices, which we call the `fringe' and `non-fringe'
regimes. These regimes are shown to exhibit different qualitative and
quantitative properties. In particular, networks in the non-fringe regime
undergo `condensation' where the root degree grows at the same rate as the
network size. Networks in the fringe regime do not exhibit condensation. A
non-trivial phase transition phenomenon is also displayed for the PageRank
distribution, which connects to the well known power-law hypothesis. In the
process, we develop a general set of techniques involving local limits,
infinite-dimensional urn models, related multitype branching processes and
corresponding Perron-Frobenius theory, branching random walks, and in
particular relating tail exponents of various functionals to the scaling
exponents of quasi-stationary distributions of associated random walks. These
techniques are expected to shed light on a variety of other co-evolving network
models.


title: Under the Hood of Transformer Networks for Trajectory Forecasting

abstract:
  Transformer Networks have established themselves as the de-facto
state-of-the-art for trajectory forecasting but there is currently no
systematic study on their capability to model the motion patterns of people,
without interactions with other individuals nor the social context. This paper
proposes the first in-depth study of Transformer Networks (TF) and
Bidirectional Transformers (BERT) for the forecasting of the individual motion
of people, without bells and whistles. We conduct an exhaustive evaluation of
input/output representations, problem formulations and sequence modeling,
including a novel analysis of their capability to predict multi-modal futures.
Out of comparative evaluation on the ETH+UCY benchmark, both TF and BERT are
top performers in predicting individual motions, definitely overcoming RNNs and
LSTMs. Furthermore, they remain within a narrow margin wrt more complex
techniques, which include both social interactions and scene contexts. Source
code will be released for all conducted experiments.


title:
Exponential Convergence of $hp$-Time-Stepping in Space-Time
  Discretizations of Parabolic PDEs

abstract:
  For linear parabolic initial-boundary value problems with self-adjoint,
time-homogeneous elliptic spatial operator in divergence form with
Lipschitz-continuous coefficients, and for incompatible, time-analytic forcing
term in polygonal/polyhedral domains $D$, we prove time-analyticity of
solutions. Temporal analyticity is quantified in terms of weighted, analytic
function classes, for data with finite, low spatial regularity and without
boundary compatibility. Leveraging this result, we prove exponential
convergence of a conforming, semi-discrete $hp$-time-stepping approach. We
combine this semi-discretization in time with first-order, so-called
"$h$-version" Lagrangian Finite Elements with corner-refinements in space into
a tensor-product, conforming discretization of a space-time formulation. We
prove that, under appropriate corner- and corner-edge mesh-refinement of $D$,
error vs. number of degrees of freedom in space-time behaves essentially (up to
logarithmic terms), to what standard FEM provide for one elliptic boundary
value problem solve in $D$. We focus on two-dimensional spatial domains and
comment on the one- and the three-dimensional case.


title:
Theoretical description of time-resolved photoemission in
  charge-density-wave materials out to long times

abstract:
  We describe coupled electron-phonon systems semiclassically - Ehrenfest
dynamics for the phonons and quantum mechanics for the electrons - using a
classical Monte Carlo approach that determines the nonequilibrium response to a
large pump field. The semiclassical approach is quite accurate, because the
phonons are excited to average energies much higher than the phonon frequency,
eliminating the need for a quantum description. The numerical efficiency of
this method allows us to perform a self-consistent time evolution out to very
long times (tens of picoseconds) enabling us to model pump-probe experiments of
a charge density wave (CDW) material. Our system is a half-filled,
one-dimensional (1D) Holstein chain that exhibits CDW ordering due to a Peierls
transition. The chain is subjected to a time-dependent electromagnetic pump
field that excites it out of equilibrium, and then a second probe pulse is
applied after a time delay. By evolving the system to long times, we capture
the complete process of lattice excitation and subsequent relaxation to a new
equilibrium, due to an exchange of energy between the electrons and the
lattice, leading to lattice relaxation at finite temperatures. We employ an
indirect (impulsive) driving mechanism of the lattice by the pump pulse due to
the driving of the electrons by the pump field. We identify two driving
regimes, where the pump can either cause small perturbations or completely
invert the initial CDW order. Our work successfully describes the ringing of
the amplitude mode in CDW systems that has long been seen in experiment, but
never successfully explained by microscopic theory.


title:
Investigating radiatively driven, magnetised plasmas with a university
  scale pulsed-power generator

abstract:
  We present first results from a novel experimental platform which is able to
access physics relevant to topics including indirect-drive magnetised ICF;
laser energy deposition; various topics in atomic physics; and laboratory
astrophysics (for example the penetration of B-fields into HED plasmas). This
platform uses the X-Rays from a wire array Z-Pinch to irradiate a silicon
target, producing an outflow of ablated plasma. The ablated plasma expands into
ambient, dynamically significant B-fields (~5 T) which are supported by the
current flowing through the Z-Pinch. The outflows have a well-defined
(quasi-1D) morphology, enabling the study of fundamental processes typically
only available in more complex, integrated schemes. Experiments were fielded on
the MAGPIE pulsed-power generator (1.4 MA, 240 ns rise time). On this machine a
wire array Z-Pinch produces an X-Ray pulse carrying a total energy of ~15 kJ
over ~30 ns. This equates to an average brightness temperature of around 10 eV
on-target.


title: Linear-depth quantum circuits for multi-qubit controlled gates

abstract:
  Quantum circuit depth minimization is critical for practical applications of
circuit-based quantum computation. In this work, we present a systematic
procedure to decompose multi-qubit-controlled unitary gate, which is essential
in many quantum algorithms, to controlled-NOT and single-qubit gates with which
quantum circuit depth only increases linearly with the number of control
qubits. Our algorithm does not require any ancillary qubits and achieves a
quadratic reduction of the circuit depth against known methods. We demonstrate
the advantage of our algorithm with proof-of-principle experiments implemented
on the IBM quantum cloud platform.


title:
Utilizing high-$p_\perp$ theory and data to constrain the initial stages
  of quark-gluon plasma

abstract:
  The scarce knowledge of the initial stages of quark-gluon plasma before the
thermalization is mostly inferred through the low-$p_\perp$ sector. We propose
a complementary approach in this report - the use of high-$p_\perp$ probes'
energy loss. We study the effects of four commonly assumed initial stages,
whose temperature profiles differ only before the thermalization, on
high-$p_\perp$ $R_{AA}$ and $v_2$ predictions. The predictions are based on our
Dynamical Radiative and Elastic ENergy-loss Approach (DREENA) framework. We
report insensitivity of $v_2$ to the initial stages, making it unable to
distinguish between different cases. $R_{AA}$ displays sensitivity to the
presumed initial stages, but current experimental precision does not allow
resolution between these cases. We further revise the commonly accepted
procedure of fitting the energy loss parameters, for each individual initial
stage, to the measured $R_{AA}$. We show that the sensitivity of $v_2$ to
various initial stages obtained through such procedure is mostly a consequence
of fitting procedure, which may obscure the physical interpretations. Overall,
the simultaneous study of high-$p_\perp$ observables, with unchanged energy
loss parametrization and restrained temperature profiles, is crucial for future
constraints on initial stages.


title:
Probing the shot behaviour in Cygnus X-1 using simultaneous
  AstroSat-NICER observation

abstract:
  We analyse the aperiodic flaring features, also known as shots, observed in
Cyg X-1 in the 0.1-80 keV energy band using a 6.39 ks simultaneous observation
with AstroSat and NICER. We detect 49 simultaneous shots in the soft and hard
X-ray bands with NICER and AstroSat-LAXPC, respectively. We observe the shot
profile for the first time in soft X-rays (0.1-3 keV), which shows a spectral
peak at $\sim$2 keV. Using time-averaged spectroscopy, we measured the
truncation of the inner accretion disk at $6.7\pm0.2$ gravitational radii. The
shot-phase resolved spectroscopy allowed us to identify the origin of some of
the brightest aperiodic peaks in the soft X-rays. We find that the accretion
rate is consistent with a constant during the shots while the inner edge of the
accretion disk moves inwards/outwards as these shots rise/decay. We discuss the
possible mechanisms causing the swing in the inner radius.


title: Biased $2 \times 2$ periodic Aztec diamond and an elliptic curve

abstract:
  We study a biased $2\times 2$ periodic random domino tilings of the Aztec
diamond and associate a linear flow on an elliptic curve to this model. Our
main result is a double integral formula for the correlation kernel, in which
the integrand is expressed in terms of this flow. For special choices of
parameters the flow is periodic, and this allows us to perform a saddle point
analysis for the correlation kernel. In these cases we compute the local
correlations in the smooth disordered (or gaseous) region. The special example
in which the flow has period six is worked out in more detail, and we show that
in that case the boundary of the rough disordered region is an algebraic curve
of degree eight.


title: Counting Abelian Squares More Efficiently

abstract:
  I present a recursive formula for calculating the number of abelian squares
of length $n+n$ over an alphabet of size $d$. The presented formula is similar
to a previously known formula but has substantially lower complexity when $d\gg
n$.


title:
Hydromagnetic waves in an expanding universe -- cosmological MHD code
  tests using analytic solutions

abstract:
  We describe how analytic solutions for linear hydromagnetic waves can be used
for testing cosmological magnetohydrodynamic (MHD) codes. We start from the
comoving MHD equations and derive analytic solutions for the amplitude
evolution of linear hydromagnetic waves in a matter-dominated, flat
Einstein-de-Sitter (EdS) universe. The waves considered are comoving, linearly
polarized Alfv\'en waves and comoving, magnetosonic (fast) waves modified by
selfgravity. The solution for compressible waves is found for a general
adiabatic index and we consider the limits of hydrodynamics without selfgravity
in addition to the full solution. In addition to these analytic solutions, the
linearized equations are solved numerically for a $\Lambda$CDM cosmology. We
use the analytic and numeric solutions to compare with results obtained using
the cosmological MHD code AREPO and find good agreement in all cases. We
envision that our examples could be useful when developing a new cosmological
MHD code or for regression testing of existing codes.


title: An Estimator for Lensing Potential from Galaxy Number Counts

abstract:
  We derive an estimator for the lensing potential from galaxy number counts
which contains a linear and a quadratic term. We show that this estimator has a
much larger signal-to-noise ratio than the corresponding estimator from
intensity mapping. This is due to the additional lensing term in the number
count angular power spectrum which is present already at linear order. We
estimate the signal-to-noise ratio for future photometric surveys. Particularly
at high redshifts, $z\gtrsim 1.5$, the signal to noise ratio can become of
order 30. Therefore, the number counts in photometric surveys would be an
excellent means to measure tomographic lensing spectra.


title: Insights From the NeurIPS 2021 NetHack Challenge

abstract:
  In this report, we summarize the takeaways from the first NeurIPS 2021
NetHack Challenge. Participants were tasked with developing a program or agent
that can win (i.e., 'ascend' in) the popular dungeon-crawler game of NetHack by
interacting with the NetHack Learning Environment (NLE), a scalable,
procedurally generated, and challenging Gym environment for reinforcement
learning (RL). The challenge showcased community-driven progress in AI with
many diverse approaches significantly beating the previously best results on
NetHack. Furthermore, it served as a direct comparison between neural (e.g.,
deep RL) and symbolic AI, as well as hybrid systems, demonstrating that on
NetHack symbolic bots currently outperform deep RL by a large margin. Lastly,
no agent got close to winning the game, illustrating NetHack's suitability as a
long-term benchmark for AI research.


title:
Efficient two-dimensional defect-free dual-species atom arrays
  rearrangement algorithm with near-fewest atom moves

abstract:
  Dual-species atoms in optical tweezer arrays have shown several advantages
over the single-species atoms array as a platform for quantum science. Hence,
designing an efficient algorithm to rearrange the stochastically loaded
dual-species atoms arrays into arbitrary demanded configuration is crucial.
Here we proposed a heuristic algorithm [the heuristic connectivity optimization
algorithm (HCOA)] which provides the near-fewest number of atom moves $M\approx
M_{min}$ ($M_{min}$ is the ideal number of atom moves supposing all atoms can
be moved freely). Moreover, our algorithm shows high success rate ($>97\%$ for
intermediate array size ($<500$), 0.5 initial filling ratio, and various
configurations), low extra atom moves ratio ($M_m/M-1\approx6\%$ for
10$\times$10 atom array, good scalability (the success rate remains at high
level with the array size increasing, and the extra atom moves ratio decays as
the array size increases), and versatility (our algorithm can be applied in
various array types, e.g. honeycomb lattice, irregular lattice and so on).
Besides, we also proposed a complementary step to solve almost all cases left
unsolved by HCOA and atom loss in experiment.


title:
The biosphere computes evolution by autoencoding interacting organisms
  into species and decoding species into ecosystems

abstract:
  Autoencoding is a machine-learning technique for extracting a compact
representation of the essential features of input data; this representation
then enables a variety of applications that rely on encoding and subsequent
reconstruction based on decoding of the relevant data. Here, we document our
discovery that the biosphere evolves by a natural process akin to computer
autoencoding. We establish the following points: (1) A species is defined by
its species interaction code. The species code consists of the fundamental,
core interactions of the species with its external and internal environments;
core interactions are encoded by multi-scale networks including
molecules-cells-organisms. (2) Evolution expresses sustainable changes in
species interaction codes; these changing codes both map and construct the
species environment. The survival of species is computed by what we term
\textit{natural autoencoding}: arrays of input interactions generate species
codes, which survive by decoding into sustained ecosystem interactions. This
group process, termed survival-of-the-fitted, supplants the Darwinian struggle
of individuals and survival-of-the-fittest only. DNA is only one element in
natural autoencoding. (3) Natural autoencoding and artificial autoencoding
techniques manifest defined similarities and differences. Biosphere
autoencoding and survival-of-the-fitted sheds a new light on the mechanism of
evolution. Evolutionary autoencoding renders evolution amenable to new
approaches to computer modeling.


title: Multiple Models for Discrete-time Adaptive Iterative Learning Control

abstract:
  This article presents a complete framework for multiple estimation models in
the discrete-time Adaptive Iterative Learning Control (ILC) problem. The use of
multiple models improves transient response and error convergence in adaptive
systems and has not been previously explored in the context of Adaptive ILC. A
new control and identification scheme with a single estimation model is
presented. This scheme enables the extension to multiple models, unlike
existing Adaptive ILC strategies. Next, the fundamentals of the Multiple
Models, Switching and Tuning (MMST) methodology are used for multiple
estimation models in Adaptive ILC. Two switching strategies are outlined, and
convergence is proved for all techniques using the properties of
square-summable sequences. Simulation results demonstrate the efficacy of the
proposed strategies, with improved convergence using multiple estimation
models.


title:
Analog quantum control of magnonic cat states on-a-chip by a
  superconducting qubit

abstract:
  We propose to directly and quantum-coherently couple a superconducting
transmon qubit to magnons - the quanta of the collective spin excitations, in a
nearby magnetic particle. The magnet's stray field couples to the qubit via a
superconducting interference device (SQUID). We predict a resonant qubit-magnon
exchange and a nonlinear radiation-pressure interaction that are both stronger
than dissipation rates and tunable by an external flux bias. We additionally
demonstrate a quantum control scheme that generates qubit-magnon entanglement
and magnonic Schr\"{o}dinger cat states with high fidelity.


title: GradViT: Gradient Inversion of Vision Transformers

abstract:
  In this work we demonstrate the vulnerability of vision transformers (ViTs)
to gradient-based inversion attacks. During this attack, the original data
batch is reconstructed given model weights and the corresponding gradients. We
introduce a method, named GradViT, that optimizes random noise into naturally
looking images via an iterative process. The optimization objective consists of
(i) a loss on matching the gradients, (ii) image prior in the form of distance
to batch-normalization statistics of a pretrained CNN model, and (iii) a total
variation regularization on patches to guide correct recovery locations. We
propose a unique loss scheduling function to overcome local minima during
optimization. We evaluate GadViT on ImageNet1K and MS-Celeb-1M datasets, and
observe unprecedentedly high fidelity and closeness to the original (hidden)
data. During the analysis we find that vision transformers are significantly
more vulnerable than previously studied CNNs due to the presence of the
attention mechanism. Our method demonstrates new state-of-the-art results for
gradient inversion in both qualitative and quantitative metrics. Project page
at https://gradvit.github.io/.


title:
A characterization of the fuzzy fractals generated by an orbital fuzzy
  iterated function system

abstract:
  Orbital fuzzy iterated function systems are obtained as a combination of the
concepts of iterated fuzzy set system and orbital iterated function system. It
turns out that, for such a system, the corresponding fuzzy operator is weakly
Picard, its fixed points being called fuzzy fractals. In this paper we present
a structure result concerning fuzzy fractals associated to an orbital fuzzy
iterated function system by proving that such an object is perfectly determined
by the action of the initial term of the Picard iteration sequence on the
closure of the orbits of certain elements.


title: Mixing times for the TASEP on the circle

abstract:
  We study mixing times for the totally asymmetric simple exclusion process
(TASEP) on a circle of length $N$ with $k$ particles. We show that the mixing
time is of order $N^2 \min(k,N-k)^{-1/2}$, and that the cutoff phenomenon does
not occur. This confirms behavior which was separately predicted by Jara,
Lacoin and Peres, and it is more broadly believed to hold for integrable models
in the KPZ-universalty class. Our arguments rely on a connection to periodic
last passage percolation with a detailed analysis of flat geodesics, as well as
a novel random extension and time shift argument for last passage percolation.


title:
A low cost plasmonic platform for photon emission engineering of two
  dimensional semiconductors

abstract:
  Although the field of 2D materials has democratized materials science by
making high quality samples accessible cheaply, due to the atomically thin
nature of these systems, an integration with nanostructures is almost always
required to obtain a significant optical response. Traditionally, these
nanostructures are fabricated via electron beam lithography or focused ion beam
milling, which are expensive and large area fabrication can be further time
consuming. In order to overcome this problem, we report the integration of 2D
semiconductors on a cost-effective and large area fabricated nanocone platform.
We show that the plasmon modes of our nanocone structures lead to
photoluminescence (PL) enhancement of monolayer WSe$_2$ by about eight to ten
times compared to the non-plasmonic case, consistent with finite-difference
time-domain simulations. Excitation power-dependent measurements reveal that
our nanocone platform enables a versatile route to engineering the relative
exciton trion contributions to the emission.


title:
Solar Anti-Hale Bipolar Magnetic Regions: A Distinct Population with
  Systematic Properties

abstract:
  Besides their causal connection with long and short-term magnetic
variability, solar bipolar magnetic regions are our chief source of insight
into the location, size, and properties of large-scale toroidal magnetic
structures in the solar interior. The great majority of these regions (~95%)
follow a systematic east-west polarity orientation (Hale's law) that reverses
in opposite hemispheres and across even and odd cycles. These regions also
present a systematic north-south polarity orientation (Joy's law) that helps
build the poloidal field that seeds the new cycle. Exceptions to Hale's law are
rare and difficult to study due to their low numbers. Here, we present a
statistical analysis of the inclination (tilt) with respect to the equator of
Hale versus anti-Hale regions spanning four solar cycles, considering two
complementary tilt definitions adopted in previous studies. Our results show
that anti-Hale regions belong to a separate population than Hale regions,
suggesting a different originating mechanism. However, we find that anti-Hale
region tilts present similar systematic tilt properties and similar latitudinal
distributions to Hale regions, implying a strong connection between the two. We
see this as evidence that they belong to a common toroidal flux system. We
speculate that anti-Hale regions originate from poloidal field sheared and
strengthened on the spot after the emergence of Hale regions with very strong
poloidal contribution. Thus, they are not in contradiction with the idea of
largely coherent toroidal flux systems inside the solar interior.


title: Transformer based ensemble for emotion detection

abstract:
  Detecting emotions in languages is important to accomplish a complete
interaction between humans and machines. This paper describes our contribution
to the WASSA 2022 shared task which handles this crucial task of emotion
detection. We have to identify the following emotions: sadness, surprise,
neutral, anger, fear, disgust, joy based on a given essay text. We are using an
ensemble of ELECTRA and BERT models to tackle this problem achieving an F1
score of $62.76\%$. Our codebase (https://bit.ly/WASSA_shared_task) and our
WandB project (https://wandb.ai/acl_wassa_pictxmanipal/acl_wassa) is publicly
available.


title: Detection, Recognition, and Tracking: A Survey

abstract:
  For humans, object detection, recognition, and tracking are innate. These
provide the ability for human to perceive their environment and objects within
their environment. This ability however doesn't translate well in computers. In
Computer Vision and Multimedia, it is becoming increasingly more important to
detect, recognize and track objects in images and/or videos. Many of these
applications, such as facial recognition, surveillance, animation, are used for
tracking features and/or people. However, these tasks prove challenging for
computers to do effectively, as there is a significant amount of data to parse
through. Therefore, many techniques and algorithms are needed and therefore
researched to try to achieve human like perception. In this literature review,
we focus on some novel techniques on object detection and recognition, and how
to apply tracking algorithms to the detected features to track the objects'
movements.


title: An Information-Theoretic View of Mixed-Delay Traffic in 5G and 6G

abstract:
  Fifth generation mobile communication systems (5G) have to accommodate both
Ultra-Reliable Low-Latency Communication (URLLC) and enhanced Mobile Broadband
(eMBB) services. While, eMBB applications support high data rates, URLLC
services aim at guaranteeing low-latencies and high-reliabilities. eMBB and
URLLC services are scheduled on the same frequency band, where the different
latency requirements of the communications render the coexistence challenging.
In this survey, we review, from an information theoretic perspective, coding
schemes that simultaneously accommodate URLLC and eMBB transmissions and show
that they outperform traditional scheduling approaches. Various communication
scenarios are considered, including point-to-point channels, broadcast
channels, interference networks, cellular models, and cloud radio access
networks (C-RANs). The main focus is on the set of rate pairs that can
simultaneously be achieved for URLLC and eMBB messages, which well captures the
tension between the two types of communications. We also discuss
finite-blocklength results where the measure of interest is the set of error
probability pairs that can simultaneously be achieved on the two communication
regimes.


title:
Electric polarization and its quantization in one-dimensional
  non-Hermitian chains

abstract:
  We generalize the modern theory of electric polarization to the case of
one-dimensional non-Hermitian systems with line-gapped spectrum. In these
systems, the electronic position operator is non-Hermitian even when projected
into the subspace of states below the energy gap. However, the associated
Wilson-loop operator is biorthogonally unitary in the thermodynamic limit,
thereby leading to real-valued electronic positions that allow for a clean
definition of polarization. Non-Hermitian polarization can be quantized in the
presence of certain symmetries, as for Hermitian insulators. Different from the
latter case, though, in this regime polarization quantization depends also on
the type of energy gap, which can be either real or imaginary, leading to a
richer variety of topological phases. The most counter-intuitive example is the
1D non-Hermitian chain with time-reversal symmetry only, where non-Hermitian
polarization is quantized in presence of an imaginary line-gap. We propose two
specific models to provide numerical evidence supporting our findings.


title:
Enabling faster and more reliable sonographic assessment of gestational
  age through machine learning

abstract:
  Fetal ultrasounds are an essential part of prenatal care and can be used to
estimate gestational age (GA). Accurate GA assessment is important for
providing appropriate prenatal care throughout pregnancy and identifying
complications such as fetal growth disorders. Since derivation of GA from
manual fetal biometry measurements (head, abdomen, femur) are
operator-dependent and time-consuming, there have been a number of research
efforts focused on using artificial intelligence (AI) models to estimate GA
using standard biometry images, but there is still room to improve the accuracy
and reliability of these AI systems for widescale adoption. To improve GA
estimates, without significant change to provider workflows, we leverage AI to
interpret standard plane ultrasound images as well as 'fly-to' ultrasound
videos, which are 5-10s videos automatically recorded as part of the standard
of care before the still image is captured. We developed and validated three AI
models: an image model using standard plane images, a video model using fly-to
videos, and an ensemble model (combining both image and video). All three were
statistically superior to standard fetal biometry-based GA estimates derived by
expert sonographers, the ensemble model has the lowest mean absolute error
(MAE) compared to the clinical standard fetal biometry (mean difference: -1.51
$\pm$ 3.96 days, 95% CI [-1.9, -1.1]) on a test set that consisted of 404
participants. We showed that our models outperform standard biometry by a more
substantial margin on fetuses that were small for GA. Our AI models have the
potential to empower trained operators to estimate GA with higher accuracy
while reducing the amount of time required and user variability in measurement
acquisition.


title: Bringing Linearly Transformed Cosines to Anisotropic GGX

abstract:
  Linearly Transformed Cosines (LTCs) are a family of distributions that are
used for real-time area-light shading thanks to their analytic integration
properties. Modern game engines use an LTC approximation of the ubiquitous GGX
model, but currently this approximation only exists for isotropic GGX and thus
anisotropic GGX is not supported. While the higher dimensionality presents a
challenge in itself, we show that several additional problems arise when
fitting, post-processing, storing, and interpolating LTCs in the anisotropic
case. Each of these operations must be done carefully to avoid rendering
artifacts. We find robust solutions for each operation by introducing and
exploiting invariance properties of LTCs. As a result, we obtain a small $8^4$
look-up table that provides a plausible and artifact-free LTC approximation to
anisotropic GGX and brings it to real-time area-light shading.


title:
Traps and transport resistance: the next frontier for stable
  state-of-the-art non-fullerene acceptor solar cells

abstract:
  Stability is one of the most important challenges facing organic solar cells
(OSC) on their path to commercialization. In the high-performance material
system PM6:Y6 studied here, investigate degradation mechanisms of inverted
photovoltaic devices. We have identified two distinct degradation pathways: one
requires presence of both illumination and oxygen and features a short-circuit
current reduction, the other one is induced thermally and marked by severe
losses of open-circuit voltage and fill factor. We focus our investigation on
the thermally accelerated degradation. Our findings show that bulk material
properties and interfaces remain remarkably stable, however, aging-induced
defect state formation in the active layer remains the primary cause of thermal
degradation. The increased trap density leads to higher non-radiative
recombination, which limits open-circuit voltage and lowers charge carrier
mobility in the photoactive layer. Furthermore, we find the trap-induced
transport resistance to be the major reason for the drop in fill factor. Our
results suggest that device lifetimes could be significantly increased by
marginally suppressing trap formation, leading to a bright future for OSC.


title: X-ray cluster cosmology

abstract:
  Formation of dark matter halos is sensitive to the expansion rate of the
Universe and to the growth of structures under gravitational collapse.
Virialization of halos heats the gaseous intra-cluster medium to high
temperatures, leading to copious emission of photons at X-ray wavelengths. We
summarize the progress of X-ray surveys in determining cosmology using galaxy
clusters. We review recent cosmological results based on cluster volume
abundance, clustering, standard candles, extreme object statistics, and present
relevant theoretical considerations. We discuss clusters as gravitation theory
probes and present an outlook on future developments.


title:
Bibliometric Profile of an Emerging Journal: Participatory Educational
  Research

abstract:
  Participatory Educational Research journal is one of the journals that
contributes to the field of education and indexed in major international
databases such as ERIC and Scopus.This study provides the bibliometric
characteristic of the total 347 articles published in PER during the period of
2014-2021 using bibliometric analysis. Publish or Perish software to collect
citation data from Google Scholar was used as an analysis instrument for the
impact of the articles.


title:
The best of both worlds: Using automatic detection and limited human
  supervision to create a homogenous magnetic catalog spanning four solar
  cycles

abstract:
  Bipolar magnetic regions (BMRs) are the cornerstone of solar variability.
They are tracers of the large-scale magnetic processes that give rise to the
solar cycle, shapers of the solar corona, building blocks of the large-scale
solar magnetic field, and significant contributors to the free-energetic budget
that gives rise to flares and coronal mass ejections. Surprisingly, no
homogeneous catalog of BMRs exists today, in spite of the existence of
systematic measurements of the magnetic field since the early 1970's. The
purpose of this work is to address this deficiency by creating a homogenous
catalog of BMRs from the 1970's until the present. For this purpose, in this
paper we discuss the strengths and weaknesses of the automatic and manual
detection of BMRs and how both methods can be combined to form the basis of our
Bipolar Active Region Detection (BARD) code and its supporting human
supervision module. At present, the BARD catalog contains more than 10,000
unique BMRs tracked and characterized during every day of their observation.
Here we also discuss our future plans for the creation of an extended
multi-scale magnetic catalog combining the SWAMIS and BARD catalogs.


title:
Temporal trapping of ultrashort pulses enables deterministic optical
  quantum computation

abstract:
  The realization of deterministic photon-photon gates is a central goal in
optical quantum computation and engineering. While solid-state nonlinear optics
offers a scalable, room temperature solution, weak material nonlinearities lead
to extremely demanding requirements on optical loss and confinement, which to
date have not been realized. In this work, we introduce a novel confinement
method, dispersion-engineered temporal trapping, which enhances the nonlinear
interaction strength by orders of magnitude by simultaneously confining photons
in both time and space. Temporal confinement is imposed by a non-resonant
"trap" pulse via cross-phase modulation, realizing a wavelength-scale "flying
cavity". Numerical simulations confirm that temporal trapping confines the
multimode nonlinear dynamics to a single-mode subspace, enabling high-fidelity
deterministic quantum gate operations. With realistic dispersion engineering
and loss figures, we show that temporally trapped ultrashort pulses could
achieve strong coupling on near-term nonlinear nanophotonic platforms. Our
scheme is compatible with traveling-wave implementations and intracavity time
multiplexing, which can further enhance the operation bandwidth and
scalability. We expect temporal trapping to open a new path to optical strong
coupling, with applications in quantum computing, simulation, and light
sources.


title:
Improving Neural Predictivity in the Visual Cortex with Gated Recurrent
  Connections

abstract:
  Computational models of vision have traditionally been developed in a
bottom-up fashion, by hierarchically composing a series of straightforward
operations - i.e. convolution and pooling - with the aim of emulating simple
and complex cells in the visual cortex, resulting in the introduction of deep
convolutional neural networks (CNNs). Nevertheless, data obtained with recent
neuronal recording techniques support that the nature of the computations
carried out in the ventral visual stream is not completely captured by current
deep CNN models. To fill the gap between the ventral visual stream and deep
models, several benchmarks have been designed and organized into the
Brain-Score platform, granting a way to perform multi-layer (V1, V2, V4, IT)
and behavioral comparisons between the two counterparts. In our work, we aim to
shift the focus on architectures that take into account lateral recurrent
connections, a ubiquitous feature of the ventral visual stream, to devise
adaptive receptive fields. Through recurrent connections, the input s
long-range spatial dependencies can be captured in a local multi-step fashion
and, as introduced with Gated Recurrent CNNs (GRCNN), the unbounded expansion
of the neuron s receptive fields can be modulated through the use of gates. In
order to increase the robustness of our approach and the biological fidelity of
the activations, we employ specific data augmentation techniques in line with
several of the scoring benchmarks. Enforcing some form of invariance, through
heuristics, was found to be beneficial for better neural predictivity.


title: Eigenvalues on Spherically Symmetric Manifolds

abstract:
  In this article we will explore Dirichlet Laplace eigenvalues on balls on
spherically symmetric manifolds. We will compare any Dirichlet Laplace
eigenvalue with the corresponding Dirichlet Laplace eigenvalue on balls in
Euclidean space with the same radius. As a special case we will show that the
Dirichlet Laplace eigenvalues on balls with small radius on the sphere are
smaller than the corresponding eigenvalues on the Euclidean ball with the same
radius. While the opposite is true for the Dirichlet Laplace eigenvalues of
hyperbolic spaces.


title:
What can we Learn Even From the Weakest? Learning Sketches for
  Programmatic Strategies

abstract:
  In this paper we show that behavioral cloning can be used to learn effective
sketches of programmatic strategies. We show that even the sketches learned by
cloning the behavior of weak players can help the synthesis of programmatic
strategies. This is because even weak players can provide helpful information,
e.g., that a player must choose an action in their turn of the game. If
behavioral cloning is not employed, the synthesizer needs to learn even the
most basic information by playing the game, which can be computationally
expensive. We demonstrate empirically the advantages of our sketch-learning
approach with simulated annealing and UCT synthesizers. We evaluate our
synthesizers in the games of Can't Stop and MicroRTS. The sketch-based
synthesizers are able to learn stronger programmatic strategies than their
original counterparts. Our synthesizers generate strategies of Can't Stop that
defeat a traditional programmatic strategy for the game. They also synthesize
strategies that defeat the best performing method from the latest MicroRTS
competition.


title:
A Primer for Telemetry Interfacing in Accordance with NASA Standards
  Using Low Cost FPGAs

abstract:
  Photon counting detector systems on sounding rocket payloads often require
interfacing asynchronous outputs with a synchronously clocked telemetry (TM)
stream. Though this can be handled with an on-board computer, there are several
low cost alternatives including custom hardware, microcontrollers and
field-programmable gate arrays (FPGAs). This paper outlines how a TM interface
(TMIF) for detectors on a sounding rocket with asynchronous parallel digital
output can be implemented using low cost FPGAs and minimal custom hardware. Low
power consumption and high speed FPGAs are available as commercial
off-the-shelf (COTS) products and can be used to develop the main component of
the TMIF. Then, only a small amount of additional hardware is required for
signal buffering and level translating. This paper also discusses how this
system can be tested with a simulated TM chain in the small laboratory setting
using FPGAs and COTS specialized data acquisition products.


title:
SPRITE: A Scalable Privacy-Preserving and Verifiable Collaborative
  Learning for Industrial IoT

abstract:
  Recently collaborative learning is widely applied to model sensitive data
generated in Industrial IoT (IIoT). It enables a large number of devices to
collectively train a global model by collaborating with a server while keeping
the datasets on their respective premises. However, existing approaches are
limited by high overheads and may also suffer from falsified aggregated results
returned by a malicious server. Hence, we propose a Scalable,
Privacy-preserving and veRIfiable collaboraTive lEarning (SPRITE) algorithm to
train linear and logistic regression models for IIoT. We aim to reduce burden
from resource-constrained IIoT devices and trust dependence on cloud by
introducing fog as a middleware. SPRITE employs threshold secret sharing to
guarantee privacy-preservation and robustness to IIoT device dropout whereas
verifiable additive homomorphic secret sharing to ensure verifiability during
model aggregation. We prove the security of SPRITE in an honest-but-curious
setting where the cloud is untrustworthy. We validate SPRITE to be scalable and
lightweight through theoretical overhead analysis and extensive testbed
experimentation on an IIoT use-case with two real-world industrial datasets.
For a large-scale industrial setup, SPRITE records 65% and 55% improved
performance over its competitor for linear and logistic regressions
respectively while reducing communication overhead for an IIoT device by 90%.


title: Angular momentum of zero-frequency gravitons

abstract:
  By following closely Weinberg's soft theorem, which captures the $1/\omega$
pole contribution to the amplitude for soft graviton emissions
($\omega<\Lambda$) on top of an arbitrary background hard process, we calculate
the expectation value of the graviton's angular momentum operator for arbitrary
collisions dressed with soft radiation. We find that the result becomes
independent of the cutoff $\Lambda$ on the graviton's frequency, effectively
localizing at $\omega=0$. In this way, our result captures the contribution to
the angular momentum that comes from the zero-frequency modes. Like the soft
theorem, our formula has an exact dependence on the kinematics of the hard
particles and is only a function of their momenta. As an example, we discuss in
some detail the case of the $2 \to 2$ scattering of spinless particles in
General Relativity and ${\cal N}=8$ supergravity.


title: Nonrationality and Principal permutation classes: the remaining case

abstract:
  We complete the proof of the fact that all principal permutation classes
generated by a pattern longer than two have a nonrational generating function.


title:
A second order upper bound for the ground state energy of a hard-sphere
  gas in the Gross-Pitaevskii regime

abstract:
  We prove an upper bound for the ground state energy of a Bose gas consisting
of $N$ hard spheres with radius $\mathfrak{a}/N$, moving in the
three-dimensional unit torus $\Lambda$. Our estimate captures the correct
asymptotics of the ground state energy, up to errors that vanish in the limit
$N \to \infty$. The proof is based on the construction of an appropriate trial
state, given by the product of a Jastrow factor (describing two-particle
correlations on short scales) and of a wave function constructed through a
(generalized) Bogoliubov transformation, generating orthogonal excitations of
the Bose-Einstein condensate and describing correlations on large scales.

categories: math-ph cond-mat.quant-gas math.AP math.MP


title:
Massive particle pair production and oscillation in Friedman Universe:
  dark energy and matter interaction

abstract:
  The classical Friedman equations of time-varying Hubble function $H$,
dark-energy and matter densities couple to quantised field equations for
massive modes $M\gg H$. Numerically solving these equations, we show the
particle-antiparticle pairs production and oscillation in microscopic time
scale ${\mathcal O}(M^{-1})$. A massive pair plasma state is formed in
macroscopic time scale ${\mathcal O}(H^{-1})$. Its density and pressure
introduce the interaction of matter and dark energy densities in the Friedman
equations. Focusing on epochs after reheating, we show that the negative dark
energy tracks down the radiation energy in the radiation epoch. Such tracking
dynamics ends, and dark energy becomes positive in the matter epoch. The matter
converts to dark energy, and their present values are comparable, explaining
the cosmic coincidence. As a result, a class of effective interacting dark
energy models is advocated to confront cosmological observations.


title:
Visualization of the challenges and limitations of the long-term sunspot
  number record

abstract:
  The solar cycle periodically reshapes the magnetic structure and radiative
output of the Sun and determines its impact on the heliosphere roughly every 11
years. Besides this main periodicity, it shows century-long variations
(including periods of abnormally low solar activity called grand minima). The
Maunder Minimum (1645-1715) has generated significant interest as the archetype
of a grand minimum in magnetic activity for the Sun and other stars, suggesting
a potential link between the Sun and changes in terrestrial climate. Recent
reanalyses of sunspot observations have yielded a conflicted view on the
evolution of solar activity during the past 400 years (a steady increase versus
a constant level). This has ignited a concerted community-wide effort to
understand the depth of the Maunder Minimum and the subsequent secular
evolution of solar activity. The goal of this Perspective is to review recent
work that uses historical data to estimate long-term solar variability, and to
provide context to users of these estimates that may not be aware of their
limitations. We propose a clear visual guide than can be used to easily assess
observational coverage for different periods, as well as the level of
disagreement between currently proposed sunspot group number series.


title: A Clique-Based Method for Improving Motif Scanning Accuracy

abstract:
  We present a new approach for improving motif scanning accuracy, based on
analysis of in-between similarity. Given a set of motifs obtained from a
scanning process, we construct an associated weighted graph. We also compute
the expected weight of an edge in such a graph. It turns out that restricting
results to the maximal clique in the graph, computed with respect to the
expected weight, greatly increases precision, hence improves accuracy of the
scan. We tested the method on an ungapped motif-characterized protein family
from five plant proteomes. The method was applied to three iterative motif
scanners - PSI-BLAST, JackHMMer and IGLOSS - with very good results


title:
Equivariant algebraic and semi-algebraic geometry of infinite affine
  space

abstract:
  We study $\textrm{Sym}(\infty)$-orbit closures of not necessarily closed
points in the Zariski spectrum of the infinite polynomial ring
$\mathbb{C}[x_{ij}:\, i\in\mathbb{N},\,j\in[n]]$. Among others, we characterize
invariant prime ideals in this ring. Furthermore, we study projections of basic
equivariant semi-algebraic sets defined by $\textrm{Sym}(\infty)$ orbits of
polynomials in $\mathbb{R}[x_{ij}:\, i\in\mathbb{N},\,j\in[n]]$. For $n=1$ we
prove a quantifier elimination type result which fails for $n>1$.


title:
Possible structural quantum criticality tuned by rare-earth ion
  substitution in infinite-layer nickelates

abstract:
  I show the infinite-layer rare-earth nickelates are near a structural quantum
critical point by mapping the energetics of their structural instabilities
using first priniciples calculations. I first confirm previous results that
show a phonon instability in the $P4/mmm$ phase leading to the $I4/mcm$
structure for $R$NiO$_2$ with $R$ = Sm--Lu. I then study the non-spin-polarized
phonon dispersions of the $I4/mcm$ phase and find that they exhibit rare-earth
size dependent instabilities at the $X$ and $M$ points for materials with $R$ =
Eu--Lu. Group-theoretical analysis was used to enumerate all the isotropy
subroups due to these instablities, and the distorted structures corresponding
to their order parameters were generated using the eigenvectors of the unstable
phonons. These structures were then fully relaxed by minimizing both the atomic
forces and lattice stresses. I was able to stabilize only five out of the
twelve possible distortions. The $Pbcn$ isotropy subgroup with the $M_5^+(a,a)$
order parameter shows noticeable energy gain relative to other distortions for
the compounds with late rare-earth ions. However, the order parameter of the
lowest-energy phase switches first to $X_2^- (0,a) + M_5^+ (b,0)$ and then to
$X_2^- (0,a)$ as the size of the rare-earth ion is progressively increased.
Additionally, several distorted structures lie close in energy for the early
members of this series. These features of the structural energetics persist
even when antiferromagnetism is allowed. Such a competition between different
order parameters that can be tuned by rare-earth ion substitution suggests that
any structural transition that could arise from the phonon instabilities
present in these materials can be suppressed to 0 K.


title:
Super-resolution of generalized spikes and spectra of confluent
  Vandermonde matrices

abstract:
  We study the problem of super-resolution of a linear combination of Dirac
distributions and their derivatives on a one-dimensional circle from noisy
Fourier measurements. Following numerous recent works on the subject, we
consider the geometric setting of "partial clustering", when some Diracs can be
separated much below the Rayleigh limit. Under this assumption, we prove sharp
asymptotic bounds for the smallest singular value of a corresponding
rectangular confluent Vandermonde matrix with nodes on the unit circle. As a
consequence, we derive matching lower and upper min-max error bounds for the
above super-resolution problem, under the additional assumption of nodes
belonging to a fixed grid.


title: On Supervised Feature Selection from High Dimensional Feature Spaces

abstract:
  The application of machine learning to image and video data often yields a
high dimensional feature space. Effective feature selection techniques identify
a discriminant feature subspace that lowers computational and modeling costs
with little performance degradation. A novel supervised feature selection
methodology is proposed for machine learning decisions in this work. The
resulting tests are called the discriminant feature test (DFT) and the relevant
feature test (RFT) for the classification and regression problems,
respectively. The DFT and RFT procedures are described in detail. Furthermore,
we compare the effectiveness of DFT and RFT with several classic feature
selection methods. To this end, we use deep features obtained by LeNet-5 for
MNIST and Fashion-MNIST datasets as illustrative examples. It is shown by
experimental results that DFT and RFT can select a lower dimensional feature
subspace distinctly and robustly while maintaining high decision performance.


title:
Unconventional pressure-driven metamagnetic transitions in topological
  van der Waals magnets

abstract:
  Activating metamagnetic transitions between ordered states in van der Waals
magnets and devices bring great opportunities in spintronics. We show that
external pressure, which enhances the interlayer hopping without introducing
chemical disorders, triggers multiple metamagnetic transitions upon cooling in
the topological van der Waals magnets Mn(Bi$_{1-x}$Sb$_x$)$_4$Te$_7$, where the
antiferromagnetic interlayer superexchange coupling competes with the
ferromagnetic interlayer coupling mediated by the antisite Mn spins. The
temperature-pressure phase diagrams reveal that while the ordering temperature
from the paramagnetic to ordered states is almost pressure-independent, the
metamagnetic transitions show non-trivial pressure and temperature dependence,
even re-entrance. For these highly anisotropic magnets, we attribute the former
to the ordering temperature being only weakly dependent on the intralayer
parameters, the latter to the parametrically different pressure and temperature
dependence of the two interlayer couplings. Our independent probing of these
disparate magnetic interactions paves an avenue for efficient magnetic
manipulations in van der Waals magnets.


title: Focal Modulation Networks

abstract:
  In this work, we propose focal modulation network (FocalNet in short), where
self-attention (SA) is completely replaced by a focal modulation module that is
more effective and efficient for modeling token interactions. Focal modulation
comprises three components: $(i)$ hierarchical contextualization, implemented
using a stack of depth-wise convolutional layers, to encode visual contexts
from short to long ranges at different granularity levels, $(ii)$ gated
aggregation to selectively aggregate context features for each visual token
(query) based on its content, and $(iii)$ modulation or element-wise affine
transformation to fuse the aggregated features into the query vector. Extensive
experiments show that FocalNets outperform the state-of-the-art SA counterparts
(e.g., Swin Transformers) with similar time and memory cost on the tasks of
image classification, object detection, and semantic segmentation.
Specifically, our FocalNets with tiny and base sizes achieve 82.3% and 83.9%
top-1 accuracy on ImageNet-1K. After pretrained on ImageNet-22K, it attains
86.5% and 87.3% top-1 accuracy when finetuned with resolution 224$\times$224
and 384$\times$384, respectively. FocalNets exhibit remarkable superiority when
transferred to downstream tasks. For object detection with Mask R-CNN, our
FocalNet base trained with 1$\times$ already surpasses Swin trained with
3$\times$ schedule (49.0 v.s. 48.5). For semantic segmentation with UperNet,
FocalNet base evaluated at single-scale outperforms Swin evaluated at
multi-scale (50.5 v.s. 49.7). These results render focal modulation a favorable
alternative to SA for effective and efficient visual modeling in real-world
applications. Code is available at https://github.com/microsoft/FocalNet.


title:
Simplicial chromatic polynomials as Hilbert series of Stanley--Reisner
  rings

abstract:
  We find families of simplicial complexes where the simplicial chromatic
polynomials defined by Cooper--de Silva--Sazdanovic \cite{CdSS} are Hilbert
series of Stanley--Reisner rings of auxiliary simplicial complexes. As a
result, such generalized chromatic polynomials are determined by $h$-vectors of
auxiliary simplicial complexes. In addition to generalizing related results on
graphs and matroids, the simplicial complexes used allow us to consider
problems that are not necessarily analogues of those considered for graphs.
Some examples include supports of cyclotomic polynomials, log concavity
properties of a polynomial or some translate of the polynomial, and symmetry
relations between a polynomial and its reciprocal polynomial.
  If the $h$-vectors involed have sufficiently large entries, the Hilbert
series are Hilbert polynomials of some $k$-algebra. As a consequence of
connections between $h$-vectors and simplicial chromatic polynomials, we also
find simplicial complexes whose $h$-vectors are determined by
addition-contraction relations of simplicial complexes analogous to
deletion-contraction relations of graphs. The constructions used involve
generalizations of relations Euler characteristics of configuration spaces and
chromatic polynomials of graphs.


title: Recursive Averaging with Application to Bio-Inspired 3D Source Seeking

abstract:
  We analyze a class of high-amplitude, high-frequency oscillatory systems in
which periodicity occurs on two distinct time scales and establish the
convergence of its trajectories to a suitably averaged system by recursively
applying the averaging theorem. Moreover, we introduce a novel bio-inspired 3D
source seeking algorithm for rigid bodies with a collocated sensor and prove
its practical stability under typical assumptions on the source signal strength
field by combining our averaging results with singular perturbation.


title: Discrete localities II

abstract: This is a continuation of Part I.


title:
Graph coloring-related properties of (generating functions of)
  Hodge-Deligne polynomials

abstract:
  Motivated by a connection between the topology of (generalized) configuration
spaces and chromatic polynomials, we show that generating functions of
Hodge-Deligne polynomials of quasiprojective varieties and colorings of acyclic
directed graphs with the complete graph $K_n$ as the underlying undirected
graph. In order to do this, we combine an interpretation of Crew-Spirkl for
plethysms involving chromatic symmetric polynomials using colorings of directed
acyclic graphs to plethystic exponentials often appearing Hodge-Deligne
polynomials of varieties. Applying this to a recent result of
Florentino-Nozad-Zamora, we find a connection between $GL_n$-character
varieties of finitely presented groups and colorings of these "complete"
directed graphs by (signed) variables used in the Hodge-Deligne polynomials of
the irreducible representations.
  As a consequence of the constructions used, generators of Hodge-Deligne
polynomials carry over to generators of the plethystic exponentials as a power
series in a way that is compatible with this graph coloring interpretation and
gives interesting combinatorial information that simplifies when we consider
Hodge-Deligne polynomials of birationally equivalent varieties to be
equivalent. Finally, we give an application of methods used to symmetries of
Hodge-Deligne polynomials of varieties and their configuration spaces and their
relation to chromatic symmetric polynomials.


title: MetaMorph: Learning Universal Controllers with Transformers

abstract:
  Multiple domains like vision, natural language, and audio are witnessing
tremendous progress by leveraging Transformers for large scale pre-training
followed by task specific fine tuning. In contrast, in robotics we primarily
train a single robot for a single task. However, modular robot systems now
allow for the flexible combination of general-purpose building blocks into task
optimized morphologies. However, given the exponentially large number of
possible robot morphologies, training a controller for each new design is
impractical. In this work, we propose MetaMorph, a Transformer based approach
to learn a universal controller over a modular robot design space. MetaMorph is
based on the insight that robot morphology is just another modality on which we
can condition the output of a Transformer. Through extensive experiments we
demonstrate that large scale pre-training on a variety of robot morphologies
results in policies with combinatorial generalization capabilities, including
zero shot generalization to unseen robot morphologies. We further demonstrate
that our pre-trained policy can be used for sample-efficient transfer to
completely new robot morphologies and tasks.


title: Dataset Distillation by Matching Training Trajectories

abstract:
  Dataset distillation is the task of synthesizing a small dataset such that a
model trained on the synthetic set will match the test accuracy of the model
trained on the full dataset. In this paper, we propose a new formulation that
optimizes our distilled data to guide networks to a similar state as those
trained on real data across many training steps. Given a network, we train it
for several iterations on our distilled data and optimize the distilled data
with respect to the distance between the synthetically trained parameters and
the parameters trained on real data. To efficiently obtain the initial and
target network parameters for large-scale datasets, we pre-compute and store
training trajectories of expert networks trained on the real dataset. Our
method handily outperforms existing methods and also allows us to distill
higher-resolution visual data.


title:
A Prompt Array Keeps the Bias Away: Debiasing Vision-Language Models
  with Adversarial Learning

abstract:
  Vision-language models can encode societal biases and stereotypes, but there
are challenges to measuring and mitigating these harms. Prior proposed bias
measurements lack robustness and feature degradation occurs when mitigating
bias without access to pretraining data. We address both of these challenges in
this paper: First, we evaluate different bias measures and propose the use of
retrieval metrics to image-text representations via a bias measuring framework.
Second, we investigate debiasing methods and show that optimizing for
adversarial loss via learnable token embeddings minimizes various bias measures
without substantially degrading feature representations.


title: Learning from All Vehicles

abstract:
  In this paper, we present a system to train driving policies from experiences
collected not just from the ego-vehicle, but all vehicles that it observes.
This system uses the behaviors of other agents to create more diverse driving
scenarios without collecting additional data. The main difficulty in learning
from other vehicles is that there is no sensor information. We use a set of
supervisory tasks to learn an intermediate representation that is invariant to
the viewpoint of the controlling vehicle. This not only provides a richer
signal at training time but also allows more complex reasoning during
inference. Learning how all vehicles drive helps predict their behavior at test
time and can avoid collisions. We evaluate this system in closed-loop driving
simulations. Our system outperforms all prior methods on the public CARLA
Leaderboard by a wide margin, improving driving score by 25 and route
completion rate by 24 points. Our method won the 2021 CARLA Autonomous Driving
challenge. Code and data are available at https://github.com/dotchen/LAV.


title: Small-scale structure in vector dark matter

abstract:
  We investigate the differences in the small-scale structure of vector dark
matter (VDM) and scalar dark matter (SDM) using 3+1 dimensional simulations of
single/multicomponent Schr\"{o}dinger-Poisson system. We find that the amount
of wave interference, core-to-halo mass ratio (and its scatter), spin of the
core, as well as the shape of the central regions of dark matter halos can
distinguish VDM and SDM. Starting with a collection of idealized halos
(self-gravitating solitons) as an initial condition, we show that the system
dynamically evolves to an approximately spherically symmetric configuration
that has a core surrounded by a halo of interference patterns in the mass
density. In the vector case, the central soliton in less dense and has a
smoother transition to an $r^{-3}$ tail compared to the scalar case. As
compared to SDM, wave interference in VDM is $\sim 1/\sqrt{3}$ times smaller,
resulting in fewer low and high density regions, and more diffuse granules in
the halo. The ratio of VDM core mass to the total halo mass is lower than that
in SDM, with a steeper dependence on the total energy of the system and a
slightly larger scatter. Finally, we also initiate a study of the evolution of
intrinsic spin angular momentum in the VDM case. We see a positive correlation
between the total intrinsic spin in the simulation and the spin of the final
central core, with significant scatter. We see large intrinsic spin in the core
being possible even with vanishing amounts total angular momentum in the
initial conditions (at least instantaneously). Our results point towards the
possibility of distinguishing VDM from SDM using astrophysical and terrestrial
observations.


title: High-Energy Extragalactic Neutrino Astrophysics

abstract:
  The detection of an astrophysical flux of neutrinos in the TeV-PeV energy
range by the IceCube observatory has opened new possibilities for the study of
extreme cosmic accelerators. The apparent isotropy of the neutrino arrival
directions favors an extragalactic origin for this flux, potentially created by
a large population of distant sources. Recent evidence for the detection of
neutrino emission from extragalactic sources include the active galaxies TXS
0506+056 and NGC 1068. We here review the current status of the search for the
sources of the high-energy neutrino flux, concentrating on its extragalactic
contribution. We discuss the implications of these observations for
multimessenger studies of cosmic sources and present an outlook for how
additional observations by current and future instruments will help address
fundamental questions in the emerging field of high-energy neutrino astronomy.


title: 4D-OR: Semantic Scene Graphs for OR Domain Modeling

abstract:
  Surgical procedures are conducted in highly complex operating rooms (OR),
comprising different actors, devices, and interactions. To date, only medically
trained human experts are capable of understanding all the links and
interactions in such a demanding environment. This paper aims to bring the
community one step closer to automated, holistic and semantic understanding and
modeling of OR domain. Towards this goal, for the first time, we propose using
semantic scene graphs (SSG) to describe and summarize the surgical scene. The
nodes of the scene graphs represent different actors and objects in the room,
such as medical staff, patients, and medical equipment, whereas edges are the
relationships between them. To validate the possibilities of the proposed
representation, we create the first publicly available 4D surgical SSG dataset,
4D-OR, containing ten simulated total knee replacement surgeries recorded with
six RGB-D sensors in a realistic OR simulation center. 4D-OR includes 6734
frames and is richly annotated with SSGs, human and object poses, and clinical
roles. We propose an end-to-end neural network-based SSG generation pipeline,
with a rate of success of 0.75 macro F1, indeed being able to infer semantic
reasoning in the OR. We further demonstrate the representation power of our
scene graphs by using it for the problem of clinical role prediction, where we
achieve 0.85 macro F1. The code and dataset will be made available upon
acceptance.


title: {\phi}-SfT: Shape-from-Template with a Physics-Based Deformation Model

abstract:
  Shape-from-Template (SfT) methods estimate 3D surface deformations from a
single monocular RGB camera while assuming a 3D state known in advance (a
template). This is an important yet challenging problem due to the
under-constrained nature of the monocular setting. Existing SfT techniques
predominantly use geometric and simplified deformation models, which often
limits their reconstruction abilities. In contrast to previous works, this
paper proposes a new SfT approach explaining 2D observations through physical
simulations accounting for forces and material properties. Our differentiable
physics simulator regularises the surface evolution and optimises the material
elastic properties such as bending coefficients, stretching stiffness and
density. We use a differentiable renderer to minimise the dense reprojection
error between the estimated 3D states and the input images and recover the
deformation parameters using an adaptive gradient-based optimisation. For the
evaluation, we record with an RGB-D camera challenging real surfaces exposed to
physical forces with various material properties and textures. Our approach
significantly reduces the 3D reconstruction error compared to multiple
competing methods. For the source code and data, see
https://4dqv.mpi-inf.mpg.de/phi-SfT/.