title:
Detecting Beyond the Standard Model Interactions of Solar Neutrinos in
  Low-Threshold Dark Matter Detectors

abstract:
  As low-threshold dark matter detectors advance in development, they will
become sensitive to recoils from solar neutrinos which opens up the possibility
to explore neutrino properties. We predict the enhancement of the event rate of
solar neutrino scattering from Beyond the Standard Model interactions in
low-threshold DM detectors, with a focus on silicon, germanium, gallium
arsenide, xenon, and argon-based detectors. We consider a set of general
neutrino interactions, which fall into five categories: the neutrino magnetic
moment as well as interactions mediated by four types of mediators (scalar,
pseudoscalar, vector, and axial vector), and consider coupling these mediators
to either quarks or electrons. Using these predictions, we place constraints on
the mass and couplings of each mediator and the neutrino magnetic moment from
current low-threshold detectors like SENSEI, Edelweiss, and SuperCDMS, as well
as projections relevant for future experiments such as DAMIC-M, Oscura, Darwin,
and ARGO. We find that such low-threshold detectors can improve current
constraints by up to two orders of magnitude for vector mediators and one order
of magnitude for scalar mediators.


title: Tensor Processing Units as Quantum Chemistry Supercomputers

abstract:
  We demonstrate the use of Google's cloud-based Tensor Processing Units (TPUs)
to both accelerate and scale up density functional theory (DFT) calculations of
electronic structure. Utilizing 512 TPU v3 cores, we perform the largest
general-purpose DFT computation to date, with $N = 247\,848$ orbitals,
corresponding to a cluster of over $10\,000$ water molecules with more than
$100\,000$ electrons. A full TPU v3 pod (2048 TPU v3 cores) and a TPU v4 pod
(8192 TPU v4 cores) are projected to handle up to $N \approx 500\,000$ and $N
\approx 1\,000\,000$ orbitals respectively. Lower-scaling (e.g. linear-scaling)
variants of DFT can consider even larger numbers of orbitals, although they
often only work for restricted classes of systems, such as insulating systems,
require additional approximations, and incur increased code complexity. As a
result, when computationally affordable, cubic-scaling DFT as considered here
is preferable due to its algorithmic simplicity and more general applicability.
Our work thus paves the way towards accessible and systematic use of
conventional -- and more broadly and straightforwardly applicable -- $O(N^3)$
DFT at unprecedented scales, with potential impact in areas such as quantum
chemistry, drug discovery, materials science, and nanotechnology.


title:
Introduction to The Dynamic Pickup and Delivery Problem Benchmark --
  ICAPS 2021 Competition

abstract:
  The Dynamic Pickup and Delivery Problem (DPDP) is an essential problem within
the logistics domain. So far, research on this problem has mainly focused on
using artificial data which fails to reflect the complexity of real-world
problems. In this draft, we would like to introduce a new benchmark from real
business scenarios as well as a simulator supporting the dynamic evaluation.
The benchmark and simulator have been published and successfully supported the
ICAPS 2021 Dynamic Pickup and Delivery Problem competition participated by 152
teams.


title:
Challenging exclusive top quark pair production at low and high
  luminosity LHC

abstract:
  The elastic production of top quark pairs in $pp$ collisions at low and high
luminosity regimes is investigated in detail. We extend the study performed in
Phys. Rev. D102, 074014 (2020) which has demonstrated that the sum of two
semi-exclusive $t\bar{t}$ production modes, namely in photon-Pomeron and
Pomeron-Pomeron interactions, can be experimentally measured when the
$t\bar{t}$ system decays semi-leptonically, $t\bar{t}\rightarrow
jjbl\nu_l\bar{b}$, both forward protons are tagged and a low amount of pile-up
is present. In this study we focus on separating individual channels and a
special attention is paid to the situation at high-luminosity LHC environment.
We observe that the separation of the pomeron-pomeron and photon-photon events
is a challenging task, especially at high amounts of pile-up even with an
optimistic 10~ps resolution of timing detectors. In contrast, the
photon-Pomeron signal is relatively well separable from all backgrounds at low
levels of pile-up, allowing us to discover the elastic $t\bar{t}$ production
and probe, for the first time, the production of a top quark pair in the
photon-Pomeron interactions. The diffractive photoproduction of such a complex
system as the $t\bar{t}$ pair hence can be used not only to study diffractive
properties of the scattering amplitude but also to search for new physics
beyond the Standard Model, and consequently to be a solid part of the physics
programme of forward proton detectors at LHC.


title: Accelerated Quality-Diversity for Robotics through Massive Parallelism

abstract:
  Quality-Diversity (QD) algorithms are a well-known approach to generate large
collections of diverse and high-quality policies. However, QD algorithms are
also known to be data-inefficient, requiring large amounts of computational
resources and are slow when used in practice for robotics tasks. Policy
evaluations are already commonly performed in parallel to speed up QD
algorithms but have limited capabilities on a single machine as most physics
simulators run on CPUs. With recent advances in simulators that run on
accelerators, thousands of evaluations can performed in parallel on single
GPU/TPU. In this paper, we present QDax, an implementation of MAP-Elites which
leverages massive parallelism on accelerators to make QD algorithms more
accessible. We first demonstrate the improvements on the number of evaluations
per second that parallelism using accelerated simulators can offer. More
importantly, we show that QD algorithms are ideal candidates and can scale with
massive parallelism to be run at interactive timescales. The increase in
parallelism does not significantly affect the performance of QD algorithms,
while reducing experiment runtimes by two factors of magnitudes, turning days
of computation into minutes. These results show that QD can now benefit from
hardware acceleration, which contributed significantly to the bloom of deep
learning.


title:
TOI-1759 b: a transiting sub-Neptune around a low mass star
  characterized with SPIRou and TESS

abstract:
  We report the detection and characterization of the transiting sub-Neptune
TOI-1759 b, using photometric time-series from TESS and near infrared
spectropolarimetric data from SPIRou on the CFHT. TOI-1759 b orbits a
moderately active M0V star with an orbital period of $18.849975\pm0.000006$ d,
and we measure a planetary radius and mass of $3.06\pm0.22$ R$_\oplus$ and
$6.8\pm2.0$ M$_\oplus$. Radial velocities were extracted from the SPIRou
spectra using both the CCF and the LBL methods, optimizing the velocity
measurements in the near infrared domain. We analyzed the broadband SED of the
star and the high-resolution SPIRou spectra to constrain the stellar parameters
and thus improve the accuracy of the derived planet parameters. A LSD analysis
of the SPIRou Stokes $V$ polarized spectra detects Zeeman signatures in
TOI-1759. We model the rotational modulation of the magnetic stellar activity
using a GP regression with a quasi-periodic covariance function, and find a
rotation period of $35.65^{+0.17}_{-0.15}$ d. We reconstruct the large-scale
surface magnetic field of the star using ZDI, which gives a predominantly
poloidal field with a mean strength of $18\pm4$ G. Finally, we perform a joint
Bayesian MCMC analysis of the TESS photometry and SPIRou RVs to optimally
constrain the system parameters. At $0.1176\pm0.0013$ au from the star, the
planet receives $6.4$ times the bolometric flux incident on Earth, and its
equilibrium temperature is estimated at $433\pm14$ K. TOI-1759 b is a likely
gas-dominated sub-Neptune with an expected high rate of photoevaporation.
Therefore, it is an interesting target to search for neutral hydrogen escape,
which may provide important constraints on the planetary formation mechanisms
responsible for the observed sub-Neptune radius desert.


title: Shannon Bounds on Lossy Gray-Wyner Networks

abstract:
  The Gray-Wyner network subject to a fidelity criterion is studied. Upper and
lower bounds for the trade-offs between the private sum-rate and the common
rate are obtained for arbitrary sources subject to mean-squared error
distortion. The bounds meet exactly, leading to the computation of the rate
region, when the source is jointly Gaussian. They meet partially when the
sources are modeled via an additive Gaussian "channel". The bounds are inspired
from the Shannon bounds on the rate-distortion problem.


title: Efficient Memory Partitioning in Software Defined Hardware

abstract:
  As programmers turn to software-defined hardware (SDH) to maintain a high
level of productivity while programming hardware to run complex algorithms,
heavy-lifting must be done by the compiler to automatically partition on-chip
arrays. In this paper, we introduce an automatic memory partitioning system
that can quickly compute more efficient partitioning schemes than prior
systems. Our system employs a variety of resource-saving optimizations and an
ML cost model to select the best partitioning scheme from an array of
candidates. We compared our system against various state-of-the-art SDH
compilers and FPGAs on a variety of benchmarks and found that our system
generates solutions that, on average, consume 40.3% fewer logic resources,
78.3% fewer FFs, 54.9% fewer Block RAMs (BRAMs), and 100% fewer DSPs.


title:
A semi-discrete numerical scheme for nonlocally regularized KdV-type
  equations

abstract:
  A general class of KdV-type wave equations regularized with a
convolution-type nonlocality in space is considered. The class differs from the
class of the nonlinear nonlocal unidirectional wave equations previously
studied by the addition of a linear convolution term involving third-order
derivative. To solve the Cauchy problem we propose a semi-discrete numerical
method based on a uniform spatial discretization, that is an extension of a
previously published work of the present authors. We prove uniform convergence
of the numerical method as the mesh size goes to zero. We also prove that the
localization error resulting from localization to a finite domain is
significantly less than a given threshold if the finite domain is large enough.
To illustrate the theoretical results, some numerical experiments are carried
out for the Rosenau-KdV equation, the Rosenau-BBM-KdV equation and a
convolution-type integro-differential equation. The experiments conducted for
three particular choices of the kernel function confirm the error estimates
that we provide.


title:
NoisyMix: Boosting Robustness by Combining Data Augmentations, Stability
  Training, and Noise Injections

abstract:
  For many real-world applications, obtaining stable and robust statistical
performance is more important than simply achieving state-of-the-art predictive
test accuracy, and thus robustness of neural networks is an increasingly
important topic. Relatedly, data augmentation schemes have been shown to
improve robustness with respect to input perturbations and domain shifts.
Motivated by this, we introduce NoisyMix, a training scheme that combines data
augmentations with stability training and noise injections to improve both
model robustness and in-domain accuracy. This combination promotes models that
are consistently more robust and that provide well-calibrated estimates of
class membership probabilities. We demonstrate the benefits of NoisyMix on a
range of benchmark datasets, including ImageNet-C, ImageNet-R, and ImageNet-P.
Moreover, we provide theory to understand implicit regularization and
robustness of NoisyMix.


title: Fixed Points in Supersymmetric Extensions of the Standard Model

abstract:
  We search for weakly interacting fixed points in extensions of the minimally
supersymmetric standard model (MSSM). Necessary conditions lead to three
distinct classes of anomaly-free extensions involving either new quark
singlets, new quark doublets, or a fourth generation. While interacting fixed
points arise prolifically in asymptotically free theories, their existence is
significantly constrained as soon as some of the non-abelian gauge sectors are
infrared free. Performing a scan over $\sim 200$k different MSSM extensions
using matter field multiplicities and the number of superpotential couplings as
free parameters, we find mostly infrared conformal fixed points, and a small
subset with ultraviolet ones. All settings predict low-scale
supersymmetry-breaking and a violation of $R$-parity. Despite of residual
interactions, the running of couplings out of asymptotically safe fixed points
is logarithmic as in asymptotic freedom. Some fixed points can be matched to
the Standard Model though the matching scale comes out too low. Prospects for
higher matching scales and asymptotic safety beyond the MSSM are indicated.


title:
Automated processing of X-ray computed tomography images via panoptic
  segmentation for modeling woven composite textiles

abstract:
  A new, machine learning-based approach for automatically generating 3D
digital geometries of woven composite textiles is proposed to overcome the
limitations of existing analytical descriptions and segmentation methods. In
this approach, panoptic segmentation is leveraged to produce instance segmented
semantic masks from X-ray computed tomography (CT) images. This effort
represents the first deep learning based automated process for segmenting
unique yarn instances in a woven composite textile. Furthermore, it improves on
existing methods by providing instance-level segmentation on low contrast CT
datasets. Frame-to-frame instance tracking is accomplished via an
intersection-over-union (IoU) approach adopted from video panoptic segmentation
for assembling a 3D geometric model. A corrective recognition algorithm is
developed to improve the recognition quality (RQ). The panoptic quality (PQ)
metric is adopted to provide a new universal evaluation metric for
reconstructed woven composite textiles. It is found that the panoptic
segmentation network generalizes well to new CT images that are similar to the
training set but does not extrapolate well to CT images of differing geometry,
texture, and contrast. The utility of this approach is demonstrated by
capturing yarn flow directions, contact regions between individual yarns, and
the spatially varying cross-sectional areas of the yarns.


title: Conciseness of compact $R$-analytic groups

abstract:
  We prove that every word is strongly concise in the class of compact
$R$-analytic groups.


title:
FedSpace: An Efficient Federated Learning Framework at Satellites and
  Ground Stations

abstract:
  Large-scale deployments of low Earth orbit (LEO) satellites collect massive
amount of Earth imageries and sensor data, which can empower machine learning
(ML) to address global challenges such as real-time disaster navigation and
mitigation. However, it is often infeasible to download all the high-resolution
images and train these ML models on the ground because of limited downlink
bandwidth, sparse connectivity, and regularization constraints on the imagery
resolution. To address these challenges, we leverage Federated Learning (FL),
where ground stations and satellites collaboratively train a global ML model
without sharing the captured images on the satellites. We show fundamental
challenges in applying existing FL algorithms among satellites and ground
stations, and we formulate an optimization problem which captures a unique
trade-off between staleness and idleness. We propose a novel FL framework,
named FedSpace, which dynamically schedules model aggregation based on the
deterministic and time-varying connectivity according to satellite orbits.
Extensive numerical evaluations based on real-world satellite images and
satellite networks show that FedSpace reduces the training time by 1.7 days
(38.6%) over the state-of-the-art FL algorithms.


title:
DASHA: Distributed Nonconvex Optimization with Communication
  Compression, Optimal Oracle Complexity, and No Client Synchronization

abstract:
  We develop and analyze DASHA: a new family of methods for nonconvex
distributed optimization problems. When the local functions at the nodes have a
finite-sum or an expectation form, our new methods, DASHA-PAGE and
DASHA-SYNC-MVR, improve the theoretical oracle and communication complexity of
the previous state-of-the-art method MARINA by Gorbunov et al. (2020). In
particular, to achieve an epsilon-stationary point, and considering the random
sparsifier RandK as an example, our methods compute the optimal number of
gradients $\mathcal{O}\left(\frac{\sqrt{m}}{\varepsilon\sqrt{n}}\right)$ and
$\mathcal{O}\left(\frac{\sigma}{\varepsilon^{3/2}n}\right)$ in finite-sum and
expectation form cases, respectively, while maintaining the SOTA communication
complexity $\mathcal{O}\left(\frac{d}{\varepsilon \sqrt{n}}\right)$.
Furthermore, unlike MARINA, the new methods DASHA, DASHA-PAGE and DASHA-MVR
send compressed vectors only and never synchronize the nodes, which makes them
more practical for federated learning. We extend our results to the case when
the functions satisfy the Polyak-Lojasiewicz condition. Finally, our theory is
corroborated in practice: we see a significant improvement in experiments with
nonconvex classification and training of deep learning models.


title:
Robust Estimation for Nonparametric Families via Generative Adversarial
  Networks

abstract:
  We provide a general framework for designing Generative Adversarial Networks
(GANs) to solve high dimensional robust statistics problems, which aim at
estimating unknown parameter of the true distribution given adversarially
corrupted samples. Prior work focus on the problem of robust mean and
covariance estimation when the true distribution lies in the family of Gaussian
distributions or elliptical distributions, and analyze depth or scoring rule
based GAN losses for the problem. Our work extend these to robust mean
estimation, second moment estimation, and robust linear regression when the
true distribution only has bounded Orlicz norms, which includes the broad
family of sub-Gaussian, sub-Exponential and bounded moment distributions. We
also provide a different set of sufficient conditions for the GAN loss to work:
we only require its induced distance function to be a cumulative density
function of some light-tailed distribution, which is easily satisfied by neural
networks with sigmoid activation. In terms of techniques, our proposed GAN
losses can be viewed as a smoothed and generalized Kolmogorov-Smirnov distance,
which overcomes the computational intractability of the original
Kolmogorov-Smirnov distance used in the prior work.

categories: cs.LG eess.SP math.ST stat.CO stat.ML stat.TH


title:
High-resolution far-infrared synchrotron FTIR spectroscopy and analysis
  of the $\nu_7$, $\nu_{19}$ and $\nu_{20}$ bands of trioxane

abstract:
  Rovibrational bands spectra of three $\nu_{20}$, $\nu_7$ and $\nu_{19}$ bands
of 1, 3, 5 -- trioxane (H$_2$CO)$_3$ were recorded in the 50--650 cm$^{-1}$
range using a long path absorption cell coupled to a high resolution Fourier
transform spectrometer and synchrotron radiation at the AILES beamline of the
SOLEIL synchrotron. More than 16000 lines were assigned with a dRMS better than
$0.17\times10^{-3}$ cm$^{-1}$. Two different formalisms (tensorial and Watson)
were used to derive accurate rotational and quartic parameters for the three
bands and for the first time, a precise determination of Coriolis parameter and
$q_+$ $l-$doubling constant for both $\nu_{20}$ and $\nu_{19}$ perpendicular
bands was obtained. Last, each set of spectroscopic parameters is compared and
discussed between both formalisms.


title: The Drinfel'd centres of String 2-groups

abstract:
  Let $G$ be a compact connected Lie group and $k \in H^4(BG,\mathbb{Z})$ a
cohomology class. The String 2-group $G_k$ is the central extension of $G$ by
the 2-group $[\ast/U(1)]$ classified by $k$. It has a close relationship to the
level $k$ extension of the loop group $LG$. We compute the Drinfel'd centre of
$G_k$ as a smooth 2-group. When $G$ is simply-connected, it is the invertible
part of the category of positive energy representations of $LG$ at level $k$.


title:
Jamming Resilient Indoor Factory Deployments: Design and Performance
  Evaluation

abstract:
  In the framework of 5G-and-beyond Industry 4.0, jamming attacks for denial of
service are a rising threat which can severely compromise the system
performance. Therefore, in this paper we deal with the problem of jamming
detection and mitigation in indoor factory deployments. We design two jamming
detectors based on pseudo-random blanking of subcarriers with orthogonal
frequency division multiplexing and consider jamming mitigation with frequency
hopping and random scheduling of the user equipments. We then evaluate the
performance of the system in terms of achievable BLER with ultra-reliable
low-latency communications traffic and jamming missed detection probability.
Simulations are performed considering a 3rd Generation Partnership Project
spatial channel model for the factory floor with a jammer stationed outside the
plant trying to disrupt the communication inside the factory. Numerical results
show that jamming resiliency increases when using a distributed access point
deployment and exploiting channel correlation among antennas for jamming
detection, while frequency hopping is helpful in jamming mitigation only for
strict BLER requirements.


title:
Beyond Images: Label Noise Transition Matrix Estimation for Tasks with
  Lower-Quality Features

abstract:
  The label noise transition matrix, denoting the transition probabilities from
clean labels to noisy labels, is crucial knowledge for designing statistically
robust solutions. Existing estimators for noise transition matrices, e.g.,
using either anchor points or clusterability, focus on computer vision tasks
that are relatively easier to obtain high-quality representations. However, for
other tasks with lower-quality features, the uninformative variables may
obscure the useful counterpart and make anchor-point or clusterability
conditions hard to satisfy. We empirically observe the failures of these
approaches on a number of commonly used datasets. In this paper, to handle this
issue, we propose a generally practical information-theoretic approach to
down-weight the less informative parts of the lower-quality features. The
salient technical challenge is to compute the relevant information-theoretical
metrics using only noisy labels instead of clean ones. We prove that the
celebrated $f$-mutual information measure can often preserve the order when
calculated using noisy labels. The necessity and effectiveness of the proposed
method is also demonstrated by evaluating the estimation error on a varied set
of tabular data and text classification tasks with lower-quality features. Code
is available at github.com/UCSC-REAL/Est-T-MI.


title: Principled Graph Management

abstract:
  Graph Generation is a recently introduced enhanced Column Generation
algorithm for solving expanded Linear Programming relaxations of mixed integer
linear programs without weakening the expanded relaxations which characterize
these methods. To apply Graph Generation we must be able to map any given
column generated during pricing to a small directed acyclic graph for which any
path from source to sink describes a feasible column. This structure is easily
satisfied for vehicle routing, crew scheduling and various logistics problems
where pricing is a constrained shortest path problem. The construction of such
graphs trades off the size/diversity of a subset of columns modeled by the
graphs versus the additional computational time required to solve the problems
induced by larger graphs.
  Graph Generation (GG) has two computational bottlenecks. The first is
pricing. Pricing in GG and Column Generation (CG) is identical because of the
structure of the problems solved. The second bottleneck is the restricted
master problem (RMP), which is more computationally intensive in GG than in CG
given the same number of columns generated. By design GG converges in fewer
iterations than CG, and hence requires fewer calls to pricing. Therefore, when
the computation time of GG is dominated by pricing, as opposed to solving the
RMP, GG converges much faster than CG in terms of time. However GG need not
converge faster than CG when the GG RMP, rather than pricing, dominates
computation.
  In this paper we introduce Principled Graph Management (PGM), which is an
algorithm to solve the GG RMP rapidly by exploiting its special structure. We
demonstrate the effectiveness of PGM inside a GG solution to the classical
Capacitated Vehicle Routing Problem. We demonstrate that PGM solves the GG RMP
hundreds of times faster than the baseline solver and that the improvement in
speed increases with problem size.


title: Topological Classification in a Wasserstein Distance Based Vector Space

abstract:
  Classification of large and dense networks based on topology is very
difficult due to the computational challenges of extracting meaningful
topological features from real-world networks. In this paper we present a
computationally tractable approach to topological classification of networks by
using principled theory from persistent homology and optimal transport to
define a novel vector representation for topological features. The proposed
vector space is based on the Wasserstein distance between persistence barcodes.
The 1-skeleton of the network graph is employed to obtain 1-dimensional
persistence barcodes that represent connected components and cycles. These
barcodes and the corresponding Wasserstein distance can be computed very
efficiently. The effectiveness of the proposed vector space is demonstrated
using support vector machines to classify simulated networks and measured
functional brain networks.


title:
Maximum likelihood reconstruction of water Cherenkov events with deep
  generative neural networks

abstract:
  Large water Cherenkov detectors have shaped our current knowledge of neutrino
physics and nucleon decay, and will continue to do so in the foreseeable
future. These highly capable detectors allow for directional and topological,
as well as calorimetric information to be extracted from signals on their
photosensors. The current state-of-the-art approach to water Cherenkov
reconstruction relies on maximum-likelihood estimation, with several
simplifying assumptions employed to make the problem tractable. In this paper,
we describe neural networks that produce probability density functions for the
signals at each photosensor, given a set of inputs that characterizes a
particle in the detector. The neural networks we propose allow for
likelihood-based approaches to event reconstruction with significantly fewer
assumptions compared to traditional methods, and are thus expected to improve
on the current performance of water Cherenkov detectors.


title: Global Optimization Networks

abstract:
  We consider the problem of estimating a good maximizer of a black-box
function given noisy examples. To solve such problems, we propose to fit a new
type of function which we call a global optimization network (GON), defined as
any composition of an invertible function and a unimodal function, whose unique
global maximizer can be inferred in $\mathcal{O}(D)$ time. In this paper, we
show how to construct invertible and unimodal functions by using linear
inequality constraints on lattice models. We also extend to \emph{conditional}
GONs that find a global maximizer conditioned on specified inputs of other
dimensions. Experiments show the GON maximizers are statistically significantly
better predictions than those produced by convex fits, GPR, or DNNs, and are
more reasonable predictions for real-world problems.


title: Determinantal Formulas for Exceptional Orthogonal Polynomials

abstract:
  We present determinantal formulas for exceptional Laguerre and exceptional
Jacobi polynomials and also for some exceptional Hermite polynomials. The
formulas resemble Vandermonde determinants and use the zeros of the classical
orthogonal polynomials.


title:
PromptSource: An Integrated Development Environment and Repository for
  Natural Language Prompts

abstract:
  PromptSource is a system for creating, sharing, and using natural language
prompts. Prompts are functions that map an example from a dataset to a natural
language input and target output. Using prompts to train and query language
models is an emerging area in NLP that requires new tools that let users
develop and refine these prompts collaboratively. PromptSource addresses the
emergent challenges in this new setting with (1) a templating language for
defining data-linked prompts, (2) an interface that lets users quickly iterate
on prompt development by observing outputs of their prompts on many examples,
and (3) a community-driven set of guidelines for contributing new prompts to a
common pool. Over 2,000 prompts for roughly 170 datasets are already available
in PromptSource. PromptSource is available at
https://github.com/bigscience-workshop/promptsource.


title: Combinatorial Gray codes-an updated survey

abstract:
  A combinatorial Gray code for a class of objects is a listing that contains
each object from the class exactly once such that any two consecutive objects
in the list differ only by a `small change'. Such listings are known for many
different combinatorial objects, including bitstrings, combinations,
permutations, partitions, triangulations, but also for objects defined with
respect to a fixed graph, such as spanning trees, perfect matchings or vertex
colorings. This survey provides a comprehensive picture of the state-of-the-art
of the research on combinatorial Gray codes. In particular, it gives an update
on Savage's influential survey [C. D. Savage. A survey of combinatorial Gray
codes. SIAM Rev., 39(4):605-629, 1997.], incorporating many more recent
developments. We also elaborate on the connections to closely related problems
in graph theory, algebra, order theory, geometry and algorithms, which embeds
this research area into a broader context. Lastly, we collect and propose a
number of challenging research problems, thus stimulating new research
endeavors.


title:
An Experience Report of Executive-Level Artificial Intelligence
  Education in the United Arab Emirates

abstract:
  Teaching artificial intelligence (AI) is challenging. It is a fast moving
field and therefore difficult to keep people updated with the state-of-the-art.
Educational offerings for students are ever increasing, beyond university
degree programs where AI education traditionally lay. In this paper, we present
an experience report of teaching an AI course to business executives in the
United Arab Emirates (UAE). Rather than focusing only on theoretical and
technical aspects, we developed a course that teaches AI with a view to
enabling students to understand how to incorporate it into existing business
processes. We present an overview of our course, curriculum and teaching
methods, and we discuss our reflections on teaching adult learners, and to
students in the UAE.


title: Upper bounds for the moduli of polynomial-like maps

abstract:
  We establish a version of the Pommerenke-Levin-Yoccoz inequality for the
modulus of a polynomial-like restriction of a global polynomial and give two
applications. First it is shown that if the modulus of a polynomial-like
restriction of an arbitrary polynomial is bounded from below then this forces
bounded combinatorics. The second application concerns parameter slices of
cubic polynomials given by a non-repelling value of a fixed point multiplier.
Namely, the intersection of the main cubioid and the multiplier slice lies in
the closure of the principal hyperbolic domain, with only possible exception of
queer components.


title:
Multivariate nonparametric regression by least squares Jacobi
  polynomials approximations

abstract:
  In this work, we study a random orthogonal projection based least squares
estimator for the stable solution of a multivariate nonparametric regression
(MNPR) problem. More precisely, given an integer $d\geq 1$ corresponding to the
dimension of the MNPR problem, a positive integer $N\geq 1$ and a real
parameter $\alpha\geq -\frac{1}{2},$ we show that a fairly large class of
$d-$variate regression functions are well and stably approximated by its random
projection over the orthonormal set of tensor product $d-$variate Jacobi
polynomials with parameters $(\alpha,\alpha).$ The associated uni-variate
Jacobi polynomials have degree at most $N$ and their tensor products are
orthonormal over $\mathcal U=[0,1]^d,$ with respect to the associated
multivariate Jacobi weights. In particular, if we consider $n$ random sampling
points $\mathbf X_i$ following the $d-$variate Beta distribution, with
parameters $(\alpha+1,\alpha+1),$ then we give a relation involving $n, N,
\alpha$ to ensure that the resulting $(N+1)^d\times (N+1)^d$ random projection
matrix is well conditioned. Moreover, we provide squared integrated as well as
$L^2-$risk errors of this estimator. Precise estimates of these errors are
given in the case where the regression function belongs to an isotropic Sobolev
space $H^s(I^d),$ with $s> \frac{d}{2}.$ Also, to handle the general and
practical case of an unknown distribution of the $\mathbf X_i,$ we use
Shepard's scattered interpolation scheme in order to generate fairly precise
approximations of the observed data at $n$ i.i.d. sampling points $\mathbf X_i$
following a $d-$variate Beta distribution. Finally, we illustrate the
performance of our proposed multivariate nonparametric estimator by some
numerical simulations with synthetic as well as real data.


title: Dr.Jit: A Just-In-Time Compiler for Differentiable Rendering

abstract:
  Dr.Jit is a new just-in-time compiler for physically based rendering and its
derivative. Dr.Jit expedites research on these topics in two ways: first, it
traces high-level simulation code (e.g., written in Python) and aggressively
simplifies and specializes the resulting program representation, producing
data-parallel kernels with state-of-the-art performance on CPUs and GPUs.
  Second, it simplifies the development of differentiable rendering algorithms.
Efficient methods in this area turn the derivative of a simulation into a
simulation of the derivative. Dr.Jit provides fine-grained control over the
process of automatic differentiation to help with this transformation.
  Specialization is particularly helpful in the context of differentiation,
since large parts of the simulation ultimately do not influence the computed
gradients. Dr.Jit tracks data dependencies globally to find and remove
redundant computation.


title:
Mott versus hybridization gap in the low-temperature phase of
  $1T$-TaS$_2$

abstract:
  We compute the correlated electronic structure of stacked $1T$-TaS$_2$
bilayers using the $GW$ + EDMFT method. Depending on the surface termination,
the semi-infinite uncorrelated system is either band-insulating or exhibits a
metallic surface state. For realistic values of the onsite and intersite
interactions, a Mott gap opens in the surface state, but this gap is smaller
than the gap originating from the bilayer structure. Our results are consistent
with recent scanning tunneling spectroscopy measurements for different
terminating layers, and with our own photoemission measurements, which indicate
the coexistence of spatial regions with different gaps in the electronic
spectrum.


title: ASR-Aware End-to-end Neural Diarization

abstract:
  We present a Conformer-based end-to-end neural diarization (EEND) model that
uses both acoustic input and features derived from an automatic speech
recognition (ASR) model. Two categories of features are explored: features
derived directly from ASR output (phones, position-in-word and word boundaries)
and features derived from a lexical speaker change detection model, trained by
fine-tuning a pretrained BERT model on the ASR output. Three modifications to
the Conformer-based EEND architecture are proposed to incorporate the features.
First, ASR features are concatenated with acoustic features. Second, we propose
a new attention mechanism called contextualized self-attention that utilizes
ASR features to build robust speaker representations. Finally, multi-task
learning is used to train the model to minimize classification loss for the ASR
features along with diarization loss. Experiments on the two-speaker English
conversations of Switchboard+SRE data sets show that multi-task learning with
position-in-word information is the most effective way of utilizing ASR
features, reducing the diarization error rate (DER) by 20% relative to the
baseline.


title: Fenrir: Physics-Enhanced Regression for Initial Value Problems

abstract:
  We show how probabilistic numerics can be used to convert an initial value
problem into a Gauss--Markov process parametrised by the dynamics of the
initial value problem. Consequently, the often difficult problem of parameter
estimation in ordinary differential equations is reduced to hyperparameter
estimation in Gauss--Markov regression, which tends to be considerably easier.
The method's relation and benefits in comparison to classical numerical
integration and gradient matching approaches is elucidated. In particular, the
method can, in contrast to gradient matching, handle partial observations, and
has certain routes for escaping local optima not available to classical
numerical integration. Experimental results demonstrate that the method is on
par or moderately better than competing approaches.


title: Imitation Learning by Estimating Expertise of Demonstrators

abstract:
  Many existing imitation learning datasets are collected from multiple
demonstrators, each with different expertise at different parts of the
environment. Yet, standard imitation learning algorithms typically treat all
demonstrators as homogeneous, regardless of their expertise, absorbing the
weaknesses of any suboptimal demonstrators. In this work, we show that
unsupervised learning over demonstrator expertise can lead to a consistent
boost in the performance of imitation learning algorithms. We develop and
optimize a joint model over a learned policy and expertise levels of the
demonstrators. This enables our model to learn from the optimal behavior and
filter out the suboptimal behavior of each demonstrator. Our model learns a
single policy that can outperform even the best demonstrator, and can be used
to estimate the expertise of any demonstrator at any state. We illustrate our
findings on real-robotic continuous control tasks from Robomimic and discrete
environments such as MiniGrid and chess, out-performing competing methods in
$21$ out of $23$ settings, with an average of $7\%$ and up to $60\%$
improvement in terms of the final reward.


title: Systems Mining with Heraklit: The Next Step

abstract:
  We suggest systems mining as the next step after process mining. Systems
mining starts with a more careful investigation of runs, and constructs a
detailed model of behavior, more subtle than classical process mining. The
resulting model is enriched with information about data. From this model, a
system model can be deduced in a systematic way.


title: Cyclical Pruning for Sparse Neural Networks

abstract:
  Current methods for pruning neural network weights iteratively apply
magnitude-based pruning on the model weights and re-train the resulting model
to recover lost accuracy. In this work, we show that such strategies do not
allow for the recovery of erroneously pruned weights. To enable weight
recovery, we propose a simple strategy called \textit{cyclical pruning} which
requires the pruning schedule to be periodic and allows for weights pruned
erroneously in one cycle to recover in subsequent ones. Experimental results on
both linear models and large-scale deep neural networks show that cyclical
pruning outperforms existing pruning algorithms, especially at high sparsity
ratios. Our approach is easy to tune and can be readily incorporated into
existing pruning pipelines to boost performance.


title: Computer sciences and synthesis: retrospective and perspective

abstract:
  The problem of synthesis in computer sciences, including cybernetics,
artificial intelligence and system analysis, is analyzed. Main methods of
realization this problem are discussed. Ways of search universal method of
creation universal synthetic science are represented. As example of such
universal method polymetric analysis is given. Perspective of further
development of this research, including application polymetric method for the
resolution main problems of computer sciences, is analyzed too.


title: Improved Regret for Differentially Private Exploration in Linear MDP

abstract:
  We study privacy-preserving exploration in sequential decision-making for
environments that rely on sensitive data such as medical records. In
particular, we focus on solving the problem of reinforcement learning (RL)
subject to the constraint of (joint) differential privacy in the linear MDP
setting, where both dynamics and rewards are given by linear functions. Prior
work on this problem due to Luyo et al. (2021) achieves a regret rate that has
a dependence of $O(K^{3/5})$ on the number of episodes $K$. We provide a
private algorithm with an improved regret rate with an optimal dependence of
$O(\sqrt{K})$ on the number of episodes. The key recipe for our stronger regret
guarantee is the adaptivity in the policy update schedule, in which an update
only occurs when sufficient changes in the data are detected. As a result, our
algorithm benefits from low switching cost and only performs $O(\log(K))$
updates, which greatly reduces the amount of privacy noise. Finally, in the
most prevalent privacy regimes where the privacy parameter $\epsilon$ is a
constant, our algorithm incurs negligible privacy cost -- in comparison with
the existing non-private regret bounds, the additional regret due to privacy
appears in lower-order terms.


title: Orthogonal Fold & Cut

abstract:
  We characterize the shapes that can be produced by "orthogonal fold & cut":
folding a rectangular sheet of paper along horizontal and vertical creases, and
then making a single straight cut (at any angle). Along the way, we solve a
handful of related problems: orthogonal fold & punch, 1D fold & cut, signed 1D
fold & cut, and 1D interval fold & cut.


title:
Relativistic coupled cluster calculations of the electron affinity and
  ionization potential of Nh(113)

abstract:
  Theoretical calculations based on the Dirac--Coulomb--Breit relativistic
coupled cluster method have been carried out for the electron affinities and
ionization potentials of the superheavy element nihonium (Nh) and its lighter
homologues In and Tl. The In and Tl calculations are in agreement with
measurement within uncertainties. For Nh, where experiment is yet unknown, we
predict the ionization potential of 7.569(48) eV and electron affinity of
0.776(30) eV.


title: Singular metrics and a conjecture by Campana and Peternell

abstract:
  A conjecture by Campana and Peternell says that if a positive multiple of
$K_X$ is linearly equivalent to an effective divisor $D$ plus a
pseudo-effective divisor, then the Kodaira dimension of $X$ should be at least
as big as the Iitaka dimension of $D$. This is a very useful generalization of
the non-vanishing conjecture (which is the case $D = 0$). We use recent work
about singular metrics on pluri-adjoint bundles to show that the
Campana-Peternell conjecture is almost equivalent to the non-vanishing
conjecture.


title: Sidon sets in a union of intervals

abstract:
  We study the maximum size of Sidon sets in unions of integers intervals. If
$A\subseteq\mathbb{N}$ is the union of two intervals and if $\left| A
\right|=n$ (where $\left| A \right|$ denotes the cardinality of $A$), we prove
that $A$ contains a Sidon set of size at least $0, 876\sqrt{n}$. On the other
hand, by using the small differences technique, we establish a bound of the
maximum size of Sidon sets in the union of $k$ intervals.


title: Age Distribution in Arbitrary Preemptive Memoryless Networks

abstract:
  We study the probability distribution of age of information (AoI) in
arbitrary networks with memoryless service times. A source node generates
packets following a Poisson process, and then the packets are forwarded across
the network in such a way that newer updates preempt older ones. This model is
equivalent to gossip networks that was recently studied by Yates, and for which
he obtained a recursive formula allowing the computation for the average AoI.
In this paper, we obtain a very simple characterization of the stationary
distribution of AoI at every node in the network. This allows for the
computation of the average of an arbitrary function of the age. In particular,
we can compute age-violation probabilities. Furthermore, we show how it is
possible to use insights from our simple characterization in order to
substantially reduce the computation time of average AoIs in some structured
networks. Finally, we describe how it is possible to use our characterization
in order to obtain faster and more accurate Monte Carlo simulations estimating
the average AoI, or the average of an arbitrary function of the age.


title:
Free noncommutative hereditary kernels: Jordan decomposition, Arveson
  extension, kernel domination

abstract:
  We discuss a (i) quantized version of the Jordan decomposition theorem for a
complex Borel measure on a compact Hausdorff space, namely, the more general
problem of decomposing a general noncommutative kernel (a quantization of the
standard notion of kernel function) as a linear combination of completely
positive noncommutative kernels (a quantization of the standard notion of
positive definite kernel). Other special cases of (i) include: the problem of
decomposing a general operator-valued kernel function as a linear combination
of positive kernels (not always possible), of decomposing a general bounded
linear Hilbert-space operator as a linear combination of positive linear
operators (always possible), of decomposing a completely bounded linear map
from a $C^*$-algebra ${\mathcal A}$ to an injective $C^*$-algebra ${\mathcal
L}({\mathcal Y})$ as a linear combination of completely positive maps from
${\mathcal A}$ to ${\mathcal L}({\mathcal Y})$ (always possible). We also
discuss (ii) a noncommutative kernel generalization of the Arveson extension
theorem (any completely positive map $\phi$ from a operator system ${\mathbb
S}$ to an injective $C^*$-algebra ${\mathcal L}({\mathcal Y})$ can be extended
to a completely positive map $\phi_e$ from a $C^*$-algebra containing ${\mathbb
S}$ to ${\mathcal L}({\mathcal Y})$), and (iii) a noncommutative kernel version
of a Positivstellensatz (i.e., finding a certificate to explain why one kernel
is positive at points where another given kernel is positive).


title: On Coded Caching Systems with Offline Users

abstract:
  Coded caching is a technique that leverages locally cached contents at the
users to reduce the network's peak-time communication load. Coded caching
achieves significant performance gains compared to uncoded caching schemes and
is thus a promising technique to boost performance in future networks. In the
original model introduced by Maddah-Ali and Niesen (MAN), a server stores
multiple files and is connected to multiple cache-aided users through an
error-free shared link; once the local caches have been filled and all users
have sent their demand to the server, the server can start sending coded
multicast messages to satisfy all users' demands. A practical limitation of the
original MAN model is that it halts if the server does not receive all users'
demands, which is the limiting case of asynchronous coded caching when the
requests of some users arrive with infinite delay. In this paper we formally
define a coded caching system where some users are offline. We propose
achievable and converse bounds for this novel setting and show under which
conditions they meet, thus providing an optimal solution, and when they are to
within a constant multiplicative gap of two. Interestingly, when optimality can
be be shown, the optimal load-memory tradeoff only depends on the number active
users, and not on the total (active plus offline) number of users.


title: Causal Inference Through the Structural Causal Marginal Problem

abstract:
  We introduce an approach to counterfactual inference based on merging
information from multiple datasets. We consider a causal reformulation of the
statistical marginal problem: given a collection of marginal structural causal
models (SCMs) over distinct but overlapping sets of variables, determine the
set of joint SCMs that are counterfactually consistent with the marginal ones.
We formalise this approach for categorical SCMs using the response function
formulation and show that it reduces the space of allowed marginal and joint
SCMs. Our work thus highlights a new mode of falsifiability through additional
variables, in contrast to the statistical one via additional data.


title: Common reducing subspaces and decompositions of contractions

abstract:
  A commuting triple of Hilbert space operators $(A,B,P)$, for which the closed
tetrablock $\overline{\mathbb E}$ is a spectral set, is called a
\textit{tetrablock-contraction} or simply an $\mathbb E$-\textit{contraction},
where \[ \mathbb E=\{(x_1,x_2,x_3)\in \C^3:\, 1-x_1z-x_2w+x_3zw \neq 0 \quad
\text{ whenever } \; |z|\leq 1, \; \; |w|\leq 1 \} \subset \mathbb C^3, \] is a
polynomially convex domain which is naturally associated with the
$\mu$-synthesis problem. By applications of the theory of $\mathbb
E$-contractions, we obtain several results on decompositions of contractions.


title:
A Comparison of Online Hate on Reddit and 4chan: A Case Study of the
  2020 US Election

abstract:
  The rapid integration of the Internet into our daily lives has led to many
benefits but also to a number of new, wide-spread threats such as online hate,
trolling, bullying, and generally aggressive behaviours. While research has
traditionally explored online hate, in particular, on one platform, the reality
is that such hate is a phenomenon that often makes use of multiple online
networks. In this article, we seek to advance the discussion into online hate
by harnessing a comparative approach, where we make use of various Natural
Language Processing (NLP) techniques to computationally analyse hateful content
from Reddit and 4chan relating to the 2020 US Presidential Elections. Our
findings show how content and posting activity can differ depending on the
platform being used. Through this, we provide initial comparison into the
platform-specific behaviours of online hate, and how different platforms can
serve specific purposes. We further provide several avenues for future research
utilising a cross-platform approach so as to gain a more comprehensive
understanding of the global hate ecosystem.


title:
Electric field screening in pair discharges and generation of pulsar
  radio emission

abstract:
  Pulsar radio emission may be generated in pair discharges which fill the
pulsar magnetosphere with plasma as an accelerating electric field is screened
by freshly created pairs. In this Letter we develop a simplified analytic
theory for the screening of the electric field in these pair discharges and use
it to estimate total radio luminosity and spectrum. The discharge has three
stages. First, the electric field is screened for the first time and starts to
oscillate. Next, a nonlinear phase occurs. In this phase, the amplitude of the
electric field experiences strong damping because the field dramatically
changes the momenta of newly created pairs. This strong damping ceases, and the
system enters a final linear phase, when the electric field can no longer
dramatically change pair momenta. Applied to pulsars, this theory may explain
several aspects of radio emission, including the observed luminosity,
$L_{\rm{rad}} \sim 10^{28} \rm{erg} \, \rm{s}^{-1}$, and the observed spectrum,
$S_\omega \sim \omega^{-1.4 \pm 1.0} $.


title:
Theorems motivated by foundations of quantum mechanics and some of their
  applications

abstract:
  This paper provides theorems aimed at shedding light on issues in the
foundations of quantum mechanics. These theorems can be used to propose new
interpretations to the theory, or to better understand, evaluate and improve
current interpretations. Some of these applications include: (1) A proof of the
existence of pilot-wave theories that are fully equivalent to standard quantum
mechanics in a path-wise sense. This equivalence is stronger than what is
entailed from the more traditional requirements of equivariance, or good mixing
properties, and is necessary to assure proper correlations across time and
proper records of the past. (2) A proposal for a minimalistic ontology for
non-collapse quantum mechanics, in which Born's rule provides the proper
predictions.(3) The observation of a close relationship between Born's rule and
a version of the superposition principle.


title: On Hecke algebras for $p$-adic reductive groups: The example of $G_2$

abstract:
  We study the endomorphism algebras attached to Bernstein components of
reductive $p$-adic groups. By using recent results of Solleveld, we prove a
reduction to depth zero case result for the components attached to regular
supercuspidal representations of Levi subgroups, and construct a correspondence
with the appropriate set of enhanced $L$-parameters.
  In particular, for Levi subgroups of maximal parabolic subgroups of the split
exceptional group of type $G_2$, we compute the explicit parameters for the
corresponding Hecke algebras, and show that they satisfy a conjecture of
Lusztig's. We also give examples for a generalized version of Yu's conjecture
using type theory for $G_2$.


title:
Harmony: Overcoming the hurdles of GPU memory capacity to train massive
  DNN models on commodity servers

abstract:
  Deep neural networks (DNNs) have grown exponentially in complexity and size
over the past decade, leaving only those who have access to massive
datacenter-based resources with the ability to develop and train such models.
One of the main challenges for the long tail of researchers who might have
access to only limited resources (e.g., a single multi-GPU server) is limited
GPU memory capacity compared to model size. The problem is so acute that the
memory requirement of training large DNN models can often exceed the aggregate
capacity of all available GPUs on commodity servers; this problem only gets
worse with the trend of ever-growing model sizes. Current solutions that rely
on virtualizing GPU memory (by swapping to/from CPU memory) incur excessive
swapping overhead. In this paper, we present a new training framework, Harmony,
and advocate rethinking how DNN frameworks schedule computation and move data
to push the boundaries of training large models efficiently on modest multi-GPU
deployments. Across many large DNN models, Harmony is able to reduce swap load
by up to two orders of magnitude and obtain a training throughput speedup of up
to 7.6x over highly optimized baselines with virtualized memory.


title:
On the phase-mixed eccentricity and inclination distributions of wide
  binaries in the Galaxy

abstract:
  Modern observational surveys allow us to probe the phase space distribution
function (DF) of wide binaries in the Solar neighbourhood. This DF exhibits
non-trivial features, in particular a superthermal distribution of
eccentricities for semimajor axes $a\gtrsim 10^3$AU. To interpret such features
we must first understand how the binary DF is affected by dynamical
perturbations, which typically fall into two classes: (i) stochastic kicks from
passing stars, molecular clouds, etc. and (ii) secular torques from the
Galactic tide. Here we isolate effect (ii) and calculate the time-asymptotic,
phase-mixed DF for an ensemble of wide binaries under quadrupole-order tides.
For binaries wide enough that the phase-mixing assumption is valid, none of our
results depend explicitly on semimajor axes, masses, etc. We show that unless
the initial DF is both isotropic in binary orientation and thermal in
eccentricity, then the final phase-mixed DF is always both anisotropic and
non-thermal. However, the only way to produce a superthermal DF under phase
mixing is for the initial DF to itself be superthermal.


title:
Impact Analysis of Harassment Against Women Using Association Rule
  Mining Approaches: Bangladesh Prospective

abstract:
  In recent years, it has been noticed that women are making progress in every
sector of society. Their involvement in every field, such as education, job
market, social work, etc., is increasing at a remarkable rate. For the last
several years, the government has been trying its level best for the
advancement of women in every sector by doing several research work and
activities and funding several organizations to motivate women. Although
women's involvement in several fields is increasing, the big concern is they
are facing several barriers in their advancement, and it is not surprising that
sexual harassment is one of them. In Bangladesh, harassment against women,
especially students, is a common phenomenon, and it is increasing. In this
paper, a survey-based and Apriori algorithm are used to analyze the several
impacts of harassment among several age groups. Also, several factors such as
frequent impacts of harassment, most vulnerable groups, women mostly facing
harassment, the alleged person behind harassment, etc., are analyzed through
association rule mining of Apriori algorithm and F.P. Growth algorithm. And
then, a comparison of performance between both algorithms has been shown
briefly. For this analysis, data have been carefully collected from all ages.


title: Multi-Resolution Factor Graph Based Stereo Correspondence Algorithm

abstract:
  A dense depth-map of a scene at an arbitrary view orientation can be
estimated from dense view correspondences among multiple lower-dimensional
views of the scene. These low-dimensional view correspondences are dependent on
the geometrical relationship among the views and the scene. Determining dense
view correspondences is difficult in part due to presence of homogeneous
regions in the scene and due to presence of occluded regions and illumination
differences among the views. We present a new multi-resolution factor
graph-based stereo matching algorithm (MR-FGS) that utilizes both intra- and
inter-resolution dependencies among the views as well as among the disparity
estimates. The proposed framework allows exchange of information among multiple
resolutions of the correspondence problem and is useful for handling larger
homogeneous regions in a scene. The MR-FGS algorithm was evaluated
qualitatively and quantitatively using stereo pairs in the Middlebury stereo
benchmark dataset based on commonly used performance measures. When compared to
a recently developed factor graph model (FGS), the MR-FGS algorithm provided
more accurate disparity estimates without requiring the commonly used
post-processing procedure known as the left-right consistency check. The
multi-resolution dependency constraint within the factor-graph model
significantly improved contrast along depth boundaries in the MR-FGS generated
disparity maps.


title: Webs and canonical bases in degree two

abstract:
  We show that Lusztig's canonical basis for the degree two part of the
Grassmannian coordinate ring is given by SL(k) web diagrams. Equivalently, we
show that every SL(2) web immanant of a plabic graph for Gr(k,n) is an SL(k)
web invariant.


title:
Influence of sub-system non-idealities on the performance of Gaussian
  modulated CV-QKD

abstract:
  We present a detailed analysis of the numerical modelling and evaluation of
sub-systems in a Gaussian modulated CV-QKD system, incorporating non-ideal
operations, and along with associated results.


title: Causal Imitation Learning under Temporally Correlated Noise

abstract:
  We develop algorithms for imitation learning from policy data that was
corrupted by temporally correlated noise in expert actions. When noise affects
multiple timesteps of recorded data, it can manifest as spurious correlations
between states and actions that a learner might latch on to, leading to poor
policy performance. To break up these spurious correlations, we apply modern
variants of the instrumental variable regression (IVR) technique of
econometrics, enabling us to recover the underlying policy without requiring
access to an interactive expert. In particular, we present two techniques, one
of a generative-modeling flavor (DoubIL) that can utilize access to a
simulator, and one of a game-theoretic flavor (ResiduIL) that can be run
entirely offline. We find both of our algorithms compare favorably to
behavioral cloning on simulated control tasks.


title:
On the importance of electron-electron and electron-phonon scatterings
  and energy renormalizations during carrier relaxation in monolayer
  transition-metal dichalcogenides

abstract:
  An $\it{ab \,\, initio}$ based fully microscopic many-body approach is used
to study the carrier relaxation dynamics in monolayer transition-metal
dichalcogenides. Bandstructures and wavefunctions as well as phonon energies
and coupling matrix elements are calculated using density functional theory.
The resulting dipole and Coulomb matrix elements are implemented in the
Dirac-Bloch equations to calculate carrier-carrier and carrier-phonon
scatterings throughout the whole Brillouin zone. It is shown that carrier
scatterings lead to a relaxation into hot quasi-Fermi distributions on a single
femtosecond timescale. Carrier cool down and inter-valley transitions are
mediated by phonon scatterings on a picosecond timescale. Strong,
density-dependent energy renormalizations are shown to be valley-dependent. For
MoTe$_2$, MoSe$_2$ and MoS$_2$ the change of energies with occupation is found
to be about 50$\%$ stronger in the $\Sigma$ and $\Lambda$ side valleys than in
the $K$ and $K'$ valleys. However, for realistic carrier densities, the
materials always maintain their direct bandgap at the $K$ points of the
Brillouin zone.


title: Gradient estimators for normalising flows

abstract:
  Recently a machine learning approach to Monte-Carlo simulations called Neural
Markov Chain Monte-Carlo (NMCMC) is gaining traction. In its most popular form
it uses neural networks to construct normalizing flows which are then trained
to approximate the desired target distribution. In this contribution we present
new gradient estimator for Stochastic Gradient Descent algorithm (and the
corresponding \texttt{PyTorch} implementation) and show that it leads to better
training results for $\phi^4$ model. For this model our estimator achieves the
same precision in approximately half of the time needed in standard approach
and ultimately provides better estimates of the free energy. We attribute this
effect to the lower variance of the new estimator. In contrary to the standard
learning algorithm our approach does not require estimation of the action
gradient with respect to the fields, thus has potential of further speeding up
the training for models with more complicated actions.

categories: stat.ML cond-mat.stat-mech cs.LG hep-lat


title: Approximating Full Conformal Prediction at Scale via Influence Functions

abstract:
  Conformal prediction (CP) is a wrapper around traditional machine learning
models, giving coverage guarantees under the sole assumption of
exchangeability; in classification problems, for a chosen significance level
$\varepsilon$, CP guarantees that the number of errors is at most
$\varepsilon$, irrespective of whether the underlying model is misspecified.
However, the prohibitive computational costs of full CP led researchers to
design scalable alternatives, which alas do not attain the same guarantees or
statistical power of full CP. In this paper, we use influence functions to
efficiently approximate full CP. We prove that our method is a consistent
approximation of full CP, and empirically show that the approximation error
becomes smaller as the training set increases; e.g., for $10^{3}$ training
points the two methods output p-values that are $<10^{-3}$ apart: a negligible
error for any practical application. Our methods enable scaling full CP to
large real-world datasets. We compare our full CP approximation ACP to
mainstream CP alternatives, and observe that our method is computationally
competitive whilst enjoying the statistical predictive power of full CP.


title:
Free-Energy Transduction in Chemical Reaction Networks: from Enzymes to
  Metabolism

abstract:
  We provide a rigorous definition of free-energy transduction and its
efficiency in arbitrary -- linear or nonlinear -- open chemical reaction
networks (CRNs) operating at steady state. Our method is based on the knowledge
of the stoichiometric matrix and of the chemostatted species (i.e. the species
maintained at constant concentration by the environment) to identify the
fundamental currents and forces contributing to the entropy production.
Transduction occurs when the current of a stoichiometrically balanced process
is driven against its spontaneous direction (set by its force) thanks to other
processes flowing along their spontaneous direction. In these regimes, open
CRNs operate as thermodynamic machines. After exemplifying these general ideas
using toy models, we analyze central energy metabolism. We relate the
fundamental currents to metabolic pathways and discuss the efficiency with
which they are able to transduce free energy.


title:
Governing online goods: Maturity and formalization in Minecraft, Reddit,
  and World of Warcraft communities

abstract:
  Building a successful community means governing active populations and
limited resources. This challenge often requires communities to design formal
governance systems from scratch. But the characteristics of successful
institutional designs are unclear. Communities that are more mature and
established may have more elaborate formal policy systems. Alternatively, they
may require less formalization precisely because of their maturity. Indeed,
scholars often downplay the role that formal rules relative to unwritten rules,
norms, and values. But in a community with formal rules, decisions are more
consistent, transparent, and legitimate. To understand the relationship of
formal institutions to community maturity and governance style, we conduct a
large-scale quantitative analysis applying institutional analysis frameworks of
self-governance scholar Elinor Ostrom to 80,000 communities across 3 platforms:
the sandbox game Minecraft, the MMO game World of Warcraft, and Reddit. We
classify communities' written rules to test predictors of institutional
formalization. From this analysis we extract two major findings. First,
institutional formalization, the size and complexity of an online community's
governance system, is generally positively associated with maturity, as
measured by age, population size, or degree of user engagement. Second, we find
that online communities employ similar governance styles across platforms,
strongly favoring "weak" norms to "strong" requirements. These findings suggest
that designers and founders of online communities converge on styles of
governance practice that are correlated with successful self-governance. With
deeper insights into the patterns of successful self-governance, we can help
more communities overcome the challenges of self-governance and create for
their members powerful experiences of shared meaning and collective
empowerment.


title: Finite orbit width effects in large aspect ratio stellarators

abstract:
  A new set of orbit-averaged equations for low-collisionality neoclassical
fluxes in large aspect ratio stellarators is derived. The equations are
self-consistently local while retaining finite orbit width effects via the use
of the second adiabatic invariant $J$ as a coordinate. These new equations have
been implemented in the orbit-averaged neoclassical code KNOSOS
\citep{velasco20, velasco21}. The new equations are used to study the $1/\nu$
regime and the regimes that exist at lower collisionality. For generic large
aspect ratio stellarators, as the collision frequency decreases, the $1/\nu$
regime transitions directly into the $\nu$ regime, without passing through a
$\sqrt{\nu}$ regime. An explicit formula for the particle and heat fluxes in
the $\nu$ regime is obtained. The formula includes the effect of particles that
transition between different types of wells. While these transitions make the
particle motion stochastic, they only produce stochastic diffusion independent
of the value of the collision frequency in velocity space. The diffusion in
real space remains proportional to the collision frequency, as it should in the
$\nu$ regime. The $\sqrt{\nu}$ regime is only recovered in large aspect ratio
stellarators close to omnigeneity, and it only exists in an interval of
collisionality that depends on the deviation from omnigeneity $\delta$:
$\delta^2/|\ln \delta| \ll \nu_{ii} R a/\rho_i v_{ti} \ll 1$. Here, $\nu_{ii}$
is the ion-ion collision frequency, $\rho_i$ is the ion gyroradius, $v_{ti}$ is
the ion thermal speed, $a$ is the minor radius and $R$ is the major radius.


title: Deep Learning for Epidemiologists: An Introduction to Neural Networks

abstract:
  Deep learning methods are increasingly being applied to problems in medicine
and healthcare. However, few epidemiologists have received formal training in
these methods. To bridge this gap, this article introduces to the fundamentals
of deep learning from an epidemiological perspective. Specifically, this
article reviews core concepts in machine learning (overfitting, regularization,
hyperparameters), explains several fundamental deep learning architectures
(convolutional neural networks, recurrent neural networks), and summarizes
training, evaluation, and deployment of models. We aim to enable the reader to
engage with and critically evaluate medical applications of deep learning,
facilitating a dialogue between computer scientists and epidemiologists that
will improve the safety and efficacy of applications of this technology.


title:
Superconductivity in the Uniform Electron Gas: Irrelevance of
  Kohn-Luttinger Mechanism

abstract:
  We study the Cooper instability in jellium model in the controlled regime of
small to intermediate values of the Coulomb parameter $r_s \leq 2$. We confirm
that superconductivity naturally emerges from purely repulsive interactions
described by the Kukkonen-Overhauser vertex function. By employing the implicit
renormalization approach we reveal that even in the small-$r_s$ limit, the
dominant mechanism behind Cooper instability is based on dynamic screening of
the Coulomb interaction--accurately captured by the random phase approximation,
whereas the Kohn-Luttinger contribution is negligibly small and, thus, not
relevant.


title:
Transverse Oscillating Bubble Enhanced Laser-driven Betatron X-ray
  Radiation Generation

abstract:
  Ultrafast high-brightness X-ray pulses have proven invaluable for a broad
range of research. Such pulses are typically generated via synchrotron emission
from relativistic electron bunches using large-scale facilities. Recently,
significantly more compact X-ray sources based on laser-wakefield accelerated
(LWFA) electron beams have been demonstrated. In particular, laser-driven
sources, where the radiation is generated by transverse oscillations of
electrons within the plasma accelerator structure (so-called betatron
oscillations) can generate highly-brilliant ultrashort X-ray pulses using a
comparably simple setup. Here, we experimentally demonstrate a method to
markedly enhance and control the parameters of LWFA-driven betatron X-ray
emission. With our novel Transverse Oscillating Bubble Enhanced Betatron
Radiation (TOBER) scheme, we show a significant increase in the number of
generated photons by specifically manipulating the amplitude of the betatron
oscillations. We realize this through an orchestrated evolution of the temporal
laser pulse shape and the accelerating plasma structure. This leads to
controlled off-axis injection of electrons that perform large-amplitude
collective transverse betatron oscillations, resulting in increased radiation
emission. Our concept holds the promise for a method to optimize the X-ray
parameters for specific applications, such as time-resolved investigations with
spatial and temporal atomic resolution or advanced high-resolution imaging
modalities, and the generation of X-ray beams with even higher peak and average
brightness.


title:
How to Improve Deep Learning for Software Analytics (a case study with
  code smell detection)

abstract:
  To reduce technical debt and make code more maintainable, it is important to
be able to warn programmers about code smells. State-of-the-art code small
detectors use deep learners, without much exploration of alternatives within
that technology.
  One promising alternative for software analytics and deep learning is GHOST
(from TSE'21) that relies on a combination of hyper-parameter optimization of
feedforward neural networks and a novel oversampling technique to deal with
class imbalance.
  The prior study from TSE'21 proposing this novel "fuzzy sampling" was
somewhat limited in that the method was tested on defect prediction, but
nothing else. Like defect prediction, code smell detection datasets have a
class imbalance (which motivated "fuzzy sampling"). Hence, in this work we test
if fuzzy sampling is useful for code smell detection.
  The results of this paper show that we can achieve better than
state-of-the-art results on code smell detection with fuzzy oversampling. For
example, for "feature envy", we were able to achieve 99+\% AUC across all our
datasets, and on 8/10 datasets for "misplaced class". While our specific
results refer to code smell detection, they do suggest other lessons for other
kinds of analytics. For example: (a) try better preprocessing before trying
complex learners (b) include simpler learners as a baseline in software
analytics (c) try "fuzzy sampling" as one such baseline.


title:
PanoDepth: A Two-Stage Approach for Monocular Omnidirectional Depth
  Estimation

abstract:
  Omnidirectional 3D information is essential for a wide range of applications
such as Virtual Reality, Autonomous Driving, Robotics, etc. In this paper, we
propose a novel, model-agnostic, two-stage pipeline for omnidirectional
monocular depth estimation. Our proposed framework PanoDepth takes one 360
image as input, produces one or more synthesized views in the first stage, and
feeds the original image and the synthesized images into the subsequent stereo
matching stage. In the second stage, we propose a differentiable Spherical
Warping Layer to handle omnidirectional stereo geometry efficiently and
effectively. By utilizing the explicit stereo-based geometric constraints in
the stereo matching stage, PanoDepth can generate dense high-quality depth. We
conducted extensive experiments and ablation studies to evaluate PanoDepth with
both the full pipeline as well as the individual modules in each stage. Our
results show that PanoDepth outperforms the state-of-the-art approaches by a
large margin for 360 monocular depth estimation.


title:
Polarization angle requirements for CMB B-mode experiments. Application
  to the LiteBIRD satellite

abstract:
  A methodology to provide the polarization angle requirements for different
sets of detectors, at a given frequency of a CMB polarization experiment, is
presented. The uncertainties in the polarization angle of each detector set are
related to a given bias on the tensor-to-scalar ratio $r$ parameter. The
approach is grounded in using a linear combination of the detector sets to
obtain the CMB polarization signal. In addition, assuming that the
uncertainties on the polarization angle are in the small angle limit (lower
than a few degrees), it is possible to derive analytic expressions to establish
the requirements. The methodology also accounts for possible correlations among
detectors, that may originate from the optics, wafers, etc. The approach is
applied to the LiteBIRD space mission. We show that, for the most restrictive
case (i.e., full correlation of the polarization angle systematics among
detector sets), the requirements on the polarization angle uncertainties are of
around 1 arcmin at the most sensitive frequency bands (i.e., $\approx 150$ GHz)
and of few tens of arcmin at the lowest (i.e., $\approx 40$ GHz) and highest
(i.e., $\approx 400$ GHz) observational bands. Conversely, for the least
restrictive case (i.e., no correlation of the polarization angle systematics
among detector sets), the requirements are $\approx 5$ times less restrictive
than for the previous scenario. At the global and the telescope levels,
polarization angle knowledge of a few arcmins is sufficient for correlated
global systematic errors and can be relaxed by a factor of two for fully
uncorrelated errors in detector polarization angle. The reported uncertainty
levels are needed in order to have the bias on $r$ due to systematics below the
limit established by the LiteBIRD collaboration.


title: On the extension of quasiplurisubharmonic functions

abstract:
  Let $(V,\omega)$ be a compact K\"ahler manifold such that $V$ admits a cover
by Zariski-open Stein sets with the property that $\omega$ has a strictly
plurisubharmonic exhaustive potential on each element of the cover. If
$X\subset V$ is an analytic subvariety, we prove that any
$\omega|_X$-plurisubharmonic function on $X$ extends to a
$\omega$-plurisubharmonic function on $V$.
  This result generalizes a previous result of ours on the extension of
singular metrics of ample line bundles. It allows one to show that any
transcendental K\"ahler class in the real Neron-Severi space $NS_{\mathbb
R}(V)$ has this extension property.


title: Singlet Fission Photovoltaics: Progress and Promising Pathways

abstract:
  Singlet fission is a form of multiple exciton generation which occurs in
organic chromophores when a high energy singlet exciton separates into two
lower energy triplet excitons, each with approximately half the singlet energy.
Since this process is spin-allowed it can proceed on an ultrafast timescale of
less than several picoseconds, outcompeting most other loss mechanisms and
reaching quantitative yields approaching 200%.
  Due to this high quantum efficiency, the singlet fission process shows
promise as a means of reducing thermalisation losses in photovoltaic cells.
This would potentially allow for efficiency improvements beyond the
thermodynamic limit in a single junction cell. Efforts to incorporate this
process into solar photovoltaic cells have spanned a wide range of device
structures over the past decade. In this review we compare and categorise these
attempts in order to assess the state of the field and identify the most
promising avenues of future research and development.


title: Adaptive Sampling Strategies to Construct Equitable Training Datasets

abstract:
  In domains ranging from computer vision to natural language processing,
machine learning models have been shown to exhibit stark disparities, often
performing worse for members of traditionally underserved groups. One factor
contributing to these performance gaps is a lack of representation in the data
the models are trained on. It is often unclear, however, how to operationalize
representativeness in specific applications. Here we formalize the problem of
creating equitable training datasets, and propose a statistical framework for
addressing this problem. We consider a setting where a model builder must
decide how to allocate a fixed data collection budget to gather training data
from different subgroups. We then frame dataset creation as a constrained
optimization problem, in which one maximizes a function of group-specific
performance metrics based on (estimated) group-specific learning rates and
costs per sample. This flexible approach incorporates preferences of
model-builders and other stakeholders, as well as the statistical properties of
the learning task. When data collection decisions are made sequentially, we
show that under certain conditions this optimization problem can be efficiently
solved even without prior knowledge of the learning rates. To illustrate our
approach, we conduct a simulation study of polygenic risk scores on synthetic
genomic data -- an application domain that often suffers from
non-representative data collection. We find that our adaptive sampling strategy
outperforms several common data collection heuristics, including equal and
proportional sampling, demonstrating the value of strategic dataset design for
building equitable models.


title: Goodman surgery and projectively Anosov flows

abstract:
  We introduce a generalization of Goodman surgery to the category of
projectively Anosov flows. This construction is performed along a knot that is
simultaneously Legendrian and transverse for a supporting bi-contact structure.
When the flow is Anosov, our operation generates the same flows of Goodman's
construction. The Anosovity of the new flow is strictly connected to contact
geometry. We use this relation to give a refinement to the sequence of
surgeries coefficients producing Anosov flows in concrete examples. We give an
interpretation of the bi-contact surgery in terms of contact-Legendrian surgery
and admissible-inadmissible transverse surgery and we deduce some
(hyper)tightness result for contact and transverse surgeries. Outside of the
realm of Anosov flows, we produce new projectively Anosov flows on hyperbolic
3-manifolds with leaves of genus g>1 in the invariant foliations.


title: Black hole sensitivities in Einstein-scalar-Gauss-Bonnet gravity

abstract:
  The post-Newtonian dynamics of black hole binaries in
Einstein-scalar-Gauss-Bonnet theories of gravity depends on the so-called
"sensitivities", quantities which characterize a black hole's adiabatic
response to the time-dependent scalar field environment sourced by its
companion. In this work, we calculate numerically the sensitivities of
nonrotating black holes, including spontaneously scalarized ones, in three
classes of Einstein-scalar-Gauss-Bonnet gravity: the shift-symmetric, dilatonic
and Gaussian theories. When possible, we compare our results against
perturbative analytical results, finding excellent agreement. Unlike their
general relativistic counterparts, black holes in Einstein-scalar-Gauss-Bonnet
gravity only exist in a restricted parameter space controlled by the theory's
coupling constant. A preliminary study of the role played by the sensitivities
in black hole binaries suggests that, in principle, black holes can be driven
outside of their domain of existence during the inspiral, for binary parameters
which we determine.


title:
Optical and JWST Mid-IR Emission Line Diagnostics for Simultaneous IMBH
  and Stellar Excitation in z~0 Dwarf Galaxies

abstract:
  Current observational facilities have yet to conclusively detect $10^3 - 10^4
M_{\odot}$ intermediate mass black holes (IMBHs) that fill in the evolutionary
gap between early universe seed black holes and $z \sim 0$ supermassive black
holes. Dwarf galaxies present an opportunity to reveal active IMBHs amidst
persistent star formation. We introduce photoionization simulations tailored to
address key physical uncertainties: coincident vs. non-coincident mixing of
IMBH and starlight excitation, open vs. closed surrounding gas cloud
geometries, and different AGN SED shapes. We examine possible AGN emission line
diagnostics in the optical and mid-IR, and find that the diagnostics are often
degenerate with respect to the investigated physical uncertainties. In spite of
these setbacks, and in contrast to recent work, we are able to show that [O
III]/H$\beta$ typically remains bright for dwarf AGN powered by IMBHs down to
$10^3 M_{\odot}$. Dwarf AGN are predicted to have inconsistent star-forming and
Seyfert/LINER classifications using the most common optical diagnostics. In the
mid-IR, [O IV] 25.9$\mu$m and [Ar II] 6.98$\mu$m are less sensitive to physical
uncertainties than are optical diagnostics. Based on these emission lines, we
provide several mid-IR emission line diagnostic diagrams with demarcations for
separating starbursts and AGN with varying levels of activity. The diagrams are
valid over a wide range of ionization parameters and metallicities out to
$z\sim0.1$, so will prove useful for future JWST observations of local dwarf
AGN in the search for IMBHs. We make our photoionization simulation suite
freely available.


title:
Fast Convex Optimization for Two-Layer ReLU Networks: Equivalent Model
  Classes and Cone Decompositions

abstract:
  We develop fast algorithms and robust software for convex optimization of
two-layer neural networks with ReLU activation functions. Our work leverages a
convex reformulation of the standard weight-decay penalized training problem as
a set of group-$\ell_1$-regularized data-local models, where locality is
enforced by polyhedral cone constraints. In the special case of
zero-regularization, we show that this problem is exactly equivalent to
unconstrained optimization of a convex "gated ReLU" network. For problems with
non-zero regularization, we show that convex gated ReLU models obtain
data-dependent approximation bounds for the ReLU training problem. To optimize
the convex reformulations, we develop an accelerated proximal gradient method
and a practical augmented Lagrangian solver. We show that these approaches are
faster than standard training heuristics for the non-convex problem, such as
SGD, and outperform commercial interior-point solvers. Experimentally, we
verify our theoretical results, explore the group-$\ell_1$ regularization path,
and scale convex optimization for neural networks to image classification on
MNIST and CIFAR-10.


title:
Training a Bidirectional GAN-based One-Class Classifier for Network
  Intrusion Detection

abstract:
  The network intrusion detection task is challenging because of the imbalanced
and unlabeled nature of the dataset it operates on. Existing generative
adversarial networks (GANs), are primarily used for creating synthetic samples
from reals. They also have been proved successful in anomaly detection tasks.
In our proposed method, we construct the trained encoder-discriminator as a
one-class classifier based on Bidirectional GAN (Bi-GAN) for detecting
anomalous traffic from normal traffic other than calculating expensive and
complex anomaly scores or thresholds. Our experimental result illustrates that
our proposed method is highly effective to be used in network intrusion
detection tasks and outperforms other similar generative methods on the NSL-KDD
dataset.


title: On Automorphism Groups of Idempotent Evolution Algebras

abstract:
  We study the automorphism group of an idempotent evolution algebra, show that
any finite group can be the automorphism group of an evolution algebra, and
describe certain evolution algebras with given automorphism groups. In
particular, we classify $n$-dimensional idempotent evolution algebras whose
automorphism group is isomorphic to the symmetric group $S_n$, and classify
idempotent evolution algebras with maximal diagonal automorphism subgroups.


title:
Adaptive Discrete Communication Bottlenecks with Dynamic Vector
  Quantization

abstract:
  Vector Quantization (VQ) is a method for discretizing latent representations
and has become a major part of the deep learning toolkit. It has been
theoretically and empirically shown that discretization of representations
leads to improved generalization, including in reinforcement learning where
discretization can be used to bottleneck multi-agent communication to promote
agent specialization and robustness. The discretization tightness of most
VQ-based methods is defined by the number of discrete codes in the
representation vector and the codebook size, which are fixed as
hyperparameters. In this work, we propose learning to dynamically select
discretization tightness conditioned on inputs, based on the hypothesis that
data naturally contains variations in complexity that call for different levels
of representational coarseness. We show that dynamically varying tightness in
communication bottlenecks can improve model performance on visual reasoning and
reinforcement learning tasks.


title:
Recommendations for Visualization Recommendations: Exploring Preferences
  and Priorities in Public Health

abstract:
  The promise of visualization recommendation systems is that analysts will be
automatically provided with relevant and high-quality visualizations that will
reduce the work of manual exploration or chart creation. However, little
research to date has focused on what analysts value in the design of
visualization recommendations. We interviewed 18 analysts in the public health
sector and explored how they made sense of a popular in-domain dataset. in
service of generating visualizations to recommend to others. We also explored
how they interacted with a corpus of both automatically- and manually-generated
visualization recommendations, with the goal of uncovering how the design
values of these analysts are reflected in current visualization recommendation
systems. We find that analysts champion simple charts with clear takeaways that
are nonetheless connected with existing semantic information or domain
hypotheses. We conclude by recommending that visualization recommendation
designers explore ways of integrating context and expectation into their
systems.


title: Can Transformers be Strong Treatment Effect Estimators?

abstract:
  In this paper, we develop a general framework based on the Transformer
architecture to address a variety of challenging treatment effect estimation
(TEE) problems. Our methods are applicable both when covariates are tabular and
when they consist of sequences (e.g., in text), and can handle discrete,
continuous, structured, or dosage-associated treatments. While Transformers
have already emerged as dominant methods for diverse domains, including natural
language and computer vision, our experiments with Transformers as Treatment
Effect Estimators (TransTEE) demonstrate that these inductive biases are also
effective on the sorts of estimation problems and datasets that arise in
research aimed at estimating causal effects. Moreover, we propose a propensity
score network that is trained with TransTEE in an adversarial manner to promote
independence between covariates and treatments to further address selection
bias. Through extensive experiments, we show that TransTEE significantly
outperforms competitive baselines with greater parameter efficiency over a wide
range of benchmarks and settings.


title:
Generalizability of Machine Learning Models: Quantitative Evaluation of
  Three Methodological Pitfalls

abstract:
  Despite the great potential of machine learning, the lack of generalizability
has hindered the widespread adoption of these technologies in routine clinical
practice. We investigate three methodological pitfalls: (1) violation of
independence assumption, (2) model evaluation with an inappropriate performance
indicator, and (3) batch effect and how these pitfalls could affect the
generalizability of machine learning models. We implement random forest and
deep convolutional neural network models using several medical imaging
datasets, including head and neck CT, lung CT, chest X-Ray, and
histopathological images, to quantify and illustrate the effect of these
pitfalls. We develop these models with and without the pitfall and compare the
performance of the resulting models in terms of accuracy, precision, recall,
and F1 score. Our results showed that violation of the independence assumption
could substantially affect model generalizability. More specifically, (I)
applying oversampling before splitting data into train, validation and test
sets; (II) performing data augmentation before splitting data; (III)
distributing data points for a subject across training, validation, and test
sets; and (IV) applying feature selection before splitting data led to
superficial boosts in model performance. We also observed that inappropriate
performance indicators could lead to erroneous conclusions. Also, batch effect
could lead to developing models that lack generalizability. The aforementioned
methodological pitfalls lead to machine learning models with over-optimistic
performance. These errors, if made, cannot be captured using internal model
evaluation, and the inaccurate predictions made by the model may lead to wrong
conclusions and interpretations. Therefore, avoiding these pitfalls is a
necessary condition for developing generalizable models.


title:
Regression Transformer: Concurrent Conditional Generation and Regression
  by Blending Numerical and Textual Tokens

abstract:
  We report the Regression Transformer (RT), a method that abstracts regression
as a conditional sequence modeling problem. The RT casts continuous properties
as sequences of numerical tokens and encodes them jointly with conventional
tokens. This yields a dichotomous model that can seamlessly transition between
solving regression tasks and conditional generation tasks; solely governed by
the mask location. We propose several extensions to the XLNet objective and
adopt an alternating training scheme to concurrently optimize property
prediction and conditional text generation based on a self-consistency loss.
  Our experiments on both chemical and protein languages demonstrate that the
performance of traditional regression models can be surpassed despite training
with cross entropy loss. Importantly, priming the same model with continuous
properties yields a highly competitive conditional generative models that
outperforms specialized approaches in a constrained property optimization
benchmark. In sum, the Regression Transformer opens the door for "swiss army
knife" models that excel at both regression and conditional generation. This
finds application particularly in property-driven, local exploration of the
chemical or protein space.


title: Understanding Cross-Domain Few-Shot Learning: An Experimental Study

abstract:
  Cross-domain few-shot learning has drawn increasing attention for handling
large differences between the source and target domains--an important concern
in real-world scenarios. To overcome these large differences, recent works have
considered exploiting small-scale unlabeled data from the target domain during
the pre-training stage. This data enables self-supervised pre-training on the
target domain, in addition to supervised pre-training on the source domain. In
this paper, we empirically investigate scenarios under which it is advantageous
to use each pre-training scheme, based on domain similarity and few-shot
difficulty: performance gain of self-supervised pre-training over supervised
pre-training increases when domain similarity is smaller or few-shot difficulty
is lower. We further design two pre-training schemes, mixed-supervised and
two-stage learning, that improve performance. In this light, we present seven
findings for CD-FSL which are supported by extensive experiments and analyses
on three source and eight target benchmark datasets with varying levels of
domain similarity and few-shot difficulty. Our code is available at
https://anonymous.4open.science/r/understandingCDFSL.


title: An Artificial Intelligence Dataset for Solar Energy Locations in India

abstract:
  Rapid development of renewable energy sources, particularly solar
photovoltaics, is critical to mitigate climate change. As a result, India has
set ambitious goals to install 300 gigawatts of solar energy capacity by 2030.
Given the large footprint projected to meet these renewable energy targets the
potential for land use conflicts over environmental and social values is high.
To expedite development of solar energy, land use planners will need access to
up-to-date and accurate geo-spatial information of PV infrastructure. The
majority of recent studies use either predictions of resource suitability or
databases that are either developed thru crowdsourcing that often have
significant sampling biases or have time lags between when projects are
permitted and when location data becomes available. Here, we address this
shortcoming by developing a spatially explicit machine learning model to map
utility-scale solar projects across India. Using these outputs, we provide a
cumulative measure of the solar footprint across India and quantified the
degree of land modification associated with land cover types that may cause
conflicts. Our analysis indicates that over 74\% of solar development In India
was built on landcover types that have natural ecosystem preservation, and
agricultural values. Thus, with a mean accuracy of 92\% this method permits the
identification of the factors driving land suitability for solar projects and
will be of widespread interest for studies seeking to assess trade-offs
associated with the global decarbonization of green-energy systems. In the same
way, our model increases the feasibility of remote sensing and long-term
monitoring of renewable energy deployment targets.


title: Robust Binary Models by Pruning Randomly-initialized Networks

abstract:
  We propose ways to obtain robust models against adversarial attacks from
randomly-initialized binary networks. Unlike adversarial training, which learns
the model parameters, we in contrast learn the structure of the robust model by
pruning a randomly-initialized binary network. Our method confirms the strong
lottery ticket hypothesis in the presence of adversarial attacks. Compared to
the results obtained in a non-adversarial setting, we in addition improve the
performance and compression of the model by 1) using an adaptive pruning
strategy for different layers, and 2) using a different initialization scheme
such that all model parameters are initialized either to +1 or -1. Our
extensive experiments demonstrate that our approach performs not only better
than the state-of-the art for robust binary networks; it also achieves
comparable or even better performance than full-precision network training
methods.


title: On minimal higher genus fillings

abstract:
  In this article, we prove that if $(M,g)$ is a genus $G$ orientable surface
with a single boundary component $S^1$, and if $(D,g_0)$ is a disc such that
interior points are connected by unique geodesics and
  $$d_{(D,g_0)}(x,y) \geq d_{(M,g)}(x,y)$$ for all $x,y \in \partial M =
\partial D$, then $$(1 + \frac{2 G}{\pi}) \textrm{Area}(M,g) \geq
\textrm{Area}(D,g_0).$$