arXiv papers from October 2022

Aurel Bulgac

We have extended significantly the microscopic description of the fission process by examining a larger set of observables. We extract neutron and proton numbers of fission fragments, their spins and fission fragment relative orbital angular momentum and their correlations, investigate neutrons emitted at or shortly after scission, excitation energy sharing

Kiril Datchev, Jacob Shapiro

We consider the initial-value problem for a one-dimensional wave equation with coefficients that are positive, constant outside of an interval, and have bounded variation (BV). Under the assumption of compact support of the initial data, we prove that the local energy decays exponentially fast in time, and provide the explicit constant to which the solution

John Carpenter, Crystal Brogan, Daisuke Iono, Tony Mroczkowski

The Wideband Sensitivity Upgrade (WSU) is the top priority initiative for the ALMA2030 Development Roadmap. The WSU will initially double, and eventually quadruple, ALMA's system bandwidth and will deliver improved sensitivity by upgrading the receivers, digital electronics and correlator. The WSU will afford significant improvements for every future ALMA ob

Mingxi Jia, Dian Wang, Guanang Su, David Klee

In robotic manipulation, acquiring samples is extremely expensive because it often requires interacting with the real world. Traditional image-level data augmentation has shown the potential to improve sample efficiency in various machine learning tasks. However, image-level data augmentation is insufficient for an imitation learning agent to learn good mani

Shin-ichi Ohta

We investigate barycenters of probability measures on Gromov hyperbolic spaces, toward development of convex optimization in this class of metric spaces. We establish a contraction property (the Wasserstein distance between probability measures provides an upper bound of the distance between their barycenters), a deterministic approximation of barycenters of

Tomas Petricek, Gerrit J. J. van den Burg, Alfredo Nazábal, Taha Ceritli

Data wrangling tasks such as obtaining and linking data from various sources, transforming data formats, and correcting erroneous records, can constitute up to 80% of typical data engineering work. Despite the rise of machine learning and artificial intelligence, data wrangling remains a tedious and manual task. We introduce AI assistants, a class of semi-au

Haojie Huang, Dian Wang, Xupeng Zhu, Robin Walters

Given point cloud input, the problem of 6-DoF grasp pose detection is to identify a set of hand poses in SE(3) from which an object can be successfully grasped. This important problem has many practical applications. Here we propose a novel method and neural network model that enables better grasp success rates relative to what is available in the literature

Fan Liu, Xihang Jiang, Lifeng Wang

Biological materials exhibit complex structure-property relationships which are only beginning to be elucidated. Understanding the underlying physical mechanisms of the structure-property relationships is the key to designing bioinspired materials. The eggshell is an excellent example because many design trade-offs are well balanced by its seemingly simple b

Fan Liu, Guangyu He, Xihang Jiang, Lifeng Wang

Artificial skin with the sense of touch can support robots to interact with the surrounding environment efficiently and accomplish complex tasks. Traditional multi-layered artificial skins require complex manufacturing processes which can result in high cost as well as limitations on the material and size of the skin. In this paper, we demonstrate a machine

Maksim Makarenko, Elnur Gasanov, Rustem Islamov, Abdurakhmon Sadiev

We propose Adaptive Compressed Gradient Descent (AdaCGD) - a novel optimization algorithm for communication-efficient training of supervised machine learning models with adaptive compression level. Our approach is inspired by the recently proposed three point compressor (3PC) framework of Richtarik et al. (2022), which includes error feedback (EF21), lazily

Paul A Cummings, Brian Ortega

For semigroup $S$, a commutative congruence $\sigma_{orient}$ on $S$ and a subsemigroup Orientable($S$) of $S$ were introduced in "Two cancellative commutative congruences and group diagrams", Semigroup Forum (2011) 82: 338-353. Here we demonstrate that when the semigroup is in fact a group $G$, then Orientable($G$) is the commutator subgroup $[G,G]$ and $ G

Peter Kagey

This proof without words demonstrates that there are $\binom{n+2}{4}$ equilateral triangles in the regular $n$-vertices-per-side triangular grid by describing a map from four-element subsets of $\{1,2, \dots, n+2\}$ into the set of equilateral triangles in this grid. Specifically, we illustrate the triangle that corresponds to the subset $\{4,5,8,11\}$ under

Wenli Yang, Guan Huang, Renjie Li, Jiahao Yu

Convolutional neural network (CNN) models have seen advanced improvements in performance in various domains, but lack of interpretability is a major barrier to assurance and regulation during operation for acceptance and deployment of AI-assisted applications. There have been many works on input interpretability focusing on analyzing the input-output relatio

Sharut Gupta, Kartik Ahuja, Mohammad Havaei, Niladri Chatterjee

Federated learning aims to train predictive models for data that is distributed across clients, under the orchestration of a server. However, participating clients typically each hold data from a different distribution, which can yield to catastrophic generalization on data from a different client, which represents a new domain. In this work, we argue that i

SHINE Collaboration, H. Adhikary, K. K. Allison, N. Amin

This paper presents multiplicity measurements of $K^0_{\textrm{S}}$, $\Lambda$, and $\bar{\Lambda}$ produced in 120 GeV/$c$ proton-carbon interactions. The measurements were made using data collected at the NA61/SHINE experiment during two different periods. Decays of these neutral hadrons impact the measured $\pi^+$, $\pi^-$, $p$ and $\bar{p}$ multiplicitie

Amaury Micheli, Patrick Peter

General relativity and its cosmological solution predicts the existence of tensor modes of perturbations evolving on top of our Friedman-Lema\^itre-Robertson-Walker expanding Universe. Being gauge invariant and not necessarily coupled to other quantum sources, they can be seen as representing pure gravity. Unambiguously showing they are indeed to be quantise

Gal Mishne, Zhengchao Wan, Yusu Wang, Sheng Yang

Given the exponential growth of the volume of the ball w.r.t. its radius, the hyperbolic space is capable of embedding trees with arbitrarily small distortion and hence has received wide attention for representing hierarchical datasets. However, this exponential growth property comes at a price of numerical instability such that training hyperbolic learning

Yan V. Fyodorov, Boris A. Khoruzhenko, Mihail Poplavskyi

Complex eigenvalues of random matrices $J=\text{GUE }+ i\gamma \diag (1, 0, \ldots, 0)$ provide the simplest model for studying resonances in wave scattering from a quantum chaotic system via a single open channel. It is known that in the limit of large matrix dimensions $N\gg 1$ the eigenvalue density of $J$ undergoes an abrupt restructuring at $\gamma = 1$

Henning Kirchberg, Abraham Nitzan

We determine the zero-frequency charge current noise in a metal-molecule-metal junction embedded in a thermal environment, e.g., a solvent, dominated by sequential charge transmission described by a classical master equation, and study its dependence of specific model parameters, i.e., the environmental reorganization energy and relaxation behavior. Interest

Ik Kyeong Jin, Krittika Kumar, Matthew J. Rendell, Jonathan Y. Huang

Holes in silicon quantum dots are receiving significant attention due to their potential as fast, tunable, and scalable qubits in semiconductor quantum circuits. Despite this, challenges remain in this material system including difficulties using charge sensing to determine the number of holes in a quantum dot, and in controlling the coupling between adjacen

Manzil Zaheer, Kenneth Marino, Will Grathwohl, John Schultz

A fundamental ability of an intelligent web-based agent is seeking out and acquiring new information. Internet search engines reliably find the correct vicinity but the top results may be a few links away from the desired target. A complementary approach is navigation via hyperlinks, employing a policy that comprehends local content and selects a link that m

Rayan Wali

Loss functions drive the optimization of machine learning algorithms. The choice of a loss function can have a significant impact on the training of a model, and how the model learns the data. Binary classification is one of the major pillars of machine learning problems, used in medical imaging to failure detection applications. The most commonly used surro

Ali K. Z. Tehrani, Ivan M. Rosado-Mendez, Hassan Rivaz

Quantitative ultrasound (QUS) allows estimating the intrinsic tissue properties. Speckle statistics are the QUS parameters that describe the first order statistics of ultrasound (US) envelope data. The parameters of Homodyned K-distribution (HK-distribution) are the speckle statistics that can model the envelope data in diverse scattering conditions. However

Suyoun Kim, Ke Li, Lucas Kabela, Rongqing Huang

Recently, there has been an increasing interest in two-pass streaming end-to-end speech recognition (ASR) that incorporates a 2nd-pass rescoring model on top of the conventional 1st-pass streaming ASR model to improve recognition accuracy while keeping latency low. One of the latest 2nd-pass rescoring model, Transformer Rescorer, takes the n-best initial out

Ke Liao, Huanchen Zhai, Evelin Martine Christlmaier, Thomas Schraivogel

We present the theory of a density matrix renormalization group (DMRG) algorithm which can solve for both the ground and excited states of non-Hermitian transcorrelated Hamiltonians, and show applications in \emph{ab initio} molecular systems. Transcorrelation (TC) accelerates the basis set convergence rate by including known physics (such as, but not limite

Ali K. Z. Tehrani, Hassan Rivaz

Convolutional Neural Networks (CNN) have been employed for displacement estimation in ultrasound elastography (USE). High-quality axial strains (derivative of the axial displacement in the axial direction) can be estimated by the proposed networks. In contrast to axial strain, lateral strain, which is highly required in Poisson's ratio imaging and elasticity

Georgios Chochlakis, Gireesh Mahajan, Sabyasachee Baruah, Keith Burghardt

The need for emotional inference from text continues to diversify as more and more disciplines integrate emotions into their theories and applications. These needs include inferring different emotion types, handling multiple languages, and different annotation formats. A shared model between different configurations would enable the sharing of knowledge and

François Charton

This paper investigates the failure cases and out-of-distribution behavior of transformers trained on matrix inversion and eigenvalue decomposition. I show that incorrect model predictions still retain deep mathematical properties of the solution (e.g. correct eigenvalues, unit norm of eigenvectors), and that almost all model failures can be attributed to, a

Vishal Abhishek, Vaibhav Srivastava

We study SIS epidemic spreading models under population dispersal on multi-layer networks. We consider a patchy environment in which each patch comprises individuals belonging to different classes. Individuals disperse to other patches on a multi-layer network in which each layer corresponds to a class. The dispersal on each layer is modeled by a Continuous

Dongcui Diao, Hengshuai Yao, Bei Jiang

Recognizing and telling similar objects apart is even hard for human beings. In this paper, we show that there is a phenomenon of class interference with all deep neural networks. Class interference represents the learning difficulty in data, and it constitutes the largest percentage of generalization errors by deep networks. To understand class interference

Ruichen Yao, Ziteng Cui, Xiaoxiao Li, Lin Gu

Fairness is a fundamental requirement for trustworthy and human-centered Artificial Intelligence (AI) system. However, deep neural networks (DNNs) tend to make unfair predictions when the training data are collected from different sub-populations with different attributes (i.e. color, sex, age), leading to biased DNN predictions. We notice that such a troubl

I Wayan Sudiarta, Hadi Susanto

We present a new power method to obtain solutions of eigenvalue problems. The method can determine not only the dominant or lowest eigenvalues but also all eigenvalues without the need for a deflation procedure. The method uses a functional of an operator (or a matrix) to select or filter an eigenvalue. The method can freely select a solution by varying a pa

Mojtaba Rajabi, Oleg Lavrentovich, Michael Shribak

Polarizing microscopy brought about many advancements in the science of liquid crystals and other soft materials, including those of biological origin. Recent developments in optics and computer-based analysis enabled a new generation of quantitative polarizing microscopy which produces spatial maps of the optic axis. Unfortunately, most of the available app

Rohan Sawhney, Daqi Lin, Markus Kettunen, Benedikt Bitterli

Monte Carlo rendering algorithms often utilize correlations between pixels to improve efficiency and enhance image quality. For real-time applications in particular, repeated reservoir resampling offers a powerful framework to reuse samples both spatially in an image and temporally across multiple frames. While such techniques achieve equal-error up to 100 t

Hilla De-Leon, Francesco Pederiva

A contact potential describing an effective interaction between atomic $^4$He reproducing the results obtained with the HFDHE2 potential by Aziz et al. is employed to study the resulting equation of state by means of Quantum Monte Carlo calculations. \cblack The energy per particle and the pair distribution functions were investigated as a function of the ul

Riashat Islam, Manan Tomar, Alex Lamb, Yonathan Efroni

Learning to control an agent from data collected offline in a rich pixel-based visual observation space is vital for real-world applications of reinforcement learning (RL). A major challenge in this setting is the presence of input information that is hard to model and irrelevant to controlling the agent. This problem has been approached by the theoretical R

Nina Galanter, Marco Carone, Ronald C. Kessler, Alex Luedtke

Individualizing treatment assignment can improve outcomes for diseases with patient-to-patient variability in comparative treatment effects. When a clinical trial demonstrates that some patients improve on treatment while others do not, it is tempting to assume that treatment effect heterogeneity exists. However, if variability in response is mainly driven b

Wenjie Xu, Yuning Jiang, Bratislav Svetozarevic, Colin N. Jones

We study the problem of constrained efficient global optimization, where both the objective and constraints are expensive black-box functions that can be learned with Gaussian processes. We propose CONFIG (CONstrained efFIcient Global Optimization), a simple and effective algorithm to solve it. Under certain regularity assumptions, we show that our algorithm

N. H. Barton, A. M. Etheridge, A. Véber

The classical infinitesimal model is a simple and robust model for the inheritance of quantitative traits. In this model, a quantitative trait is expressed as the sum of a genetic and a non-genetic (environmental) component and the genetic component of offspring traits within a family follows a normal distribution around the average of the parents' trait val

Valeria d'Andrea, Michele Puppin, Manlio De Domenico

Recurrent networks are a special class of artificial neural systems that use their internal states to perform computing tasks for machine learning. One of its state-of-the-art developments, i.e. reservoir computing (RC), uses the internal structure -- usually a static network with random structure -- to map an input signal into a nonlinear dynamical system d

Dmitrii Zinoviev, Victor Zinoviev

Using the ideas of concatenation construction of codes over the $q$-ary alphabet, we modify the known generalized Sylvester-type construction of the Hadamard matrices. The new construction is based on two collections of the Hadamard matrices. In particular this construction involves $m$ Hadamard matrices of order $k$ and $k$ Hadamard matrices of order $m$. T

C. R. O'Dell, G. J. Ferland, J. E. Mendez-Delgado

Examination of emission lines in high-velocity resolution optical spectra of the Orion Nebula confirms that the velocity component on the red wing of the main ionization front emission line is due to backscattering in the Photon Dominated Region. This scattered light component has a weak wavelength dependence that is consistent with either general interstell

Julia R. K. Cline, Vera M. Schäfer, Zhijing Niu, Dylan J. Young

We demonstrate continuous loading of strontium atoms into a high finesse ring cavity and observe continuous strong collective coupling in the form of a vacuum Rabi splitting between the atoms and the cavity on the 7.5 kHz transition $^1{\rm S}_0$ to $^3{\rm P}_1$. The atoms are loaded into the cavity from a 3D narrow linewidth molasses, thus avoiding large m

Martin Lumiste, Andrei Ilie

Accurate traffic forecasting is of the utmost importance for optimal travel planning and for efficient city mobility. IARAI (The Institute of Advanced Research in Artificial Intelligence) organizes Traffic4cast, a yearly traffic prediction competition based on real-life data [https://www.iarai.ac.at/traffic4cast/], aiming to leverage artificial intelligence

Peter P. Pedersen, C. A. Murray, D. Queloz, M. Gillon

The variability induced by precipitable water vapour (PWV) can heavily affect the accuracy of time-series photometric measurements gathered from the ground, especially in the near-infrared. We present here a novel method of modelling and mitigating this variability, as well as open-sourcing the developed tool -- Umbrella. In this study, we evaluate the exten

K. Ray, D. Harnett, T. G. Steele

We calculated next-to-leading-order (NLO) QCD perturbative contributions to a $J^{PC}=0^{+-}$, $d u\bar d\bar u$ tetraquark (diquark-antidiquark) correlator in the chiral limit of massless $u$ and $d$ quarks. At NLO, there are four quark self-energy diagrams and six gluon-exchange diagrams. Nonlocal divergences were cancelled using diagrammatic renormalizati

Fan Liu, Zian Jia, Xihang Jiang, Lifeng Wang

This paper presents a mechanically bistable mechanism of the compressed pre-curved beam. A governing equation is proposed which can be used to predict and explain the bistability of the compressed pre-curved beam. FE simulations and experimental tests are performed to validate the analytical solution. The beams unique tunable and asymmetrical potential energ

Priyanka Sukumaran, Conor Houghton, Nina Kazanina

LSTMs trained on next-word prediction can accurately perform linguistic tasks that require tracking long-distance syntactic dependencies. Notably, model accuracy approaches human performance on number agreement tasks (Gulordava et al., 2018). However, we do not have a mechanistic understanding of how LSTMs perform such linguistic tasks. Do LSTMs learn abstra

Junhao Liu, Qizhou Zhang, Patrick M. Koch, Hauyu Baobab Liu

We present ALMA dust polarization and molecular line observations toward 4 clumps (I(N), I, IV, and V) in the massive star-forming region NGC 6334. In conjunction with large-scale dust polarization and molecular line data from JCMT, Planck, and NANTEN2, we make a synergistic analysis of relative orientations between magnetic fields ($\theta_{\mathrm{B}}$), c

Yangyi Chen, Lifan Yuan, Ganqu Cui, Zhiyuan Liu

Pre-trained language models (PLMs) may fail in giving reliable estimates of their predictive uncertainty. We take a close look into this problem, aiming to answer two questions: (1) Do PLMs learn to become calibrated in the training process? (2) How effective are existing calibration methods? For the first question, we conduct fine-grained control experiment

Yousu Chen, Liwei Wang, Xiaoyuan Fan, Dexin Wang

5G wireless technology can deliver higher data speeds, ultra low latency, more reliability, massive network capacity, increased availability, and a more uniform user experience to users. It brings additional power to help address the challenges brought by renewable integration and decarbonization. In this paper, a 5G enabled adaptive computing workflow has b

Titas Chakraborty, Akshay Bhagat, Henggang Cui

Robust motion forecasting of the dynamic scene is a critical component of an autonomous vehicle. It is a challenging problem due to the heterogeneity in the scene and the inherent uncertainties in the problem. To improve the accuracy of motion forecasting, in this work, we identify a new consistency constraint in this task, that is an agent's future trajecto

Grace Floring, Mohnish Peswani, Brian Maxwell

The problem of detonation attenuation in stoichiometric methane-oxygen and its re-establishment following its interaction with obstacles was investigated using high resolution numerical simulation. The main focus was on the role of the transverse detonation on the re-establishment of the detonation wave. We applied an efficient thermochemically derived four-

Aman Alok, Rahul Gupta, Shankar Ananthakrishnan

Spoken Language Understanding (SLU) systems typically consist of a set of machine learning models that operate in conjunction to produce an SLU hypothesis. The generated hypothesis is then sent to downstream components for further action. However, it is desirable to discard an incorrect hypothesis before sending it downstream. In this work, we present two de

Randy J. Chase, David R. Harrison, Gary Lackmann, Amy McGovern

Over the past decade the use of machine learning in meteorology has grown rapidly. Specifically neural networks and deep learning have been used at an unprecedented rate. In order to fill the dearth of resources covering neural networks with a meteorological lens, this paper discusses machine learning methods in a plain language format that is targeted for t

Matthieu Fortin-Deschênes, Kenji Watanabe, Takashi Taniguchi, Fengnian Xia

The unique physics found in moir\'e superlattices of twisted or lattice-mismatched atomic layers hold great promise for future quantum technologies. However, twisted configurations are typically thermodynamically unfavorable, making the accurate twist angle control in direct growth implausible. While rotationally aligned moir\'e superlattices based on lattic

Dariusz Buraczewski, Congzao Dong, Alexander Iksanov, Alexander Marynych

We prove a functional limit theorem in a space of analytic functions for the random Dirichlet series $D(\alpha;z)=\sum_{n\geq 2}(\log n)^{\alpha}(\eta_n+{\rm i} \theta_n)/n^z$, properly scaled and normalized, where $(\eta_n,\theta_n)_{n\in\mathbb{N}}$ is a sequence of independent copies of a centered $\mathbb{R}^2$-valued random vector $(\eta,\theta)$ with a

Adam B Kashlak

Symmetry is a cornerstone of much of mathematics, and many probability distributions possess symmetries characterized by their invariance to a collection of group actions. Thus, many mathematical and statistical methods rely on such symmetry holding and ostensibly fail if symmetry is broken. This work considers under what conditions a sequence of probability

J. H. Béjanin, C. T. Earnest, M. Mariantoni

Building large-scale superconducting quantum computers requires two complimentary elements: scalable wiring techniques and multiplex architectures. In our previous work [B\'ejanin et al., Phys. Rev. Applied 6, 044010 (2016)], we have introduced and characterized a truly vertical interconnect named the quantum socket. In this paper, we exercise the quantum so

Sebastian Gehrmann, Sebastian Ruder, Vitaly Nikolaev, Jan A. Botha

Existing data-to-text generation datasets are mostly limited to English. To address this lack of data, we create Table-to-Text in African languages (TaTa), the first large multilingual table-to-text dataset with a focus on African languages. We created TaTa by transcribing figures and accompanying text in bilingual reports by the Demographic and Health Surve

Yatir Halevi, Assaf Hasson, Ya'acov Peterzil

Let $K$ be a $p$-adically closed field and $G$ a group interpretable in $K$. We show that if $G$ is definably semisimple (i.e. $G$ has no definable infinite normal abelian subgroups) then there exists a finite normal subgroup $H$ such that $G/H$ is definably isomorphic to a $K$-linear group. The result remains true in models of $\mathrm{Th}(\mathbb{Q}_p^{an}

Slavomír Hanzely, Dmitry Kamzolov, Dmitry Pasechnyuk, Alexander Gasnikov

In this paper, we present the first stepsize schedule for Newton method resulting in fast global and local convergence guarantees. In particular, a) we prove an $O\left( \frac 1 {k^2} \right)$ global rate, which matches the state-of-the-art global rate of cubically regularized Newton method of Polyak and Nesterov (2006) and of regularized Newton method of Mi

Yuki Kobayashi, Christian Heide, Amalya C. Johnson, Fang Liu

Interactions of quantum materials with strong-laser fields can induce exotic nonequilibrium electronic states. Monolayer transition-metal dichalcogenides, a new class of direct-gap semiconductors with prominent quantum confinement, offer exceptional opportunities toward Floquet engineering of quasiparticle electron-hole states, or excitons. Strong-field driv

Georgios Efstathiadis

Inferring how an epidemic will progress and what actions to take when presented with limited information is of critical importance for epidemiologists and health professionals. In real world settings, epidemiology data can be scarce or subject to reporting errors. In this project there are different epidemic scenarios simulated and, using hidden Markov Chain

Fan Liu, Xihang Jiang, Zian Jia, Lifeng Wang

The rapidly developing robotics industry demands structures with novel mechanical functions. Traditional approaches, developing new materials and designing new structures, face two challenges. Highly complex force-displacement functions can hardly be realized and one fabricated structure only has one or limited function. We perform theoretical calculations a

Vahid Nikkhah, Mario Junior Mencagli, Nader Engheta

In recent years, wave-based analog computing has been at the center of attention for providing ultra-fast and power-efficient signal processing enabled by wave propagation through artificially engineered structures. Building on these structures, various proposals have been put forward for performing computations with waves. Most of these proposals have been

Ulrich Ratzinger, Huifang Wang

A geometric model based on a spherical Fermi - surface and using the equivalent skin-layer model allows to calculate the surface resistance, which is relevant for the RF power losses in the cavity walls. An exact solution for this conduction electron model in skin layers was derived. It is compared with measurements and with predictions from the traditional

Christian M. Jensen, Mathias C. Frederiksen, Carsten S. Kallesøe, Jeppe N. Jensen

A computationally efficient Model-Predictive Control (MPC) approach is proposed for systems with unknown delay using only input/output data. We use the Koopman operator framework and the related Hankel Alternative View of Koopman (HAVOK) algorithm to identify a model in a basis of projected time-delay coordinates and demonstrate a novel MPC structure that re

Phillip C. Lotshaw, Kevin D. Battles, Bryan Gard, Gilles Buchs

Many-qubit M{\o}lmer-S{\o}rensen (MS) interactions applied to trapped ions offer unique capabilities for quantum information processing, with applications including quantum simulation and the quantum approximate optimization algorithm (QAOA). Here, we develop a physical model to describe many-qubit MS interactions under four sources of experimental noise: vi

Gregorio Diaz

In this paper we study the so-called large solutions of elliptic semilinear equations with non null sources term, thus solutions blowing up on the boundary of the domain for which reason they are greater than any other solution whenever Weak Maximum Principle holds. The main topic about large solutions is uniqueness results and their behavior near the bounda

Kazuo Sano

In the prospect theory, value function is typically concave for gains, commonly convex for losses, with losses usually having a steeper slope than gains. The neural system largely differs from the loss and gains sides. Five new studies on neurons related to this issue have examined neuronal responses to losses, gains, and reference points. This study investi

Diego Marcondes, Adilson Simonis, Junior Barrera

Science consists on conceiving hypotheses, confronting them with empirical evidence, and keeping only hypotheses which have not yet been falsified. Under deductive reasoning they are conceived in view of a theory and confronted with empirical evidence in an attempt to falsify it, and under inductive reasoning they are conceived based on observation, confront

Arnab Sarkar, Scott Randall, Yuanyuan Su, Gabriella E. Alvarez

We present deep Chandra observations of the pre-merger galaxy cluster Abell 98. Abell 98 is a complex merging system. While the northern (A98N) and central subclusters (A98S) are merging along the north-south direction, A98S is undergoing a separate late-stage merger, with two distinct X-ray cores. We report detection of gas sloshing spirals in A98N and in t

Anoop Krishnan, Brian Neas, Ajita Rattani

Published academic research and media articles suggest face recognition is biased across demographics. Specifically, unequal performance is obtained for women, dark-skinned people, and older adults. However, these published studies have examined the bias of facial recognition in the visible spectrum (VIS). Factors such as facial makeup, facial hair, skin col

Jianqing Fan, Yingying Fan, Jinchi Lv, Fan Yang

Large-scale network inference with uncertainty quantification has important applications in natural, social, and medical sciences. The recent work of Fan, Fan, Han and Lv (2022) introduced a general framework of statistical inference on membership profiles in large networks (SIMPLE) for testing the sharp null hypothesis that a pair of given nodes share the s

Sam Coates, Toranosuke Matsubara, Akihisa Koga

We present a multi-edge-length aperiodic tiling which exhibits 6--fold rotational symmetry. The edge lengths of the tiling are proportional to 1:$\tau$, where $\tau$ is the golden mean $\frac{1+\sqrt{5}}{2}$. We show how the tiling can be generated using simple substitution rules for its three constituent tiles, which we then use to demonstrate the bipartite

P. Nazari, B. Tabone, G. P. Rosotti

Complex organic molecules are only detected toward a fraction of high-mass protostars. The goal of this work is to investigate whether high-mass disks can explain the lack of methanol emission from some massive protostellar systems. We consider an envelope-only and an envelope-plus-disk model and use RADMC-3D to calculate the methanol emission. High and low

Matilde Lalín, Siva Sankar Nair

We exhibit a change of variables that maintains the Mahler measure of a given polynomial. This method leads to the construction of highly non-trivial polynomials with given Mahler measure and settles some conjectural numerical formulas due to Boyd and Brunault.

R. Chapai, M. P. Smylie, H. Hebbeker, D. Y. Chung

We present a study of the superconducting properties of the candidate topological superconductor Ti_(3)Sb. Electrical transport measurements show zero resistance with a T_(c,onset) of ~ 5.9 K with a transition width {\Delta}T_c~ 0.6 K. The superconducting phase boundaries as derived from magneto-transport and magnetic susceptibility measurements agree well.

Ronan Kerr, Adam L. Kraus, Simon J. Murphy, Daniel M. Krolikowski

The study of young associations is essential for building a complete record of local star formation processes. The Fornax-Horologium association (FH), including the $\chi^1$ Fornacis cluster, represents one of the nearest young stellar populations to the Sun. This association has recently been linked to the Tuc-Hor, Carina, and Columba associations, building

Caitlin Ward, Rob Deardon, Alexandra M. Schmidt

For many infectious disease outbreaks, the at-risk population changes their behavior in response to the outbreak severity, causing the transmission dynamics to change in real-time. Behavioral change is often ignored in epidemic modeling efforts, making these models less useful than they could be. We address this by introducing a novel class of data-driven ep

Jacob R. Lamb, Edward N. Ward, Clemens F. Kaminski

Oblique plane microscopy, OPM, is a form of lightsheet microscopy that permits volumetric imaging of biological samples at high temporal and spatial resolution. However, the imaging geometry of OPM, and related variants of light sheet microscopy, distorts the coordinate frame of the presented image sections with respect to real space coordinate frame in whic

Philipp Schmoll, Christian Balz, Bella Lake, Jens Eisert

Aimed at a more realistic classical description of natural quantum systems, we present a two-dimensional tensor network algorithm to study finite temperature properties of frustrated model quantum systems and real quantum materials. For this purpose, we introduce the infinite projected entangled simplex operator ansatz to study thermodynamic properties. To o

Darlington Akogo, Benjamin Dabo Sarkodie, Issah Abubakari Samori, Bashiru Babatunde Jimah

A rapid and accurate diagnosis of cardiomegaly and pleural effusion is of the utmost importance to reduce mortality and medical costs. Artificial Intelligence has shown promise in diagnosing medical conditions. With this study, we seek to evaluate how well Artificial Intelligence (AI) systems, developed my minoHealth AI Labs, will perform at diagnosing cardi

Muhammad Sami Irfan, Mizanur Rahman, Travis Atkison, Sagar Dasgupta

In a connected transportation system, adaptive traffic signal controllers (ATSC) utilize real-time vehicle trajectory data received from vehicles through wireless connectivity (i.e., connected vehicles) to regulate green time. However, this wirelessly connected ATSC increases cyber-attack surfaces and increases their vulnerability to various cyber-attack mod

Ingo Wald

We present an algorithm that allows for building left-balanced and complete k-d trees over k-dimensional points in a trivially parallel and GPU friendly way. Our algorithm requires exactly one int per data point as temporary storage, and uses O(log N) iterations, each of which performs one parallel sort, and one trivially parallel CUDA per-node update kernel

Daniel Webber, Yujie Zhang, Michel Picard, Jonathan Boisvert

Tomographic volumetric additive manufacturing (VAM) is an optical 3D printing technique where an object is formed by photopolymerizing resin via tomographic projections. Currently, these projections are calculated using the Radon transform from computed tomography but it ignores two fundamental properties of real optical projection systems: finite etendue an

Harlin Lee, Aaqib Saeed, Andrea L. Bertozzi

Pretraining neural networks with massive unlabeled datasets has become popular as it equips the deep models with a better prior to solve downstream tasks. However, this approach generally assumes that the downstream tasks have access to annotated data of sufficient size. In this work, we propose ALOE, a novel system for improving the data- and label-efficien

Kohei Kishida

The Fourth International Conference on Applied Category Theory took place at the Computer Laboratory of the University of Cambridge on 12--16 July 2021. It was a hybrid event, with physical attendees present in Cambridge and other participants taking part online. All the talks were recorded and the videos have been posted online, links to which can be found

Nicholas P. Ballering, Colette I. Levens, Kate Y. L. Su, L. Ilsedore Cleeves

Many white dwarfs host disks of dust produced by disintegrating planetesimals and revealed by infrared excesses. The disk around G29-38 was the first to be discovered and is now well-observed, yet we lack a cohesive picture of its geometry and dust properties. Here we model the G29-38 disk for the first time using radiative transfer calculations that account

Roman Shvydkoy

Many classical examples of models of self-organized dynamics, including the Cucker-Smale, Motsch-Tadmor, multi-species, and several others, include an alignment force that is based upon density-weighted averaging protocol. Those protocols can be viewed as special cases of `environmental averaging'. In this paper we formalize this concept and introduce a unif

Tom Benhamou, Shimon Garti, Moti Gitik, Alejandro Poveda

We address the question of the consistency strength of certain filters and ultrafilters which fail to satisfy the Galvin property. We answer questions \cite[Questions 7.8,7.9]{TomMotiII}, \cite[Question 5]{NegGalSing} and improve theorem \cite[Theorem 2.3]{NegGalSing}.

Zihan Wang, Qi Meng, HaiFeng Lan, XinRui Zhang

Speech emotion recognition (SER) classifies audio into emotion categories such as Happy, Angry, Fear, Disgust and Neutral. While Speech Emotion Recognition (SER) is a common application for popular languages, it continues to be a problem for low-resourced languages, i.e., languages with no pretrained speech-to-text recognition models. This paper firstly prop

Xinjian Li, Ye Jia, Chung-Cheng Chiu

Research on speech-to-speech translation (S2ST) has progressed rapidly in recent years. Many end-to-end systems have been proposed and show advantages over conventional cascade systems, which are often composed of recognition, translation and synthesis sub-systems. However, most of the end-to-end systems still rely on intermediate textual supervision during

Jungang Zou, Sijian Wang, Qixuan Chen

Multiple imputation is widely used for handling missing data in real-world applications. For variable selection on multiply-imputed datasets, however, if selection is performed on each imputed dataset separately, it can result in different sets of selected variables across datasets. MI-LASSO, one of the most commonly used approaches to this problem, regards

Avery Ma, Nikita Dvornik, Ran Zhang, Leila Pishdad

Data augmentation is a key element for training accurate models by reducing overfitting and improving generalization. For image classification, the most popular data augmentation techniques range from simple photometric and geometrical transformations, to more complex methods that use visual saliency to craft new training examples. As augmentation methods ge

Abheek Ghosh, Dheeraj Nagaraj, Manish Jain, Milind Tambe

We study the problem of planning restless multi-armed bandits (RMABs) with multiple actions. This is a popular model for multi-agent systems with applications like multi-channel communication, monitoring and machine maintenance tasks, and healthcare. Whittle index policies, which are based on Lagrangian relaxations, are widely used in these settings due to t

Théo Morales, Gerard Lacey

Understanding hand-object pose with computer vision opens the door to new applications in mixed reality, assisted living or human-robot interaction. Most methods are trained and evaluated on balanced datasets. This is of limited use in real-world applications; how do these methods perform in the wild on unknown objects? We propose a novel benchmark for objec

Zexu Pan, Wupeng Wang, Marvin Borsdorf, Haizhou Li

The speaker extraction technique seeks to single out the voice of a target speaker from the interfering voices in a speech mixture. Typically an auxiliary reference of the target speaker is used to form voluntary attention. Either a pre-recorded utterance or a synchronized lip movement in a video clip can serve as the auxiliary reference. The use of visual c

Matthias Geyer, Jan Hausmann, Konrad Kitzing, Madlyn Senkyr

Using Maxwell's mental imagery of a tube of fluid motion of an imaginary fluid, we derive his equations $\operatorname{curl} \mathbf{E} = -\frac{\partial \mathbf{B}}{\partial t}$, $\operatorname{curl} \mathbf{H} = \frac{\partial \mathbf{D}}{\partial t} + \mathbf{j}$, $\operatorname{div} \mathbf{D} = \varrho$, $\operatorname{div} \mathbf{B} = 0$, which togeth