May 2022 arXiv papers — page 53

Utkarsh R. Patel, Yiqian Mao, Jack Hamel, Eric Michielssen

The Wigner-Smith (WS) time delay matrix relates an acoustic system's scattering matrix to its wavenumber derivative. The entries of the WS time delay matrix can be expressed in terms of energy density-like volume integrals, which cannot be efficiently evaluated in a boundary element method framework. This paper presents two schemes for efficiently populating

Qing-Yang Qiu, Ying Wu, Xin-You Lü

We investigate the non-Markovian dynamics of two giant artificial atoms interacting with a continuum of bosonic modes in a one-dimensional (1D) waveguide. Based on the diagrammatic method, we present the exact analytical solutions, which predict the rich phenomena of collective radiance. For the certain collective states, the decay rates are found to be far

Salvador Balkus, Donghui Yan

GPT-3 is a large-scale natural language model developed by OpenAI that can perform many different tasks, including topic classification. Although researchers claim that it requires only a small number of in-context examples to learn a task, in practice GPT-3 requires these training examples to be either of exceptional quality or a higher quantity than easily

Qingying Wang, Ruoxi Wang, Jiacheng Liu, Fan Jiang

Radiation therapy treatment planning can be viewed as an iterative hyperparameter tuning process to balance conflicting clinical goals. In this work, we investigated the performance of modern Bayesian Optimization (BO) methods on automated treatment planning problems in high-dimensional settings. 20 locally advanced rectal cancer patients treated with intens

Du hyung Kim, Minsik Kong, Myeongjun Kang, Minjae Kim

Delafossite oxide CuAlO2 has engaged great attention as a promising p-type conducting oxide. In this work, high-quality CuAlO2 single crystals with a size of several millimeters (mm) are successfully achieved with a reactive crucible melting method. The crystals are characterized by X-ray diffraction, scanning electron microscopy with energy-dispersive spect

Fulvio Melia

Spontaneously broken symmetries in particle physics may have produced several phase transitions in cosmology, e.g., at the GUT energy scale (~10^15 GeV), resulting in a quasi-de Sitter inflationary expansion, solving the background temperature horizon problem. This transition would have occurred at t~10^-36 to 10^-33 seconds, leading to a separation of the s

Thomas Tuloup, Raditya Weda Bomantara, Jiangbin Gong

The dynamics of solitons driven in a nonlinear Thouless pump and its connection with the system's topology were recently explored for both weak and strong nonlinear strength. This work uncovers the fate of nonlinear Thouless pumping in the regime of intermediate nonlinearity, thus establishing a fascinating crossover from the observation of nonzero and quant

Yubin Ge, Ziang Xiao, Jana Diesner, Heng Ji

Generating follow-up questions on the fly could significantly improve conversational survey quality and user experiences by enabling a more dynamic and personalized survey structure. In this paper, we proposed a novel task for knowledge-driven follow-up question generation in conversational surveys. We constructed a new human-annotated dataset of human-writt

Hugues Beauchesne, Cheng-Wei Chiang

Many attempts have been made to observe the decay of the Higgs boson to a photon and an invisible massless dark photon. For this decay to be potentially observable at the LHC, new mediators that communicate between the Standard Model and the dark photon must exist. In this Letter, we study bounds on such mediators coming from the Higgs signal strengths, obli

Vladimir I. Kruglov, Houria Triki

We study the dynamics of femtosecond light pulse propagation in a cubic-quintic medium exhibiting dispersive effect up to the fourth order as well as self-frequency shift and self-steepening nonlinearity. A rich variety of periodic and solitary wave solutions are derived for the governing generalized higher-order nonlinear Schr\"{o}dinger equation in the pre

Byeongyeob Choi, Jehyun Baek, Donghyun You

A second-order total variation diminishing (TVD) method with variable flux limiters is proposed to overcome the non-realizability issue, which has been one of major obstacles in applying the conventional second-order TVD schemes to the moment transport equations. In the present method, a realizable moment set at a cell face is reconstructed by allowing the f

Xiaojia Wang, Shrihari D. Pande, Ivan C. Christov

We investigate the steady-state fluid--structure interaction between a Newtonian fluid flow and a deformable microtube in two novel geometric configurations arising in recent microfluidics experiments. The first configuration is a cylindrical fluidic channel surrounded by an annulus of soft material with a rigid outer wall, while the second one is a cylindri

Ignacio Lopez-Gomez, Amy McGovern, Shreya Agrawal, Jason Hickey

Heat waves are projected to increase in frequency and severity with global warming. Improved warning systems would help reduce the associated loss of lives, wildfires, power disruptions, and reduction in crop yields. In this work, we explore the potential for deep learning systems trained on historical data to forecast extreme heat on short, medium and subse

Xiangong Tang, Can Wang, Weihua Deng

In this work, based on the complete Bernstein function, we propose a generalized regularity analysis including maximal $\mathrm{L}^p$ regularity for the Fokker--Planck equation, which governs the subordinated Brownian motion with the inverse tempered stable subordinator that has a drift. We derive a generalized time--stepping finite element scheme based on t

Bowen Xing, Ivor W. Tsang

Recent years have witnessed the emerging success of leveraging syntax graphs for the target sentiment classification task. However, we discover that existing syntax-based models suffer from two issues: noisy information aggregation and loss of distant correlations. In this paper, we propose a novel model termed Neural Subgraph Explorer, which (1) reduces the

Nikolai Krivulin

We consider a decision-making problem to find absolute ratings of alternatives that are compared in pairs under multiple criteria, subject to constraints in the form of two-sided bounds on ratios between the ratings. Given matrices of pairwise comparisons made according to the criteria, the problem is formulated as the log-Chebyshev approximation of these ma

Oliver Knill

We prove that each eigenvalue l(k) of the Kirchhoff Laplacian K of a graph or quiver is bounded above by d(k)+d(k-1) for all k in {1,...,n}. Here l(1),...,l(n) is a non-decreasing list of the eigenvalues of K and d(1),..,d(n) is a non-decreasing list of vertex degrees with the additional assumption d(0)=0. We also prove that in general the weak Brouwer-Haeme

Yusuke Mori, Hiroaki Yamane, Yusuke Mukuta, Tatsuya Harada

Storytelling has always been vital for human nature. From ancient times, humans have used stories for several objectives including entertainment, advertisement, and education. Various analyses have been conducted by researchers and creators to determine the way of producing good stories. The deep relationship between stories and emotions is a prime example.

George Grätzer

Let $L$ be a slim, planar, semimodular lattice (slim means that it does not contain an ${\mathsf M}_3$-sublattice). We call the interval $I = [o, i]$ of $L$ \emph{rectangular}, if there are complementary $a, b \in I$ such that $a$ is to the left of $b$. We claim that a rectangular interval of a slim rectangular lattice is also a slim rectangular lattice. We

Katherine Owens, J. Nathan Kutz

We leverage data-driven model discovery methods to determine the governing equations for the emergent behavior of heterogeneous networked dynamical systems. Specifically, we consider networks of coupled nonlinear oscillators whose collective behaviour approaches a limit cycle. Stable limit-cycles are of interest in many biological applications as they model

Tyler A. Chang, Zhuowen Tu, Benjamin K. Bergen

We assess how multilingual language models maintain a shared multilingual representation space while still encoding language-sensitive information in each language. Using XLM-R as a case study, we show that languages occupy similar linear subspaces after mean-centering, evaluated based on causal effects on language modeling performance and direct comparisons

Liwei Guo, Kaiyang Zhao, Yiying Zhang, Felix Xiaozhu Lin

A TrustZone TEE often invokes an external filesystem. While filedata can be encrypted, the revealed file activities can leak secrets. To hide the file activities from the filesystem and its OS, we propose Enigma, a deception-based defense injecting sybil file activities as the cover of the actual file activities. Enigma contributes three new designs. (1) To

Hasan Al Shaikh, Mohammad Bin Monjil, Shigang Chen, Navid Asadizanjani

The expansive globalization of the semiconductor supply chain has introduced numerous untrusted entities into different stages of a device's lifecycle. To make matters worse, the increase complexity in the design as well as aggressive time to market requirements of the newer generation of integrated circuits can lead either designers to unintentionally intro

Eric Wagner, Roman Matzutt, Jan Pennekamp, Lennart Bader

Blockchain technology promises to overcome trust and privacy concerns inherent to centralized information sharing. However, current decentralized supply chain management systems do either not meet privacy and scalability requirements or require a trustworthy consortium, which is challenging for increasingly dynamic supply chains with constantly changing part

Mark E. Ziffer, Lucas Huber, Feifan Wang, Victoria A. Posey

Many crystalline solids possess strongly anharmonic soft phonon modes characterized by diminishing frequency as temperature approaches a critical point associated with a symmetry breaking phase transition. While electron-soft phonon coupling can introduce unique scattering channels for charge carriers in ferroelectrics, recent studies on the non-ferroelectri

Ran Finkelstein, Samir Bali, Ofer Firstenberg, Irina Novikova

This tutorial introduces the theoretical and experimental basics of Electromagnetically Induced Transparency (EIT) in thermal alkali vapors. We first introduce a brief phenomenological description of EIT in simple three-level systems of stationary atoms and derive analytical expressions for optical transmission and dispersion under EIT conditions. Then we fo

Christopher D. Parkinson, Stephen W. Bougher, Franklin P. Mills, Amanda Brecht

The chemical evolution of an exoplanetary Venus-like atmosphere is dependent upon the ultraviolet to near ultraviolet (FUV-NUV) radiation ratio from the parent star, the balance between CO$_{2}$ photolysis and recombination via reactions that depend on the water abundance, and various catalytic chemical cycles. In this study, we use a three-dimensional (3-D)

Morteza Banagar, Harpreet S. Dhillon

In this paper, we develop an impairments-aware air-to-ground unified channel model that incorporates the effect of both wobbling and hardware impairments, where the former is caused by random physical fluctuations of unmanned aerial vehicles (UAVs), and the latter by intrinsic radio frequency (RF) nonidealities at both the transmitter and receiver, such as p

Wei Yuan, Quanjun Zhang, Tieke He, Chunrong Fang

Automatic Program Repair (APR) aims at fixing buggy source code with less manual debugging efforts, which plays a vital role in improving software reliability and development productivity. Recent APR works have achieved remarkable progress via applying deep learning (DL), particularly neural machine translation (NMT) techniques. However, we observe that exis

Michael A. Beck, Christopher P. Bidinosti, Christopher J. Henry, Manisha Ajmani

In the context of supervised machine learning a learning curve describes how a model's performance on unseen data relates to the amount of samples used to train the model. In this paper we present a dataset of plant images with representatives of crops and weeds common to the Manitoba prairies at different growth stages. We determine the learning curve for a

Linh Nguyen, Akshay Iyer, Shweta Khushu

Wind energy's ability to compete with fossil fuels on a market level depends on lowering wind's high operational costs. Since damages on wind turbine blades are the leading cause for these operational problems, identifying blade damages is critical. However, recent works in visual identification of blade damages are still experimental and focus on optimizing

Nader Zare, Arad Firouzkouhi, Omid Amini, Mahtab Sarvmaili

Soccer Simulation 2D League is one of the major leagues of RoboCup competitions. In a Soccer Simulation 2D (SS2D) game, two teams of 11 players and one coach compete against each other. The players are only allowed to communicate with the server that is called Soccer Simulation Server. This paper introduces the previous and current research of the CYRUS socc

Chuanzheng Wang, Yiming Meng, Stephen L. Smith, Jun Liu

Control barrier functions are widely used to synthesize safety-critical controls. The existence of Gaussian-type noise may lead to unsafe actions and result in severe consequences. While studies are widely done in safety-critical control for stochastic systems, in many real-world applications, we do not have the knowledge of the stochastic component of the d

Jung Hoon Lee, Sujith Vijayan

Deep neural networks (DNNs), the agents of deep learning (DL), require a massive number of parallel/sequential operations, which makes it difficult to comprehend them and impedes proper diagnosis. Without better knowledge of DNNs' internal process, deploying DNNs in high-stakes domains may lead to catastrophic failures. Therefore, to build more reliable DNNs

Shuyu Kong, You Li, Hai Zhou

Federated Learning is an emerging distributed collaborative learning paradigm used by many of applications nowadays. The effectiveness of federated learning relies on clients' collective efforts and their willingness to contribute local data. However, due to privacy concerns and the costs of data collection and model training, clients may not always contribu

Stephen C. Anco, Sara Seifi, Amanullah Dar

Conserved integrals and invariants (advected scalars) are studied for the equations of radial compressible fluid/gas flow in $n>1$ dimensions. Apart from entropy, which is a well-know invariant, three additional invariants are found from an explicit determination of invariants up to first-order. One holds for a general equation of state, and the two others h

Kanav Vats, Mehrnaz Fani, David A. Clausi, John S. Zelek

Tracking and identifying players is an important problem in computer vision based ice hockey analytics. Player tracking is a challenging problem since the motion of players in hockey is fast-paced and non-linear. There is also significant player-player and player-board occlusion, camera panning and zooming in hockey broadcast video. Prior published research

M. Lovisetto, M. C. Braidotti, R. Prizia, C. Michel

Large scale structures in the Universe, ranging from globular clusters to entire galaxies, are the manifestation of relaxation to out-of-equilibrium states that are not described by standard statistical mechanics at equilibrium. Instead, they are formed through a process of a very different nature, i.e. violent relaxation. However, astrophysical time-scales

Mohammad R. Rezaei, Milos R. Popovic, Milad Lankarany, Ali Yousefi

The state-space models (SSMs) are widely utilized in the analysis of time-series data. SSMs rely on an explicit definition of the state and observation processes. Characterizing these processes is not always easy and becomes a modeling challenge when the dimension of observed data grows or the observed data distribution deviates from the normal distribution.

Joshua Ritchie

Pattern formation, arising from systems of autonomous reaction-diffusion equations, on networks has become a common topic of study in the scientific literature. In this work we focus primarily on directed networks. Although some work prior has been done to understand how patterns arise on directed networks, these works have restricted their attentions to net

Sergio González-Andrade, María de los Ángeles Silva

In this paper we are concerned with a class of optimization problems involving the $p(x)$-Laplacian operator, which arise in imaging and signal analysis. We study the well-posedness of this kind of problems in an amalgam space considering that the variable exponent $p(x)$ is a log-H\"older continuous function. Further, we propose a preconditioned descent alg

Houria En-Naciri, Lahoussine Lafhim, Alain Zemkoho

In this paper, we exploit the so-called value function reformulation of the bilevel optimization problem to develop duality results for the problem. Our approach builds on Fenchel-Lagrange-type duality to establish suitable results for the bilevel optimization problem. First, we overview some standard duality results to show that they are not applicable to o

Mary Lai O. Salvaña, Amanda Lenzi, Marc G. Genton

When analyzing the spatio-temporal dependence in most environmental and earth sciences variables such as pollutant concentrations at different levels of the atmosphere, a special property is observed: the covariances and cross-covariances are stronger in certain directions. This property is attributed to the presence of natural forces, such as wind, which ca

Nick Arnosti, Carlos Bonet

We study a setting where tickets for an experience are allocated by lottery. Each agent belongs to a group, and a group is successful if and only if its members receive enough tickets for everyone. A lottery is efficient if it maximizes the number of agents in successful groups, and fair if it gives every group the same chance of success. We study the effici

Alain Zemkoho

The aim of this paper is to present a set of Python-based tools to develop forecasts using time series data sets. The material is based on a four week course that the author has taught for seven years to students on operations research, management science, analytics, and statistics one-year MSc programmes. However, it can easily be adapted to various other a

Sarah Aguasvivas Manzano, Vani Sundaram, Artemis Xu, Khoi Ly

We present formulation and open-source tools to achieve in-material model predictive control of sensor/actuator systems using learned forward kinematics and on-device computation. Microcontroller units (MCUs) that compute the prediction and control task while colocated with the sensors and actuators enable in-material untethered behaviors. In this approach,

B. Abareshi, J. Aguilar, S. Ahlen, Shadab Alam

The Dark Energy Spectroscopic Instrument (DESI) has embarked on an ambitious five-year survey to explore the nature of dark energy with spectroscopy of 40 million galaxies and quasars. DESI will determine precise redshifts and employ the Baryon Acoustic Oscillation method to measure distances from the nearby universe to z > 3.5, as well as measure the growth

Han Huang, Konstantin Tikhomirov

The Gaussian Elimination with Partial Pivoting (GEPP) is a classical algorithm for solving systems of linear equations. Although in specific cases the loss of precision in GEPP due to roundoff errors can be very significant, empirical evidence strongly suggests that for a {\it typical} square coefficient matrix, GEPP is numerically stable. We obtain a (parti

Erika Ye, Nuno F. G. Loureiro

Kinetic simulations of collisionless (or weakly collisional) plasmas using the Vlasov equation are often infeasible due to high resolution requirements and the exponential scaling of computational cost with respect to dimension. Recently, it has been proposed that matrix product state (MPS) methods, a quantum-inspired but classical algorithm, can be used to

Yotam Perlitz, Liat Ein-Dor, Dafna Sheinwald, Noam Slonim

Generating natural language statements to convey logical inferences from tabular data (i.e., Logical NLG) is a process with one input and a variety of valid outputs. This characteristic underscores the need for a method to produce a diverse set of valid outputs, presenting different perspectives of the input data. We propose a simple yet effective diversity-

Andrea Gesmundo, Jeff Dean

Most uses of machine learning today involve training a model from scratch for a particular task, or sometimes starting with a model pretrained on a related task and then fine-tuning on a downstream task. Both approaches offer limited knowledge transfer between different tasks, time-consuming human-driven customization to individual tasks and high computation

Andrea Tirinzoni, Rémy Degenne

Elimination algorithms for bandit identification, which prune the plausible correct answers sequentially until only one remains, are computationally convenient since they reduce the problem size over time. However, existing elimination strategies are often not fully adaptive (they update their sampling rule infrequently) and are not easy to extend to combina

Daniel Kapec, Y. T. Albert Law, Sruthi A. Narayanan

Known examples of the holographic dictionary in asymptotically Anti-de Sitter spacetimes equate moduli spaces of bulk vacua with conformal manifolds in the dual quantum field theory. We demonstrate that the same identification holds for gravity in asymptotically flat spacetimes in any dimension, in accord with expectations derived from the celestial conforma

Yongqiang Wang, Angelia Nedic

Decentralized optimization enables a network of agents to cooperatively optimize an overall objective function without a central coordinator and is gaining increased attention in domains as diverse as control, sensor networks, data mining, and robotics. However, the information sharing among agents in decentralized optimization also discloses agents' informa

Michael Villarreal, Bibek Poudel, Ryan Wickman, Yu Shen

With the growing use of machine learning algorithms and ubiquitous sensors, many `perception-to-control' systems are being developed and deployed. To ensure their trustworthiness, improving their robustness through adversarial training is one potential approach. We propose a gradient-free adversarial training technique, named AutoJoin, to effectively and eff

Piyawat Lertvittayakumjorn, Francesca Toni

Recent works in Explainable AI mostly address the transparency issue of black-box models or create explanations for any kind of models (i.e., they are model-agnostic), while leaving explanations of interpretable models largely underexplored. In this paper, we fill this gap by focusing on explanations for a specific interpretable model, namely pattern-based l

Francesco Zagaria, Giovanni P. Rosotti, Cathie J. Clarke, Benoît Tabone

For many years proto-planetary discs have been thought to evolve viscously: angular momentum redistribution leads to accretion and outward disc spreading. Recently, the hypothesis that accretion is due, instead, to angular momentum removal by magnetic winds gained new popularity: no disc spreading is expected in this case. In this paper, we run several one-d

Joshua Ritchie

In this work we investigate the asymptotic behaviour of solutions to the Einstein equations with a minimally coupled scalar field. The primary focus of the present paper here establishing under what conditions a solution becomes "asymptotically Kasner" sufficiently close to the initial singularity. To address this question we restrict our attention to Bianch

Alain Tchana, Raphael Colin, Adrien Le Berre, Vincent Berger

The General Data Protection Regulation (GDPR) forces IT companies to comply with a number of principles when dealing with European citizens' personal data. Non-compliant companies are exposed to penalties which may represent up to 4% of their turnover. Currently, it is very hard for companies driven by personal data to make their applications GDPR-compliant,

Emília Alves, Victor Goulart, Nicolau C. Saldanha

Locally convex (or nondegenerate) curves in the sphere $S^n$ have been studied for several reasons, including the study of linear ordinary differential equations of order $n+1$. Taking Frenet frames allows us to obtain corresponding curves $\Gamma$ in the group $Spin_{n+1}$. Let $L_n(z_0;z_1)$ be the space of such curves $\Gamma$ with prescribed endpoints $\

Ping Li, Weijie Zhao

The work in ICML'09 showed that the derivatives of the classical multi-class logistic regression loss function could be re-written in terms of a pre-chosen "base class" and applied the new derivatives in the popular boosting framework. In order to make use of the new derivatives, one must have a strategy to identify/choose the base class at each boosting ite

Emin Ucer, Mithat Kisacikoglu, Murat Yuksel, Ali C. Gurbuz

Electric vehicles (EVs) are finally making their way onto the roads. However, the challenges concerning their long charging times and their impact on congestion of the power distribution grid are still waiting to be resolved. With historical measurement data, EV chargers can take better-informed actions while staying mostly off-line. Proposed solutions that

Wan-Jin Yeo, Samu Taulu, J. Nathan Kutz

Magnetoencephalography (MEG) is a noninvasive method for measuring magnetic flux signals caused by brain activity using sensor arrays located on or above the scalp. A common strategy for monitoring brain activity is to place sensors on a nearly uniform grid, or sensor array, around the head. By increasing the total number of sensors, higher spatial-frequency

Etienne Granet

We construct a family of hermitian potentials in 1D quantum mechanics that converges in the zero-range limit to a $\delta$ interaction with an energy-dependent coupling. It falls out of the standard four-parameter family of pointlike interactions in 1D. Such classification was made by requiring the pointlike interaction to be hermitian. But we show that alth

Lyuben Lichev, Bas Lodewijks, Dieter Mitsche, Bruno Schapira

The percolated random geometric graph $G_n(\lambda, p)$ has vertex set given by a Poisson Point Process in the square $[0,\sqrt{n}]^2$, and every pair of vertices at distance at most 1 independently forms an edge with probability $p$. For a fixed $p$, Penrose proved that there is a critical intensity $\lambda_c = \lambda_c(p)$ for the existence of a giant co

George Grätzer

In a 2121 paper with G\'abor Cz\'edli, we introduced and verified the Three-pendant Three-crown Property, 3P3C, for congruence lattices of slim, planar, semimodular lattices. The proof is very long; in part, because it relies on Cz\'edli's 2021 paper on lamps. This paper verifies 3P3C using the Swing Lemma, an elementary and short approach.

Bin Guo, Samir D. Mathur

In several approaches to evading the information paradox, the semiclassical black hole is replaced by an Exotic Compact Object (ECO). It has been conjectured that gravitational waves emitted by the merger of ECOs can reflect off the ECOs and produce a detectable `echo'. We argue that while a part of the wave can indeed reflect off the surface of an ECO, this

Liangze Jiang, Tao Lin

Federated Learning (FL) is a machine learning paradigm where many clients collaboratively learn a shared global model with decentralized training data. Personalized FL additionally adapts the global model to different clients, achieving promising results on consistent local training and test distributions. However, for real-world personalized FL applications

Chao Feng, John J. Molina, Matthew S. Turner, Ryoichi Yamamoto

Few simulations exist for microswimmers near deformable interfaces. Here, we present numerical simulations of the hydrodynamic flows associated with a single microswimmer embedded in a binary fluid mixture. The two fluids demix, separated by a penetrable and deformable interface that we assume to be initially prepared in its planar ground-state. We find that

Andrzej Niedźwiecki, Michał Szanecki, Andrzej A. Zdziarski, Fu-Guo Xie

We have developed an improved model of X-ray emission from optically thin, two-temperature accretion flows, \texttt{kerrflow}, using an exact Monte Carlo treatment of global Comptonization as well as with a fully general relativistic description of both the radiative and hydrodynamic processes. It also includes pion-decay electrons, whose synchrotron emissio

Steve Awodey

We have another look at the construction by Hofmann and Streicher of a universe $(U,{\mathsf{E}l})$ for the interpretation of Martin-L\"of type theory in a presheaf category $\psh{\C}$. It turns out that $(U,{\mathsf{E}l})$ can be described as the \emph{categorical nerve} of the classifier $\dot{\Set}^{\mathsf{op}} \to \op{\Set}$ for discrete fibrations in $

Kaixiang Zhang, Kaian Chen, Zhaojian Li, Jun Chen

Data-driven predictive control of connected and automated vehicles (CAVs) has received increasing attention as it can achieve safe and optimal control without relying on explicit dynamical models. However, employing the data-driven strategy involves the collection and sharing of privacy-sensitive vehicle information, which is vulnerable to privacy leakage an

Chenyang Xu, Ziquan Zhuang

For any Kawamata log terminal (klt) singularity and any minimizer of its normalized volume function, we prove that the associated graded ring is always finitely generated, as conjectured by Chi Li. As a consequence, we complete the last step of establishing the Stable Degeneration Conjecture proposed by Chi Li and the first named author for an arbitrary klt

Nils M. Kriege

Random walk kernels have been introduced in seminal work on graph learning and were later largely superseded by kernels based on the Weisfeiler-Leman test for graph isomorphism. We give a unified view on both classes of graph kernels. We study walk-based node refinement methods and formally relate them to several widely-used techniques, including Morgan's al

Bishal Lamichhane, Navaraj Neupane

Technological advancements have led to significant improvements in healthcare for prevention, diagnosis, treatments, and care. While resourceful regions can capitalize on state-of-the-art healthcare technologies, there might be barriers and delays in technology-enabled healthcare availability for a low-resource region. Unique innovations guided by the constr

Diksha Gupta, Carlos D. Brody

Trial history biases in decision-making tasks are thought to reflect systematic updates of decision variables, therefore their precise nature informs conclusions about underlying heuristic strategies and learning processes. However, random drifts in decision variables can corrupt this inference by mimicking the signatures of systematic updates. Hence, identi

Melissa Chapman, Caleb Scoville, Marcus Lapeyrolerie, Carl Boettiger

Machine learning (ML) methods already permeate environmental decision-making, from processing high-dimensional data on earth systems to monitoring compliance with environmental regulations. Of the ML techniques available to address pressing environmental problems (e.g., climate change, biodiversity loss), Reinforcement Learning (RL) may both hold the greates

Deniz Kattwinkel, Axel Niemeyer, Justus Preusser, Alexander Winter

A principal must decide between two options. Which one she prefers depends on the private information of two agents. One agent always prefers the first option; the other always prefers the second. Transfers are infeasible. One application of this setting is the efficient division of a fixed budget between two competing departments. We first characterize all

Vahid Taghiloo

We study three dimensional topologically massive gravity (TMG) in presence of a generic codimension one null boundary. The existence of the boundary is accounted for by enlarging the Hilbert space of the theory by degrees of freedom which only reside at the boundary, the boundary degrees of freedom. The solution phase space of this theory in addition to bulk

Tomasz Bochacik

We study the stability of randomized Taylor schemes for ODEs. We consider three notions of probabilistic stability: asymptotic stability, mean-square stability, and stability in probability. We prove fundamental properties of the probabilistic stability regions and benchmark them against the absolute stability regions for deterministic Taylor schemes.

Sarit Agami

High-dimensional reduction methods are powerful tools for describing the main patterns in big data. One of these methods is the topological data analysis (TDA), which modeling the shape of the data in terms of topological properties. This method specifically translates the original data into two-dimensional system, which is graphically represented via the 'p

Pasquale Antonante, Heath Nilsen, Luca Carlone

This paper investigates runtime monitoring of perception systems. Perception is a critical component of high-integrity applications of robotics and autonomous systems, such as self-driving cars. In these applications, failure of perception systems may put human life at risk, and a broad adoption of these technologies requires the development of methodologies

Changrim Ahn, André LeClair

It is well understood that 2d conformal field theory (CFT) deformed by an irrelevant $T\bar{T}$ perturbation of dimension $4$ has universal properties. In particular, for the most interesting cases, the theory develops a singularity in the ultra-violet (UV), signifying a shortest possible distance, with a Hagedorn transition in applications to string theory.

Youjia Wu, Sebastian Baum, Katherine Freese, Luca Visinelli

Dark matter annihilation might power the first luminous stars in the Universe. These types of stars, known as dark stars, could form in $(10^6\mathrm{-}10^8)\,M_\odot$ protohalos at redshifts $z \sim 20$, and they could be much more luminous and larger in size than ordinary stars powered by nuclear fusion. We investigate the formation of dark stars in the se

Yi-Ming Wu, Shang-Shun Zhang, Artem Abanov, Andrey V. Chubukov

We analyze a possibility for odd-frequency pairing near a quantum critical point(QCP) in a metal. We consider a model with dynamical pairing interaction $V(\Omega_n)\sim 1/|\Omega_n|^\gamma$ (the $\gamma$-model). This interaction gives rise to a non-Fermi liquid in the normal state and is attractive for pairing. The two trends compete with each other. We sea

Marcelo Viridiano, Tiago Timponi Torrent, Oliver Czulo, Arthur Lorenzi Almeida

This paper argues in favor of the adoption of annotation practices for multimodal datasets that recognize and represent the inherently perspectivized nature of multimodal communication. To support our claim, we present a set of annotation experiments in which FrameNet annotation is applied to the Multi30k and the Flickr 30k Entities datasets. We assess the c

Sabrina Pasterski

In this note we show how the 1-loop exact correction to the subleading soft graviton theorem arising from IR divergences of scattering amplitudes matches onto the quadratic corrections to the soft charges computed from the BMS flux algebra. In the process, we examine how the BMS flux construction extends the celestial diamond framework to non-linear order an

Mirtha Lucas, Miguel Lerma, Jacob Furst, Daniela Raicu

Neural networks are becoming increasingly better at tasks that involve classifying and recognizing images. At the same time techniques intended to explain the network output have been proposed. One such technique is the Gradient-based Class Activation Map (Grad-CAM), which is able to locate features of an input image at various levels of a convolutional neur

Tobias Fritz

Given two finite-dimensional representations $\rho$ and $\sigma$ of $\mathsf{SU}(n)$, when is there $n \in \mathbb{N}$ such that $\rho^{\otimes n}$ is isomorphic to a subrepresentation of $\sigma^{\otimes n}$? When is there a third representation $\eta$ such that $\rho \otimes \eta$ is a subrepresentation of $\sigma \otimes \eta$? We call these the questions

Abhinav Singh, Alejandra Foggia, Pietro Incardona, Ivo F. Sbalzarini

We present a meshfree collocation scheme to discretize intrinsic surface differential operators over scalar fields on smooth curved surfaces with given normal vectors and a non-intersecting tubular neighborhood. The method is based on Discretization-Corrected Particle Strength Exchange (DC-PSE), which generalizes finite difference methods to meshfree point c

Liu Cao, Lyutianyang Zhang, Sian Jin, Sumit Roy

As most existing work investigate the PHY layer abstraction under an assumption of perfect channel estimation, it may become unreliable if there exists channel estimation error in a real communication system. This letter improves an efficient PHY layer method, EESM-log-SGN PHY layer abstraction, by considering the presence of channel estimation error. We dev

Zhenning Cai, Geshuo Wang, Siyao Yang

We present two diagrammatic Monte Carlo methods for quantum systems coupled with harmonic baths, whose dynamics are described by integro-differential equations. The first approach can be considered as a reformulation of Dyson series, and the second one, called "bold-thin-bold diagrammatic Monte Carlo", is based on resummation of the diagrams in the Dyson ser

Botao Hao, Tor Lattimore, Chao Qin

Information-directed sampling (IDS) has recently demonstrated its potential as a data-efficient reinforcement learning algorithm. However, it is still unclear what is the right form of information ratio to optimize when contextual information is available. We investigate the IDS design through two contextual bandit problems: contextual bandits with graph fee

Prerna Juneja, Tanushree Mitra

Increasing demands for fact-checking has led to a growing interest in developing systems and tools to automate the fact-checking process. However, such systems are limited in practice because their system design often does not take into account how fact-checking is done in the real world and ignores the insights and needs of various stakeholder groups core t

Albert Q. Jiang, Wenda Li, Szymon Tworkowski, Konrad Czechowski

In theorem proving, the task of selecting useful premises from a large library to unlock the proof of a given conjecture is crucially important. This presents a challenge for all theorem provers, especially the ones based on language models, due to their relative inability to reason over huge volumes of premises in text form. This paper introduces Thor, a fr

Mahan Mj, Balarka Sen

We define a notion of a symplectic structure on stratified spaces, and demonstrate that given a symplectic structure on a stratified space $X$ with integral cohomology class, $X$ can be symplectically embedded in some complex projective space equipped with the standard K\"ahler form. This extends a theorem, due to Gromov and Tischler for manifolds, to strati

G. Bezhanishvili, S. Melzer

We use Priestley duality to give a new proof of the Hofmann-Mislove Theorem.

Jukka Corander, Ulpu Remes, Ida Holopainen, Timo Koski

Likelihood-free inference for simulator-based statistical models has recently attracted a surge of interest, both in the machine learning and statistics communities. The primary focus of these research fields has been to approximate the posterior distribution of model parameters, either by various types of Monte Carlo sampling algorithms or deep neural netwo

Robert Guirado, Abbas Rahimi, Geethan Karunaratne, Eduard Alarcón

Hyperdimensional computing (HDC) is an emerging computing paradigm that represents, manipulates, and communicates data using very long random vectors (aka hypervectors). Among different hardware platforms capable of executing HDC algorithms, in-memory computing (IMC) systems have been recently proved to be one of the most energy-efficient options, due to hyp

Mahan Mj, Balarka Sen

We extend Gromov and Eliashberg-Mishachev's h-principle on manifolds to stratified spaces. This is done in both the sheaf-theoretic framework of Gromov and the smooth jets framework of Eliashberg-Mishachev. The generalization involves developing 1) the notion of stratified continuous sheaves to extend Gromov's theory, 2) the notion of smooth stratified bundl