May 2023 arXiv papers — page 189

Hayato Futami, Jessica Huynh, Siddhant Arora, Shih-Lun Wu

This paper describes our system for the low-resource domain adaptation track (Track 3) in Spoken Language Understanding Grand Challenge, which is a part of ICASSP Signal Processing Grand Challenge 2023. In the track, we adopt a pipeline approach of ASR and NLU. For ASR, we fine-tune Whisper for each domain with upsampling. For NLU, we fine-tune BART on all t

Linghao Chen, Yuzhe Qin, Xiaowei Zhou, Hao Su

Hand-eye calibration is a critical task in robotics, as it directly affects the efficacy of critical operations such as manipulation and grasping. Traditional methods for achieving this objective necessitate the careful design of joint poses and the use of specialized calibration markers, while most recent learning-based approaches using solely pose regressi

Yuelang Xu, Hongwen Zhang, Lizhen Wang, Xiaochen Zhao

Existing approaches to animatable NeRF-based head avatars are either built upon face templates or use the expression coefficients of templates as the driving signal. Despite the promising progress, their performances are heavily bound by the expression power and the tracking accuracy of the templates. In this work, we present LatentAvatar, an expressive neur

Matthew Rupert

We derive sufficient conditions for exact functors on locally finite abelian categories to preserve Loewy diagrams of objects. We apply our results to determine sufficient conditions for induction functors associated to simple current extensions of vertex algebras to preserve Loewy diagrams.

Cecilia Ka Yuk Chan, Wenxin Zhou

This study examines the relationship between student perceptions and their intention to use generative AI in higher education. Drawing on Expectancy-Value Theory (EVT), a questionnaire was developed to measure students' knowledge of generative AI, perceived value, and perceived cost. A sample of 405 students participated in the study, and confirmatory factor

Cecilia Ka Yuk Chan, Louisa H. Y. Tsi

This paper explores the potential of artificial intelligence (AI) in higher education, specifically its capacity to replace or assist human teachers. By reviewing relevant literature and analysing survey data from students and teachers, the study provides a comprehensive perspective on the future role of educators in the face of advancing AI technologies. Fi

Chenyang Lyu, Zefeng Du, Jitao Xu, Yitao Duan

Machine Translation (MT) has greatly advanced over the years due to the developments in deep neural networks. However, the emergence of Large Language Models (LLMs) like GPT-4 and ChatGPT is introducing a new phase in the MT domain. In this context, we believe that the future of MT is intricately tied to the capabilities of LLMs. These models not only offer

David Durfee

In this work we consider the problem of differentially private computation of quantiles for the data, especially the highest quantiles such as maximum, but with an unbounded range for the dataset. We show that this can be done efficiently through a simple invocation of $\texttt{AboveThreshold}$, a subroutine that is iteratively called in the fundamental Spar

A. S. G. Robotham, J. C. J. D'Silva, R. A. Windhorst, R. A. Jansen

The James Webb Space Telescope (JWST) near-infrared camera (NIRCam) has been found to exhibit serious wisp-like structures in four of its eight short-wavelength detectors. The exact structure and strength of these wisps is highly variable with the position and orientation of JWST, so the use of static templates is non-optimal. Here we investigate a dynamic s

Joshua E. S. Socolar

Tiling models can reveal unexpected ways in which local constraints give rise to exotic long-range spatial structure. The recently discovered Hat monotile (and its mirror image) has been shown to be aperiodic~[Smith et al., arXiv:2303.10798 (2023)]; it can tile the plane with no holes or overlaps, but cannot do so periodically. We show that the structure enf

Jonghyun Kim, Bosang Kim, Hyotae Lee, Jungpyo Kim

In general, human pose estimation methods are categorized into two approaches according to their architectures: regression (i.e., heatmap-free) and heatmap-based methods. The former one directly estimates precise coordinates of each keypoint using convolutional and fully-connected layers. Although this approach is able to detect overlapped and dense keypoint

Asad Aali, Marius Arvinte, Sidharth Kumar, Jonathan I. Tamir

We present SURE-Score: an approach for learning score-based generative models using training samples corrupted by additive Gaussian noise. When a large training set of clean samples is available, solving inverse problems via score-based (diffusion) generative models trained on the underlying fully-sampled data distribution has recently been shown to outperfo

Kazuhiko Tsutsumi, Yoshimichi Teratani, Kaiji Motoyama, Rui Sakano

We study how bias and tunneling asymmetries affect nonlinear current through a quantum dot with $N$ discrete levels in the Fermi liquid regime, using an exact low-energy expansion of the current derived up to terms of order $V^3$ with respect to the bias voltage. The expansion coefficients are described in terms of the phase shift, the linear susceptibilitie

Min-Kook Suh, Seung-Woo Seo

Although contrastive learning methods have shown prevailing performance on a variety of representation learning tasks, they encounter difficulty when the training dataset is long-tailed. Many researchers have combined contrastive learning and a logit adjustment technique to address this problem, but the combinations are done ad-hoc and a theoretical backgrou

Chia-Min Lin

In this work, we calculate the average equation of state for the oscillating inflaton field of the simplest $α$-attractor E-model. We show that the average equation of state can be solved analytically. We discover that when $α$ is small, the average equation of state of the oscillating inflaton field approaches that of a cosmological constant. This is the ph

Nurendra Choudhary, Chandan K. Reddy

Reasoning over knowledge graphs (KGs) is a challenging task that requires a deep understanding of the complex relationships between entities and the underlying logic of their relations. Current approaches rely on learning geometries to embed entities in vector space for logical query operations, but they suffer from subpar performance on complex queries and

Chun-Jie Yang, Xin-Yue Liu, Shi-Qiang Xia, Si-Yuan Bai

Allowing the generation of effective interactions between distant quantum emitters (QEs) via flying photons, quantum interconnect (QI) is essentially a light-matter interface and acts as a building block in quantum technologies. A surface plasmon polariton (SPP) supported by a metallic waveguide provides an ideal interface to explore strong light-matter coup

Md Zarif Hossain, Ahmed Imteaj

Federated Learning (FL), a distributed machine learning technique has recently experienced tremendous growth in popularity due to its emphasis on user data privacy. However, the distributed computations of FL can result in constrained communication and drawn-out learning processes, necessitating the client-server communication cost optimization. The ratio of

A. B. Balantekin, Michael J. Cervia, Amol V. Patwardhan, Ermal Rrapaj

In extreme astrophysical environments such as core-collapse supernovae and binary neutron star mergers, neutrinos play a major role in driving various dynamical and microphysical phenomena, such as baryonic matter outflows, the synthesis of heavy elements, and the supernova explosion mechanism itself. The interactions of neutrinos with matter in these enviro

Yupeng Yang, Xiujuan Li, Gang Li

The matter power spectrum has been strongly constrained by astronomical measurements at large scales, but only weakly at small scales. Compared with the standard scenario, the deviation of the matter power spectrum at small scales has influence on the cosmological structure formation, e.g., the comoving number density of dark matter halos. The thermal histor

Chen Li, Yang Cao, Ye Zhu, Debo Cheng

Using knowledge graphs to assist deep learning models in making recommendation decisions has recently been proven to effectively improve the model's interpretability and accuracy. This paper introduces an end-to-end deep learning model, named RKGCN, which dynamically analyses each user's preferences and makes a recommendation of suitable items. It co

Dave Van Veen, Cara Van Uden, Maayane Attias, Anuj Pareek

We systematically investigate lightweight strategies to adapt large language models (LLMs) for the task of radiology report summarization (RRS). Specifically, we focus on domain adaptation via pretraining (on natural language, biomedical text, or clinical text) and via discrete prompting or parameter-efficient fine-tuning. Our results consistently achieve be

Naonori S. Sugiyama, Daisuke Yamauchi, Tsutomu Kobayashi, Tomohiro Fujita

We present, for the first time, an observational test of the consistency relation for the large-scale structure (LSS) of the Universe through a joint analysis of the anisotropic two- and three-point correlation functions (2PCF and 3PCF) of galaxies. We parameterise the breakdown of the LSS consistency relation in the squeezed limit by $E_{\rm s}$, which repr

Jiefeng Chen, Jayaram Raghuram, Jihye Choi, Xi Wu

Recently, there is an emerging interest in adversarially training a classifier with a rejection option (also known as a selective classifier) for boosting adversarial robustness. While rejection can incur a cost in many applications, existing studies typically associate zero cost with rejecting perturbed inputs, which can result in the rejection of numerous

Ning An

The global aging population presents significant challenges for societies worldwide, particularly in an increasingly digitalized era. The Learning Society is crucial in preparing different societies and their people to address these challenges effectively. This paper extends this concept and proposes a new conceptual framework, Learning Societies for Digital

Kyle Harlow, Hyesu Jang, Timothy D. Barfoot, Ayoung Kim

We survey the current state of millimeterwave (mmWave) radar applications in robotics with a focus on unique capabilities, and discuss future opportunities based on the state of the art. Frequency Modulated Continuous Wave (FMCW) mmWave radars operating in the 76--81GHz range are an appealing alternative to lidars, cameras and other sensors operating in the

Subrata Das, Swaroop Ghosh

The success of quantum circuits in providing reliable outcomes for a given problem depends on the gate count and depth in near-term noisy quantum computers. Quantum circuit compilers that decompose high-level gates to native gates of the hardware and optimize the circuit play a key role in quantum computing. However, the quality and time complexity of the op

Michele Fornea

Looking for a geometric framework to study plectic Heegner points, we define a collection of abelian varieties - called plectic Jacobians - using the middle degree cohomology of quaternionic Shimura varieties (QSVs). The construction is inspired by the definition of Griffiths' intermediate Jacobians and rests on Nekovar-Scholl's notion of plectic Hod

Atalia Navarro-Boullosa, Argelia Bernal, J. Alberto Vazquez

Using Low Brightness Surface Galaxies (LBSG) rotational curves we inferred the free parameters of $\ell$-boson stars as a dark matter component. The $\ell$-boson stars are numerical solutions to the non-relativistic limit of the Einstein-Klein-Gordon system, the Schrödinger-Poisson (SP) system. These solutions are parametrized by an angular momentum number $

Andre Costa, Tianchen Hu, John Dolbow

Over the past few decades, the phase-field method for fracture has seen widespread appeal due to the many benefits associated with its ability to regularize a sharp crack geometry. Along the way, several different models for including the effects of pressure loads on the crack faces have been developed. This work investigates the performance of these models

Lukas Gehring

A triangulation of a polytope into simplices is refined recursively. In every refinement round, some simplices which have been marked by an external algorithm are bisected and some others around also must be bisected to retain regularity of the triangulation. The ratio of the total number of marked simplices and the total number of bisected simplices is boun

Christoph Striegel, Göran Kauermann, Jonas Biehler

In this work we propose a low rank approximation of high fidelity finite element simulations by utilizing weights corresponding to areas of high stress levels for an abdominal aortic aneurysm, i.e. a deformed blood vessel. We focus on the van Mises stress, which corresponds to the rupture risk of the aorta. This is modeled as a Gaussian Markov random field a

Simona Ramos, Joshua Ellul

In this article, we develop an interdisciplinary analysis of MEV which desires to merge the gap that exists between technical and legal research supporting policymakers in their regulatory decisions concerning blockchains, DeFi and associated risks. Consequently, this article is intended for both technical and legal audiences, and while we abstain from a det

Xuming Hu, Zhaochen Hong, Zhijiang Guo, Lijie Wen

Real-world fact verification task aims to verify the factuality of a claim by retrieving evidence from the source document. The quality of the retrieved evidence plays an important role in claim verification. Ideally, the retrieved evidence should be faithful (reflecting the model's decision-making process in claim verification) and plausible (convincing

Xuming Hu, Zhaochen Hong, Chenwei Zhang, Irwin King

Relation extraction (RE) aims to extract potential relations according to the context of two entities, thus, deriving rational contexts from sentences plays an important role. Previous works either focus on how to leverage the entity information (e.g., entity types, entity verbalization) to inference relations, but ignore context-focused content, or use coun

Jin Zhao, Huang Zhao Zhang, Ming-Zhe Chong, Yue-Yi Zhang

Recently, metasurfaces have experienced revolutionary growth in the sensing and superresolution imaging field, due to their enabling of subwavelength manipulation of electromagnetic waves. However, the addition of metasurfaces multiplies the complexity of retrieving target information from the detected fields. Besides, although the deep learning method affor

Victor Atanasov

The interrelationship between energy and probability conservation is explored from the point of view of statistical physics and non-relativistic quantum mechanics. The simultaneous validity of the law of conservation of energy and the continuity equation (probability conservation) breaks for an interacting dynamical system. A separate and independent descrip

T. Klatzer, U. Bachhiesl, S. Wogrin, A. Tomasgard

With the transition towards a decarbonized society, energy system integration is becoming ever more essential. In this transition, the energy vector hydrogen is expected to play a key role as it can be produced from (renewable) power and utilized in a plethora of applications and processes across sectors. To date, however, there is no infrastructure for the

Xiaozhen Yang, Erda Wen, Daniel F. Sievenpiper

Breaking reciprocity in the microwave frequency range will have important implications for modern electronic systems. Since it usually involves bulky biasing magnets or complex spatial-temporal modulations, exploring a lightweight, all-passive approach becomes intriguing. Starting from a circuit model, we theoretically demonstrate the nonreciprocal behaviour

Yiqian He, Benrong Mu

In~\cite{Johnson:2019mdp}, Clifford put forward that the specific heat at constant volume $C_{V}$ is always negetive for the super-entropy black hole. Later in~\cite{Cong:2019bud}, Robert et al. found $C_{V}>0$ in certain region for the generalized exotic BTZ black holes. Futhermore, they proposed a new conjecture that as the $C_{V}>0$, the specific heat at

Chen Tessler, Yoni Kasten, Yunrong Guo, Shie Mannor

In this work, we present Conditional Adversarial Latent Models (CALM), an approach for generating diverse and directable behaviors for user-controlled interactive virtual characters. Using imitation learning, CALM learns a representation of movement that captures the complexity and diversity of human motion, and enables direct control over character movement

Mariam Elgamal, Doug Carmean, Elnaz Ansari, Okay Zed

As computing hardware becomes more specialized, designing environmentally sustainable computing systems requires accounting for both hardware and software parameters. Our goal is to design low carbon computing systems while maintaining a competitive level of performance and operational efficiency. Despite previous carbon modeling efforts for computing system

Xu-hui Wang, Xue-song Li, Nan-jing Huang

This paper focuses on the study of the finite-time consensus (FTC) and fixed-time consensus (FXC) issues of multi-agent systems (MASs) driven by parabolic partial differential equations (PDEs). Compared with the study in the existing literature, the topic of FTC and FXC control is first embodied in MASs driven by parabolic PDEs. Based on the Lyapunov theorem

Isabella Novik, Hailun Zheng

We define a certain merging operation that given two $d$-polytopes $P$ and $Q$ such that $P$ has a simplex facet $F$ and $Q$ has a simple vertex $v$ produces a new $d$-polytope $P\hspace{0.1em}\triangleright Q$ with $f_0(P)+f_0(Q)-(d+1)$ vertices. We show that if for some $1\leq i\leq d-1$, $P$ and $Q$ are $(d-i)$-simplicial $i$-simple $d$-polytopes, then so

Hitesh Poddar, Tomoki Yoshimura, Matteo Pagin, Theodore S Rappaport

The next generation of wireless networks will use sub-THz frequencies alongside mmWave frequencies to enable multi-Gbps and low-latency applications. To enable different verticals and use cases, engineers must take a holistic approach to build, analyze, and study different parts of the network and the interplay among the lower and higher layers of the protoc

Karthik Gopinath, Douglas N. Greve, Sudeshna Das, Steve Arnold

Surface analysis of the cortex is ubiquitous in human neuroimaging with MRI, e.g., for cortical registration, parcellation, or thickness estimation. The convoluted cortical geometry requires isotropic scans (e.g., 1mm MPRAGEs) and good gray-white matter contrast for 3D reconstruction. This precludes the analysis of most brain MRI scans acquired for clinical

Bruno H. S. Mendonça, Elizane E. de Moraes, Alexsandro Kirch, Ronaldo J. C. Batista

In this study, using non-equilibrium molecular dynamics simulation, the flow of water in deformed carbon nanotubes is studied for two water models TIP4P/2005 and SPC/FH. The results demonstrated a non-uniform dependence of the flow on the tube deformation and the flexibility imposed on the water molecules, leading to an unexpected increase in the flow in som

Mojdeh Saadati, Aditya Balu, Shivani Chiranjeevi, Talukder Zaki Jubery

Deep learning-based approaches have produced models with good insect classification accuracy; Most of these models are conducive for application in controlled environmental conditions. One of the primary emphasis of researchers is to implement identification and classification models in the real agriculture fields, which is challenging because input images t

Tobias Bischoff, Katherine Deck

We present a method to downscale idealized geophysical fluid simulations using generative models based on diffusion maps. By analyzing the Fourier spectra of images drawn from different data distributions, we show how one can chain together two independent conditional diffusion models for use in domain translation. The resulting transformation is a diffusion

Yannik Schälte, Fabian Fröhlich, Paul J. Jost, Jakob Vanhoefer

Mechanistic models are important tools to describe and understand biological processes. However, they typically rely on unknown parameters, the estimation of which can be challenging for large and complex systems. We present pyPESTO, a modular framework for systematic parameter estimation, with scalable algorithms for optimization and uncertainty quantificat

Antonin Sojka, Brad D. Price, Mark S. Sherwin

In this paper, we present a rapidly-prototyped, costefficient, 3D-printed quasioptical sample holder for improving the signal-to-noise ratio (SNR) in modern, resonator-free, highfield electron paramagnetic resonance (EPR) spectrometers. Such spectrometers typically operate in induction mode: the detected EPR (cross-polar) signal is polarized orthogonal to th

Alice Cortinovis, Lexing Ying

This work proposes algorithms for computing additive and multiplicative free convolutions of two given measures. We consider measures with compact support whose free convolution results in a measure with a density function that exhibits a square-root decay at the boundary (for example, the semicircle distribution or the Marchenko-Pastur distribution). A key

Fabio Pavanello, Elena Ioana Vatajelu, Alberto Bosio, Thomas Van Vaerenbergh

The field of neuromorphic computing has been rapidly evolving in recent years, with an increasing focus on hardware design and reliability. This special session paper provides an overview of the recent developments in neuromorphic computing, focusing on hardware design and reliability. We first review the traditional CMOS-based approaches to neuromorphic har

Yifei Sun, Ying Sheng

Proportional rate models are among the most popular methods for analyzing the rate function of counting processes. Although providing a straightforward rate-ratio interpretation of covariate effects, the proportional rate assumption implies that covariates do not modify the shape of the rate function. When such an assumption does not hold, we propose describ

Juan Ignacio Ibañez, Alexander Freier

For more than a decade, Bitcoin has gained as much adoption as it has received criticism. Fundamentally, Bitcoin is under fire for the high carbon footprint that results from the energy-intensive proof-of-work (PoW) consensus algorithm. There is a trend however for Bitcoin mining to adopt a trajectory toward achieving carbon-negative status, notably due to t

Nolan Samboy

We drop a circular disk magnet through a thin coil of wire, record the induced voltage, and compare the results to an analytic model based on the dipole approximation and Faraday's law, which predicts that the difference between the voltage peak magnitudes corresponding to the entry and exit of the magnet should be in proportion to $z_0^{-1/2}$, where $z

Neeraj Varshney, Chitta Baral

Despite remarkable progress made in natural language processing, even the state-of-the-art models often make incorrect predictions. Such predictions hamper the reliability of systems and limit their widespread adoption in real-world applications. 'Selective prediction' partly addresses the above concern by enabling models to abstain from answering wh

Yichuan Li, Jialong Han, Kyumin Lee, Chengyuan Ma

In recent years, Pre-trained Language Models (PLMs) have shown their superiority by pre-training on unstructured text corpus and then fine-tuning on downstream tasks. On entity-rich textual resources like Wikipedia, Knowledge-Enhanced PLMs (KEPLMs) incorporate the interactions between tokens and mentioned entities in pre-training, and are thus more effective

Jiayu Peng, James Damewood, Rafael Gómez-Bombarelli

The structural tunability and compositional diversity of multicomponent perovskite oxides have enabled their various applications, including catalysis and electronics. The cation ordering in these oxides, ranging from disordered (i.e., high-entropy) to ordered (e.g., rocksalt), profoundly influences their properties. While computational design tools can typi

A. M. Vibhakar, D. D. Khalyavin, P. Manuel, N. J. Steinke

A combination of competing exchange interactions and substitutional disorder gives rise to magnetic inhomogeneities in the [Y$_{2-x}$Mn$_x$]MnMnMn$_4$O$_{12}$ $x = 0.23$ and $x = 0.16$ quadruple perovskite manganites. Our neutron powder scattering measurements show that both the $x = 0.23$ and $x = 0.16$ samples separate into two distinct magnetic phases; be

Wolfgang Wieland

In perturbative gravity, it is straight-forward to characterize the two local degrees of freedom of the gravitational field in terms of a mode expansion of the linearized perturbation. In the non-perturbative regime, we are in a more difficult position. It is not at all obvious how to construct Dirac observables that can separate the gauge orbits. Standard p

Breanna A. Binder, Ashley K. Anderson, Kristen Garofali, Margaret Lazzarini

We present an analysis of the two-point spatial correlation functions of high-mass X-ray binary (HMXB) and young star cluster (YSC) populations in M31 and M33. We find evidence that HMXBs are spatially correlated with YSCs to a higher degree than would be expected from random chance in both galaxies. When supplemented with similar studies in the Milky Way, S

Yushun Dong, Jundong Li, Tobias Schnabel

In recent years, neural models have been repeatedly touted to exhibit state-of-the-art performance in recommendation. Nevertheless, multiple recent studies have revealed that the reported state-of-the-art results of many neural recommendation models cannot be reliably replicated. A primary reason is that existing evaluations are performed under various incon

Marek Michalewicz

In 2015 the Interdisciplinary Centre for Mathematical and Computational Modelling (ICM), University of Warsaw built a modern datacenter and installed three substantial HPC systems as part of a 168 M PLN (36 M Euro) OCEAN project. Some of the systems were ill-conceived, badly architected and for the five years of their life span have brought minimal ROI. This

Bingzhi Zhang, Quntao Zhuang

Bosonic continuous-variable Variational quantum circuits (VQCs) are crucial for information processing in cavity quantum electrodynamics and optical systems, widely applicable in quantum communication, sensing and error correction. The trainability of such VQCs is less understood, hindered by the lack of theoretical tools such as $t$-design due to the infini

David E. Kaplan, Tom Melia, Surjeet Rajendran

In this and a companion paper, we show that quantum field theories with gauge symmetries permit a broader class of classical dynamics than typically assumed. In this article, we show that the dynamics extracted from the path integral or Hamiltonian formulation of general relativity allows for classical states that do not satisfy the full set of Einstein'

Milica Banic, J. E. Sipe, Marco Liscidini

We demonstrate that genuine multipartite entangled states can be generated using frequency bin encoding in integrated photonic platforms. We introduce a source of four-photon GHZ states, and a source of three-photon W states. We predict generation rates on the order of 10$^4$ Hz for a silicon microring source with milliwatt pump powers. These results, along

Yujie Lu, Pan Lu, Zhiyu Chen, Wanrong Zhu

Embodied agents have achieved prominent performance in following human instructions to complete tasks. However, the potential of providing instructions informed by texts and images to assist humans in completing tasks remains underexplored. To uncover this capability, we present the multimodal procedural planning (MPP) task, in which models are given a high-

Zhiqi Bu, Zongyu Dai, Yiliang Zhang, Qi Long

Multiple imputation (MI) has been widely applied to missing value problems in biomedical, social and econometric research, in order to avoid improper inference in the downstream data analysis. In the presence of high-dimensional data, imputation models that include feature selection, especially $\ell_1$ regularized regression (such as Lasso, adaptive Lasso,

Xintong Li, Peng Zhou, Xuan Hu, Ethan Rivers

Ambipolar dual-gate transistors based on two-dimensional (2D) materials, such as graphene, carbon nanotubes, black phosphorus, and certain transition metal dichalcogenides (TMDs), enable reconfigurable logic circuits with suppressed off-state current. These circuits achieve the same logical output as CMOS with fewer transistors and offer greater flexibility

Ronaldo García Reyes, Ying Wang, Min Li, Marlis Ontiviero Ortega

The paper introduces a novel non-parametric Riemannian regression method using Isometric Riemannian Manifolds (IRMs). The proposed technique, Intrinsic Local Polynomial Regression on IRMs (ILPR-IRMs), enables global data mapping between Riemannian manifolds while preserving underlying geometries. The ILPR method is generalized to handle multivariate covariat

Emily Aiken, Esther Rolf, Joshua Blumenstock

Poverty maps derived from satellite imagery are increasingly used to inform high-stakes policy decisions, such as the allocation of humanitarian aid and the distribution of government resources. Such poverty maps are typically constructed by training machine learning algorithms on a relatively modest amount of ``ground truth" data from surveys, and then

Alexander Fominyh

The paper explores the differential inclusion of a special form. It is supposed that the support function of the set in the right-hand side of an inclusion may contain the maximum of the finite number of continuously differentiable (in phase coordinates) functions. It is required to find a trajectory that would satisfy the differential inclusion with the bou

Gustavo de O. Luiz, Caique C. Rodrigues, Thiago P. Mayer Alegre, Gustavo S. Wiederhecker

Recent exploration of collective phenomena in oscillator arrays has highlighted its potential for accessing a range of physical phenomena, from fundamental quantum many-body dynamics to the solution of practical optimization problems using photonic Ising machines. Spontaneous oscillations often arise in these oscillator arrays as an imbalance between gain an

Vadim Semenov

We show that if the Gauss Image Measure of submeasure $λ$ via convex body $K$ agrees with the Gauss Image Measure of $λ$ via convex body $L$, then the radial Gauss Image maps of their duals, are equal to each other almost everywhere as multivalued maps with respect to $λ$. As an application of this result, we establish that, in this case, dual bodies, $K^*$

Biao Zhang, Mathias Müller, Rico Sennrich

Despite recent successes with neural models for sign language translation (SLT), translation quality still lags behind spoken languages because of the data scarcity and modality gap between sign video and text. To address both problems, we investigate strategies for cross-modality representation sharing for SLT. We propose SLTUNET, a simple unified neural mo

Jared Katzman, Angelina Wang, Morgan Scheuerman, Su Lin Blodgett

In this paper, we examine computational approaches for measuring the "fairness" of image tagging systems, finding that they cluster into five distinct categories, each with its own analytic foundation. We also identify a range of normative concerns that are often collapsed under the terms "unfairness," "bias," or even "discriminat

Andreas Look, Melih Kandemir, Barbara Rakitsch, Jan Peters

Graph neural networks are often used to model interacting dynamical systems since they gracefully scale to systems with a varying and high number of agents. While there has been much progress made for deterministic interacting systems, modeling is much more challenging for stochastic systems in which one is interested in obtaining a predictive distribution o

Francesco Gavazzo

Moving from the mathematical theory of (abstract) syntax, we develop a general relational theory of symbolic manipulation parametric with respect to, and accounting for, general notions of syntax. We model syntax relying on categorical notions, such as free algebras and monads, and show that a general theory of symbolic manipulation in the style of rewriting

Xinyan Li, Cheng Qian, Lucas Glass

Respiratory syncytial virus (RSV) is one of the most dangerous respiratory diseases for infants and young children. Due to the nonpharmaceutical intervention (NPI) imposed in the COVID-19 outbreak, the seasonal transmission pattern of RSV has been discontinued in 2020 and then shifted months ahead in 2021 in the northern hemisphere. It is critical to underst

Łukasz Janeczko, Piotr Faliszewski

We study the complexity of deciding whether there is a tie in a given approval-based multiwinner election, as well as the complexity of counting tied winning committees. We consider a family of Thiele rules, their greedy variants, Phragmen's sequential rule, and Method of Equal Shares. For most cases, our problems are computationally hard, but for sequen

Hana Najmanová, Enrico Ronchi

This article presents experimental data sets and information about the pre-movement behaviour and specific evacuation conditions observed in 15 evacuation drills in 10 nursery schools in the Czech Republic involving 970 children (3-7 years of age) and 87 staff members. Based on the analysis of video recordings, over 1800 data points describing pre-movement t

Masaya Amo, Keisuke Izumi, Yoshimune Tomikawa, Tetsuya Shiromizu

In our previous papers [arXiv:2106.03150, arXiv:2110.10917, arXiv:2208.00822], we analyzed the asymptotic behavior of future directed null geodesics near future null infinity and then we showed a proposition on the accessibility of the null geodesics to future null infinity in a specific class of asymptotically flat spacetimes. In this paper, we adopt the re

Agustín M. Rodríguez-Medrano, Federico A. Stasyszyn, Dante J. Paz, Volker Springel

Magnetic fields are one of most concealed components of the universe. They are observed as part of the intergalactic medium and on galaxy cluster scales, however their origin and evolution is unclear. In this work we use the IllustrisTNG simulation to investigate the effects of magnetic fields in cosmic voids, the least dense regions of the universe. We find

Zhiheng Lyu, Zhijing Jin, Justus Mattern, Rada Mihalcea

NLP datasets are richer than just input-output pairs; rather, they carry causal relations between the input and output variables. In this work, we take sentiment classification as an example and look into the causal relations between the review (X) and sentiment (Y). As psychology studies show that language can affect emotion, different psychological process

Xingbo Fu, Chen Chen, Yushun Dong, Anil Vullikanti

An antibiogram is a periodic summary of antibiotic resistance results of organisms from infected patients to selected antimicrobial drugs. Antibiograms help clinicians to understand regional resistance rates and select appropriate antibiotics in prescriptions. In practice, significant combinations of antibiotic resistance may appear in different antibiograms

Wojciech Różowski, Tobias Kappé, Dexter Kozen, Todd Schmid

We introduce Probabilistic Guarded Kleene Algebra with Tests (ProbGKAT), an extension of GKAT that allows reasoning about uninterpreted imperative programs with probabilistic branching. We give its operational semantics in terms of special class of probabilistic automata. We give a sound and complete Salomaa-style axiomatisation of bisimilarity of ProbGKAT e

Aik Rui Tan, Shingo Urata, Samuel Goldman, Johannes C. B. Dietschreit

Neural networks (NNs) often assign high confidence to their predictions, even for points far out-of-distribution, making uncertainty quantification (UQ) a challenge. When they are employed to model interatomic potentials in materials systems, this problem leads to unphysical structures that disrupt simulations, or to biased statistics and dynamics that do no

Anisur Rahaman Molla, Kaushik Mondal, William K. Moses

Over the years, much research involving mobile computational entities has been performed. From modeling actual microscopic (and smaller) robots, to modeling software processes on a network, many important problems have been studied in this context. Gathering is one such fundamental problem in this area. The problem of gathering $k$ robots, initially arbitrar

Dominik Bohnert, Christian Winter

A $k$-graph $\mathcal{G}$ is asymmetric if there does not exist an automorphism on $\mathcal{G}$ other than the identity, and $\mathcal{G}$ is called minimal asymmetric if it is asymmetric but every non-trivial induced sub-hypergraph of $\mathcal{G}$ is non-asymmetric. Extending a result of Jiang and Nešetřil, we show that for every $k$-graph, $k\ge3$, there

German Dzyubenko, Kirill A. Kopotun

Given $k\in N$, a nonnegative function $f\in C^r[a,b]$, $r\ge 0$, an arbitrary finite collection of points $\big\{α_i\big\}_{i\in J} \subset [a,b]$, and a corresponding collection of nonnegative integers $\big\{m_i\big\}_{i\in J}$ with $0\le m_i \le r$, $i\in J$, is it true that, for sufficiently large $n\in N$, there exists a polynomial $P_n$ of degree $n$

Jaehyun Cho, Luke L. Hsiung, Robert E. Rudd, Sylvie Aubry

In this study, we present the first simulation results of the formation of dislocation cell wall microstructures in tantalum subjected to shock loading. Dislocation patterns and cell wall formation are important to understanding the mechanical properties of the materials in which they spontaneously arise, and yet the processing and self-assembly mechanisms l

Luocheng Huang, Quentin A. A. Tanguy, Johannes E. Froch, Saswata Mukherjee

Light's ability to perform massive linear operations parallelly has recently inspired numerous demonstrations of optics-assisted artificial neural networks (ANN). However, a clear advantage of optics over purely digital ANN in a system-level has not yet been established. While linear operations can indeed be optically performed very efficiently, the lack

Carmen Gomez-Fayren, Patrick Meessen, Tomas Ortin, Matteo Zatti

We study the thermodynamics of the 4-dimensional electrically charged black-hole solutions of the simplest 5-dimensional Kaluza-Klein theory using Wald's formalism. We show how the electric work term present in the 4-dimensional first law of black-hole thermodynamics arises in the purely gravitational 5-dimensional framework. In particular, we find an in

Somnath Biswas, Fatimah Alowa, Ruyan Zhao, Marios Zacharias

The efficiency of two-dimensional Dion-Jacobson-type materials relies on the complex interplay between electronic and lattice dynamics; however, questions remain about the functional role of exciton-phonon interactions. This study establishes the robust polaronic nature of the excitons in these materials at room temperature by combining ultrafast spectroscop

Mateusz Wiśniewski, Jakub Spiechowicz

We reinvestigate a paradigmatic model of nonequilibrium statistical physics consisting of an inertial Brownian particle in a symmetric periodic potential subjected to both a time--periodic force and a static bias. In doing so we focus on the negative mobility phenomenon in which the average velocity of the particle is opposite to the constant force acting on

Shengpu Tang, Maggie Makar, Michael W. Sjoding, Finale Doshi-Velez

Many reinforcement learning (RL) applications have combinatorial action spaces, where each action is a composition of sub-actions. A standard RL approach ignores this inherent factorization structure, resulting in a potential failure to make meaningful inferences about rarely observed sub-action combinations; this is particularly problematic for offline sett

Yuexi Zhang, Dan Luo, Balaji Sundareshan, Octavia Camps

Cross view action recognition (CVAR) seeks to recognize a human action when observed from a previously unseen viewpoint. This is a challenging problem since the appearance of an action changes significantly with the viewpoint. Applications of CVAR include surveillance and monitoring of assisted living facilities where is not practical or feasible to collect

Aakash Rajpal, Noshaba Cheema, Klaus Illgner-Fehns, Philipp Slusallek

Accurate depth maps are essential in various applications, such as autonomous driving, scene reconstruction, point-cloud creation, etc. However, monocular-depth estimation (MDE) algorithms often fail to provide enough texture & sharpness, and also are inconsistent for homogeneous scenes. These algorithms mostly use CNN or vision transformer-based architectur

Martin P. Martinov, Elizabeth M. Fletcher, Rowan M. Thomson

Purpose: To develop and apply multiscale Monte Carlo (MC) simulations to assess variations in nucleus and cytoplasm dose enhancement factors (n,cDEFs) over tumor-scale volumes. Methods: The intrinsic variation of n,cDEFs (due to variations in local gold concentration and cell/nucleus size) are estimated via MC modeling of varied cellular GNP uptake and cell/