December 2023 arXiv papers — page 34

Xin Hao, Changyang She, Phee Lep Yeoh, Yuhong Liu

In this paper, we develop a deep learning-based bandwidth allocation policy that is: 1) scalable with the number of users and 2) transferable to different communication scenarios, such as non-stationary wireless channels, different quality-of-service (QoS) requirements, and dynamically available resources. To support scalability, the bandwidth allocation pol

Zefeng Qiu, Prashanth Umapathy, Qingquan Zhang, Guanqun Song

This document is the final project report for our advanced operating system class. During this project, we mainly focused on applying multiprocessing and multi-threading technology to our whole project and utilized the map-reduce algorithm in our data cleaning and data analysis process. In general, our project can be divided into two components: data scrapin

Manish Shetty, Koushik Sen, Ion Stoica

Programmers often search for usage examples for API methods. A tool that could generate realistic, idiomatic, and contextual usage examples for one or more APIs would be immensely beneficial to developers. Such a tool would relieve the need for a deep understanding of the API landscape, augment existing documentation, and help discover interactions among API

Denis Shchepakin, Sreecharan Sankaranarayanan, Dawn Zimmaro

Bayesian Knowledge Tracing (BKT) is a probabilistic model of a learner's state of mastery corresponding to a knowledge component. It considers the learner's state of mastery as a "hidden" or latent binary variable and updates this state based on the observed correctness of the learner's response using parameters that represent transition probabilities betwee

Mingyang Song, Xuelian Geng, Songfang Yao, Shilong Lu

Zero-shot keyphrase extraction aims to build a keyphrase extractor without training by human-annotated data, which is challenging due to the limited human intervention involved. Challenging but worthwhile, zero-shot setting efficiently reduces the time and effort that data labeling takes. Recent efforts on pre-trained large language models (e.g., ChatGPT and

Yusuke Iguchi, Joshua A. Straquadine, Chaitanya Murthy, Steven A. Kivelson

We image local superfluid density in single crystals of Pd-intercalated ErTe$_3$ below the superconducting critical temperature, $T_c$, well below the onset temperature, $T_{CDW}$, of (disordered) charge-density-wave order. We find no detectable inhomogeneities. We observe a rapid increase of the superfluid density below $T_c$, deviating from the behavior ex

May Cai, Cecilie Olesen Recke, Thomas Yahl

Completion problems, of recovering a point from a set of observed coordinates, are abundant in applications to image reconstruction, phylogenetics, and data science. We consider a completion problem coming from algebraic statistics: to describe the completions of a point to a probability distribution lying in a given log-linear model. When there are finitely

Qin Sun, Grace McKenzie, Guanqun Song, Ting Zhu

Virtual file systems are a tool to centralize and mobilize a file system that could otherwise be complex and consist of multiple hierarchies, hard disks, and more. In this paper, we discuss the design of Unix-based file systems and how this type of file system layout using inode data structures and a disk emulator can be implemented as a single-file virtual

Anuja Dixit, Shreya Byreddy, Guanqun Song, Ting Zhu

Classification is one of the most important tasks in Machine Learning (ML) and with recent advancements in artificial intelligence (AI) it is important to find efficient ways to implement it. Generally, the choice of classification algorithm depends on the data it is dealing with, and accuracy of the algorithm depends on the hyperparameters it is tuned with.

Abshir Ali, Guanqun Song, Ting Zhu

As 5G networks become more mainstream, privacy has come to the forefront of end users. More scrutiny has been shown to previous generation cellular technologies such as 3G and 4G on how they handle sensitive metadata transmitted from an end user mobile device to base stations during registration with a cellular network. These generation cellular networks do

Yifeng Lyu, Han Hu, Rongfei Fan, Zhi Liu

The integrated satellite-terrestrial network (ISTN) system has experienced significant growth, offering seamless communication services in remote areas with limited terrestrial infrastructure. However, designing a routing scheme for ISTN is exceedingly difficult, primarily due to the heightened complexity resulting from the inclusion of additional ground sta

Geoffroy Leconte, Dominique Orban

We develop a worst-case evaluation complexity bound for trust-region methods in the presence of unbounded Hessian approximations. We use the algorithm of arXiv:2103.15993v3 as a model, which is designed for nonsmooth regularized problems, but applies to unconstrained smooth problems as a special case. Our analysis assumes that the growth of the Hessian appro

Ashvini A Kulshrestha, Guanqun Song, Ting Zhu

The year 2022 saw a significant increase in Microsoft vulnerabilities, reaching an all-time high in the past decade. With new vulnerabilities constantly emerging, there is an urgent need for proactive approaches to harden systems and protect them from potential cyber threats. This project aims to investigate the vulnerabilities of the Windows Operating Syste

Xinlin Cao, Ahcene Ghandriche, Mourad Sini

We derive the electromagnetic medium equivalent to a cluster of all-dielectric nanoparticles (i.e. enjoying high refractive indices), distributed periodically in a smooth domain $\Omega$, while excited at nearly resonating dielectric incident frequencies (i.e. subwavelength Mie-resonant frequencies). This effective medium is an alteration of the permeability

Zihan Chen, Jundong Li, Cong Shen

Personalized Federated Learning (PFL) relies on collective data knowledge to build customized models. However, non-IID data between clients poses significant challenges, as collaborating with clients who have diverse data distributions can harm local model performance, especially with limited training data. To address this issue, we propose FedACS, a new PFL

Xin Liu, Zheng Li, Yifan Gao, Jingfeng Yang

The goal of session-based recommendation in E-commerce is to predict the next item that an anonymous user will purchase based on the browsing and purchase history. However, constructing global or local transition graphs to supplement session data can lead to noisy correlations and user intent vanishing. In this work, we propose the Frequent Attribute Pattern

Yoon-Joo Kim, Radu Laza, Olivier Martin

This article initiates the study of isotrivial Lagrangian fibrations of compact hyper-K\"ahler manifolds. We present four foundational results that extend well-known facts about isotrivial elliptic fibrations of K3 surfaces. First, we prove that smooth fibers of an isotrivial Lagrangian fibration are isogenous to a power of an elliptic curve. Second, we exhi

Alex Correia, Collin D. Capano

Direct observation of gravitational waves from binary black hole (BBH) mergers has made it possible to test the laws of black hole thermodynamics using real astrophysical sources. These tests rely on accurate and unbiased parameter estimates from the pre and postmerger portions of a signal. Due to numerical complications, previous analyses have fixed the sky

Chris Jia

This article, produced as part of an undergraduate research class, aims to provide an overview of gravitational waves, though it does not offer a comprehensive review. We begin with a brief discussion regarding the history of gravitational waves, beginning even before Albert Einstein's theory of general relativity, highlighting important developments and mil

Prosenjit Bose, Tyler Tuttle

In this article, we present a construction of a spanner on a set of $n$ points in $\mathbf{R}^d$ that we call a heavy path WSPD spanner. The construction is parameterized by a constant $s > 2$ called the separation ratio. The size of the graph is $O(s^dn)$ and the spanning ratio is at most $1 + 2/s + 2/(s - 1)$. We also show that this graph has a hop spannin

Shaojie Zhang, Jianqin Yin, Yonghao Dang

Skeleton-based action recognition is a central task in computer vision and human-robot interaction. However, most previous methods suffer from overlooking the explicit exploitation of the latent data distributions (i.e., the intra-class variations and inter-class relations), thereby leading to confusion about ambiguous samples and sub-optimum solutions of th

Andrew Hou, Feng Liu, Zhiyuan Ren, Michel Sarkis

We propose INFAMOUS-NeRF, an implicit morphable face model that introduces hypernetworks to NeRF to improve the representation power in the presence of many training subjects. At the same time, INFAMOUS-NeRF resolves the classic hypernetwork tradeoff of representation power and editability by learning semantically-aligned latent spaces despite the subject-sp

Chonghyuk Cho, Jonghwan Park, Haecheon Choi

We introduce a novel recursive process to a neural-network-based subgrid-scale (NN-based SGS) model for large eddy simulation (LES) of high Reynolds number turbulent flow. This process is designed to allow an SGS model to be applicable to a hierarchy of different grid sizes without requiring an expensive filtered direct numerical simulation (DNS) data: 1) tr

Amin Hakimi, Kasra Rouhi, Tatiana G. Rappoport, Mario G. Silveirinha

Materials with Berry curvature dipoles (BDs) support a non-Hermitian electro-optic (EO) effect that is investigated here for lasing at terahertz (THz) frequencies. Such a system is here conceived as a stack of low-symmetry 2D materials. We show that a cavity made of such a material supports a nonreciprocal growing mode with elliptical polarization that gener

Peter J. Ehlers, Hendra I. Nurdin, Daniel Soh

Reservoir computing, using nonlinear dynamical systems, offers a cost-effective alternative to neural networks for complex tasks involving processing of sequential data, time series modeling, and system identification. Echo state networks (ESNs), a type of reservoir computer, mirror neural networks but simplify training. They apply fixed, random linear trans

Ahad N. Zehmakan

We study a majority based preference diffusion model in which the members of a social network update their preferences based on those of their connections. Consider an undirected graph where each node has a strict linear order over a set of $\alpha$ alternatives. At each round, a node randomly selects two adjacent alternatives and updates their relative orde

Changsong Lei, Mengfei Xia, Shaofeng Wang, Yaqian Liang

Tooth arrangement is a crucial step in orthodontics treatment, in which aligning teeth could improve overall well-being, enhance facial aesthetics, and boost self-confidence. To improve the efficiency of tooth arrangement and minimize errors associated with unreasonable designs by inexperienced practitioners, some deep learning-based tooth arrangement method

Kazuki Sunaga, Keisuke Sugiura, Hiroki Matsutani

A graph embedding is an emerging approach that can represent a graph structure with a fixed-length low-dimensional vector. node2vec is a well-known algorithm to obtain such a graph embedding by sampling neighboring nodes on a given graph with a random walk technique. However, the original node2vec algorithm typically relies on a batch training of graph struc

Mar Bastero-Gil, Paulo B. Ferraz, Lorenzo Ubaldi, Roberto Vega-Morales

Building on recently constructed inflationary vector dark matter production mechanisms as well as studies of magnetogenesis, we show that an inflationary dark Schwinger mechanism can generate the observed dark matter relic abundance for `dark electron' masses as light as $\sim 0.1$ eV and as heavy as $10^{12}$ GeV. The dark matter can interact very weakl

Gabe Guo, Judah Goldfeder, Ling Lan, Aniv Ray

The revolution in materials in the past century was built on a knowledge of the atomic arrangements and the structure-property relationship. The sine qua non for obtaining quantitative structural information is single crystal crystallography. However, increasingly we need to solve structures in cases where the information content in our input signal is signi

Bernd S. W. Schröder

For every ordered set, we reconstruct the deck obtained by removal of the elements of rank r that are neither minimal nor maximal. Consequently, we also reconstruct the deck obtained by removal of the extremal, that is, minimal or maximal, elements. Finally, we reconstruct the ordered sets with a minmax pair of pseudo-similar points.

J. Köry, P. S. Stewart, N. A. Hill, X. Y. Luo

We introduce a discrete mathematical model for the mechanical behaviour of a planar slice of human corneal tissue, in equilibrium under the action of physiological intraocular pressure (IOP). The model considers a regular (two-dimensional) network of structural elements mimicking a discrete number of parallel collagen lamellae connected by proteoglycan-based

Shujuan Li, Junsheng Zhou, Baorui Ma, Yu-Shen Liu

Point cloud upsampling aims to generate dense and uniformly distributed point sets from a sparse point cloud, which plays a critical role in 3D computer vision. Previous methods typically split a sparse point cloud into several local patches, upsample patch points, and merge all upsampled patches. However, these methods often produce holes, outliers or nonun

Qi Huang, Lucas Jordao, Siyuan Lu, Swarnabha Chattaraj

Realization of the long sought on-chip scalable photonic quantum information processing networks has been thwarted by the absence of spatially-ordered and scalable on-demand single photon emitters with emission figures-of-merit exceeding the required thresholds across large numbers. The positioning must meet the required degree of accuracy that enables fabri

Shifeng Qian, Yongpan Li, Cheng-Cheng Liu

We demonstrate that a class of stable $\mathbb{Z}_2$ monopole charge Dirac point ($\mathbb{Z}_2$DP) phases can robustly exist in real materials, which surmounts the understanding: that is, a $\mathbb{Z}_2$DP is unstable and generally considered to be only the critical point of a $\mathbb{Z}_2$ nodal line ($\mathbb{Z}_2$NL) characterized by a $\mathbb{Z}_2$ m

Yang You, Kai Xiong, Zhening Yang, Zhengxiang Huang

We introduce PACE (Pose Annotations in Cluttered Environments), a large-scale benchmark designed to advance the development and evaluation of pose estimation methods in cluttered scenarios. PACE provides a large-scale real-world benchmark for both instance-level and category-level settings. The benchmark consists of 55K frames with 258K annotations across 30

Xinran Yang, Guoyun Shao, Weibo He

We calculate the density fluctuations of net baryon number up to sixth order induced by the interactions of nuclear matter, and explore their relationship with the nuclear liquid-gas phase transition (LGPT), including the stable and metastable phase as well as the region far from the phase transition. The results show that dramatic density fluctuations exist

Michael Cerchia, Jesse Franklin, Evan O'Dorney

We compute generators and relations for the section ring of a rational divisor on an elliptic curve. Our technique generalizes the work of O'Dorney (in genus zero) and Voight--Zureick-Brown (for specific divisors arising from the study of stacky curves). For effective divisors supported on at most two points, we give explicit descriptions of the generators a

Yihang Yao, Zuxin Liu, Zhepeng Cen, Peide Huang

Online safe reinforcement learning (RL) involves training a policy that maximizes task efficiency while satisfying constraints via interacting with the environments. In this paper, our focus lies in addressing the complex challenges associated with solving multi-constraint (MC) safe RL problems. We approach the safe RL problem from the perspective of Multi-O

Michael Maroun

In the framework of distributionally generalized quantum theory, the object $H\psi$ is defined as a distribution. The mathematical significance is a mild generalization for the theory of para- and pseudo-differential operators (as well as a generalization of the weak eigenvalue problem), where the $\psi$-do symbol (which is not a proper linear operator in th

Jason Kumar, Carsten Rott, Pearl Sandick, Natalia Tapia-Arellano

We use publicly-available data to perform a search for correlations of high energy neutrino candidate events detected by IceCube and high-energy photons seen by the HAWC collaboration. Our search is focused on unveiling such correlations outside of the Galactic plane. This search is sensitive to correlations in the neutrino candidate and photon skymaps which

Weijie Xiong, Giorgio Facelli, Mehrad Sahebi, Owen Agnel

Quantum Extreme Learning Machines (QELMs) have emerged as a promising framework for quantum machine learning. Their appeal lies in the rich feature map induced by the dynamics of a quantum substrate - the quantum reservoir - and the efficient post-measurement training via linear regression. Here we study the expressivity of QELMs by decomposing the predictio

Hao Xu, Juan A. Colmenares

To provide quick responses to users, Internet companies rely on online data systems able to answer queries in milliseconds. These systems employ complementary overload management techniques to ensure they provide a continued, acceptable service through-out traffic surges, where 'acceptable' partly means that serviced queries meet or track closely their respo

Moritz Harmel, Anubhav Paras, Andreas Pasternak, Nicholas Roy

Reinforcement learning has been demonstrated to outperform even the best humans in complex domains like video games. However, running reinforcement learning experiments on the required scale for autonomous driving is extremely difficult. Building a large scale reinforcement learning system and distributing it across many GPUs is challenging. Gathering experi

Abhishake Rastogi, Tapio Helin, Nicole Mücke

We provide an overview of recent progress in statistical inverse problems with random experimental design, covering both linear and nonlinear inverse problems. Different regularization schemes have been studied to produce robust and stable solutions. We discuss recent results in spectral regularization methods and regularization by projection, exploring both

Cainã de Oliveira, Leonardo Paulo Maia, Vitor de Souza

We present a solution to Liouville's equation for an ensemble of charged particles propagating in magnetic fields. The solution is presented using an expansion in spherical harmonics of the phase space density, allowing a direct interpretation of the distribution of arrival directions of cosmic rays. The results are found for chosen conditions of variabi

Abhishek Kumar, Alban Pothérat

This paper presents a method to stabilise oscillations occurring in a mixed convective flow in a nearly hemispherical cavity, using actuation based on the receptivity map of the unstable mode. This configuration models the continuous casting of metallic alloys, where hot liquid metal is poured at the top of a hot sump with cold walls pulled in a solid phase

Nic Brody, Kasia Jankiewicz

A countable group is residually finite if every nontrivial element can act nontrivially on a finite set. When a group fails to be residually finite, we might want to measure how drastically it fails - it could be that only finitely many conjugacy classes of elements fail to act nontrivially on a finite set, or it could be that the group has no nontrivial act

Gabriel R. Palma, Rodrigo F. Mello, Wesley A. C. Godoy, Eduardo Engel

Implementing insect monitoring systems provides an excellent opportunity to create accurate interventions for insect control. However, selecting the appropriate time for an intervention is still an open question due to the inherent difficulty of implementing on-site monitoring in real-time. This decision is even more critical with insect species that can abr

Andreas Søjmark, Fabrice Wunderlich

We present a simple unifying treatment of a broad class of applications from statistical mechanics, econometrics, mathematical finance, and insurance mathematics, where (possibly subordinated) L\'evy noise arises as a scaling limit of some form of continuous-time random walk (CTRW). For each application, it is natural to rely on weak convergence results for

S. Borsányi, B. B. Brandt, G. Endrődi, J. Guenther

Peripheral heavy-ion collisions are expected to exhibit magnetic fields with magnitudes comparable to the QCD scale, as well as non-zero baryon densities. Whereas QCD at finite magnetic fields can be simulated directly with standard lattice algorithms, the implementation of real chemical potentials is hindered by the infamous sign problem. Aiming to shed lig

Alexander Berkovich, Aritram Dhar

In 2009, Berkovich and Garvan introduced a new partition statistic called the GBG-rank modulo $t$ which is a generalization of the well-known BG-rank. In this paper, we use the Littlewood decomposition of partitions to study partitions with bounded largest part and fixed integral value of GBG-rank modulo primes. As a consequence, we obtain new elegant genera

Wassim Kabbani, Marcel Grimmer, Christoph Busch

Generative Adversarial Networks (GANs) have witnessed significant advances in recent years, generating increasingly higher quality images, which are non-distinguishable from real ones. Recent GANs have proven to encode features in a disentangled latent space, enabling precise control over various semantic attributes of the generated facial images such as pos

Kasia Jankiewicz, Kevin Schreve

We prove that for every prime $p$ algebraically clean graphs of groups are virtually residually $p$-finite and cohomologically $p$-complete. We also prove that they are cohomologically good. We apply this to certain $2$-dimensional Artin groups.

J. C. Vega, E. Choreño, D. Ojeda-Guillén, R. D. Mota

We study the non-degenerate parametric amplifier problem from an algebraic approach of the $SU(1,1)$ group. We write the Hamiltonian of this problem in terms of the boson generators of the $SU(1,1)$ group and the difference operator. We apply the tilting transformation to our results to exactly solve this Hamiltonian and obtain its energy spectrum and eigenf

Tianyi Li, Joshua Klavins, Te Xu, Niaz Mahmud Zafri

Many factors influence the yielding result of a driver-pedestrian interaction, including traffic volume, vehicle speed, roadway characteristics, etc. While individual aspects of these interactions have been explored, comprehensive, naturalistic studies, particularly those considering the built environment's influence on driver-yielding behavior, are lacking.

Chengming Hu, Haolun Wu, Xuan Li, Chen Ma

Knowledge distillation aims to train a compact student network using soft supervision from a larger teacher network and hard supervision from ground truths. However, determining an optimal knowledge fusion ratio that balances these supervisory signals remains challenging. Prior methods generally resort to a constant or heuristic-based fusion ratio, which oft

Aidan Herderschee, Juan Maldacena

We show that the Banks-Fischler-Shenker-Susskind matrix model for M-theory obeys the leading and subleading soft theorems expected from eleven-dimensional supergravity. The subleading soft theorem implies the amplitude is Lorentz symmetric. This is argued for general four point amplitudes, but only for restricted kinematics for five and higher point amplitud

Shuoxing Zhou

We establish a hypertrace characterization of property (T) for $\mathrm{II}_1$ factors: Given a $\mathrm{II}_1$ factors $M$, $M$ does not have property (T) if and only if there exists a von Neumann algebra $\mathcal{A}$ with $M\subset \mathcal{A}$ such that $\mathcal{A}$ admits a $M$-hypertrace but no normal hypertrace. For $M$ without property (T), such an

Yuxiang Zhao, Benhao Lu, Mohamad Alipour

Unmanned Aerial Systems (UAS) have gained significant traction for their application in infrastructure inspections. However, considering the enormous scale and complex nature of infrastructure, automation is essential for improving the efficiency and quality of inspection operations. One of the core problems in this regard is electing an optimal automated fl

Vanlin Sathya, Aasawaree Deshmukh, Mohit Goyal, Mehmet Yavuz

Private network deployment is gaining momentum in warehouses, retail, automation, health care, and many such use cases to guarantee mission-critical services with less latency. Guaranteeing the delay-sensitive application in Wi-Fi is always challenging due to the nature of unlicensed spectrum. As the device ecosystem keeps growing and expanding, all the curr

Andrea Mucchietto, Korbinian Baumgaertl, Dirk Grundler

Magnetic bit writing by short-wave magnons without conversion to the electrical domain is expected to be a game-changer for in-memory computing architectures. Recently, the reversal of nanomagnets by propagating magnons was demonstrated. However, experiments have not yet explored different wavelengths and the nonlinear excitation regime of magnons required f

Joern Ploennigs, Markus Berger

Recent generative AI platforms are able to create texts or impressive images from simple text prompts. This makes them powerful tools for summarizing knowledge about architectural history or deriving new creative work in early design tasks like ideation, sketching and modelling. But, how good is the understanding of the generative AI models of the history of

Rajat Subhra Hazra, Frank den Hollander, Azadeh Parvaneh

Let $G_n$ be an undirected finite graph on $n\in\mathbb{N}$ vertices labelled by $[n] = \{1,\ldots,n\}$. For $i \in [n]$, let $\Delta_{i,n}$ be the friendship bias of vertex $i$, defined as the difference between the average degree of the neighbours of vertex $i$ and the degree of vertex $i$ itself when $i$ is not isolated, and zero when $i$ is isolated. Let

J. Kvapil, G. Borca-Tasciuc, H. Bossi, K. Chen

The RHIC interaction rate at sPHENIX will reach around 3 MHz in pp collisions and requires the detector readout to reject events by a factor of over 200 to fit the DAQ bandwidth of 15 kHz. Some critical measurements, such as heavy flavor production in pp collisions, often require the analysis of particles produced at low momentum. This prohibits adopting the

Benjamin Scellier, Maxence Ernoult, Jack Kendall, Suhas Kumar

Energy-based learning algorithms have recently gained a surge of interest due to their compatibility with analog (post-digital) hardware. Existing algorithms include contrastive learning (CL), equilibrium propagation (EP) and coupled learning (CpL), all consisting in contrasting two states, and differing in the type of perturbation used to obtain the second

Wassim Kabbani, Christoph Busch, Kiran Raja

Face image quality assessment (FIQA) is crucial for obtaining good face recognition performance. FIQA algorithms should be robust and insensitive to demographic factors. The eye sclera has a consistent whitish color in all humans regardless of their age, ethnicity and skin-tone. This work proposes a robust sclera segmentation method that is suitable for face

Nikolaos Louloudakis, Perry Gibson, José Cano, Ajitha Rajan

Converting deep learning models between frameworks is a common step to maximize model compatibility across devices and leverage optimization features that may be exclusively provided in one deep learning framework. However, this conversion process may be riddled with bugs, making the converted models either undeployable or problematic, considerably degrading

Carl R. Gwinn

I find nearly plane-wave solutions for the Gauss-Ampere law for the 4-vector potential, subject to the Lorenz gauge condition, in the spacetime of a plane gravitational plane wave. I assume that the gravitational wave is weak, in the sense that the dimensionless strain amplitude h is much less than 1. I find a solution for the homogeneous scalar wave equatio

Nishant Vishwamitra, Keyan Guo, Farhan Tajwar Romit, Isabelle Ondracek

Online hate is an escalating problem that negatively impacts the lives of Internet users, and is also subject to rapid changes due to evolving events, resulting in new waves of online hate that pose a critical threat. Detecting and mitigating these new waves present two key challenges: it demands reasoning-based complex decision-making to determine the prese

Jay Kejriwal, Stefan Benus, Lina M. Rojas-Barahona

Speakers tend to engage in adaptive behavior, known as entrainment, when they become similar to their interlocutor in various aspects of speaking. We present an unsupervised deep learning framework that derives meaningful representation from textual features for developing semantic entrainment. We investigate the model's performance by extracting features us

Junqi Jiang, Antonio Rago, Francesco Leofante, Francesca Toni

Model Multiplicity (MM) arises when multiple, equally performing machine learning models can be trained to solve the same prediction task. Recent studies show that models obtained under MM may produce inconsistent predictions for the same input. When this occurs, it becomes challenging to provide counterfactual explanations (CEs), a common means for offering

Irina Kostitsyna, Tim Ophelders, Irene Parada, Tom Peters

The sliding cubes model is a well-established theoretical framework that supports the analysis of reconfiguration algorithms for modular robots consisting of face-connected cubes. The best algorithm currently known for the reconfiguration problem, by Abel and Kominers [arXiv, 2011], uses O(n3) moves to transform any n-cube configuration into any other n-cube

Hrant Topchyan, Vasilii Iugov, Mkhitar Mirumyan, Tigran S. Hakobyan

We systematically study gapless edge modes corresponding to $\mathbb{Z}_3$ symmetry-protected topological (SPT) phases of two-dimensional three-state Potts paramagnets on a triangular lattice. First, we derive microscopic lattice models for the gapless edge and, using the density-matrix renormalization group (DMRG) approach, investigate the finite-size scali

Jan Charatan, Eleanor Birrell

The California Privacy Rights Act (CPRA) was a ballot initiative that revised the California Consumer Privacy Act (CCPA). Although often framed as expanding and enhancing privacy rights, a close analysis of textual revisions -- both changes from the earlier law and changes from earlier drafts of the CPRA guidelines -- suggest that the reality might be more n

Aram Bingham, Néstor Díaz Morera

In this paper, we show that the Bruhat order on any sect of a symmetric variety of type $AIII$ is lexicographically shellable. Our proof proceeds from a description of these posets as rook placements in a partition shape which fits in a $p \times q$ rectangle. This allows us to extend an EL-labeling of the rook monoid given by Can to an arbitrary sect. As a

Shravan Pradeep, Paulo E. Arratia, Douglas J. Jerolmack

When wet soil becomes fully saturated by intense rainfall, or is shaken by an earthquake, it may fluidize catastrophically. Sand-rich slurries are treated as granular suspensions, where the failure is related to an unjamming transition. Mud flows are modeled as gels, where yielding and shear-thinning behaviors arise from inter-particle attraction and cluster

Huizhen Yu, Yi Wan, Richard S. Sutton

In this paper, we study asynchronous stochastic approximation algorithms without communication delays. Our main contribution is a stability proof for these algorithms that extends a method of Borkar and Meyn by accommodating more general noise conditions. We also derive convergence results from this stability result and discuss their application in important

Brandon L. Butler, Domagoj Fijan, Sharon C. Glotzer

Particle tracking is commonly used to study time-dependent behavior in many different types of physical and chemical systems involving constituents that span many length scales, including atoms, molecules, nanoparticles, granular particles, etc. Behaviors of interest studied using particle tracking information include disorder-order transitions, thermodynami

Shovasis Kumar Biswas, Wihan Adi, Aidana Beisenova, Samir Rosas

Resonant metasurfaces present extraordinary subwavelength light trapping capabilities, which have been critical to the development of high-performance biochemical sensors and surface-enhanced spectroscopy techniques. To date, metasurface-enhanced light-matter interactions in the mid-infrared region have been primarily leveraged in the weak coupling regime. N

Yuechun Gu, Jiajie He, Keke Chen

With increasingly deployed deep neural networks in sensitive application domains, such as healthcare and security, it's essential to understand what kind of sensitive information can be inferred from these models. Most known model-targeted attacks assume attackers have learned the application domain or training data distribution to ensure successful attacks.

Eshan Chattopadhyay, Mohit Gurumukhani, Noam Ringach

We prove several new results for seedless condensers in the context of three related classes of sources: Non-Oblivious Symbol Fixing (NOSF) sources, online NOSF (oNOSF) sources [AORSV, EUROCRYPT'20], and adversarial Chor-Goldreich (aCG) source [DMOZ, STOC'23]. We think of these sources as a sequence of random variables $\mathbf{X}=\mathbf{X}_1,\dots,\mathbf{

Nikhil Mehta, Kevin J Liang, Jing Huang, Fu-Jen Chu

Out-of-distribution (OOD) detection is an important topic for real-world machine learning systems, but settings with limited in-distribution samples have been underexplored. Such few-shot OOD settings are challenging, as models have scarce opportunities to learn the data distribution before being tasked with identifying OOD samples. Indeed, we demonstrate th

Joanna Perido, Kevin Denis, Jason Glenn, Nicholas F. Cothard

The far-infrared (IR) region is rich with information needed to characterize interstellar dust and to investigate the cold outer planets of the solar system and their icy moons. The proposed sub-orbital observatory the Balloon Experiment for Galactic INfrared Science (BEGINS) will utilize cryogenic instruments to map spectral energy distributions (SEDs) of i

Binbin Xiang, Maciej Wielgosz, Theodora Kontogianni, Torben Peters

Detailed forest inventories are critical for sustainable and flexible management of forest resources, to conserve various ecosystem services. Modern airborne laser scanners deliver high-density point clouds with great potential for fine-scale forest inventory and analysis, but automatically partitioning those point clouds into meaningful entities like indivi

Suman K. Dhakal, Jie Zhang

Flare productivity varies among solar active regions (ARs). This study analyzed 20 ARs of contrasting sunspot areas and flare productivities to understand the super flare productivity of certain ARs. We used the flare index (FI) as an indicator of flare activity. We examined the pattern of morphological evolution of magnetic features. Further, we derived a s

Stephen Whitelam

Landauer's principle states that erasing a bit of information at fixed temperature T costs at least kT ln 2 units of work. Here we investigate erasure at varying temperature, to which Landauer's result does not apply. We formulate bit erasure as a stochastic nonequilibrium process involving a compression of configuration space, with physical and logical stat

Michael J. Friedel, Nicole Lautze, Erin Wallin, Roland Gritto

We present a multimodal machine learning (MML) workflow to assimilate and simultaneously predict the 3d distribution of numeric and categorical features along a groundwater-geothermal continuum. Success of the MML workflow relies on a transductive learning algorithm that projects field modalities onto a single embedding space (hypersurface). Multimodalities

H. Chen, R. Gibbons, S. J. Haselschwardt, S. Kravitz

Experiments searching for weakly interacting massive particle dark matter are now detecting background events from solar neutrino-electron scattering. However, the dominant background in state-of-the-art experiments such as LZ and XENONnT is beta decays from radon contamination. In spite of careful detector material screening, radon progenitor atoms are ubiq

Arjun Seshadri, Stephen Ragain, Johan Ugander

Although the foundations of ranking are well established, the ranking literature has primarily been focused on simple, unimodal models, e.g. the Mallows and Plackett-Luce models, that define distributions centered around a single total ordering. Explicit mixture models have provided some tools for modelling multimodal ranking data, though learning such model

Emmanuel Fromager, Benjamin Lasorne

This work presents an alternative, general, and in-principle exact extension of electronic Kohn-Sham density functional theory (KS-DFT) to the fully quantum-mechanical molecular problem. Unlike in existing multi-component or exact-factorization-based DFTs of electrons and nuclei, both nuclear and electronic densities are mapped onto a fictitious electronical

Wenxuan Guo, Panos Toulis

In this paper, we develop invariance-based procedures for testing and inference in high-dimensional regression models. These procedures, also known as randomization tests, provide several important advantages. First, for the global null hypothesis of significance, our test is valid in finite samples. It is also simple to implement and comes with finite-sampl

Isabel Donoso Leiva, Eric Goles, Martín Ríos-Wilson, Sylvain Sené

Among the fundamental questions in computer science is that of the impact of synchronism/asynchronism on computations, which has been addressed in various fields of the discipline: in programming, in networking, in concurrence theory, in artificial learning, etc. In this paper, we tackle this question from a standpoint which mixes discrete dynamical system t

Jeremy R. Rouxel, Riccardo Mincigrucci, Danny Fainozzi, Claudio Masciovecchio

Measuring natural circular dichroism in the X-ray regime to extract stereochemical information from chiral molecules in solution remains a challenge. This is primarily due to technical limitations of the existing synchrotron sources. It hinders access to measurements of local chirality by exploiting core hole electronic transitions. In response to this chall

Xiaoxue Gao, Edwin Jiang, Andrew Pike, Eldred Lee

High entropy alloys (HEAs) have attracted substantial interest in recent years. Thus far, most investigations have focused on their applications as structural materials rather than functional materials. In this paper, we show that FeMnNiAlCr HEAs can potentially be applied as both a structural and functional material for high-efficiency concentrated solar th

W. C. Smith, A. Borgognoni, M. Villiers, E. Roverc'h

Inducing interactions between individual photons is key for photonic quantum information and studying many-body photon states. Superconducting circuits are well suited to combine strong interactions with low losses. Typically, microwave photons are stored in an $LC$ oscillator shunted by a Josephson junction, where the zero-point phase fluctuations across th

Olivier Gingras, Antoine Georges, Olivier Parcollet

We investigate the possible superconducting instabilities of strongly correlated electron materials using a generalization of linear response theory to external pairing fields depending on frequency. We compute a pairing susceptibility depending on two times, allowing us to capture dynamical pairing and in particular odd-frequency solutions. We first benchma

Jianxin Sun, David Lenz, Hongfeng Yu, Tom Peterka

Considering the challenges posed by the space and time complexities in handling extensive scientific volumetric data, various data representations have been developed for the analysis of large-scale scientific data. Multivariate functional approximation (MFA) is an innovative data model designed to tackle substantial challenges in scientific data analysis. I

Deyi Zhuo, Ling-Jie Zhou, Yi-Fan Zhao, Ruoxi Zhang

The plateau phase transition in quantum anomalous Hall (QAH) insulators corresponds to a quantum state wherein a single magnetic domain gives way to multiple magnetic domains and then re-converges back to a single magnetic domain. The layer structure of the sample provides an external knob for adjusting the Chern number C of the QAH insulators. Here, we empl

Akhil Padmanabha, Janavi Gupta, Chen Chen, Jehan Yang

Teleoperation of mobile manipulators within a home environment can significantly enhance the independence of individuals with severe motor impairments, allowing them to regain the ability to perform self-care and household tasks. There is a critical need for novel teleoperation interfaces to offer effective alternatives for individuals with impairments who m