October 2023 arXiv papers — page 139

Yiming Chen, Chi Chen, Inhui Hwang, Michael J. Davis

X-ray absorption spectroscopy (XAS) is a commonly-employed technique for characterizing functional materials. In particular, x-ray absorption near edge spectra (XANES) encodes local coordination and electronic information and machine learning approaches to extract this information is of significant interest. To date, most ML approaches for XANES have primari

Liangqi Yuan, Dong-Jun Han, Vishnu Pandi Chellapandi, Stanislaw H. Żak

Multimodal federated learning (FL) aims to enrich model training in FL settings where devices are collecting measurements across multiple modalities (e.g., sensors measuring pressure, motion, and other types of data). However, key challenges to multimodal FL remain unaddressed, particularly in heterogeneous network settings: (i) the set of modalities collect

Nuria Gómez-Vargas, Sebastián Maldonado, Carla Vairetti

In this paper, we introduce a novel predict-and-optimize method for profit-driven churn prevention. We frame the task of targeting customers for a retention campaign as a regret minimization problem. The main objective is to leverage individual customer lifetime values (CLVs) to ensure that only the most valuable customers are targeted. In contrast, many pro

N. V. Krylov

In 1990 F. Chiarenza and M. Frasca published a paper in which they generalized a result of C. Fefferman on estimates of the integral of $|bu|^{p}$ through the integral of $|Du|^{p}$ for $p>1$. Formally their proof is valid only for $d\geq 3$. We present here further generalization with a different proof in which $D $ is replaced with the fractional power of

Nathan A. Harper, Emily Y. Hwang, Ryoto Sekine, Luis Ledezma

Efficient on-chip entangled photon pair generation at telecom wavelengths is an integral aspect of emerging quantum optical technologies, particularly for quantum communication and computing. However, moving to shorter wavelengths enables the use of more accessible silicon detector technology and opens up applications in imaging and spectroscopy. Here, we pr

David Hartman, Tomáš Hons, Jaroslav Nešetřil

Structural convergence is a framework for convergence of graphs by Ne\v{s}et\v{r}il and Ossona de Mendez that unifies the dense (left) graph convergence and Benjamini-Schramm convergence. They posed a problem asking whether for a given sequence of graphs $(G_n)$ converging to a limit $L$ and a vertex $r$ of $L$ it is possible to find a sequence of vertices $

Qucheng Gao, Pengfei Zhang, Xiao Chen

It is well established that the presence of single impurity can have a substantial impact on the transport properties of quantum many-body systems at low temperature. In this work, we investigate a close analog of this problem from the perspective of quantum information dynamics. We construct Brownian circuits and Clifford circuits consisting of a free fermi

Po-Ning Chen, Michael McNulty, Birgit Schörkhuber

This paper concerns the formation of singularities in the classical $(5+1)$-dimensional, co-rotational Skyrme model. While it is well established that blowup is excluded in $(3+1)$-dimensions, nothing appears to be known in the higher dimensional case. We prove that the model, in the so-called strong field limit, admits an explicit self-similar solution whic

Ekaterina Kompantseva, Askar Tuganbaev

We consider the class $\mathcal{A}_0$ of Abelian block-rigid $CRQ$-groups of ring type. A subgroup $A$ of an Abelian group $G$ is called an \textsf{absolute ideal} of the group $G$ if $A$ is an ideal in any ring on $G$. We describe principal absolute ideals of groups in $\mathcal{A}_0$. This allows to prove that any group in $\mathcal{A}_0$ is an $afi$-group

Zsolt Bartha, Júlia Komjáthy, Daniel Valesin

We study degree-penalized contact processes on Galton-Watson trees (GW) and the configuration model. The model we consider is a modification of the usual contact process on a graph. In particular, each vertex can be either infected or healthy. When infected, each vertex heals at rate one. Also, when infected, a vertex $v$ with degree $d_v$ infects its neighb

Julien Walden Huang, Stephen Roberts, Jan-Peter Calliess

This paper examines the asymptotic convergence properties of Lipschitz interpolation methods within the context of bounded stochastic noise. In the first part of the paper, we establish probabilistic consistency guarantees of the classical approach in a general setting and derive upper bounds on the uniform convergence rates. These bounds align with well-est

D. Caprioli, G. Zacharegkas, C. Haggerty, S. Gupta

The non-resonant (Bell) streaming instability driven by energetic particles is crucial for producing amplified magnetic fields that are key to the acceleration of cosmic rays (CRs) in supernova remnants, around Galactic and extra-galactic CR sources, and for the CR transport. We present a covariant theory for the saturation of the Bell instability, substanti

Georgy E. Sambarov, Anna P. Kartashova, Tatyana Yu. Galushina

The paper presents numerical models of mean-motion resonances detected in the Quadrantid meteoroid stream consisting of particles of mass 0.003 to 0.03 g, which helps to prove the presence of the following mean-motion resonances: the 1:9 resonance with Venus, 1:5 resonance with Earth, 1:3 and 3:8 resonances with Mars, and 2:1,7:3, 9:4 resonances with Jupiter

Luc Dessart, Taeho Ryu, Pau Amaro Seoane, Andrew M. Taylor

High-velocity stellar collisions driven by a supermassive black hole (BH) or BH-driven disruptive collisions, in dense, nuclear clusters can rival the energetics of supergiant star explosions following gravitational collapse of their iron core. Here, starting from a sample of red-giant star collisions simulated with the hydrodynamics code AREPO, we generate

Saransh Singh, Martin G. Gorman, Patrick G. Heighway, Joel V. Bernier

The traditional picture of solid-solid phase transformations assumes an ordered parent phase transforms into an ordered daughter phase via a single unique pathway. Zirconium and its prototypical phase transition from hexagonal close-packed (hcp) to simple hexagonal (hex-3) structure has generated considerable controversy over several decades regarding which

Thiago Bomfim, Afonso Fernandes

It is known that if $f: \mathbb{S}^{1} \rightarrow \mathbb{S}^{1}$ is a transitive $C^{1+\alpha}$-local diffeomorphism non-invertible and non-uniformly expanding, then there is a unique parameter $t_{0} \in (0 , 1]$ such that the topological pressure function $\mathbb{R} \ni t \mapsto P_{top}(f , -t\log|Df|)$ is not analytic, in particular $f$ has a phase tr

Gabriele Campanella, Ricky Kwan, Eugene Fluder, Jennifer Zeng

Recent breakthroughs in self-supervised learning have enabled the use of large unlabeled datasets to train visual foundation models that can generalize to a variety of downstream tasks. While this training paradigm is well suited for the medical domain where annotations are scarce, large-scale pre-training in the medical domain, and in particular pathology,

Ross Geuy, Nate Rising, Tiancheng Shi, Meng Ling

We present results from a pilot experiment to measure if machine recommendations can debias human perceptual biases in visualization tasks. We specifically studied the ``pull-down'' effect, i.e., people underestimate the average position of lines, for the task of estimating the ensemble average of data points in line charts. These line charts can show for ex

Karim Helwani, Erfan Soltanmohammadi, Michael M. Goodwin

Improving the interpretability of deep neural networks has recently gained increased attention, especially when the power of deep learning is leveraged to solve problems in physics. Interpretability helps us understand a model's ability to generalize and reveal its limitations. In this paper, we introduce a causal interpretable deep structure for modeling dy

Gabriella Pantaleão, Rúben Queirós, Hélder Fontes, Rui Campos

With the growing connectivity demands, Unmanned Aerial Vehicles (UAVs) have emerged as a prominent component in the deployment of Next Generation On-demand Wireless Networks. However, current UAV positioning solutions typically neglect the impact of Rate Adaptation (RA) algorithms or simplify its effect by considering ideal and non-implementable RA algorithm

Aakash Varma Nadimpalli, Ajita Rattani

Facial forgery by deepfakes has caused major security risks and raised severe societal concerns. As a countermeasure, a number of deepfake detection methods have been proposed. Most of them model deepfake detection as a binary classification problem using a backbone convolutional neural network (CNN) architecture pretrained for the task. These CNN-based meth

Che Liu, Anand Shah, Wenjia Bai, Rossella Arcucci

Medical Vision-Language Pre-training (VLP) learns representations jointly from medical images and paired radiology reports. It typically requires large-scale paired image-text datasets to achieve effective pre-training for both the image encoder and text encoder. The advent of text-guided generative models raises a compelling question: Can VLP be implemented

Farhang Bayat, Brian Miller, Yeonok Park, Zhelin Yu

Online dose calculations before radiation treatment have applications in dose delivery verification, plan adaptation, and treatment planning. We propose a novel CBCT imaging pipeline to enhance accuracy. Our approach aims to improve HU accuracy in CBCT images for more precise dose calculations. A quantitative CBCT pipeline was implemented, combining data cor

Ahmad Mokhtar

We study the geometry of the Fano schemes $\mathrm{\textbf{F}}_{k}(\mathrm{SD}_n^r)$ of the projective variety $\mathrm{SD}_n^r$ defined by the $r\times r$ minors of a symmetric $n\times n$ matrix filled with indeterminates. These schemes are fine moduli spaces parameterizing $(k+1)$-dimensional linear spaces of $n\times n$ symmetric matrices of rank less th

Jacopo G. Chen

We present an algorithm that computes Friedl and L\"uck's twisted $L^2$-Euler characteristic for a suitable regular CW complex, employing Oki's matrix expansion algorithm to indirectly evaluate the Dieudonn\'e determinant. The algorithm needs to run for an extremely long time to certify its outputs, but a truncated, human-assisted version produces very good

Forrest Huang, Gang Li, Tao Li, Yang Li

Macros are building block tasks of our everyday smartphone activity (e.g., "login", or "booking a flight"). Effectively extracting macros is important for understanding mobile interaction and enabling task automation. These macros are however difficult to extract at scale as they can be comprised of multiple steps yet hidden within programmatic components of

Hassan Almubarak, Nader Sadegh, Evangelos A. Theodorou

Multi-objective safety-critical control entails a diligent design to avoid possibly conflicting scenarios and ensure safety. This paper addresses multi-objective safety-critical control through a novel approach utilizing barrier states (BaS) to integrate safety into control design. It introduces the concept of safety embedded systems, where the safety condit

Sophia Gu

With the recent rise of widely successful deep learning models, there is emerging interest among professionals in various math and science communities to see and evaluate the state-of-the-art models' abilities to collaborate on finding or solving problems that often require creativity and thus brainstorming. While a significant chasm still exists between cur

Vishnu Dutt Sharma, Anukriti Singh, Pratap Tokekar

2D top-down maps are commonly used for the navigation and exploration of mobile robots through unknown areas. Typically, the robot builds the navigation maps incrementally from local observations using onboard sensors. Recent works have shown that predicting the structural patterns in the environment through learning-based approaches can greatly enhance task

Amelie Menges

Given n convex bodies in the real space of dimension d, we consider the set of homogeneous polynomials of degree d in n variables that can be represented as their volume polynomial. This set is a subset of the set of Lorentzian polynomials. Using our knowledge of operations that preserve the Lorentzian property, we give a complete classification of the cases

Quan Ze Chen, Amy X. Zhang

From moderating content within an online community to producing socially-appropriate generative outputs, decision-making tasks -- conducted by either humans or AI -- often depend on subjective or socially-established criteria. To ensure such decisions are consistent, prevailing processes primarily make use of high-level rules and guidelines to ground decisio

Yi Ru Wang, Jiafei Duan, Dieter Fox, Siddhartha Srinivasa

Large Language Models (LLMs), through their contextualized representations, have been empirically proven to encapsulate syntactic, semantic, word sense, and common-sense knowledge. However, there has been limited exploration of their physical reasoning abilities, specifically concerning the crucial attributes for comprehending everyday objects. To address th

Matthias Neumann, Tom Philipp, Marcel Häringer, Gregor Neusser

A data-driven modeling approach is presented to quantify the influence of morphology on effective properties in nanostructured sodium vanadium phosphate $\mathrm{Na}_3\mathrm{V}_2(\mathrm{PO}_4)_3$/ carbon composites (NVP/C), which are used as cathode material in sodium-ion batteries. This approach is based on the combination of advanced imaging techniques,

V. Roshan Joseph, William E. Lewis, Henry S. Yuchi, Kathryn A. Maupin

This article aims at discovering the unknown variables in the system through data analysis. The main idea is to use the time of data collection as a surrogate variable and try to identify the unknown variables by modeling gradual and sudden changes in the data. We use Gaussian process modeling and a sparse representation of the sudden changes to efficiently

Ferran Alet, Erica Weng, Tomás Lozano Pérez, Leslie Pack Kaelbling

\textit{Graph neural networks} (GNNs) are effective models for many dynamical systems consisting of entities and relations. Although most GNN applications assume a single type of entity and relation, many situations involve multiple types of interactions. \textit{Relational inference} is the problem of inferring these interactions and learning the dynamics f

Akram Hasseine

In the effective theory of loop quantum cosmology LQC, the influence of the holonomy correction (with $\overline{\mu}$-scheme) on the homogeneous and the inhomogeneous cosmological models have been extensively studied in the case of flat space. In this paper,using the K-Quantization method and the $\overline{\mu}$-scheme,in the same framework of LQC,we const

Elizabeth J. Iles, Alex R. Pettitt, Takashi Okamoto, Daisuke Kawata

Different mechanisms driving bar structure formation indicate that bar origins should be distinguishable in the stellar populations of galaxies. To study how these origins affect different bar morphologies and impact stellar orbits and migration, we analyse three simulated discs which are representative of bar formation under isolated evolution motivated by

James Currie, Narad Rampersad

We find the lexicographically least infinite binary rich word having critical exponent $2+\sqrt{2}/2$

Dan Li, Chunmei Wang, Shangyou Zhang

This paper presents a new weak Galerkin (WG) method for elliptic interface problems on general curved polygonal partitions. The method's key innovation lies in its ability to transform the complex interface jump condition into a more manageable Dirichlet boundary condition, simplifying the theoretical analysis significantly. The numerical scheme is designed

Mikhail Salnikov, Maria Lysyuk, Pavel Braslavski, Anton Razzhigaev

Pre-trained Text-to-Text Language Models (LMs), such as T5 or BART yield promising results in the Knowledge Graph Question Answering (KGQA) task. However, the capacity of the models is limited and the quality decreases for questions with less popular entities. In this paper, we present a novel approach which works on top of the pre-trained Text-to-Text QA sy

Thierry Serafin Nadeau, Marten H. van Kerkwijk, Cees G. Bassa, Ben W. Stappers

Using archival data from the 42 foot telescope at the Jodrell Bank Observatory, we produce daily stacks of aligned giant pulses for the Crab pulsar, to study changes to the daily profiles between April 2012 to December 2016. From these, we identify echoes, where intervening material away from the line of sight causes pulsed emission to be redirected towards

Tony Feng, Bao Le Hung

The Breuil-M\'{e}zard Conjecture predicts the existence of hypothetical "Breuil-Mezard cycles" in the moduli space of mod $p$ Galois representations of $\mathrm{Gal}(\overline{\mathbb{Q}}_q/\mathbb{Q}_q)$ that should govern congruences between mod $p$ automorphic forms. For generic parameters, we propose a construction of Breuil-M\'{e}zard cycles in arbitrar

Rodolfo Valentim, Idilio Drago, Marco Mellia, Federico Cerutti

Sound-squatting is a phishing attack that tricks users into malicious resources by exploiting similarities in the pronunciation of words. Proactive defense against sound-squatting candidates is complex, and existing solutions rely on manually curated lists of homophones. We here introduce Sound-skwatter, a multi-language AI-based system that generates sound-

C. J. O. Reichhardt, C. Reichhardt

We examine a charge lattice coupled to a one-dimensional asymmetric potential in the presence of an applied magnetic field, which induces gyrotropic effects in the charge motion. This system could be realized for Wigner crystals in nanostructured samples, dusty plasmas, or other classical charge-ordered states where gyrotropic motion and damping can arise. F

Philip Protter, Andrés Riveros Valdevenito

In this short paper, we connect the procedure of constructing a totally inaccessible stopping time for a given process using the well-known Cox construction, dependent on an independent exponential random variable; with naturally occurring jump times of Feller processes. Ultimately, we show that these two phenomena are not only related, but are in fact two e

Alex Cooper, Aki Vehtari, Catherine Forbes, Lauren Kennedy

Brute force cross-validation (CV) is a method for predictive assessment and model selection that is general and applicable to a wide range of Bayesian models. Naive or `brute force' CV approaches are often too computationally costly for interactive modeling workflows, especially when inference relies on Markov chain Monte Carlo (MCMC). We propose overcoming

Pavel Salev, Elliot Kisiel, Dayne Sasaki, Brandon Gunn

Electrical triggering of a metal-insulator transition (MIT) often results in the formation of characteristic spatial patterns such as a metallic filament percolating through an insulating matrix or an insulating barrier splitting a conducting matrix. When the MIT triggering is driven by electrothermal effects, the temperature of the filament or barrier can b

Sumukh Vasisht Shankar, Evangelos K Oikonomou, Rohan Khera

In the rapidly evolving landscape of modern healthcare, the integration of wearable & portable technology provides a unique opportunity for personalized health monitoring in the community. Devices like the Apple Watch, FitBit, and AliveCor KardiaMobile have revolutionized the acquisition and processing of intricate health data streams. Amidst the variety of

Andrea Alcaraz, Federico Rodriguez Cairoli, Carla Colaci, Constanza Silvestrini

Objectives: Lung cancer remains a significant global public health challenge and is still one of the leading cause of cancer-related death in Argentina. This study aims to assess the disease and economic burden of lung cancer in the country. Study design: Burden of disease study Methods. A mathematical model was developed to estimate the disease burden and d

G. -Q. Zhang, D. Bégué, A. Pe'er, B. -B. Zhang

The study of Magnetically Arrested Disks (MAD) attract strong interest in recent years, as these disk configurations were found to generate strong jets as observed in many accreting systems. Here, we present the results of 14 general relativistic magnetohydrodynamic(GRMHD) simulations of advection dominated accretion flow in the MAD state across black hole s

Pratik Brahma, Krishnakumar Bhattaram, Sayeef Salahuddin

Modern microelectronic devices are composed of interfaces between a large number of materials, many of which are in amorphous or polycrystalline phases. Modeling such non-crystalline materials using first-principles methods such as density functional theory is often numerically intractable. Recently, graph neural networks (GNNs) have shown potential to achie

Ertza Warraich, Omer Shabtai, Khalid Manaa, Shay Vargaftik

We present OptiReduce, a new collective-communication system for the cloud with bounded, predictable completion times for deep-learning jobs in the presence of varying computation (stragglers) and communication (congestion and gradient drops) variabilities. OptiReduce exploits the inherent resiliency and the stochastic nature of distributed deep-learning (DD

Wen-Hsuan Chu, Adam W. Harley, Pavel Tokmakov, Achal Dave

Object tracking is central to robot perception and scene understanding. Tracking-by-detection has long been a dominant paradigm for object tracking of specific object categories. Recently, large-scale pre-trained models have shown promising advances in detecting and segmenting objects and parts in 2D static images in the wild. This begs the question: can we

O. R. Dehghan

The aim of this paper is to introduce the notion of bipolar fuzzy soft hypervector spaces and study their basic properties. In this regard, at first some new operation and external hyperoperation are defined on bipolar fuzzy soft sets over hypervector space V, related to the operation and external hyperoperation of V. Then the notion of bipolar fuzzy soft hy

Minhui Zou, Zhenhua Zhu, Tzofnat Greenberg-Toledo, Orian Leitersdorf

The execution of deep neural network (DNN) algorithms suffers from significant bottlenecks due to the separation of the processing and memory units in traditional computer systems. Emerging memristive computing systems introduce an in situ approach that overcomes this bottleneck. The non-volatility of memristive devices, however, may expose the DNN weights s

Javier H Nicolau, Xishuo Wei, Pengfei Liu, Gyungjin Choi

Global gyrokinetic simulations find a strong helically-trapped electron mode (HTEM) driven by density gradients in the optimized stellarator W7-X fusion experiment. The eigenmode structure localizes in the inner side of the torus with an unfavorable magnetic curvature and weak magnetic field, where there is a large fraction of helically-trapped electrons. Th

Yangsibo Huang, Samyak Gupta, Mengzhou Xia, Kai Li

The rapid progress in open-source large language models (LLMs) is significantly advancing AI development. Extensive efforts have been made before model release to align their behavior with human values, with the primary goal of ensuring their helpfulness and harmlessness. However, even carefully aligned models can be manipulated maliciously, leading to unint

Tim Haubold, Sven Beuchler, Joachim Schöberl

From the literature, it is known that the choice of basis functions in hp-FEM heavily influences the computational cost in order to obtain an approximate solution. Depending on the choice of the reference element, suitable tensor product like basis functions of Jacobi polynomials with different weights lead to optimal properties due to condition number and s

N. Jannsen, J. De Ridder, D. Seynaeve, S. Regibo

PLAnetary Transits and Oscillations of stars (PLATO) is the ESA M3 space mission dedicated to detect and characterise transiting exoplanets including information from the asteroseismic properties of their stellar hosts. The uninterrupted and high-precision photometry provided by space-borne instruments such as PLATO require long preparatory phases. An exhaus

Le Chen, Weirong Chen, Rui Wang, Marc Pollefeys

As a promising fashion for visual localization, scene coordinate regression (SCR) has seen tremendous progress in the past decade. Most recent methods usually adopt neural networks to learn the mapping from image pixels to 3D scene coordinates, which requires a vast amount of annotated training data. We propose to leverage Neural Radiance Fields (NeRF) to ge

Qianqian Zhang, Chi-Jiun Su

Quick UDP Internet Connection (QUIC) is an emerging end-to-end encrypted, transport-layer protocol, which has been increasingly adopted by popular web services to improve communication security and quality of experience (QoE) towards end-users. However, this tendency makes the traffic analysis more challenging, given the limited information in the QUIC packe

Courtland Leer, Vincent Trost, Vineeth Voruganti

Recent research shows that Large Language Models (LLMs) exhibit a compelling level of proficiency in Theory of Mind (ToM) tasks. This ability to impute unobservable mental states to others is vital to human social cognition and may prove equally important in principal-agent relations between individual humans and Artificial Intelligences (AIs). In this paper

Xuxi Chen, Yu Yang, Zhangyang Wang, Baharan Mirzasoleiman

Dataset distillation aims to minimize the time and memory needed for training deep networks on large datasets, by creating a small set of synthetic images that has a similar generalization performance to that of the full dataset. However, current dataset distillation techniques fall short, showing a notable performance gap when compared to training on the or

Federico Carollo, Igor Lesanovsky, Juan P. Garrahan

We consider quantum jump trajectories of Markovian open quantum systems subject to stochastic in time resets of their state to an initial configuration. The reset events provide a partitioning of quantum trajectories into consecutive time intervals, defining sequences of random variables from the values of a trajectory observable within each of the intervals

Francisco Martín, Mariel Sáez, Raphael Tsiamis

We prove a conjecture by Hoffman, White, and the first author regarding the uniqueness of pitchfork and helicoid translators of the mean curvature flow in $\mathbb{R}^3$. We employ an arc-counting argument motivated by Morse-Rad\'o theory for translators and a rotational maximum principle. Applications to the classification of semigraphical translators in $\

Daphné Lubert-Perquel, Byeong Wook Cho, Alan J. Philips, Young Hee Lee

Combining the synthetic tunability of molecular compounds with the optical selection rules of transition metal dichalcogenides (TMDC) that derive from spin-valley coupling could provide interesting opportunities for the readout of quantum information. However, little is known about the electronic and spin interactions at such interfaces and the influence on

Ankit Bhojak, Surjeet Singh Choudhary, Saurabh Shrivastava, Kalachand Shuin

The primary goal of this paper is to introduce bilinear analogues of uncentered spherical averages, Nikodym averages associated with spheres and the associated bilinear maximal functions. We obtain $L^p$-estimates for uncentered bilinear maximal functions for dimensions $d\geq2$. Moreover, we also discuss the one-dimensional case. In the process of developin

Timothee Mickus, Raúl Vázquez

A recent body of work has demonstrated that Transformer embeddings can be linearly decomposed into well-defined sums of factors, that can in turn be related to specific network inputs or components. There is however still a dearth of work studying whether these mathematical reformulations are empirically meaningful. In the present work, we study representati

Laurens Walleghem, Rafael Wagner, Yìlè Yīng, David Schmid

Extended Wigner's friend no-go theorems provide a modern lens for investigating the measurement problem, by making precise the challenges that arise when one attempts to model agents as dynamical quantum systems. Most such no-go theorems studied to date, such as the Frauchiger-Renner argument and the Local Friendliness argument, are explicitly constructed us

Jose Carlos Marinello Filho, Taufik Abrao, Ekram Hossain, Amine Mezghani

Channel state information (CSI) estimation is a critical issue in the design of modern massive multiple-input multiple-output (mMIMO) networks. With the increasing number of users, assigning orthogonal pilots to everyone incurs a large overhead that strongly penalizes the system's spectral efficiency (SE). It becomes thus necessary to reuse pilots, giving ri

Oliver Schumann, Michael Buchholz, Klaus Dietmayer

Large environments are challenging for path planning algorithms as the size of the configuration space increases. Furthermore, if the environment is mainly unexplored, large amounts of the path are planned through unknown areas. Hence, a complete replanning of the entire path occurs whenever the path collides with newly discovered obstacles. We propose a nov

Rod Rofougaran, Shinjae Yoo, Huan-Hsin Tseng, Samuel Yen-Chi Chen

The preservation of privacy is a critical concern in the implementation of artificial intelligence on sensitive training data. There are several techniques to preserve data privacy but quantum computations are inherently more secure due to the no-cloning theorem, resulting in a most desirable computational platform on top of the potential quantum advantages.

Aleksandr Azatov, Giulio Barni, Rudin Petrossian-Byrne, Miguel Vanvlasselaer

We quantise from first principles field theories living on the background of a bubble wall in the planar limit with particular focus on the case of spontaneous breaking of gauge symmetry. Using these tools, we compute the average momentum transfer from transition radiation: the soft emission of radiation by an energetic particle passing across the wall, with

Edgar Costa, Kiran S. Kedlaya, David Roe

For a fixed positive integer $e$, we describe an algorithm for computing, for all primes $p \leq X$, the mod-$p^e$ reduction of the trace of Frobenius at $p$ of a fixed hypergeometric motive over $\mathbb{Q}$ in time quasilinear in $X$. This extends our previous work for the mod-$p$ reduction, again combining the Beukers--Cohen--Mellit trace formula with ave

Joël Mathys, Florian Grötschla, Kalyan Varma Nadimpalli, Roger Wattenhofer

Most Graph Neural Networks follow the standard message-passing framework where, in each step, all nodes simultaneously communicate with each other. We want to challenge this paradigm by aligning the computation more closely to the execution of distributed algorithms and propose the Flood and Echo Net. A single round of a Flood and Echo Net consists of an ori

Pan Zhao, Antoine Chambaz, Julie Josse, Shu Yang

Policy learning utilizing observational data is pivotal across various domains, with the objective of learning the optimal treatment assignment policy while adhering to specific constraints such as fairness, budget, and simplicity. This study introduces a novel positivity-free (stochastic) policy learning framework designed to address the challenges posed by

Alec Helbling, Evan Montoya, Duen Horng Chau

Recent text-to-image generative models can generate high-fidelity images from text prompts. However, these models struggle to consistently generate the same objects in different contexts with the same appearance. Consistent object generation is important to many downstream tasks like generating comic book illustrations with consistent characters and setting.

Pushpendra Gupta, In Jun Park, Anupama Swain, Abhisek Mishra

The efficient generation of spin currents and spin torques via spin-orbit coupling is an important goal of spintronics research. One crucial metric for spin current generation is the spin Hall angle, which is the ratio of the spin Hall current to the transversely flowing charge current. A typical approach to measure the spin Hall angle in nonmagnetic materia

Omid Davoodi, Shayan Mohammadizadehsamakosh, Majid Komeili

Part-prototype networks have recently become methods of interest as an interpretable alternative to many of the current black-box image classifiers. However, the interpretability of these methods from the perspective of human users has not been sufficiently explored. In this work, we have devised a framework for evaluating the interpretability of part-protot

Viet-Anh Le, Vaishnav Tadiparthi, Behdad Chalaki, Hossein Nourkhiz Mahjoub

In this paper, we develop a control framework for the coordination of multiple robots as they navigate through crowded environments. Our framework comprises of a local model predictive control (MPC) for each robot and a social long short-term memory model that forecasts pedestrians' trajectories. We formulate the local MPC formulation for each individual rob

S. Hassani, J-M. Maillard, N. Zenine

This paper is a plea for diagonals and telescopers of rational, or algebraic, functions using creative telescoping, in a computer algebra experimental mathematics learn-by-examples approach. We show that diagonals of rational functions (and this is also the case with diagonals of algebraic functions) are left invariant when one performs an infinite set of bi

Thomas H. M. Roos, Edwin Versteeg, Dennis W. J. Klomp, Jeroen C. W. Siero

Purpose: This work aims to address the limitations faced by researchers in developing and sharing new MRI sequences by implementing an interpreter for the open-source MRI pulse sequence format, Pulseq, on a Philips MRI scanner. Methods: The implementation involved modifying a few source code files to create a Pulseq interpreter for the Philips MRI system. Va

Horacio Casini, Leandro Martinek

Twist operators implement symmetries in bounder regions of the space. Standard twists are a special class of twists constructed using modular tools. The twists corresponding to translations have interesting special properties. They can move continuously an operator from a region to a disjoint one without ever passing through the gap separating the two. In ad

Bin Chen, Gregory Debruyne, Jasson Vindas

We study the range of validity of the density hypothesis for the zeros of $L$-functions associated with cusp Hecke eigenforms $f$ of even integral weight and prove that $N_{f}(\sigma, T) \ll T^{2(1-\sigma)+\varepsilon}$ holds for $\sigma \geq 1407/1601$. This improves upon a result of Ivi\'{c}, who had previously shown the zero-density estimate in the narrow

Venkat Venkatasubramanian, N. Sanjeevrajan, Manasi Khandekar

We present a novel theory of the microstructure of deep neural networks. Using a theoretical framework called statistical teleodynamics, which is a conceptual synthesis of statistical thermodynamics and potential game theory, we predict that all highly connected layers of deep neural networks have a universal microstructure of connection strengths that is di

Cosmo Viola, Max Fan, Talia Ringer

Proofs in proof assistants like Rocq can be brittle, breaking easily in response to changes. To address this, recent work introduced an algorithm and tool in Rocq to automatically repair broken proofs in response to changes that correspond to type equivalences. However, many changes remained out of the scope of this algorithm and tool -- especially changes i

Anastasia Antsiferova, Khaled Abud, Aleksandr Gushchin, Ekaterina Shumitskaya

Nowadays, neural-network-based image- and video-quality metrics perform better than traditional methods. However, they also became more vulnerable to adversarial attacks that increase metrics' scores without improving visual quality. The existing benchmarks of quality metrics compare their performance in terms of correlation with subjective quality and calcu

Alexia Bravo, Adam C. Schneider, Daniella Bardalez Gagliuffi, Adam J. Burgasser

We present three new brown dwarf spectral binary candidates: CWISE J072708.09$-$360729.2, CWISE J103604.84$-$514424.4, and CWISE J134446.62$-$732053.9, discovered by citizen scientists through the Backyard Worlds: Planet 9 project. Follow-up near-infrared spectroscopy shows that each of these objects is poorly fit by a single near-infrared standard. We const

Charles Dawson, Chuchu Fan

To ensure safe, reliable operation of the electrical grid, we must be able to predict and mitigate likely failures. This need motivates the classic security-constrained AC optimal power flow (SCOPF) problem. SCOPF is commonly solved using adversarial optimization, where the dispatcher and an adversary take turns optimizing a robust dispatch and adversarial a

Jeffrey T. Mullen, James A. Boulton, Minghao Pan, Ki Wook Kim

Using first principles calculations, this work investigates the suitability of diamond/c-BN heterojunctions for high frequency, high power device applications. The key quantities of band offsets and interface charge polarization are examined for different crystallographic orientations [(110), (111), or (100)], bond terminations (C-B or C-N), and substrates (

Andrei Khrennikov

We start with methodological analysis of the notion of scientific theory and its interrelation with reality. This analysis is based on the works of Helmholtz, Hertz, Boltzmann, and Schr\"odinger (and reviews of D' Agostino). Following Helmholtz, Hertz established the "Bild concept" for scientific theories. Here "Bild" ("picture") carries the meaning "model"

Andrea Pinamonti, Gareth Speight, Scott Zimmerman

In the setting of horizontal curves in the Heisenberg group, we prove a $C^{m,\omega}$ finiteness principle, a $C^{m,\omega}$ Lusin approximation result, a $C^{\infty}$ Whitney extension result, and a $C^{\infty}$ Lusin approximation result. Combined with previous work, this completes the study of Whitney extension and Lusin approximation for horizontal curv

Andrei Dumitrasc, Carola Kruse, Ulrich Ruede

The generalized Golub-Kahan bidiagonalization has been used to solve saddle-point systems where the leading block is symmetric and positive definite. We extend this iterative method for the case where the symmetry condition no longer holds. We do so by relying on the known connection the algorithm has with the Conjugate Gradient method and following the line

Preston K. Robinette, Daniel Moyer, Taylor T. Johnson

Information hiding is the process of embedding data within another form of data, often to conceal its existence or prevent unauthorized access. This process is commonly used in various forms of secure communications (steganography) that can be used by bad actors to propagate malware, exfiltrate victim data, and discreetly communicate. Recent work has utilize

Amir Naseredini

Rowhammer poses a significant security challenge for modern computers, specifically affecting Dynamic Random Access Memory(DRAM). Given society's growing reliance on computer systems, ensuring the reliability of hardware is of utmost importance. This paper provides a comprehensive survey of Rowhammer, examining the literature from various angles. We categori

Wenjun Xia, Yongyi Shi, Chuang Niu, Wenxiang Cong

Computed tomography (CT) involves a patient's exposure to ionizing radiation. To reduce the radiation dose, we can either lower the X-ray photon count or down-sample projection views. However, either of the ways often compromises image quality. To address this challenge, here we introduce an iterative reconstruction algorithm regularized by a diffusion prior

Navid Aftabi, Dan Li, Paritosh Ramanan

Cybersecurity of Industrial Cyber-Physical Systems is drawing significant concerns as data communication increasingly leverages wireless networks. A lot of data-driven methods were develope for detecting cyberattacks, but few are focused on distinguishing them from equipment faults. In this paper, we develop a data-driven framework that can be used to detect

Rocío Carratalá-Sáez, Francisco J. andújar, Yuri Torres, Arturo Gonzalez-Escribano

Computational platforms for high-performance scientific applications are becoming more heterogenous, including hardware accelerators such as multiple GPUs. Applications in a wide variety of scientific fields require an efficient and careful management of the computational resources of this type of hardware to obtain the best possible performance. However, th

Carla Cederbaum, Markus Wolff

It is a well-known fact that the Schwarzschild spacetime admits a maximal spacetime extension in null coordinates which extends the exterior Schwarzschild region past the Killing horizon, called the Kruskal-Szekeres extension. This method of extending the Schwarzschild spacetime was later generalized by Brill-Hayward to a class of spacetimes of "profile $h$"

Praneet Singh, Edward J. Delp, Amy R. Reibman

Practical video analytics systems that are deployed in bandwidth constrained environments like autonomous vehicles perform computer vision tasks such as face detection and recognition. In an end-to-end face analytics system, inputs are first compressed using popular video codecs like HEVC and then passed onto modules that perform face detection, alignment, a