October 2023 arXiv papers — page 9

Vidyesh Rao Anisetti, Sharath Ananthamurthy, J. M. Schwarz

We explore rectification phenomena in a system where two-dimensional random walkers interact with a funnel-shaped ratchet under two distinct classes of reflection rules. The two classes include the angle of reflection exceeding the angle of incidence ($\theta_{reflect} > \theta_{incident}$), or vice versa ($\theta_{reflect} < \theta_{incident}$). These gener

Javier Chagoya, I. Díaz-Saldaña, J. C. López-Domínguez, C. Martínez-Robles

We apply Wald's formalism to a Lagrangian within generalised Proca gravity that admits a Schwarzschild black hole with a non-trivial vector field. The resulting entropy differs from that of the same black hole in General Relativity by a logarithmic correction modulated by the only independent charge of the vector field. We find conditions on this charge to g

Jia-Yi Yang, Di-Chang Chen, Ji-Wei Xie, Ji-Lin Zhou

One of the fundamental questions in astronomy is how planetary systems form and evolve. Measuring the planetary occurrence and architecture as a function of time directly addresses this question. In the fourth paper of the Planets Across Space and Time (PAST) series, we investigate the occurrence and architecture of Kepler planetary systems as a function of

James Cheung

In this short paper, we prove that the Bochner integral form of the operator-valued Riccati equation has a unique solution if and only if its mild form has a unique solution. This implies that the mild and Bochner integral forms of this equation are equivalent. The result is obtained through an operator representation argument.

Dong-Ho Lee, Jay Pujara, Mohit Sewak, Ryen W. White

Although large language models (LLMs) have advanced the state-of-the-art in NLP significantly, deploying them for downstream applications is still challenging due to cost, responsiveness, control, or concerns around privacy and security. As such, trainable models are still the preferred option in some cases. However, these models still require human-labeled

Ningyuan Yao, Zhentao Zhang

Let $X$ be a definable group definable over a small model $M_0$. Recall that a global type $p$ on $X$ is definable $f$-generic over $M_0$ if every left translate of $p$ is definable over $M_0$. We call $p$ strongly $f$-generic over $M_0$ if every left translate of $p$ does not fork over $M_0$. Let $H$ be a group definable over the field ${\mathbb Q}_p$ of $p

Zhendong Chu, Nan Wang, Hongning Wang

Conversational Recommender Systems (CRS) actively elicit user preferences to generate adaptive recommendations. Mainstream reinforcement learning-based CRS solutions heavily rely on handcrafted reward functions, which may not be aligned with user intent in CRS tasks. Therefore, the design of task-specific rewards is critical to facilitate CRS policy learning

Taro Kanao, Hayato Goto

Kerr parametric oscillators (KPOs) can stabilize the superpositions of coherent states, which can be utilized as qubits, and are promising candidates for realizing hardware-efficient quantum computers. Although elementary gates for universal quantum computation with KPO qubits have been proposed, these gates are usually based on adiabatic operations and thus

Vadim Makarov, Alexey Abrikosov, Poompong Chaiwongkhot, Aleksey K. Fedorov

A commercial quantum key distribution (QKD) system needs to be formally certified to enable its wide deployment. The certification should include the system's robustness against known implementation loopholes and attacks that exploit them. Here we ready a fiber-optic QKD system for this procedure. The system has a prepare-and-measure scheme with decoy-state

Peng-Cheng Li, Cheng-Long Zhang, Zong-Yang Yue, Xiao-Bao Deng

Development related to PandABox-based fly scans is an important part of the active work on Mamba, the software framework for beamline experiments at the High Energy Photon Source (HEPS); presented in this paper is the progress of our development, and some outlook for advanced fly scans based on knowledge learned during the process. By treating fly scans as a

Jaromir Savelka, Paul Denny, Mark Liffiton, Brad Sheese

The accurate classification of student help requests with respect to the type of help being sought can enable the tailoring of effective responses. Automatically classifying such requests is non-trivial, but large language models (LLMs) appear to offer an accessible, cost-effective solution. This study evaluates the performance of the GPT-3.5 and GPT-4 model

Oscar Karnalim, Hapnes Toba, Meliana Christianti Johan, Erico Darmawan Handoyo

In programming education, plagiarism and misuse of artificial intelligence (AI) assistance are emerging issues. However, not many relevant studies are focused on web programming. We plan to develop automated tools to help instructors identify both misconducts. To fully understand the issues, we conducted a controlled experiment to observe the unfair benefits

Weiwei Gao, Zhao Tang, Jijun Zhao, James R. Chelikowsky

The GW approximation is widely used for reliable and accurate modeling of single-particle excitations. It also serves as a starting point for many theoretical methods, such as its use in the Bethe-Salpeter equation (BSE) and dynamical mean-field theory. However, full-frequency GW calculations for large systems with hundreds of atoms remain computationally ch

Ziqiao Wang, Yongyi Mao

We present new information-theoretic generalization guarantees through the a novel construction of the "neighboring-hypothesis" matrix and a new family of stability notions termed sample-conditioned hypothesis (SCH) stability. Our approach yields sharper bounds that improve upon previous information-theoretic bounds in various learning scenarios. Notably, th

Guanfang Dong, Anup Basu

Denoising algorithms play a crucial role in medical image processing and analysis. However, classical denoising algorithms often ignore explanatory and critical medical features preservation, which may lead to misdiagnosis and legal liabilities. In this work, we propose a new denoising method for medical images that not only efficiently removes various types

Jeremy Avigad

This essay considers ways that recent uses of computers in mathematics challenge contemporary views on the nature of mathematical understanding. It also puts these challenges in a historical perspective and offers speculation as to a possible resolution.

Kevin A. McKinnon, Andrés del Pino, Constance M. Rockosi, Miranda Apfel

We present a hierarchical Bayesian pipeline, BP3M, that measures positions, parallaxes, and proper motions (PMs) for cross-matched sources between Hubble~Space~Telescope (HST) images and Gaia -- even for sparse fields ($N_*<10$ per image) -- expanding from the recent GaiaHub tool. This technique uses Gaia-measured astrometry as priors to predict the location

Pengfei Li, Jianyi Yang, Adam Wierman, Shaolei Ren

We study a challenging form of Smoothed Online Convex Optimization, a.k.a. SOCO, including multi-step nonlinear switching costs and feedback delay. We propose a novel machine learning (ML) augmented online algorithm, Robustness-Constrained Learning (RCL), which combines untrusted ML predictions with a trusted expert online algorithm via constrained projectio

Kenneth Gill

We show that in contrast to the Rado graph, the Henson graphs are not computably indivisible.

Sai Srivatsa Ravindranath, Yanchen Jiang, David C. Parkes

The $\textit{data market design}$ problem is a problem in economic theory to find a set of signaling schemes (statistical experiments) to maximize expected revenue to the information seller, where each experiment reveals some of the information known to a seller and has a corresponding price [Bergemann et al., 2018]. Each buyer has their own decision to make

Yesom Park, Taekyung Lee, Jooyoung Hahn, Myungjoo Kang

The aim of this paper is the reconstruction of a smooth surface from an unorganized point cloud sampled by a closed surface, with the preservation of geometric shapes, without any further information other than the point cloud. Implicit neural representations (INRs) have recently emerged as a promising approach to surface reconstruction. However, the reconst

David A. King

My model, it has three layers, Three layers is nematic. And had it just two layers, it would be a smectic. We study a reduced model of the smectic transition in two dimensions where the particles occupy three equally spaced layers. The role of particle geometry comes in through the interactions between particles on the central layer and those above and below

Héctor Javier Vázquez Martínez, Annika Lea Heuser, Charles Yang, Jordan Kodner

The success of neural language models (LMs) on many technological tasks has brought about their potential relevance as scientific theories of language despite some clear differences between LM training and child language acquisition. In this paper we argue that some of the most prominent benchmarks for evaluating the syntactic capacities of LMs may not be su

Sergio Calvo-Ordonez, Chun-Wun Cheng, Jiahao Huang, Lipei Zhang

Diffusion Probabilistic Models stand as a critical tool in generative modelling, enabling the generation of complex data distributions. This family of generative models yields record-breaking performance in tasks such as image synthesis, video generation, and molecule design. Despite their capabilities, their efficiency, especially in the reverse process, re

Haolun Wu, Ofer Meshi, Masrour Zoghi, Fernando Diaz

Accurate modeling of the diverse and dynamic interests of users remains a significant challenge in the design of personalized recommender systems. Existing user modeling methods, like single-point and multi-point representations, have limitations w.r.t.\ accuracy, diversity, and adaptability. To overcome these deficiencies, we introduce density-based user re

Mingxuan Yi, Song Liu

Variational inference is a technique that approximates a target distribution by optimizing within the parameter space of variational families. On the other hand, Wasserstein gradient flows describe optimization within the space of probability measures where they do not necessarily admit a parametric density function. In this paper, we bridge the gap between

Eugenia Alleva, Isotta Landi, Leslee J Shaw, Erwin Böttinger

Clinical note classification is a common clinical NLP task. However, annotated data-sets are scarse. Prompt-based learning has recently emerged as an effective method to adapt pre-trained models for text classification using only few training examples. A critical component of prompt design is the definition of the template (i.e. prompt text). The effect of t

Jeremy Avigad

Since the early twentieth century, it has been understood that mathematical definitions and proofs can be represented in formal systems systems with precise grammars and rules of use. Building on such foundations, computational proof assistants now make it possible to encode mathematical knowledge in digital form. This article enumerates some of the ways tha

F Sgaramella, D Sirghi, L Abbene, F Artibani

In this paper we present the results of a new kaonic helium-4 measurement with a 1.37 g/l gaseous target by the SIDDHARTA-2 experiment at the DAΦNE collider. We measured, for the first time, the energies and yields of three transitions belonging to the Mseries. Moreover, we improved by a factor about three, the statistical precision of the 2p level energy sh

Léna Kläy, Léo Girardin, Vincent Calvez, Florence Débarre

Understanding the temporal spread of gene drive alleles -- alleles that bias their own transmission -- through modeling is essential before any field experiments. In this paper, we present a deterministic reaction-diffusion model describing the interplay between demographic and allelic dynamics, in a one-dimensional spatial context. We focused on the traveli

Hang Zhou, Hans-Georg Müller

We develop statistical models for samples of distribution-valued stochastic processes featuring time-indexed univariate distributions, with emphasis on functional principal component analysis. The proposed model presents an intrinsic rather than transformation-based approach. The starting point is a transport process representation for distribution-valued pr

Dehua Bi, Tianjian Zhou, Wei Zhong, Yuan Ji

It is highly desirable to borrow information from external data to augment a control arm in a randomized clinical trial, especially in settings where the sample size for the control arm is limited. However, a main challenge in borrowing information from external data is to accommodate potential heterogeneous subpopulations across the external and trial data.

Chi-Ming Chang, Ying-Hsuan Lin, Jingxiang Wu

We investigate the existence of $\frac18$-BPS black hole microstates in the $\mathfrak{su}(1,1|2)$ sector of Type IIB string theory on $\mathrm{AdS}_5 \times \mathrm{S}^5$. As will be explained, these states are in one-to-one correspondence with the Schur operators comprising the chiral algebra of $\mathcal{N}=4$ super-Yang-Mills, and a conjecture of Beem et

Sesha Sai Behara, John C. Thomas, Brian Puchala, Anton Van der Ven

We describe a first-principles statistical mechanics method to calculate the free energies of crystalline alloys that depend on temperature, composition, and strain. The approach relies on an extension of the alloy cluster expansion to include an explicit dependence on homogeneous strain in addition to site occupation variables that track the degree of chemi

Yasha Gindikin, Alex Kamenev

We present a bunch of novel phenomena stemming from the pair spin-orbit interaction (PSOI), which does not rely on structure inversion asymmetry but instead arises from Coulomb fields of interacting electrons in materials with a strong Rashba effect. First, PSOI can induce $p-$wave superconducting order without the need for any mediators of attraction. Depen

Shuvam Keshari, Tanusree Dutta, Raju Mullick, Ashish Rathor

Choosing the right person for the right job makes the personnel interview process a cognitively demanding task. Psychometric tests, followed by an interview, have often been used to aid the process although such mechanisms have their limitations. While psychometric tests suffer from faking or social desirability of responses, the interview process depends on

Beatrice Bevilacqua, Moshe Eliasof, Eli Meirom, Bruno Ribeiro

Subgraph GNNs are provably expressive neural architectures that learn graph representations from sets of subgraphs. Unfortunately, their applicability is hampered by the computational complexity associated with performing message passing on many subgraphs. In this paper, we consider the problem of learning to select a small subset of the large set of possibl

Chris Richardson, Yao Zhang, Kellen Gillespie, Sudipta Kar

Personalization, the ability to tailor a system to individual users, is an essential factor in user experience with natural language processing (NLP) systems. With the emergence of Large Language Models (LLMs), a key question is how to leverage these models to better personalize user experiences. To personalize a language model's output, a straightforward ap

Dev R Sadaula, Timothy R Kallman

Time dependent photoionization modeling of warm absorber outflows in active galactic nuclei can play an important role in understanding the interaction between warm absorbers and the central black hole. The warm absorber may be out of the equilibrium state because of the variable nature of the central continuum. In this paper, with the help of time dependent

Allen M. Wang, Darren T. Garnier, Cristina Rea

While fusion reactors known as tokamaks hold promise as a firm energy source, advances in plasma control, and handling of events where control of plasmas is lost, are needed for them to be economical. A significant bottleneck towards applying more advanced control algorithms is the need for better plasma simulation, where both physics-based and data-driven a

Qidong Su, Chuqin Geng, Gennady Pekhimenko, Xujie Si

Static and dynamic computational graphs represent two distinct approaches to constructing deep learning frameworks. The former prioritizes compiler-based optimizations, while the latter focuses on programmability and user-friendliness. The recent release of PyTorch 2.0, which supports compiling arbitrary deep learning programs in Python, signifies a new dire

Subhadra Vadlamannati, Ryan Solgi

The transformer architecture has revolutionized Natural Language Processing (NLP) and other machine-learning tasks, due to its unprecedented accuracy. However, their extensive memory and parameter requirements often hinder their practical applications. In this work, we study the effect of tensor-train decomposition to improve the accuracy and compress transf

Rishab Dutta, Fei Gao, Armin Khamoshi, Thomas M. Henderson

We develop an efficient algorithm to implement the recently introduced binary tree state (BTS) ansatz on a classical computer. BTS allows a simple approximation to permanents arising from the computationally intractable antisymmetric product of interacting geminals and respects size-consistency. We show how to compute BTS overlap and reduced density matrices

Kirankumar Shiragur, Jiaqi Zhang, Caroline Uhler

Learning causal structures from interventional data is a fundamental problem with broad applications across various fields. While many previous works have focused on recovering the entire causal graph, in practice, there are scenarios where learning only part of the causal graph suffices. This is called $targeted$ causal discovery. In our work, we focus on t

V. Cavalheri Pereira, J. C. A. Barata

Recent works have proposed the use of the formalism of Positive Operator Valued Measures to describe time measurements in quantum mechanics. This work aims to expand on the work done by other authors, by generalizing the previously proposed construction method of such measures to include causal Poincar\'e transformations, in order to construct measures which

Qian-Hui Chen, Kathryn Grasha, Andrew J. Battisti, Emily Wisnioski

Spiral structures are important drivers of the secular evolution of disc galaxies, however, the origin of spiral arms and their effects on the development of galaxies remain mysterious. In this work, we present two three-armed spiral galaxies at z~0.3 in the Middle Age Galaxy Properties with Integral Field Spectroscopy (MAGPI) survey. Taking advantage of the

Shuhaib Mehri, Vered Shwartz

Automatic evaluation of natural language generation has long been an elusive goal in NLP.A recent paradigm fine-tunes pre-trained language models to emulate human judgements for a particular task and evaluation criterion. Inspired by the generalization ability of instruction-tuned models, we propose a learned metric based on instruction tuning. To test our a

Shengzhong Liu, Tomoyoshi Kimura, Dongxin Liu, Ruijie Wang

This paper proposes a novel contrastive learning framework, called FOCAL, for extracting comprehensive features from multimodal time-series sensing signals through self-supervised training. Existing multimodal contrastive frameworks mostly rely on the shared information between sensory modalities, but do not explicitly consider the exclusive modality informa

Jan Dereziński, Ben Li, Marcin Napiórkowski

According to the Bogoliubov theory the low energy behaviour of the Bose gas at zero temperature can be described by non-interacting bosonic quasiparticles called phonons. In this work the damping rate of phonons at low momenta, the so-called Beliaev damping, is explained and computed with simple arguments involving the Fermi Golden Rule and Bogoliubov's quas

Robert Goldblatt

Propositional temporal logic over the real number time flow is finitely axiomatisable, but its first-order counterpart is not recursively axiomatisable. We study the logic that combines the propositional axiomatisation with the usual axioms for first-order logic with identity, and develop an alternative ``admissible'' semantics for it, showing that it is str

Connor Henley, Joseph Hollmann, Colin Meyer, Ramesh Raskar

Diffuse optical spectroscopy (DOS) techniques aim to characterize scattering media by examining their optical response to laser illumination. Time-domain DOS methods involve illuminating the medium with a laser pulse and using a fast photodetector to measure the time-dependent intensity of light that exits the medium after multiple scattering events. While D

Shuo Liu, Gail Kaiser

Vulnerability identification is crucial to protect software systems from attacks for cyber-security. However, huge projects have more than millions of lines of code, and the complex dependencies make it hard to carry out traditional static and dynamic methods. Furthermore, the semantic structure of various types of vulnerabilities differs greatly and may occ

Alex Edison, Michèle Levi

We establish and develop a novel methodology to treat higher-order non-linear effects of gravitational radiation that is scattered from binary inspirals, which employs modern scattering-amplitudes methods on the effective picture of the binary as a composite particle. We spell out our procedure to study such effects: assembling tree amplitudes via generalize

Arjun Narayanan, Fanwei Kong, Shawn Shadden

We present a deep learning model to automatically generate computer models of the human heart from patient imaging data with an emphasis on its capability to generate thin-walled cardiac structures. Our method works by deforming a template mesh to fit the cardiac structures to the given image. Compared with prior deep learning methods that adopted this appro

Kevin Zhang, Sakshum Kulshrestha, Christopher Metzler

Despite recent advances, developing general-purpose universal denoising and artifact-removal networks remains largely an open problem: Given fixed network weights, one inherently trades-off specialization at one task (e.g.,~removing Poisson noise) for performance at another (e.g.,~removing speckle noise). In addition, training such a network is challenging d

Oswaldo Lezama, Claudia Gallego

The classical commutative coding theory has been recently extended to noncommutative rings of polynomial type. There are many interesting works in coding theory over single Ore extensions. In this review article we present the most relevant algebraic tools and properties of single Ore extensions used in noncommutative coding theory. The last section represen

Vishal Ramesh, Rui Zhao, Naman Goel

Data on the Web has fueled much of the recent progress in AI. As more high-quality data becomes difficult to access, synthetic data is emerging as a promising solution for privacy-friendly data release and complementing real datasets in developing robust and safe AI. But there is limited work on decentralised, scalable and contributor-centric synthetic data

Lex Beattie, Isabel Corpus, Lucy H. Lin, Praveen Ravichandran

We present a novel evaluation framework for representation bias in latent factor recommendation (LFR) algorithms. Our framework introduces the concept of attribute association bias in recommendations allowing practitioners to explore how recommendation systems can introduce or amplify stakeholder representation harm. Attribute association bias (AAB) occurs w

João Victor Galvão da Mata, Martin S. Andersen

We introduce AdaSub, a stochastic optimization algorithm that computes a search direction based on second-order information in a low-dimensional subspace that is defined adaptively based on available current and past information. Compared to first-order methods, second-order methods exhibit better convergence characteristics, but the need to compute the Hess

Sunayana Rane, Mark Ho, Ilia Sucholutsky, Thomas L. Griffiths

Value alignment is essential for building AI systems that can safely and reliably interact with people. However, what a person values -- and is even capable of valuing -- depends on the concepts that they are currently using to understand and evaluate what happens in the world. The dependence of values on concepts means that concept alignment is a prerequisi

Adrian de Luis, Minh Tran, Taisei Hanyu, Anh Tran

As climate change intensifies, the global imperative to shift towards sustainable energy sources becomes more pronounced. Photovoltaic (PV) energy is a favored choice due to its reliability and ease of installation. Accurate mapping of PV installations is crucial for understanding their adoption and informing energy policy. To meet this need, we introduce th

Ismael Ben-Yelun, Luis Irastorza-Valera, Luis Saucedo-Mora, Francisco Javier Montáns

In the emerging field of mechanical metamaterials, using periodic lattice structures as a primary ingredient is relatively frequent. However, the choice of aperiodic lattices in these structures presents unique advantages regarding failure, e.g., buckling or fracture, because avoiding repeated patterns prevents global failures, with local failures occurring

Oliver Davis Johns

Vector calculus in three dimensions with a Euclidian metric is the lingua franca of classical physics, including classical electrodynamics. This article corrects some long-standing imprecision in a fundamental result. Some textbooks assert that a vector function defined in the whole of a three dimensional space is uniquely determined by its divergence, its c

Kun Qian, Anton Belyi, Fei Wu, Samira Khorshidi

The quality of a knowledge graph directly impacts the quality of downstream applications (e.g. the number of answerable questions using the graph). One ongoing challenge when building a knowledge graph is to ensure completeness and freshness of the graph's entities and facts. In this paper, we introduce ODKE, a scalable and extensible framework that sources

Arec Jamgochian, Hugo Buurmeijer, Kyle H. Wray, Anthony Corso

Optimal plans in Constrained Partially Observable Markov Decision Processes (CPOMDPs) maximize reward objectives while satisfying hard cost constraints, generalizing safe planning under state and transition uncertainty. Unfortunately, online CPOMDP planning is extremely difficult in large or continuous problem domains. In many large robotic domains, hierarch

Szilvia Ujváry, Gergely Flamich, Vincent Fortuin, José Miguel Hernández Lobato

An important yet underexplored question in the PAC-Bayes literature is how much tightness we lose by restricting the posterior family to factorized Gaussian distributions when optimizing a PAC-Bayes bound. We investigate this issue by estimating data-independent PAC-Bayes bounds using the optimal posteriors, comparing them to bounds obtained using MFVI. Conc

Anton Lee, Yaqian Zhang, Heitor Murilo Gomes, Albert Bifet

Continual learning aims to create artificial neural networks capable of accumulating knowledge and skills through incremental training on a sequence of tasks. The main challenge of continual learning is catastrophic interference, wherein new knowledge overrides or interferes with past knowledge, leading to forgetting. An associated issue is the problem of le

Zhao Song, Guangyi Xu, Junze Yin

Large language models (LLMs) have significantly improved various aspects of our daily lives. These models have impacted numerous domains, from healthcare to education, enhancing productivity, decision-making processes, and accessibility. As a result, they have influenced and, to some extent, reshaped people's lifestyles. However, the quadratic complexity of

Yixiao Zhou, Jørgen Christensen-Dalsgaard, Martin Asplund, Yaguang Li

Solar-like oscillations have been detected in thousands of stars thanks to modern space missions. These oscillations have been used to measure stellar masses and ages, which have been widely applied in Galactic archaeology. One of the pillars of such applications is the $\nu_{\max}$ scaling relation: the frequency of maximum power $\nu_{\max}$, assumed to be

Stefan Künzli, Florian Grötschla, Joël Mathys, Roger Wattenhofer

Simulating fluid dynamics is crucial for the design and development process, ranging from simple valves to complex turbomachinery. Accurately solving the underlying physical equations is computationally expensive. Therefore, learning-based solvers that model interactions on meshes have gained interest due to their promising speed-ups. However, it is unknown

Wenqi Yue, Georg A. Gottwald

We perform a stochastic model reduction of the Kuramoto-Sakaguchi model for finitely many coupled phase oscillators with phase frustration. Whereas in the thermodynamic limit coupled oscillators exhibit stationary states and a constant order parameter, finite-size networks exhibit persistent temporal fluctuations of the order parameter. These fluctuations ar

Alexander Kastner, Clark Lyons

We prove that every Schreier graph of a free Borel action of a finitely generated non-amenable group has a Baire measurable perfect matching. This result was previously only known in the bipartite setting. We also prove that every Borel non-amenable bounded degree graph with only even degrees has a Baire measurable balanced orientation.

Costas Mavromatis, Balasubramaniam Srinivasan, Zhengyuan Shen, Jiani Zhang

Large Language Models (LLMs) can adapt to new tasks via in-context learning (ICL). ICL is efficient as it does not require any parameter updates to the trained LLM, but only few annotated examples as input for the LLM. In this work, we investigate an active learning approach for ICL, where there is a limited budget for annotating examples. We propose a model

Alberto Landi

We study the stack $\mathcal{H}_{r,g,n}$ of $n$-pointed smooth cyclic covers of degree $r$ between smooth curves of genus $g$ and the projective line. We give two presentations of an open substack of $\mathcal{H}_{r,g,n}$ as a quotient stack, and we study its complement. Using this, we compute the integral Picard group of $\mathcal{H}_{r,g,n}$. Moreover, we

Djuna Croon, Jeremy Sakstein

We study the evolution of heavy stars ($M\ge40{\rm M}_\odot$) undergoing pair-instability in the presence of annihilating dark matter. Focusing on the scenario where the dark matter is in capture-annihilation equilibrium, we model the profile of energy injections in the local thermal equilibrium approximation. We find that significant changes to masses of as

Koushik Chatterjee, Ziri Younsi, Prashant Kocherlakota, Ramesh Narayan

The launching of astrophysical jets provides the most compelling observational evidence for direct extraction of black hole (BH) spin energy via the Blandford-Znajek (BZ) mechanism. Whilst it is known that spinning Kerr BHs within general relativity (GR) follow the BZ jet power relation, the nature of BH energy extraction in general theories of gravity has n

Rafael F. Toledo, Joanne M. Atlee, Rui Ming Xiong

Analyses of a software product line (SPL) typically report variable results that are annotated with logical expressions indicating the set of product variants for which the results hold. These expressions can get complicated and difficult to reason about when the SPL has lots of features and product variants. Previous work introduced a visualizer that suppor

Kang Liu, Laurent Pfeiffer

This article investigates the convergence of the Generalized Frank-Wolfe (GFW) algorithm for the resolution of potential and convex second-order mean field games. More specifically, the impact of the discretization of the mean-field-game system on the effectiveness of the GFW algorithm is analyzed. The article focuses on the theta-scheme introduced by the au

Ruya Karagulle, Nikos Arechiga, Andrew Best, Jonathan DeCastro

This work introduces a preference learning method that ensures adherence to given specifications, with an application to autonomous vehicles. Our approach incorporates the priority ordering of Signal Temporal Logic (STL) formulas describing traffic rules into a learning framework. By leveraging Parametric Weighted Signal Temporal Logic (PWSTL), we formulate

Koushik Chatterjee, Prashant Kocherlakota, Ziri Younsi, Ramesh Narayan

We perform the first numerical simulations modeling the inflow and outflow of magnetized plasma in the Kerr-Sen spacetime, which describes classical spinning black holes (BHs) in string theory. We find that the Blandford-Znajek (BZ) mechanism, which is believed to power astrophysical relativistic outflows or ``jets'', is valid even for BHs in an alternate th

Yoichi Watanabe, Rukhsora Akramova

In high-quality radiotherapy delivery, precise segmentation of targets and healthy structures is essential. This study proposes Radiomics features as a superior measure for assessing the segmentation ability of physicians and auto-segmentation tools, in comparison to the widely used Dice Similarity Coefficient (DSC). The research involves selecting reproduci

Murat Babek Salman, Emil Björnson, Gokhan Muzaffer Guvensen, Tolga Ciloglu

Nonlinear distortion stemming from low-cost power amplifiers may severely affect wireless communication performance through out-of-band (OOB) radiation and in-band distortion. The distortion is correlated between different transmit antennas in an antenna array, which results in a beamforming gain at the receiver side that grows with the number of antennas. I

Kang Liu, Laurent Pfeiffer

We formulate and investigate a mean field optimization (MFO) problem over a set of probability distributions $\mu$ with a prescribed marginal $m$. The cost function depends on an aggregate term, which is the expectation of $\mu$ with respect to a contribution function. This problem is of particular interest in the context of Lagrangian potential mean field g

Prakhyat Khati, Ajay Kumar Shrestha, Julita Vassileva

In this paper, we propose a certificate sharing system based on blockchain that gives students authority and control over their academic certificates. Our strategy involves developing blockchain-based NFT certifications that can be shared with institutions or employers using blockchain addresses. Students may access the data created by each individual instit

Avalon Vinella, Margaret Capetz, Rebecca Pattichis, Christina Chance

In recent years, climate change repercussions have increasingly captured public interest. Consequently, corporations are emphasizing their environmental efforts in sustainability reports to bolster their public image. Yet, the absence of stringent regulations in review of such reports allows potential greenwashing. In this study, we introduce a novel prelimi

T. C. Adorno, S. P. Gavrilov, D. M. Gitman

Effective field theory of low-energy exitations-magnons that describes antiferromagnets is mapped into scalar electrodynamics of a charged scalar field interacting with an external electromagnetic potential. In the presence of a constant inhomogeneous external magnetic field the latter problem is technically reduced to the problem of charged-particle creatio

Moritz A. Graule, Volkan Isler

A robot in a human-centric environment needs to account for the human's intent and future motion in its task and motion planning to ensure safe and effective operation. This requires symbolic reasoning about probable future actions and the ability to tie these actions to specific locations in the physical environment. While one can train behavioral models ca

Prakamya Mishra, Zonghai Yao, Shuwei Chen, Beining Wang

Large Language Models (LLMs) like the GPT and LLaMA families have demonstrated exceptional capabilities in capturing and condensing critical contextual information and achieving state-of-the-art performance in the summarization task. However, community concerns about these models' hallucination issues continue to rise. LLMs sometimes generate factually hallu

Nicolò Cogno, Cristian Axenie, Roman Bauer, Vasileios Vavourakis

Computational models and simulations are not just appealing because of their intrinsic characteristics across spatiotemporal scales, scalability, and predictive power, but also because the set of problems in cancer biomedicine that can be addressed computationally exceeds the set of those amenable to analytical solutions. Agent-based models and simulations a

Aaron Lou, Minkai Xu, Stefano Ermon

Riemannian diffusion models draw inspiration from standard Euclidean space diffusion models to learn distributions on general manifolds. Unfortunately, the additional geometric complexity renders the diffusion transition term inexpressible in closed form, so prior methods resort to imprecise approximations of the score matching training objective that degrad

Felipe García-Ramos, Gerardo González Robert, Mumtaz Hussain

We study the topological, dynamical, and descriptive set theoretic properties of Hurwitz continued fractions. Hurwitz continued fractions associate an infinite sequence of Gaussian integers to every complex number which is not a Gaussian rational. The resulting space of sequences of Gaussian integers $\Omega$ is not closed. By means of an algorithm, we show

Maurizio La Rocca, Tiziana La Rocca, Francesco Fasano, Javier Sanchez-Vidal

Corruption, fraud, and unethical activities have emerged as significant obstacles to global economic, political, and social progress. Although many empirical studies have focused on country-level corruption metrics, this study is the first to utilize a substantial international dataset to assess the effects of illicit and unethical managerial practices on fi

Thomas O'Hare

Let $f,g$ be $C^2$ expanding maps on the circle which are topologically conjugate. We assume that the derivatives of $f$ and $g$ at corresponding periodic points coincide for some large period $N$. We show that $f$ and $g$ are "approximately smoothly conjugate." Namely, we construct a $C^2$ conjugacy $h_N$ such that $h_N$ is exponentially close to $h$ in the

Jaren N. Ashcraft, Ewan S. Douglas, Daewook Kim, A. J. E. Riggs

For diffraction-limited optical systems an accurate physical optics model is necessary to properly evaluate instrument performance. Astronomical observatories outfitted with coronagraphs for direct exoplanet imaging require physical optics models to simulate the effects of misalignment and diffraction. Accurate knowledge of the observatory's PSF is integral

Mianchu Wang, Rui Yang, Xi Chen, Hao Sun

Offline Goal-Conditioned RL (GCRL) offers a feasible paradigm for learning general-purpose policies from diverse and multi-task offline datasets. Despite notable recent progress, the predominant offline GCRL methods, mainly model-free, face constraints in handling limited data and generalizing to unseen goals. In this work, we propose Goal-conditioned Offlin

L. Varriano, G. Savard, J. A. Clark, D. P. Burdette

The Beta-decay Paul Trap is an open-geometry, linear trap used to measure the decays of $^8$Li and $^8$B to search for a tensor contribution to the weak interaction. In the latest $^8$Li measurement of Burkey et al. (2022), $\beta$ scattering was the dominant experimental systematic uncertainty. The Beta-decay Paul Trap Mk IV reduces the prevalence of $\beta

Matin Macktoobian, Zhan Shu, Qing Zhao

Faults occurring in ad-hoc robot networks may fatally perturb their topologies leading to disconnection of subsets of those networks. Optimal topology synthesis is generally resource-intensive and time-consuming to be done in real time for large ad-hoc robot networks. One should only perform topology re-computations if the probability of topology recoverabil

Hung Truong, Abderahmane Marouf, Jan B. Vos, Alain Gehri

The study described in this paper was conducted as part of the European Funded CleanSky2 project AFC4TR (Active Flow Control for Tilt-Rotor aircraft). High Fidelity numerical simulations were made to study various approaches of using Active Flow Control (AFC) actuators to delay flow separation at near stall conditions of the Next Generation Civil Tilt Rotor

Thomas Sayer, Andrés Montoya-Castillo

Modern 4-wave mixing spectroscopies are expensive to obtain experimentally and computationally. In certain cases, the unfavorable scaling of quantum dynamics problems can be improved using a generalized quantum master equation (GQME) approach. However, the inclusion of multiple (light-matter) interactions complicates the equation of motion and leads to seemi

Stanley Yao Xiao

In this paper we prove that for all degree $6$ polynomials with rational coefficients that $F(\mathbb{Z}^2) \neq \mathbb{Z}_{\geq 0}$. The answers a question of B. Poonen and J. S. Lew in the degree 6 case. This work builds on previous work with S. Yamagishi.