October 2024 arXiv papers — page 69

Leonardo Limongi, Francesco Martini, Thu Ha Dao, Alessandro Gaggero

Photon Number Resolving Detectors (PNRDs) are devices capable of measuring the number of photons present in an incident optical beam, enabling light sources to be measured and characterized at the quantum level. In this paper, we explore the performance and design considerations of a linearly multiplexed photon number-resolving single-photon detector array,

Omkar Patil, Anant Sah, Nakul Gopalan

Humans can perform various combinations of physical skills without having to relearn skills from scratch every single time. For example, we can swing a bat when walking without having to re-learn such a policy from scratch by composing the individual skills of walking and bat swinging. Enabling robots to combine or compose skills is essential so they can lea

Ankur Garg, Tushar Shukla, Sunita Arya, Ghansham Sangar

The Ocean Color Monitor-3, launched aboard Oceansat-3, represents a significant advancement in ocean observation technology, building upon the capabilities of its predecessors. With thirteen spectral bands, OCM-3 enhances feature identification and atmospheric correction, enabling precise data collection from a sun-synchronous orbit. With thirteen spectral b

Matthew Han, Hannah Rana

Recent advancements in the development of Applied-Field Magnetoplasmadynamic thrusters (AF-MPDTs) present themselves to be an increasingly promising propulsion technology for deep space exploration missions. Various entities, ranging from state-sponsored institutions to privately-owned startups, have developed AF-MPDTs across a wide range of power levels. Cu

Jerry Huang, Prasanna Parthasarathi, Mehdi Rezagholizadeh, Boxing Chen

The growth in prominence of large language models (LLMs) in everyday life can be largely attributed to their generative abilities, yet some of this is also owed to the risks and costs associated with their use. On one front is their tendency to hallucinate false or misleading information, limiting their reliability. On another is the increasing focus on the

Jureeporn Yuennan, Piyachat Panyasiripan, Phongpichit Channuie

In this study, we investigate traversable wormholes inspired by the Kiselev framework, which extends classical black hole solutions by incorporating anisotropic fluids. These exotic fluids play a crucial role in cosmology, particularly in explaining phenomena such as the accelerated expansion of the universe. We generalize the Kiselev framework to static, sp

Antonio Herrera-Martin, Radu V. Craiu, Gwendolyn M. Eadie, David C. Stenning

An important task in the study of fast radio bursts (FRBs) remains the automatic classification of repeating and non-repeating sources based on their morphological properties. We propose a statistical model that considers a modified logistic regression to classify FRB sources. The classical logistic regression model is modified to accommodate the small propo

Catie LeDesma, Kendall Mehling, John Drew Wilson, Marco Nicotra

In this paper, we propose a new paradigm for atom interferometry and demonstrate that there exists a universal set of atom optic components for inertial sensing. These components constitute gates with which we carry out quantum operations and represent input-output matterwave transformations between lattice eigenstates. Each gate is associated with a modulat

Keir Adams, Kento Abeywardane, Jenna Fromer, Connor W. Coley

Engineering molecules to exhibit precise 3D intermolecular interactions with their environment forms the basis of chemical design. In ligand-based drug design, bioisosteric analogues of known bioactive hits are often identified by virtually screening chemical libraries with shape, electrostatic, and pharmacophore similarity scoring functions. We instead hypo

Lili Li, Santosh Kumar, Malvika Garikapati, Yu-Ping Huang

Quantum techniques are expected to revolutionize how information is acquired, exchanged, and processed. Yet it has been a challenge to realize and measure their values in practical settings. We present first photon machine learning as a new paradigm of neural networks and establish the first unambiguous advantage of quantum effects for artificial intelligenc

Stefan Trandafir, Adán Cabello

Bipartite perfect quantum strategies (BPQSs) allow two players isolated from each other to win every trial of a nonlocal game. BPQSs have crucial roles in recent developments in quantum information and quantum computation. However, only few BPQSs with a small number of inputs are known and only one of them has been experimentally tested. It has recently been

Abdelmonem Elrefaey, Rong Pan

This paper presents a novel Integer Programming (IP) approach for discovering the Markov Equivalent Class (MEC) of Bayesian Networks (BNs) through observational data. The MEC-IP algorithm utilizes a unique clique-focusing strategy and Extended Maximal Spanning Graphs (EMSG) to streamline the search for MEC, thus overcoming the computational limitations inher

Amr Gomaa, Michael Sargious, Antonio Krüger

The increasing integration of machine learning across various domains has underscored the necessity for accessible systems that non-experts can utilize effectively. To address this need, the field of automated machine learning (AutoML) has developed tools to simplify the construction and optimization of ML pipelines. However, existing AutoML solutions often

Young Hyun Cho, Jordan Awan

Motivated by the 2020 US Census products, this paper extends differential privacy (DP) to address the joint release of DP outputs and nonprivate statistics, referred to as invariant. Our framework, Semi-DP, redefines adjacency by focusing on datasets that conform to the given invariant, ensuring indistinguishability between adjacent datasets within invariant

Tanaporn Na Narong, Zoe N. Zachko, Steven B. Torrisi, Simon J. L. Billinge

We used interpretable machine learning to combine information from multiple heterogeneous spectra: X-ray absorption near-edge spectra (XANES) and atomic pair distribution functions (PDFs) to extract local structural and chemical environments of transition metal cations in oxides. Random forest models were trained on simulated XANES, PDF, and both combined to

Yann Bouteiller, Karthik Soma, Giovanni Beltrame

The universe involves many independent co-learning agents as an ever-evolving part of our observed environment. Yet, in practice, Multi-Agent Reinforcement Learning (MARL) applications are typically constrained to small, homogeneous populations and remain computationally intensive. We propose a methodology that enables simulating populations of Reinforcement

Jacopo Tagliabue, Tyler Caraza-Harter, Ciro Greco

Chaining functions for longer workloads is a key use case for FaaS platforms in data applications. However, modern data pipelines differ significantly from typical serverless use cases (e.g., webhooks and microservices); this makes it difficult to retrofit existing pipeline frameworks due to structural constraints. In this paper, we describe these limitation

Ali Azizpour, Nicolas Zilberstein, Santiago Segarra

Graphons are continuous models that represent the structure of graphs and allow the generation of graphs of varying sizes. We propose Scalable Implicit Graphon Learning (SIGL), a scalable method that combines implicit neural representations (INRs) and graph neural networks (GNNs) to estimate a graphon from observed graphs. Unlike existing methods, which face

Minhua Lin, Zhengzhang Chen, Yanchi Liu, Xujiang Zhao

Time series data is ubiquitous across various domains, including manufacturing, finance, and healthcare. High-quality annotations are essential for effectively understanding time series and facilitating downstream tasks; however, obtaining such annotations is challenging, particularly in mission-critical domains. In this paper, we propose TESSA, a multi-agen

D. C. Chen, D. Rainer, J. A. Sauls

The structure of the order parameter and the excitation spectrum are investigated for isolated impurities in d-wave superconductors. Atomic scale impurities, or defects, scatter quasiparticles and lead to local suppression (pair-breaking) near the impurity. The pair-breaking effect arises from the formation of quasiparticle states bound to the impurity. The

Thuan Pham, Xingpeng Li

Optimal power flow (OPF) has been used for real-time grid operations. Prior efforts demonstrated that utilizing flexibility from dynamic topologies will improve grid efficiency. However, this will convert the linear OPF into a mixed-integer linear programming network-reconfigured OPF (NR-OPF) problem, substantially increasing the computing time. Thus, a mach

Mahesh Vaijainthymala Krishnamoorthy

As AI systems increasingly integrate into critical societal sectors, the demand for robust privacy-preserving methods has escalated. This paper introduces Data Obfuscation through Latent Space Projection (LSP), a novel technique aimed at enhancing AI governance and ensuring Responsible AI compliance. LSP uses machine learning to project sensitive data into a

Josue Avila

For a real quadratic field $K=\mathbb{Q}(\sqrt{D})$, let $K_{\infty}$ denote the cyclotomic $\mathbb{Z}_{p}$-extension of $K$. Greenberg conjectured that the corresponding Iwasawa module $X_{\infty}$ is finite. Building on the work of Mouhib and Movahhedi, we provide new examples of real quadratic fields for which the conjecture holds, when $X_{\infty}$ is c

Suryoday Basak, Abhijeeth Padarthi, Mahanth Gowda

This paper introduces mmWave-Whisper, a system that demonstrates the feasibility of full-corpus automated speech recognition (ASR) on phone calls eavesdropped remotely using off-the-shelf frequency modulated continuous wave (FMCW) millimeter-wave radars. Operating in the 77-81 GHz range, mmWave-Whisper captures earpiece vibrations from smartphones, converts

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

Optical metasurfaces provide novel solutions to label-free biochemical sensing by localizing light resonantly beyond the diffraction limit, thereby selectively enhancing light-matter interactions for improved analytical performance. However, high-Q resonances in metasurfaces are usually achieved in the reflection mode, which impedes metasurface integration i

Siju Zhang, Tie Liu, Ke Wang, Annie Zavagno

Fragmentation and evolution for the molecular shells of the compact HII regions are less explored compared to their evolved counterparts. We map nine compact HII regions with a typical diameter of 0.4 pc that are surrounded by molecular shells traced by CCH. Several to a dozen dense gas fragments probed by H13CO+ are embedded in these molecular shells. These

David Henry, Rossen I. Ivanov, Zisis N. Sakellaris

In this paper we derive a higher-order KdV equation (HKdV) as a model to describe the unidirectional propagation of waves on an internal interface separating two fluid layers of varying densities. Our model incorporates underlying currents by permitting a sheared current in both fluid layers, and also accommodates the effect of the Earth's rotation by includ

Sodre Hiago, Barcelona Sebastian, Sandin Vincent, Moraes Pablo

This article presents initial configuration work and use of the robotic platform and manipulator in question. The development of the ideal configuration for using this robot serves as a guide for new users and also validates its functionality for use in projects. Husky is a large payload capacity and power systems robotics development platform that accommoda

Th. Lamprou, P. Stammer, J. Rivera-Dean, N. Tsatrafyllis

Non-classical and entangled light states are of fundamental interest in quantum mechanics and they are a powerful tool for the emergence of new quantum technologies. The development of methods that can lead to the generation of such light states is therefore of high importance. Recently, it has been demonstrated that intense laser-matter interactions can ser

Sonja Cwik, Chandralekha Singh

Student beliefs in introductory physics courses can influence their course outcomes and retention in STEM disciplines and future career aspirations. This study used survey data from 501 students in the first of two-semester algebra-based introductory physics courses primarily taken by bioscience majors, in which women make up approximately 65% of the class.

Moraes Pablo, Rodríguez Mónica, Peters Christopher, Sodre Hiago

This study investigates the interaction between humanoid robots NAO and Pepper, emphasizing their potential applications in educational settings. NAO, widely used in education, and Pepper, designed for social interactions, of er new opportunities for autonomous communication and collaboration. Through a series of programmed interactions, the robots demonstra

Parth Garud, Connor Johnson, Alfonso Lagares de Toledo

Material Girl was a two-stage sounding rocket designed and flown during the 2022-2023 academic year. Weighing a total of 197 lbs and measuring 176 inches in length, the vehicle incorporated many improvements from the team's previous rocket, Mr. Blue Sky. The rocket used a Student Researched and Developed (SRAD) P-class motor on each stage, a mechanical stagi

N. Buyukcizmeci, T. Reichert, A. S. Botvina, M. Bleicher

We extend the theoretical approach which includes the dynamical and statistical stages for the description of the nucleosynthesis in central collisions of relativistic ions. Previously, this approach was successfully applied to describe experimental data on both normal nuclei and single strange hypernuclei production in the GSI and RHIC-BES energy range. We

Gabriela Flores, Ahilen Mazondo, Pablo Moraes, Hiago Sodre

This research examines the impact of robotics groups in higher education, focusing on how these activities influence the development of transversal skills and academic motivation. While robotics goes beyond just technical knowledge, participation in these groups has been observed to significantly improve skills such as teamwork, creativity, and problem-solvi

Samiha Sharlin, Tyler R. Josephson

Large Language Models (LLMs) are transformer-based machine learning models that have shown remarkable performance in tasks for which they were not explicitly trained. Here, we explore the potential of LLMs to perform symbolic regression -- a machine-learning method for finding simple and accurate equations from datasets. We prompt GPT-4 and GPT-4o models to

Takashi Owada, Gennady Samorodnitsky

We extend the classical preferential attachment random graph model to random simplicial complexes. At each stage of the model, we choose one of the existing $k$-simplices with probability proportional to its $k$-degree. The chosen $k$-simplex then forms a $(k+1)$-simplex with a newly arriving vertex. We establish a strong law of large numbers for the degree

Laurence Keefe, Austin McDaniel, Max Cubillos, Ilya Zilberter

The vector electric-field Helmholtz equation, containing cross-polarization terms, is factored to produce both pseudo-differential and exponential operator forms of a three-dimensional, one-way, vector, wave equation for propagation through inhomogeneous media. From this operator factorization we develop a high-order approximate, vector Helmholtz propagator

Anthony Baez, Wang Zhang, Ziwen Ma, Subhro Das

Physics-informed neural networks (PINNs) incorporate physical laws into their training to efficiently solve partial differential equations (PDEs) with minimal data. However, PINNs fail to guarantee adherence to conservation laws, which are also important to consider in modeling physical systems. To address this, we proposed PINN-Proj, a PINN-based model that

Ilya Hajiaghayi, MohammadTaghi Hajiaghayi, Gary Peng, Suho Shin

We study bilateral trade with a broker, where a buyer and seller interact exclusively through the broker. The broker strategically maximizes her payoff through arbitrage by trading with the buyer and seller at different prices. We study whether the presence of the broker interferes with the mechanism's gains-from-trade (GFT) achieving a constant-factor appro

Carolyn Engelhardt, Seth Hovland

In this paper we study the relationships between links in plat position, the dynamics of the braid group, and Heegaard splittings of double branched covers of $S^3$ over a link. These relationships offer new ways to view links in plat position and a new tool kit for analyzing links. In particular, we show that the Hempel distance of the Heegaard splitting of

Mohammad Ghassemi, Han Zhang, Ali Afana, Akram Bin Sediq

Beam management is an important technique to improve signal strength and reduce interference in wireless communication systems. Recently, there has been increasing interest in using diverse sensing modalities for beam management. However, it remains a big challenge to process multi-modal data efficiently and extract useful information. On the other hand, the

Furkan Mumcu, Yasin Yilmaz

Deep Neural Networks (DNNs) have been shown to be vulnerable to adversarial examples. While numerous successful adversarial attacks have been proposed, defenses against these attacks remain relatively understudied. Existing defense approaches either focus on negating the effects of perturbations caused by the attacks to restore the DNNs' original predictions

Bernhard A. Moser, Michael Lunglmayr

In contrast to the traditional principle of periodic sensing neuromorphic engineering pursues a paradigm shift towards bio-inspired event-based sensing, where events are primarily triggered by a change in the perceived stimulus. We show in a rigorous mathematical way that information encoding by means of Threshold-Based Representation based on either Leaky I

Heather LeClair, Tim Spilde, Sarah Anderson, Brenda Kroschel

Zero forcing is a graph propagation process for which vertices fill-in (or propagate information to) neighbor vertices if all neighbors except for one, are filled. The zero-forcing number is the smallest number of vertices that must be filled to begin the process so that the entire graph or network becomes filled. In this paper, bounds are provided on the ze

Yang Zhong, Jiangang Hao, Michael Fauss, Chen Li

The rapid advancement of large language models (LLMs) has enabled the generation of coherent essays, making AI-assisted writing increasingly common in educational and professional settings. Using large-scale empirical data, we examine and benchmark the characteristics and quality of essays generated by popular LLMs and discuss their implications for two key

Oleg Igouchkine, Xingchen Liu

Multiscale structures are becoming increasingly prevalent in the field of mechanical design. The variety of fine-scale structures and their respective representations results in an interoperability challenge. To address this, a query-based API was recently proposed which allows different representations to be combined across the scales for multiscale structu

Samarth Bhargav, Alexander Gu

Understanding the internal mechanisms of GPT-style transformers, particularly their capacity to perform in-context learning (ICL), is critical for advancing AI alignment and interpretability. In-context learning allows transformers to generalize during inference without modifying their weights, yet the precise operations driving this capability remain largel

Alexander Polok, Santosh Kesiraju, Karel Beneš, Lukáš Burget

This paper proposes a simple yet effective way of regularising the encoder-decoder-based automatic speech recognition (ASR) models that enhance the robustness of the model and improve the generalisation to out-of-domain scenarios. The proposed approach is dubbed as $\textbf{De}$coder-$\textbf{C}$entric $\textbf{R}$egularisation in $\textbf{E}$ncoder-$\textbf

José Nicolás Orce

Throughout the nuclear chart, particle-hole correlations give rise to giant resonances and, together with the proton-neutron interaction, deformation and rotational bands. In order to shed light on many-body correlations in open-shell nuclei, I explore macroscopic properties that could manifest from the collective behavior of protons and neutrons. Intuitivel

Yihong Zhou, Angel Paredes, Chaimaa Essayeh, Thomas Morstyn

The recent growth of Artificial Intelligence (AI), particularly large language models, requires energy-demanding high-performance computing (HPC) data centers, which poses a significant burden on power system capacity. Scheduling data center computing jobs to manage power demand can alleviate network stress with minimal infrastructure investment and contribu

Xiaoqian Shen, Yunyang Xiong, Changsheng Zhao, Lemeng Wu

Multimodal Large Language Models (MLLMs) have shown promising progress in understanding and analyzing video content. However, processing long videos remains a significant challenge constrained by LLM's context size. To address this limitation, we propose LongVU, a spatiotemporal adaptive compression mechanism thats reduces the number of video tokens while pr

Abdoul Jalil Djiberou Mahamadou, Artem A. Trotsyuk

Efforts to mitigate bias and enhance fairness in the artificial intelligence (AI) community have predominantly focused on technical solutions. While numerous reviews have addressed bias in AI, this review uniquely focuses on the practical limitations of technical solutions in healthcare settings, providing a structured analysis across five key dimensions aff

Jian-Wei Qiu, Zhite Yu

We give a brief overview on the theory and phenomenology of generalized parton distributions (GPDs), including the recently developed framework of single-diffractive hard exclusive process for matching GPDs to experimental observables. We concentrate on the extraction of GPDs from experimental processes, especially on the challenges and potential solutions r

Dante Everaert, Rohit Patki, Tianqi Zheng, Christopher Potts

Query Autocomplete (QAC) is a critical feature in modern search engines, facilitating user interaction by predicting search queries based on input prefixes. Despite its widespread adoption, the absence of large-scale, realistic datasets has hindered advancements in QAC system development. This paper addresses this gap by introducing AmazonQAC, a new QAC data

Tao Li, Henger Li, Yunian Pan, Tianyi Xu

Federated learning (FL) is susceptible to a range of security threats. Although various defense mechanisms have been proposed, they are typically non-adaptive and tailored to specific types of attacks, leaving them insufficient in the face of multiple uncertain, unknown, and adaptive attacks employing diverse strategies. This work formulates adversarial fede

Haotong Liang, Chuangye Wang, Heshan Yu, Dylan Kirsch

Iterative cycles of theoretical prediction and experimental validation are the cornerstone of the modern scientific method. However, the proverbial "closing of the loop" in experiment-theory cycles in practice are usually ad hoc, often inherently difficult, or impractical to repeat on a systematic basis, beset by the scale or the time constraint of computati

Giuseppe Spallitta, Roberto Sebastiani, Armin Biere

All-Solution Satisfiability (AllSAT) and its extension, All-Solution Satisfiability Modulo Theories (AllSMT), have become more relevant in recent years, mainly in formal verification and artificial intelligence applications. The goal of these problems is the enumeration of all satisfying assignments of a formula (for SAT and SMT problems, respectively), maki

Alek Hutson, Rene Bellwied

Recent studies have shown a potential correlation between the entanglement of initial state partons in elementary particle collisions, as conceptualized by contemporary quantum and particle theory, and the final state multiplicity distribution of hadrons produced in experiments like those at the Large Hadron Collider (LHC). It has been proposed that this rel

Ömer Veysel Çağatan, Barış Akgün

In this study, we investigate the effect of SSL objective modifications within the SPR framework, focusing on specific adjustments such as terminal state masking and prioritized replay weighting, which were not explicitly addressed in the original design. While these modifications are specific to RL, they are not universally applicable across all RL algorith

Ömer Veysel Çağatan

We propose SigCLR: Sigmoid Contrastive Learning of Visual Representations. SigCLR utilizes the logistic loss that only operates on pairs and does not require a global view as in the cross-entropy loss used in SimCLR. We show that logistic loss shows competitive performance on CIFAR-10, CIFAR-100, and Tiny-IN compared to other established SSL objectives. Our

Seth Pettie, Dingyu Wang

In the $d$-dimensional turnstile streaming model, a frequency vector $\mathbf{x}=(\mathbf{x}(1),\ldots,\mathbf{x}(n))\in (\mathbb{R}^d)^n$ is updated entry-wisely over a stream. We consider the problem of $f$-moment estimation, where one wants to estimate $$f(\mathbf{x})=\sum_{v\in[n]}f(\mathbf{x}(v))$$ with a small-space sketch. In this work we present a si

Tomohiro Hirano, Alexis Akira Toda

A rational bubble is a situation in which the asset price exceeds its fundamental value defined by the present discounted value of dividends in a rational equilibrium model. We discuss the recent development of the theory of rational bubbles attached to real assets, emphasizing the following three points. (i) There exist plausible economic models in which bu

A. Itin

Design of photonic crystals having large bandgaps above a prescribed band is a well-known physical problem with many applications. A connection to an interesting mathematical construction was pointed out some time ago: it had been conjectured that optimal structures for gaps between bands n and n+1 correspond, in case of transverse magnetic polarisation, to

Raphael Hernandes, Giulio Corsi

The current surge in Artificial Intelligence (AI) interest, reflected in heightened media coverage since 2009, has sparked significant debate on AI's implications for privacy, social justice, workers' rights, and democracy. The media plays a crucial role in shaping public perception and acceptance of AI technologies. However, research into how AI appears in

Wen Jiang, Boshu Lei, Katrina Ashton, Kostas Daniilidis

We present an active mapping system that plans for both long-horizon exploration goals and short-term actions using a 3D Gaussian Splatting (3DGS) representation. Existing methods either do not take advantage of recent developments in multimodal Large Language Models (LLM) or do not consider challenges in localization uncertainty, which is critical in embodi

Gunter Bombaerts, Tom Hannes, Martin Adam, Alessandra Aloisi

As the signatories of this manifesto, we denounce the attention economy as inhumane and a threat to our sociopolitical and ecological well-being. We endorse policymakers' efforts to address the negative consequences of the attention economy's technology, but add that these approaches are often limited in their criticism of the systemic context of human atten

Martina Conte, Romina Travaglini

We propose a mathematical kinetic framework to investigate interactions between tumor cells and the immune system, focusing on the spatial dynamics of tumor progression and immune responses. We develop two kinetic models: one describes a conservative scenario where immune cells switch between active and passive states without proliferation, while the other i

Emiliano Rasztocky, Matias Rolf Hampel, Rodrigo Reeves, Jacques R. D. Lepine

Many modern radio telescopes employ an observational strategy that involves maximizing the use of their available spaces (cabins), outfitting them with various receivers at different frequencies to detect incoming signals from the sky simultaneously or individually. The Large Latin American Millimeter Array (LLAMA), is a joint venture between Argentina and B

Natalie Blot, Caelan Brooks, Daniel W. Swartz, Eslam Abdelaleem

Viruses like influenza have long coevolved with host immune systems, gradually shaping the evolutionary trajectory of these pathogens. Host immune systems develop immunity against circulating strains, which in turn avoid extinction by exploiting antigenic escape mutations that render new strains immune from existing antibodies in the host population. Infecte

Delilah Milner, Oliver Fiehn

Thousands of metabolomic papers are published each year, creating challenges for scientists to combine results and yield conclusions that span across studies. Literature databases such as the Human Metabolome Database provide summaries of metabolite detections and relevance, but it does not focus on specific processes, such as aging. Another database, Metabo

Chandra Irugalbandara

Extending Large Language Models (LLMs) to advanced applications requires reliable structured output generation. Existing methods which often rely on rigid JSON schemas, can lead to unreliable outputs, diminished reasoning capabilities, and increased computational overhead, limiting LLMs' adaptability for complex tasks. We introduce Meaning Typed Prompting (M

Daniel J. Ratliff, Olga Trichtchenko, Thomas J. Bridges

For Stokes waves in finite depth within the neighbourhood of the Benjamin-Feir stability transition, there are two families of periodic waves, one modulationally unstable and the other stable. In this paper we show that these two families can be joined by a heteroclinic connection, which manifests in the fluid as a travelling front. By shifting the analysis

My H Dinh, James Kotary, Lauryn P. Gouldin, William Yeoh

Criminal courts across the United States handle millions of cases every year, and the scheduling of those cases must accommodate a diverse set of constraints, including the preferences and availability of courts, prosecutors, and defense teams. When criminal court schedules are formed, defendants' scheduling preferences often take the least priority, althoug

Rudro R. Biswas, Charles S. Wright, Kunaal Joshi, Srividya Iyer-Biswas

What are the signatures of the onset of catastrophe? Here we present the rich system physics characterizing the transition from homeostasis to stochasticity driven breakdown in an experimentally motivated minimal model. Recent high-precision experiments on individual bacterial cells, growing and dividing repeatedly in a variety of environments, have revealed

Tyler A. Chang, Dheeraj Rajagopal, Tolga Bolukbasi, Lucas Dixon

Training data attribution (TDA) methods aim to attribute model outputs back to specific training examples, and the application of these methods to large language model (LLM) outputs could significantly advance model transparency and data curation. However, it has been challenging to date to apply these methods to the full scale of LLM pretraining. In this pa

David Hubbard

Existing results of Fu show that, if two finite sets of roots of unity are projectively equivalent by a projective automorphism that does not act bijectively on the set of all roots of unity, then these sets consist of at most 14 points. Moreover, Fu constructs the two possible maximal sets, which are unique up to projective equivalence. In this article, we

Narges Mahyar

The urgency of climate change is now recognized globally. As humanity confronts the critical need to mitigate climate change and foster sustainability, data visualization emerges as a powerful tool with a unique capacity to communicate insights crucial for understanding environmental complexities. This paper explores the critical need for designing and inves

Meiby Ortiz-Bouza, Duc Vu, Abdullah Karaaslanli, Selin Aviyente

Over the past two decades, tools from network science have been leveraged to characterize the organization of both structural and functional networks of the brain. One such measure of network organization is hub node identification. Hubs are specialized nodes within a network that link distinct brain units corresponding to specialized functional processes. C

Sara Honarvar, Yancy Diaz-Mercado

Modeling human trajectories in crowded environments is challenging due to the complex nature of pedestrian behavior and interactions. This paper proposes a geometric graph neural network (GNN) architecture that integrates domain knowledge from psychological studies to model pedestrian interactions and predict future trajectories. Unlike prior studies using c

Juan P. Mendez, Xujiao Gao, Jeffrey Ivie, James H. G. Owen

We investigate the different transport mechanisms that can occur in pn junction devices made using atomic precision advanced manufacturing (APAM) at temperatures ranging from cryogenic to room temperature. We first elucidate the potential cause of the anomalous behavior observed in the forward-bias response of these devices in recent cryogenic temperature me

Joeri De Ro

Given a locally compact quantum group $\mathbb{G}$ and two $\mathbb{G}$-$W^*$-algebras $\alpha: A\curvearrowleft \mathbb{G}$ and $\beta: B\curvearrowleft \mathbb{G}$, we study the notion of equivariant $W^*$-Morita equivalence $(A, \alpha)\sim_{\mathbb{G}} (B, \beta)$, which is an equivariant version of Rieffel's notion of $W^*$-Morita equivalence. We prove

Reza Fayyazi, Stella Hoyos Trueba, Michael Zuzak, Shanchieh Jay Yang

In cybersecurity, security analysts constantly face the challenge of mitigating newly discovered vulnerabilities in real-time, with over 300,000 vulnerabilities identified since 1999. The sheer volume of known vulnerabilities complicates the detection of patterns for unknown threats. While LLMs can assist, they often hallucinate and lack alignment with recen

Elliot Blackstone, Louise Gassot, Patrick Gérard, Peter D. Miller

The leading-order asymptotic behavior of the solution of the Cauchy initial-value problem for the Benjamin-Ono equation in $L^2(\mathbb{R})$ is obtained explicitly for generic rational initial data $u_0$. An explicit asymptotic wave profile $u^\mathrm{ZD}(t,x;\epsilon)$ is given, in terms of the branches of the multivalued solution of the inviscid Burgers eq

Jacqueline Malayter, Christopher Brinton, David Love

Deep learning aided codes have been shown to improve code performance in feedback codes in high noise regimes due to the ability to leverage non-linearity in code design. In the additive white Gaussian broadcast channel (AWGN-BC), the addition of feedback may allow the capacity region to extend far beyond the capacity region of the channel without feedback,

Chandra Chekuri, Rhea Jain

We consider Directed Steiner Forest (DSF), a fundamental problem in network design. The input to DSF is a directed edge-weighted graph $G = (V, E)$ and a collection of vertex pairs $\{(s_i, t_i)\}_{i \in [k]}$. The goal is to find a minimum cost subgraph $H$ of $G$ such that $H$ contains an $s_i$-$t_i$ path for each $i \in [k]$. DSF is NP-Hard and is known t

Qi Xiao, Lidong Song, Jongha Woo, Rongxing Hu

This paper introduces "invisible manipulation," an innovative cyber-attack mechanism achieved through strategically timed stealthy false data injection attacks (SFDIAs). By stealthily manipulating measurements of a critical asset prior to the target time period, the attacker can subtly guide the engineering system toward a predetermined operational state wit

Chejian Xu, Mintong Kang, Jiawei Zhang, Zeyi Liao

Foundation model-based agents are increasingly used to automate complex tasks, enhancing efficiency and productivity. However, their access to sensitive resources and autonomous decision-making also introduce significant security risks, where successful attacks could lead to severe consequences. To systematically uncover these vulnerabilities, we propose Adv

R. Oguz Araz, Xavier Serra, Dmitry Bogdanov

Current version identification (VI) datasets often lack sufficient size and musical diversity to train robust neural networks (NNs). Additionally, their non-representative clique size distributions prevent realistic system evaluations. To address these challenges, we explore the untapped potential of the rich editorial metadata in the Discogs music database

Zhu Shen, Ambarish Chattopadhyay, Yuzhou Lin, Jose R. Zubizarreta

In recent decades, event studies have emerged as a central methodology in health and social research for evaluating the causal effects of staggered interventions. In this paper, we analyze event studies from experimental design principles for observational studies, with a focus on information borrowing across measurements. We develop robust weighting estimat

Nathan E. Glatt-Holtz, Andrew J. Holbrook, Justin A. Krometis, Cecilia F. Mondaini

In the first edition of this Handbook, two remarkable chapters consider seemingly distinct yet deeply connected subjects ...

Borja Aizpurua, Fernando Loren, Saeed S. Jahromi, Sukhbinder Singh

We introduce a framework for seamlessly integrating quantum computing into pretrained large language models (LLMs). The key idea is to construct a hybrid quantum-classical representation that exactly reproduces the original model, providing a principled starting point from which quantum resources can only improve performance. Our approach replaces the weight

Victor Le Coz, Michael Benzaquen, Damien Challet

We propose a minimal model of the secured interbank network able to shed light on recent money markets puzzles. We find that excess liquidity emerges due to the interactions between the reserves and liquidity ratio constraints; the appearance of evergreen repurchase agreements and collateral re-use emerges as a simple answer to banks' counterparty risk and l

Arrun Sivasubramanian, Divya Sasidharan, Sowmya V, Vinayakumar Ravi

Ultrasound fetal imaging is beneficial to support prenatal development because it is affordable and non-intrusive. Nevertheless, fetal plane classification (FPC) remains challenging and time-consuming for obstetricians since it depends on nuanced clinical aspects, which increases the difficulty in identifying relevant features of the fetal anatomy. Thus, to

Yifan Qin, Zhenge Jia, Zheyu Yan, Jay Mok

This paper proposes an ultra-low power, mixed-bit-width sparse convolutional neural network (CNN) accelerator to accelerate ventricular arrhythmia (VA) detection. The chip achieves 50% sparsity in a quantized 1D CNN using a sparse processing element (SPE) architecture. Measurement on the prototype chip TSMC 40nm CMOS low-power (LP) process for the VA classif

Rohit Agarwal, Karaka Prasanth Naidu, Alexander Horsch, Krishna Agarwal

We study the online learning problem characterized by the varying input feature space of streaming data. Although LSTMs have been employed to effectively capture the temporal nature of streaming data, they cannot handle the dimension-varying streams in an online learning setting. Therefore, we propose a dynamic LSTM-based novel method, called packetLSTM, to

Yuanmin Tang, Jing Yu, Keke Gai, Jiamin Zhuang

Zero-Shot Composed Image Retrieval (ZS-CIR) supports diverse tasks with a broad range of visual content manipulation intentions that can be related to domain, scene, object, and attribute. A key challenge for ZS-CIR is to accurately map image representation to a pseudo-word token that captures the manipulation intention relevant image information for general

Nicholas Rios, Jie Xu

Companies like Amazon and UPS are heavily invested in last-mile delivery problems. Optimizing last-delivery operations not only creates tremendous cost savings for these companies but also generate broader societal and environmental benefits in terms of better delivery service and reduced air pollutants and greenhouse gas emissions. Last-mile delivery is rea

Xinming Du, Shan Zhang, Eric Zou

A century of plastic usage has led to an accumulation of plastic waste in waterways and oceans. Over time, these wastes break down into particles smaller than 5 microns -- or ''microplastics'' -- which can infiltrate human biological systems. Despite decades of research into this emerging source of environmental pollution, there is a paucity of direct eviden

Alessandro Galeazzi, Pujan Paudel, Mauro Conti, Emiliano De Cristofaro

In recent years, the opaque design and the limited public understanding of social networks' recommendation algorithms have raised concerns about potential manipulation of information exposure. Reducing content visibility, aka shadow banning, may help limit harmful content; however, it can also be used to suppress dissenting voices. This prompts the need for

Muhan Lin, Shuyang Shi, Yue Guo, Behdad Chalaki

The correct specification of reward models is a well-known challenge in reinforcement learning. Hand-crafted reward functions often lead to inefficient or suboptimal policies and may not be aligned with user values. Reinforcement learning from human feedback is a successful technique that can mitigate such issues, however, the collection of human feedback ca