October 2024 arXiv papers — page 52

Shuchen Meng, Andi Chen, Chihang Wang, Mengyao Zheng

Accurate exchange rate prediction is fundamental to financial stability and international trade, positioning it as a critical focus in economic and financial research. Traditional forecasting models often falter when addressing the inherent complexities and non-linearities of exchange rate data. This study explores the application of advanced deep learning m

C. Luke Nelson, Grant Nations, Daniel S. Drew

Electroaerodynamic propulsion, where force is produced via the momentum-transferring collisions between accelerated ions and neutral air molecules, is a promising alternative mechanism for flight at the micro air vehicle scale due to its silent and solid-state nature. Its relatively low efficiency, however, has thus far precluded its use in a power-autonomou

Ankur Garg, Nitesh Thapa, Ghansham Sangar, Neha Gaur

The Geo High Resolution Camera (GHRC) aboard ISRO GSAT-29 satellite is a state-of-the-art 6-band Visible and Near Infrared (VNIR) imager in geostationary orbit at 55degE longitude. It provides a ground sampling distance of 55 meters at nadir, covering 110x110 km at a time, and can image the entire Earth disk using a scan mirror mechanism. To cover India, GHR

Pengcheng Zhang, Xiaohan Yu, Xiao Bai, Jin Zheng

The development of person search techniques has been greatly promoted in recent years for its superior practicality and challenging goals. Despite their significant progress, existing person search models still lack the ability to continually learn from increaseing real-world data and adaptively process input from different domains. To this end, this work in

Farid Ariai, Joel Mackenzie, Gianluca Demartini

Natural Language Processing (NLP) is revolutionising the way both professionals and laypersons operate in the legal field. The considerable potential for NLP in the legal sector, especially in developing computational assistance tools for various legal processes, has captured the interest of researchers for years. This survey follows the Preferred Reporting

Muhua Huang, Xijuan Zhang, Christopher Soto, James Evans

We introduce a methodology for assigning quantifiable and psychometrically validated personalities to AI-Agents using the Big Five framework. Across three studies, we evaluate its feasibility and limitations. In Study 1, we show that large language models (LLMs) capture semantic similarities among Big Five measures, providing a basis for personality assignme

Neil MacVicar

Let $C$ be the attractor of the IFS $\{f_{d}(z) = (-n+i)^{-1}(z+d): d\in D\}$, $D\subset\{0, 1, \ldots, n^{2}\}$ and let $\dim$ denote the box-counting dimension. It is known that for all $\lambda\in[0, 1]$, that the set of complex numbers $\alpha$ for which $\dim(C\cap(C+\alpha)) = \lambda\dim(C)$ is dense in the set of $\alpha$ for which $C \cap (C + \alph

Yixin Xu, Xing Xiang, Zhigang Li, Yanguang Zhou

In this letter, we systematically investigate the microscopic dynamics of collective vibrational excitations in simple liquids. The thermodynamic states of simple liquids are unified to the mean atomic free volume. Our results show that longitudinal acoustic collective vibrational excitations are always observed in simple liquids even when the liquids are vi

Darin Tsui, Aryan Musharaf, Yigit Efe Erginbas, Justin Singh Kang

The growing adoption of machine learning models for biological sequences has intensified the need for interpretable predictions, with Shapley values emerging as a theoretically grounded standard for model explanation. While effective for local explanations of individual input sequences, scaling Shapley-based interpretability to extract global biological insi

Malek Aburub, Cristian C. Beltran-Hernandez, Tatsuya Kamijo, Masashi Hamaya

Robots hold great promise for performing repetitive or hazardous tasks, but achieving human-like dexterity, especially in contact-rich and dynamic environments, remains challenging. Rigid robots, which rely on position or velocity control, often struggle with maintaining stable contact and applying consistent force in force-intensive tasks. Learning from Dem

Man Yiu Tsang, Karmel S. Shehadeh

We propose and analyze a new data-driven trade-off (TRO) approach for modeling uncertainty that serves as a middle ground between the optimistic approach, which adopts a distributional belief, and the pessimistic distributionally robust optimization approach, which hedges against distributional ambiguity. We equip the TRO model with a TRO ambiguity set chara

Shuaijia Chen, Ronald T. Leon, Rahmat Qambari, Yan Yan

Interfacial energy dissipation during stick-slip motion of a liquid drop on a non-conductive polymer substrate is shown to lead to an irreversible increase in electrical charge. This previously unobserved phenomenon occurs during surface wetting, in contrast to the previously reported charge separation mechanism that occurs during dewetting. Understanding th

Kiwan Park

We investigate the transport coefficients $\alpha$ and $\beta$ in plasma systems with varying Reynolds numbers while maintaining a unit magnetic Prandtl number. {The $\alpha$ and $\beta$ tensors parameterize the turbulent electromotive force (EMF) in terms of the large-scale magnetic field ${\bf \overline{B}}$ and current density ${\bf \overline{}}$ as follo

Menna Fateen, Tsunenori Mine

Recent advances in large language models (LLMs) have shown promise for scalable educational applications, but their use in dialog-based tutoring systems remains challenging due to the need for effective pedagogical strategies and the high costs associated with expert-curated datasets. Our study explores the use of smaller, more affordable LLMs for one-on-one

Tianchun Wang, Yuanzhou Chen, Zichuan Liu, Zhanwen Chen

The advent of large language models (LLMs) has revolutionized the field of text generation, producing outputs that closely mimic human-like writing. Although academic and industrial institutions have developed detectors to prevent the malicious usage of LLM-generated texts, other research has doubt about the robustness of these systems. To stress test these

Ishani Agarwal, Miroslav Saraivanov, Ali Al-Bayaty, Marek Perkowski

The decomposition from the group theory-based methods of Sasao and Saraivanov is extended to design binary quantum cascades, using the quantum rotational gates by the X-axis (CNOT and RX), Y-axis (RY), and Z-axis (controlled-Z) of the Bloch sphere. A class of local transformations is also presented to simplify the final canonical cascade circuits. Our propos

Francesca Schironi

In this article I discuss the fragments of Hipparchus' star catalogue and compare them to Hipparchus' Exegesis to trace the early history of stellar catalogues in Greek astronomy.

Morgan Thinel, Simon Turkel, Sebastian E. Rossi, Christie S. Koay

Bound states in the continuum (BICs) are quantum states that remain localized despite existing within a continuum of extended, delocalized states. They defy conventional wave theories and could be instrumental for quantum technologies that rely on the precise control of quantum states. While optical BICs have been realized in photonic systems, achieving elec

Tong Wang, Shunqin Zhang, Sanguo Zhang, Jian Huang

There has been a significant recent surge in deep neural network (DNN) techniques. Most of the existing DNN techniques have restricted model formats/assumptions. To overcome their limitations, we propose the nonparametric transformation model, which encompasses many popular models as special cases and hence is less sensitive to model mis-specification. This

Weikai Li, Ding Wang, Zijian Ding, Atefeh Sohrabizadeh

High-level synthesis (HLS) is a widely used tool in designing Field Programmable Gate Array (FPGA). HLS enables FPGA design with software programming languages by compiling the source code into an FPGA circuit. The source code includes a program (called "kernel") and several pragmas that instruct hardware synthesis, such as parallelization, pipeline, etc. Wh

Saurabh Bansal, Subhajit Ghosh, Matthew Low, Yuhsin Tsai

In this work, we study the cosmological effects of a tower of warm dark matter states on the cosmic microwave background (CMB) and on large-scale structure (LSS). For concreteness, we consider the $N$naturalness model, which is a proposed mechanism to solve the Higgs hierarchy problem. In this framework, the sector of particles of the Standard Model is copie

Mingyu Zong, Arvin Hekmati, Michael Guastalla, Yiyi Li

This paper identifies and analyzes applications in which Large Language Models (LLMs) can make Internet of Things (IoT) networks more intelligent and responsive through three case studies from critical topics: DDoS attack detection, macroprogramming over IoT systems, and sensor data processing. Our results reveal that the GPT model under few-shot learning ac

Aayush Shah, Chakradhar Guntuboina, Amir Barati Farimani

In recent years, natural language processing (NLP) models have demonstrated remarkable capabilities in various domains beyond traditional text generation. In this work, we introduce PeptideGPT, a protein language model tailored to generate protein sequences with distinct properties: hemolytic activity, solubility, and non-fouling characteristics. To facilita

Vahid Sadiri Javadi, Johanne R. Trippas, Yash Kumar Lal, Lucie Flek

Narratives are widely recognized as a powerful tool for structuring information and facilitating comprehension of complex ideas in various domains such as science communication. This paper investigates whether incorporating narrative elements can assist Large Language Models (LLMs) in solving complex problems more effectively. We propose a novel approach, St

Mitchell Linegar, R. Michael Alvarez

What are the opinions of American registered voters about election fraud and types of election fraud as we head into the final stages of the 2024 Presidential election? In this paper we use data from an online national survey of 2,211 U.S. registered voters interviewed between June 26 - July 3, 2024. Respondents were asked how common they thought that ten di

Kunal Dey, Mansi Goyal, Bupendra Singh, Aditi Kar Gangopadhyay

An undeniable signature scheme is type of digital signature where the signer retains control over the signature's verifiability. Therefore with the approval of the signer, only an authenticated verifier can verify the signature. In this work, we develop a module lattice-based post-quantum undeniable signature system. Our method is based on the GPV framework

Maithili Patel, Sonia Chernova

As service robots become more general-purpose, they will need to adapt to their users' preferences over a large set of all possible tasks that they can perform. This includes preferences regarding which actions the users prefer to delegate to robots as opposed to doing themselves. Existing personalization approaches require task-specific data for each user.

SeongKu Kang, Yunyi Zhang, Pengcheng Jiang, Dongha Lee

Academic paper search is an essential task for efficient literature discovery and scientific advancement. While dense retrieval has advanced various ad-hoc searches, it often struggles to match the underlying academic concepts between queries and documents, which is critical for paper search. To enable effective academic concept matching for paper search, we

Paul Steinacker, Nard Dumoulin Stuyck, Wee Han Lim, Tuomo Tanttu

Fabrication of quantum processors in advanced 300 mm wafer-scale complementary metal-oxide-semiconductor (CMOS) foundries provides a unique scaling pathway towards commercially viable quantum computing with potentially millions of qubits on a single chip. Here, we show precise qubit operation of a silicon two-qubit device made in a 300 mm semiconductor proce

Fumiaki Nakai, Kiwamu Yoshii

We numerically examine binary-sized granular mixtures confined between two parallel walls subjected to vertical vibration using the discrete element method. For a size ratio of $3$ between large and small particles, we study the structure of large particles in moderately dense regimes where the combined two-dimensional packing fractions of both particle size

Gregor Svindland, Alexander Voß

We introduce a decision-making framework tailored for the management of systemic risk in networks. This framework is constructed upon three fundamental components: (1) a set of acceptable network configurations, (2) a set of interventions aimed at risk mitigation, and (3) a cost function quantifying the expenses associated with these interventions. While our

Alex Degtyarev, Sławomir Rams

We combine classical Vinberg's algorithms with the lattice-theoretic/arithmetic approach from arXiv:1706.05734 [math.AG] to give a method of classifying large line configurations on complex quasi-polarized K3-surfaces. We apply our method to classify all complex K3-octic surfaces with at worst Du Val singularities and at least 32 lines. The upper bound o

Changlong Wu, Ananth Grama, Wojciech Szpankowski

Generative models have shown impressive capabilities in synthesizing high-quality outputs across various domains. However, a persistent challenge is the occurrence of "hallucinations", where the model produces outputs that are plausible but invalid. While empirical strategies have been explored to mitigate this issue, a rigorous theoretical understanding rem

Renata Kallosh

D-dimensional maximal supergravities type I with G/H coset spaces have global G-symmetry and local H symmetry, which can be gauge-fixed in symmetric or Iwasawa-type gauges. Maximal D-dimensional supergravities type II derived from higher dimensions without dualization have less local and global symmetries. In 4D, Gaillard-Zumino duality group Sp(56) enhances

Xueqi Cheng, Catherine Yang, Yuying Zhao, Yu Wang

The rapid rise of online social networks underscores the need to understand the heterogeneous strengths of online relationships. Yet, efforts to assess tie strength (TS) are hindered by the lack of ground-truth labels, differing research perspectives, and limited model performance in real-world settings. To address this gap, we introduce BTS, a comprehensive

Haiyang Zheng, Nan Pu, Wenjing Li, Nicu Sebe

In this paper, we study a practical yet challenging task, On-the-fly Category Discovery (OCD), aiming to online discover the newly-coming stream data that belong to both known and unknown classes, by leveraging only known category knowledge contained in labeled data. Previous OCD methods employ the hash-based technique to represent old/new categories by hash

Andreas Petrou-Zeniou, Azeem M. Shaikh

This paper considers the problem of inference after ranking. In our setting, we are interested in any population whose rank according to some random quantity, such as an estimated treatment effect, a measure of value-added, or benefit (net of cost), falls in a pre-specified range of values. As such, this framework generalizes the inference on winners setting

Zhen Xu, Jingming Pan, Siyuan Han, Hongju Ouyang

With the global economic integration and the high interconnection of financial markets, financial institutions are facing unprecedented challenges, especially liquidity risk. This paper proposes a liquidity coverage ratio (LCR) prediction model based on the gated recurrent unit (GRU) network to help financial institutions manage their liquidity risk more eff

Zane Peterkovic, Avinash Upadhya, Christopher Perrella, Admir Bajraktarevic

Low-light optical imaging refers to the use of cameras to capture images with minimal photon flux. This area has broad application to diverse fields, including optical microscopy for biological studies. In such studies, it is important to reduce the intensity of illumination to reduce adverse effects such as photobleaching and phototoxicity that may perturb

Alexander J Healey, Cheng Tan, Boris Gross, Sam C Scholten

Stacking van der Waals magnets holds promise for creating new hybrid materials with properties that do not exist in bulk materials. Here we investigate orthogonally twisted stacks of the van der Waals antiferromagnet CrSBr, aiming to exploit an extreme misalignment of magnetic anisotropy across the twisted interface.Using nitrogen-vacancy centre microscopy,

Morgan Jones, James Anderson

This paper presents two algorithms that compute approximate Positive Semidefinite (PSD) projections of real symmetric matrices using Randomized Numerical Linear Algebra (RNLA). Classical PSD projection of an $n\times n$ matrix relies on a deterministic eigen-decomposition with computation that scales as $\mathcal{O}(n^3)$. Our approach leverages RNLA to cons

Antesh Upadhyay, Abolfazl Hashemi

Federated learning is a prominent distributed learning paradigm that incorporates collaboration among diverse clients, promotes data locality, and thus ensures privacy. These clients have their own technological, cultural, and other biases in the process of data generation. However, the present standard often ignores this bias/heterogeneity, perpetuating bia

Sadat Shahriar, Zheng Qi, Nikolaos Pappas, Srikanth Doss

Aligning Large Language Models (LLM) to address subjectivity and nuanced preference levels requires adequate flexibility and control, which can be a resource-intensive and time-consuming procedure. Existing training-time alignment methods require full re-training when a change is needed and inference-time ones typically require access to the reward model at

Ajitesh Srivastava, Shang-Hua Teng

Given a network with an ongoing epidemic, the network immunization problem seeks to identify a fixed number of nodes to immunize in order to maximize the number of infections prevented. A fundamental computational challenge in network immunization is that the objective function is generally neither submodular nor supermodular. Consequently, no efficient algo

Ahmed H. Biby, Benjamin S. Rich, Charles B. Musgrave

Selecting materials for hybrid cathodes for batteries, which combine intercalation and conversion materials, has gained interest due to their unique synergistic properties, which are not achievable by homogeneous materials. Here, we present a data-driven, chemistry-agnostic, inverse material design framework to discover hybrid cathode materials (HCMs) for me

Lucas R. C. Farias, Aluizio F. R. Araújo

This paper introduces the inverse modeling constrained multi-objective evolutionary algorithm based on decomposition (IM-C-MOEA/D) for addressing constrained real-world optimization problems. Our research builds upon the advancements made in evolutionary computing-based inverse modeling, and it strategically bridges the gaps in applying inverse models based

Mitchell Linegar, Betsy Sinclair, Sander van der Linden, R. Michael Alvarez

We present a generalizable AI-assisted framework for rapidly generating effective "prebunking" interventions against misinformation. Like mRNA vaccine platforms, our approach uses a stable template structure that can be quickly adapted to counter emerging false narratives. In a preregistered two-wave experiment with 4,293 U.S. registered voters, we test this

Shiping Cao, Zhen-Qing Chen

Starting with a transient irreducible diffusion process $X^0$ on a locally compact separable metric space $(D, d)$, one can construct a canonical symmetric reflected diffusion process $\bar X$ on a completion $D^*$ of $(D, d)$ through the theory of reflected Dirichlet spaces. The boundary trace process $\check X$ of $X$ on the boundary $\partial D:=D^*\setmi

Dimitris Bertsimas, Vasiliki Stoumpou

Random Forests have been one of the most popular bagging methods in the past few decades, especially due to their success at handling tabular datasets. They have been extensively studied and compared to boosting models, like XGBoost, which are generally considered more performant. Random Forests adopt several simplistic assumptions, such that all samples and

Suparna De, Ionut Bostan, Nishanth Sastry

Recent studies have outlined the accessibility challenges faced by blind or visually impaired, and less-literate people, in interacting with social networks, in-spite of facilitating technologies such as monotone text-to-speech (TTS) screen readers and audio narration of visual elements such as emojis. Emotional speech generation traditionally relies on huma

Xinran Wang, Qi Le, Ammar Ahmed, Enmao Diao

Ensuring that generative AI systems align with human values is essential but challenging, especially when considering multiple human values and their potential trade-offs. Since human values can be personalized and dynamically change over time, the desirable levels of value alignment vary across different ethnic groups, industry sectors, and user cohorts. Wi

Xiao Liu, Xinhao Xiang, Zizhong Li, Yongheng Wang

The growing capabilities of AI in generating video content have brought forward significant challenges in effectively evaluating these videos. Unlike static images or text, video content involves complex spatial and temporal dynamics which may require a more comprehensive and systematic evaluation of its contents in aspects like video presentation quality, s

Abraham Levitan, Klaus Wakonig, Zirui Gao, Adam Kubec

Single-shot ptychography is a quantitative phase imaging method wherein overlapping beams of light arranged in a grid pattern simultaneously illuminate a sample, allowing a full ptychographic dataset to be collected in a single shot. It is primarily used at optical wavelengths, but there is interest in using it for X-ray imaging. However, the constraints imp

Anton Husakou, Nicholas Karpowicz, Vladislav S. Yakovlev, Misha Ivanov

We present an all-optical concept for measuring the electric field of light spanning from infrared to extreme ultraviolet with multi-petahertz detection bandwidth. Our approach employs a heterodyne detection of light produced by a highly nonlinear light-matter interaction gate. We establish a numerical model of a complex spectral response for unambiguous ele

Yue Li, Zhixue Zhao, Carolina Scarton

In-context learning (ICL) performance is highly sensitive to prompt design, yet the impact of class label options (e.g. lexicon or order) in zero-shot classification remains underexplored. This study proposes LOADS (Label set Optimization via Activation Distribution kurtosiS), a post-hoc method for selecting optimal label sets in zero-shot ICL with large lan

Kal Backman, Ben Beck, Dana Kulić

While cycling offers an attractive option for sustainable transportation, many potential cyclists are discouraged from taking up cycling due to the lack of suitable and safe infrastructure. Efficiently mapping cycling infrastructure across entire cities is necessary to advance our understanding of how to provide connected networks of high-quality infrastruct

Bruno Croso Cunha da Silva, Thomas Palmeira Ferraz, Roseli De Deus Lopes

Disinformation on social media poses both societal and technical challenges, requiring robust detection systems. While previous studies have integrated textual information into propagation networks, they have yet to fully leverage the advancements in Transformer-based language models for high-quality contextual text representations. This work addresses this

Duc Kieu, Tung Kieu, Peng Han, Bin Yang

Due to the global trend towards urbanization, people increasingly move to and live in cities that then continue to grow. Traffic forecasting plays an important role in the intelligent transportation systems of cities as well as in spatio-temporal data mining. State-of-the-art forecasting is achieved by deep-learning approaches due to their ability to contend

Yuan Huang, Valentin De Bortoli, Fugen Zhou, Jerome Gilles

Wavelet-based segmentation approaches are widely used for texture segmentation purposes because of their ability to characterize different textures. In this paper, we assess the influence of the chosen wavelet and propose to use the recently introduced empirical wavelets. We show that the adaptability of the empirical wavelet permits to reach better results

Xiaoming Xu, Dhrubajyoti Ghosh, Sheng Luo

Neurodegenerative disorders such as Alzheimer's disease (AD) present a significant global health challenge, characterized by cognitive decline, functional impairment, and other debilitating effects. Current AD clinical trials often assess multiple longitudinal primary endpoints to comprehensively evaluate treatment efficacy. Traditional methods, however, may

Gergely Bérczi, Jonas Klüver

We propose a conjectural counting formula for the coefficients of the chromatic symmetric function of unit interval graphs using reinforcement learning. The formula counts specific disjoint cycle-tuples in the graphs, referred to as Eschers, which satisfy certain concatenation conditions. These conditions are identified by a reinforcement learning model and

Deepesh Bhamre, Shrey Gogia, Anuradha Misra

We address the issue of cancellation of infrared (IR) divergences at the amplitude level in Light Front Quantum Chromodynamics (LFQCD) using the coherent state formalism. We consider the process $e^{+}e^{-}\rightarrow q\bar{q}g$ upto $\mathcal{O}(g^3)$ in light-cone-time-ordered Hamiltonian perturbation theory and show that IR divergences in S-matrix element

Basile Hurat, Zariluz Alvarado, Jerome Gilles

The empirical wavelet transform is an adaptive multiresolution analysis tool based on the idea of building filters on a data-driven partition of the Fourier domain. However, existing 2D extensions are constrained by the shape of the detected partitioning. In this paper, we provide theoretical results that permits us to build 2D empirical wavelet filters base

Shaun Cooper, Timothy Huber, Jeffery Opoku

We investigate the question of when an eta quotient is a derivative of a formal power series with integer coefficients and present an analysis in the case of level 10. As a consequence, we establish and classify an infinite number of integral evaluations such as $$ \int_0^{e^{-2\pi/\sqrt{10}}} q\prod_{j=1}^\infty \frac{(1-q^j)^3(1-q^{10j})^8}{(1-q^{5j})^7} \

Danyal Aftab, Steven Davy

Large language models demonstrate impressive proficiency in language understanding and generation. Nonetheless, training these models from scratch, even the least complex billion-parameter variant demands significant computational resources rendering it economically impractical for many organizations. With large language models functioning as general-purpose

Israel Fama, Bárbara Bueno, Alexandre Alcoforado, Thomas Palmeira Ferraz

In a context where the Brazilian judiciary system, the largest in the world, faces a crisis due to the slow processing of millions of cases, it becomes imperative to develop efficient methods for analyzing legal texts. We introduce uBERT, a hybrid model that combines Transformer and Recurrent Neural Network architectures to effectively handle long legal text

Chenhan Zhang, Weiqi Wang, Zhiyi Tian, James Jianqiao Yu

Although recent advancements in end-to-end learning-based link prediction (LP) methods have shown remarkable capabilities, the significance of traditional similarity-based LP methods persists in unsupervised scenarios where there are no known link labels. However, the selection of node features for similarity computation in similarity-based LP can be challen

I. Alekseev, V. Belov, A. Bystryakov, M. Danilov

The yields of the inverse beta decay events produced by antineutrinos from a certain nuclear reactor fuel component are used by many experiments to check various model predictions. Yet measurements of the absolute yields feature significant uncertainties coming, mainly, from the understanding of the antineutrino detection efficiency. This work presents a sim

Anna Jaśkiewicz, Andrzej S. Nowak

This paper investigates discrete-time Markov decision processes with recursive utilities (or payoffs) defined by the classic CES aggregator and the Kreps-Porteus certainty equivalent operator. According to the classification introduced by Marinacci and Montrucchio, some aggregators that we consider are Thompson and some of them are neither Thompson nor Black

Xiaxia Wang, XueSong Leng, Guoping Xu

The escalating significance of information security has underscored the per-vasive role of encryption technology in safeguarding communication con-tent. Morse code, a well-established and effective encryption method, has found widespread application in telegraph communication and various do-mains. However, the transmission of Morse code images faces challeng

Shiuli Subhra Ghosh, Anmol Dwivedi, Ali Tajer, Kyongmin Yeo

Causal inference provides an analytical framework to identify and quantify cause-and-effect relationships among a network of interacting agents. This paper offers a novel framework for analyzing cascading failures in power transmission networks. This framework generates a directed latent graph in which the nodes represent the transmission lines and the direc

Haoru Ju, Daniel Leifer, Steven J. Miller, Sooraj A. Padmanabhan

We study variants of a stochastic game inspired by backgammon where players may propose to double the stake, with the game state dictated by a one-dimensional random walk. Our variants allow for different numbers of proposals and different multipliers to the stake. We determine the optimal game state for proposing and accepting, giving analytic solutions in

Kamand Kalashi, Sajjad Saed, Babak Teimourpour

Network analysis is increasingly important across various fields, including the fragrance industry, where perfumes are represented as nodes and shared user preferences as edges in perfume networks. Community detection can uncover clusters of similar perfumes, providing insights into consumer preferences, enhancing recommendation systems, and informing target

Dachun Sun, Ruijie Wang, Jinning Li, Ruipeng Han

This paper addresses the problem of optimizing the allocation of labeling resources for semi-supervised belief representation learning in social networks. The objective is to strategically identify valuable messages on social media graphs that are worth labeling within a constrained budget, ultimately maximizing the task's performance. Despite the progress i

Shira Faigenbaum-Golovin, Alon Kipnis, Axel Bühler, Eli Piasetzky

The Bible, a product of an extensive and intricate process of oral-written transmission spanning centuries, obscures the contours of its earlier recensions. Debate rages over determining the existing layers and identifying the date of composition and historical background of the biblical texts. Traditional manual methodologies have grappled with authorship c

Philipp Berghofer

QBism is currently one of the most widely discussed 'subjective' interpretations of quantum mechanics. Its key move is to say that quantum probabilities are personalist Bayesian probabilities and that the quantum state represents subjective degrees of belief. Even probability-one predictions are considered subjective assignments expressing the agent's highes

Maren Eckhoff, Valmir Selimi, Alexander Aranovitch, Ian Lyons

Many therapies are effective in treating multiple diseases. We present an approach that leverages methods developed in natural language processing and real-world data to prioritize potential, new indications for a mechanism of action (MoA). We specifically use representation learning to generate embeddings of indications and prioritize them based on their pr

Richard Yu, Jorge Ramirez, Elaine Wong

In commuting parametric quantum circuits, the Fourier series of the pairwise fidelity can be expressed as the characteristic function of random variables. Furthermore, expressiveness can be cast as the recurrence probability of a random walk on a lattice. This construction has been successfully applied to the group composed only of Pauli-Z rotations, and we

Philipp W. A. Schönhöfer, Sharon C. Glotzer

In recent years the functionality of synthetic active microparticles has edged even closer to that of their biological counterparts. However, we still lack the understanding needed to recreate at the microscale key features of autonomous behavior exhibited by microorganisms or swarms of macroscopic robots. In this study, we propose a model for a three-dimens

N. V. Antonov, N. M. Gulitskiy, P. I. Kakin, A. S. Romanchuk

We study a model of random walk on a fluctuating rough surface using the field-theoretic renormalization group (RG). The surface is modelled by the well-known Kardar--Parisi--Zhang (KPZ) stochastic equation while the random walk is described by the standard diffusion equation for a particle in a uniform gravitational field. In the RG approach, possible types

Mayur Jhamnani, Sajith V Sadasivan, Sheetal Kumar Jain, Asif Equbal

Dynamic Nuclear Polarization (DNP) is transforming NMR and MRI by significantly enhancing sensitivity through the transfer of polarization from electron spins to nuclear spins via microwave irradiation. However, the use of monochromatic continuous-wave (CW) irradiation limits the efficiency of DNP for systems with heterogeneous broad EPR lines. Broad-band te

Luyang Zhao, Yitao Jiang, Chun-Yi She, Muhao Chen

Soft robots offer adaptability and safe interaction with complex environments. Rapid prototyping kits that allow soft robots to be assembled easily will allow different geometries to be explored quickly to suit different environments or to mimic the motion of biological organisms. We introduce SoftSnap modules: snap-together components that enable the rapid

S Sakshi, Utkarsh Tyagi, Sonal Kumar, Ashish Seth

The ability to comprehend audio--which includes speech, non-speech sounds, and music--is crucial for AI agents to interact effectively with the world. We present MMAU, a novel benchmark designed to evaluate multimodal audio understanding models on tasks requiring expert-level knowledge and complex reasoning. MMAU comprises 10k carefully curated audio clips p

Jerome Gilles

The recently proposed empirical wavelet transform was based on a particular type of filter. In this paper, we aim to propose a general framework for the construction of empirical wavelet systems in the continuous case. We define a well-suited formalism and then investigate some general properties of empirical wavelet systems. In particular, we provide some s

Mahtab Ranjbar, Mehdi Mohebbi, Mahdi Cherakhloo, Bijan Vosoughi. Vahdat

In recent years, the integration of advanced imaging techniques and deep learning methods has significantly advanced computer-aided diagnosis (CAD) systems for breast cancer detection and classification. Transformers, which have shown great promise in computer vision, are now being applied to medical image analysis. However, their application to histopatholo

Aidan Hunt, Gregory Talpey, Gemma Calandra, Brian Polagye

While confinement is understood to increase the power and thrust coefficients of cross-flow turbines, how the optimal rotor geometry changes with the blockage ratio -- defined as the ratio between the array projected area and the channel cross-sectional area -- has not been systematically explored. Here, the interplay between rotor geometry and the blockage

Burak Ercan, Onur Eker, Aykut Erdem, Erkut Erdem

Low-light environments pose significant challenges for image enhancement methods. To address these challenges, in this work, we introduce the HUE dataset, a comprehensive collection of high-resolution event and frame sequences captured in diverse and challenging low-light conditions. Our dataset includes 106 sequences, encompassing indoor, cityscape, twiligh

MohammadTaghi Hajiaghayi, Shayan Chashm Jahan, Mohammad Sharifi, Suho Shin

The online bipartite matching problem, extensively studied in the literature, deals with the allocation of online arriving vertices (items) to a predetermined set of offline vertices (agents). However, little attention has been given to the concept of class fairness, where agents are categorized into different classes, and the matching algorithm must ensure

A. A. Shchepkin, D. V. Grosman, I. I. Shkarupa, D. V. Karlovets

The work investigates absorption of a twisted photon, which possesses quantized total angular momentum (TAM), by a relativistic electron with the Lorentz factor $\gamma \sim 1-10$ in a strong magnetic field up to the Schwinger limit, $H_c = 4.4\cdot 10^{13}$ G. We examine the absorption cross sections and their dependence on the parameters of the incident ph

Daniel Falkowski, Carl-Fredrik Lidgren

We consider the question of, given operators $A$, $Z$ and a sequence of invertible operators $U_n\to Z$, whether the sequence $U_nAU_n^{-1}$ is bounded in norm, as well as generalizations of this where $U_nAU_n^{-1}$ is modified by some bounded linear map on bounded linear operators. In the setting of Hilbert spaces, we provide a complete classification in t

Samuel Jacob Chacko, Sajib Biswas, Chashi Mahiul Islam, Fatema Tabassum Liza

As powerful Large Language Models (LLMs) are now widely used for numerous practical applications, their safety is of critical importance. While alignment techniques have significantly improved overall safety, LLMs remain vulnerable to carefully crafted adversarial inputs. Consequently, adversarial attack methods are extensively used to study and understand t

Shiqing Wei, Rooholla Khorrambakht, Prashanth Krishnamurthy, Vinicius Mariano Gonçalves

Ensuring the safety of dynamical systems is crucial, where collision avoidance is a primary concern. Recently, control barrier functions (CBFs) have emerged as an effective method to integrate safety constraints into control synthesis through optimization techniques. However, challenges persist when dealing with convex primitives and tasks requiring torque c

Yueh-Chun Wu, Gábor B. Halász, Joshua T. Damron, Zheng Gai

Thermally driven transitions between ferromagnetic and paramagnetic phases are characterized by critical behavior with divergent susceptibilities, long-range correlations, and spin dynamics that can span kHz to GHz scales as the material approaches the critical temperature $\mathrm{T_c}$, but it has proven technically challenging to probe the relevant length

Vincenzo Di Vito, Mostafa Mohammadian, Kyri Baker, Ferdinando Fioretto

Recent developments in applying machine learning to address Alternating Current Optimal Power Flow (AC OPF) problems have demonstrated significant potential in providing close to optimal solutions for generator dispatch in near real-time. While these learning to optimize methods have demonstrated remarkable performance on steady-state operations, practical a

Leonard M. Sander

We study a system of coupled oscillators of the Sakaguchi-Kuramoto type with interactions including a phase delay. We consider the case of a coupling matrix such that oscillators with large natural frequencies drive all slower ones but not the reverse. This scheme is inspired by Hebbian learning in neuroscience. We propose a simple model which is partly solv

Mohit Chandra, Siddharth Sriraman, Gaurav Verma, Harneet Singh Khanuja

Adverse Drug Reactions (ADRs) from psychiatric medications are the leading cause of hospitalizations among mental health patients. With healthcare systems and online communities facing limitations in resolving ADR-related issues, Large Language Models (LLMs) have the potential to fill this gap. Despite the increasing capabilities of LLMs, past research has n

Linwei Hu, Ye Jin Choi, Vijayan N. Nair

In today's machine learning world for tabular data, XGBoost and fully connected neural network (FCNN) are two most popular methods due to their good model performance and convenience to use. However, they are highly complicated, hard to interpret, and can be overfitted. In this paper, we propose a new modeling framework called cross spline net (CSN) that is

Roozbeh Bassirian, Bill Fefferman, Itai Leigh, Kunal Marwaha

We find a modification to QMA where having one quantum proof is strictly less powerful than having two unentangled proofs, assuming EXP $\ne$ NEXP. This gives a new route to prove QMA(2) = NEXP that overcomes the primary drawback of a recent approach [arXiv:2402.18790 , arXiv:2306.13247] (QIP 2024). Our modification endows each proof with a form of *multipar

Aniket Das, Ayushman Singh, Nishant, Sharad Prakash

Gastrointestinal (GI) diseases represent a significant global health concern, with Capsule Endoscopy (CE) offering a non-invasive method for diagnosis by capturing a large number of GI tract images. However, the sheer volume of video frames necessitates automated analysis to reduce the workload on doctors and increase the diagnostic accuracy. In this paper,

Abraham Israeli, David Jurgens, Daniel Romero

The Internet has significantly expanded the potential for global collaboration, allowing millions of users to contribute to collective projects like Wikipedia. While prior work has assessed the success of online collaborations, most approaches are time-agnostic, evaluating success without considering its longevity. Research on the factors that ensure the lon