December 2024 arXiv papers — page 196

Daniel Seal, Rossella Arcucci, Salva Rühling-Cachay, César Quilodrán-Casas

Climate change is increasing the frequency of extreme precipitation events, making weather disasters such as flooding and landslides more likely. The ability to accurately nowcast precipitation is therefore becoming more critical for safeguarding society by providing immediate, accurate information to decision makers. Motivated by the recent success of gener

Vincent A. Cicirello

When it was first introduced, the Chips-n-Salsa Java library provided stochastic local search and related algorithms, with a focus on self-adaptation and parallel execution. For the past four years, we expanded its scope to include evolutionary computation. This paper concerns the evolutionary algorithms that Chips-n-Salsa now provides, which includes multip

Touseef Haider, Anastasiia Tsvietkova

Link equivalence up to isotopy in a 3-space is the problem that lies at the root of knot theory, and is important in 3-dimensional topology and geometry. We consider its restriction to alternating links, given by two alternating diagrams with $n_1$ and $n_2$ crossings, and show that this problem has polynomial algorithm in terms of $max\{n_1, n_2\}$. For the

Boyuan Liu, Yves Sibony, Georges Meynet, Volker Bromm

The James Webb Space Telescope (JWST) has revealed an unexpectedly high abundance of UV luminous galaxies at redshifts $z\gtrsim 10$, challenging `standard' galaxy formation models. This study investigates the role of rapidly rotating (massive) stars undergoing chemically homogeneous evolution (CHE) in reconciling this potential tension. These stars are more

J. C. Bellizotti Souza, N. P. Vizarim, C. J. O. Reichhardt, C. Reichhardt

We compare the driven dynamics of skyrmions, antiskyrmions, and skyrmionium interacting with random disorder, circular defects, and asymmetric potentials. When interacting with a line defect at a constant drive, skyrmions and antiskyrmions show an acceleration effect for motion along the wall and a drop in velocity when they can cross the barrier. In contras

Christoph Leiter, Jonas Belouadi, Yanran Chen, Ran Zhang

The NLLG (Natural Language Learning & Generation) arXiv reports assist in navigating the rapidly evolving landscape of NLP and AI research across cs.CL, cs.CV, cs.AI, and cs.LG categories. This fourth installment captures a transformative period in AI history - from January 1, 2023, following ChatGPT's debut, through September 30, 2024. Our analysis reveals

Trenton Chang, Lindsay Warrenburg, Sae-Hwan Park, Ravi B. Parikh

In many settings, machine learning models may be used to inform decisions that impact individuals or entities who interact with the model. Such entities, or agents, may game model decisions by manipulating their inputs to the model to obtain better outcomes and maximize some utility. We consider a multi-agent setting where the goal is to identify the "worst

Tejas Mane, Xiao Li, Mohammad Sadoghi, Mohsen Lesani

Fault-tolerant replicated database systems consume less energy than the compute-intensive proof-of-work blockchain. Thus, they are promising technologies for the building blocks that assemble global financial infrastructure. To facilitate global scaling, clustered replication protocols are essential in orchestrating nodes into clusters based on proximity. Ho

Kaumudibikash Goswami, Abhinash Kumar Roy, Varun Srivastava, Barr Perez

A central problem in open quantum systems is the characterization of non-Markovian processes, where an environment retains the memory of its interaction with the system. A key distinction is whether or not this memory can be simulated classically, as this can lead to efficient modelling and noise mitigation. Powerful tools have been developed recently within

Artur Jorge Marinho, Kanishka Perera

In this paper we prove new multiplicity results for a critical growth anisotropic quasilinear elliptic system that is coupled through a subcritical perturbation term. We identify a certain scaling for the system and a parameter {\gamma} related to this scaling that determines the geometry of the associated variational functional. This leads to a natural clas

Jesús Monge-Alvarez, Carlos Hoyos-Barceló, Luis M. San-José-Revuelta, Pablo Casaseca-de-la-Higuera

Cough is a protective reflex conveying information on the state of the respiratory system. Cough assessment has been limited so far to subjective measurement tools or uncomfortable (i.e., non-wearable) cough monitors. This limits the potential of real-time cough monitoring to improve respiratory care. Objective: This paper presents a machine hearing system f

Julio Backhoff, Zhizhang Wang, Xin Zhang

We consider a competition between $d+1$ players, and aim to identify the "most exciting game'' of this kind. This is translated, mathematically, into a stochastic optimization problem over martingales that live on the $d$-dimensional subprobability simplex $\Delta$ and terminate on the vertices of $\Delta$ (so-called win-martingales), with a cost function re

Michele Moresco

As revealed by Hubble in 1928, our Universe is expanding. This discovery was fundamental to widening our horizons and our conception of space, and since then determining the rate at which our Universe is expanding has become one of the crucial measurements in cosmology. At the beginning of this century, these measurements revealed the unexpected behavior tha

Mert Gurbuzbalaban, Mohammad Rafiqul Islam, Xiaoyu Wang, Lingjiong Zhu

Langevin algorithms are popular Markov Chain Monte Carlo methods for Bayesian learning, particularly when the aim is to sample from the posterior distribution of a parametric model, given the input data and the prior distribution over the model parameters. Their stochastic versions such as stochastic gradient Langevin dynamics (SGLD) allow iterative learning

Benjamin Klieger, Charis Charitsis, Miroslav Suzara, Sierra Wang

We explore the potential for productive team-based collaboration between humans and Artificial Intelligence (AI) by presenting and conducting initial tests with a general framework that enables multiple human and AI agents to work together as peers. ChatCollab's novel architecture allows agents - human or AI - to join collaborations in any role, autonomously

Amit Moryossef

Sign Language Processing (SLP) is an interdisciplinary field comprised of Natural Language Processing (NLP) and Computer Vision. It is focused on the computational understanding, translation, and production of signed languages. Traditional approaches have often been constrained by the use of gloss-based systems that are both language-specific and inadequate

Francesco Monticone, Dimitrios Sounas, Matteo Ciabattoni

The study of classical waves in time-periodic systems is experiencing a resurgence of interest, motivated by their rich physics and the new engineering opportunities they enable, with several analogies to parallel efforts in other branches of physics, e.g., Floquet-engineered quantum materials and time crystals. Here, we first briefly review some of the most

Zhuquan Zhang, Yu-Che Chien, Man Tou Wong, Frank Y. Gao

In condensed matter systems, interactions between collective modes offer avenues for nonlinear coherent manipulation of coupled excitations and quantum phases. Antiferromagnets, with their inherently coupled magnon modes, provide a promising platform for nonlinear control of microscopic spin waves and macroscopic magnetization. However, nonlinear magnon-magn

Michael Filaseta, Jonah Klein, Cihan Sabuncu

For a given integer $m$ and any residue $a \pmod{m}$ that can be written as a sum of 3 squares modulo $m$, we show the existence of infinitely many integers $n \equiv a \pmod{m}$ such that the number of representations of $n$ as a sum of three squares, $r_3(n)$, satisfies $r_3(n) \gg_m \sqrt{n} \log \log n$. Consequently, we establish that there are infinite

Tomáš Souček, Prajwal Gatti, Michael Wray, Ivan Laptev

The goal of this work is to generate step-by-step visual instructions in the form of a sequence of images, given an input image that provides the scene context and the sequence of textual instructions. This is a challenging problem as it requires generating multi-step image sequences to achieve a complex goal while being grounded in a specific environment. P

Armin Shafiee Sarvestani, Sheyang Tang, Zhou Wang

Mesh quality assessment (MQA) models play a critical role in the design, optimization, and evaluation of mesh operation systems in a wide variety of applications. Current MQA models, whether model-based methods using topology-aware features or projection-based approaches working on rendered 2D projections, often fail to capture the intricate interactions bet

Jean-Christophe Pain, Djamel Benredjem

In this work, we investigate the ionization of silicon by electron impacts in hot plasmas. Our calculations of the cross sections and rates rely on the Coulomb-Born-Exchange, Binary-Encounter-Dipole and Distorted-Wave methods implemented in the Flexible Atomic Code (FAC), and are compared with measurements and other theoretical values. We use a semi-empirica

Siddarth Malreddy, Matthew Lawhon, Usha Amrutha Nookala, Aditya Mantha

Adopting advances in recommendation systems is often challenging in industrial settings due to unique constraints. This paper aims to highlight these constraints through the lens of feature interactions. Feature interactions are critical for accurately predicting user behavior in recommendation systems and online advertising. Despite numerous novel technique

Rongrong Liu, Chris Nagele, Julian H Krolik, Brooks E Kinch

Data derived from general relativistic magnetohydrodynamic simulations of accretion onto black holes can be used as input to a postprocessing scheme that predicts the radiated spectrum. Combining a relativistic Compton scattering radiation transfer solution in the corona with detailed local atmosphere solutions incorporating local ionization and thermal bala

Gustavo P. C. P. da Luz, Gabriel Massuyoshi Sato, Luis Fernando Gomez Gonzalez, Juliana Freitag Borin

The increasing urbanization and the growing number of vehicles in cities have underscored the need for efficient parking management systems. Traditional smart parking solutions often rely on sensors or cameras for occupancy detection, each with its limitations. Recent advancements in deep learning have introduced new YOLO models (YOLOv8, YOLOv9, YOLOv10, and

Roel J. Leenhouts, Nathan Morgan, Emad Al Ibrahim, William H. Green

Solvation free energy is an important design parameter in reaction kinetics and separation processes, making it a critical property to predict during process development. In previous research, directed message passing neural networks (D-MPNN) have successfully been used to predict solvation free energies and enthalpies in organic solvents. However, solvent m

Lifan Yuan, Wendi Li, Huayu Chen, Ganqu Cui

Different from its counterpart outcome reward models (ORMs), which evaluate the entire responses, a process reward model (PRM) scores a reasoning trajectory step by step, providing denser and more fine grained rewards. However, training a PRM requires labels annotated at every intermediate step, presenting significant challenges for both manual and automatic

Yi-Min Huang

Centrifugal confinement fusion, a promising alternative to toroidal confinement devices like tokamaks and stellarators, leverages supersonic plasma rotation within a magnetic mirror configuration to achieve simplified coil design, compactness, and enhanced stability. This brief article explores the potential of centrifugal confinement fusion for propulsion a

Jinming Xing, Chang Xue, Dongwen Luo, Ruilin Xing

Missing data is a pervasive challenge in wireless networks and many other domains, often compromising the performance of machine learning and deep learning models. To address this, we propose a novel framework, FGATT, that combines the Fuzzy Graph Attention Network (FGAT) with the Transformer encoder to perform robust and accurate data imputation. FGAT lever

David Helmer, Michael Boardman, S. Kate Conroy, Adam J. Hepworth

The REAIM 2024 Blueprint for Action states that AI applications in the military domain should be ethical and human-centric and that humans must remain responsible and accountable for their use and effects. Developing rigorous test and evaluation, verification and validation (TEVV) frameworks will contribute to robust oversight mechanisms. TEVV in the develop

Ali Naseri Sadr

We use the square peg problem for smooth curves to prove a generalized table Theorem for real valued functions on Riemannian surfaces with odd Euler characteristic. We then use this result to prove the table conjecture for even functions on the two sphere.

Arpan Kundu, Francesco Martinelli, Giulia Galli

Ultrasmall nanodiamonds (USNDs) are promising platforms for fluorescent and quantum sensing applications. Here we present first-principles electronic structure calculations of color centers in USNDs, specifically the nitrogen-vacancy (NV-) and we investigate their optical addressability as a function of the surface termination. We consider both isolated nano

Sumukha Udupa, Ahmed Hemida, Charles A. Kamhoua, Jie Fu

In many security applications of cyber-physical systems, a system designer must guarantee that critical missions are satisfied against attacks in the sensors and actuators of the CPS. Traditional security design of CPSs often assume that attackers have complete knowledge of the system. In this article, we introduce a class of deception techniques and study h

Elżbieta Krawczyk

We study automatic sequences and automatic systems generated by general constant length (nonprimitive) substitutions. While an automatic system is typically uncountable, the set of automatic sequences is countable, implying that most sequences within an automatic system are not themselves automatic. We provide a complete and succinct classification of automa

Xueyang Li, Han Xiao, Weixiang Weng, Xiaowei Xu

Colorectal cancer is a prevalent form of cancer, and many patients develop colorectal cancer liver metastasis (CRLM) as a result. Early detection of CRLM is critical for improving survival rates. Radiologists usually rely on a series of multi-phase contrast-enhanced computed tomography (CECT) scans done during follow-up visits to perform early detection of t

Charlotte Kristjansen, Konstantin Zarembo

We set up a perturbative framework for the 't Hooft line in the N=4 super-Yang-Mills theory, and apply it to correlators thereof with Wilson loops and local operators. Using this formalism we obtain a number of perturbative and non-perturbative results that directly connect to localization, holography and integrability.

Panpan Chen, Seonyeong Park, Refik Mert Cam, Hsuan-Kai Huang

In certain three-dimensional (3D) applications of photoacoustic computed tomography (PACT), including \textit{in vivo} breast imaging, hemispherical measurement apertures that enclose the object within their convex hull are employed for data acquisition. Data acquired with such measurement geometries are referred to as \textit{half-scan} data, as only half o

Gabriel Taton, Frederic Fauquet, Ilyes Betka, Jean-Paul Guillet

Terahertz (THz) generation via photomixing on photoconductive antenna using twin delayed chirped pulses provides a long THz pulse with a narrow bandwidth. To generate a long pulse with broad bandwidth, we propose a new method that combines two long optical pulses with opposite chirps. The pulses exhibit temporal distributions of their instantaneous frequenci

Saverio Rossi, Leonardo Di Bari, Martin Weigt, Francesco Zamponi

Protein evolution involves mutations occurring across a wide range of time scales. In analogy with disordered systems in statistical physics, this dynamical heterogeneity suggests strong correlations between mutations happening at distinct sites and times. To quantify these correlations, we examine the role of various fluctuation sources in protein evolution

Hannaneh Akrami, Bhaskar Ray Chaudhury, Jugal Garg, Aniket Murhekar

The immense success of ML systems relies heavily on large-scale, high-quality data. The high demand for data has led to many paradigms that involve selling, exchanging, and sharing data, motivating the study of economic processes with data as an asset. However, data differs from classical economic assets in terms of free duplication: there is no concept of l

Prajakta Prabhune, Anlan Chen, Yigitcan Comlek, Wei Chen

For polymer nanocomposites, disordered microstructural nature makes processing control and tailoring properties to desired values a challenge. Understanding process-structure-property relation can provide guidelines for process and constituents design. Our work explores nuances of PSP relation for polymer nanocomposites with attractive pairing between partic

Sergio A. Ortega, Pablo Fernández, Miguel A. Martin-Delgado

As cloud services continue to expand, the security of private data stored and processed in these environments has become paramount. This work delves into quantum homomorphic encryption (QHE), an emerging technology that facilitates secure computation on encrypted quantum data without revealing the underlying information. We reinterpret QHE schemes through cl

Sukhdev Mouraya, Supratik Banerjee, Nandita Pan

In ferrofluids, the vorticity is balanced by the rate of particle rotation, which is known here as critical balance. The universal energy cascade is investigated for a stationary and non-stationary incompressible ferrofluid turbulent system using exact relations. The findings reveal that under moderate magnetic fields, kinetic and total energy cascades occur

Cristian López, Keegan J. Moore

The energy-based dual-phase dynamics identification (EDDI) method is a new data-driven technique for the discovery of equations of motion (EOMs) of strongly nonlinear single-degree-of-freedom (SDOF) oscillators. This research uses the EDDI method to obtain mathematical models for SDOF systems with clearance nonlinearities. The first key aspect of the EDDI me

Sourav Banerjee, Ayushi Agarwal, Eishkaran Singh

The pursuit of leaderboard rankings in Large Language Models (LLMs) has created a fundamental paradox: models excel at standardized tests while failing to demonstrate genuine language understanding and adaptability. Our systematic analysis of NLP evaluation frameworks reveals pervasive vulnerabilities across the evaluation spectrum, from basic metrics to com

F. Javier Marañón-González, José Navarro-Salas

The adiabatic regularization method is likely the most direct and intuitive renormalization scheme for FLRW cosmologies. The method requires one to start with a nonvanishing mass, but massless theories can be studied by taking the massless limit at the end of the calculations. For spin-$1$, however, this limit changes the number of degrees of freedom, so it

Pramod N. Achar, Andrea Bourque

We give a "lattice-theoretic" description of the global Schubert variety for $\mathrm{GL}_n$ associated to any dominant coweight.

John R. Lister, Tim-Frederik Dauck

The linear stability of miscible displacement for radial source flow at infinite P\'eclet number in a Hele-Shaw cell is calculated theoretically. The axisymmetric self-similar flow is shown to be unstable to viscous fingering if the viscosity ratio $m$ between ambient and injected fluids exceeds $3\over2$ and to be stable if $m<{3\over2}$. If $1<m<{3\over2}$

Elena Cordero, Gianluca Giacchi, Luigi Rodino

The integration of operator kernels with the Wigner distribution, first conceptualized by E. Wigner in 1932 and later extended by L. Cohen and others, has opened new avenues in time-frequency analysis and operator calculus. Despite substantial advancements, the presence of ``ghost frequencies" in Wigner kernels continues to pose significant challenges, parti

Guang Yang

The current Domain Name System (DNS), as a core infrastructure of the internet, exhibits several shortcomings: its centralized architecture leads to censorship risks and single points of failure, making domain name resolution vulnerable to attacks. The lack of encryption in the resolution process exposes it to DNS hijacking and cache poisoning attacks. Addit

Said Togru, Youssef Sameh Mostafa, Karim Lotfy

This paper evaluates the use of metamorphic relations to enhance the robustness and real-world performance of machine learning models. We propose a Metamorphic Retraining Framework, which applies metamorphic relations to data and utilizes semi-supervised learning algorithms in an iterative and adaptive multi-cycle process. The framework integrates multiple s

Elizabeth M. Daly, Sean Rooney, Seshu Tirupathi, Luis Garces-Erice

Evaluating the safety of AI Systems is a pressing concern for organizations deploying them. In addition to the societal damage done by the lack of fairness of those systems, deployers are concerned about the legal repercussions and the reputational damage incurred by the use of models that are unsafe. Safety covers both what a model does; e.g., can it be use

Ben Allanach

We confront the malaphoric $B_3-L_2$ model with bounds coming from a search for resonances in the di-lepton channels at the 13~TeV LHC. In contrast to the original $B_3-L_2$ model, the $Z^\prime$ of the malaphoric $B_3-L_2$ model has sizeable couplings to the lighter two families; these originate from order unity kinetic mixing with the hypercharge gauge bos

Mingye Gao, Aman Varshney, Shan Chen, Vikram Goddla

Cancer clinical trials often face challenges in recruitment and engagement due to a lack of participant-facing informational and educational resources. This study investigated the potential of Large Language Models (LLMs), specifically GPT4, in generating patient-friendly educational content from clinical trial informed consent forms. Using data from Clinica

Shinjan Ghosh, Julian Busch, Georgia Olympia Brikis, Biswadip Dey

Design exploration or optimization using computational fluid dynamics (CFD) is commonly used in the industry. Geometric variation is a key component of such design problems, especially in turbulent flow scenarios, which involves running costly simulations at every design iteration. While parametric RANS-PINN type approaches have been proven to make effective

Philippe Brouillard, Chandler Squires, Jonas Wahl, Konrad P. Kording

Causal discovery aims to automatically uncover causal relationships from data, a capability with significant potential across many scientific disciplines. However, its real-world applications remain limited. Current methods often rely on unrealistic assumptions and are evaluated only on simple synthetic toy datasets, often with inadequate evaluation metrics.

Natesh S. Pillai, Aaron Smith, Azeem Zaman

As sample sizes grow, scalability has become a central concern in the development of Markov chain Monte Carlo (MCMC) methods. One general approach to this problem, exemplified by the popular stochastic gradient Langevin dynamics (SGLD) algorithm, is to use a small random subsample of the data at every time step. This paper, building on recent work such as \c

Audrey Huang, Adam Block, Dylan J. Foster, Dhruv Rohatgi

Recent work in language modeling has raised the possibility of self-improvement, where a language models evaluates and refines its own generations to achieve higher performance without external feedback. It is impossible for this self-improvement to create information that is not already in the model, so why should we expect that this will lead to improved c

Junbo Shen, Bing Xue, Thomas Kannampallil, Chenyang Lu

Early detection of surgical complications allows for timely therapy and proactive risk mitigation. Machine learning (ML) can be leveraged to identify and predict patient risks for postoperative complications. We developed and validated the effectiveness of predicting postoperative complications using a novel surgical Variational Autoencoder (surgVAE) that un

Mateusz Stolarski, Adam Piróg, Piotr Bródka

The identification of key nodes in complex networks is an important topic in many network science areas. It is vital to a variety of real-world applications, including viral marketing, epidemic spreading and influence maximization. In recent years, machine learning algorithms have proven to outperform the conventional, centrality-based methods in accuracy an

Chengzhang Zhu, Luobin Cui, Ying Tang, Jiacun Wang

Artificial intelligence generated content (AIGC), a rapidly advancing technology, is transforming content creation across domains, such as text, images, audio, and video. Its growing potential has attracted more and more researchers and investors to explore and expand its possibilities. This review traces AIGC's evolution through four developmental milestone

Alberto Megías, Sergio Contreras Arredondo, Cheng Giuseppe Chen, Chenyu Tang

This contribution introduces a neural-network-based approach to discover meaningful transition pathways underlying complex biomolecular transformations in coherence with the committor function. The proposed path-committor-consistent artificial neural network (PCCANN) iteratively refines the transition pathway by aligning it to the gradient of the committor.

Aidan Peppin, Anka Reuel, Stephen Casper, Elliot Jones

To accurately and confidently answer the question 'could an AI model or system increase biorisk', it is necessary to have both a sound theoretical threat model for how AI models or systems could increase biorisk and a robust method for testing that threat model. This paper provides an analysis of existing available research surrounding two AI and biorisk thr

Thomas C. Rossi, Lu Qiao, Conner P. Dykstra, Ronaldo Rodrigues Pela

Ultrafast X-ray spectroscopy has been revolutionized in recent years due to the advent of fourth-generation X-ray facilities. In solid-state materials, core excitons determine the energy and line shape of absorption features in core-level spectroscopies such as X-ray absorption spectroscopy. The screening of core excitons is an inherent many-body process tha

Sanjiv Kumar Bariwal, Rajesh Kumar

The phenomenon of collisional breakage in particulate processes has garnered significant interest due to its wide-ranging applications in fields such as milling, astrophysics, and disk formation. This study investigates the analysis of the pure collisional breakage equation (CBE), characterized by its nonlinear nature with presence of locally bounded collisi

Martin Stanek

A preliminary attempt to use cryptographic keywords and analyze vulnerabilities published in the National Vulnerability Database is presented. Basic statistics and visualizations are included.

Alberto Gonzalo Rodriguez Salgado, Maying Shen, Philipp Harzig, Peter Mayer

Robustness to out-of-distribution data is crucial for deploying modern neural networks. Recently, Vision Transformers, such as SegFormer for semantic segmentation, have shown impressive robustness to visual corruptions like blur or noise affecting the acquisition device. In this paper, we propose Channel Wise Feature Augmentation (CWFA), a simple yet efficie

Blaise Munyampirwa, Vihan Lakshman, Benjamin Coleman

Driven by recent breakthrough advances in neural representation learning, approximate near-neighbor (ANN) search over vector embeddings has emerged as a critical computational workload. With the introduction of the seminal Hierarchical Navigable Small World (HNSW) algorithm, graph-based indexes have established themselves as the overwhelmingly dominant parad

Peter Cholak, Rodney Downey, Noam Greenberg

A longstanding question is to characterize the lattice of supersets (modulo finite sets), $\mathcal{L}^*(A)$, of a low$_2$ computably enumerable (c.e.) set. The conjecture is that $\mathcal{L}^*(A)\cong {\mathcal E}^*$. In spite of claims in the literature, this longstanding question/conjecture remains open. We contribute to this problem by solving one of th

Charles Dunkl, Vadim Gorin

Heckman-Polychronakos operators form a prominent family of commuting differential-difference operators defined in terms of the Dunkl operators $\mathcal D_i$ as $\mathcal P_m= \sum_{i=1}^N (x_i \mathcal D_i)^m$. They have been known since 1990s in connection with trigonometric Calogero-Moser-Sutherland Hamiltonian and Jack symmetric polynomials. We explicitl

Nono SC Merleau, Miguel O'Malley, Érika Roldán, Sayan Mukherjee

Higher-dimensional sliding puzzles are constructed on the vertices of a $d$-dimensional hypercube, where $2^d-l$ vertices are distinctly coloured. Rings with the same colours are initially set randomly on the vertices of the hypercube. The goal of the puzzle is to move each of the $2^d-l$ rings to pre-defined target vertices on the cube. In this setting, the

Hossein Moosaei, Milan Hladík, Ahmad Mousavi, Zheming Gao

Binary classification tasks with imbalanced classes pose significant challenges in machine learning. Traditional classifiers often struggle to accurately capture the characteristics of the minority class, resulting in biased models with subpar predictive performance. In this paper, we introduce a novel approach to tackle this issue by leveraging Universum po

Manpreet Kaur, Ankur Tomar, Srijan Mishra, Shashwat Verma

Deep Learning methods are highly local and sensitive to the domain of data they are trained with. Even a slight deviation from the domain distribution affects prediction accuracy of deep networks significantly. In this work, we have investigated a set of techniques aimed at increasing accuracy of generator networks which perform translation from one domain t

Alexandra Chouldechova, Chad Atalla, Solon Barocas, A. Feder Cooper

The valid measurement of generative AI (GenAI) systems' capabilities, risks, and impacts forms the bedrock of our ability to evaluate these systems. We introduce a shared standard for valid measurement that helps place many of the disparate-seeming evaluation practices in use today on a common footing. Our framework, grounded in measurement theory from the s

Weihan Xu

Intensive Care Units (ICUs) provide critical care and life support for most severely ill and injured patients in the hospital. With the need for ICUs growing rapidly and unprecedentedly, especially during COVID-19, accurately identifying the most critical patients helps hospitals to allocate resources more efficiently and save more lives. The Predicting Inte

Juntao Huang, Liu Liu, Kunlun Qi, Jiayu Wan

The Boltzmann equation, a fundamental equation in kinetic theory, serves as a bridge between microscopic particle dynamics and macroscopic continuum mechanics. However, deriving closed macroscopic moment systems from the Boltzmann equation remains a long-standing challenge due to the intrinsic non-closure of the moment hierarchy. In this paper, we propose a

Farhad G. Zanjani, Hong Cai, Hanno Ackermann, Leila Mirvakhabova

This paper presents Planar Gaussian Splatting (PGS), a novel neural rendering approach to learn the 3D geometry and parse the 3D planes of a scene, directly from multiple RGB images. The PGS leverages Gaussian primitives to model the scene and employ a hierarchical Gaussian mixture approach to group them. Similar Gaussians are progressively merged probabilis

Anirudh S Chakravarthy, Shuai Kyle Zheng, Xin Huang, Sachithra Hemachandra

The fine-tuning of pre-trained models has become ubiquitous in generative AI, computer vision, and robotics. Although much attention has been paid to improving the efficiency of fine-tuning model, there has been less scholarship around fine-tuning specifically for improved model performance. To remedy this gap, we present PROFIT, one of the first optimizers

Himadri Barman

We demonstrate how the quantum teleportation protocol of a single qubit can be understood by designing a simple game that can be played by three participants: Alice, Bob, and *Quantum God*.

Mateusz Kasprzyk, Paweł Pełka, Boris N. Oreshkin, Grzegorz Dudek

This paper presents an enhanced N-BEATS model, N-BEATS*, for improved mid-term electricity load forecasting (MTLF). Building on the strengths of the original N-BEATS architecture, which excels in handling complex time series data without requiring preprocessing or domain-specific knowledge, N-BEATS* introduces two key modifications. (1) A novel loss function

Poorya Aghaomidi, Ge Wang

Accurate sleep stage classification is essential for understanding sleep disorders and improving overall health. This study proposes a novel three-stage approach for sleep stage classification using ECG signals, offering a more accessible alternative to traditional methods that often rely on complex modalities like EEG. In Stages 1 and 2, we initialize the w

Sumeet Ramesh Motwani, Chandler Smith, Rocktim Jyoti Das, Rafael Rafailov

Large Language Models (LLMs) often produce answers with a single chain-of-thought, which restricts their ability to explore reasoning paths or self-correct flawed outputs in complex tasks. In this paper, we introduce MALT (Multi-Agent LLM Training), a novel post-training strategy that divides the reasoning process into generation, verification, and refinemen

Federico D. Halpern, Min-Gu Yoo, Brendan Lyons, Juan Diego Colmenares

Diffusive transport processes in magnetized plasmas are highly anisotropic, with fast parallel transport along the magnetic field lines sometimes faster than perpendicular transport by orders of magnitude. This constitutes a major challenge for describing non-grid-aligned magnetic structures in Eulerian (grid-based) simulations. The present paper describes a

David Zollikofer, Béni Egressy, Frederik Benzing, Matthias Otth

Several self-supervised learning (SSL) approaches have shown that redundancy reduction in the feature embedding space is an effective tool for representation learning. However, these methods consider a narrow notion of redundancy, focusing on pairwise correlations between features. To address this limitation, we formalize the notion of embedding space redund

W. Ishibashi, M. Gröbner

Binary black hole (BBH) evolution in the discs of active galactic nuclei (AGN) is a promising channel for gravitational wave (GW)-driven mergers. It is however unclear whether binaries interacting with the surrounding disc undergo orbital contraction or expansion. We develop a simple analytic model of accreting BBHs in AGN discs to follow the orbital evoluti

Pradip Kattel, Abay Zhakenov, Natan Andrei

We consider a model describing a system where the superconductivity competes with the overscreened Kondo effect. The model consists of a single spin$-\frac{1}{2}$ quantum impurity at the edge of a quantum wire where spin$-1$ bulk fermions interact attractively, generating a (superconducting) mass gap. The competition between the Kondo screening and the super

Paolo Arnaudo, Benjamin Withers

We obtain exact expressions for correlation functions of charged scalar operators at finite density and low temperature in CFT$_4$ dual to the RN-AdS$_5$ black brane. We use recent developments in the Heun connection problem in black hole perturbation theory arising from Liouville CFT and the AGT correspondence. The connection problem is solved perturbativel

Anthony L. Piro, Brenna Mockler

Observations of tidal disruption events (TDEs) on a timescale of years after the main flare show evidence of continued activity in the form of optical/UV emission, quasi-periodic eruptions, and delayed radio flares. Motivated by this, we explore the time evolution of these disks using semi-analytic models to follow the changing disk properties and feeding ra

K. Ohnaka, K. -H. Hofmann, G. Weigelt, J. Th. van Loon

Significant mass loss in the red supergiant (RSG) phase has great influence on the evolution of massive stars and their final fate as supernovae. We present near-infrared interferometric imaging of the circumstellar environment of the dust-enshrouded RSG WOH G64 in the Large Magellanic Cloud. WOH G64 was observed with the GRAVITY instrument at ESO's Very Lar

Remi Delaporte-Mathurin, Nikola Goles, John Ball, Collin Dunn

In the pursuit of fusion power, achieving tritium self-sufficiency stands as a pivotal challenge. Tritium breeding within molten salts is a critical aspect of next-generation fusion reactors, yet experimental measurements of \gls{tbr} have remained elusive. Here we present the results of the \gls{baby} experiment, which represents a pioneering effort in trit

S. Amitonov, A. Aprà, M. Asker, R. Bals

We characterize single- and two-qubit operations in a SiGe quantum dot array, from the perspective of its quantum information processing capabilities. The analysis includes rigorous randomized benchmarking of single- and two-qubit gates, SPAM characterization, and Bell's state tomography, which are all basic functionality required for universal quantum compu

Diaa E. Habibi, Gert Aarts, Lingxiao Wang, Kai Zhou

The probability distribution effectively sampled by a complex Langevin process for theories with a sign problem is not known a priori and notoriously hard to understand. Diffusion models, a class of generative AI, can learn distributions from data. In this contribution, we explore the ability of diffusion models to learn the distributions created by a comple

Joseph W. Jerome

This article develops and applies results, originally introduced in earlier work, for the existence of homotopy curves, terminating at a desired solution. We describe the principal hypotheses and results in section two; right inverse approximation is at the core of the theory. We apply this theory in section three to the basic drift-diffusion equations. The

Pallabi Chatterjee, Ranjan Modak

It is well known that unitary evolution tends to increase entanglement, whereas continuous monitoring counteracts this growth by pinning the wavefunction trajectories to the eigenstates of the measurement operators. In this work, we investigate the fate of the measurement-induced phase transition in a periodically driven free-fermionic quantum system, where

Vinícius Barros da Silva, João Peres Vieira, Edson Denis Leonel

Recently, the covariant formulation of the geometric bifurcation theory, developed in a previous paper, has been applied to two elementary problems: the study of limit cycles of dynamical systems and the second part of Hilbert's sixteenth problem. First, it has been shown that dynamical systems with more than one limit cycle are understood to be those in whi

Christopher Stith

The Bianchi identities for the Weyl curvature tensor of a spacetime $(M, g)$ solving the vacuum Einstein equations in a double null foliation exhibit a hyperbolic structure, which can be used to obtain detailed nonlinear estimates on the null Weyl tensor components. The aim of this paper is twofold. First we discuss existence and uniqueness for solutions of

Steven Abel, Ivano Basile, Viktor Matyas

It appears to be difficult within string theory to obtain genuine scale separation between spacetime and the internal sector. In this paper, we propose a novel mechanism for scale-separated vacua which hinges on stringy effects that are invisible at the level of effective field theory. We show that (meta)stable vacua can form if a super no-scale one-loop pot

Arnas Volčokas, Justin Ball, Giovanni Di Giannatale, Stephan Brunner

In this paper, we investigate the effects of ion-scale turbulence-generated currents on the local safety factor profile under conditions of low magnetic shear and proximity to rational surfaces, relevant to Internal Transport Barrier (ITB) formation. Our results show that turbulent currents can generate stationary zonal magnetic potential corrugations, produ

Amit Giveon, Akikazu Hashimoto, David Kutasov

In \cite{Giveon:2024fhz}, we argued that the (0,2) heterotic string gives rise in spacetime to left and right-moving symmetric product CFT's. In this paper we confirm this claim by showing that it computes correlation functions in these CFT's.

Maxime Lucas, Damien Francois, Laurent Mombaerts, Cristina Donato

Epilepsy is known to drastically alter brain dynamics during seizures (ictal periods), but its effects on background (non-ictal) brain dynamics remain poorly understood. To investigate this, we analyzed an in-house dataset of brain activity recordings from epileptic zebrafish, focusing on two controlled genetic conditions across two fishlines. After using ma