February 2024 arXiv papers — page 137

Valentin Bauer, Tommaso Padovano, Mattia Gianotti, Giacomo Caslini

Painting and music therapy approaches can help to foster social interaction for autistic people. However, the tools sometimes lack of flexibility and fail to keep people's attention. Unknowns also remain about the effect of combining these approaches. Though, very few studies have investigated how Multisensory Environments (MSEs) could help to address these

Hui Sun, Feng Bao

We report two methods for solving FBSDEs of path dependent types of high dimensions. Specifically, we propose a deep learning framework for solving such problems using path signatures as underlying features. Our two methods (forward/backward) demonstrate comparable/better accuracy and efficiency compared to the state of the art techniques. More importantly,

Beatrice Savoldi, Andrea Piergentili, Dennis Fucci, Matteo Negri

Gender-neutral translation (GNT) that avoids biased and undue binary assumptions is a pivotal challenge for the creation of more inclusive translation technologies. Advancements for this task in Machine Translation (MT), however, are hindered by the lack of dedicated parallel data, which are necessary to adapt MT systems to satisfy neutral constraints. For s

Arthur Ferraz, Cheikh Ahmed, Quentin Cappart, Thibaut Vidal

Districting-and-routing is a strategic problem aiming to aggregate basic geographical units (e.g., zip codes) into delivery districts. Its goal is to minimize the expected long-term routing cost of performing deliveries in each district separately. Solving this stochastic problem poses critical challenges since repeatedly evaluating routing costs on a set of

Valentin Boettcher, Richard Hartmann, Konstantin Beyer, Walter T. Strunz

We present a fully quantum dynamical treatment of a quantum heat engine and its baths based on the Hierarchy of Pure States (HOPS), an exact and general method for open quantum system dynamics. We show how the change of the bath energy and the interaction energy can be determined within HOPS, for arbitrary coupling strength and smooth time dependence of the

Anish Acharya, Li Jing, Bhargav Bhushanam, Dhruv Choudhary

Pretext Invariant Representation Learning (PIRL) followed by Supervised Fine-Tuning (SFT) has become a standard paradigm for learning with limited labels. We extend this approach to the Positive Unlabeled (PU) setting, where only a small set of labeled positives and a large unlabeled pool -- containing both positives and negatives are available. We study thi

Brigitte Strohmaier

Berta Karlik was an Austrian physicist who was not only among the early radioactivity researchers and nuclear physicists in Vienna, but also pioneered a woman's academic career in Austria. She was the first woman at the University of Vienna to acquire the venia legendi in physics, and the first full professor at a philosophical faculty in Austria. For almost

Renan Lima Baima, Loïck Chovet, Johannes Sedlmeir, Gilbert Fridgen

In the emerging space economy, autonomous robotic missions with specialized goals such as mapping and mining are gaining traction, with agencies and enterprises increasingly investing resources. Multirobot systems (MRS) research has provided many approaches to establish control and communication layers to facilitate collaboration from a technical perspective

Eman Abdullah AlOmar, Benjamin Knobloch, Thomas Kain, Christopher Kalish

AntiCopyPaster is an IntelliJ IDEA plugin, implemented to detect and refactor duplicate code interactively as soon as a duplicate is introduced. The plugin only recommends the extraction of a duplicate when it is worth it. In contrast to current Extract Method refactoring approaches, our tool seamlessly integrates with the developer's workflow and actively p

Hrishav Bakul Barua, Ganesh Krishnasamy, KokSheik Wong, Abhinav Dhall

High Dynamic Range (HDR) imaging aims to replicate the high visual quality and clarity of real-world scenes. Due to the high costs associated with HDR imaging, the literature offers various data-driven methods for HDR image reconstruction from Low Dynamic Range (LDR) counterparts. A common limitation of these approaches is missing details in regions of the r

Haoyu Yang, Anthony Agnesina, Haoxing Ren

Exploding predictive AI has enabled fast yet effective evaluation and decision-making in modern chip physical design flows. State-of-the-art frameworks typically include the objective of minimizing the mean square error (MSE) between the prediction and the ground truth. We argue the averaging effect of MSE induces limitations in both model training and deplo

Ling Liang, Zusen Xu, Kim-Chuan Toh, Jia-Jie Zhu

The Halpern iteration for solving monotone inclusion problems has gained increasing interests in recent years due to its simple form and appealing convergence properties. In this paper, we investigate the inexact variants of the scheme in both deterministic and stochastic settings. We conduct extensive convergence analysis and show that by choosing the inexa

Katerina Rigana, Ernst C. Wit, Samantha Cook

Accurately defining, measuring and mitigating risk is a cornerstone of financial risk management, especially in the presence of financial contagion. Traditional correlation-based risk assessment methods often struggle under volatile market conditions, particularly in the face of external shocks, highlighting the need for a more robust and invariant predictiv

Daniel Zhengyu Huang, Nicholas H. Nelsen, Margaret Trautner

Computationally efficient surrogates for parametrized physical models play a crucial role in science and engineering. Operator learning provides data-driven surrogates that map between function spaces. However, instead of full-field measurements, often the available data are only finite-dimensional parametrizations of model inputs or finite observables of mo

Chirag Chhablani, Sarthak Jain, Akshay Channesh, Ian A. Kash

Graph Neural Networks (GNNs) have been a powerful tool for node classification tasks in complex networks. However, their decision-making processes remain a black-box to users, making it challenging to understand the reasoning behind their predictions. Counterfactual explanations (CFE) have shown promise in enhancing the interpretability of machine learning m

Shalosh B. Ekhad, Doron Zeilberger

In their recent claimed computer-free proof of the Four Color Theorem, David Jackson and Bruce Richmond attempted to use sophisticated "asymptotic analysis" to explicitly compute a certain number whose positivity (according to them) implies this famous theorem. While the jury is still out whether their valiant attempt holds water, we prove, in this modest no

Oliver Maßmeyer, Jürgen Belz, Badrosadat Ojaghi Dogahe, Maximilian Widemann

Two-dimensional (2D) materials have received a lot of interest over the past decade. Especially van der Waals (vdW) 2D materials, such as transition metal dichalcogenides (TMDCs), and their heterostructures exhibit semiconducting properties that make them highly suitable for novel device applications. Controllable mixing and matching of the 2D materials with

Peikai Qi

We compute Iwasawa $\lambda$ invariant in terms of Massey products in Galois cohomology with restricted ramification. When applied to imaginary quadratic fields and cyclotomic fields, we obtain a new proof and generalization of results of Gold and McCallum-Sharifi. The main tool is the generalized Bockstein map introduced by Lam-Liu-Sharifi-Wake-Wang.

Renan Lima Baima, Iván Abellán Álvarez, Ivan Pavić, Emanuela Podda

In response to the European Commission's aim of cutting carbon emissions by 2050, there is a growing need for cutting-edge solutions to promote low-carbon energy consumption in public infrastructures. This paper introduces a Proof of Concept (PoC) that integrates the transparency and immutability of blockchain and the Internet of Things (IoT) to enhance ener

Ali Behcet Alpat, Giovanni Bartolini, Talifujiang Wusimanjiang, Goesta Mattausch

Materials to be deployed in space applications have to undergo a variety of different test scenarios, simulating actual space conditions. Among these materials solar photovoltaic cells, optics, meta-materials and more will be directly exposed to space radiation and must be tested accordingly. From the design phase of such target materials to the final produc

Lucas Friedrich, Jonas Maziero

The rapid development of quantum computers promises transformative impacts across diverse fields of science and technology. Quantum neural networks (QNNs), as a forefront application, hold substantial potential. Despite the multitude of proposed models in the literature, persistent challenges, notably the vanishing gradient (VG) and cost function concentrati

Wasu Top Piriyakulkij, Cassidy Langenfeld, Tuan Anh Le, Kevin Ellis

We give a model of how to infer natural language rules by doing experiments. The model integrates Large Language Models (LLMs) with Monte Carlo algorithms for probabilistic inference, interleaving online belief updates with experiment design under information-theoretic criteria. We conduct a human-model comparison on a Zendo-style task, finding that a critic

Richard Wedeen

We develop the axiom system proposed by Kontsevich and Segal to define volume-dependent field theories (VFTs), a class of non-topological quantum field theories whose dependence on the background metric factors through the associated density. We construct a well-defined Lorentzian limit of a Wick-rotated VFT defined on smooth, possibly degenerate Lorentzian

Tobias Ettling, Sari Saba-Sadiya, Gemma Roig

Machine learning is a rapidly evolving field with a wide range of applications, including biological signal analysis, where novel algorithms often improve the state-of-the-art. However, robustness to algorithmic variability - measured by different algorithms, consistently uncovering similar findings - is seldom explored. In this paper we investigate whether

Isaac Lara, Sergio Rajsbaum, Armajac Raventós-Pujol

To the best of our knowledge, a complete characterization of the domains that escape the famous Arrow's impossibility theorem remains an open question. We believe that different ways of proving Arrovian theorems illuminate this problem. This paper presents a new combinatorial topology proof of Arrow's theorem. In PODC 2022, Rajsbaum and Ravent\'os-Pujol prov

Zelin Wan, Jin-Hee Cho, Mu Zhu, Ahmed H. Anwar

This paper introduces a novel approach, Decision Theory-guided Deep Reinforcement Learning (DT-guided DRL), to address the inherent cold start problem in DRL. By integrating decision theory principles, DT-guided DRL enhances agents' initial performance and robustness in complex environments, enabling more efficient and reliable convergence during learning. O

Cheng Ma, Omar Malik, G. Korniss

We investigate quantum persistence by analyzing amplitude and phase fluctuations of the wave function governed by the time-dependent free-particle Schr\"odinger equation. The quantum system is initialized with local random uncorrelated Gaussian amplitude and phase fluctuations. In analogy with classical diffusion, the persistence probability is defined as th

Yanxiao Liu, Cheuk Ting Li

We present a unified one-shot coding framework designed for the communication and compression of messages among multiple nodes across a general acyclic noisy network. Our setting can be seen as a one-shot version of the acyclic discrete memoryless network studied by Lee and Chung, and noisy network coding studied by Lim, Kim, El Gamal and Chung. We design a

Renan Lima Baima, Tiago Miguel Barao Caetano, Ana Carolina Oliveira Lima, Emilia Oliveira Lima Leal

The primary objective is to emphasize the merits of active methodologies and cross-disciplinary curricula in Requirement Engineering. This direction promises a holistic and applied trajectory for Computer Engineering education, supported by the outcomes of our case study, where artifact-centric learning proved effective, with 73% of students achieving the hi

Nicolas Gillis, Robert Luce

The sufficiently scattered condition (SSC) is a key condition in the study of identifiability of various matrix factorization problems, including nonnegative, minimum-volume, symmetric, simplex-structured, and polytopic matrix factorizations. The SSC allows one to guarantee that the computed matrix factorization is unique/identifiable, up to trivial ambiguit

Nikolaus Correll, Dylan Kriegman, Stephen Otto, James Watson

We describe a force-controlled robotic gripper with built-in tactile and 3D perception. We also describe a complete autonomous manipulation pipeline consisting of object detection, segmentation, point cloud processing, force-controlled manipulation, and symbolic (re)-planning. The design emphasizes versatility in terms of applications, manufacturability, use

Adeniyi Adekola, Ayotunde Ayorinde, Hisham Muhammed, Francis Okewole

A computationally inexpensive, analytically simple, and remarkably efficient rain attenuation prediction algorithm is presented in this paper. The algorithm, here referred to as the Quasi-Moment-Method, has only two main requirements for its implementation. First, rain attenuation measurement data for bit terrestrial or slant paths for the site of interest m

Siva Rajesh Kasa, Hu Yijie, Santhosh Kumar Kasa, Vaibhav Rajan

\texttt{Mixture-Models} is an open-source Python library for fitting Gaussian Mixture Models (GMM) and their variants, such as Parsimonious GMMs, Mixture of Factor Analyzers, MClust models, Mixture of Student's t distributions, etc. It streamlines the implementation and analysis of these models using various first/second order optimization routines such as G

Meirav Amram, Cheng Gong, JiaLi Mo

This paper considers some algebraic surfaces that can deform to planar Zappatic stable surfaces with a unique singularity of type En. We prove that the Galois covers of these surfaces are all simply connected of general type, for n >= 4, and we give a formula for Chern numbers of such Galois covers. As an application, we prove that such surfaces do not exist

L. Colomban, M. Kretzschmar, V. Krasnoselkikh, O. V. Agapitov

The evolution of the solar wind electron distribution function with heliocentric distance exhibits different features that are still unexplained, in particular, the increase of the Strahl pitch angle width. Wave-particle interactions between electrons and whistler waves are often proposed to explain these phenomena. We aim at quantifying the effect of whistl

Yong Cao, Wenyan Li, Jiaang Li, Yifei Yuan

Pretrained large Vision-Language models have drawn considerable interest in recent years due to their remarkable performance. Despite considerable efforts to assess these models from diverse perspectives, the extent of visual cultural awareness in the state-of-the-art GPT-4V model remains unexplored. To tackle this gap, we extensively probed GPT-4V using the

Renan Lima Baima, Loïck Chovet, Eduard Hartwich, Abhishek Bera

In the new space economy, space agencies, large enterprises, and start-ups aim to launch space multi-robot systems (MRS) for various in-situ resource utilization (ISRU) purposes, such as mapping, soil evaluation, and utility provisioning. However, these stakeholders' competing economic interests may hinder effective collaboration on a centralized digital pla

Sun-Kai Leung

Assuming a uniform $q$-variant of the prime $k$-tuple conjecture, we compute moments of the number of primes in arithmetic progressions to a large modulus $q$ as the residue classes vary. Consequently, depending on the size of $\varphi(q)$, the prime count follows either a Gaussian or a Poisson distribution. In particular, the least prime in progressions fol

Eman Abdullah AlOmar, Anushkrishna Venkatakrishnan, Mohamed Wiem Mkaouer, Christian D. Newman

Large Language Models (LLMs), like ChatGPT, have gained widespread popularity and usage in various software engineering tasks, including refactoring, testing, code review, and program comprehension. Despite recent studies delving into refactoring documentation in commit messages, issues, and code review, little is known about how developers articulate their

Jasan Zughaibi, Bradley J. Nelson, Michael Muehlebach

Magnetic navigation offers wireless control over magnetic objects, which has important medical applications, such as targeted drug delivery and minimally invasive surgery. Magnetic navigation systems are categorized into systems using permanent magnets and systems based on electromagnets. Electromagnetic Navigation Systems (eMNSs) are believed to have a supe

Kristina Radivojevic, Nicholas Clark, Paul Brenner

The emergence of Large Language Models (LLMs) has great potential to reshape the landscape of many social media platforms. While this can bring promising opportunities, it also raises many threats, such as biases and privacy concerns, and may contribute to the spread of propaganda by malicious actors. We developed the "LLMs Among Us" experimental framework o

Víctor Araña-Pulido, Eugenio Jiménez-Yguácel, Francisco Cabrera-Almeida, Pedro Quintana-Morales

This article presents a radio frequency system that can be used to perform precise vertical landings of drones. The system is based on the three-way phase shift detection of a signal transmitted from the landing point. The antenna system is designed by taking into account parameters such as landing tracking area, analog-to-digital converter (ADC) resolution,

Junhong Zhang, Zhihui Lai, Jie Zhou, Guangfei Liang

This paper focuses on a specific family of classifiers called nonparallel support vector classifiers (NPSVCs). Different from typical classifiers, the training of an NPSVC involves the minimization of multiple objectives, resulting in the potential concerns of feature suboptimality and class dependency. Consequently, no effective learning scheme has been est

The LHAASO Collaboration, Zhen Cao, F. Aharonian, Axikegu

On October 9, 2022, the Large High Altitude Air Shower Observatory (LHAASO) reported the observation of the very early TeV afterglow of the brightest-of-all-time GRB 221009A, recording the highest photon statistics in the TeV band ever from a gamma-ray burst. We use this unique observation to place stringent constraints on an energy dependence of the speed o

Jelena Sedlar, Riste Škrekovski

A proper abelian coloring of a cubic graph G by a finite abelian group A is any proper edge-coloring of G by the non-zero elements of A such that the sum of the colors of the three edges incident to any vertex v of G equals zero. It is known that cyclic groups of order smaller than 10 do not color all bridgeless cubic graphs, and that all abelian groups of o

Joshua Jeishing Wen

We describe a way to study and compute Pieri rules for wreath Macdonald polynomials using the quantum toroidal algebra. The Macdonald pairing can be naturally generalized to the wreath setting, but the wreath Macdonald polynomials are no longer collinear with their duals. We establish the relationship between these dual polynomials and the quantum toroidal a

Yiannis N. Petridis, Morten S. Risager

We consider equidistribution of angles for certain hyperbolic lattice points in the upper half-plane. Extending work of Friedlander and Iwaniec we show that for the full modular group equidistribution persists for matrices with $a^2+b^2+c^2+d^2=p$ with $p$ prime; at least if we assume sufficiently good lower bounds in the hyperbolic prime number theorem by F

Christopher Cebra, Alexander Strang

While generic competitive systems exhibit mixtures of hierarchy and cycles, real-world systems are predominantly hierarchical. We demonstrate and extend a mechanism for hierarchy; systems with similar agents approach perfect hierarchy in expectation. A variety of evolutionary mechanisms plausibly select for nearly homogeneous populations, however, extant wor

Seyedarmin Azizi, Mahdi Nazemi, Massoud Pedram

As Vision Transformers (ViTs) increasingly set new benchmarks in computer vision, their practical deployment on inference engines is often hindered by their significant memory bandwidth and (on-chip) memory footprint requirements. This paper addresses this memory limitation by introducing an activation-aware model compression methodology that uses selective

Tejas Prasanna, Matthew S. B. Coleman, Todd A. Thompson

The neutrino-driven wind cooling phase of proto-neutron stars (PNSs) follows successful supernovae. Wind models without magnetic fields or rotation fail to achieve the necessary conditions for production of the third $r-$process peak, but robustly produce a weak $r-$process in neutron-rich winds. Using 2D magnetohydrodynamic simulations with magnetar-strengt

Riley W. Clarke, James R. A. Davenport, John Gizis, Melissa L. Graham

Due to their short timescale, stellar flares are a challenging target for the most modern synoptic sky surveys. The upcoming Vera C. Rubin Legacy Survey of Space and Time (LSST), a project designed to collect more data than any precursor survey, is unlikely to detect flares with more than one data point in its main survey. We developed a methodology to enabl

Troels Harmark

We propose a thermal scalar equation of motion (EOM) that takes into account curvature corrections for backgrounds supported by Ramond-Ramond fluxes. This can be used to obtain the Hagedorn temperature for type II string theory on AdS and pp-wave backgrounds. For Ramond-Ramond flux supported pp-waves we show that the proposed thermal scalar EOM reproduces th

Giorgio Laverda, Javier Rubio

In this paper we investigate the vacuum stability of the non-minimally coupled Standard-Model Higgs during a phase of kinetic domination following the end of inflation. The non-minimal coupling to curvature stabilises the Higgs fluctuations during inflation while driving them towards the instability scale during kination, when they can classically overcome t

Yaniv Donath, Enrico Pajer

Cosmological correlators, the natural observables of the primordial universe, have been extensively studied in the past two decades using the in-in formalism pioneered by Schwinger and Keldysh for the study of dissipative open systems. Ironically, most applications in cosmology have focused on non-dissipative closed systems. We show that, for non-dissipative

Dominika Ďurovčíková, Vivishek Sudhir

The detection of weak forces is a central problem in physics and engineering, ranging in importance from fundamental pursuits such as precision tests of gravity, gravitational-wave detection, and searches for dark matter, to applications such as force microscopy. These pursuits require a low-mass mechanical force transducer with a high quality factor, whose

Jayana A. Saes, Raissa F. P. Mendes, Nicolás Yunes

The accurate observations of neutron stars have deepened our knowledge of both general relativity and the properties of nuclear physics at large densities. Relating observations to the microphysics that govern these stars can sometimes be aided by approximate universal relations. One such relation connects the ratio of the central pressure to the central ene

V. Markov, S. Gallerani, A. Ferrara, A. Pallottini

A sizable fraction of the heavy elements synthesized by stars in galaxies condenses into sub-micron-sized solid-state particles, known as dust grains. Dust produces a wavelength-dependent attenuation, $A_\lambda$, of the galaxy emission, thereby significantly altering its observed properties. Locally, $A_\lambda$ is in general the sum of a power-law and a UV

Giuliano Giacalone, Benjamin Bally, Govert Nijs, Shihang Shen

Whether or not femto-scale droplets of quark-gluon plasma (QGP) are formed in so-called small systems at high-energy colliders is a pressing question in the phenomenology of the strong interaction. For proton-proton or proton-nucleus collisions the answer is inconclusive due to the large theoretical uncertainties plaguing the description of these processes.

Fabrizio Gentile, Margherita Talia, Emanuele Daddi, Marika Giulietti

We present the first spectroscopic ALMA follow-up for a pilot sample of nine Radio-Selected NIRdark galaxies in the COSMOS field. These sources were initially selected as radio-detected sources (S(3GHz)>12.65 uJy), lacking an optical/NIR counterpart in the COSMOS2015 catalog (Ks>24.7 mag), with just three of them subsequently detected in the deeper COSMOS202

José Diogo Simão

We propose an explicit spin-foam amplitude for Lorentzian gravity in three dimensions, allowing for both space- and time-like boundaries. The model is based on two main requirements: that it should be structurally similar to its well-known Euclidean analog, and that geometricity should be recovered in the semiclassical regime. To this end we introduce new co

Satoshi Yamada, Yoshihiro Ueda, Taiki Kawamuro, Claudio Ricci

We study "buried" active galactic nuclei (AGNs) almost fully covered by circumnuclear material in ultra-/luminous infrared galaxies (U/LIRGs), which show weak ionized lines from narrow line regions. Employing an indicator of [O IV] 25.89-um or [Ne V] 14.32-um line to 12-um AGN luminosity ratio, we find 17 buried AGN candidates that are [O IV]-weak ($L_{\rm [

Alexandra S. Mahajan, Jason D. Eastman, James Kirk

Using JWST observations of a primary transit and two secondary eclipses for GJ 1214b, we determine an eccentricity that is more precise than a decade of HARPS data, which enables us to measure the stellar density to 2.62%. Coupled with a prior on the stellar mass from a dynamically calibrated K-$M_*$ relation, we determine $R_*$ to 1.13% -- 3 times more prec

Heidi Benham, Andrew De Lapo, Damir Dzhafarov, Reed Solomon

The Ginsburg--Sands theorem from topology states that every infinite topological space has an infinite subspace homeomorphic to exactly one of the following five topologies on $\omega$: indiscrete, discrete, initial segment, final segment, and cofinite. The original proof is nonconstructive, and features an interesting application of Ramsey's theorem for pai

Chengjian Feng, Yujie Zhong, Zequn Jie, Weidi Xie

In this paper, we present a novel paradigm to enhance the ability of object detector, e.g., expanding categories or improving detection performance, by training on synthetic dataset generated from diffusion models. Specifically, we integrate an instance-level grounding head into a pre-trained, generative diffusion model, to augment it with the ability of loc

Dimitrios G. Konstantinides, Charalampos D. Passalidis, Nikolaos E. Porichis

We consider closure properties in the class of positively decreasing distributions. Our results stem from different types of dependence, but each type belongs in the family of asymptotically independent dependence structure. Namely we examine the closure property with respect to minimum, maximum, convolution product and convolution. Furthermore, we take into

Dongyang Liu, Renrui Zhang, Longtian Qiu, Siyuan Huang

We propose SPHINX-X, an extensive Multimodality Large Language Model (MLLM) series developed upon SPHINX. To improve the architecture and training efficiency, we modify the SPHINX framework by removing redundant visual encoders, bypassing fully-padded sub-images with skip tokens, and simplifying multi-stage training into a one-stage all-in-one paradigm. To f

Daniel Winter, Niv Cohen, Yedid Hoshen

Graph neural networks (GNNs) are the dominant paradigm for classifying nodes in a graph, but they have several undesirable attributes stemming from their message passing architecture. Recently, distillation methods succeeded in eliminating the use of GNNs at test time but they still require them during training. We perform a careful analysis of the role that

Jonathan Crabbé, Nicolas Huynh, Jan Stanczuk, Mihaela van der Schaar

Fourier analysis has been an instrumental tool in the development of signal processing. This leads us to wonder whether this framework could similarly benefit generative modelling. In this paper, we explore this question through the scope of time series diffusion models. More specifically, we analyze whether representing time series in the frequency domain i

Boyi Li, Yue Wang, Jiageng Mao, Boris Ivanovic

Adapting driving behavior to new environments, customs, and laws is a long-standing problem in autonomous driving, precluding the widespread deployment of autonomous vehicles (AVs). In this paper, we present LLaDA, a simple yet powerful tool that enables human drivers and autonomous vehicles alike to drive everywhere by adapting their tasks and motion plans

Steffen Bollmann, Jukka I. Väyrynen, Elio J. König

Motivated by the importance of studying topological superconductors beyond the mean-field approximation, we here investigate mesoscopic islands of time reversal invariant topological superconductors (TRITOPS). We characterize the spectrum in the presence of strong order parameter fluctuations in the presence of an arbitrary number of Kramers pairs of Majoran

Xing Han Lù, Zdeněk Kasner, Siva Reddy

We propose the problem of conversational web navigation, where a digital agent controls a web browser and follows user instructions to solve real-world tasks in a multi-turn dialogue fashion. To support this problem, we introduce WEBLINX - a large-scale benchmark of 100K interactions across 2300 expert demonstrations of conversational web navigation. Our ben

Zane Durante, Bidipta Sarkar, Ran Gong, Rohan Taori

The development of artificial intelligence systems is transitioning from creating static, task-specific models to dynamic, agent-based systems capable of performing well in a wide range of applications. We propose an Interactive Agent Foundation Model that uses a novel multi-task agent training paradigm for training AI agents across a wide range of domains,

Ingvar Ziemann, Stephen Tu, George J. Pappas, Nikolai Matni

In this work, we study statistical learning with dependent ($\beta$-mixing) data and square loss in a hypothesis class $\mathscr{F}\subset L_{\Psi_p}$ where $\Psi_p$ is the norm $\|f\|_{\Psi_p} \triangleq \sup_{m\geq 1} m^{-1/p} \|f\|_{L^m} $ for some $p\in [2,\infty]$. Our inquiry is motivated by the search for a sharp noise interaction term, or variance pr

Mattia Freguglia, Andrea Malchiodi

We prove existence of Yamabe metrics on singular manifolds with conical points and conical links of Einstein type that include orbifold structures. We deal with metrics of generic type and derive a counterpart of Aubin's classical result. Interestingly, the singular nature of the metric determines a different condition on the dimension, compared to the regul

Zhenqing Ling, Daoyuan Chen, Liuyi Yao, Yaliang Li

The confluence of Federated Learning (FL) and Large Language Models (LLMs) is ushering in a new era in privacy-preserving natural language processing. However, the intensive memory requirements for fine-tuning LLMs pose significant challenges, especially when deploying on clients with limited computational resources. To circumvent this, we explore the novel

D. Cornu, P. Salomé, B. Semelin, A. Marchal

The upcoming Square Kilometer Array (SKA) will set a new standard regarding data volume generated by an astronomical instrument, which is likely to challenge widely adopted data-analysis tools that scale inadequately with the data size. The aim of this study is to develop a new source detection and characterization method for massive radio astronomical datas

Isabella Bueno, Gabriel R. Palma, Idemauro A. R. Lara, Rafael A. Moral

Understanding social immunity mechanisms in ant colonies remains crucial to comprehending the evolution of defense strategies in eusocial organisms. This study assumes the absence of the role of memory in the ants' defense strategy, considering that they can make a new exploration of collective cleaning behaviors. We investigate how worker interactions and p

Guosheng Fu, Hangjie Ji, Will Pazner, Wuchen Li

Liquid droplet dynamics are widely used in biological and engineering applications, which contain complex interfacial instabilities and pattern formation such as droplet merging, splitting, and transport. This paper studies a class of mean field control formulations for these droplet dynamics, which can be used to control and manipulate droplets in applicati

M. E. Sharina, M. I. Maricheva, A. Y. Kniazev, V. V. Shimansky

This paper presents the results of analysing the integrated light (IL) low-resolution spectra of globular clusters (GCs) in the M31 and Centaurus A groups of galaxies. The sample consists of eight very metal-poor GCs ($\rm [Fe/H]\le -2$ dex) with high signal-to-noise ratio spectra acquired with the telescopes: the 6-m SAO RAS (BTA), the Southern African Larg

Roberto San Millán-Castillo, Eduardo Morgado, Rebeca Goya Esteban

This article focuses on the prediction of the vibration frequency response of handheld probes. A novel approach that involves machine learning and readily available data from probes was explored. Vibration probes are efficient and affordable devices that provide information about testing airborne sound insulation in building acoustics. However, fixing a prob

Dmitry Galakhov, Alexei Morozov, Nikita Tselousov

BPS algebras are the symmetries of a wide class of brane-inspired models. They are closely related to Yangians -- the peculiar and somewhat sophisticated limit of DIM algebras. Still they possess some simple and explicit representations. We explain here that for $Y(\widehat{\mathfrak{gl}}_r)$ these representations are related to Uglov polynomials, whose fami

Shimon Vainer, Mark Boss, Mathias Parger, Konstantin Kutsy

Graphics pipelines require physically-based rendering (PBR) materials, yet current 3D content generation approaches are built on RGB models. We propose to model the PBR image distribution directly, avoiding photometric inaccuracies in RGB generation and the inherent ambiguity in extracting PBR from RGB. As existing paradigms for cross-modal fine-tuning are n

Abhinav Sinha, Dwaipayan Mukherjee, Shashi Ranjan Kumar

This work proposes a cooperative strategy that employs deviated pursuit guidance to simultaneously intercept a moving (but not manoeuvring) target. As opposed to many existing cooperative guidance strategies which use estimates of time-to-go, based on proportional-navigation guidance, the proposed strategy uses an exact expression for time-to-go to ensure si

Idil Esen Zulfikar, Sabarinath Mahadevan, Paul Voigtlaender, Bastian Leibe

Current state-of-the-art Video Object Segmentation (VOS) methods rely on dense per-object mask annotations both during training and testing. This requires time-consuming and costly video annotation mechanisms. We propose a novel Point-VOS task with a spatio-temporally sparse point-wise annotation scheme that substantially reduces the annotation effort. We ap

David D. Baek, Ziming Liu, Max Tegmark

We present GenEFT: an effective theory framework for shedding light on the statics and dynamics of neural network generalization, and illustrate it with graph learning examples. We first investigate the generalization phase transition as data size increases, comparing experimental results with information-theory-based approximations. We find generalization i

Vo Hong Minh Phan, Enrico Peretti, Pierre Cristofari, Antoine Gusdorf

Cosmic rays are the only agent capable of ionizing the interior of dense molecular clouds and, thus, they are believed to play an essential role in determining the physical and chemical evolution of star-forming regions. In this work, we aim to study cosmic-ray induced ionization rates in starburst environments using non-thermal emissions of cosmic rays from

Víctor Araña-Pulido, Eugenio Jiménez-Yguácel, Francisco Cabrera-Almeida, Pedro Quintana-Morales

This paper presents a circuit for precise vertical landing of drones based on a three phase-shifts detection of a single frequency transmitted from the landing point. The circuit can be considered as a new navigation sensor that assists in guidance corrections for landing at a specific point. The circuit has three inputs to which the signal transmitted from

Abhishek Panigrahi, Nikunj Saunshi, Kaifeng Lyu, Sobhan Miryoosefi

Recent developments in large language models have sparked interest in efficient pretraining methods. Stagewise training approaches to improve efficiency, like gradual stacking and layer dropping (Reddi et al, 2023; Zhang & He, 2020), have recently garnered attention. The prevailing view suggests that stagewise dropping strategies, such as layer dropping, are

Valerio Bellucci, Sarlota Birnsteinova, Tokushi Sato, Romain Letrun

X-ray Multi-Projection Imaging (XMPI) is an emerging technology that allows for the acquisition of millions of 3D images per second in samples opaque to visible light. This breakthrough capability enables volumetric observation of fast stochastic phenomena, which were inaccessible due to the lack of a volumetric X-ray imaging probe with kHz to MHz repetition

Arnab Sarkar, Patrick Eggenberger, Lev Yungelson, Christopher A. Tout

We extend our model of magnetic braking (MB), driven by an $\alpha-\Omega$ dynamo mechanism, from fully convective M-dwarfs (FCMDs) to explain the surface and internal spin $P_\mathrm{spin}$ evolution of partly convective dwarfs (PCDs) starting from the disc-dispersal stage to the main-sequence turnoff. In our model, the spin of the core is governed by shear

Víctor Alonso, Juan Antonio González, Isaías García de la Fuente, José Carlos Cobos

Relative permittivities, $\varepsilon_{r}$, and refractive indices, $n_{D}$, have been measured at (293.15-303.15) K, for the mixtures 1-pentanol + 2,5,8,11,14-pentaoxapentadecane (TEGDME) or TEGDME + octane. These data have been used, together with density measurements available in the literature, to calculate the correlation factors, $g_{K}$, according to

A. Bunde, J. Ludescher, H. J. Schellnhuber

El Ni\~no episodes are part of the El Ni\~no-Southern Oscillation (ENSO), which is the strongest driver of interannual climate variability, and can trigger extreme weather events and disasters in various parts of the globe. Previously we have described a network approach that allows to forecast El Ni\~no events about 1 year ahead. Here we evaluate the real-t

Chris Akers, Annie Y. Wei

Conventional holographic tensor networks can be described as toy holographic maps constructed from many small linear maps acting in a spatially local way, all connected together with ``background entanglement'', i.e. links of a fixed state, often the maximally entangled state. However, these constructions fall short of modeling real holographic maps. One rea

Wahab Khawaja, Martins Ezuma, Vasilii Semkin, Fatih Erden

The use of unmanned aerial vehicles (UAVs) for a variety of commercial, civilian, and defense applications has increased many folds in recent years. While UAVs are expected to transform future air operations, there are instances where they can be used for malicious purposes. In this context, the detection, classification, and tracking (DCT) of UAVs (DCT-U) f

Evangelos A. Paraskevas, Arman Cam, Leandros Perivolaropoulos, Ozgur Akarsu

We explore the predictions of $\Lambda_{\rm s}$CDM, a novel framework suggesting a rapid anti-de Sitter (AdS) to de Sitter (dS) vacua transition in the late Universe, on bound cosmic structures. In its simplest version, the cosmological constant, $\Lambda_{\rm s}$, abruptly switches sign from negative to positive, attaining its present-day value at a redshif

Francisco C. Caramello, Henrique A. Puel Martins, Ivan P. Costa e Silva

We prove a transverse diameter theorem in the context of Lorentzian foliations, which can be interpreted as a Hawking--Penrose-type singularity theorem for timelike geodesics transverse to the foliation. In order to develop the necessary machinery we introduce and study a novel causality structure on the leaf space via the transverse Lorentzian geometry on t

Hafez Ghaemi, Hamed Kebriaei, Alireza Ramezani Moghaddam, Majid Nili Ahamdabadi

Classical multi-agent reinforcement learning (MARL) assumes risk neutrality and complete objectivity for agents. However, in settings where agents need to consider or model human economic or social preferences, a notion of risk must be incorporated into the RL optimization problem. This will be of greater importance in MARL where other human or non-human age

Sebastian Debus, Cordian Riener, Robin Schabert

Univariate polynomials are called stable with respect to a domain $D$ if all of their roots lie in $D$. We study linear slices of the space of stable univariate polynomials with respect to a half-plane. We show that a linear slice always contains a stable polynomial with only a few distinct roots. Subsequently, we apply these results to symmetric polynomials

Eun Cheol Choi, Emilio Ferrara

Our society is facing rampant misinformation harming public health and trust. To address the societal challenge, we introduce FACT-GPT, a system leveraging Large Language Models (LLMs) to automate the claim matching stage of fact-checking. FACT-GPT, trained on a synthetic dataset, identifies social media content that aligns with, contradicts, or is irrelevan

Lauren Miako Beede, Giuseppe Vinci

Researchers continue exploring neurons' intricate patterns of activity in the cerebral visual cortex in response to visual stimuli. The way neurons communicate and optimize their interactions with each other under different experimental conditions remains a topic of active investigation. Probabilistic Graphical Models are invaluable tools in neuroscience res