April 2024 arXiv papers — page 171

Zecheng Zhang

The study of operator learning involves the utilization of neural networks to approximate operators. Traditionally, the focus has been on single-operator learning (SOL). However, recent advances have rapidly expanded this to include the approximation of multiple operators using foundation models equipped with millions or billions of trainable parameters, lea

Pratim Saha, Chengcui Zhang

Translation-based Video Synthesis (TVS) has emerged as a vital research area in computer vision, aiming to facilitate the transformation of videos between distinct domains while preserving both temporal continuity and underlying content features. This technique has found wide-ranging applications, encompassing video super-resolution, colorization, segmentati

T. A. Movsessian, T. Yu. Magakian, B. Reipurth, H. R. Andreasyan

We continue to present the results of the Byurakan Narrow Band Imaging Survey (BNBIS). The main goal of this survey is to search for Herbig-Haro (HH) objects and jets in Galactic dark clouds. In this work we present the results of the search in the vicinity of infrared sources that are bright in the WISE survey and embedded in the dark clouds. The survey is

Matjaž Kebrič, Ulrich Schollwöck, Fabian Grusdt

Lattice gauge theories (LGTs) provide valuable insights into problems in strongly correlated many-body systems. Confinement which arises when matter is coupled to gauge fields is just one of the open problems, where LGT formalism can explain the underlying mechanism. However, coupling gauge fields to dynamical charges complicates the theoretical and experime

Qi Cui, Ruohan Meng, Chaohui Xu, Chip-Hong Chang

Ensuring the legal usage of deep models is crucial to promoting trustable, accountable, and responsible artificial intelligence innovation. Current passport-based methods that obfuscate model functionality for license-to-use and ownership verifications suffer from capacity and quality constraints, as they require retraining the owner model for new users. The

Benjamin Bonnefont, Michel Pain, Olivier Zindy

In comparison with Derrida's REM, we investigate the influence of the so-called decoration processes arising in the limiting extremal processes of numerous log-correlated Gaussian fields. In particular, we focus on the branching Brownian motion and two specific quantities from statistical physics in the vicinity of the critical temperature. The first one is

Clemens Schwarke, Victor Klemm, Joshua Bagajo, Jean-Pierre Sleiman

Differentiable simulators promise to improve sample efficiency in robot learning by providing analytic gradients of the system dynamics. Yet, their application to contact-rich tasks like locomotion is complicated by the inherently non-smooth nature of contact, impeding effective gradient-based optimization. Existing works thus often rely on soft contact mode

Jonah Berggren, Khrystyna Serhiyenko

A dimer model is a quiver with faces embedded into a disk. A consistent dimer model gives rise to a strand diagram, and hence to a positroid. The Gorenstein-projective module category over the completed boundary algebra of a dimer model was shown by Pressland to categorify a cluster structure on the corresponding positroid variety. Outside of the Grassmannia

Jing Liang, Zhuo Deng, Zheming Zhou, Omid Ghasemalizadeh

We present a novel end-to-end algorithm (PoCo) for the indoor RGB-D place recognition task, aimed at identifying the most likely match for a given query frame within a reference database. The task presents inherent challenges attributed to the constrained field of view and limited range of perception sensors. We propose a new network architecture, which gene

Julian Fischer, Sebastian Hensel, Alice Marveggio, Maximilian Moser

We prove a weak-strong uniqueness principle for varifold-BV solutions to planar multiphase mean curvature flow beyond a circular topology change: Assuming that there exists a classical solution with an interface that becomes increasingly circular and shrinks to a point, any varifold-BV solution with the same initial interface must coincide with it, and any v

Hao Li, Yang Zou, Ying Wang, Orchid Majumder

Scaling up model and data size has been quite successful for the evolution of LLMs. However, the scaling law for the diffusion based text-to-image (T2I) models is not fully explored. It is also unclear how to efficiently scale the model for better performance at reduced cost. The different training settings and expensive training cost make a fair model compa

Weigao Sun, Zhen Qin, Dong Li, Xuyang Shen

Sequence parallelism (SP) serves as a prevalent strategy to handle long sequences that exceed the memory limit of a single device. However, for linear sequence modeling methods like linear attention, existing SP approaches do not take advantage of their right-product-first feature, resulting in sub-optimal communication efficiency and usability. In this pape

Simon Apers, Sander Gribling, Aaron Sidford

In this note we provide and analyze a simple method that given an $n \times d$ matrix, outputs approximate $\ell_p$-Lewis weights, a natural measure of the importance of the rows with respect to the $\ell_p$ norm, for $p \geq 2$. More precisely, we provide a simple post-processing procedure that turns natural one-sided approximate $\ell_p$-Lewis weights into

Jian Zheng, Ge Gao

Everyone spends some time waiting every day. HCI research has developed tools for boosting productivity while waiting. However, little is known about how people naturally spend their waiting time. We conducted an experience sampling study with 21 working adults who used a mobile app to report their daily waiting time activities over two weeks. The aim of thi

Anirban Chakraborty, Tirthankar Roy Choudhury

Early results from the JWST observations have reported a surprisingly high number of UV-bright galaxies at $z \geq 10$, which appears to challenge the theoretical predictions from standard galaxy formation models in the $\Lambda$CDM framework at these redshifts. To alleviate this tension, several cosmological and astrophysical interpretations have been advan

I. A. Aleksandrov, A. Kudlis

We investigate the phenomenon of electron-positron pair production from vacuum in the presence of a strong electric field of circular polarization. By means of a nonperturbative approach based on the quantum kinetic equations (QKEs), we numerically calculate helicity-resolved momentum distributions of the particles produced and analyze the corresponding heli

Safouane El Ghazouali, Arnaud Gucciardi, Francesca Venturini, Nicola Venturi

Object detection in remotely sensed satellite pictures is fundamental in many fields such as biophysical, and environmental monitoring. While deep learning algorithms are constantly evolving, they have been mostly implemented and tested on popular ground-based taken photos. This paper critically evaluates and compares a suite of advanced object detection alg

Yue Yu, Adam J. Thorpe, Jesse Milzman, David Fridovich-Keil

Data-poisoning attacks can disrupt the efficient operations of transportation systems by misdirecting traffic flows via falsified data. One challenge in countering these attacks is to reduce the uncertainties on the types of attacks, such as the distribution of their targets and intensities. We introduce a resource allocation method in transportation network

L. Ramirez-Piscina, J. M. Sancho

A physical model for a structured tetrameric pore is studied. The pore is modeled as a device composed of four subunits, each one exhibiting two possible states (open and closed). The pore is located within a membrane that separates two reservoirs with ionic solutions. All variables of the model follow physical dynamical equations accounting for the internal

Claire Schultzberg, Brammert Ottens

Over the last decades has emerged a rich literature on the evaluation of recommendation systems. However, less is written about how to efficiently combine different evaluation methods from this rich field into a single efficient evaluation funnel. In this paper we aim to build intuition for how to choose evaluation methods, by presenting a novel framework th

Kye A. Stalpes, David C. Collins, Kevin M. Huffenberger

The polarization of the Cosmic Microwave Background (CMB) is rich in information but obscured by foreground emission from the Milky Way's interstellar medium (ISM). To uncover relationships between the underlying turbulent ISM and the foreground power spectra, we simulated a suite of driven, magnetized, turbulent models of the ISM, varying the fluid properti

Didem Kochan, Xiu Yang

Gaussian process (GP) regression is a non-parametric, Bayesian framework to approximate complex models. Standard GP regression can lead to an unbounded model in which some points can take infeasible values. We introduce a new GP method that enforces the physical constraints in a probabilistic manner. This GP model is trained by the quantum-inspired Hamiltoni

Ashutosh Gupta, John Komp, Abhay Singh Rajput, Krishna Shankaranarayanan

This paper investigates whether recent advances in Large Language Models (LLMs) can assist in translating human explanations into a format that can robustly support learning Linear Temporal Logic (LTL) from demonstrations. Both LLMs and optimization-based methods can extract LTL specifications from demonstrations; however, they have distinct limitations. LLM

Joscha Prochno, Carsten Schütt, Mathias Sonnleitner, Elisabeth M. Werner

While there is extensive literature on approximation, deterministic as well as random, of general convex bodies $K$ in the symmetric difference metric, or other metrics arising from intrinsic volumes, very little is known for corresponding random results in the Hausdorff distance when the approximant $K_n$ is given by the convex hull of $n$ independent rando

Mayur Sonawane, Sahil Rajesh Dhayalkar, Siddesh Waje, Soyal Markhelkar

Human Activity Recognition is a subject of great research today and has its applications in remote healthcare, activity tracking of the elderly or the disables, calories burnt tracking etc. In our project, we have created an Android application that recognizes the daily human activities and calculate the calories burnt in real time. We first captured labeled

Jon Hermes, Josh Minor, Minjun Wu, Adarsh Patil

Upcoming CXL-based disaggregated memory devices feature special purpose units to offload compute to near-memory. In this paper, we explore opportunities for offloading compute to general purpose cores on CXL memory devices, thereby enabling a greater utility and diversity of offload. We study two classes of popular memory intensive applications: ML inference

Efraín Rojas, G. Cruz, J. C. Natividad

Within the framework of the modified geodetic brane gravity, conformed by the Regge-Teitelboim model and enhanced with a linear term in the extrinsic curvature of the brane, the possibility that under an FRW geometry this theory emulates the so-called dark energy is discussed. The cosmological behavior of this model displays a self-(non-self)-accelerated exp

Kamalika Chaudhuri, Chuan Guo, Laurens van der Maaten, Saeed Mahloujifar

Protecting privacy during inference with deep neural networks is possible by adding noise to the activations in the last layers prior to the final classifiers or other task-specific layers. The activations in such layers are known as "features" (or, less commonly, as "embeddings" or "feature embeddings"). The added noise helps prevent reconstruction of the i

Boje Deforce, Meng-Chieh Lee, Bart Baesens, Estefanía Serral Asensio

Time series anomaly detection (TSAD) finds many applications such as monitoring environmental sensors, industry KPIs, patient biomarkers, etc. A two-fold challenge for TSAD is a versatile and unsupervised model that can detect various different types of time series anomalies (spikes, discontinuities, trend shifts, etc.) without any labeled data. Modern neura

Stanislav Babak, Mikel Falxa, Gabriele Franciolini, Mauro Pieroni

Pulsar Timing Array (PTA) observations hinted towards the existence of a stochastic gravitational wave background (SGWB) in the nHz frequency band. Still, the nature of the SGWB signal cannot be confidently inferred from current data, and the leading explanation invokes mergers of supermassive black holes. If confirmed, such discovery would not only represen

Xiaobo Jin, Xu Zhang, Wenbo Wan, Hanru Wang

Thermal Hall effect (THE) in insulator is a remarkable phenomenon that arises from the motion of chargeless quasi-particles under a magnetic field. While magnons or exotic spin excitations were considered as the origin of THE in some magnetic materials, there are more and more evidences suggesting that phonons play a significant role. However, the mechanism

Manan Bhatia

The directed landscape is a prominent model of random geometry which is believed to be the universal scaling limit of all planar random geometries in the Kardar-Parisi-Zhang universality class. It comes equipped with a few different natural simple curves associated to it, such as geodesics and interfaces. Given such a curve, one might wonder whether the geom

Tullio Valent

In this paper we attempt to present a very general approach to the study of structures (somehow) defined on a set $X$ by a family of maps $d: X \times X \mapsto \mathbb{R}^+$. It will be shown how the assignment of a preorder $\prec_{\Pi}$ on a set $\Pi$ of families of maps from $X \times X$ into $\mathbb{R}^+$ defines a structure on $X$. The structures obta

Oscar Garcia-Montero, Aleksas Mazeliauskas, Philip Plaschke, Sören Schlichting

We use QCD kinetic theory to compute photon and dilepton production in the chemically equilibrating out-of-equilibrium quark-gluon plasma created in the early stages of high-energy heavy-ion collisions. We derive universal scaling functions for the pre-equilibrium spectra of photons and dileptons. These scaling functions can be used to make realistic predict

Feng Li

The difference between the spin alignments of $K^\ast$ and those of $\phi$ at the low collision energies is a puzzle raised by the recent experiments. Unlike $\phi$ meson, $K^\ast$, carrying a unit strange charge, should react to strange chemical potential $\mu_S$. In this paper, we shall first convince you that $\mu_S$ is not small in a brayon-rich medium f

Zhiyuan Geng

We prove the uniqueness of $L^1$ blow-down limit at infinity for an entire minimizing solution $u:\mathbb{R}^2\rightarrow\mathbb{R}^2$ of a planar Allen-Cahn system with a triple-well potential. Consequently, $u$ can be approximated by a triple junction map at infinity. The proof exploits a careful analysis of energy upper and lower bounds, ensuring that the

G. Apicella, A. Molent, M. Gaudenzi

The state-of-the-art proposes Life Care Annuities, that have been recently designed as variable annuity contracts with Long-Term Care payouts and Guaranteed Lifelong Withdrawal Benefits. In this paper, we propose more general features for these insurance products and refine their pricing methods. We name our proposed product ``GLWB-LTC''. In particular, as t

Arijit Manna, Sabyasachi Pal, Tapas Baug, Sougata Mondal

The observation of oxygen (O)- and nitrogen (N)-bearing molecules gives an idea about the complex prebiotic chemistry in the interstellar medium (ISM). In this article, we present the identification of the rotational emission lines of N-bearing molecules ethyl cyanide (C$_{2}$H$_{5}$CN), cyanoacetylene (HC$_{3}$N), and O-bearing molecules methyl formate (CH$

Dedi Wang, Yunrui Qiu, Eric Beyerle, Xuhui Huang

Markov state models (MSMs) are valuable for studying dynamics of protein conformational changes via statistical analysis of molecular dynamics (MD) simulations. In MSMs, the complex configuration space is coarse-grained into conformational states, with the dynamics modeled by a series of Markovian transitions among these states at discrete lag times. Constru

Vincent Divol, Jonathan Niles-Weed, Aram-Alexandre Pooladian

Entropic Brenier maps are regularized analogues of Brenier maps (optimal transport maps) which converge to Brenier maps as the regularization parameter shrinks. In this work, we prove quantitative stability bounds between entropic Brenier maps under variations of the target measure. In particular, when all measures have bounded support, we establish the opti

Mitsuo Higaki

We prove the existence of solutions for the axisymmetric steady Navier-Stokes system around an infinite cylinder under external forces. The solutions are constructed to be decaying at the horizontal infinity, despite an analogue of the Stokes paradox for the linearized system, and having neither periodicity nor decay in the vertical direction. The proof is b

Sergio Bermudo, Iztok Peterin, Jelena Sedlar, Riste Škrekovski

The modular product $G\diamond H$ of graphs $G$ and $H$ is a graph on vertex set $V(G)\times V(H)$. Two vertices $(g,h)$ and $(g^{\prime},h^{\prime})$ of $G\diamond H$ are adjacent if $g=g^{\prime}$ and $hh^{\prime}\in E(H)$, or $gg^{\prime}\in E(G)$ and $h=h^{\prime}$, or $gg^{\prime}\in E(G)$ and $hh^{\prime}\in E(H)$, or (for $g\neq g^{\prime}$ and $h\neq

Longfei Yun, Yonghao Zhuang, Yao Fu, Eric P Xing

Mixture-of-Expert (MoE) based large language models (LLMs), such as the recent Mixtral and DeepSeek-MoE, have shown great promise in scaling model size without suffering from the quadratic growth of training cost of dense transformers. Like dense models, training MoEs requires answering the same question: given a training budget, what is the optimal allocati

Omid Zabeti

For an arbitrary topological space $X$, assume that $S(X)$ is the vector lattice of all equivalence classes of real-valued continuous functions on open dense subsets of $X$; it is a laterally complete vector lattice but not a normed lattice, certainly. Nevertheless, we can have the extended unbounded norm topology ($un$-topology) on it. On the other hand, by

Luca Michel, Lex Reiter, Antoine Sanner, Robert J. Flatt

Structural build-up in fresh cement paste at rest is characterized by time evolutions of storage modulus and yield stress, which both increase linearly in time during the induction period of hydration, followed by an exponential evolution after entering the acceleration period. While it is understood that C-S-H formation at contact points between cement part

Adam M. Krajewski, Jonathan W. Siegel, Zi-Kui Liu

Structure-informed materials informatics is a rapidly evolving discipline of materials science relying on the featurization of atomic structures or configurations to construct vector, voxel, graph, graphlet, and other representations useful for machine learning prediction of properties, fingerprinting, and generative design. This work discusses how current f

Pratik Khandagale, Carlos Garcia-Cervera, Gal deBotton, Timothy Breitzman

The electromechanical response of polymeric soft matter to applied electric fields is of fundamental scientific interest as well as relevant to technologies for sensing and actuation. Several existing theoretical and numerical approaches for polarizable polymers subject to a combined applied electric field and stretch are based on discrete monomer models. In

Sajid Ali, Hassan Azad, Indranil Biswas, Fazal M. Mahomed

A local classification of semisimple algebras of vector fields on $\mathbb{C}^{3}$ is given, using the canonical forms of the Heisenberg algebra and of $sl(2,\mathbb{C})\times sl(2,\mathbb{C})$.

You-Hung Hsu, Chun-Ju Lai

We establish a Bruhat decomposition indexed by the wreath product $\Sigma_m\wr \Sigma_d$ between two symmetric groups -- note that $\Sigma_m\wr \Sigma_d$ is not a Coxeter group in general. We show that such a decomposition affords a geometric variant in terms of the Bialynicki-Birula decomposition for varieties with $\mathbb{C}^*$-actions. Next, we construct

Xiaoshuang Huang, Hongxiang Li, Meng Cao, Long Chen

Recent developments underscore the potential of textual information in enhancing learning models for a deeper understanding of medical visual semantics. However, language-guided medical image segmentation still faces a challenging issue. Previous works employ implicit and ambiguous architectures to embed textual information. This leads to segmentation result

Timon Schapeler, Robert Schade, Michael Lass, Christian Plessl

At large scales, quantum systems may become advantageous over their classical counterparts at performing certain tasks. Developing tools to analyse these systems at the relevant scales, in a manner consistent with quantum mechanics, is therefore critical to benchmarking performance and characterising their operation. While classical computational approaches

Oskar Kędzierski

We give a constructive characterization of matrices satisfying the reverse-order law for the Moore--Penrose pseudoinverse. In particular, for a given matrix $A$ we construct another matrix $B$, of arbitrary compatible size and chosen rank, in terms of the right singular vectors of $A$, such that the reverse order law for $AB$ is satisfied. Moreover, we show

Romina Eramo, Hamzeh Eyal Salman, Matteo Spezialetti, Darko Stern

The risen complexity of automotive systems requires new development strategies and methods to master the upcoming challenges. Traditional methods need thus to be changed by an increased level of automation, and a faster continuous improvement cycle. In this context, current vehicle performance tests represent a very time-consuming and expensive task due to t

Sheng Di, Jinyang Liu, Kai Zhao, Xin Liang

Error-bounded lossy compression has been effective in significantly reducing the data storage/transfer burden while preserving the reconstructed data fidelity very well. Many error-bounded lossy compressors have been developed for a wide range of parallel and distributed use cases for years. They are designed with distinct compression models and principles,

Nikolaos Tziolas

This is a survey paper on algebraic surfaces in positive characteristic based on a series of lectures that the author gave at the University of Edinburgh in March 2023. It is focused on certain positive characteristic phenomena like infinitesimal group schemes and their actions on algebraic surfaces as well as the failure in positive characteristic of certai

Ata Çelen, Guo Han, Konrad Schindler, Luc Van Gool

Interior design allows us to be who we are and live how we want - each design is as unique as our distinct personality. However, it is not trivial for non-professionals to express and materialize this since it requires aligning functional and visual expectations with the constraints of physical space; this renders interior design a luxury. To make it more ac

Wanyun Cui, Qianle Wang

This paper reveals the phenomenon of parameter heterogeneity in large language models (LLMs). We find that a small subset of "cherry" parameters exhibit a disproportionately large influence on model performance, while the vast majority of parameters have minimal impact. This heterogeneity is found to be prevalent across different model families, scales, and

B. T. Draine

A previous study (Paper I) investigated the polarization properties of a variety of simple convex grain shapes, some of which were found to be consistent with the observed polarization properties of interstellar dust from far-ultraviolet to far-infrared. Here we study the optical properties of 45 non-convex shapes, all aggregates of $N$ equal-sized spheres.

Maxime Bouthors, Josep Crego, Francois Yvon

Retrieval-Augmented Neural Machine Translation (RAMT) architectures retrieve examples from memory to guide the generation process. While most works in this trend explore new ways to exploit the retrieved examples, the upstream retrieval step is mostly unexplored. In this paper, we study the effect of varying retrieval methods for several translation architec

Ling Long, Yifan Yang

In this paper, based on the toric hypergeometric model given in a paper by Beukers--Cohen--Mellit, we provide two other ways to explain why the zig-zag diagram method can be used to compute Hodge numbers for hypergeometric data defined over $\mathbb Q$.

Kazumi Okuyama

We continue our study of the doubled Hilbert space formalism of double-scaled SYK model initiated in [arXiv:2401.07403]. We show that the 1-particle Hilbert space introduced by Lin and Stanford in [arXiv:2307.15725] is related to the doubled Hilbert space $\mathcal{H}_0\otimes \mathcal{H}_0$ of the 0-particle Hilbert space $\mathcal{H}_0$ by some linear isom

Ying Yuan, Qingying Hao, Giovanni Apruzzese, Mauro Conti

Machine learning based phishing website detectors (ML-PWD) are a critical part of today's anti-phishing solutions in operation. Unfortunately, ML-PWD are prone to adversarial evasions, evidenced by both academic studies and analyses of real-world adversarial phishing webpages. However, existing works mostly focused on assessing adversarial phishing webpages

Shanghua Gao, Ada Fang, Yepeng Huang, Valentina Giunchiglia

We envision "AI scientists" as systems capable of skeptical learning and reasoning that empower biomedical research through collaborative agents that integrate AI models and biomedical tools with experimental platforms. Rather than taking humans out of the discovery process, biomedical AI agents combine human creativity and expertise with AI's ability to ana

Poulami Sinhamahapatra, Suprosanna Shit, Anjany Sekuboyina, Malek Husseini

Vertebral fracture grading classifies the severity of vertebral fractures, which is a challenging task in medical imaging and has recently attracted Deep Learning (DL) models. Only a few works attempted to make such models human-interpretable despite the need for transparency and trustworthiness in critical use cases like DL-assisted medical diagnosis. Moreo

Jingxu Wu, Chenjia Li, Zhenzhou Lei, Tuerdi Wumaier

The Legendre transformation is a crucial tool in theoretical physics, known for its symmetry, especially when applied to multivariate functions. In statistical mechanics, ensembles represent the central focus. Leveraging the dimensionless aspect of Legendre transformation, this paper explores the transformation process from the entropy characteristic functio

Michiel de Bondt

A camel can carry $B$ bananas on its back. It can have $2$ bananas at a time in its stomach. For each mile the camel walks, the amount of bananas in its stomach decreases $1$. As soon as the amount of bananas in the camel's stomach is at most $1$, it can eat a new banana. When the camel's stomach is empty, the camel must eat a new banana (in order to be able

Qijun Luo, Hengxu Yu, Xiao Li

This work presents BAdam, an optimization method that leverages the block coordinate descent (BCD) framework with Adam's update rule. BAdam offers a memory efficient approach to the full parameter finetuning of large language models. We conduct a theoretical convergence analysis for BAdam in the deterministic case. Experimentally, we apply BAdam to finetune

Congrong Ren, Sheng Di, Longtao Zhang, Kai Zhao

This paper presents error-bounded lossy compression tailored for particle datasets from diverse scientific applications in cosmology, fluid dynamics, and fusion energy sciences. As today's high-performance computing capabilities advance, these datasets often reach trillions of points, posing significant visualization, analysis, and storage challenges. While

Giacomo Albi, Sara Bicego, Dante Kalise

Modeling and control of agent-based models is twice cursed by the dimensionality of the problem, as both the number of agents and their state space dimension can be large. Even though the computational barrier posed by a large ensemble of agents can be overcome through a mean field formulation of the control problem, the feasibility of its solution is genera

Alec Wills, Anthony Mannino, Isidro Losada, Sara G. Mayo

The free energy of ion solvation can be decomposed into enthalpic and entropic contributions. This helps to understand the connection between the dielectric properties and the underlying forces. We present a simple linear-response model of screened charge interactions that provides an alternative understanding of solvation barriers. Moreover, it explains the

Haoran Sun, Lixin Liu, Junjie Li, Fengyu Wang

The ability of large language models (LLMs) to follow instructions is crucial to real-world applications. Despite recent advances, several studies have highlighted that LLMs struggle when faced with challenging instructions, especially those that include complex constraints, hindering their effectiveness in various tasks. To address this challenge, we introd

Matyas Juhasz, Tina Marchand, Roshan Melwani, Kalyan Dutia

Quantified policy targets are a fundamental element of climate policy, typically characterised by domain-specific and technical language. Current methods for curating comprehensive views of global climate policy targets entail significant manual effort. At present there are few scalable methods for extracting climate targets from national laws or policies, w

Rahul Gupta, Chloé Bouard, Fabian Kammerbauer, J. Omar Ledesma-Martin

Spin-Orbit Torque (SOT) Magnetic Random-Access Memory (MRAM) devices offer improved power efficiency, nonvolatility, and performance compared to static RAM, making them ideal, for instance, for cache memory applications. Efficient magnetization switching, long data retention, and high-density integration in SOT MRAM require ferromagnets (FM) with perpendicul

Danilo Saccani, Leonardo Massai, Luca Furieri, Giancarlo Ferrari-Trecate

This paper proposes a novel approach to improve the performance of distributed nonlinear control systems while preserving stability by leveraging Deep Neural Networks (DNNs). We build upon the Neural System Level Synthesis (Neur-SLS) framework and introduce a method to parameterize stabilizing control policies that are distributed across a network topology.

Julien Zylberman, Ugo Nzongani, Andrea Simonetto, Fabrice Debbasch

Unitary and non-unitary diagonal operators are fundamental building blocks in quantum algorithms with applications in the resolution of partial differential equations, Hamiltonian simulations, the loading of classical data on quantum computers (quantum state preparation) and many others. In this paper, we introduce a general approach to implement unitary and

M. H. Macêdo, J. Furtado, G. Alencar, R. R. Landim

In this study, we investigate the thermodynamic properties and quasinormal modes of Dymnikova black holes within the context of higher dimensions in Einstein's general theory of relativity. We calculate the thermodynamic parameters, including the Hawking temperature and heat capacity, which allowed us to investigate the black hole's stability. Lastly the qua

Zhigen Zhao, Shuo Cheng, Yan Ding, Ziyi Zhou

Task and Motion Planning (TAMP) integrates high-level task planning and low-level motion planning to equip robots with the autonomy to effectively reason over long-horizon, dynamic tasks. Optimization-based TAMP focuses on hybrid optimization approaches that define goal conditions via objective functions and are capable of handling open-ended goals, robotic

Bernd Kolar, Johannes Schrotshamer, Markus Schöberl

Forward-flatness is a generalization of static feedback linearizability and a special case of a more general flatness concept for discrete-time systems. Recently, it has been shown that this practically quite relevant property can be checked by computing a unique sequence of involutive distributions which generalizes the well-known static feedback linearizat

Stephen M. Wilkins, Jussi K. Kuusisto, Dimitrios Irodotou, Shihong Liao

Understanding the co-evolution of super-massive black holes (SMBHs) and their host galaxies remains a key challenge of extragalactic astrophysics, particularly the earliest stages at high-redshift. However, studying SMBHs at high-redshift with cosmological simulations, is challenging due to the large volumes and high-resolution required. Through its innovati

Yao Shen, Wei-Min Shang, Chi-Chun Zhou, Fu-Lin Zhang

The structure of quantum mechanics forbids a bipartite scenario for masking quantum information, however, it allows multipartite maskers. The Latin squares are found to be closely related to a series of tripartite maskers. This adds another item, significantly different from the original no-cloning theorem, to the no-go theorems. On the other hand, anyonic e

Meher Niger, Helya Goharbavang, Taeyong Ahn, Emily K. Alley

Microvascular networks are challenging to model because these structures are currently near the diffraction limit for most advanced three-dimensional imaging modalities, including confocal and light sheet microscopy. This makes semantic segmentation difficult, because individual components of these networks fluctuate within the confines of individual pixels.

Johannes Scheffler

In this note, we generalize a mean-value inequality of Guo-Phong-Sturm to the setting of a compact K\"ahler orbifold. This shows that their reasoning is insensitive to quotient singularities. As we aim for a self-contained exposition, we generalize some fundamental results, namely the $\alpha$-invariant estimate by H\"ormander and Tian, an approximation resu

Yunhui Guo, Yang Zhang, Zhaolin Wang, Yuanwei Liu

The beamforming performance of the uniform circular array (UCA) in near-field wideband communication systems is investigated. Compared to uniform linear array (ULA), UCA exhibits uniform effective array aperture in all directions, thus enabling more users to benefit from near-field communications. In this paper, the unique beam squint effect in near-field wi

Yu Wang, Lei Sang, Yi Zhang, Yiwen Zhang

Heterogeneous Graphs (HGs) effectively model complex relationships in the real world through multi-type nodes and edges. In recent years, inspired by self-supervised learning (SSL), contrastive learning (CL)-based Heterogeneous Graphs Neural Networks (HGNNs) have shown great potential in utilizing data augmentation and contrastive discriminators for downstre

Cheng-Han Hsieh, Héctor G. Arce, María José Maureira, Jaime E. Pineda

We surveyed nearly all the embedded protostars in seven nearby clouds (Corona Australis, Aquila, Chamaeleon I & II, Ophiuchus North, Ophiuchus, Serpens) with the Atacama Large Millimeter/submillimeter Array at 1.3mm observations with a resolution of 0.1$"$. This survey detected 184 protostellar disks, 90 of which were observed at a resolution of 14-18 au, ma

Niklas Becker

In this dissertation, we look at environmental effects in extreme and intermediate mass ratio inspirals into massive black holes. In these systems, stellar mass compact objects orbit massive black holes and lose orbital energy due to gravitational wave emission and other dissipative forces. We explore environmental interactions with dark matter spikes, stell

Lei Shi, Ian Chen, Hiroo Takayama, Vijay Vedula

Personalized cardiac mechanics modeling is a powerful tool for understanding the biomechanics of cardiac function in health and disease and assisting in treatment planning. However, current models are limited to using medical images acquired at a single cardiac phase, often limiting their applicability for processing dynamic image acquisitions. This study in

Hussein Mozannar, Valerie Chen, Mohammed Alsobay, Subhro Das

Evaluation of large language models for code has primarily relied on static benchmarks, including HumanEval (Chen et al., 2021), or more recently using human preferences of LLM responses. As LLMs are increasingly used as programmer assistants, we study whether gains on existing benchmarks or more preferred LLM responses translate to programmer productivity w

Franco Maria Nardini, Cosimo Rulli, Rossano Venturini

Dense retrieval techniques employ pre-trained large language models to build a high-dimensional representation of queries and passages. These representations compute the relevance of a passage w.r.t. to a query using efficient similarity measures. In this line, multi-vector representations show improved effectiveness at the expense of a one-order-of-magnitud

Abhinav Jha

In this note, we extend the analysis for the residual-based a posteriori error estimators in the energy norm defined for the algebraic flux correction (AFC) schemes [Jha20.CAMWA] to the newly proposed algebraic stabilization schemes [JK21.NM, Kn23.NA]. Numerical simulations on adaptively refined grids are performed in two dimensions showing the higher effici

Thomas Renault, David Restrepo Amariles, Aurore Troussel

We build a novel database of around 285,000 notes from the Twitter Community Notes program to analyze the causal influence of appending contextual information to potentially misleading posts on their dissemination. Employing a difference in difference design, our findings reveal that adding context below a tweet reduces the number of retweets by almost half.

Sylvain Chevallier, Igor Carrara, Bruno Aristimunha, Pierre Guetschel

Objective. This study conduct an extensive Brain-computer interfaces (BCI) reproducibility analysis on open electroencephalography datasets, aiming to assess existing solutions and establish open and reproducible benchmarks for effective comparison within the field. The need for such benchmark lies in the rapid industrial progress that has given rise to undi

S. Lo Franco, G. Montani

We investigate the emergence of a quantum Big-Bounce in the context of an isotropic Universe, filled by a self-interacting scalar field, which plays the role of a physical clock. The bouncing cosmology is the result of a scattering process, driven by the scalar field potential, which presence breaks down the frequency separation of the Wheeler-DeWitt equatio

Alessandro Giuliano, S. Andrew Gadsden, Waleed Hilal, John Yawney

The volume of remote sensing data is experiencing rapid growth, primarily due to the plethora of space and air platforms equipped with an array of sensors. Due to limited hardware and battery constraints the data is transmitted back to Earth for processing. The large amounts of data along with security concerns call for new compression and encryption techniq

Andrea Ianniccari, Antonio J. Iovino, Alex Kehagias, Davide Perrone

We provide evidence for a correspondence between the formation of black holes and the stability of circular null geodesics around the collapsing perturbation. We first show that the critical threshold of the compaction function to form a black hole in radiation is well approximated by the critical threshold for the appearance of the first unstable circular o

Bernardo Leite, Henrique Lopes Cardoso

Question Generation aims to automatically generate questions based on a given input provided as context. A controllable question generation scheme focuses on generating questions with specific attributes, allowing better control. In this study, we propose a few-shot prompting strategy for controlling the generation of question-answer pairs from children's na

Szymon Mazurek, Monika Pytlarz, Sylwia Malec, Alessandro Crimi

Artificial intelligence have contributed to advancements across various industries. However, the rapid growth of artificial intelligence technologies also raises concerns about their environmental impact, due to associated carbon footprints to train computational models. Fetal brain segmentation in medical imaging is challenging due to the small size of the

Evan Toler, Antoine Cerfon, Dhairya Malhotra

In a recent publication (Toler et al. 2023), we demonstrated that for axisymmetric geometries, the Kapur-Rokhlin quadrature rule provided an efficient and high-order accurate method for computing the normal component, on the plasma surface, of the magnetic field due to the toroidal current flowing in the plasma, via the virtual-casing principle. The calculat

Kristjan-Julius Laak, Jaan Aru

Personalized learning (PL) aspires to provide an alternative to the one-size-fits-all approach in education. Technology-based PL solutions have shown notable effectiveness in enhancing learning performance. However, their alignment with the broader goals of modern education is inconsistent across technologies and research areas. In this paper, we examine the

Fanxu Meng, Zhaohui Wang, Muhan Zhang

To parameter-efficiently fine-tune (PEFT) large language models (LLMs), the low-rank adaptation (LoRA) method approximates the model changes $\Delta W \in \mathbb{R}^{m \times n}$ through the product of two matrices $A \in \mathbb{R}^{m \times r}$ and $B \in \mathbb{R}^{r \times n}$, where $r \ll \min(m, n)$, $A$ is initialized with Gaussian noise, and $B$ w