May 2024 arXiv papers — page 31

Joakim Færgeman

For a reductive group $G$, we prove that complex irreducible rigid $G$-local systems with quasi-unipotent monodromies and finite order abelianization on a smooth curve are motivic, generalizing a theorem of Katz for $GL_n$. We do so by showing that the Hecke eigensheaf corresponding to such a local system is itself motivic. Unlike other works in the subject,

Reihaneh Teimouri, Marta Kersten-Oertel, Yiming Xiao

Efficient and accurate brain ventricle segmentation from clinical CT scans is critical for emergency surgeries like ventriculostomy. With the challenges in poor soft tissue contrast and a scarcity of well-annotated databases for clinical brain CTs, we introduce a novel uncertainty-aware ventricle segmentation technique without the need of CT segmentation gro

Harim Jin, Norbert Langer, Daniel J. Lennon, Charles R. Proffitt

The evolution and fate of massive stars are thought to be affected by rotationally induced internal mixing. The surface boron abundance is a sensitive tracer of this in early B-type main sequence stars. We test current stellar evolution models of massive main sequence stars which include rotational mixing through a systematic study of their predicted surface

Bakhao Dioum, Virginia D'Auria, Alessandro Zavatta, Olivier Pfister

The frequency comb of a multimode interferometer offers exceptional scalability potential for field-encoded quantum information. However, the staple field detection method, homodyne detection, cannot access quantum information in the whole comb because some spectral quadratures (and their asymmetries with respect to the LO) are out of reach. We present here

Piyush Sharda, Shyam H. Menon

Turbulence, magnetic fields and radiation feedback are key components that shape the formation of stars, especially in the metal-free environments at high redshifts where Population III stars form. Yet no 3D numerical simulations exist that simultaneously take all of these into account. We present the first suite of radiation-magnetohydrodynamics (RMHD) simu

Xinbu Cheng, Zixiang Xu

A widely open conjecture proposed by Bollob\'as, Erd\H{o}s, and Tuza in the early 1990s states that for any $n$-vertex graph $G$, if the independence number $\alpha(G) = \Omega(n)$, then there is a subset $T \subseteq V(G)$ with $|T| = o(n)$ such that $T$ intersects all maximum independent sets of $G$. In this paper, we prove that this conjecture holds for g

Ardavan Farahvash, Mayank Agrawal, Adam P. Willard, Andrew A. Peterson

The adsorption and desorption of reactants and products from a solid surface is essential for achieving sustained surface chemical reactions. At a liquid-solid interface, these processes can involve the collective reorganization of interfacial solvent molecules in order to accommodate the adsorbing or desorbing species. Identifying the role of solvent in ads

Sabine Frittella, Daniil Kozhemiachenko

We consider two expansions of G\"{o}del logic $\mathsf{G}$ with two versions of paraconsistent negation. The first one is $\mathsf{G_{inv}}$ -- the expansion of $\mathsf{G}$ with an involuitive negation ${\sim_\mathsf{i}}$ defined via $v({\sim_\mathsf{i}}\phi)=1-v(\phi)$. The second one is $\mathsf{G}^2_{(\rightarrow,-\!<)}$ -- an expansion with a so-called

Pengxiang Zhang, Wilfried Haensch, Charudatta M. Phatak, Supratik Guha

We propose a new type of multi-bit and energy-efficient magnetic memory based on current-driven, field-free, and highly controlled domain wall motion. A meandering domain wall channel with precisely interspersed pinning regions provides the multi-bit capability of a magnetic tunnel junction. The magnetic free layer of the memory device has perpendicular magn

Weijiang Lai, Beihong Jin, Beibei Li, Yiyuan Zheng

Existing micro-video recommendation models exploit the interactions between users and micro-videos and/or multi-modal information of micro-videos to predict the next micro-video a user will watch, ignoring the information related to vloggers, i.e., the producers of micro-videos. However, in micro-video scenarios, vloggers play a significant role in user-vide

Aravind Sankaran, Lars Karlsson, Paolo Bientinesi

We consider the problem of ranking a set of objects based on their performance when the measurement of said performance is subject to noise. In this scenario, the performance is measured repeatedly, resulting in a range of measurements for each object. If the ranges of two objects do not overlap, then we consider one object as 'better' than the other, and we

Qing Zhou, Junlin Huang, Qiang Li, Junyu Gao

From paired image-text training to text-only training for image captioning, the pursuit of relaxing the requirements for high-cost and large-scale annotation of good quality data remains consistent. In this paper, we propose Text-only Synthesis for Image Captioning (ToCa), which further advances this relaxation with fewer human labor and less computing time.

Michela Esposito, Stefano Borgani, Giuseppe Murante

The identification of substructures within halos in cosmological hydrodynamical simulations is a fundamental step to identify the simulated counterparts of real objects, namely galaxies. For this reason, substructure finders play a crucial role in extracting relevant information from the simulation outputs. They are based on physically-motivated definitions

Shengyao Li, Sabpreet Bhatti, Siew Lang Teo, Ming Lin

In alignment with the increasing demand for larger storage capacity and longer data retention, electrical control of magnetic anisotropy has been a research focus in the realm of spintronics. Typically, magnetic anisotropy is determined by grain dimensionality, which is set during the fabrication of magnetic thin films. Despite the intrinsic correlation betw

Wenlong Gou, Chuanhang Yu, Juntao Ma, Gang Wu

A variety of ranging threats represented by Ghost Peak attack have raised concerns regarding the security performance of Ultra-Wide Band (UWB) systems with the finalization of the IEEE 802.15.4z standard. Based on channel reciprocity, this paper proposes a low complexity attack detection scheme that compares Channel Impulse Response (CIR) features of both ra

S. Mahata, M. Mandal, H. Mahapatra, S. Biswas

Motivated by the recent experimental results of branching fractions for D_((s))^+ arrows {\eta}^((')) l^+ {\nu}_l decays, which deviate from their SM predictions, we have investigated these decays in W' model and scalar leptoquark model to find possible signatures of new physics (NP) in semileptonic charm decays induced by c arrow(s,d)l {nu}_l transitions. U

Shuran Zheng, Xuan Qi, Rui Ray Chen, Yongchan Kwon

Data plays a central role in advancements in modern artificial intelligence, with high-quality data emerging as a key driver of model performance. This has prompted the development of principled and effective data curation methods in recent years. However, existing methods largely rely on heuristics, and whether they are truly effective remains unclear. For

Matheus Guedes de Andrade, Emily A. Van Milligen, Leonardo Bacciottini, Aparimit Chandra

We evaluate the performance of two-way quantum repeater chains with sequential entanglement swapping. Within the analysis we consider memory decoherence, gate imperfections, and imperfect link-level entanglement generation. Our main results include closed-form expressions for the average entanglement fidelity of the generated end-to-end entangled states. We

Kehan Long, Yinzhuang Yi, Zhirui Dai, Sylvia Herbert

We introduce a novel method for mobile robot navigation in dynamic, unknown environments, leveraging onboard sensing and distributionally robust optimization to impose probabilistic safety constraints. Our method introduces a distributionally robust control barrier function (DR-CBF) that directly integrates noisy sensor measurements and state estimates to de

Shao-Zhen Lin, Frank Jülicher, Jacques Prost, Jean-Francois Rupprecht

We construct a hydrodynamic theory of active smectics A in two-dimensional space, including the creation/annihilation and motility of dislocations with Burgers' number $\pm1$. We derive analytical criteria on the set of parameters that lead to flows. We show that the motility of dislocations can lead to flow transitions with distinct features from the previo

G. Lugones, A. G. Grunfeld

We study cold strange quark stars employing an enhanced version of the quark-mass density-dependent model which incorporates excluded volume effects to address non-perturbative QCD repulsive interactions. We provide a comparative analysis of our mass formula parametrization with previous models from the literature. We identify the regions within the paramete

Masataro Asai, Stephen Wissow

Despite being successful in board games and reinforcement learning (RL), Monte Carlo Tree Search (MCTS) combined with Multi Armed Bandits (MABs) has seen limited success in domain-independent classical planning until recently. Previous work (Wissow and Asai 2024) showed that UCB1, designed for bounded rewards, does not perform well as applied to cost-to-go e

Devon Graham, Kevin Leyton-Brown

Utilitarian algorithm configuration is a general-purpose technique for automatically searching the parameter space of a given algorithm to optimize its performance, as measured by a given utility function, on a given set of inputs. Recently introduced utilitarian configuration procedures offer optimality guarantees about the returned parameterization while p

Abhinandan

We show that the category of analytic/completed prismatic $F$-crystals on the absolute prismatic site of a small (unramified at $p$) base ring is naturally equivalent to the category of relative Wach modules from the theory of $(\varphi, \Gamma)$-modules. The result is obtained by showing that the data of the Galois action on a Wach module is equivalent to t

Jackson Davis, Pilar Beccar-Varela, Soumyodip Banerjee, Maxime A. Siegler

Magnetic metal-organic frameworks (MMOFs), where magnetic metal nodes are connected into a crystal structure by organic linkers, have a potential to host exotic magnetic states. We present a study of bulk magnetic properties of four metal-organic frameworks with the same boron imidazolate linkers, Cu-BIF, Co-BIF, Ni-BIF, and newly synthesized Zn-BIF, display

Erik Mainellis, Bouzid Mosbahi, Ahmed Zahari

A compatible associative algebra is a vector space equipped with two associative multiplication structures that interact in a certain natural way. This article presents the classification of these algebras with dimension less than four, as well as the classifications of their corresponding derivations, centroids, automorphisms, and quasi-centroids. We then c

David Richter, Timon Böhler, Pascal Weisenburger, Mira Mezini

Linear algebra computations are foundational for neural networks and machine learning, often handled through arrays. While many functional programming languages feature lists and recursion, arrays in linear algebra demand constant-time access and bulk operations. To bridge this gap, some languages represent arrays as (eager) functions instead of lists. In th

Wei Liu, Ming Xiang, Nai Ding

Understanding how sentences are internally represented in the human brain, as well as in large language models (LLMs) such as ChatGPT, is a major challenge for cognitive science. Classic linguistic theories propose that the brain represents a sentence by parsing it into hierarchically organized constituents. In contrast, LLMs do not explicitly parse linguist

Wenzhuo Liu, Fei Zhu, Shijie Ma, Cheng-Lin Liu

Although Vision Transformers (ViTs) have recently advanced computer vision tasks significantly, an important real-world problem was overlooked: adapting to variable input resolutions. Typically, images are resized to a fixed resolution, such as 224x224, for efficiency during training and inference. However, uniform input size conflicts with real-world scenar

A. A. Snarskii

Introduced the quantitative measure of the structural complexity of the graph (complex network, etc.) based on a procedure similar to the renormalization process, considering the difference between actual and averaged graph structures on different scales. The proposed concept of the graph structural complexity corresponds to qualitative comprehension of the

H. W. Willie Wong

A tournament $T$ is a tournament completion of a bipartite tournament $D$ if $D$ is a spanning subdigraph of $T$, i.e., $V(D)=V(T)$ and $A(D)\subseteq A(T)$. If $C$ is a $k$-dicycle (i.e., directed cycle of length $k$) in a tournament completion $T$ of $D$ and $C$ is not a dicycle in $D$, i.e., $A(C)\subseteq A(T)$ and $A(C)\not\subseteq A(D)$, then we call

Gun Woo Warren Park, Payod Panda, Lev Tankelevitch, Sean Rintel

Effective meetings are effortful, but traditional videoconferencing systems offer little support for reducing this effort across the meeting lifecycle. Generative AI (GenAI) has the potential to radically redefine meetings by augmenting intentional meeting behaviors. CoExplorer, our novel adaptive meeting prototype, preemptively generates likely phases that

Jack Holligan, Huey-Wen Lin

We present the first lattice calculation of the nucleon isovector helicity parton distribution function (PDF) in the framework of large-momentum effective theory (LaMET) that uses the hybrid scheme with self-renormalization. We use ensembles generated by the MILC collaboration at lattice spacings $a=\{0.1207,0.0888,0.0582\}$ fm, with $N_f=2+1+1$ flavors of h

Zhankun Luo, Abolfazl Hashemi

We study the trajectory of iterations and the convergence rates of the Expectation-Maximization (EM) algorithm for two-component Mixed Linear Regression (2MLR). The fundamental goal of MLR is to learn the regression models from unlabeled observations. The EM algorithm finds extensive applications in solving the mixture of linear regressions. Recent results h

Ivan Petrukha, Nataliia Stulova, Sergii Kryvoblotskyi

Background. The recent surge in phishing attacks keeps undermining the effectiveness of the traditional anti-phishing blacklist approaches. On-device anti-phishing solutions are gaining popularity as they offer faster phishing detection locally. Aim. We aim to eliminate the delay in recognizing and recording phishing campaigns in databases via on-device solu

Marcin Bownik

We show an extension of a probabilistic result of Marcus, Spielman, and Srivastava, which resolved the Kadison-Singer problem, for block diagonal positive semidefinite random matrices. We use this result to show several selector results, which generalize their partition counterparts. This includes a selector form of Weaver's KS$_r$ conjecture for block diago

Changrui Liu, Sven U. Pfeiffer, Guido C. H. E. de Croon

Relative localization (RL) is essential for the successful operation of micro air vehicle (MAV) swarms. Achieving accurate 3-D RL in infrastructure-free and GPS-denied environments with only distance information is a challenging problem that has not been satisfactorily solved. In this work, based on the range-based peer-to-peer RL using the ultra-wideband (U

Nicholas J. Horton, Sara Stoudt

In 2022, the Journal of Statistics and Data Science Education (JSDSE) instituted augmented requirements for authors to post deidentified data and code underlying their papers. These changes were prompted by an increased focus on reproducibility and open science (NASEM 2019). A recent review of data availability practices noted that "such policies help increa

Eric Y. Chen

The relative Langlands program introduced by Ben-Zvi--Sakellaridis--Venkatesh posits a duality structure exchanging automorphic periods and L-functions, which can be encoded by pairs of dual Hamiltonian actions. In work of the author and Venkatesh, an extension of the definitions to certain singular spaces was made with the objective of restoring duality in

Yongcheng Ding, Yue Ban, Mikel Sanz, José D. Martín-Guerrero

Quantum machine learning, as an extension of classical machine learning that harnesses quantum mechanics, facilitates effiient learning from data encoded in quantum states. Training a quantum neural network typically demands a substantial labeled training set for supervised learning. Human annotators, often experts, provide labels for samples through additio

M. Yu. Melnikov, A. A. Shashkin, S. -H. Huang, C. W. Liu

We have developed a technique that dramatically reduces the contact resistances and depletes a shunting channel between the contacts outside the Hall bar in ultra-high mobility SiGe/Si/SiGe heterostructures. It involves the creation of three overlapping independent gates deposited on top of the structure and allows transport measurements to be performed at m

Dejiang Yin, Li-yun Zhang, Lei Qian, Ralph P. Eatough

We present the discovery of 8 isolated millisecond pulsars in Globular Cluster (GC) NGC 6517 using the Five-Hundred-meter Aperture Spherical radio Telescope (FAST). The spin periods of those pulsars (namely PSR J1801-0857K to R, or, NGC 6517K to R) are all shorter than 10 ms. With these discoveries, NGC 6517 is currently the GC with the most known pulsars in

Luca Comisso

Particle acceleration and pitch-angle anisotropy resulting from magnetic reconnection are investigated in highly magnetized ion-electron plasmas. By means of fully kinetic particle-in-cell simulations, we demonstrate that magnetic reconnection generates anisotropic particle distributions $f_s \left( {|\cos \alpha|,\varepsilon} \right)$, characterized by brok

Song Li, Jin Min Yang, Mengchao Zhang, Rui Zhu

Motivated by the null results of current dark matter searches and the small-scale problems, we study a dark sector charged by a spontaneous broken gauge $U(1)'$. To explore the parameter space of this model, in addition to the consideration of the small-scale data, we also consider the theoretical bounds on the dark Higgs mass, with the upper bound coming fr

Andrzej Kukla, Piotr Miska

Let $A$ be a set of positive integers. We define a positive integer $n$ as an $A$-practical number if every positive integer from the set $\left\{1,\ldots ,\sum_{d\in A, d\mid n}d\right\}$ can be written as a sum of distinct divisors of $n$ that belong to $A$. Denote the set of $A$-practical numbers as $\text{Pr}(A)$. The aim of the paper is to explore the p

Yitao Zhao, Turgay Celik, Nanqing Liu, Feng Gao

In conventional remote sensing change detection (RS CD) procedures, extensive manual labeling for bi-temporal images is first required to maintain the performance of subsequent fully supervised training. However, pixel-level labeling for CD tasks is very complex and time-consuming. In this paper, we explore a novel self-supervised contrastive framework appli

Yihang Dong, Xuhang Chen, Yanyan Shen, Michael Kwok-Po Ng

Emotion recognition based on Electroencephalography (EEG) has gained significant attention and diversified development in fields such as neural signal processing and affective computing. However, the unique brain anatomy of individuals leads to non-negligible natural differences in EEG signals across subjects, posing challenges for cross-subject emotion reco

Piyush Sharda, Yuan-Sen Ting, Neige Frankel

In recent years, the study of the Milky Way has significantly advanced due to extensive spectroscopic surveys of its stars, complemented by astroseismic and astrometric data. However, it remains disjoint from recent advancements in understanding the physics of the Galactic interstellar medium (ISM). This paper introduces a new model for the chemical evolutio

Camille Castera, Peter Ochs

Towards designing learned optimization algorithms that are usable beyond their training setting, we identify key principles that classical algorithms obey, but have up to now, not been used for Learning to Optimize (L2O). Following these principles, we provide a general design pipeline, taking into account data, architecture and learning strategy, and thereb

Semih Cayci, Atilla Eryilmaz

Solving partially observable Markov decision processes (POMDPs) remains a fundamental challenge in reinforcement learning (RL), primarily due to the curse of dimensionality induced by the non-stationarity of optimal policies. In this work, we study a natural actor-critic (NAC) algorithm that integrates recurrent neural network (RNN) architectures into a natu

Mobina Tousian, Christian Solis Calero, Julio Cesar Perez Sansalvador

The tumor microenvironment (TME) plays a critical role in cancer cell proliferation, invasion, and resistance to therapy. A principal component of the TME is the tumor immune microenvironment (TIME), which includes various immune cells such as macrophages. Depending on the signals received from environmental elements like IL-4 or IFN-$\gamma$, macrophages ca

Sabrina Khurshid, Mohammed Shahid Abdulla, Gourab Ghatak

Sharpe Ratio (SR) is a critical parameter in characterizing financial time series as it jointly considers the reward and the volatility of any stock/portfolio through its variance. Deriving online algorithms for optimizing the SR is particularly challenging since even offline policies experience constant regret with respect to the best expert Even-Dar et al

Keisuke Fujii, Ryuichi Sano, Tomohide Nakano, Jae-Sun Park

It has been theoretically predicted that the \ExB drift caused by the spontaneously generated potential in scrape-off-layers (SOLs) and divertors in tokamaks is of a similar size to the poloidal component of the parallel flow and turbulent flow, thereby it significantly impacts on the plasma transport there. Many experiments indeed have implied the role of t

Yang Zhang, Yawei Li, Xinpeng Wang, Qianli Shen

Overparametrized transformer networks are the state-of-the-art architecture for Large Language Models (LLMs). However, such models contain billions of parameters making large compute a necessity, while raising environmental concerns. To address these issues, we propose FinerCut, a new form of fine-grained layer pruning, which in contrast to prior work at the

Naveen Raman, Mateo Espinosa Zarlenga, Mateja Jamnik

Concept-based explainability methods provide insight into deep learning systems by constructing explanations using human-understandable concepts. While the literature on human reasoning demonstrates that we exploit relationships between concepts when solving tasks, it is unclear whether concept-based methods incorporate the rich structure of inter-concept re

Zezhou Yang, Cuiyun Gao, Zhaoqiang Guo, Zhenhao Li

Over the past decade, modern code review (MCR) has been established as a cornerstone of software quality assurance and a vital channel for knowledge transfer within development teams. However, the manual inspection of increasingly complex systems remains a cognitively demanding and resource-intensive activity, often leading to significant workflow bottleneck

Shou-shan Bao, Wenhai Gao, Hong Zhang, Jian Zhou

The axion-gluon coupling can be constrained directly through hard exclusive processes at the LHC. Specifically, we study the associated production of a long-lived axion with a $\rho^0$ meson in ultra-peripheral $AA$ collisions and in $pp$ collisions. With the axion escaped from the detector, the final state is characterized by a mono-hadron signature. The ma

Miquel Oller, Dmitry Berenson, Nima Fazeli

In this paper, we consider the problem of non-prehensile manipulation using grasped objects. This problem is a superset of many common manipulation skills including instances of tool-use (e.g., grasped spatula flipping a burger) and assembly (e.g., screwdriver tightening a screw). Here, we present an algorithmic approach for non-prehensile manipulation lever

Amandine Brunetto, Sascha Hornauer, Fabien Moutarde

Sound plays a major role in human perception. Along with vision, it provides essential information for understanding our surroundings. Despite advances in neural implicit representations, learning acoustics that align with visual scenes remains a challenge. We propose NeRAF, a method that jointly learns acoustic and radiance fields. NeRAF synthesizes both no

Eric Y. Chen, Akshay Venkatesh

Relative Langlands duality structures the study of automorphic periods around a putative duality between certain group actions of Langlands dual reductive groups. In this article, after giving a self-contained exposition of the relevant ingredients from relative Langlands duality, we examine this proposal for some interesting pairs of singular spaces: one pa

Maria M. Martins, Piyush Kumar, Marianne E. Bathen, Zaher Salman

Using low-energy muons, we map the charge carrier concentration as a function of depth and electric field across the \SiOSi interface up to a depth of \SI{100}{\nano\meter} in Si-based MOS capacitors. The results show that the formation of the anisotropic bond-centered muonium \MuBCz state in Si serves as a direct measure of the local changes in electronic s

Conghui Li

In this note, we give a new proof by module-theoretic methods for a result of Puig asserting that blocks which are stable equivalent of Morita type to nilpotent blocks are also nilpotent.

Zhi Zheng, Shangding Gu

Ensuring safety in MARL, particularly when deploying it in real-world applications such as autonomous driving, emerges as a critical challenge. To address this challenge, traditional safe MARL methods extend MARL approaches to incorporate safety considerations, aiming to minimize safety risk values. However, these safe MARL algorithms often fail to model oth

Chengxing Xie, Difan Zou

Recent studies have highlighted their proficiency in some simple tasks like writing and coding through various reasoning strategies. However, LLM agents still struggle with tasks that require comprehensive planning, a process that challenges current models and remains a critical research issue. In this study, we concentrate on travel planning, a Multi-Phases

Máté Kiss, Roland Tóth, Maarten Schoukens

The quality of a model resulting from (black-box) system identification is highly dependent on the quality of the data that is used during the identification procedure. Designing experiments for linear time-invariant systems is well understood and mainly focuses on the power spectrum of the input signal. Performing experiment design for nonlinear system iden

Christoph Kern, Michael Kim, Angela Zhou

Estimating heterogeneous treatment effects is important to tailor treatments to those individuals who would most likely benefit. However, conditional average treatment effect predictors may often be trained on one population but possibly deployed on different, possibly unknown populations. We use methodology for learning multi-accurate predictors to post-pro

Haijun Zhang, Bowen Chen, Xiangnan Liu, Chao Ren

The possibility of jointly optimizing location sensing and communication resources, facilitated by the existence of communication and sensing spectrum sharing, is what promotes the system performance to a higher level. However, the rapid mobility of user equipment (UE) can result in inaccurate location estimation, which can severely degrade system performanc

Timo Zwettler, Giulia del Pace, Filip Marijanovic, Sambuddha Chattopadhyay

A fundamental problem of out-of-equilibrium physics is the speed at which the order parameter grows upon crossing a phase transition. Here, we investigate the dynamics of ordering in a Fermi gas undergoing a density-wave phase transition induced by quenching of long-range, cavity-mediated interactions. We observe in real-time the exponential rise of the orde

Wei Zhu, Aaron Xuxiang Tian, Congrui Yin, Yuan Ni

Soft prompt tuning is a widely studied parameter-efficient fine-tuning method. However, it has a clear drawback: many soft tokens must be inserted into the input sequences to guarantee downstream performance. As a result, soft prompt tuning is less considered than Low-rank adaptation (LoRA) in the large language modeling (LLM) era. In this work, we propose a

Ismail Nejjar, Faez Ahmed, Olga Fink

Regression models often fail to generalize effectively in regions characterized by highly imbalanced label distributions. Previous methods for deep imbalanced regression rely on gradient-based weight updates, which tend to overfit in underrepresented regions. This paper proposes a paradigm shift towards in-context learning as an effective alternative to conv

János Kollár

Let $P\to X$ be a ${\mathbb P}^1$-bundle over a variety $X$. The aim of this note is to understand all connected, algebraic groups $$ \operatorname{Aut}^\circ(P)\subset G\subset \operatorname{Bir}( X\times {\mathbb P}^1). $$ We get a quite complete answer if $\operatorname{Aut}^\circ(X)$ is a maximal, connected, algebraic subgroup of $\operatorname{Bir}(X)$,

Eva Löcherbach, Kádmo Laxa

We consider a model for a social network with N interacting social actors. This model is a system of interacting marked point processes in which each point process indicates the successive times in which a social actor expresses a "favorable" (+1) or "contrary" (-1) opinion. The orientation and the rate at which an actor expresses an opinion is influenced by

Kwangjun Ahn, Ashok Cutkosky

In this work, we offer a theoretical analysis of two modern optimization techniques for training large and complex models: (i) adaptive optimization algorithms, such as Adam, and (ii) the model exponential moving average (EMA). Specifically, we demonstrate that a clipped version of Adam with model EMA achieves the optimal convergence rates in various nonconv

Federico Mungari, Corrado Puligheddu, Andres Garcia-Saavedra, Carla Fabiana Chiasserini

The Open Radio Access Network (O-RAN) architecture aims to support a plethora of network services, such as beam management and network slicing, through the use of third-party applications called xApps. To efficiently provide network services at the radio interface, it is thus essential that the deployment of the xApps is carefully orchestrated. In this paper

Jiaqing Shan, Maohua Li

In this paper, the Darboux transformation (DT) of the reverse space-time (RST) nonlocal short pulse equation is constructed by a hodograph transformation and the eigenfunctions of its Lax pair. The multi-soliton solutions of the RST nonlocal short pulse equation are produced through the DT, which can be expressed in terms of determinant representation. By ta

Vitalis Vosylius, Younggyo Seo, Jafar Uruç, Stephen James

In the field of Robot Learning, the complex mapping between high-dimensional observations such as RGB images and low-level robotic actions, two inherently very different spaces, constitutes a complex learning problem, especially with limited amounts of data. In this work, we introduce Render and Diffuse (R&D) a method that unifies low-level robot actions and

Ivan N. Burenev, Satya N. Majumdar, Alberto Rosso

In this paper, we consider the problem of numerical investigation of the counting statistics for a class of one-dimensional systems. Importance sampling, the cornerstone technique usually implemented for such problems, critically hinges on selecting an appropriate biased distribution. While exponential tilt in the observable stands as the conventional choice

Youlong Ding, Xueyang Wu, Yining Meng, Yonggang Luo

Deep learning with differential privacy (DP) has garnered significant attention over the past years, leading to the development of numerous methods aimed at enhancing model accuracy and training efficiency. This paper delves into the problem of training Transformer models with differential privacy. Our treatment is modular: the logic is to `reduce' the probl

Sharut Gupta, Chenyu Wang, Yifei Wang, Tommi Jaakkola

At the core of self-supervised learning for vision is the idea of learning invariant or equivariant representations with respect to a set of data transformations. This approach, however, introduces strong inductive biases, which can render the representations fragile in downstream tasks that do not conform to these symmetries. In this work, drawing insights

L. Lesniak, P. Zenczykowski

We study B+- -- > K+-K+K- decays using the QCD factorization model with final state interactions between K+ and K- mesons taken into account. The parameters of the model are fitted to the data of the BABAR and LHCb collaborations. We describe the K Kbar effective mass distributions and examine the CP-violating asymmetry effects in the full range of the Dalit

Ludovico T. Giorgini, Ulrich D. Jentschura, Enrico M. Malatesta, Tommaso Rizzo

We discuss numerical aspects of instantons in two- and three-dimensional $\phi^4$ theories with an internal $O(N)$ symmetry group, the so-called $N$-vector model. Combining asymptotic transseries expansions for large argument with convergence acceleration techniques, we obtain high-precision values for certain integrals of the instanton that naturally occur

Johann Bauer, Sheldon West, Eduardo Alonso, Mark Broom

We present two variants of a multi-agent reinforcement learning algorithm based on evolutionary game theoretic considerations. The intentional simplicity of one variant enables us to prove results on its relationship to a system of ordinary differential equations of replicator-mutator dynamics type, allowing us to present proofs on the algorithm's convergenc

Deepshikha, Aniruddha Samanta

Erasures are a common problem that arises while signals or data are being transmitted. A profound challenge in frame theory is to find the optimal dual frames ($OD$-frames) to minimize the reconstruction error if erasures occur. In this paper, we study the optimal duals of frames generated by graphs. First, we characterize walk-regular graphs. Then, it is sh

Zheng-Hang Sun, Yong-Yi Wang, Jian Cui, Heng Fan

Characterizing the nature of many-body localization transitions (MBLTs) and their potential critical behaviors has remained a challenging problem. In this work, we study the dynamics of the displacement, quantifying the spread of the radial probability distribution in the Fock space, for three systems with MBLTs, i.e., the Hamiltonian models with quasiperiod

Longxiang He, Li Shen, Xueqian Wang

Implicit Q-learning (IQL) serves as a strong baseline for offline RL, which learns the value function using only dataset actions through quantile regression. However, it is unclear how to recover the implicit policy from the learned implicit Q-function and why IQL can utilize weighted regression for policy extraction. IDQL reinterprets IQL as an actor-critic

Cesare Donati, Martina Mammarella, Fabrizio Dabbene, Carlo Novara

In this paper, we propose a unified framework for identifying interpretable nonlinear dynamical models that preserve physical properties. The proposed approach integrates physical principles with black-box basis functions to compensate for unmodeled dynamics, ensuring accuracy over long prediction horizons and computational efficiency. Additionally, we intro

L. K. R. Duarte, L. G. Rizzi

Usually, the strain-induced softening behaviour observed in the differential modulus $K(T,\gamma)$ of hydrogels has been attributed to the breakage of internal structures of the network, such as the cross-links that bind together the polymer chains. Here we consider a stress-strain relationship that we have recently derived from a coarse-grained model to dem

Cyrille Chevalier, Selma Youcef Khodja

The oscillator bases expansion stands as an efficient approximation method for the time-independent Schr\"odinger equation. The method, originally formulated with one non-linear variational parameter, can be extended to incorporate two such parameters. It handles both non- and semi-relativistic kinematics with generic two-body interactions. In the current wo

Solenne Gaucher, Martino Bernasconi, Matteo Castiglioni, Andrea Celli

Bilateral trade models the problem of facilitating trades between a seller and a buyer having private valuations for the item being sold. In the online version of the problem, the learner faces a new seller and buyer at each time step, and has to post a price for each of the two parties without any knowledge of their valuations. We consider a scenario where,

Bart Jacobs

Drawing (a multiset of) coloured balls from an urn is one of the most basic models in discrete probability theory. Three modes of drawing are commonly distinguished: multinomial (draw-replace), hypergeometric (draw-delete), and Polya (draw-add). These drawing operations are represented as maps from urns to distributions over multisets of draws. The set of ur

Bianca Löhnert, Nikolaus Augsten, Cem Okulmus, Magdalena Ortiz

Despite the many advantages that ontology-based data access (OBDA) has brought to a range of application domains, state-of-the-art OBDA systems still do not support popular graph database management systems such as Neo4j. Algorithms for query rewriting focus on languages like conjunctive queries and their unions, which are fragments of first-order logic and

Melek Türkmen, Sanem Meral, Baris Yilmaz, Melis Cikis

This paper explores the application of deep learning (DL) techniques to strong motion records for single-station epicenter localization. Often underutilized in seismology-related studies, strong motion records offer a potential wealth of information about seismic events. We investigate whether DL-based methods can effectively leverage this data for accurate

Daniel Bethell, Simos Gerasimou, Radu Calinescu, Calum Imrie

Empowering safe exploration of reinforcement learning (RL) agents during training is a critical challenge towards their deployment in many real-world scenarios. When prior knowledge of the domain or task is unavailable, training RL agents in unknown, black-box environments presents an even greater safety risk. We introduce ADVICE (Adaptive Shielding with a C

Giulio Crisanti, Sid Smith

We present a prescription for choosing orthogonal bases of differential $n$-forms belonging to quadratic twisted period integrals, with respect to the intersection number inner product. To evaluate these inner products, we additionally propose a new closed formula for intersection numbers beyond $\mathrm{d} \log$ forms. These findings allow us to systematica

Haritha T, Chithra A

For a connected graph $G$, its resistance distance matrix is denoted by $R(G)$. A graph is called resistance regular if all the row (or column) sums of $R(G)$ are equal. We provide a necessary and sufficient condition for a simple connected graph to be resistance regular. Additionally, we establish sharp bounds for the resistance spectral radius and present

Ilia Azizi, Marc-Olivier Boldi, Valérie Chavez-Demoulin

This work introduces the Supervised Expectation-Maximization Framework (SEMF), a versatile and model-agnostic approach for generating prediction intervals with any ML model. SEMF extends the Expectation-Maximization algorithm, traditionally used in unsupervised learning, to a supervised context, leveraging latent variable modeling for uncertainty estimation.

Jarett LeVan, Marco Acciarri, Scott Baalrud

Bulk viscosity of a plasma consisting of strongly coupled diatomic ions is computed using molecular dynamics simulations. The simulations are based on the rigid rotor one-component plasma, which is introduced as a model system that adds two degrees of molecular rotation to the traditional one-component plasma. It is characterized by two parameters: the Coulo

Ilya Yegorov, Georgy Savidov

Crash report accumulation is a necessary step during continuous fuzzing. Dynamic software analysis techniques like fuzzing and dynamic symbolic execution generate a large number of crashes for analysis. However, the time and resource constraints often lead to the postponement of fixing some less critical issues, potentially introducing new errors in future r

Yuanyang Hu, Mingxin Wang

Let $G=(V,E)$ be a locally finite connected graph. We develop the first eigenvalue method on $G$ introduced in 1963 by Kaplan \cite{Kaplan} on Euclidean space, the discrete Phragm\'{e}n-Lindel\"{o}f principle of parabolic equations and upper and lower solutions method on $G$. Using these methods, we establish the estimates and asymptotic behaviour of the lif

Yuhan Li, Hao Zhou, Wenxiang Shang, Ran Lin

While image-based virtual try-on has made significant strides, emerging approaches still fall short of delivering high-fidelity and robust fitting images across various scenarios, as their models suffer from issues of ill-fitted garment styles and quality degrading during the training process, not to mention the lack of support for various combinations of at