May 2024 arXiv papers — page 20

Keyan Gootkin, Marc Hon, Daniel Huber, Daniel R. Hey

{\delta} Scuti variables are found at the intersection of the classical instability strip and the main sequence on the Hertzsprung-Russell diagram. With space-based photometry providing millions of light-curves of A-F type stars, we can now probe the occurrence rate of {\delta} Scuti pulsations in detail. Using 30-min cadence light-curves from NASA's Transit

Hanrong Ye, De-An Huang, Yao Lu, Zhiding Yu

We introduce X-VILA, an omni-modality model designed to extend the capabilities of large language models (LLMs) by incorporating image, video, and audio modalities. By aligning modality-specific encoders with LLM inputs and diffusion decoders with LLM outputs, X-VILA achieves cross-modality understanding, reasoning, and generation. To facilitate this cross-m

Yingqing He, Zhaoyang Liu, Jingye Chen, Zeyue Tian

With the recent advancement in large language models (LLMs), there is a growing interest in combining LLMs with multimodal learning. Previous surveys of multimodal large language models (MLLMs) mainly focus on multimodal understanding. This survey elaborates on multimodal generation and editing across various domains, comprising image, video, 3D, and audio.

Feipeng Ma, Hongwei Xue, Guangting Wang, Yizhou Zhou

Most multi-modal tasks can be formulated into problems of either generation or embedding. Existing models usually tackle these two types of problems by decoupling language modules into a text decoder for generation, and a text encoder for embedding. To explore the minimalism of multi-modal paradigms, we attempt to achieve only one model per modality in this

Shenao Zhang, Donghan Yu, Hiteshi Sharma, Han Zhong

Preference optimization, particularly through Reinforcement Learning from Human Feedback (RLHF), has achieved significant success in aligning Large Language Models (LLMs) to adhere to human intentions. Unlike offline alignment with a fixed dataset, online feedback collection from humans or AI on model generations typically leads to more capable reward models

Simon Giebenhain, Tobias Kirschstein, Martin Rünz, Lourdes Agapito

The creation of high-fidelity, digital versions of human heads is an important stepping stone in the process of further integrating virtual components into our everyday lives. Constructing such avatars is a challenging research problem, due to a high demand for photo-realism and real-time rendering performance. In this work, we propose Neural Parametric Gaus

B. Világos, B. Cseh, A. Yagüe López, M. Joyce

Barium (Ba) stars help to verify asymptotic giant branch (AGB) star nucleosynthesis models since they experienced pollution from an AGB binary companion and thus their spectra carry the signatures of the slow neutron capture process (s process). For 180 Ba stars, we searched for AGB stellar models that match the observed abundance patterns. We employed three

Chao Duan, Rui Wang

Polyelectrolyte (PE) brushes are widely used in biomaterials and nanotechnology to regulate surface properties and interactions. Here, we apply the electrostatic correlation augmented self-consistent field theory to investigate the interactions between opposing PE brushes in a mixture of 1:1 and 3:1 salt solutions. Our theory predicts hysteretic feature of t

Atrisha Sarkar, Andrei Ioan Muresanu, Carter Blair, Aaryam Sharma

Generative agents, which implement behaviors using a large language model (LLM) to interpret and evaluate an environment, has demonstrated the capacity to solve complex tasks across many social and technological domains. However, when these agents interact with other agents and humans in presence of social structures such as existing norms, fostering coopera

Ge Zhang, Scott Qu, Jiaheng Liu, Chenchen Zhang

Large Language Models (LLMs) have made great strides in recent years to achieve unprecedented performance across different tasks. However, due to commercial interest, the most competitive models like GPT, Gemini, and Claude have been gated behind proprietary interfaces without disclosing the training details. Recently, many institutions have open-sourced sev

Moshira Abdalla, Sajid Javed, Muaz Al Radi, Anwaar Ulhaq

Video anomaly detection (VAD) holds immense importance across diverse domains such as surveillance, healthcare, and environmental monitoring. While numerous surveys focus on conventional VAD methods, they often lack depth in exploring specific approaches and emerging trends. This survey explores deep learning-based VAD, expanding beyond traditional supervise

Tianrun Chen, Chunan Yu, Jing Li, Jianqi Zhang

In this paper, we introduce a new task: Zero-Shot 3D Reasoning Segmentation for parts searching and localization for objects, which is a new paradigm to 3D segmentation that transcends limitations for previous category-specific 3D semantic segmentation, 3D instance segmentation, and open-vocabulary 3D segmentation. We design a simple baseline method, Reasoni

Minghan Li, Xilun Chen, Ari Holtzman, Beidi Chen

Large language models (LLMs) often hallucinate and lack the ability to provide attribution for their generations. Semi-parametric LMs, such as kNN-LM, approach these limitations by refining the output of an LM for a given prompt using its nearest neighbor matches in a non-parametric data store. However, these models often exhibit slow inference speeds and pr

Mingmeng Geng, Sihong He, Roberto Trotta

Can large language models (LLMs) simulate social surveys? To answer this question, we conducted millions of simulations in which LLMs were asked to answer subjective questions. A comparison of different LLM responses with the European Social Survey (ESS) data suggests that the effect of prompts on bias and variability is fundamental, highlighting major cultu

Thomas Chalopin, Petar Bojović, Dominik Bourgund, Si Wang

Quantum simulations of Hubbard models with ultracold atoms rely on the exceptional control of coherent motion provided by optical lattices. Here we demonstrate enhanced tunability using an optical superlattice in a fermionic quantum gas microscope. With our phase-stable bichromatic design, we achieve a precise control of tunneling and tilt throughout the lat

Isaac Labe, Noam Issachar, Itai Lang, Sagie Benaim

We tackle the task of learning dynamic 3D semantic radiance fields given a single monocular video as input. Our learned semantic radiance field captures per-point semantics as well as color and geometric properties for a dynamic 3D scene, enabling the generation of novel views and their corresponding semantics. This enables the segmentation and tracking of a

Shicong Cen, Jincheng Mei, Katayoon Goshvadi, Hanjun Dai

Reinforcement learning from human feedback (RLHF) has demonstrated great promise in aligning large language models (LLMs) with human preference. Depending on the availability of preference data, both online and offline RLHF are active areas of investigation. A key bottleneck is understanding how to incorporate uncertainty estimation in the reward function le

Moritz Cygorek, Erik M. Gauger

We describe a general-purpose computational toolkit for simulating open quantum systems, which provides numerically exact solutions for composites of zero-dimensional quantum systems that may be strongly coupled to multiple, quite general non-Markovian environments. It is based on process tensor matrix product operators (PT-MPOs), which efficiently encapsula

Chloe R. Bennett, Heather Cole-Lewis, Stephanie Farquhar, Naama Haamel

The field of artificial intelligence (AI) is rapidly influencing health and healthcare, but bias and poor performance persists for populations who face widespread structural oppression. Previous work has clearly outlined the need for more rigorous attention to data representativeness and model performance to advance equity and reduce bias. However, there is

P. C. Stavrinos, A. Triantafyllopoulos

In this article, we review some aspects of gravitational field and cosmology based on Finsler and Finsler-like generalized metric structures. The geometrical framework of these spaces allows further investigation of locally-anisotropic phenomena related to the gravitational field and cosmological considerations, e.g the extracted geodesics, deflection of lig

Masahiro Kato

This study investigates an asymptotically locally minimax optimal algorithm for fixed-budget best-arm identification (BAI). We propose the Generalized Neyman Allocation (GNA) algorithm and demonstrate that its worst-case upper bound on the probability of misidentifying the best arm aligns with the worst-case lower bound under the small-gap regime, where the

Adam Fisch, Jacob Eisenstein, Vicky Zayats, Alekh Agarwal

Language model (LM) post-training (or alignment) involves maximizing a reward function that is derived from preference annotations. Direct Preference Optimization (DPO) is a popular offline alignment method that trains a policy directly on preference data without the need to train a reward model or apply reinforcement learning. However, the empirical evidenc

Wenbo Hu, Zi-Yi Dou, Liunian Harold Li, Amita Kamath

Large Vision-Language Models (LVLMs) typically encode an image into a fixed number of visual tokens (e.g., 576) and process these tokens with a language model. Despite their strong performance, LVLMs face challenges in adapting to varying computational constraints. This raises the question: can we achieve flexibility in the number of visual tokens to suit di

Emmanuel Addai, Delfim F. M. Torres, Zalia Abdul-Hamid, Mary Nwaife Mezue

We propose and analyze a deterministic mathematical model for the transmission of food-borne diseases in a population consisting of humans and flies. We employ the Caputo operator to examine the impact of governmental actions and online food delivery services on the transmission of food-borne diseases. The proposed model investigates important aspects such a

Paolo Andreetto, Nazar Bartosik, Laura Buonincontri, Daniele Calzolari

The Muon Collider is one of the most promising future collider facilities with the potential to reach multi-TeV center-of-mass energy and high luminosity. Due to the significant Higgs boson production cross section in muon collisions at these high energies, the collider can be considered a Higgs factory. It holds the capability to significantly advance our u

Jian-Qiao Zhu, Haijiang Yan, Thomas L. Griffiths

The observed similarities in the behavior of humans and Large Language Models (LLMs) have prompted researchers to consider the potential of using LLMs as models of human cognition. However, several significant challenges must be addressed before LLMs can be legitimately regarded as cognitive models. For instance, LLMs are trained on far more data than humans

Taehyeon Koo, Nicole E. Pashley

Researchers often turn to block randomization to increase the precision of their inference or due to practical considerations, such as in multisite trials. However, if the number of treatments under consideration is large it might not be feasible or practical to assign all treatments within each block. We develop novel inference results under the finite-popu

William Luszczak, Leigh Orf

Tornadoes are severe weather phenomena characterized by a violently rotating column of air connecting the ground to a parent storm. Within the United States, hundreds of tornadoes occur every year. Despite this, the dynamics of tornado formation and propagation are not particularly well understood, in part due to the challenge of instrumentation: many existi

Arunabh Srivastava, Sennur Ulukus

We consider a gossip network consisting of a source generating updates and $n$ nodes connected according to a given graph structure. The source keeps updates of a process, that might be generated or observed, and shares them with the gossiping network. The nodes in the network communicate with their neighbors and disseminate these version updates using a pus

Connor Holmes, Frederike Dümbgen, Timothy D. Barfoot

A recent set of techniques in the robotics community, known as certifiably correct methods, frames robotics problems as polynomial optimization problems (POPs) and applies convex, semidefinite programming (SDP) relaxations to either find or certify their global optima. In parallel, differentiable optimization allows optimization problems to be embedded into

Ellis Thompson, Keng Tou Chu, Florie Mesple, Xiao-Wei Zhang

In moir\'e materials with flat electronic bands and suitable quantum geometry, strong correlations can give rise to novel topological states of matter. The nontrivial band topology of twisted molybdenum ditelluride (tMoTe$_2$) -- responsible for its fractional quantum anomalous Hall (FQAH) states -- is predicted to arise from a layer-pseudospin skyrmion latt

Abhay Deshpande, Liyiming Ke, Quinn Pfeifer, Abhishek Gupta

We consider imitation learning with access only to expert demonstrations, whose real-world application is often limited by covariate shift due to compounding errors during execution. We investigate the effectiveness of the Continuity-based Corrective Labels for Imitation Learning (CCIL) framework in mitigating this issue for real-world fine manipulation task

Armand Bernou, Mitia Duerinckx

We consider a system of classical Brownian particles interacting via a smooth long-range potential in the mean-field regime, and we analyze the propagation of chaos in form of sharp, uniform-in-time estimates on many-particle correlation functions. Our results cover both the kinetic Langevin setting and the corresponding overdamped Brownian dynamics. The app

Marco Introvigne, Andrea Ramazzina, Stefanie Walz, Dominik Scheuble

Current autonomous driving technologies are being rolled out in geo-fenced areas with well-defined operation conditions such as time of operation, area, weather conditions and road conditions. In this way, challenging conditions as adverse weather, slippery road or densely-populated city centers can be excluded. In order to lift the geo-fenced restriction an

Riccardo Gozzi, Olivier Bournez

In a recent article, we introduced and studied a precise class of dynamical systems called solvable systems. These systems present a dynamic ruled by discontinuous ordinary differential equations with solvable right-hand terms and unique evolution. They correspond to a class of systems for which a transfinite method exist to compute the solution. We also pre

Abhinav Natarajan, Thomas Chaplin, Adam Brown, Maria-Jose Jimenez

The chromatic alpha filtration is a generalization of the alpha filtration that can encode spatial relationships among classes of labelled point cloud data, and has applications in topological data analysis of multi-species data. In this paper we introduce the chromatic Delaunay-\v{C}ech and chromatic Delaunay-Rips filtrations, which are computationally favo

Vadym Kliuchnikov, Sebastian Schönnenbeck

We present new optimal and heuristic algorithms for exact synthesis of multi-qubit unitaries and isometries. For example, our algorithms find Clifford and T circuits for unitaries with entries in $\mathbb{Z}[i,1/\sqrt{2}]$. The optimal algorithms are the A* search instantiated with a new data structure for graph vertices and new consistent heuristic function

Frederike Dümbgen

In order to operate in and interact with the physical world, robots need to have estimates of the current and future state of the environment. We thus equip robots with sensors and build models and algorithms that, given some measurements, produce estimates of the current or future states. Environments can be unpredictable and sensors are not perfect. Theref

Manh Khoi Duong, Stefan Conrad

Motivated by the recital (67) of the current corrigendum of the AI Act in the European Union, we propose and present measures and mitigation strategies for discrimination in tabular datasets. We specifically focus on datasets that contain multiple protected attributes, such as nationality, age, and sex. This makes measuring and mitigating bias more challengi

Xueyao Sun, Kaize Shi, Haoran Tang, Guandong Xu

Large language models (LLMs) can elicit social bias during generations, especially when inference with toxic prompts. Controlling the sensitive attributes in generation encounters challenges in data distribution, generalizability, and efficiency. Specifically, fine-tuning and retrieval demand extensive unbiased corpus, while direct prompting requires meticul

Hanwei Zhu, Haoning Wu, Yixuan Li, Zicheng Zhang

While recent advancements in large multimodal models (LMMs) have significantly improved their abilities in image quality assessment (IQA) relying on absolute quality rating, how to transfer reliable relative quality comparison outputs to continuous perceptual quality scores remains largely unexplored. To address this gap, we introduce Compare2Score-an all-ar

Johannes Buchner, Hattie Starck, Mara Salvato, Hagai Netzer

The assembly and co-evolution of supermassive black holes (SMBH) and their host galaxy stellar population is a key open questions in galaxy evolution. Stellar mass ($M_\star$) and star formation rate (SFR), are inferred by modeling the spectral energy distribution (SED). For galaxies triggering SMBH activity, the active galactic nucleus (AGN) contaminates th

Thomas W. Mitchel, Michael Taylor, Vincent Sitzmann

Real-world geometry and 3D vision tasks are replete with challenging symmetries that defy tractable analytical expression. In this paper, we introduce Neural Isometries, an autoencoder framework which learns to map the observation space to a general-purpose latent space wherein encodings are related by isometries whenever their corresponding observations are

Lei Li, Songyou Peng, Zehao Yu, Shaohui Liu

Real-world objects and environments are predominantly composed of edge features, including straight lines and curves. Such edges are crucial elements for various applications, such as CAD modeling, surface meshing, lane mapping, etc. However, existing traditional methods only prioritize lines over curves for simplicity in geometric modeling. To this end, we

Sofie Martins, Erik Kjellgren, Emiliano Molinaro, Claudio Pica

We are improving one of the available lattice software packages HiRep by adding GPU acceleration supporting highly-optimized simulations on both NVIDIA and AMD GPUs. HiRep allows lattice simulations of theories with fermions in higher representations and a variable number of colors in the gauge group. The development is accompanied by an overall software qua

L. Spagnoli, A. Roggero, N. Wiebe

We show in this paper that a strong and easy connection exists between quantum error correction and Lattice Gauge Theories (LGT) by using the Gauge symmetry to construct an efficient error-correcting code for Abelian LGTs. We identify the logical operations on this gauge covariant code and show that the corresponding Hamiltonian can be expressed in terms of

Hamzah I. Khan, Adam J. Thorpe, David Fridovich-Keil

Autonomous agents operating around human actors must consider how their behaviors might affect those humans, even when not directly interacting with them. To this end, it is often beneficial to be predictable and appear naturalistic. Existing methods to address this problem use human actor intent modeling or imitation learning techniques, but these approache

Yi-Lin Wei, Jian-Jian Jiang, Chengyi Xing, Xian-Tuo Tan

This paper explores a novel task "Dexterous Grasp as You Say" (DexGYS), enabling robots to perform dexterous grasping based on human commands expressed in natural language. However, the development of this field is hindered by the lack of datasets with natural human guidance; thus, we propose a language-guided dexterous grasp dataset, named DexGYSNet, offeri

Langlin Huang, Yang Feng

Subword tokenization is a common method for vocabulary building in Neural Machine Translation (NMT) models. However, increasingly complex tasks have revealed its disadvantages. First, a vocabulary cannot be modified once it is learned, making it hard to adapt to new words. Second, in multilingual translation, the imbalance in data volumes across different la

Avedis Neehus, Frank Pollmann, Johannes Knolle

We investigate a model of Dirac fermions with Haldane type mass impurities which open a global topological gap even in the dilute limit. Surprisingly, we find that the chirality of this mass term, i.e., the sign of the Chern number, can be reversed by tuning the magnitude of the single-impurity scattering. Consequently, the disorder induces a phase disconnec

Abhijeet Anand, Julien Guy, Stephen Bailey, John Moustakas

We present a computationally efficient galaxy archetype-based redshift estimation and spectral classification method for the Dark Energy Survey Instrument (DESI) survey. The DESI survey currently relies on a redshift fitter and spectral classifier using a linear combination of PCA-derived templates, which is very efficient in processing large volumes of DESI

Maxime Dion, Tania Belabbas, Nolan Bastien

Pauli matrices and Pauli strings are widely used in quantum computing. These mathematical objects are useful to describe or manipulate the quantum state of qubits. They offer a convenient basis to express operators and observables used in different problem instances such as molecular simulation and combinatorial optimization. Therefore, it is important to ha

Emily I. Gray, Justin I. Read, Ethan Taylor, Matthew D. A. Orkney

Nuclear Star Clusters (NSCs) are amongst the densest stellar systems in the Universe and are found at the centres of many bright spiral and elliptical galaxies, and up to ${\sim}$40% of dwarf galaxies. However, their formation mechanisms, and possible links to globular clusters (GCs), remain debated. This paper uses the EDGE simulations - a collection of zoo

Michael Regan, Shira Wein, George Baker, Emilio Monti

Abstract Meaning Representation (AMR) is a semantic formalism that captures the core meaning of an utterance. There has been substantial work developing AMR corpora in English and more recently across languages, though the limited size of existing datasets and the cost of collecting more annotations are prohibitive. With both engineering and scientific quest

Viviane Potocnik, Luca Colagrande, Tim Fischer, Luca Bertaccini

Transformer-based foundation models have become crucial for various domains, most notably natural language processing (NLP) or computer vision (CV). These models are predominantly deployed on high-performance GPUs or hardwired accelerators with highly customized, proprietary instruction sets. Until now, limited attention has been given to RISC-V-based genera

Hanchao Liu, Xiaohang Zhan, Shaoli Huang, Tai-Jiang Mu

Character animation in real-world scenarios necessitates a variety of constraints, such as trajectories, key-frames, interactions, etc. Existing methodologies typically treat single or a finite set of these constraint(s) as separate control tasks. They are often specialized, and the tasks they address are rarely extendable or customizable. We categorize thes

E. A. Kurianovich, A. I. Mikhailov, I. V. Volovich

The approach to the theory of a relativistic random process is considered by the path integral method as Brownian motion taking into account the boundedness of speed. An attempt was made to build a relativistic analogue of the Wiener measure as a weak limit of finite-difference approximations. A formula has been proposed for calculating the probability parti

Michael Gennari, Mehdi Drissi, Mikhail Gorchtein, Petr Navratil

We report the first \textit{ab initio} calculation of the nuclear-structure-dependent radiative correction $ \delta_{ \mathrm{NS} } $ to the $ {}^{10}\mathrm{C} \rightarrow {}^{10}\mathrm{B} $ superallowed transition, computed with the no-core shell model and chiral effective field theory. We obtain $\delta_{ \mathrm{NS} } = - 0.422 (29)_{ \mathrm{nuc} } (12

Eduardo Fernández, Javier Martínez-Aguinaga, Francisco Presas

This article introduces two new constructions at the higher homotopy level in the space of Legendrian embeddings in $(\mathbb{R}^3, \xi_{\operatorname{std}})$. We first introduce the parametric Legendrian satellite construction, showing that the satellite operation works for parametric families of Legendrian embeddings. This yields new invariants at the high

Bobby He, Lorenzo Noci, Daniele Paliotta, Imanol Schlag

Outlier Features (OFs) are neurons whose activation magnitudes significantly exceed the average over a neural network's (NN) width. They are well known to emerge during standard transformer training and have the undesirable effect of hindering quantisation in afflicted models. Despite their practical importance, little is known behind why OFs emerge during t

Pedro Lauand, Bereket Ngussie Bekele, Elie Wolfe

From the modern perspective of causal inference, Bell's theorem -- a fundamental signature of quantum theory -- is a particular case where quantum correlations are incompatible with the classical theory of causality, and the generalization of Bell's theorem to quantum networks has led to several breakthrough results and novel applications. Here, we consider

Khuong Vo

A deep latent variable model is a powerful method for capturing complex distributions. These models assume that underlying structures, but unobserved, are present within the data. In this dissertation, we explore high-dimensional problems related to physiological monitoring using latent variable models. First, we present a novel deep state-space model to gen

Xiang Fu, Andrew Rosen, Kyle Bystrom, Rui Wang

In density functional theory, charge density is the core attribute of atomic systems from which all chemical properties can be derived. Machine learning methods are promising in significantly accelerating charge density prediction, yet existing approaches either lack accuracy or scalability. We propose a recipe that can achieve both. In particular, we identi

Mathilde Neugnot-Cerioli, Olga Muss Laurenty

This report explores the potential implications of rapidly integrating Artificial Intelligence (AI) applications into children's environments. The introduction of AI in our daily lives necessitates scrutiny considering the significant role of the environment in shaping cognition, socio-emotional skills, and behaviors, especially during the first 25 years of

Sutanoya Chakraborty, Arijit Ghosh

Given a drawing $D$ of a graph $G$, we define the crossing number between any two cycles $C_{1}$ and $C_{2}$ in $D$ to be the number of crossings that involve at least one edge from each of $C_1$ and $C_2$ except the crossings between edges that are common to both cycles. We show that if the crossing number between every two cycles in $G$ is even in a drawin

Zhiyuan Chen

We prove that for a locally stable family of klt singularities with constant local volume, the ideal sequences of the minimizing valuations for the normalized volume function form a family of ideals with flat cosupport, which induces a degeneration to a locally stable family of K-semistable log Fano cone singularities. Our proof is a family version of the me

Saber Malekmohammadi, Afaf Taik, Golnoosh Farnadi

Federated learning (FL), which is a decentralized machine learning (ML) approach, often incorporates differential privacy (DP) to provide rigorous data privacy guarantees. Previous works attempted to address high structured data heterogeneity in vanilla FL settings through clustering clients (a.k.a clustered FL), but these methods remain sensitive and prone

Luis E. Padilla, Juan Carlos Hidalgo, Karim A. Malik, David Mulryne

After primordial inflation, the universe may have experienced a prolonged reheating epoch, potentially leading to a phase of matter domination supported by the oscillating inflaton field. During such an epoch, perturbations in the inflaton virialize upon reentering the cosmological horizon, forming inflaton structures. If the primordial overdensities are suf

Salvatore Mercuri

The adele ring of a number field is a central object in modern number theory. Its status as a locally compact topological ring is one of the key reasons why. We describe a formal proof that the adele ring of a number field is locally compact implemented in the Lean 4 theorem prover. Our work includes the formalisations of new types, including the completion

Yuda Song, Lili Wu, Dylan J. Foster, Akshay Krishnamurthy

Sample-efficiency and reliability remain major bottlenecks toward wide adoption of reinforcement learning algorithms in continuous settings with high-dimensional perceptual inputs. Toward addressing these challenges, we introduce a new theoretical framework, RichCLD (Rich-Observation RL with Continuous Latent Dynamics), in which the agent performs control ba

Blake R. Duschatko, Xiang Fu, Cameron Owen, Yu Xie

We present a differentiable formalism for learning free energies that is capable of capturing arbitrarily complex model dependencies on coarse-grained coordinates and finite-temperature response to variation of general system parameters. This is done by endowing models with explicit dependence on temperature and parameters and by exploiting exact differentia

Victor Tomno, Linety Muhati

We study sign symmetric $P_{0,1}^+$-matrix completion problem. It is shown that any non-asymmetric incomplete digraph lacks sign symmetric $P_{0,1}^+$-completion, digraphs of order at most four are completely classified and finally comparisons between sign symmetric $P_{0,1}^+$-completion and other matrix completions was given.

Subhaskar Mandal, Ziyao Wang, Rimi Banerjee, Hau Tian Teo

Photonic Chern insulators can be implemented in gyromagnetic photonic crystals with broken time-reversal (TR) symmetry. They exhibit gapless chiral edge states (CESs), enabling unidirectional propagation and demonstrating exceptional resilience to localization even in the presence of defects or disorders. However, when two Chern insulators with opposite Cher

Dingkang Yang, Jinjie Wei, Dongling Xiao, Shunli Wang

Developing intelligent pediatric consultation systems offers promising prospects for improving diagnostic efficiency, especially in China, where healthcare resources are scarce. Despite recent advances in Large Language Models (LLMs) for Chinese medicine, their performance is sub-optimal in pediatric applications due to inadequate instruction data and vulner

Zifan Song, Yudong Wang, Wenwei Zhang, Kuikun Liu

Open-source Large Language Models (LLMs) and their specialized variants, particularly Code LLMs, have recently delivered impressive performance. However, previous Code LLMs are typically fine-tuned on single-source data with limited quality and diversity, which may insufficiently elicit the potential of pre-trained Code LLMs. In this paper, we present Alchem

Andrei Rabusov, Daniel Greenwald, Stephan Paul

We present preliminary results of a partial-wave analysis of $\tau^-\to\pi^-\pi^-\pi^+\nu_\tau$ using data from the Belle experiment at the KEKB $\mathrm{e}^+\mathrm{e}^-$ collider. We validate our model with a model-independent analysis. We see the $\mathrm{a}_1(1420)$ and a G-parity-violating $1^-[\omega(782)\pi]_\mathrm{P}$ wave in tauon decays. Our resul

Johanna I. Hütner, Andrea Conti, David Kugler, Florian Mittendorfer

Macroscopic properties of materials stem from fundamental atomic-scale details, yet for insulators, resolving surface structures remains a challenge. The basal (0001) plane of ${\alpha}$-Al$_{2}$O$_{3}$ was imaged with noncontact atomic force microscopy with an atomically-defined tip apex. The surface forms a complex $({\sqrt31} {\times} {\sqrt31})R{\pm}9{\d

Zhanhui Zhou, Zhixuan Liu, Jie Liu, Zhichen Dong

Large language models are usually fine-tuned to align with human preferences. However, fine-tuning a large language model can be challenging. In this work, we introduce $\textit{weak-to-strong search}$, framing the alignment of a large language model as a test-time greedy search to maximize the log-probability difference between small tuned and untuned model

Harikrishna Narasimhan, Wittawat Jitkrittum, Ankit Singh Rawat, Seungyeon Kim

Cascades and speculative decoding are two common approaches to improving language models' inference efficiency. Both approaches involve interleaving models of different sizes, but via fundamentally distinct mechanisms: cascades employ a deferral rule that invokes the larger model only for "hard" inputs, while speculative decoding uses speculative execution t

Rahel L. Baumgartner, Luca V. Delacrétaz, Pranjal Nayak, Julian Sonner

Random matrix theory (RMT) universality is the defining property of quantum mechanical chaotic systems, and can be probed by observables like the spectral form factor (SFF). In this paper, we describe systematic deviations from RMT behaviour at intermediate time scales in systems with approximate symmetries. At early times, the symmetries allow us to organiz

Seyni Kane, Anis Bkakria

Graph encryption schemes play a crucial role in facilitating secure queries on encrypted graphs hosted on untrusted servers. With applications spanning navigation systems, network topology, and social networks, the need to safeguard sensitive data becomes paramount. Existing graph encryption methods, however, exhibit vulnerabilities by inadvertently revealin

Zekai Sun, Xiuxian Guan, Junming Wang, Haoze Song

The rapid advancements in machine learning techniques have led to significant achievements in various real-world robotic tasks. These tasks heavily rely on fast and energy-efficient inference of deep neural network (DNN) models when deployed on robots. To enhance inference performance, distributed inference has emerged as a promising approach, parallelizing

Zhiqiang Cai, Yu Cao, Yuanfei Huang, Xiang Zhou

Sampling invariant distributions from an It\^o diffusion process presents a significant challenge in stochastic simulation. Traditional numerical solvers for stochastic differential equations require both a fine step size and a lengthy simulation period, resulting in biased and correlated samples. The current deep learning-based method solves the stationary

Jose Tupayachi, Haowen Xu, Olufemi A. Omitaomu, Mustafa Can Camur

The incorporation of Artificial Intelligence (AI) models into various optimization systems is on the rise. Yet, addressing complex urban and environmental management problems normally requires in-depth domain science and informatics expertise. This expertise is essential for deriving data and simulation-driven for informed decision support. In this context,

Felix Finster, Johannes Kleiner, Claudio F. Paganini

It is shown that, in the non-relativistic limit, causal fermion systems give rise to an effective collapse theory. The nonlinear and stochastic correction terms to the Schr\"odinger equation are derived from the causal action principle. The dynamics of the statistical operator is described by a deterministic equation of Kossakowski-Lindblad form. Moreover, t

Rohini S. Giles, John R. Spencer, Constantine C. C. Tsang, Thomas K. Greathouse

Between 2001 and 2023, we obtained high spectral resolution mid-infrared observations of Io using the TEXES instrument at NASA's Infrared Telescope Facility. These observations were centered at 529.8 cm-1 (18.88 {\mu}m) and include several SO2 absorption lines. By modeling the shapes and strengths of these absorption lines, we are able to determine how Io's

Pedro Lauand, Bereket Ngussie Bekele, Elie Wolfe

Bell's theorem, a cornerstone of quantum theory, shows that quantum correlations are incompatible with a classical theory of cause and effect. Through the lens of causal inference, it can be understood as a particular case of causal compatibility, which delves into the alignment of observational data with a given causal structure. Here, we explore the proble

Domenico Scordino, Ignazio Bombaci

We study the effects of dark matter on the structural properties of neutron stars. In particular we investigate how the presence of a dark matter component influences the mass-radius relation, the value of the maximum mass of a neutron star and other stellar properties. To model ordinary matter we use a state-of-the-art equation of state of $\beta$-stable nu

Sergey Titov, Mikhail Evtikhiev, Anton Shapkin, Oleg Smirnov

In this technical report, we present three novel datasets of Kotlin code: KStack, KStack-clean, and KExercises. We also describe the results of fine-tuning CodeLlama and DeepSeek models on this data. Additionally, we present a version of the HumanEval benchmark rewritten by human experts into Kotlin - both the solutions and the tests. Our results demonstrate

Jacob Bedrossian, Siming He, Sameer Iyer, Fei Wang

We consider the 2D, incompressible Navier-Stokes equations near the Couette flow, $\omega^{(NS)} = 1 + \epsilon \omega$, set on the channel $\mathbb{T} \times [-1, 1]$, supplemented with Navier boundary conditions on the perturbation, $\omega|_{y = \pm 1} = 0$. We are simultaneously interested in two asymptotic regimes that are classical in hydrodynamic stab

Christian Furrer, Jacob Juhl Sørensen, Jorge Yslas

In this paper, we consider the problem of experience rating within the classic Markov chain life insurance framework. We begin by establishing a link between mixed Poisson distributions and the problem of pricing group disability insurance contracts that exhibit heterogeneity. We focus on shrinkage estimation of disability and recovery rates, taking into acc

Zhuang Qi, Junlin Zhang, Xiaming Chen, Xin Qi

The neighbor-based method has become a powerful tool to handle the outlier detection problem, which aims to infer the abnormal degree of the sample based on the compactness of the sample and its neighbors. However, the existing methods commonly focus on designing different processes to locate outliers in the dataset, while the contributions of different type

Chunhui Chen, Jing Chen, Baojia Luo, Shi Jin

Numerically solving multi-marginal optimal transport (MMOT) problems is computationally prohibitive, even for moderate-scale instances involving $l\ge4$ marginals with support sizes of $N\ge1000$. The cost in MMOT is represented as a tensor with $N^l$ elements. Even accessing each element once incurs a significant computational burden. In fact, many algorith

Wenhao He, Tongyang Li, Xiantao Li, Zecheng Li

The optimal control problem for open quantum systems can be formulated as a time-dependent Lindbladian that is parameterized by a number of time-dependent control variables. Given an observable and an initial state, the goal is to tune the control variables so that the expected value of some observable with respect to the final state is maximized. In this pa

Vincenzo Minissale, Vincenzo Greco, Salvatore Plumari

Recent experimental data from $pp$ collisions have shown a significant increase in heavy baryon production leading to a baryon over meson ratio which is one order of magnitude higher than elementary collisions ($e^+e^-$, $ep$). From a theoretical point of view this large production of baryon can be explained with hadronization via quark coalescence assuming

Matías Bilkis, Joan Moya Kohler, Fernando Vilariño

The advent of Artificial Intelligence is expected to imply profound changes in the short-term. It is therefore imperative for Academia, and particularly for the Computer Science scope, to develop cross-disciplinary tools that bond AI developments to their social dimension. To this aim, we introduce the Challenge-Device-Synthesis methodology (CDS), in which a

Tiago S. A. N. Simões, Antonio Coniglio, Hans J. Herrmann, Lucilla de Arcangelis

We study the average long-time behavior of the binary opinions of a social group with peer-to-peer interactions under the influence of an external bias and a persuadable leader, a strongly-biased agent with a dynamic opinion with the intention of spreading it across the system. We use a generalized, fully-connected Ising model, with each spin representing th

Daofei Zhang

We establish an improved criterion for rapid mixing of hyperbolic flows by weakening the requirement on the temporal distance function from positive box dimension to the existence of two values whose ratio is Diophantine. We also demonstrate the applicability of our results through explicit examples where the previous dimension condition were either too rest

Yu-Chen Zhou, Hong-Bo Jin, Cong-Feng Qiao, Yue-Liang Wu

The intermediate-mass-ratio inspirals (IMRIs) may be surrounded by dark matter (DM) minispikes. The dynamical friction from these DM minispike structures can affect the dynamics and the gravitational wave (GW) emission of the IMRIs. We analyze the effects of general dynamical friction, with a particular contribution from DM particles moving faster than the s

J. W. Bruce, M. A. C. Fernandes, F. Tari

Given a germ of a smooth plane curve $(\{f(x,y)=0\},0)\subset (\mathbb K^2,0), \mathbb K=\mathbb R, \mathbb C$, with an isolated singularity, we define two invariants $I_f$ and $V_f\in \mathbb N\cup\{\infty\}$ which count the number of inflections and vertices (suitably interpreted in the complex case) concentrated at the singular point; the first is an affi