May 2024 arXiv papers — page 34

John Regan, Marta Volonteri

The pathway(s) to seeding the massive black holes (MBHs) that exist at the heart of galaxies in the present and distant Universe remains an unsolved problem. Here we categorise, describe and quantitatively discuss the formation pathways of both $\textit{light}$ and $\textit{heavy}$ seeds. We emphasise that the most recent computational models suggest that ra

Yi-Pei Chen, Noriki Nishida, Hideki Nakayama, Yuji Matsumoto

Enhancing user engagement through personalization in conversational agents has gained significance, especially with the advent of large language models that generate fluent responses. Personalized dialogue generation, however, is multifaceted and varies in its definition -- ranging from instilling a persona in the agent to capturing users' explicit and impli

Oleg Kaptsov

We consider higher symmetries and operator symmetries of linear partial differential equations. The higher symmetries form a Lie algebra, and operator ones form an associative algebra. The relationship between these symmetries is established. We show that symmetries of linear equations sometimes generate symmetries of nonlinear ones. New symmetries of two-di

Adeline Samson, Massimiliano Tamborrino, Irene Tubikanec

The stochastic FitzHugh-Nagumo (FHN) model is a two-dimensional nonlinear stochastic differential equation with additive degenerate noise, whose first component, the only one observed, describes the membrane voltage evolution of a single neuron. Due to its low-dimensionality, its analytical and numerical tractability and its neuronal interpretation, it has b

Thomas Cory, Wolf Rieder, Thu-My Huynh

Mobile Health (mHealth) applications have become a crucial part of health monitoring and management. However, the proliferation of these applications has also raised concerns over the privacy and security of Personally Identifiable Information and Protected Health Information. Addressing these concerns, this paper introduces a novel framework for the qualita

Anna-Lee Jessop, Primoz Pirih, Limin Wang, Nipam Patel

Biophotonic nanostructures in butterfly wing scales remain fascinating examples of biological functional materials, with intriguing open questions in regards to formation and evolutionary function. One particularly interesting butterfly species, Erora opisena (Lycaenidae: Theclinae), develops wing scales that contain three-dimensional photonic crystals that

Yunzhi Yao, Ningyu Zhang, Zekun Xi, Mengru Wang

The remarkable capabilities of modern large language models are rooted in their vast repositories of knowledge encoded within their parameters, enabling them to perceive the world and engage in reasoning. The inner workings of how these models store knowledge have long been a subject of intense interest and investigation among researchers. To date, most stud

Ruo-Chun Tzeng, Naoto Ohsaka, Kaito Ariu

We study the matroid semi-bandits problem, where at each round the learner plays a subset of $K$ arms from a feasible set, and the goal is to maximize the expected cumulative linear rewards. Existing algorithms have per-round time complexity at least $\Omega(K)$, which becomes expensive when $K$ is large. To address this computational issue, we propose Faste

F. D. Amaro, E. Baracchini, L. Benussi, S. Bianco

The CYGNO collaboration is developing next generation directional Dark Matter (DM) detection experiments, using gaseous Time Projection Chambers (TPCs), as a robust method for identifying Weakly Interacting Massive Particles (WIMPs) below the Neutrino Fog. SF6 is potentially ideal for this since it provides a high fluorine content, enhancing sensitivity to s

Jinyan Chen, Jackson Tiong, Lin Htoo Zaw, Valerio Scarani

We introduce an even-parity precession protocol that can detect the nonclassicality of some quantum states using only measurements of a uniformly-precessing variable at different points in time. Depending on the system under study, the protocol may detect the Wigner negativity of a single quantum harmonic oscillator or of a single spin $j\geq 2$; the non-Gau

Junjie Shentu, Matthew Watson, Noura Al Moubayed

Text-to-image (T2I) customization empowers users to adapt the T2I diffusion model to new concepts absent in the pre-training dataset. On this basis, capturing multiple new concepts from a single image has emerged as a new task, allowing the model to learn multiple concepts simultaneously or discard unwanted concepts. However, multiple-concept disentanglement

Teodor-George Marchitan, Claudiu Creanga, Liviu P. Dinu

This paper describes the approach of the UniBuc - NLP team in tackling the SemEval 2024 Task 8: Multigenerator, Multidomain, and Multilingual Black-Box Machine-Generated Text Detection. We explored transformer-based and hybrid deep learning architectures. For subtask B, our transformer-based model achieved a strong \textbf{second-place} out of $77$ teams wit

M. Maneesh Kumar, Sanjay Sarkar, Amit Agarwal

Electric field-induced modulation of the optical properties is crucial for amplitude and phase modulators used in photonic devices. Here, we present a comprehensive study of the band geometry-induced electro-optic effect, specifically focusing on the Fermi surface and disorder-induced contributions. These contributions are crucial for metallic and semimetall

P. E. Collin Aldia, Jiayang Chen, Jonas K. C. Ballentin, Lukas W. Perner

We present a compact, reliable, and robust free-running all-polarization-maintaining erbium (Er) single-cavity dual-comb laser generated via polarization multiplexing with gain sharing. Polarization multiplexing exploits the fast and slow axes of the fiber, while modelocking is achieved through a nonlinear amplifying loop mirror scheme using readily availabl

Liyuan Suo

In this paper, we mainly investigate a class of Kolmogorov-Fokker-Planck operator with 4 different scalings in nondivergence form. And we assume the coefficients $a^{ij}$ are only measurable in $t$ and satisfy the vanishing mean oscillation in space variables. We establish a global priori estimates of $\nabla_x^u$, $( -\Delta_y )^{1/3} u$ and $( -\Delta_z )^

Thibaud Gloaguen, Nikola Jovanović, Robin Staab, Martin Vechev

Watermarking has emerged as a promising way to detect LLM-generated text, by augmenting LLM generations with later detectable signals. Recent work has proposed multiple families of watermarking schemes, several of which focus on preserving the LLM distribution. This distribution-preservation property is motivated by the fact that it is a tractable proxy for

Massimo Bilancioni, Massimiliano Esposito

Similarly to gear systems in vehicles, most chemical reaction networks (CRNs) involved in energy transduction have at their disposal multiple transduction pathways, each characterized by distinct efficiencies. We conceptualize these pathways as `chemical gears' and demonstrate their role in refining the second law of thermodynamics. This allows us to determi

Hyungtaik Oh, Wonkeun Jo, Dongil Kim

Sequential recommendation systems that model dynamic preferences based on a use's past behavior are crucial to e-commerce. Recent studies on these systems have considered various types of information such as images and texts. However, multimodal data have not yet been utilized directly to recommend products to users. In this study, we propose an attention-ba

Yunsong Wang, Tianxin Huang, Hanlin Chen, Gim Hee Lee

Empowering 3D Gaussian Splatting with generalization ability is appealing. However, existing generalizable 3D Gaussian Splatting methods are largely confined to narrow-range interpolation between stereo images due to their heavy backbones, thus lacking the ability to accurately localize 3D Gaussian and support free-view synthesis across wide view range. In t

Xiaobao Wu, Xinshuai Dong, Liangming Pan, Thong Nguyen

Dynamic topic models track the evolution of topics in sequential documents, which have derived various applications like trend analysis and opinion mining. However, existing models suffer from repetitive topic and unassociated topic issues, failing to reveal the evolution and hindering further applications. To address these issues, we break the tradition of

Anirudhan Badrinath, Prabhat Agarwal, Jiajing Xu

For aligning large language models (LLMs), prior work has leveraged reinforcement learning via human feedback (RLHF) or variations of direct preference optimization (DPO). While DPO offers a simpler framework based on maximum likelihood estimation, it compromises on the ability to easily tune language models to maximize auxiliary, non-preferential objectives

O. Deniz Akyildiz, Mark Girolami, Andrew M. Stuart, Arnaud Vadeboncoeur

Bayesian inversion is central to the quantification of uncertainty within problems arising from numerous applications in science and engineering. To formulate the approach, four ingredients are required: a forward model mapping the unknown parameter to an element of a solution space, often the solution space for a differential equation; an observation operat

Antonio Martín Andrés, Pedro Femia Marzo

Positive predictive value and negative predictive value are two widely used parameters to assess the clinical usefulness of a medical diagnostic test. When there are two diagnostic tests, it is recommendable to make a comparative assessment of the values of these two parameters after applying the two tests to the same subjects (paired samples). The objective

Erik D. Demaine, Yael Kirkpatrick, Rebecca Lin

How should we thread a single string through a set of tubes so that pulling the string taut self-assembles the tubes into a desired graph? While prior work [ITCS 2024] solves this problem with the goal of minimizing the length of string, we study here the objective of minimizing the total turn cost. The frictional force required to pull the string through th

Louisa Seelbach Benkner

We study the average height of random trees generated by leaf-centric binary tree sources as introduced by Zhang, Yang and Kieffer. A leaf-centric binary tree source induces for every $n \geq 2$ a probability distribution on the set of binary trees with $n$ leaves. Our results generalize a result by Devroye, according to which the average height of a random

Leon Götz, Marcel Kollovieh, Stephan Günnemann, Leo Schwinn

Despite recent advances in subquadratic attention mechanisms or state-space models, processing long token sequences still imposes significant computational requirements. Token merging has emerged as a solution to increase computational efficiency in computer vision architectures. In this work, we perform the first investigations of token merging in time seri

Zangir Iklassov, Yali Du, Farkhad Akimov, Martin Takac

Large Language Models (LLMs) have become pivotal in addressing reasoning tasks across diverse domains, including arithmetic, commonsense, and symbolic reasoning. They utilize prompting techniques such as Exploration-of-Thought, Decomposition, and Refinement to effectively navigate and solve intricate tasks. Despite these advancements, the application of LLMs

A. Nayak, D. Rajak, B. Farkas, C. Granados

Strongly laser-driven semiconductor crystals offer substantial advantages for the study of many-body physics and ultrafast optoelectronics via the high harmonic generation process. While this phenomenon has been employed to investigate the dynamics of solids in the presence of strong laser fields, its potential to be utilized as an attosecond light source ha

Louise Leclerc

We introduce in this paper a new formalisation of positive opetopes where faces are organised in a poset. Then we show that our definition is equivalent to that of positives opetopes as given by Marek Zawadowski.

Alexandra Carvalho, Vivek Nair, Sergio G. Echeverrigaray, and Antonio H. Castro Neto

We have investigated the lithium capacity of the 2H phase of niobium sulphide (NbS2) using density functional theory calculations and experiments. Theoretically, this material is found to allow the intercalation of a double layer of Li in between each NbS2 layer when in equilibrium with metal Li. The resulting specific capacity (340.8 mAh/g for the pristine

Giulio Chiribella, Saptarshi Roy, Tamal Guha, Sutapa Saha

A fundamental limitation of quantum communication is that a single qubit can carry at most 1 bit of classical information. For an important class of quantum communication channels, known as entanglement-breaking, this limitation holds even if the sender and receiver share entangled particles. But does this mean that, for the purpose of communicating classica

Leszek Hadasz, Rikard von Unge

We give a tentative definition of the recently introduced Root-$T\bar{T}$ operator in a generic, two dimensional quantum conformal field theory with continuous spectrum of scaling weights. The definition assumes certain factorization properties and uses Schwinger parametrization to introduce the square root. Properties of the operator thus defined are invest

Tianyang Chi, Ningyu He, Xiaohui Hu, Haoyu Wang

Maximal Extractable Value (MEV) drives the prosperity of the blockchain ecosystem. By strategically including, excluding, or reordering transactions within blocks, block producers can extract additional value, which in turn incentivizes them to keep the decentralization of the whole blockchain platform. Before September 2022, around $675M was extracted in te

Aleksandar Aksentijević, Suzana Aleksić

This paper has the characteristics of a review paper in which results of shift-invariant subspaces of Sobolev type are summarized without proofs. The structure of shift-invariant spaces $V_s$, $s\in\mathbb{R}$, generated by at most countable family of generators, which are subspaces of Sobolev spaces $H^s(\mathbb{R}^n)$, are announced in \cite{aap} and Besse

Xiaohao Xu, Ye Li, Tianyi Zhang, Jinrong Yang

Constructing large-scale labeled datasets for multi-modal perception model training in autonomous driving presents significant challenges. This has motivated the development of self-supervised pretraining strategies. However, existing pretraining methods mainly employ distinct approaches for each modality. In contrast, we focus on LiDAR-Camera 3D perception

Kasper H. Nielsen, Ying Wang, Edward Deacon, Patrik I. Sund

The lack of interactions between single photons prohibits direct nonlinear operations in quantum optical circuits, representing a central obstacle in photonic quantum technologies. Here, we demonstrate multi-mode nonlinear photonic circuits where both linear and direct nonlinear operations can be programmed with high precision at the single-photon level. Det

Hongbin Lin, Bin Li, Chun Wai Wong, Juan Rojas

Intelligent vision control systems for surgical robots should adapt to unknown and diverse objects while being robust to system disturbances. Previous methods did not meet these requirements due to mainly relying on pose estimation and feature tracking. We propose a world-model-based deep reinforcement learning framework "Grasp Anything for Surgery" (GAS), t

Alessandro Carones

The detection of primordial polarization $B$ modes of the Cosmic Microwave Background (CMB) requires exquisite control of Galactic foreground contamination. The Needlet Internal Linear Combination (NILC) method has proven effective in reconstructing CMB $B$ modes without suffering from mis-modeling errors of Galactic emission. However, with the most complex

Yuxin Liu, Deepika Tiwari, Cristian Bogdan, Benoit Baudry

JavaScript packages are notoriously prone to bloat, a factor that significantly impacts the performance and maintainability of web applications. While web bundlers and tree-shaking can mitigate this issue in client-side applications, state-of-the-art techniques have limitations on the detection and removal of bloat in server-side applications. In this paper,

Seong-Hyeon Hwang, Minsu Kim, Steven Euijong Whang

We study the problem of robust data augmentation for regression tasks in the presence of noisy data. Data augmentation is essential for generalizing deep learning models, but most of the techniques like the popular Mixup are primarily designed for classification tasks on image data. Recently, there are also Mixup techniques that are specialized to regression

CUORE Collaboration, D. Q. Adams, C. Alduino, K. Alfonso

We present the model we developed to reconstruct the CUORE radioactive background based on the analysis of an experimental exposure of 1038.4 kg yr. The data reconstruction relies on a simultaneous Bayesian fit applied to energy spectra over a broad energy range. The high granularity of the CUORE detector, together with the large exposure and extended stable

Eyup Yalcinkaya

This paper investigates the geometric structures and properties of 8-dimensional manifolds with Spin(7)-holonomy. We focus on the characterization and implications of 4-planes within these manifolds, which are endowed with an almost symplectic structure compatible with the Spin(7)-structure. We provide a detailed analysis of the differential forms defining t

Changle Qu, Sunhao Dai, Xiaochi Wei, Hengyi Cai

Recently, tool learning with large language models (LLMs) has emerged as a promising paradigm for augmenting the capabilities of LLMs to tackle highly complex problems. Despite growing attention and rapid advancements in this field, the existing literature remains fragmented and lacks systematic organization, posing barriers to entry for newcomers. This gap

Zhenjie Zhang, Yuyang Rao, Hao Xiao, Xiaokui Xiao

Generative AI models, such as GPT-4 and Stable Diffusion, have demonstrated powerful and disruptive capabilities in natural language and image tasks. However, deploying these models in decentralized environments remains challenging. Unlike traditional centralized deployment, systematically guaranteeing the integrity of AI model services in fully decentralize

Jinbo Xing, Hanyuan Liu, Menghan Xia, Yong Zhang

We introduce ToonCrafter, a novel approach that transcends traditional correspondence-based cartoon video interpolation, paving the way for generative interpolation. Traditional methods, that implicitly assume linear motion and the absence of complicated phenomena like dis-occlusion, often struggle with the exaggerated non-linear and large motions with occlu

Xiumei Deng, Jun Li, Kang Wei, Long Shi

Adaptive moment estimation (Adam), as a Stochastic Gradient Descent (SGD) variant, has gained widespread popularity in federated learning (FL) due to its fast convergence. However, federated Adam (FedAdam) algorithms suffer from a threefold increase in uplink communication overhead compared to federated SGD (FedSGD) algorithms, which arises from the necessit

Keming Lu, Bowen Yu, Fei Huang, Yang Fan

Effectively aligning Large Language Models (LLMs) with human-centric values while preventing the degradation of abilities acquired through Pre-training and Supervised Fine-tuning (SFT) poses a central challenge in Reinforcement Learning from Human Feedback (RLHF). In this paper, we first discover that interpolating RLHF and SFT model parameters can adjust th

Katariina Perkonoja, Joni Virta

In the contemporary data landscape characterized by multi-source data collection and third-party sharing, ensuring individual privacy stands as a critical concern. While various anonymization methods exist, their utility preservation and privacy guarantees remain challenging to quantify. In this work, we address this gap by studying the utility and privacy o

Maxime Fairon

Around 20 years ago, M. Van den Bergh introduced double Poisson brackets as operations on associative algebras inducing Poisson brackets under the representation functor. Weaker versions of these operations, called modified double Poisson brackets, were later introduced by S. Arthamonov in order to induce a Poisson bracket on moduli spaces of representations

Zhenxing Niu, Yuyao Sun, Qiguang Miao, Rong Jin

Deep Neural Networks (DNNs) are known to be vulnerable to both backdoor and adversarial attacks. In the literature, these two types of attacks are commonly treated as distinct robustness problems and solved separately, since they belong to training-time and inference-time attacks respectively. However, this paper revealed that there is an intriguing connecti

Juntae Kim, Sungwon Woo, Jongho Nang

Image copy detection is the task of detecting edited copies of any image within a reference database. While previous approaches have shown remarkable progress, the large size of their networks and descriptors remains a disadvantage, complicating their practical application. In this paper, we propose a novel method that achieves competitive performance by usi

Shakti N. Wadekar, Abhishek Chaurasia, Aman Chadha, Eugenio Culurciello

This work uniquely identifies and characterizes four prevalent multimodal model architectural patterns in the contemporary multimodal landscape. Systematically categorizing models by architecture type facilitates monitoring of developments in the multimodal domain. Distinct from recent survey papers that present general information on multimodal architecture

Huyen Le, Khiet Dang, Tien Lai, Nhung Nguyen

Quantifying sarcomere structure organization in human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) is crucial for understanding cardiac disease pathology, improving drug screening, and advancing regenerative medicine. Traditional methods, such as manual annotation and Fourier transform analysis, are labor-intensive, error-prone, and lack

Ivan Iudice, Domenico Pascarella, Gianluca Corraro, Giovanni Cuciniello

In aviation, the impact of threats is becoming increasingly significant, particularly for global navigation satellite system (GNSS). Two relevant GNSS threats are represented by jamming and spoofing. In order to evaluate the technological solutions to counter GNSS attacks, such attacks should be assessed by means of a proper GNSS threat simulator. This work

Ning Li, Huaikang Zhou, Kris Mikel-Hong

Recent advancements in generative artificial intelligence (AI) have transformed collaborative work processes, yet the impact on team performance remains underexplored. Here we examine the role of generative AI in enhancing or replacing traditional team dynamics using a randomized controlled experiment with 435 participants across 122 teams. We show that team

Abhijit Kumar Kushwaha, Sankara Arunachalam, Ville Jokinen, Dan Daniel

This paper explores the friction forces encountered by droplets on non-wetting surfaces, specifically focusing on superhydrophobic and superheated substrates. Employing a combination of experimental techniques, including inclined plane tests and cantilever force sensor measurements, we quantify friction forces across a broad range of velocities and surface t

Qiang Li, Hoi-To Wai

This paper studies a risk minimization problem with decision dependent data distribution. The problem pertains to the performative prediction setting in which a trained model can affect the outcome estimated by the model. Such dependency creates a feedback loop that influences the stability of optimization algorithms such as stochastic gradient descent (SGD)

Mingxuan Liu, Yilin Ning, Salinelat Teixayavong, Xiaoxuan Liu

The ethical integration of Artificial Intelligence (AI) in healthcare necessitates addressing fairness-a concept that is highly context-specific across medical fields. Extensive studies have been conducted to expand the technical components of AI fairness, while tremendous calls for AI fairness have been raised from healthcare. Despite this, a significant di

Lara Ost, Sebastiano Cultrera di Montesano, Herbert Edelsbrunner

In numerous fields, dynamic time series data require continuous updates, necessitating efficient data processing techniques for accurate analysis. This paper examines the banana tree data structure, specifically designed to efficiently maintain persistent homology -- a multi-scale topological descriptor -- for dynamically changing time series data. We implem

Kanti V. Mardia

It will not be an exaggeration to say that R A Fisher is the Albert Einstein of Statistics. He pioneered almost all the main branches of statistics, but it is not as well known that he opened the area of Directional Statistics with his 1953 paper introducing a distribution on the sphere which is now known as the Fisher distribution. He stressed that for sphe

Dong Bok Lee, Aoxuan Silvia Zhang, Byungjoo Kim, Junhyeon Park

In this paper, we address the problem of cost-sensitive multi-fidelity Bayesian Optimization (BO) for efficient hyperparameter optimization (HPO). Specifically, we assume a scenario where users want to early-stop the BO when the performance improvement is not satisfactory with respect to the required computational cost. Motivated by this scenario, we introdu

Waqar Mirza, Nikhil Karamchandani, Niranjan Balachandran

In this paper, we introduce a variation of the group testing problem where each test is specified by an ordered subset of items and returns the first defective item in the specified order or returns null if there are no defectives. We refer to this as cascaded group testing and the goal is to identify a small set of $K$ defective items amongst a collection o

Leo Shan Wenzhang Zhou Grace Zhao

Image matting aims to obtain an alpha matte that separates foreground objects from the background accurately. Recently, trimap-free matting has been well studied because it requires only the original image without any extra input. Such methods usually extract a rough foreground by itself to take place trimap as further guidance. However, the definition of 'f

Longze Chen, Ziqiang Liu, Wanwei He, Yunshui Li

Long-context modeling capabilities are important for large language models (LLMs) in various applications. However, directly training LLMs with long context windows is insufficient to enhance this capability since some training samples do not exhibit strong semantic dependencies across long contexts. In this study, we propose a data mining framework \textbf{

Xiumei Deng, Jun Li, Long Shi, Kang Wei

Digital twin (DT) has emerged as a promising solution to enhance manufacturing efficiency in industrial Internet of Things (IIoT) networks. To promote the efficiency and trustworthiness of DT for wireless IIoT networks, we propose a blockchain-enabled DT (B-DT) framework that employs deep neural network (DNN) partitioning technique and reputation-based conse

Junjie Wang, Bin Chen, Bin Kang, Yulin Li

Open-vocabulary detection aims to detect objects from novel categories beyond the base categories on which the detector is trained. However, existing open-vocabulary detectors trained on base category data tend to assign higher confidence to trained categories and confuse novel categories with the background. To resolve this, we propose OV-DQUO, an \textbf{O

Pengyu Shi, Jie Zhang, Jacques Magnaudet

The buoyancy-driven motion of a deformable bubble rising near a vertical hydrophilic wall is studied numerically. We focus on moderately inertial regimes in which the bubble undergoes low-to-moderate deformations and would rise in a straight line in the absence of the wall. Three different types of near-wall motion are observed, depending on the buoyancy-to-

M. Wiśniewski, J. Spiechowicz

Non-Markovian systems form a broad area of physics that remains greatly unexplored despite years of intensive investigations. The spotlight is on memory as a source of effects that are absent in their Markovian counterparts. In this work we dive into this problem and analyze a driven Brownian particle moving in a spatially periodic potential and exposed to c

Étienne Fournier, Christine Jeoffrion, Belal Hmedan, Damien Pellier

The 5.0 industry promotes collaborative robots (cobots). This research studies the impacts of cobot collaboration using an experimental setup. 120 participants realized a simple and a complex assembly task. 50% collaborated with another human (H/H) and 50% with a cobot (H/C). The workload and the acceptability of the cobotic collaboration were measured. Work

Xin Tong

We extend the Langlands program in various subprograms with certain different generalizations: (1) Mixed-parity functorial perturbation of the usual Langlands program after Fargues-Scholze in all characteristics; (2) Robba-Frobenius sheafified functorial perturbation of the usual Langlands program after Fargues-Scholze and Kedlaya-Liu in all characteristics;

Giulia Ventagli, Ippocratis D. Saltas

We present a pipeline to infer the equation of state of neutron stars from observations based on deep neural networks. In particular, using the standard (deterministic), as well as Bayesian (probabilistic) deep networks, we explore how one can infer the interior speed of sound of the star given a set of mock observations of total stellar mass, stellar radius

Viktor Abramov

In this paper we study ternary algebras of third-order hypermatrices. By hypermatrix we mean a complex-valued variable with three indices, which is also called a three-dimensional matrix or spatial matrix. We assume that a hypermatrix is defined in three-dimensional Euclidean space and when this space is rotated, it transforms as a SO(3)-tensor. We consider

Shimpei Kajiwara, Eiji Hase, Shota Nakano, Keishiro Ootani

We introduce a novel laser-scanning optical microscopy technique that employs optical-frequency-comb (OFC) lasers. This method facilitates multimodal spectroscopic imaging by analyzing interferograms produced via a dual-comb spectroscopic approach. Such interferograms capture comprehensive light information, including amplitude, phase, polarization, frequenc

Zhengji Li, Xi Xiao, Jiacheng Xie, Yuxiao Fan

With the development of modern society, traffic volume continues to increase in most countries worldwide, leading to an increase in the rate of pavement damage Therefore, the real-time and highly accurate pavement damage detection and maintenance have become the current need. In this paper, an enhanced pavement damage detection method with CycleGAN and impro

Ryoko Kino, Sho Nagao, Takeru Akiyama, Hiroyuki Fujioka

A real-time beam profile monitoring system is proposed for GeV photon beams at the BM4 beamline of the Mikamine site, Research Center for Accelerator and Radioisotope Science (RARiS; previously known as ELPH) at Tohoku University. This monitoring system enhances the capability to monitor the entire beamline by incorporating newly developed beam profile monit

Hongze Sun, Rui Liu, Wuque Cai, Jun Wang

Visual object tracking, which is primarily based on visible light image sequences, encounters numerous challenges in complicated scenarios, such as low light conditions, high dynamic ranges, and background clutter. To address these challenges, incorporating the advantages of multiple visual modalities is a promising solution for achieving reliable object tra

Yaoyao Xu, Xinjian Zhao, Xiaozhuang Song, Benyou Wang

We introduce a pioneering methodology for boosting large language models in the domain of protein representation learning. Our primary contribution lies in the refinement process for correlating the over-reliance on co-evolution knowledge, in a way that networks are trained to distill invaluable insights from negative samples, constituted by protein pairs so

Irem Ulku, O. Ozgur Tanriover, Erdem Akagündüz

Plant health can be monitored dynamically using multispectral sensors that measure Near-Infrared reflectance (NIR). Despite this potential, obtaining and annotating high-resolution NIR images poses a significant challenge for training deep neural networks. Typically, large networks pre-trained on the RGB domain are utilized to fine-tune infrared images. This

Haoxiang Shi, Xulong Zhang, Ning Cheng, Yong Zhang

The purpose of emotion recognition in conversation (ERC) is to identify the emotion category of an utterance based on contextual information. Previous ERC methods relied on simple connections for cross-modal fusion and ignored the information differences between modalities, resulting in the model being unable to focus on modality-specific emotional informati

Lukas Sporrer, Guojun Zhou, Mingchao Wang, Vasileios Balos

Two-dimensional conjugated metal-organic frameworks (2D c-MOFs) are emerging as a unique class of 2D electronic materials. However, intrinsically semiconducting 2D c-MOFs with gaps in the Vis-NIR and high charge carrier mobility have been rare. Most of the reported semiconducting 2D c-MOFs are metallic (i.e. gapless), which limits their use in applications w

Zhonghang Li, Lianghao Xia, Yong Xu, Chao Huang

The objective of traffic prediction is to accurately forecast and analyze the dynamics of transportation patterns, considering both space and time. However, the presence of distribution shift poses a significant challenge in this field, as existing models struggle to generalize well when faced with test data that significantly differs from the training distr

Donato Crisostomi, Marco Fumero, Daniele Baieri, Florian Bernard

In this paper, we present a novel data-free method for merging neural networks in weight space. Differently from most existing works, our method optimizes for the permutations of network neurons globally across all layers. This allows us to enforce cycle consistency of the permutations when merging $N \geq 3$ models, allowing circular compositions of permuta

Luciano Piersanti, Lev R. Yungelson, Eduardo Bravo

Binary systems made by a low-mass CO WD and a He-donor represent possible progenitors of explosive events via He-detonation, producing low-luminosity thermonuclear Supernovae with a peculiar nucleosynthetis. Recently, the binary system PTF J223857.11+743015.1 has been suggested as one. We investigate the evolution of the PTF J223857.11+743015.1 system, compo

Young-Pil Choi, Houzhi Tang, Weiyuan Zou

This paper investigates the global well-posedness and large-time behavior of solutions for a coupled fluid model in $\mathbb{R}^3$ consisting of the isothermal compressible Euler-Poisson system and incompressible Navier-Stokes equations coupled through the drag force. Notably, we exploit the dissipation effects inherent in the Poisson equation to achieve a f

Ruofan Wang, Xingjun Ma, Hanxu Zhou, Chuanjun Ji

Recent advancements in Large Vision-Language Models (VLMs) have underscored their superiority in various multimodal tasks. However, the adversarial robustness of VLMs has not been fully explored. Existing methods mainly assess robustness through unimodal adversarial attacks that perturb images, while assuming inherent resilience against text-based attacks. D

Xiaocheng Yang, Bingsen Chen, Yik-Cheung Tam

Instructing large language models (LLMs) to solve elementary school math problems has shown great success using Chain of Thought (CoT). However, the CoT approach relies on an LLM to generate a sequence of arithmetic calculations which can be prone to cascaded calculation errors. We hypothesize that an LLM should focus on extracting predicates and generating

Akhil S Anand, Shambhuraj Sawant, Dirk Reinhardt, Sebastien Gros

In this paper, we explore the interplay between Predictive Control and closed-loop optimality, spanning from Model Predictive Control to Data-Driven Predictive Control. Predictive Control in general relies on some form of prediction scheme on the real system trajectories. However, these predictions may not accurately capture the real system dynamics, for e.g

Bin Zhang, Bi Zeng, Zexin Peng

In recent years, Neural Radiance Fields (NeRF) has revolutionized three-dimensional (3D) reconstruction with its implicit representation. Building upon NeRF, 3D Gaussian Splatting (3D-GS) has departed from the implicit representation of neural networks and instead directly represents scenes as point clouds with Gaussian-shaped distributions. While this shift

Wujiang Xu, Qitian Wu, Zujie Liang, Jiaojiao Han

Sequential Recommendation (SR) task involves predicting the next item a user is likely to interact with, given their past interactions. The SR models examine the sequence of a user's actions to discern more complex behavioral patterns and temporal dynamics. Recent research demonstrates the great impact of LLMs on sequential recommendation systems, either vie

Severi Rissanen, Markus Heinonen, Arno Solin

In the domains of image and audio, diffusion models have shown impressive performance. However, their application to discrete data types, such as language, has often been suboptimal compared to autoregressive generative models. This paper tackles the challenge of improving discrete diffusion models by introducing a structured forward process that leverages t

Jiaxiang Li, Siliang Zeng, Hoi-To Wai, Chenliang Li

Aligning human preference and value is an important requirement for contemporary foundation models. State-of-the-art techniques such as Reinforcement Learning from Human Feedback (RLHF) often consist of two stages: 1) supervised fine-tuning (SFT), where the model is fine-tuned by learning from human demonstration data; 2) Preference learning, where preferenc

Yichao Zhang, Yang Zhou

Over any fixed totally real number field with narrow class number one, we prove that the Rankin-Cohen bracket of two Hecke eigenforms for the Hilbert modular group can only be a Hecke eigenform for dimension reasons, except for a couple of cases where the Rankin-Selberg method does not apply. We shall also prove a conjecture of Freitag on the volume of Hilbe

Jiri Mekyska, Katarina Safarova, Tomas Urbanek, Jirina Bednarova

Graphomotor and handwriting disabilities (GD and HD, respectively) could significantly reduce children's quality of life. Effective remediation depends on proper diagnosis; however, current approaches to diagnosis and assessment of GD and HD have several limitations and knowledge gaps, e.g. they are subjective, they do not facilitate identification of specif

Takaaki Nomura, Xinran Xu

We discuss degenerate vector dark matter and dark photon that are induced from hidden $SU(2)_H$ gauge sector where it is spontaneously broken by vacuum expectation value of $SU(2)_H$ doublet. Kinetic mixing between $SU(2)_H$ and $U(1)_Y$ gauge fields can be generated by introducing dimension six operator realizing dark photon interactions. In estimating reli

Mikhail Korobkov, Xiao Ren

We consider some new estimates for general steady Navier-Stokes solutions in plane domains. According to our main result, if the domain is convex, then the difference between mean values of the velocity over two concentric circles is bounded (up to a constant factor) by the square-root of the Dirichlet integral in the annulus between the circles. The constan

Zixuan Zeng, Shuhua Deng, Shoukang Yang, Bo Yan

As a heavy molecule, barium monofluoride (BaF) presents itself as a promising candidate for measuring permanent electric dipole moment. The precision of such measurements can be significantly enhanced by utilizing a cold molecular sample. Here we report the realization of three-dimensional magneto-optical trapping (MOT) of BaF molecules. Through the repumpin

Yige Hong, Qiaomin Xie, Yudong Chen, Weina Wang

We consider the infinite-horizon average-reward restless bandit problem. We propose a novel \emph{two-set policy} that maintains two dynamic subsets of arms: one subset of arms has a nearly optimal state distribution and takes actions according to an Optimal Local Control routine; the other subset of arms is driven towards the optimal state distribution and

Onur Boyar, Yanheng Gu, Yuji Tanaka, Shunsuke Tonogai

Generative modeling of crystal structures is significantly challenged by the complexity of input data, which constrains the ability of these models to explore and discover novel crystals. This complexity often confines de novo design methodologies to merely small perturbations of known crystals and hampers the effective application of advanced optimization t

Byeonghu Na, Yeongmin Kim, Minsang Park, Donghyeok Shin

Recent advances in powerful pre-trained diffusion models encourage the development of methods to improve the sampling performance under well-trained diffusion models. This paper introduces Diffusion Rejection Sampling (DiffRS), which uses a rejection sampling scheme that aligns the sampling transition kernels with the true ones at each timestep. The proposed

Ziqi Zhang, Zifeng Zhuang, Jingzehua Xu, Yiyuan Yang

We propose a novel one-step supervised imitation learning (IL) framework called Adversarial Density Regression (ADR). This IL framework aims to correct the policy learned on unknown-quality to match the expert distribution by utilizing demonstrations, without relying on the Bellman operator. Specifically, ADR addresses several limitations in previous IL algo