April 2024 arXiv papers — page 23

Jian Shen, Jiaxin Huang, Zhigong Song

Dual-arm robots have great application prospects in intelligent manufacturing due to their human-like structure when deployed with advanced intelligence algorithm. However, the previous visuomotor policy suffers from perception deficiencies in environments where features of images are impaired by the various conditions, such as abnormal lighting, occlusion a

Oubaida Chouchane, Christoph Busch, Chiara Galdi, Nicholas Evans

When decisions are made and when personal data is treated by automated processes, there is an expectation of fairness -- that members of different demographic groups receive equitable treatment. This expectation applies to biometric systems such as automatic speaker verification (ASV). We present a comparison of three candidate fairness metrics and extend pr

Qingyang Zhang, Yake Wei, Zongbo Han, Huazhu Fu

Multimodal fusion focuses on integrating information from multiple modalities with the goal of more accurate prediction, which has achieved remarkable progress in a wide range of scenarios, including autonomous driving and medical diagnosis. However, the reliability of multimodal fusion remains largely unexplored especially under low-quality data settings. T

Guozheng Li, Peng Wang, Wenjun Ke, Yikai Guo

Relation extraction (RE) aims to identify relations between entities mentioned in texts. Although large language models (LLMs) have demonstrated impressive in-context learning (ICL) abilities in various tasks, they still suffer from poor performances compared to most supervised fine-tuned RE methods. Utilizing ICL for RE with LLMs encounters two challenges:

Haoran Lian, Yizhe Xiong, Jianwei Niu, Shasha Mo

Byte Pair Encoding (BPE) serves as a foundation method for text tokenization in the Natural Language Processing (NLP) field. Despite its wide adoption, the original BPE algorithm harbors an inherent flaw: it inadvertently introduces a frequency imbalance for tokens in the text corpus. Since BPE iteratively merges the most frequent token pair in the text corp

Guozheng Li, Peng Wang, Jiajun Liu, Yikai Guo

Relation extraction (RE) is an important task that aims to identify the relationships between entities in texts. While large language models (LLMs) have revealed remarkable in-context learning (ICL) capability for general zero and few-shot learning, recent studies indicate that current LLMs still struggle with zero and few-shot RE. Previous studies are mainl

Yi Yuan, Zhuo Chen, Xubo Liu, Haohe Liu

Contrastive language-audio pretraining~(CLAP) has been developed to align the representations of audio and language, achieving remarkable performance in retrieval and classification tasks. However, current CLAP struggles to capture temporal information within audio and text features, presenting substantial limitations for tasks such as audio retrieval and ge

Nannan Wu, Zhuo Kuang, Zengqiang Yan, Li Yu

Due to escalating privacy concerns, federated learning has been recognized as a vital approach for training deep neural networks with decentralized medical data. In practice, it is challenging to ensure consistent imaging quality across various institutions, often attributed to equipment malfunctions affecting a minority of clients. This imbalance in image q

Rama Carl Hoetzlein

The aerial flocking of birds, or murmurations, has fascinated observers while presenting many challenges to behavioral study and simulation. We examine how the periphery of murmurations remain well bounded and cohesive. We also investigate agitation waves, which occur when a flock is disturbed, developing a plausible model for how they might emerge spontaneo

S. V. Pilipenko, S. A. Drozdov, M. V. Tkachev, A. G. Doroshkevich

The initial power spectrum of density perturbations, generated during the inflationary epoch, is now constrained by observations on scales $\lambda>5$~Mpc and has a power-law form. The peculiarities of the inflationary process can lead to the appearance of non-power-law contributions to this spectrum, such as peaks. The exact size and shape of the peak canno

Guozheng Li, Zijie Xu, Ziyu Shang, Jiajun Liu

Dialogue relation extraction (DRE) aims to extract relations between two arguments within a dialogue, which is more challenging than standard RE due to the higher person pronoun frequency and lower information density in dialogues. However, existing DRE methods still suffer from two serious issues: (1) hard to capture long and sparse multi-turn information,

Weiming Xu, Tao Yang, Peng Zhang

Combustion instability in gas turbines and rocket engines, as one of the most challenging problems in combustion research, arises from the complex interactions among flames, which are also influenced by chemical reactions, heat and mass transfer, and acoustics. Identifying and understanding combustion instability is essential to ensure the safe and reliable

Koji Kobashi

The purpose of this article is to compute the radial distribution function of liquid CO2 at 250 K and 50 bar and its expansion coefficients by spherical harmonics as a function of intermolecular distance up to 20 angstroms for all possible combinations of angular indices l, l prime, and m less than or equal to 8, in contrast to past works of limited combinat

Jaewon Jang, Bonjun Choi

Federated learning ensures the privacy of clients by conducting distributed training on individual client devices and sharing only the model weights with a central server. However, in real-world scenarios, the heterogeneity of data among clients necessitates appropriate personalization methods. In this paper, we aim to address this heterogeneity using a form

Patrick Das Gupta

The space-time geometry in any inertial frame is described by the line-element $ds^2= \eta_{\mu \nu} dx^\mu dx^\nu$. Now, not only the Minkowski metric $\eta_{\mu \nu} $ is invariant under proper Lorentz transformations, the totally antisymmetric Levi-Civita tensor $e_{\mu \nu \alpha \beta} $ too is. In general relativity (GR), $\eta_{\mu \nu} $ of the flat

Honghui Sun, Zhenwei Yao

Understanding the mechanical instabilities of two-dimensional membranes has strong connection to the subjects of structure instabilities, morphology control and materials failures. In this work, we investigate the plastic mechanism developed in the annular crystalline membrane system for adapting to the shrinking space, which is caused by the controllable gr

Geno Nikolov, Petar Nikolov

We study two modifications of the trapezoidal product cubature formulae, approximating double integrals over the square domain $[a,b]^2=[a,b]\times [a,b]$. Our modified cubature formulae use mixed type data: except evaluations of the integrand on the points forming a uniform grid on $[a,b]^2$, they involve two or four univariate integrals. An useful property

J. Oppliger, J. Küspert, A. -C. Dippel, M. v. Zimmermann

Spectacular quantum phenomena such as superconductivity often emerge in flat-band systems where Coulomb interactions overpower electron kinetics. Engineering strategies for flat-band physics is therefore of great importance. Here, using high-energy grazing-incidence x-ray diffraction, we demonstrate how in-situ temperature annealing of the infinite-layer nic

Daijin Yang, Erica Kleinman, Casper Harteveld

This paper introduces a scoping review of 55 articles to explore GPT's potential for games, offering researchers a comprehensive understanding of the current applications and identifying both emerging trends and unexplored areas. We identify five key applications of GPT in current game research: procedural content generation, mixed-initiative game design, mi

Mohammad Pedramfar, Vaneet Aggarwal

This paper introduces the notion of upper-linearizable/quadratizable functions, a class that extends concavity and DR-submodularity in various settings, including monotone and non-monotone cases over different convex sets. A general meta-algorithm is devised to convert algorithms for linear/quadratic maximization into ones that optimize upper-linearizable/qu

Junyi Gu, Mauro Bellone, Tomáš Pivoňka, Raivo Sell

Critical research about camera-and-LiDAR-based semantic object segmentation for autonomous driving significantly benefited from the recent development of deep learning. Specifically, the vision transformer is the novel ground-breaker that successfully brought the multi-head-attention mechanism to computer vision applications. Therefore, we propose a vision-t

Gerhard Tutz

A general random effects model is proposed that allows for continuous as well as discrete distributions of the responses. Responses can be unrestricted continuous, bounded continuous, binary, ordered categorical or given in the form of counts. The distribution of the responses is not restricted to exponential families, which is a severe restriction in genera

Florenz Graf, Jochen Lindermayr, Birgit Graf, Werner Kraus

Taking over arbitrary tasks like humans do with a mobile service robot in open-world settings requires a holistic scene perception for decision-making and high-level control. This paper presents a human-inspired scene perception model to minimize the gap between human and robotic capabilities. The approach takes over fundamental neuroscience concepts, such a

Kazuki Fujii, Taishi Nakamura, Mengsay Loem, Hiroki Iida

Cross-lingual continual pre-training of large language models (LLMs) initially trained on English corpus allows us to leverage the vast amount of English language resources and reduce the pre-training cost. In this study, we constructed Swallow, an LLM with enhanced Japanese capability, by extending the vocabulary of Llama 2 to include Japanese characters an

Ray Zirui Zhang, Christopher E. Miles, Xiaohui Xie, John S. Lowengrub

We propose a new neural network based method for solving inverse problems for partial differential equations (PDEs) by formulating the PDE inverse problem as a bilevel optimization problem. At the upper level, we minimize the data loss with respect to the PDE parameters. At the lower level, we train a neural network to locally approximate the PDE solution op

Atefeh Habibpourmoghadam, Andrey Evlyukhin, Antonio Calà Lesina

Dielectric metasurfaces can show multi-resonant response with high Q-factors in the visible range due to multipole coupling. This work suggests that these collective resonances in all-dielectric metasurfaces can be modified if adjacent to an anisotropic liquid crystal medium, opening a new path toward the dynamic metasurface effects. Using numerical full-wav

Hamidreza Rahmati, Farhad Rahmati

Blockchain is a decentralized network to increase trust, integrity, and transparency of transactions. With the exponential growth of transactions in the realm of Blockchain, especially in Bitcoin, Blockchain size increases as all transactions must be stored and verified. In Bitcoin, validating M of N transactions involves the necessity of M authentic signatu

Dohyun Kim

Geometrical interpretation on U(1) gage theory of Dirac monopole, introduced here from the line integral\cite{Brandt} form of vector potentials, shows the gauge representation be multi-valued. In this paper, we construct Euclidean form of U(1) gauge theory on Dirac monopole based on the geometrical interpretation, derive the Dirac quantization condition geom

Yizhe Xiong, Xiansheng Chen, Xin Ye, Hui Chen

Recently, Large Language Models (LLMs) have been widely adopted in a wide range of tasks, leading to increasing attention towards the research on how scaling LLMs affects their performance. Existing works, termed Scaling Laws, have discovered that the final test loss of LLMs scales as power-laws with model size, computational budget, and dataset size. Howeve

Ahmed S. Mohamed, Anurag Dhungel, Md Sakib Hasan, Joseph S. Najem

Reservoir computing is a brain-inspired machine learning framework for processing temporal data by mapping inputs into high-dimensional spaces. Physical reservoir computers (PRCs) leverage native fading memory and nonlinearity in physical substrates, including atomic switches, photonics, volatile memristors, and, recently, memcapacitors, to achieve efficient

Guillermo Badia, Manfred Droste, Carles Noguera, Erik Paul

Fagin's seminal result characterizing $\mathsf{NP}$ in terms of existential second-order logic started the fruitful field of descriptive complexity theory. In recent years, there has been much interest in the investigation of quantitative (weighted) models of computations. In this paper, we start the study of descriptive complexity based on weighted Turing m

Rohan Gandhi, Srinivas Narayana

Layer-4 load balancer (LB) is a key building block of online services. In this paper, we empower such LBs to adapt to different and dynamic performance of backend instances (DIPs). Our system, KNAPSACKLB, is generic (can work with variety of LBs), does not require agents on DIPs, LBs or clients, and scales to large numbers of DIPs. KNAPSACKLB uses judicious

Dennis Bonatsos, Andriana Martinou, S. K. Peroulis, T. J. Mertzimekis

Prolate to oblate shape transitions have been predicted in an analytic way in the framework of the Interacting Boson Model (IBM), determining O(6) as the symmetry at the critical point. Parameter-independent predictions for prolate to oblate transitions in various regions on the nuclear chart have been made in the framework of the proxy-SU(3) and pseudo-SU(3

Ian P. Swift, Debaleena Chattopadhyay

The creative industry is both concerned and enthusiastic about how generative AI will reshape creativity. How might these tools interact with the workflow values of creative artists? In this paper, we adopt a value-sensitive design framework to examine how generative AI, particularly Photoshop's Generative Fill (GF), helps or hinders creative professionals'

Dapeng Li, Hang Dong, Lu Wang, Bo Qiao

In recent years, multi-agent reinforcement learning algorithms have made significant advancements in diverse gaming environments, leading to increased interest in the broader application of such techniques. To address the prevalent challenge of partial observability, communication-based algorithms have improved cooperative performance through the sharing of

Masoud Monajatipoor, Zi-Yi Dou, Aichi Chien, Nanyun Peng

Vision-language models have become increasingly powerful for tasks that require an understanding of both visual and linguistic elements, bridging the gap between these modalities. In the context of multimodal clinical AI, there is a growing need for models that possess domain-specific knowledge, as existing models often lack the expertise required for medica

Yunyi Liu, Zhanyu Wang, Yingshu Li, Xinyu Liang

In recent years, automated radiology report generation has experienced significant growth. This paper introduces MRScore, an automatic evaluation metric tailored for radiology report generation by leveraging Large Language Models (LLMs). Conventional NLG (natural language generation) metrics like BLEU are inadequate for accurately assessing the generated rad

Kenta Higuchi, Takuya Watanabe

In this paper, the asymptotic behaviors of the transition probability for two-level avoided crossings are studied under the limit where two parameters (adiabatic parameter and energy gap parameter) tend to zero. This is a continuation of our previous works where avoided crossings are generated by tangential intersections and obey a non-adiabatic regime. The

Martijn S. S. L. Oei, Reinout J. van Weeren, Martin J. Hardcastle, Aivin R. D. J. G. I. B. Gast

Giant radio galaxies (GRGs, giant RGs, or giants) are megaparsec-scale, jet-driven outflows from accretion disks of supermassive black holes, and represent the most extreme pathway by which galaxies can impact the Cosmic Web around them. A long-standing but unresolved question is why giants are so much larger than other radio galaxies. It has been proposed t

Kingsley Yung

The random $k$-XORSAT problem is a random constraint satisfaction problem of $n$ Boolean variables and $m=rn$ clauses, which a random instance can be expressed as a $G\mathbb{F}(2)$ linear system of the form $Ax=b$, where $A$ is a random $m \times n$ matrix with $k$ ones per row, and $b$ is a random vector. It is known that there exist two distinct threshold

Pinxuan Dai, Jiamin Xu, Wenxiang Xie, Xinguo Liu

We propose a novel point-based representation, Gaussian surfels, to combine the advantages of the flexible optimization procedure in 3D Gaussian points and the surface alignment property of surfels. This is achieved by directly setting the z-scale of 3D Gaussian points to 0, effectively flattening the original 3D ellipsoid into a 2D ellipse. Such a design pr

Dou Liu, Ying Han, Xiandi Wang, Xiaomei Tan

The integration of Artificial Intelligence (AI) in healthcare presents a transformative potential for enhancing operational efficiency and health outcomes. Large Language Models (LLMs), such as ChatGPT, have shown their capabilities in supporting medical decision-making. Embedding LLMs in medical systems is becoming a promising trend in healthcare developmen

Qiuyi Chen, Cashen Diniz, Mark Fuge

This paper introduces Least Volume (LV)--a simple yet effective regularization method inspired by geometric intuition--that reduces the number of latent dimensions required by an autoencoder without prior knowledge of the dataset's intrinsic dimensionality. We show that its effectiveness depends on the Lipschitz continuity of the decoder, prove that Principa

Sergey G. Fedosin

A four-dimensional differential Euler-Lagrange equation for continuously distributed materials is derived based on the principle of least action, and instead of Lagrangian, this equation contains the Lagrangian density. This makes it possible to determine the density of generalized four-momentum in covariant form as derivative of the Lagrangian density with

Xiao Jiang, Fei Zhou

Dynamic Vision Sensors (DVS) have recently generated great interest because of the advantages of wide dynamic range and low latency compared with conventional frame-based cameras. However, the complicated behaviors in dim light conditions are still not clear, restricting the applications of DVS. In this paper, we analyze the typical DVS circuit, and find tha

Nadia Bihari Padhan, Rahul Pandit

We investigate the role of interfaces as transport barriers in binary-fluid turbulence by employing Lagrangian tracer particles. The Cahn-Hilliard-Navier-Stokes (CHNS) system of partial differential equations provides a natural theoretical framework for our investigations. For specificity, we utilize the two-dimensional (2D) CHNS system. We capture efficient

Ji-Cai Liu

Andrews and Merca [J. Combin. Theory Ser. A 203 (2024), Art. 105849] recently obtained two interesting results on the sum of the parts with the same parity in the partitions of $n$ (the modulo $2$ case), the proof of which relies on generating functions. Motivated by Andrews and Merca's results, we define six statistics related to the partitions of $n$ and s

Yunpeng Xu, Mufang Ying, Wenge Guo, Zhi Wei

Practical machine learning systems often operate in multiple sequential stages, as seen in ranking and recommendation systems, which typically include a retrieval phase followed by a ranking phase. Effectively assessing prediction uncertainty and ensuring effective risk control in such systems pose significant challenges due to their inherent complexity. To

Dang Nguyen, Paymon Haddad, Eric Gan, Baharan Mirzasoleiman

Can we modify the training data distribution to encourage the underlying optimization method toward finding solutions with superior generalization performance on in-distribution data? In this work, we approach this question for the first time by comparing the inductive bias of gradient descent (GD) with that of sharpness-aware minimization (SAM). By studying

Zichen Yin, Bin He, Yuzhe Ying, Shuwei Zhang

Pathological features are the gold standard for tumor diagnosis, guiding treatment and prognosis. However, standard histopathological process is labor-intensive and time-consuming, while frozen sections have lower accuracy. Dynamic full-field optical coherence tomography (D-FFOCT) offers rapid histologic information by measuring the subcellular dynamics of f

Yinbin Ma, Daniela Tuninetti

This paper presents a novel achievable scheme for coded caching systems with $N$ files and $K$ users, specifically when $N \leq K$. This new scheme employs linear coding both during the placement phase - where cache contents are linear combinations of files from the library - and the delivery phase. The multi-step delivery phase enables users to decode the c

Liekang Zeng, Shengyuan Ye, Xu Chen, Yang Yang

Big Artificial Intelligence (AI) models have emerged as a crucial element in various intelligent applications at the edge, such as voice assistants in smart homes and autonomous robotics in smart factories. Training big AI models, e.g., for personalized fine-tuning and continual model refinement, poses significant challenges to edge devices due to the inhere

Yuhang Gan, Wenjie Xuan, Hang Chen, Juhua Liu

Change Detection is a crucial but extremely challenging task of remote sensing image analysis, and much progress has been made with the rapid development of deep learning. However, most existing deep learning-based change detection methods mainly focus on intricate feature extraction and multi-scale feature fusion, while ignoring the insufficient utilization

Tiema Qian, Chaowei Hu, Jazmine C. Green, Erxi Feng

Magnetic topological insulators provide a platform for emergent phenomena arising from the interplay between magnetism and band topology. Here we report the single crystal growth, crystal structure, magnetic and transport properties, as well as the neutron scattering studies of topological insulator series (Ge$_{1-\delta-x}$Mn$_x$)$_2$Bi$_2$Te$_5$ ($x\leq 0.

Yujie Chen, Anindya Bhadra, Antik Chakraborty

Probabilistic graphical models that encode an underlying Markov random field are fundamental building blocks of generative modeling to learn latent representations in modern multivariate data sets with complex dependency structures. Among these, the exponential family graphical models are especially popular, given their fairly well-understood statistical pro

Puyi Wang, Wei Sun, Zicheng Zhang, Jun Jia

Traditional deep neural network (DNN)-based image quality assessment (IQA) models leverage convolutional neural networks (CNN) or Transformer to learn the quality-aware feature representation, achieving commendable performance on natural scene images. However, when applied to AI-Generated images (AGIs), these DNN-based IQA models exhibit subpar performance.

Gary Reyes, Laura Lanzarini, César Estrebou, Aurelio F. Bariviera

Clustering algorithms or methods for GPS trajectories are in constant evolution due to the interest aroused in part of the scientific community. With the development of clustering algorithms considered traditional, improvements to these algorithms and even unique methods considered as "novelty" for science have emerged. This work aims to analyze the scientif

Marina Fuster, Ignacio Vidaurreta

In this paper we investigate the vulnerability that facial recognition systems present to adversarial examples by introducing a new methodology from the attacker perspective. The technique is based on the use of the autoencoder latent space, organized with principal component analysis. We intend to analyze the potential to craft adversarial examples suitable

Prasanna Sriganesh, James Maier, Adam Johnson, Burhanuddin Shirose

Field robotics applications, such as search and rescue, involve robots operating in large, unknown areas. These environments present unique challenges that compound the difficulties faced by a robot operator. The use of multi-robot teams, assisted by carefully designed autonomy, help reduce operator workload and allow the operator to effectively coordinate r

Mark Jensen, Giacomo De Colle, Sean Kindya, Cameron More

The Common Core Ontologies (CCO) are designed as a mid-level ontology suite that extends the Basic Formal Ontology. CCO has since been increasingly adopted by a broad group of users and applications and is proposed as the first standard mid-level ontology. Despite these successes, documentation of the contents and design patterns of the CCO has been comparat

John Beverley, Giacomo De Colle, Mark Jensen, Carter Benson

Mid-level ontologies are used to integrate terminologies and data across disparate domains. There are, however, no clear, defensible criteria for determining whether a given ontology should count as mid-level, because we lack a rigorous characterization of what the middle level of generality is supposed to contain. Attempts to provide such a characterization

Christian P. N. Tanner, Vivian R. K. Wall, Mumtaz Gababa, Joshua Portner

Understanding and ultimately controlling the transformations and properties of nanoscale systems, from proteins to synthetic nanomaterial assemblies, is limited by the inability to uncover their dynamics on their characteristic length and time scales. Here, we nevertheless demonstrate this ability using MHz X-ray photon correlation spectroscopy (XPCS) -- dir

Kecheng Zhang, Weijie Yuan, Pingzhi Fan, Xianbin Wang

Integrated sensing and communication (ISAC) is viewed as a key technology in future wireless networks. One of the main challenges in realizing ISAC is developing dual-functional waveforms that can communicate with communication receivers and perform radar sensing simultaneously. In this paper, we consider the joint design of a dual-functional orthogonal time

Tsuyoshi Ichimura, Kohei Fujita, Ryota Kusakabe, Hiroyuki Fujiwara

The ground structure can substantially influence seismic ground motion underscoring the need to develop a ground structure model with sufficient reliability in terms of ground motion estimation for earthquake damage mitigation. While many methods for generating ground structure models have been proposed and used in practice, there remains room for enhancing

Chao Yi, Lu Ren, De-Chuan Zhan, Han-Jia Ye

CLIP showcases exceptional cross-modal matching capabilities due to its training on image-text contrastive learning tasks. However, without specific optimization for unimodal scenarios, its performance in single-modality feature extraction might be suboptimal. Despite this, some studies have directly used CLIP's image encoder for tasks like few-shot classifi

Robbie A. Watt, Laura A. Mansfield

Downscaling, or super-resolution, provides decision-makers with detailed, high-resolution information about the potential risks and impacts of climate change, based on climate model output. Machine learning algorithms are proving themselves to be efficient and accurate approaches to downscaling. Here, we show how a generative, diffusion-based approach to dow

Fabio Bellini, Tiantian Mao, Ruodu Wang, Qinyu Wu

We introduce an axiom of disappointment-concordance (disco) aversion for a preference relation over acts in an Anscombe-Aumann setting. This axiom means that the decision maker, facing the sum of two acts, dislikes the situation where both acts realize simultaneously as disappointments. Our main result is that, under strict monotonicity and continuity, the a

Zhiqiang Cai, Anastassia Doktorova, Robert D. Falgout, César Herrera

This paper studies the shallow Ritz method for solving the one-dimensional diffusion problem. It is shown that the shallow Ritz method improves the order of approximation dramatically for non-smooth problems. To realize this optimal or nearly optimal order of the shallow Ritz approximation, we develop a damped block Newton (dBN) method that alternates betwee

Parth Vashisht, Abhilasha Lodha, Mukta Maddipatla, Zonghai Yao

This paper presents our team's participation in the MEDIQA-ClinicalNLP2024 shared task B. We present a novel approach to diagnosing clinical dermatology cases by integrating large multimodal models, specifically leveraging the capabilities of GPT-4V under a retriever and a re-ranker framework. Our investigation reveals that GPT-4V, when used as a retrieval a

Tongou Yang

In this short expository note, we prove the following result, which is a special case of the main theorem in arXiv:2011.09451. For each $n \ge 2$ and $p, q \in [2, \infty]$, we prove upper bounds of $\ell^q(L^p)$ decoupling constants for paraboloids in $\mathbb R^n$, as well as presenting extremisers for each case. Both are sharp up to $\varepsilon$-losses.

Jingxue Huang, Xilai Li, Tianshu Tan, Xiaosong Li

Multi-modal image fusion (MMIF) maps useful information from various modalities into the same representation space, thereby producing an informative fused image. However, the existing fusion algorithms tend to symmetrically fuse the multi-modal images, causing the loss of shallow information or bias towards a single modality in certain regions of the fusion

Evzenie Coupkova, Mireille Boutin

Given a classification problem and a family of classifiers, the Rashomon ratio measures the proportion of classifiers that yield less than a given loss. Previous work has explored the advantage of a large Rashomon ratio in the case of a finite family of classifiers. Here we consider the more general case of an infinite family. We show that a large Rashomon r

Yunzhen Feng, Tim G. J. Rudner, Nikolaos Tsilivis, Julia Kempe

Adversarial examples have been shown to cause neural networks to fail on a wide range of vision and language tasks, but recent work has claimed that Bayesian neural networks (BNNs) are inherently robust to adversarial perturbations. In this work, we examine this claim. To study the adversarial robustness of BNNs, we investigate whether it is possible to succ

Olivier Brochu Dufour, Abolfazl Mohebbi, Sofiane Achiche

Drones are increasingly used in fields like industry, medicine, research, disaster relief, defense, and security. Technical challenges, such as navigation in GPS-denied environments, hinder further adoption. Research in visual odometry is advancing, potentially solving GPS-free navigation issues. Traditional visual odometry methods use geometry-based pipelin

Pavel Pokhilko, Chia-Nan Yeh, Miguel A. Morales, Dominika Zgid

We apply tensor hypercontraction (THC) to reduce the computational scaling of expensive fully self-consistent Green's function methods. We present an efficient MPI-parallel algorithm and its implementation for evaluating the correlated second-order exchange term (SOX). This approach enabled us to conduct the largest fully self-consistent calculations with ov

Anton Hilado, Finn McGlade, Pan Yan

We establish a theory of scalar Fourier coefficients for a class of non-holomorphic, automorphic forms on the quaternionic real Lie group $\mathrm{U}(2,n)$. By studying the theta lifts of holomorphic modular forms from $\mathrm{U}(1,1)$, we apply this theory to obtain examples of non-holomorphic cusp forms on $\mathrm{U}(2,n)$ whose Fourier coefficients are

Zheyuan Zhang, Ulas Bagci

Current medical image segmentation relies on the region-based (Dice, F1-score) and boundary-based (Hausdorff distance, surface distance) metrics as the de-facto standard. While these metrics are widely used, they lack a unified interpretation, particularly regarding volume agreement. Clinicians often lack clear benchmarks to gauge the "goodness" of segmentat

P. Ye. Minaiev, O. O. Pypka, I. V. Shyshenko

One of the classic results of group theory is the so-called Schur theorem. It states that if the central factor-group $G/\zeta(G)$ of a group $G$ is finite, then its derived subgroup $[G,G]$ is also finite. This result has numerous generalizations and modifications in group theory. At the same time, similar investigations were conducted in other algebraic st

P. Ye. Minaiev, O. O. Pypka

One of the classic results of group theory is the so-called Schur theorem. It states that if the central factor-group $G/\zeta(G)$ of a group $G$ is finite, then its derived subgroup $[G,G]$ is also finite. This result has numerous generalizations and modifications in group theory. At the same time, similar investigations were conducted in other algebraic st

Beian Wang, Chong Wang, Peng Liang, Bing Li

Since the emergence of GPT-3, Large Language Models (LLMs) have caught the eyes of researchers, practitioners, and educators in the field of software engineering. However, there has been relatively little investigation regarding the performance of LLMs in assisting with requirements analysis and UML modeling. This paper explores how LLMs can assist novice an

Mohsen Moazzami Gudarzi, Sergey Slizovskiy, Boyang Mao, Endre Tóvári

Understanding and controlling the electrical properties of solution-processed 2D materials is key to further printed electronics progress. Here we demonstrate that the thermolysis of the aromatic intercalants utilized in nanosheet exfoliation for graphene laminates opens the route to achieving high intrinsic mobility and simultaneously controlling doping typ

Joseph Rilling

In the absence of historical data for use as forecasting inputs, decision makers often ask a panel of judges to predict the outcome of interest, leveraging the wisdom of the crowd (Surowiecki 2005). Even if the crowd is large and skilled, shared information can bias the simple mean of judges' estimates. Addressing the issue of bias, Palley and Soll (2019) in

Mingyu Yang, Bowen Liu, Boyang Wang, Hun-Seok Kim

Deep learning-based joint source-channel coding (deep JSCC) has been demonstrated to be an effective approach for wireless image transmission. Nevertheless, most existing work adopts an autoencoder framework to optimize conventional criteria such as Mean Squared Error (MSE) and Structural Similarity Index (SSIM) which do not suffice to maintain the perceptua

Aneesh Komanduri, Chen Zhao, Feng Chen, Xintao Wu

Diffusion probabilistic models (DPMs) have become the state-of-the-art in high-quality image generation. However, DPMs have an arbitrary noisy latent space with no interpretable or controllable semantics. Although there has been significant research effort to improve image sample quality, there is little work on representation-controlled generation using dif

Zhe Chen, Min Haeng Cho, Bo Zhang

Regionalization of intensive care for premature babies refers to a triage system of mothers with high-risk pregnancies to hospitals of varied capabilities based on risks faced by infants. Due to the limited capacity of high-level hospitals, which are equipped with advanced expertise to provide critical care, understanding the effect of delivering premature b

Naoaki Okazaki, Kakeru Hattori, Hirai Shota, Hiroki Iida

Open Japanese large language models (LLMs) have been trained on the Japanese portions of corpora such as CC-100, mC4, and OSCAR. However, these corpora were not created for the quality of Japanese texts. This study builds a large Japanese web corpus by extracting and refining text from the Common Crawl archive (21 snapshots of approximately 63.4 billion page

Tsung-Cheng Lu

We study the entanglement structure of topological orders subject to decoherence on the bipartition boundary. Focusing on the toric codes in $d$ space dimensions for $d=2,3,4$, we explore whether the boundary decoherence may be able to induce a disentangling transition, characterized by the destruction of mixed-state long-range entanglement across the bipart

Longzhen Li, Guang Li, Ren Togo, Keisuke Maeda

In this paper, we propose a new dataset distillation method that considers balancing global structure and local details when distilling the information from a large dataset into a generative model. Dataset distillation has been proposed to reduce the size of the required dataset when training models. The conventional dataset distillation methods face the pro

Tahmim Hossain, Faisal Sayed, Yugesh Rai, Kalpak Bansod

The "MedBike" is an innovative project in the field of pediatric cardiac rehabilitation. It is a 2D interactive game created specifically for children under the age of 18 who have cardiac conditions. This game is part of the MedBike system, a novel rehabilitation tool combining physical exercise with the spirit of gaming. The MedBike game provides children w

Lara J. Martin, Malathy Nagalakshmi

High-tech Augmentative and Alternative Communication (AAC) has been rapidly advancing in recent years due to the increased use of large language models (LLMs) like ChatGPT, but many of these techniques are integrated without the inclusion of the users' perspectives. Autistic adults have been particularly neglected in the design of AAC tools. We conducted in-

Pei Chen, Boran Han, Shuai Zhang

Large Language Models (LLMs) have shown great ability in solving traditional natural language tasks and elementary reasoning tasks with appropriate prompting techniques. However, their ability is still limited in solving complicated science problems. In this work, we aim to push the upper bound of the reasoning capability of LLMs by proposing a collaborative

Futoshi Minato, Osamu Iwamoto

Fission fragment yield evaluations are one of the important nuclear data studies. Fission accompanies various physical observables such as prompt fission neutron, prompt fission gamma, and delayed-neutrons. When evaluating fission fragment yields, a study including correlations among those observables is essentially required. However, fission fragment yield

Mustapha Anis Younes, Sofia Zebboudj, Abdelhakim Gharbi

In this paper, we propose a new efficient mediated semi-quantum key distribution (MSQKD) protocol, facilitating the establishment of a shared secret key between two classical participants with the assistance of an untrusted third party (TP). Unlike existing MSQKD protocols, our approach significantly reduces the quantum requirements for TP, who only needs to

Valerio Assenza, James Marshall Reber, Ivo Terek

Using the notion of magnetic curvature recently introduced by the first author, we extend E. Hopf's theorem to the setting of magnetic systems. Namely, we prove that if the magnetic flow on the s-sphere bundle is without conjugate points, then the total magnetic curvature is non-positive, and vanishes if and only if the magnetic system is magnetically flat.

Osher Lerner

This paper expands on existing learned models of human behavior via a measured step in structured irrationality. Specifically, by replacing the suboptimality constant $\beta$ in a Boltzmann rationality model with a function over states $\beta(s)$, we gain natural expressivity in a computationally tractable manner. This paper discusses relevant mathematical t

Mikhail Lomonosov, Vladimir Shiltsev

This English translation of seminal work of Russian polymath Mikhail Lomonosov. It is derived from its Russian and Latin originals (1757) and continues the series of English translations of Lomonosov nine most important scientific works, which were included by Lomonosov himself in the convolute Lomonosow Opera Academica sent for distribution among Academies

Zhentao Xu, Mark Jerome Cruz, Matthew Guevara, Tie Wang

In customer service technical support, swiftly and accurately retrieving relevant past issues is critical for efficiently resolving customer inquiries. The conventional retrieval methods in retrieval-augmented generation (RAG) for large language models (LLMs) treat a large corpus of past issue tracking tickets as plain text, ignoring the crucial intra-issue

Vanessa J. Meraz, Ziyue Zou, Pratyush Tiwary

We investigate crystal nucleation in supersaturated colloid suspensions using enhanced molecular dynamics simulations augmented with machine learning techniques. The simulations reveal that crystallization in the model colloidal system studied here, with particles interacting through a repulsive screened Coulomb Yukawa potential, proceeds from vapor to dense

Abdulazeez AlAli, George Theodorakopoulos

Recent advances in deep learning have enabled the creation of natural-sounding synthesised speech. However, attackers have also utilised these tech-nologies to conduct attacks such as phishing. Numerous public datasets have been created to facilitate the development of effective detection models. How-ever, available datasets contain only entirely fake audio;

Johanna Casado, Beatriz García

In the last decade, the multi-sensory approach to data analysis has gained relevance. The possibility of including people with vision difficulties in the field of education and the dissemination of science is part of it. However, in the field of scientific research, the topic is not yet accepted, mainly due to the lack of conclusive evidence related to its r