April 2024 arXiv papers — page 106

Chizhi Zhang, Hua-Peng Chen

Lifecycle assessment of wind turbines is essential to improve their design and to optimum maintenance plans for preventing failures during the design life. A critical element of wind turbines is the composite blade due to uncertain cyclic wind loads with relatively high frequency and amplitude in offshore environments. It is critical to detect the wind fatig

Nawrin Tabassum, Ka-Ho Chow, Xuyu Wang, Wenbin Zhang

Recent studies have revealed severe privacy risks in federated learning, represented by Gradient Leakage Attacks. However, existing studies mainly aim at increasing the privacy attack success rate and overlook the high computation costs for recovering private data, making the privacy attack impractical in real applications. In this study, we examine privacy

Xiaolei Zhang

In this paper, we introduce and study the $q$-Krull dimension of a commutative ring via its $q$-operation. A new characterization of $\tau_q$-von Neumann regular rings is obtained, and some properties of rings $q$-Krull dimension 0 are studied. Moreover, we characterize the $q$-Krull dimension of $\tau_q$-Noetherian rings in terms of Krull dimension of some

Jinhyeok Ryu, Jaeyoon Cho

The effect of boundaries on the bulk properties of quantum many-body systems is an intriguing subject of study. One can define a boundary effect function, which quantifies the change in the ground state as a function of the distance from the boundary. This function serves as an upper bound for the correlation functions and the entanglement entropies in the t

Yuqin Mao, Chaoze Zhang, Ligang Huang, Lei Gao

Microcavity-based microlasers are the kernel light sources for integrating photonics and optoelectronics. The traditional pump light frequency locking mainly utilizes a complex system with optoelectronic feedback, which requires a high-cost narrow-linewidth pump laser and limits the application of microlasers in integrated optoelectronic systems. We propose

Tianhan Xu, Takuya Ikeda, Koichi Nishiwaki

In this paper, we propose a method to segment and recover a static, clean background and multiple 360$^\circ$ objects from observations of scenes at different timestamps. Recent works have used neural radiance fields to model 3D scenes and improved the quality of novel view synthesis, while few studies have focused on modeling the invisible or occluded parts

Cheng Jiang, Alexander Gedeon, Yiwei Lyu, Eric Landgraf

Volumetric biomedical microscopy has the potential to increase the diagnostic information extracted from clinical tissue specimens and improve the diagnostic accuracy of both human pathologists and computational pathology models. Unfortunately, barriers to integrating 3-dimensional (3D) volumetric microscopy into clinical medicine include long imaging times,

Osama Khalil

We show that the Fourier transform of Patterson-Sullivan measures associated to convex cocompact groups of isometries of real hyperbolic space decays polynomially quickly at infinity. The proof is based on the $L^2$-flattening theorem obtained in prior work of the author, combined with a method based on dynamical self-similarity for ruling out the sparse set

Yu-Yuan Chen, Jia-Heng Wang, Lu Ning Song, Yu-xi Liu

The quantized surface acoustic wave (SAW) in the piezoelectric medium has recently been studied, and is used to control electric dipoles of quantum systems via the electric field produced through piezoelectric effect. However, it is not easy and convenient to manipulate magnetic moments directly by the electric field. We here study a quantum theory of SAW in

Jing Wang, Xuchen Lin, Dong Yang, Lister Staveley-Smith

We present a statistical study of the properties of diffuse HI in ten nearby galaxies, comparing the HI detected by the single-dish telescope FAST (FEASTS program) and the interferometer VLA (THINGS program), respectively. The THINGS' observation missed HI with a median of 23% due to the short-spacing problem of interferometry and limited sensitivity. We ext

Xiangcheng Zheng

Variable-exponent fractional models attract increasing attentions in various applications, while the rigorous analysis is far from well developed. This work provides general tools to address these models. Specifically, we first develop a convolution method to study the well-posedness, regularity, an inverse problem and numerical approximation for the sundiff

A. I. Milstein

The problem of finding the frequencies of small longitudinal oscillations of a spring having a finite mass and stiffness, attached at one end to a wall and at the other end to a body of finite mass, is discussed. This problem was repeatedly proposed at Olympiads for schoolchildren, in various lessons on the Internet, and even on tests in mechanics for studen

Keiko Hamano, Cedric Gillmann, Gregor J. Golabek, Diogo Lourenço

Mars, Venus and Earth are expected to have started in a hot molten state. Here, we discuss how these three terrestrial planets diverged in their evolution and what mechanisms could be the cause. We discuss that early-on after magma ocean crystallization the mantle/surface redox state and water inventory may already differ considerably, depending on planetary

Lin Jian, Yu Liu, Ming-Cheng Cheng

The classical proper orthogonal decomposition (POD) with the Galerkin projection (GP) has been revised for chip-level thermal simulation of microprocessors with a large number of cores. An ensemble POD-GP methodology (EnPOD-GP) is introduced to significantly improve the training effectiveness and prediction accuracy by dividing a large number of heat sources

Qi Guo, Minzhi Kong, P. F. Wang, Y. Yan

Polarized radio emission of RRAT J1854+0306 is investigated with single pulses using Five-hundred-meter-Aperture Spherical Telescope. Its emission is characterized by nulls, narrow and weak pulses, and occasional wide and intense bursts with a nulling fraction of 53.2%. Its burst emission is typically of one rotation, and occasionally of two or three or even

Yachong An, Hao Ding, Fred D. Richards, Weiping Jiang

Detecting the Earth's inner core motions relative to the mantle presents a considerable challenge due to their indirect accessibility. Seismological observations initially provided evidence for differential/super-rotation of the inner core, but recently demonstrated a possibly about 70-year periodic oscillation. The contrasting results underscore the ongoing

Jie Zhou, Xin Chen, Hang Zhang, Zhe Li

In this paper, we explore the application of cognitive intelligence in legal knowledge, focusing on the development of judicial artificial intelligence. Utilizing natural language processing (NLP) as the core technology, we propose a method for the automatic construction of case knowledge graphs for judicial cases. Our approach centers on two fundamental NLP

Nurul Rafi, Pablo Rivas

Dust storms are associated with certain respiratory illnesses across different areas in the world. Researchers have devoted time and resources to study the elements surrounding dust storm phenomena. This paper reviews the efforts of those who have investigated dust aerosols using sensors onboard of satellites using machine learning-based approaches. We have

Shunichiro Orihara, Tomotaka Momozaki, Tomoyuki Nakagawa

In observational studies, the propensity score plays a central role in estimating causal effects of interest. The inverse probability weighting (IPW) estimator is commonly used for this purpose. However, if the propensity score model is misspecified, the IPW estimator may produce biased estimates of causal effects. Previous studies have proposed some robust

Jiachun Li, David Simchi-Levi, Yining Wang

Contextual bandit with linear reward functions is among one of the most extensively studied models in bandit and online learning research. Recently, there has been increasing interest in designing \emph{locally private} linear contextual bandit algorithms, where sensitive information contained in contexts and rewards is protected against leakage to the gener

Tong Wu, Jia-Mu Sun, Yu-Kun Lai, Yuewen Ma

Reconstructing and editing 3D objects and scenes both play crucial roles in computer graphics and computer vision. Neural radiance fields (NeRFs) can achieve realistic reconstruction and editing results but suffer from inefficiency in rendering. Gaussian splatting significantly accelerates rendering by rasterizing Gaussian ellipsoids. However, Gaussian splat

Doron Haviv, Russell Zhang Kunes, Thomas Dougherty, Cassandra Burdziak

Optimal transport (OT) and the related Wasserstein metric (W) are powerful and ubiquitous tools for comparing distributions. However, computing pairwise Wasserstein distances rapidly becomes intractable as cohort size grows. An attractive alternative would be to find an embedding space in which pairwise Euclidean distances map to OT distances, akin to standa

Thomas Y. Hou, Van Tien Nguyen, Yixuan Wang

We propose an alternative proof of the classical result of Type-I blowup with log correction for the semilinear heat equation. Compared with previous proofs, we use a novel idea of enforcing stable normalizations for perturbations around the approximate profile and we establish a weighted $H^k$ stability, thereby avoiding the use of a topological argument an

Wei-Kuo Chen, Heejune Kim, Arnab Sen

In a recent breakthrough [arXiv:2301.04112], Chatterjee proved site disorder chaos in the Edwards-Anderson (EA) short-range spin glass model utilizing the Hermite spectral method. In this paper, we demonstrate the further usefulness of this Hermite spectral approach by extending the validity of site disorder chaos in three related spin glass models. The firs

Xinyu Liang, Ruiying Du, Jing Chen, Yu Zhang

As cross-chain technology continues to advance, the scale of cross-chain transactions is experiencing significant expansion. To improve scalability, researchers have turned to the study of cross-chain state channels. However, most of the existing schemes rely on trusted parties to support channel operations. To address this issue, we present Interpipe: a dis

Chao Wang, Peipei Zhang, Yu-Xian Li, Juntao Song

Using the tight-binding model, a multi-terminal superconductor(S) device is proposed, where the structures of the center region are primitive grpahene(G) and Y-shaped Kekul\'{e} graphene superlattice(GS), respectively. The intravalley Andreev reflection is studied in this model through the utilization of the non-equilibrium Green's function method. In the G/

Scarlett Raine, Ross Marchant, Brano Kusy, Frederic Maire

Marine surveys by robotic underwater and surface vehicles result in substantial quantities of coral reef imagery, however labeling these images is expensive and time-consuming for domain experts. Point label propagation is a technique that uses existing images labeled with sparse points to create augmented ground truth data, which can be used to train a sema

Thomas Produit, Jérôme Kasparian, Farhad Rachidi, Marcos Rubinstein

The recent development of high average, high peak power lasers has revived the effort of using lasers as a potential tool to influence natural lightning. Although impressive, the current progress in laser lightning control technology may only be the beginning of a new area involving a positive feedback between powerful laser development and atmospheric resea

Xinwei Chen, Kun Li, Tianyou Song, Jiangjian Guo

StackOverflow, with its vast question repository and limited labeled examples, raise an annotation challenge for us. We address this gap by proposing RoBERTa+MAML, a few-shot named entity recognition (NER) method leveraging meta-learning. Our approach, evaluated on the StackOverflow NER corpus (27 entity types), achieves a 5% F1 score improvement over the ba

Wenxuan Zeng, Tianshi Xu, Meng Li, Runsheng Wang

Private convolutional neural network (CNN) inference based on secure two-party computation (2PC) suffers from high communication and latency overhead, especially from convolution layers. In this paper, we propose EQO, a quantized 2PC inference framework that jointly optimizes the CNNs and 2PC protocols. EQO features a novel 2PC protocol that combines Winogra

Xiongye Xiao, Gengshuo Liu, Gaurav Gupta, Defu Cao

Integrating and processing information from various sources or modalities are critical for obtaining a comprehensive and accurate perception of the real world in autonomous systems and cyber-physical systems. Drawing inspiration from neuroscience, we develop the Information-Theoretic Hierarchical Perception (ITHP) model, which utilizes the concept of informa

Haoming Yang, Ali Hasan, Yuting Ng, Vahid Tarokh

McKean-Vlasov stochastic differential equations (MV-SDEs) provide a mathematical description of the behavior of an infinite number of interacting particles by imposing a dependence on the particle density. As such, we study the influence of explicitly including distributional information in the parameterization of the SDE. We propose a series of semi-paramet

Peifei Zhu, Tsubasa Takahashi, Hirokatsu Kataoka

Diffusion Models (DMs) have shown remarkable capabilities in various image-generation tasks. However, there are growing concerns that DMs could be used to imitate unauthorized creations and thus raise copyright issues. To address this issue, we propose a novel framework that embeds personal watermarks in the generation of adversarial examples. Such examples

Peter Olamide Olanipekun

We extend the notion of convexity of functions defined on global nonpositive curvature spaces by introducing (geodesically) $h$-convex functions. We prove estimates of Hermite-Hadamard type via Katugampola's fractional integrals. We obtain an important corollary which gives an essentially sharp estimate involving squared distance mappings between points in a

Ying-Hui Yang, Shuang Yao, Shi-Jiao Geng, Xiao-Li Wang

The characterization of Kirkwood-Dirac (KD) classicality or non-classicality is very important in quantum information processing. In general, the set of KD classical states with respect to two bases is not a convex polytope[J. Math. Phys. \textbf{65} 072201 (2024)], which makes us interested in finding out in which circumnstances they do form a polytope. In

Yang Chen, Reyhaneh Jabbarvand

Test flakiness, a non-deterministic behavior of builds irrelevant to code changes, is a major and continuing impediment to delivering reliable software. The very few techniques for the automated repair of test flakiness are specifically crafted to repair either Order-Dependent (OD) or Implementation-Dependent (ID) flakiness. They are also all symbolic approa

Runze Yan, Cheng Ding, Ran Xiao, Aleksandr Fedorov

Atrial fibrillation (AF), a common cardiac arrhythmia, significantly increases the risk of stroke, heart disease, and mortality. Photoplethysmography (PPG) offers a promising solution for continuous AF monitoring, due to its cost efficiency and integration into wearable devices. Nonetheless, PPG signals are susceptible to corruption from motion artifacts and

J. U. Lange, C. Blake, C. Saulder, N. Jeffrey

The Dark Energy Spectroscopic Instrument (DESI) survey will measure spectroscopic redshifts for millions of galaxies across roughly $14,000 \, \mathrm{deg}^2$ of the sky. Cross-correlating targets in the DESI survey with complementary imaging surveys allows us to measure and analyze shear distortions caused by gravitational lensing in unprecedented detail. I

Átila Jones, Vilmar Trevisan, Cybele T. M. Vinagre

In this note, we present a structural description of certain connected cographs having $k \geq 2$ main signless Laplacian eigenvalues. This result allows us to characterize the cographs which are quasi-threshold graphs with two main $\mathbf{Q}$-eigenvalues. In addition, we describe all the quasi-threshold graphs belonging to the subclass of generalized core

Dmitriy Beznosko, Keith Driscoll, Fernando Guadarrama, Steven Mai

High quality random numbers are necessary in the modern world. Ranging from encryption keys in cyber security to models and simulations for scientific use: it's important that these random numbers are of high quality and quickly attainable. One common solution to the generation of random numbers is that of pseudo-random number generators, or PRNGs. PRNGs gen

Shota Ono, Ravinder Pawar

The 2H, 1T, and their distorted structures are known as prototype structures of $AB_2$ monolayers. Here, we study a puckered structure that is truncated from the (110) surface of fluorite-type materials. 53 fluorite-type materials are investigated based on first-principles approach. The formation energy calculations indicate that seven systems form the pucke

Amir Arjomand, Amin Boudesh, Farnoush Bayatmakou, Kenneth B. Kent

Recent statistics indicate that approximately 1.3 billion individuals worldwide suffer from hypertension, a leading cause of premature death globally. Blood Pressure (BP) serves as a critical health indicator for accurate and timely diagnosis and/or treatment of hypertension. Traditional BP measurement methods rely on cuff-based approaches, which lack real-t

Morteza Maleki, Foad Haeri

Cardiovascular diseases are the leading cause of mortality globally, necessitating advancements in diagnostic techniques. This study explores the application of wavelet transformation for classifying electrocardiogram (ECG) signals to identify various cardiovascular conditions. Utilizing the MIT-BIH Arrhythmia Database, we employed both continuous and discre

Matthew Sigit

This paper explores the development and viability of an alternative pseudorandom number generator (PRNG) that leverages the chaotic dynamics of multiple pendulum systems. Some traditional PRNGs, notably the one implemented in the Java.Random class, suffer from predictability which gives rise to exploitability. This study identifies these vulnerabilities and

Yujia Mu, Xizixiang Wei, Cong Shen

Wireless federated learning (FL) relies on efficient uplink communications to aggregate model updates across distributed edge devices. Over-the-air computation (a.k.a. AirComp) has emerged as a promising approach for addressing the scalability challenge of FL over wireless links with limited communication resources. Unlike conventional methods, AirComp allow

Shahin Mirshekari, Mohammadreza Moradi, Hossein Jafari, Mehdi Jafari

This research employs Gaussian Process Regression (GPR) with an ensemble kernel, integrating Exponential Squared, Revised Mat\'ern, and Rational Quadratic kernels to analyze pharmaceutical sales data. Bayesian optimization was used to identify optimal kernel weights: 0.76 for Exponential Squared, 0.21 for Revised Mat\'ern, and 0.13 for Rational Quadratic. Th

Mengmeng Yang, Ming Ding, Youyang Qu, Wei Ni

The worldwide adoption of machine learning (ML) and deep learning models, particularly in critical sectors, such as healthcare and finance, presents substantial challenges in maintaining individual privacy and fairness. These two elements are vital to a trustworthy environment for learning systems. While numerous studies have concentrated on protecting indiv

Xue-Feng Pan, Xin-Lei Hei, Xiao-Yu Yao, Jia-Qiang Chen

Skyrmion qubits are a new highly promising logic element for quantum information processing. However, their scalability to multiple interacting qubits remains challenging. We propose a hybrid quantum setup with skyrmion qubits strongly coupled to nanomechanical cantilevers via magnetic coupling, which harnesses phonons as quantum interfaces for the manipulat

Hamadi Chihaoui, Paolo Favaro

We introduce a novel approach to single image denoising based on the Blind Spot Denoising principle, which we call MAsked and SHuffled Blind Spot Denoising (MASH). We focus on the case of correlated noise, which often plagues real images. MASH is the result of a careful analysis to determine the relationships between the level of blindness (masking) of the i

Xue-Feng Pan, Peng-Bo Li, Xin-Lei Hei, Xichao Zhang

Coherent and dissipative interactions between different quantum systems are essential for the construction of hybrid quantum systems and the investigation of novel quantum phenomena. Here, we propose and analyze a magnon-skyrmion hybrid quantum system, consisting of a micromagnet and nearby magnetic skyrmions. We predict a strong coupling mechanism between t

Yiming Zhang, Zhuokai Zhao, Zhaorun Chen, Zhili Feng

Self-supervised contrastive learning models, such as CLIP, have set new benchmarks for vision-language models in many downstream tasks. However, their dependency on rigid one-to-one mappings overlooks the complex and often multifaceted relationships between and within texts and images. To this end, we introduce RankCLIP, a novel pre-training method that exte

Shahin Mirshekari, Negin Hayeri Motedayen, Mohammad Ensaf

This study introduces an innovative Gaussian Process (GP) model utilizing an ensemble kernel that integrates Radial Basis Function (RBF), Rational Quadratic, and Mat\'ern kernels for product sales forecasting. By applying Bayesian optimization, we efficiently find the optimal weights for each kernel, enhancing the model's ability to handle complex sales data

Shu-wen Yang, Heng-Jui Chang, Zili Huang, Andy T. Liu

The foundation model paradigm leverages a shared foundation model to achieve state-of-the-art (SOTA) performance for various tasks, requiring minimal downstream-specific modeling and data annotation. This approach has proven crucial in the field of Natural Language Processing (NLP). However, the speech processing community lacks a similar setup to explore th

Yahya Shahsavari, Oussama A. Dambri, Yaser Baseri, Abdelhakim Senhaji Hafid

Wearable devices and medical sensors revolutionize health monitoring, raising concerns about data privacy in ML for healthcare. This tutorial explores FL and BC integration, offering a secure and privacy-preserving approach to healthcare analytics. FL enables decentralized model training on local devices at healthcare institutions, keeping patient data local

Víctor A. Braberman, Flavia Bonomo-Braberman, Yiannis Charalambous, Juan G. Colonna

The emergence of prompting as the dominant paradigm for leveraging Large Language Models (LLMs) has led to a proliferation of LLM-native software, where application behavior arises from complex, stochastic data transformations. However, the engineering of such systems remains largely exploratory and ad-hoc, hampered by the absence of conceptual frameworks, e

Ryan Cotterell, Kevin Duh

Low-resource named entity recognition is still an open problem in NLP. Most state-of-the-art systems require tens of thousands of annotated sentences in order to obtain high performance. However, for most of the world's languages, it is unfeasible to obtain such annotation. In this paper, we present a transfer learning scheme, whereby we train character-

Mohamed Boujoudar, Abdelaziz Beljadid, Ahmed Taik

Modeling unsaturated flow through soils with water uptake by plan root has many applications in agriculture and water resources management. In this study, our aim is to develop efficient numerical techniques for solving the Richards equation with a sink term due to plant root water uptake. The Feddes model is used for water absorption by plant roots, and the

Poorvesh Dongre, Majid Behravan, Kunal Gupta, Mark Billinghurst

This paper explores enhancing empathy in Large Language Models (LLMs) by integrating them with physiological data. We propose a physiological computing approach that includes developing deep learning models that use physiological data for recognizing psychological states and integrating the predicted states with LLMs for empathic interaction. We showcase the

Daniel J. Price, David Liptai, Ilya Mandel, Joanna Shepherd

Stars falling too close to massive black holes in the centres of galaxies can be torn apart by the strong tidal forces. Simulating the subsequent feeding of the black hole with disrupted material has proved challenging because of the range of timescales involved. Here we report a set of simulations that capture the relativistic disruption of the star, follow

T. Adkins, R. Meyrand, J. Squire

Understanding the partitioning of turbulent energy between ions and electrons in weakly collisional plasmas is crucial for the accurate interpretation of observations and modelling of various astrophysical phenomena. Many such plasmas are "imbalanced", wherein the large-scale energy input is dominated by Alfv\'enic fluctuations propagating in a single direct

Sveva Castello, Zhuangfei Wang, Lawrence Dam, Camille Bonvin

The standard approach to test for deviations from general relativity on cosmological scales is to combine measurements of the growth rate of structure with gravitational lensing. In this study, we show that this method suffers from an important limitation with regard to these two probes: models of dark matter with additional interactions can lead to the very

Sam Cantrill, David Ahmedt-Aristizabal, Lars Petersson, Hanna Suominen

Camera-based remote photoplethysmography (rPPG) enables contactless measurement of important physiological signals such as pulse rate (PR). However, dynamic and unconstrained subject motion introduces significant variability into the facial appearance in video, confounding the ability of video-based methods to accurately extract the rPPG signal. In this stud

Che-Yu Chen, Antonio De Felice, Shinji Tsujikawa

Horndeski's vector-tensor (HVT) gravity is described by a Lagrangian in which the field strength $F_{\mu \nu}=\partial_{\mu} A_{\nu}-\partial_{\nu} A_{\mu}$ of a vector field $A_{\mu}$ interacts with a double dual Riemann tensor $L^{\mu \nu \alpha \beta}$ in the form $\beta L^{\mu \nu \alpha \beta} F_{\mu \nu} F_{\alpha \beta}$, where $\beta$ is a constant.

David Geissbühler, Sushil Bhattacharjee, Ketan Kotwal, Guillaume Clivaz

Current finger-vein or palm-vein recognition systems usually require direct contact of the subject with the apparatus. This can be problematic in environments where hygiene is of primary importance. In this work we present a contactless vascular biometrics sensor platform named \sweet which can be used for hand vascular biometrics studies (wrist, palm, and f

Karim Benharrak, Tim Zindulka, Daniel Buschek

Large Language Models have become an integral part of new intelligent and interactive writing assistants. Many are offered commercially with a chatbot-like UI, such as ChatGPT, and provide little information about their inner workings. This makes this new type of widespread system a potential target for deceptive design patterns. For example, such assistants

Gokhan Alkac, Mehmet Kemal Gumus, Mehmet Ali Olpak

We give a novel formulation of classical double copy in the mini-superspace of static, spherically symmetric black holes where the map between the solutions of general relativity and Maxwell's theory can be realized in Boyer-Lindsquit coordinates. By employing the reduced action principle, we show that the double copy structure can be generalized to Lovelock

Kori E. Smyser, Alec White, Sandeep Sharma

In this article, we present an interpolative separable density fitting (ISDF) based algorithm to calculate exact exchange in periodic mean field calculations. In the past, decomposing the two-electron integrals into tensor hypercontraction (THC) form using ISDF was the most expensive step of the entire mean field calculation. Here we show that by using a mul

Anupam Sharma, Krishna Miyapuram

Electroencephalography (EEG) classification is a versatile and portable technique for building non-invasive Brain-computer Interfaces (BCI). However, the classifiers that decode cognitive states from EEG brain data perform poorly when tested on newer domains, such as tasks or individuals absent during model training. Researchers have recently used complex st

Zoey Liu, Bonnie J. Dorr

Recent work to enhance data partitioning strategies for more realistic model evaluation face challenges in providing a clear optimal choice. This study addresses these challenges, focusing on morphological segmentation and synthesizing limitations related to language diversity, adoption of multiple datasets and splits, and detailed model comparisons. Our stu

German P. Barletta, Rika Tandiana, Miguel Soler, Sara Fortuna

Motivation: Engineering high-affinity binders targeting specific antigenic determinants remains a challenging and often daunting task, requiring extensive experimental screening. Computational methods have the potential to accelerate this process, reducing costs and time, but only if they demonstrate broad applicability and efficiency in exploring mutations,

Márcio Batista, Allan Freitas, Márcio Santos

On a Riemannian manifold with a smooth function $f: M\to \mathbb{R}$, we consider the linearization of the Perelman scalar curvature $\mathcal{R}$ and its $L^2$-formal adjoint operator $\delta\mathcal{R}^*$. A manifold endowed with a metric $g$ whose operator $\delta\mathcal{R}^*$ has a nontrivial kernel is called a Perelman singular manifold. In this paper,

Louise C. Head, Yair A. G. Fosado, Davide Marenduzzo, Tyler N. Shendruk

Colloids dispersed in nematic liquid crystals form topological composites in which colloid-associated defects mediate interactions while adhering to fundamental topological constraints. Better realising the promise of such materials requires numerical methods that model nematic inclusions in dynamic and complex scenarios. We employ a mesoscale approach for s

Taylor Webb, Keith J. Holyoak, Hongjing Lu

We recently reported evidence that large language models are capable of solving a wide range of text-based analogy problems in a zero-shot manner, indicating the presence of an emergent capacity for analogical reasoning. Two recent commentaries have challenged these results, citing evidence from so-called `counterfactual' tasks in which the standard sequence

Wenjing Shi, Phuong Tran, Sylvia Celedón-Pattichis, Marios S. Pattichis

The paper develops datasets and methods to assess student participation in real-life collaborative learning environments. In collaborative learning environments, students are organized into small groups where they are free to interact within their group. Thus, students can move around freely causing issues with strong pose variation, move out and re-enter th

Arav Agarwal, Karthik Mittal, Aidan Doyle, Pragnya Sridhar

We conduct a preliminary study of the effect of GPT's temperature parameter on the diversity of GPT4-generated questions. We find that using higher temperature values leads to significantly higher diversity, with different temperatures exposing different types of similarity between generated sets of questions. We also demonstrate that diverse question genera

Roshni G. Iyer, Wei Wang, Yizhou Sun

We present Bi-Level Attention-Based Relational Graph Convolutional Networks (BR-GCN), unique neural network architectures that utilize masked self-attentional layers with relational graph convolutions, to effectively operate on highly multi-relational data. BR-GCN models use bi-level attention to learn node embeddings through (1) node-level attention, and (2

S. V. Bolokhov

We compute the quasinormal modes of massive scalar and Dirac fields within the framework of asymptotically de Sitter black holes in Euler-Heisenberg non-linear electrodynamics. We pay particular attention to the regime $\mu M/m_{P}^2 \gg 1$, where $\mu$ and $M$ denote the masses of the field and the black hole, respectively, and $m_{P}$ represents the Planck

Cédric M. Campos, David Martín de Diego, José Torrente-Teruel

Polyak's Heavy Ball (PHB; Polyak, 1964), a.k.a. Classical Momentum, and Nesterov's Accelerated Gradient (NAG; Nesterov, 1983) are well-established momentum-descent methods for optimization. Although the latter generally outperforms the former, primarily, generalizations of PHB-like methods to nonlinear spaces have not been sufficiently explored in th

Zhenwei Yang, Dimitris Rizopoulos, Eveline A. M. Heijnsdijk, Lisa F. Newcomb

In active surveillance of prostate cancer, cancer progression is interval-censored and the examination to detect progression is subject to misclassification, usually false negatives. Meanwhile, patients may initiate early treatment before progression detection, constituting a competing risk. We developed the Misclassification-Corrected Interval-censored Caus

Arpan Ghosh, Saurabh Sharma, Joe Philip Ninan, Devendra K. Ojha

We present here initial results of our spectro-photometric monitoring of XZ Tau. During our monitoring period, XZ Tau exhibited several episodes of brightness variations in timescales of months at optical wavelengths in contrast to the mid-infrared wavelengths. The color evolution of XZ Tau during this period suggest that the brightness variations are driven

Arun Singh Dev, Dileep Kumar

Magnetic anisotropy is a fundamental property of magnetic materials that determines the alignment of the spins along the preferential direction, called the easy axis of magnetization. Magnetic polycrystalline thin films offer several advantages over magnetic epitaxial thin films because of fabrication flexibility, higher coercivity and improved magnetic stab

Edgar J. Fuller

Mathematics as an area of study occupies an important place in higher education. Due in part to its utility in other disciplines as well as its role in student learning, institutions of higher education (IHEs) often have large numbers of mathematics faculty with different balances of teaching and research in different ranks and appointment structures. Most f

Tal Hakim

The application of machine-learning solutions to movement assessment from skeleton videos has attracted significant research attention in recent years. This advancement has made rehabilitation at home more accessible, utilizing movement assessment algorithms that can operate on affordable equipment for human pose detection and analysis from 2D or 3D videos.

Nate Wiecha, Jane A. Hoppin, Brian J. Reich

Public health data are often spatially dependent, but standard spatial regression methods can suffer from bias and invalid inference when the independent variable is associated with spatially-correlated residuals. This could occur if, for example, there is an unmeasured environmental contaminant associated with the independent and outcome variables in a spat

Michelle Espinoza

Cybercrime is a pervasive threat that impacts every facet of society. Its reach transcends geographic borders and extends far beyond the digital realm, often serving as the catalyst for offline crimes. As modern conflicts become increasingly intertwined with cyber warfare, the need for interdisciplinary cooperation to grasp and combat this escalating threat

Jacoby Johnson, Subash Kharel, Alan Mannamplackal, Amr S. Abdelfattah

Cloud-native and microservice architectures have taken over the development world by storm. While being incredibly scalable and resilient, microservice architectures also come at the cost of increased overhead to build and maintain. Google's Service Weaver aims to simplify the complexities associated with implementing cloud-native systems by introducing the

Yu Wang, Shu-Rui Zhang, Aidin Momtaz, Rahim Moradi

ChatGPT has been the most talked-about concept in recent months, captivating both professionals and the general public alike, and has sparked discussions about the changes that artificial intelligence (AI) will bring to the world. As physicists and astrophysicists, we are curious about if scientific data can be correctly analyzed by large language models (LL

Vishwas Sathish, Hannah Lin, Aditya K Kamath, Anish Nyayachavadi

Large Language Models (LLMs) are a powerful technology that augment human skill to create new opportunities, akin to the development of steam engines and the internet. However, LLMs come with a high cost. They require significant computing resources and energy to train and serve. Inequity in their control and access has led to concentration of ownership and

Akash R. Wasil, Joshua Clymer, David Krueger, Emily Dardaman

Prominent AI experts have suggested that companies developing high-risk AI systems should be required to show that such systems are safe before they can be developed or deployed. The goal of this paper is to expand on this idea and explore its implications for risk management. We argue that entities developing or deploying high-risk AI systems should be requ

Geunwoong Jeon, Justin Fagnoni, Hao Wan, Maria M. Santore

Motivated by recent studies of two-phase lipid vesicles possessing 2D solid domains integrated within a fluid bilayer phase, we study the shape equilibria of closed vesicles possessing a single planar, circular inclusion. While 2D solid elasticity tends to expel Gaussian curvature, topology requires closed vesicles to maintain an average, non-zero Gaussian c

Abderrahmen Mtibaa

While the success of edge and fog computing increased with the proliferation of the Internet of Things (IoT) solutions, such novel computing paradigm, that moves compute resources closer to the source of data and services, must address many challenges such as reducing communication overhead to/from datacenters, the latency to compute and receive results, as

Xiufan Yu, Linjun Zhang, Arun Srinivasan, Min-ge Xie

We introduce a novel meta-analysis framework to combine dependent tests under a general setting, and utilize it to synthesize various microbiome association tests that are calculated from the same dataset. Our development builds upon the classical meta-analysis methods of aggregating $p$-values and also a more recent general method of combining confidence di

Branislav Bosansky, Lada Hospodkova, Michal Najman, Maria Rigaki

The accuracy of deployed malware-detection classifiers degrades over time due to changes in data distributions and increasing discrepancies between training and testing data. This phenomenon is known as the concept drift. While the concept drift can be caused by various reasons in general, new malicious files are created by malware authors with a clear inten

Eduard Roure Perdices

We present a multi-variable extension of Rubio de Francia's restricted weak-type extrapolation theory that does not involve Rubio de Francia's iteration algorithm; instead, we rely on the following Sawyer-type inequality for the weighted Hardy-Littlewood maximal operator $M_u$: $$ \left \Vert \frac{M_u (fv)}{v} \right \Vert_{L^{1,\infty}(uv)} \leq C_{u,v} \V

Johannes Sahlmann, Pablo Gómez

The third Gaia data release (DR3) contains $\sim$170\,000 astrometric orbit solutions of two-body systems located within $\sim$500 pc of the Sun. Determining component masses in these systems, in particular of stars hosting exoplanets, usually hinges on incorporating complementary observations in addition to the astrometry, e.g. spectroscopy and radial veloc

Brian R. Bartoldson, James Diffenderfer, Konstantinos Parasyris, Bhavya Kailkhura

This paper revisits the simple, long-studied, yet still unsolved problem of making image classifiers robust to imperceptible perturbations. Taking CIFAR10 as an example, SOTA clean accuracy is about $100$%, but SOTA robustness to $\ell_{\infty}$-norm bounded perturbations barely exceeds $70$%. To understand this gap, we analyze how model size, dataset size,

Nathan Dalaklis

Let $\Phi = \{\phi_e\}_{e\in E}$ be a finitely irreducible conformal graph directed Markov system (CGDMS) with symbolic representation $E_A^{\infty}$ and limit set $J$. Under a mild condition on the system, we give a multifractal analysis of level sets of Birkhoff averages with respect to Hausdorff dimension for a large family of functions. We then apply the

E. Yu. Lerner

In 1977, Yu. V. Matiyasevich proposed a formula expressing the chromatic polynomial of an arbitrary graph as a linear combination of flow polynomials of subgraphs of the original graph. In this paper, we prove that this representation is a particular case of one (easily verifiable) formula, namely, the representation of the characteristic polynomial of an ar

Kazumasa Nomura, Paul Terwilliger

Let $\Gamma$ denote a $Q$-polynomial distance-regular graph, with vertex set $X$ and diameter $D\geq 3$. The standard module $V$ has a basis $\lbrace {\hat x} \vert x \in X\rbrace$, where ${\hat x}$ denotes column $x$ of the identity matrix $I \in {\rm Mat}_X(\mathbb C)$. Let $E$ denote a $Q$-polynomial primitive idempotent of $\Gamma$. The eigenspace $EV$ i

Marek Maciaszek, Lukas Razinkovas

Single photon emitters in hexagonal boron nitride offer a gateway to the future of quantum technologies, yet their identification remains challenging and subject to ongoing debate. We demonstrate through ab initio calculations that the optical properties of a carbon chain tetramer are in excellent agreement with the characteristics of a blue quantum emitter