April 2024 arXiv papers — page 66

Heinrich Peters, Joseph B. Bayer, Sandra C. Matz, Yikun Chi

The present paper introduces a novel approach to studying social media habits through predictive modeling of sequential smartphone user behaviors. While much of the literature on media and technology habits has relied on self-report questionnaires and simple behavioral frequency measures, we examine an important yet understudied aspect of media and technolog

Qiujie Dong, Rui Xu, Pengfei Wang, Shuangmin Chen

Despite recent advances in reconstructing an organic model with the neural signed distance function (SDF), the high-fidelity reconstruction of a CAD model directly from low-quality unoriented point clouds remains a significant challenge. In this paper, we address this challenge based on the prior observation that the surface of a CAD model is generally compo

Shengxin Hong, Xiuyi Fan

Explainable Artificial Intelligence (XAI) has become critical in enhancing the transparency and trustworthiness of AI systems, especially as these systems are increasingly deployed in high-stakes domains such as healthcare and finance. Despite the progress made in developing explanation generation techniques for individual machine learning (ML) models, signi

Hideki Kawahara, Masanori Morise

We generalized a voice morphing algorithm capable of handling temporally variable, multiple-attributes, and multiple instances. The generalized morphing provides a new strategy for investigating speech diversity. However, excessive complexity and the difficulty of preparation have prevented researchers and students from enjoying its benefits. To address this

Quoc Khanh Nguyen, Truong Thanh Hung Nguyen, Vo Thanh Khang Nguyen, Van Binh Truong

To address the challenges of providing quick and plausible explanations in Explainable AI (XAI) for object detection models, we introduce the Gaussian Class Activation Mapping Explainer (G-CAME). Our method efficiently generates concise saliency maps by utilizing activation maps from selected layers and applying a Gaussian kernel to emphasize critical image

Vasily Melnikov

Let $E$ be a Banach space such that $E'$ has the Radon-Nikod\'ym property. The aim of this work is to connect relative weak compactness in the $E$-valued martingale Hardy space $H^{1}(\mu,E)$ to a convex compactness criterion in a weaker topology, such as the topology of uniform convergence on compacts in measure. These results represent a dynamic version of

Mark J Hagmann

We used analytical methods to study the interaction of electrons with shunted models consisting of a rectangular, triangular, or delta function. potential barrier in series with a pre-barrier region at zero potential. In each model the shunted boundary conditions cause the matrix equation to have only zeros in the right-hand column vector. Thus, the determin

Wenhan Lyu, Yimeng Wang, Tingting, Chung

The integration of AI assistants, especially through the development of Large Language Models (LLMs), into computer science education has sparked significant debate. An emerging body of work has looked into using LLMs in education, but few have examined the impacts of LLMs on students in entry-level programming courses, particularly in real-world contexts an

F. C. E. Lima, C. A. S. Almeida, Rodolfo Casana

We study a generalized $\phi^4$ model that gives rise to BPS kink/antikink configurations with compacton-like profiles. One observes that the positive parameter controlling the generalizing function promotes an infinity degenerescence of the BPS solutions. We then use the Differential Configurational Complexity technique to distinguish the degenerate configu

Adel Abbout

We investigate the spin torque in the topological phase of the 3Q antiferromagnetic (AFM) configuration. We first obtain the band structure to identify the topology and nature of the different gaps of the system and then calculate the spin density in the whole system. We demonstrate the presence of a non-vanishing spin-torque component at the edge of the sys

Patrizio Dazzi, Luca Ferrucci, Marco Danelutto, Konstantinos Tserpes

This position paper introduces Urgent Edge Computing (UEC) as a paradigm shift addressing the evolving demands of time-sensitive applications in distributed edge environments, in time-critical scenarios. With a focus on ultra-low latency, availability, resource management, decentralization, self-organization, and robust security, UEC aims to facilitate opera

Zaizheng Li, Susanna Terracini

We analyze the bifurcation phenomenon for the following two-component competition system: \begin{equation*} \begin{cases} -\Delta u_1=\mu u_1(1-u_1)-\beta \alpha u_1u_2,& \text{in}\ B_1\subset \mathbb{R}^N, -\Delta u_2=\sigma u_2(1-u_2)-\beta \gamma u_1u_2,& \text{in}\ B_1\subset \mathbb{R}^N, \frac{\partial u_1}{\partial n}= \frac{\partial u_2}{\partial n}

Ziqi Yang, Xuhai Xu, Bingsheng Yao, Jiachen Li

Patient-provider communication has been crucial to cancer patients' survival after their cancer treatments. However, the research community and patients themselves often overlook the communication challenges after cancer treatments as they are overshadowed by the severity of the patient's illness and the variety and rarity of the cancer disease itself. Meanw

Yang Yang, Shunyi Zheng

The advancement of deep learning has driven notable progress in remote sensing semantic segmentation. Attention mechanisms, while enabling global modeling and utilizing contextual information, face challenges of high computational costs and require window-based operations that weaken capturing long-range dependencies, hindering their effectiveness for remote

Anis Bkakria, Reda Yaich

Privacy in Location-Based Services (LBS) has become a paramount concern with the ubiquity of mobile devices and the increasing integration of location data into various applications. This paper presents several novel contributions to advancing the understanding and management of privacy leakage in LBS. Our contributions provide a more comprehensive framework

Sefika Efeoglu, Zongxiong Chen, Sonja Schimmler, Bianca Wentzel

Research Data Management (RDM) is essential in handling and organizing data in the research field. The Berlin Open Science Platform (BOP) serves as a case study that exemplifies the significance of standardization within the Berlin University Alliance (BUA), employing different vocabularies when publishing their data, resulting in data heterogeneity. The met

Zelalem Abebe Bekele, Yuan-Yuan Jiang, Kun Lei, Xiukai Lan

Spin-orbit torques (SOTs) generated through the conventional spin Hall effect (SHE) and/or Rashba-Edelstein effect offer potential for magnetization manipulation. However, deterministic switching of perpendicular ferromagnets via SOTs requires a strong symmetry-breaking perturbation, typically an external magnetic field. Here, we demonstrate that field-free

Sota Nishiyama, Masayuki Ohzeki

The storage capacity of a binary classification model is the maximum number of random input-output pairs per parameter that the model can learn. It is one of the indicators of the expressive power of machine learning models and is important for comparing the performance of various models. In this study, we analyze the structure of the solution space and the

Yaiza Asensio, Sergio Marras, Davide Spirito, Marco Gobbi

Understanding the structural and magnetic properties in layered hybrid organic-inorganic metal halide perovskites (HOIPs) is key for their design and integration in spin-electronic devices. Here, we have conducted a systematic study on ten compounds to understand the effect of the transition metal (Cu$^{2+}$, Mn$^{2+}$, Co$^{2+}$), organic spacer (alkyl- and

Jiongliang Lin, Yiwen Guo, Hao Chen

Intrusion detection is a long standing and crucial problem in security. A system capable of detecting intrusions automatically is on great demand in enterprise security solutions. Existing solutions rely heavily on hand-crafted rules designed by security operators, which suffer from high false negative rates and poor generalization ability to new, zero-day a

Qingyuan Yang, Hu Ding

Wasserstein Barycenter (WB) is one of the most fundamental optimization problems in optimal transportation. Given a set of distributions, the goal of WB is to find a new distribution that minimizes the average Wasserstein distance to them. The problem becomes even harder if we restrict the solution to be ``$k$-sparse''. In this paper, we study the $k$-sparse

Boyu Ning

One of the national treasures, Mahjong, is a strategic card game originating from China, played competitively by constructing and optimizing card hands among four players. In the game, players aim to achieve specific combinations of cards, known as "winning hands," through actions such as drawing, discarding, pung, and kong. Mahjong not only tests memory and

Linhui Xiao, Xiaoshan Yang, Fang Peng, Yaowei Wang

Visual grounding, which aims to ground a visual region via natural language, is a task that heavily relies on cross-modal alignment. Existing works utilized uni-modal pre-trained models to transfer visual or linguistic knowledge separately while ignoring the multimodal corresponding information. Motivated by recent advancements in contrastive language-image

Shuyu Ou, Mahyar Hassanifar, Martin Votava, Marius Langwasser

The modular multilevel converter (MMC) is a topology that consists of a high number of capacitors, and degradation of capacitors can lead to converter malfunction, limiting the overall system lifetime. Condition monitoring methods can be applied to assess the health status of capacitors and realize predictive maintenance to improve reliability. Current resea

C. Swastik, Ravinder K. Banyal, Mayank Narang, Athira Unni

There is growing evidence from stellar kinematics and galactic chemical evolution (GCE) suggesting that giant planets (M$_{P}\geq$0.3$M_{J}$) are relatively young compared to the most commonly occurring population of small planets (M$_{P} <$0.3$M_{J}$). To further test the validity of these results, we analyzed the ages for a large number of 2336 exoplanet h

Sefika Efeoglu, Adrian Paschke

Information Extraction (IE) is a transformative process that converts unstructured text data into a structured format by employing entity and relation extraction (RE) methodologies. The identification of the relation between a pair of entities plays a crucial role within this framework. Despite the existence of various techniques for relation extraction, the

Long Cheng, Mingrui Bao, Jingxian Zhang, Xue Zhang

Chirality in solid-state materials has sparked significant interest due to potential applications of topologically-protected chiral states in next-generation information technology. The electrical magneto-chiral effect (eMChE), arising from relativistic spin-orbit interactions, shows great promise for developing chiral materials and devices for electronic in

Dries Vandenabeele, Nikolaus Doppelhammer, Sambhu Radhakrishnan, Vinod Chandran C.

Crystallizing zeolites with isotropic properties is critical to the chemical industry but can be extremely challenging as small deviations in the synthesis conditions can have extreme effects on the final products. Easily implemented in-situ monitoring systems could make a real difference, but very few experimental methodologies cater to the specific needs o

Xiaolei Zhang

The small finitistic dimension $\fPD(R)$ of a ring $R$ is defined to be the supremum of projective dimensions of $R$-modules with finite projective resolutions. In this paper, we investigate the small finitistic dimensions of four types of ring constructions: polynomial rings, formal power series rings, trivial extensions and amalgamations. Besides, we show

Thorren Kirschbaum, Annika Bande

Machine learning has emerged as a new tool in chemistry to bypass expensive experiments or quantum-chemical calculations, for example, in high-throughput screening applications. However, many machine learning studies rely on small data sets, making it difficult to efficiently implement powerful deep learning architectures such as message passing neural netwo

Kareem M. Attiah, Wei Yu

This paper presents a novel optimization framework for beamforming design in integrated sensing and communication systems where a base station seeks to minimize the Bayesian Cram\'er-Rao bound of a sensing problem while satisfying quality of service constraints for the communication users. Prior approaches formulate the design problem as a semidefinite progr

Jianyu Xu, Qiuzhuang Sun, Yang Yang, Huadong Mo

The 2019-20 Australia bushfire incurred numerous economic losses and significantly affected the operations of power systems. A power station or transmission line can be significantly affected due to bushfires, leading to an increase in operational costs. We study a fundamental but challenging problem of planning the optimal power flow (OPF) for power systems

J. Carlos Martínez Mori

In this expository article I describe classical results in the combinatorics of parking functions. Its English-Spanish translation is included. -- -- En este art\'iculo de difusi\'on matem\'atica describo resultados cl\'asicos en la combinatoria de funciones de parqueo. Su traducci\'on espa\~nol-ingl\'es est\'a incluida.

Jianxiang Zang, Hui Liu

The robustness of Transformer-based Natural Language Inference encoders is frequently compromised as they tend to rely more on dataset biases than on the intended task-relevant features. Recent studies have attempted to mitigate this by reducing the weight of biased samples during the training process. However, these debiasing methods primarily focus on iden

Mingqing Zhai, Longfei Fang, Huiqiu Lin

Let $spex(n,H_{minor})$ denote the maximum spectral radius of $n$-vertex $H$-minor free graphs. The problem on determining this extremal value can be dated back to the early 1990s. Up to now, it has been solved for $n$ sufficiently large and some special minors, such as $\{K_{2,3},K_4\}$, $\{K_{3,3},K_5\}$, $K_r$ and $K_{s,t}$. In this paper, we find some un

Yong Liu, Mengtian Kang, Shuo Gao, Chi Zhang

Fundus diseases are major causes of visual impairment and blindness worldwide, especially in underdeveloped regions, where the shortage of ophthalmologists hinders timely diagnosis. AI-assisted fundus image analysis has several advantages, such as high accuracy, reduced workload, and improved accessibility, but it requires a large amount of expert-annotated

Ruiyang Chen, Yi-Han Luo, Jinbao Long, Baoqi Shi

Photon-pair sources are critical building blocks for photonic quantum systems. Leveraging Kerr nonlinearity and cavity-enhanced spontaneous four-wave mixing, chip-scale photon-pair sources can be created using microresonators built on photonic integrated circuit. For practical applications, a high microresonator quality factor $Q$ is mandatory to magnify pho

Jiaqi Wang, Mengtian Kang, Yong Liu, Chi Zhang

Machine learning-based fundus image diagnosis technologies trigger worldwide interest owing to their benefits such as reducing medical resource power and providing objective evaluation results. However, current methods are commonly based on supervised methods, bringing in a heavy workload to biomedical staff and hence suffering in expanding effective databas

Hui Li, Jia-Kai Shi, Li-Bao Fan, Zi-Min Li

The interplay of three light-matter interaction processes - rotating and counter-rotating interactions and two-photon relaxation of the light field - is a topic of interest in quantum optics and quantum information processing. In this work, we theoretically investigate the three light-matter interaction processes using the anisotropic Rabi model, which accou

Sajad Hajikhani, Ramin Mehrabifard, Hamed Soltani Ahmadi

Plasma technology has undeniably revolutionized industrial processes in recent decades. Atmospheric pressure plasma (APP) has emerged as a prominent and widely applicable tool in various scientific disciplines. Notably, plasma-assisted flow control has become a subject of intense interest, particularly applying surface dielectric barrier discharge (SDBD) pla

Yue Li, Yanyong Hong

Pre-Novikov algebras and quasi-Frobenius Novikov algebras naturally appear in the theory of Novikov bialgebras. In this paper, we show that there is a natural pre-Novikov algebra structure associated to a quasi-Frobenius Novikov algebra. Then we introduce the definition of double constructions of quasi-Frobenius Novikov algebras associated to two pre-Novikov

Stefania Bellavia, Benedetta Morini, Simone Rebegoldi

This work elaborates on the TRust-region-ish (TRish) algorithm, a stochastic optimization method for finite-sum minimization problems proposed by Curtis et al. in [Curtis2019, Curtis2022]. A theoretical analysis that complements the results in the literature is presented, and the issue of tuning the involved hyper-parameters is investigated. Our study also f

Rob Romijnders, Christos Louizos, Yuki M. Asano, Max Welling

The COVID19 pandemic had enormous economic and societal consequences. Contact tracing is an effective way to reduce infection rates by detecting potential virus carriers early. However, this was not generally adopted in the recent pandemic, and privacy concerns are cited as the most important reason. We substantially improve the privacy guarantees of the cur

Gan Ren

The Stokes-Einstein-Debye (SED) relation is proposed to be breakdown in supercooled liquids by many studies. However, the conclusions are usually drawn by testing some variants of the SED relation rather than its original form. In this work, the rationality of the SED relation and its variants is examined by performing molecular dynamics simulations with the

Shuhei Hatanaka, Jun Yamasaki

We developed a reconstruction method for the density matrix and Wigner function of electron beams through analysis of the Airy pattern intensity profile. The density matrix in a transmission electron microscope object plane was calculated using the coherence function and the electron wave amplitude and phase distributions. The Wigner function was then recons

Quancheng Du, Xiao Wang, Shouguo Yin, Lingxi Li

Accurate prediction of agent motion trajectories is crucial for autonomous driving, contributing to the reduction of collision risks in human-vehicle interactions and ensuring ample response time for other traffic participants. Current research predominantly focuses on traditional deep learning methods, including convolutional neural networks (CNNs) and recu

Yaqing Hou, Wenqiang Ma, Abhishek Gupta, Kavitesh Kumar Bali

In recent years, the field of Transfer Evolutionary Optimization (TrEO) has witnessed substantial growth, fueled by the realization of its profound impact on solving complex problems. Numerous algorithms have emerged to address the challenges posed by transferring knowledge between tasks. However, the recently highlighted ``no free lunch theorem'' in transfe

Xiuqiang He, Maitraya Avadhut Desai, Linbin Huang, Florian Dörfler

This article proposes a "cross-forming" control concept for grid-forming inverters operating against grid faults. Cross-forming refers to voltage angle forming and current magnitude forming. It differs from classical grid-forming and grid-following paradigms that feature voltage magnitude-and-angle forming and voltage magnitude-and-angle following (or curren

Ghassan Zubi, Maximilian Kuhn, Sofoklis Makridis, Savio Coutinho

The UAE aviation sector is vital for its economy and is forecast to grow substantially in the coming decades, increasing thereby fuel consumption. At the same time, the country is committed to cutting greenhouse gas emissions to mitigate climate change. Liquid green hydrogen is expected to emerge as an important aviation fuel in the future. The UAE can use i

Aixin Sun

Recommender Systems (RecSys) have become indispensable in numerous applications, profoundly influencing our everyday experiences. Despite their practical significance, academic research in RecSys often abstracts the formulation of research tasks from real-world contexts, aiming for a clean problem formulation and more generalizable findings. However, it is o

L. Siqueira, B. W. Freitas, D. B. de Freitas

This study presents photometric solutions for a carefully selected sample of 181 detached eclipsing binary systems recently observed by the Transiting Exoplanet Survey Satellite (TESS) mission. Our findings contribute to discussions about OBA-type systems and the orbital circularization influenced by tidal interactions. The results encompass crucial paramete

V. H. Binh, Cesar Bonilla, A. E. Cárcamo Hernández, D. T. Huong

We propose two models based on the $SU(3)_C \times SU(3)_L \times U(1)_X$ gauge symmetry, each incorporating distinct inverse seesaw mechanisms for generating neutrino masses at the radiative level. Therefore, neutrino masses are suppressed by the radiative nature of the mass generation mechanism, which occurs after the spontaneous breaking of the global lep

Lei Lu, Yanyue Xie, Wei Jiang, Wei Wang

This paper investigates the challenging problem of learned image compression (LIC) with extreme low bitrates. Previous LIC methods based on transmitting quantized continuous features often yield blurry and noisy reconstruction due to the severe quantization loss. While previous LIC methods based on learned codebooks that discretize visual space usually give

Aman Kumar, Muhammad Ul Haque Khan, Bijitendra Mittra

Modern System-on-Chip (SoC) designs are becoming more and more complex due to the technology upscaling. SoC designs often operate on multiple asynchronous clock domains, further adding to the complexity of the overall design. To make the devices power efficient, designers take a Globally-Asynchronous Locally-Synchronous (GALS) approach that creates multiple

Aman Kumar, Mark Litterick, Samuele Candido

As the demand for Internet of Things (IoT) and Human-to-Machine Interaction (HMI) increases, modern System-on-Chips (SoCs) offering such solutions are becoming increasingly complex. This intricate design poses significant challenges for verification, particularly when time-to-market is a crucial factor for consumer electronics products. This paper presents a

Chung-Han Hsieh, Yi-Shan Wong

This paper studies a risk-sensitive decision-making problem under uncertainty. It considers a decision-making process that unfolds over a fixed number of stages, in which a decision-maker chooses among multiple alternatives, some of which are deterministic and others are stochastic. The decision-maker's cumulative value is updated at each stage, reflecting t

Zihao Yue, Yepeng Zhang, Ziheng Wang, Qin Jin

Automatic movie narration aims to generate video-aligned plot descriptions to assist visually impaired audiences. Unlike standard video captioning, it involves not only describing key visual details but also inferring plots that unfold across multiple movie shots, presenting distinct and complex challenges. To advance this field, we introduce Movie101v2, a l

Leonid V. Skripnikov, Sergey D. Prosnyak, Aleksei V. Malyshev, Michail Athanasakis-Kaklamanakis

A method for calculating the field shift contribution to isotope shifts in many-electron atoms, incorporating quantum electrodynamics (QED) effects, is introduced. We also implement the model QED approach to incorporate QED contribution to the nuclear recoil effect at the high-order correlation effects treatment level. The proposed computational scheme is us

Lifeng Xu, Jian Jiang

The machine learning force field has achieved significant strides in accurately reproducing the potential energy surface with quantum chemical accuracy. However, it still faces significant challenges, e.g., extrapolating to uncharted chemical spaces, interpreting long-range electrostatics, and mapping complex macroscopic properties. To address these issues,

Lei Zhang, Ming Xu

In this paper, we introduce the metric operator for a compact homogeneous Finsler space, and use it to investigate the geodesic orbit property. We define the notion of standard homogeneous $(\alpha_1,\cdots,\alpha_s)$-metric which generalizes the notion of standard homogeneous $(\alpha_1,\alpha_2)$-metric. We classify all connected simply connected homogeneo

Hongtao Zhang, Keaven M Anderson, Zachary Zimmer, Gregory Golm

With the robust uptick in the applications of Bayesian external data borrowing, eliciting a prior distribution with the proper amount of information becomes increasingly critical. The prior effective sample size (ESS) is an intuitive and efficient measure for this purpose. The majority of ESS definitions have been proposed in the context of borrowing control

Luis E. Young-S., S. K. Adhikari

A Bose-Einstein condensate (BEC) formed on a curved surface with a distinct topology has been a hot topic of intense research, in search of new phenomena in quantum physics as well as for its possible application in quantum computing. In addition to the study of a spherical-shell-shaped BEC, we studied the formation of a cylindrical-shell-shaped harmonically

Aman Kumar, Sebastian Simon

Nowadays, a majority of System-on-Chips (SoCs) make use of Intellectual Property (IP) in order to shorten development cycles. When such IPs are developed, one of the main focuses lies in the high configurability of the design. This flexibility on the design side introduces the challenge of covering a huge state space of IP configurations on the verification

Ruturaj Ghatage, Aditya Kulkarni, Rajlaxmi Patil, Sharvi Endait

Question-answering systems have revolutionized information retrieval, but linguistic and cultural boundaries limit their widespread accessibility. This research endeavors to bridge the gap of the absence of efficient QnA datasets in low-resource languages by translating the English Question Answering Dataset (SQuAD) using a robust data curation approach. We

Daniel Cebrián-Lacasa, Marcin Leda, Andrew B. Goryachev, Lendert Gelens

We explore a biomimetic model that simulates a cell, with the internal cytoplasm represented by a two-dimensional circular domain and the external cortex by a surrounding ring, both modeled using FitzHugh-Nagumo systems. The external ring is dynamically influenced by a pacemaker-driven wave originating from the internal domain, leading to the emergence of th

Samuael Adnew, Paul Pu Liang

Automatic Speech Recognition (ASR) can play a crucial role in enhancing the accessibility of spoken languages worldwide. In this paper, we build a set of ASR tools for Amharic, a language spoken by more than 50 million people primarily in eastern Africa. Amharic is written in the Ge'ez script, a sequence of graphemes with spacings denoting word boundaries. T

Ivan Chajda, Miroslav Kolařík, Helmut Länger

On an arbitrary meet-semilattice S with 0 we define an orthogonality relation and investigate the lattice Cl(S) of all subsets of S closed under this orthogonality. We show that if S is atomic then Cl(S) is a complete atomic Boolean algebra. If S is a pseudocomplemented lattice, this orthogonality relation can be defined by means of the pseudocomplementation

Daniel Marris, Luca Giuggioli

The presence of temporal correlations in random movement trajectories is a widespread phenomenon across biological, chemical and physical systems. The ubiquity of persistent and anti-persistent motion in many natural and synthetic systems has led to a large literature on the modelling of temporally correlated movement paths. Despite the substantial body of w

Aun Raza, Hamish Nicholson, Ioanna Tsakalidou, Anna Herlihy

Implementing concurrent data structures is challenging and requires a deep understanding of concurrency concepts and careful design to ensure correctness, performance, and scalability. Further, composing operations on two or more concurrent data structures often requires a synchronization wrapper to ensure the operations are applied together atomically, resu

Zhengcong Fei, Mingyuan Fan, Junshi Huang

Consistency models have exhibited remarkable capabilities in facilitating efficient image/video generation, enabling synthesis with minimal sampling steps. It has proven to be advantageous in mitigating the computational burdens associated with diffusion models. Nevertheless, the application of consistency models in music generation remains largely unexplore

Rohan Agarwal

Addressing the challenge of modular architectural design, this study presents a novel approach through the implementation of a shape grammar system using functional and object-oriented programming principles from computer science. The focus lies on the modular generation of plates in the style of French Neoclassical architect Jean-Nicolas-Louis Durand, known

Carlos Lassance, Hervé Dejean, Stéphane Clinchant, Nicola Tonellotto

Learned sparse models such as SPLADE have successfully shown how to incorporate the benefits of state-of-the-art neural information retrieval models into the classical inverted index data structure. Despite their improvements in effectiveness, learned sparse models are not as efficient as classical sparse model such as BM25. The problem has been investigated

Patrick Rehill

This methodological review examines the use of the causal forest method by applied researchers across 133 peer-reviewed papers. It shows that the emerging best practice relies heavily on the approach and tools created by the original authors of the causal forest such as their grf package and the approaches given by them in examples. Generally researchers use

Philippe G. LeFloch, Jean-Marc Mercier, Shohruh Miryusupov

For three applications of central interest in finance, we demonstrate the relevance of numerical algorithms based on reproducing kernel Hilbert space (RKHS) techniques. Three use cases are investigated. First, we show that extrapolating from few pricer examples leads to sufficiently accurate and computationally efficient results so that our algorithm can ser

Biplab Dutta, Joao Carlos de Aquino Carvalho, Guadalupe Garcia-Arellano, Paolo Pedri

In the extreme near-field, when the spatial extension of the atomic wavefunction is no longer negligible compared to the atom-surface distance, the dipole approximation is no longer sufficient to describe Casimir-Polder interactions. Here we calculate the higher-order, quadrupole and octupole, contributions to Casimir-Polder energy shifts of Rydberg atoms cl

Pengzhi Li, Baijuan Li

In recent years, the rapid development of large-scale models has made new possibilities for interdisciplinary fields such as architecture. In this paper, we present a novel daylight-driven AI-aided architectural design method. Firstly, we formulate a method for generating massing models, producing architectural massing models using random parameters quickly.

Wojciech Różowski

Deterministic automata have been traditionally studied through the point of view of language equivalence, but another perspective is given by the canonical notion of shortest-distinguishing-word distance quantifying the of states. Intuitively, the longer the word needed to observe a difference between two states, then the closer their behaviour is. In this p

Ali Ghubaish, Zebo Yang, Aiman Erbad, Raj Jain

Intrusion detection systems (IDS) for the Internet of Things (IoT) systems can use AI-based models to ensure secure communications. IoT systems tend to have many connected devices producing massive amounts of data with high dimensionality, which requires complex models. Complex models have notorious problems such as overfitting, low interpretability, and hig

Chelsea Fox, Kyle Chen, Micaela Antonini, Tommaso Magrini

As a result of evolution, many biological materials have developed irregular structures that lead to outstanding mechanical properties, like high stiffness-to-weight ratios and good energy absorption. To reproduce these properties in synthetic materials, biomimicry typically replicates the irregular natural structure, often leading to fabrication challenges.

Maneesha U. Athukorala, Deshan K. Sumanathilaka

Machine Transliteration provides the ability to transliterate a basic language into different languages in a computational way. Transliteration is an important technical process that has caught the attention most recently. The Sinhala transliteration has many constraints because of the insufficiency of resources in the Sinhala language. Due to these limitati

Yebo Wu, Li Li, Chengzhong Xu

This paper presents ProFL, a new framework that effectively addresses the memory constraints in FL. Rather than updating the full model during local training, ProFL partitions the model into blocks based on its original architecture and trains each block in a progressive fashion. It first trains the front blocks and safely freezes them after convergence. Tra

Xiaofei Wang, Yunfeng Zhao, Chao Qiu, Qinghua Hu

Amidst the robust impetus from artificial intelligence (AI) and big data, edge intelligence (EI) has emerged as a nascent computing paradigm, synthesizing AI with edge computing (EC) to become an exemplary solution for unleashing the full potential of AI services. Nonetheless, challenges in communication costs, resource allocation, privacy, and security cont

Hamidreza Mirkhani, Behzad Khamidehi, Kasra Rezaee

Trajectory augmentation serves as a means to mitigate distributional shift in imitation learning. However, imitating trajectories that inadequately represent the original expert data can result in undesirable behaviors, particularly in safety-critical scenarios. We propose a trajectory augmentation method designed to maintain similarity with expert trajector

Guanghao Li, Qi Chen, YuXiang Yan, Jian Pu

We introduce EC-SLAM, a real-time dense RGB-D simultaneous localization and mapping (SLAM) system leveraging Neural Radiance Fields (NeRF). While recent NeRF-based SLAM systems have shown promising results, they have yet to fully exploit NeRF's potential to constrain pose optimization. EC-SLAM addresses this by using sparse parametric encodings and Truncated

Kerstin Lenk, Audrey Denizot, Barbara Genocchi, Ippa Seppälä

At tripartite synapses, astrocytes are in close contact with neurons and contribute to various functions, from synaptic transmission, maintenance of ion homeostasis and glutamate uptake to metabolism. However, disentangling the precise contribution of astrocytes to those phenomena and the underlying biochemical mechanisms is remarkably challenging. This nota

Moshe Eliasof, Beatrice Bevilacqua, Carola-Bibiane Schönlieb, Haggai Maron

In recent years, significant efforts have been made to refine the design of Graph Neural Network (GNN) layers, aiming to overcome diverse challenges, such as limited expressive power and oversmoothing. Despite their widespread adoption, the incorporation of off-the-shelf normalization layers like BatchNorm or InstanceNorm within a GNN architecture may not ef

Ana-Cristina Rogoz, Radu Tudor Ionescu

This work explores a novel data augmentation method based on Large Language Models (LLMs) for predicting item difficulty and response time of retired USMLE Multiple-Choice Questions (MCQs) in the BEA 2024 Shared Task. Our approach is based on augmenting the dataset with answers from zero-shot LLMs (Falcon, Meditron, Mistral) and employing transformer-based m

Yidan Liu, Weiying Xie, Kai Jiang, Jiaqing Zhang

The majority of existing hyperspectral anomaly detection (HAD) methods use the low-rank representation (LRR) model to separate the background and anomaly components, where the anomaly component is optimized by handcrafted sparse priors (e.g., $\ell_{2,1}$-norm). However, this may not be ideal since they overlook the spatial structure present in anomalies and

Mohameden Ahmedou, Mohamed Ben Ayed, Khalil El Mehdi

In this paper, we extend the analysis of the subcritical approximation of the Nirenberg problem on spheres recently conducted in \cite{MM19, MM}. Specifically, we delve into the scenario where the sequence of blowing up solutions exhibits a non-zero weak limit, which necessarily constitutes a solution of the Nirenberg problem itself. Our focus lies in provid

Clement Etienam, Yang Juntao, Issam Said, Oleg Ovcharenko

We have developed an advanced workflow for reservoir characterization, effectively addressing the challenges of reservoir history matching through a novel approach. This method integrates a Physics Informed Neural Operator (PINO) as a forward model within a sophisticated Cluster Classify Regress (CCR) framework. The process is enhanced by an adaptive Regular

Kefan Li, Yuan Yuan

Code generation with Large Language Models (LLMs) has been extensively studied and achieved remarkable progress. As a complementary aspect to code generation, test case generation is of crucial importance in ensuring the quality and reliability of code. However, using LLMs as test case generators has been much less explored. Current research along this line

Anant Mathur, Sarat Moka, Benoit Liquet, Zdravko Botev

We present a new optimization method for the group selection problem in linear regression. In this problem, predictors are assumed to have a natural group structure and the goal is to select a small set of groups that best fits the response. The incorporation of group structure in a predictor matrix is a key factor in obtaining better estimators and identify

M. Pudlak, R. G. Nazmitdinov

Graphene corrugations affect hybridization of $\pi$ and $\sigma$ orbitals of carbon atoms in graphene based systems. It can as well break differently the symmetry of the electron transfer integrals for different strip boundaries. Using these facts, we found that the momentum distribution of electrons in ballistically propagating beam can be selective without

Bruno Ramos-Cruz, Javier Andreu-Pérez, Francisco J. Quesada, Luis Martínez

Blockchain technology has become a trusted method for establishing secure and transparent transactions through a distributed, encrypted network. The operation of blockchain is governed by consensus algorithms, among which Proof of Stake (PoS) is popular yet has its drawbacks, notably the potential for centralising power in nodes with larger stakes or higher

Yuqin Luo, Xintong Dong, Shiqi Dong, Tie Zhong

The perfectly matched layer(PML) is commonly used in wave propagation, radiation and diffraction problems in unbounded space domains. A new implementation scheme of PML is presented. The PML formulation is pre-defined, and the wave field absorption is achieved by calculating the residual between the PML equation and original equation through backward inducti

Dániel Gerbner

Given graphs $H$ and $F$, the generalized Tur\'an number $\mathrm{ex}(n,H,F)$ is the largest number of copies of $H$ in $n$-vertex $F$-free graphs. We study the case when either $H$ or $F$ is a matching. We obtain several asymptotic and exact results.

Ilan Doron-Arad, Joseph, Naor

We formulate and study non-linear paging - a broad model of online paging where the size of subsets of pages is determined by a monotone non-linear set function of the pages. This model captures the well-studied classic weighted paging and generalized paging problems, and also submodular and supermodular paging, studied here for the first time, that have a r

Jianan Pan, Kunyang He, Kai Wang, Qing Mu

The substantial threat of concurrent air pollutants to public health is increasingly severe under climate change. To identify the common drivers and extent of spatio-temporal similarity of PM2.5 and ozone, this paper proposed a log Gaussian-Gumbel Bayesian hierarchical model allowing for sharing a SPDE-AR(1) spatio-temporal interaction structure. The propose

Erik Schnaubelt, Mariusz Wozniak, Julien Dular, Idoia Cortes Garcia

High-temperature superconductors (HTS) have the potential to enable magnetic fields beyond the current limits of low-temperature superconductors in applications like accelerator magnets. However, the design of HTS-based magnets requires computationally demanding transient multi-physics simulations with highly non-linear material properties. To reduce the sol

Kaushik Bhattacharya, Dipanjan Dey, Priyanka Saha

It is known that in Friedmann-Lemaitre-Robertson-Walker cosmology one has more number of dynamical equations, compared to the number of unknown variables. This fact makes some equations redundant. The situation becomes complicated because all the relevant differential equations in cosmology are not of the same order. In this article we study the fate of the