May 2024 arXiv papers — page 95

Mounes Zaval, Sedat Ozer

In the evolving field of Explainable AI (XAI), interpreting the decisions of deep neural networks (DNNs) in computer vision tasks is an important process. While pixel-based XAI methods focus on identifying significant pixels, existing concept-based XAI methods use pre-defined or human-annotated concepts. The recently proposed Segment Anything Model (SAM) ach

Natascha Stumpp, Doris Aschenbrenner, Manuel Stahl, Andreas Aßmuth

To support maintenance and servicing of industrial machines, service processes are even today often performed manually and analogously, although supportive technologies such as augmented reality, virtual reality and digital platforms already exist. In many cases, neither technicians on-site nor remote experts have all the essential information and options fo

Ray Ito, Junichiro Takahashi

This paper presents a demonstration of our monster battle game, in which the game agents fight in accordance with their player's language commands. The commands were translated into the knowledge expression called behavior branches by a code-generation large language model. This work facilitated the design of the commanding system more easily, enabling the g

Katarzyna Skowronek, Marek Arendarczyk, Radosław Zimroz, Agnieszka Wyłomańska

In this paper, we explore the modified Greenwood statistic, which, in contrast to the classical Greenwood statistic, is properly defined for random samples from any distribution. The classical Greenwood statistic, extensively examined in the existing literature, has found diverse and interesting applications across various domains. Furthermore, numerous modi

Rohitash Chandra, Baicheng Zhu, Qingying Fang, Eka Shinjikashvili

During the COVID-19 pandemic, the news media coverage encompassed a wide range of topics that includes viral transmission, allocation of medical resources, and government response measures. There have been studies on sentiment analysis of social media platforms during COVID-19 to understand the public response given the rise of cases and government strategie

A. Hariki, T. Okauchi, Y. Takahashi, J. Kuneš

We present a numerical simulation of the X-ray magnetic circular dichroism (XMCD) at the $L_{2,3}$ edge of Mn in altermagnetic MnF$_2$ using a combination of density functional + exact diagonlization of an atomic model. We explore how the dichroic spectra vary with the light propagation vector and the N\'eel vector. We show how XMCD in rutile structures can

Yongxu Fu, Yi Zhang

There has been much recent interest and progress on topological structures of the non-Hermitian Bloch bands. Here, we study the topological structures of non-Bloch bands of non-Hermitian multiband quantum systems under open boundary conditions, which has received limited attention in prior studies. Using a continuity criterion and an efficient sub-generalize

Marcos Kalinowski, Daniel Mendez, Görkem Giray, Antonio Pedro Santos Alves

Context: Machine learning (ML)-enabled systems are being increasingly adopted by companies aiming to enhance their products and operational processes. Objective: This paper aims to deliver a comprehensive overview of the current status quo of engineering ML-enabled systems and lay the foundation to steer practically relevant and problem-driven academic resea

Siavash Shams, Sukru Samet Dindar, Xilin Jiang, Nima Mesgarani

Transformers have revolutionized deep learning across various tasks, including audio representation learning, due to their powerful modeling capabilities. However, they often suffer from quadratic complexity in both GPU memory usage and computational inference time, affecting their efficiency. Recently, state space models (SSMs) like Mamba have emerged as a

Jingyi Zhuang, Renata Wentzcovitch

Iron partitioning among the main lower mantle phases, bridgmanite (Bm) and ferropericlase (Fp), has non-monotonic behavior owing to the high-spin to low-spin crossover in ferrous iron (Fe2+) in Fp. Results of previous studies of the iron partitioning coefficient between these phases, $K_D$, still have considerable uncertainty. Here, we investigate the Fe2+ p

Hikmat Khan, Ghulam Rasool, Nidhal Carla Bouaynaya

Continual learning focuses on learning non-stationary data distribution without forgetting previous knowledge. Rehearsal-based approaches are commonly used to combat catastrophic forgetting. However, these approaches suffer from a problem called "rehearsal memory overfitting, " where the model becomes too specialized on limited memory samples and loses its a

Adiba Orzikulova, Jaehyun Kwak, Jaemin Shin, Sung-Ju Lee

Many healthcare sensing applications utilize multimodal time-series data from sensors embedded in mobile and wearable devices. Federated Learning (FL), with its privacy-preserving advantages, is particularly well-suited for health applications. However, most multimodal FL methods assume the availability of complete modality data for local training, which is

Wei Liu, Xiang-Dong Shi, Xiao-Hui Fang, Qi-Shan Wang

We report the photometric analysis of SU UMa based on the observations of the Transiting Exoplanet Survey Satellite (TESS). TESS has released a large amount of data, which contains the light curves of a complete superoutburst and three normal outbursts of SU UMa. Based on the observations, the evolution of superhumps during the superoutburst was analyzed. By

Zhen Cao, F. Aharonian, Axikegu, Y. X. Bai

The KM2A is the largest sub-array of the Large High Altitude Air Shower Observatory (LHAASO). It consists of 5216 electromagnetic particle detectors (EDs) and 1188 muon detectors (MDs). The data recorded by the EDs and MDs are used to reconstruct primary information of cosmic ray and gamma-ray showers. This information is used for physical analysis in gamma-

Dionysios Sklavenitis, Dimitris Kalles

Advances in AI have led to new types of technical debt in software engineering projects. AI-based competition platforms face challenges due to rapid prototyping and a lack of adherence to software engineering principles by participants, resulting in technical debt. Additionally, organizers often lack methods to evaluate platform quality, impacting sustainabi

Kohei Kobayashi

Control of open quantum dynamics is of great interest for realizing quantum technologies. Therefore, it is an important task to quantify and characterize the entropy for open quantum systems under decoherence. In this paper, we study the time evolution of the von Neumann entropy for open quantum systems described by the Lindblad master equation. Note that, i

Aryan Garg, Raghav Mallampali, Akshat Joshi, Shrisudhan Govindarajan

Dual pixels contain disparity cues arising from the defocus blur. This disparity information is useful for many vision tasks ranging from autonomous driving to 3D creative realism. However, directly estimating disparity from dual pixels is less accurate. This work hypothesizes that distilling high-precision dark stereo knowledge, implicitly or explicitly, to

Sunyuan Qiang, Xuxin Lin, Yanyan Liang, Jun Wan

Continual learning (CL) aims to extend deep models from static and enclosed environments to dynamic and complex scenarios, enabling systems to continuously acquire new knowledge of novel categories without forgetting previously learned knowledge. Recent CL models have gradually shifted towards the utilization of pre-trained models (PTMs) with parameter-effic

Subhadeep Chakrabarti, Saish Shinde

Pyrolysis techniques have served to be a groundbreaking technique for effectively utilising natural and man-made biomass products like plastics, wood, crop residue, fruit peels etc. Recent advancements have shown a greater yield of essential products like biochar, bio-oil and other non-condensable gases by blending different biomasses in a certain ratio. Thi

Arindam Das, Puja Das, Nobuchika Okada

The origin of tiny neutrino mass is a long standing unsolved puzzle of the Standard Model (SM), which allows us to consider scenarios beyond the Standard Model (BSM) in a variety of ways. One of them being a gauge extension of the SM may be realized as in the form of an anomaly free, general $U(1)_X$ extension of the SM, where an $SU(2)_L$ triplet scalar wit

Chris Emezue

Structured prediction tasks, like machine translation, involve learning functions that map structured inputs to structured outputs. Recurrent Neural Networks (RNNs) have historically been a popular choice for such tasks, including in natural language processing (NLP) applications. However, training RNNs using Maximum Likelihood Estimation (MLE) has its limit

Jiakun Liu, H. Vincent Poor, Iickho Song, Wenyi Zhang

A composite source, consisting of multiple subsources and a memoryless switch, outputs one symbol at a time from the subsource selected by the switch. If some data should be encoded more accurately than other data from an information source, the composite source model is suitable because in this model different distortion constraints can be put on the subsou

Jungwoo Kim, Ji-Su Lee, Huijae Kim, Taesik Lee

This paper presents an analytical framework for conducting academic reviews in the field of Healthcare Systems Engineering, employing ChatGPT, a state-of-the-art tool among recent language models. We utilized 9,809 abstract paragraphs from conference presentations to systematically review the field. The framework comprises distinct analytical processes, each

Anastasios Foliadis, Mario H. Castañeda, Richard A. Stirling-Gallacher, Reiner S. Thomä

Utilizing deep learning (DL) techniques for radio-based positioning of user equipment (UE) through channel state information (CSI) fingerprints has demonstrated significant potential. DL models can extract complex characteristics from the CSI fingerprints of a particular environment and accurately predict the position of a UE. Nonetheless, the effectiveness

Yuta Sakamoto, Takahiro Sakaue

Statistics of stochastic processes are crucially influenced by the boundary conditions. In one spatial dimension, for example, the first passage time distribution in semi-infinite space (one absorbing boundary) is markedly different from that in a finite interval with two absorbing boundaries. Here, we propose a method, which we refer to as a method of filtr

Masato Sakai, Marcus Freitag, Akihisa Sakurai, Conrad M Albrecht

Preservation of the Nasca geoglyphs at the UNESCO World Heritage Site in Peru is urgent as natural and human impact accelerates. More frequent weather extremes such as flashfloods threaten Nasca artifacts. We demonstrate that runoff models based on (sub-)meter scale, LiDAR-derived digital elevation data can highlight AI-detected geoglyphs that are in danger

Lingli Hu, Zhaoyang Yin

In this paper, we first establish the local well-posednesss of a Two-Component b-Family equations in nonhomogeneous Besov spaces $B^{1+\frac 1 p}_{p,1}$ with $1\leq p<+\infty.$ Then we present a new blow-up result for the Two-Component b-Family equations.

Yu-Guo Liu, Shu Chen

The Lindblad master equation (LME) describing the Markovian dynamics of the quantum open system can be understood as the evolution of the effective non-Hermitian Hamiltonian balanced with random quantum jumps. Here we investigate the balance-breaking dynamics by partly eliminating jumps from postselection experiments. To describe this dynamics, a non-linear

Liuzhi Zhou, Yu He, Kun Zhai, Xiang Liu

Federated learning (FL) has emerged as a prominent approach for collaborative training of machine learning models across distributed clients while preserving data privacy. However, the quest to balance acceleration and stability becomes a significant challenge in FL, especially on the client-side. In this paper, we introduce FedCAda, an innovative federated

Haruki Takezawa, Han Hsu, Kei Hirose, Fumiya Sakai

We report the formations of fcc and distorted hcp iron-helium compounds with x in FeHex up to 0.13 and 0.48, respectively, based on experiments at 5-54 GPa and ~1000-2820 K. Upon releasing pressure under room temperature, these fcc and distorted hcp FeHex were still observed by XRD and SIMS measurements. Our first-principles calculations indicate that fcc an

Baiyu Pan, Jichao Jiao, Jianxing Pang, Jun Cheng

In recent years, numerous real-time stereo matching methods have been introduced, but they often lack accuracy. These methods attempt to improve accuracy by introducing new modules or integrating traditional methods. However, the improvements are only modest. In this paper, we propose a novel strategy by incorporating knowledge distillation and model pruning

Kento Kawaharazuka, Kei Okada, Masayuki Inaba

While the musculoskeletal humanoid has various biomimetic benefits, its complex modeling is difficult, and many learning control methods have been developed. However, for the actual robot, the hysteresis of its joint angle tracking is still an obstacle, and realizing target posture quickly and accurately has been difficult. Therefore, we develop a feedback c

Seongjun Kang, Gwangbin Kim, Seokhyun Hwang, Jeongju Park

This paper introduces a motor-driven Peltier device designed to deliver immediate thermal sensations within extended reality (XR) environments. The system incorporates eight motor-driven Peltier elements, facilitating swift transitions between warm and cool sensations by rotating preheated or cooled elements to opposite sides. A multi-layer structure, compri

Lei Niu, Susu Wang

In this paper, we study the dynamics of a discrete Kolmogorov predator-prey model with Ricker-type prey growth. We give the sufficient and necessary condition to guarantee the existence and uniqueness of the positive fixed point. Using the center manifold theory, we prove that the period-doubling bifurcations can occur at the positive fixed point. Furthermor

Samuel Shuman

In this paper, we expand on previous work describing partial derivatives and metric component estimators to define tangent spaces on causal sets. Partial derivative operators are the basis vectors of the tangent space, and the metric defines the inner product. First, we use partial derivatives of the metric components to define the connection and partial der

Minghao Wu, Jiahao Xu, Yulin Yuan, Gholamreza Haffari

Literary translation remains one of the most challenging frontiers in machine translation due to the complexity of capturing figurative language, cultural nuances, and unique stylistic elements. In this work, we introduce TransAgents, a novel multi-agent framework that simulates the roles and collaborative practices of a human translation company, including

Kento Kawaharazuka, Yoshimoto Ribayashi, Akihiro Miki, Yasunori Toshimitsu

The musculoskeletal humanoid is difficult to modelize due to the flexibility and redundancy of its body, whose state can change over time, and so balance control of its legs is challenging. There are some cases where ordinary PID controls may cause instability. In this study, to solve these problems, we propose a method of learning a correlation model among

Minwoo Seong, Gwangbin Kim, Yumin Kang, Junhyuk Jang

This study proposes a framework for enhancing the stroke quality of badminton players by generating personalized motion guides, utilizing a multimodal wearable dataset. These guides are based on counterfactual algorithms and aim to reduce the performance gap between novice and expert players. Our approach provides joint-level guidance through visualizable da

Li Sun, Zhenhao Huang, Hao Peng, Yujie Wang

Graph clustering is a fundamental problem in machine learning. Deep learning methods achieve the state-of-the-art results in recent years, but they still cannot work without predefined cluster numbers. Such limitation motivates us to pose a more challenging problem of graph clustering with unknown cluster number. We propose to address this problem from a fre

Yueqiao Jin, Lixiang Yan, Vanessa Echeverria, Dragan Gašević

Integrating generative AI (GAI) into higher education is crucial for preparing a future generation of GAI-literate students. Yet a thorough understanding of the global institutional adoption policy remains absent, with most of the prior studies focused on the Global North and the promises and challenges of GAI, lacking a theoretical lens. This study utilizes

Shanmuka Shivashankara, Grace Gogliettino

Regularized quantum information metrics are calculated for the scattering process $e^-e^+ \rightarrow \gamma,Z\rightarrow \mu^-\mu^+$ that has a witness photon entangled with the initial electron-positron state. Unitarity implies the correct regularization of divergences that appear in both the final density matrix and von Neumann entanglement entropies. The

Kento Kawaharazuka, Naoaki Kanazawa, Kei Okada, Masayuki Inaba

In this study, we investigate object grasping by visual servoing in a low-rigidity robot. It is difficult for a low-rigidity robot to handle its own body as intended compared to a rigid robot, and calibration between vision and body takes some time. In addition, the robot must constantly adapt to changes in its body, such as the change in camera position and

Leiye Xu, Zongrui Hu

We show that a minimal toplogical dynamical system that is frequently stable if and only if it is almost automorphic.

Nobuyuki Okuma

In the field of fractional Chern insulators, a great deal of effort has been devoted to characterizing Chern bands that exhibit properties similar to the Landau levels. Among them, the concept of the vortex function, which generalizes the complex coordinate used for the symmetric-gauge Landau-level basis, allows for a concise description. In this paper, we d

Shun Okumura, Masayuki Ohzeki, Masaya Abe

Despite proposing a quantum generative model for time series that successfully learns correlated series with multiple Brownian motions, the model has not been adapted and evaluated for financial problems. In this study, a time-series generative model was applied as a quantum generative model to actual financial data. Future data for two correlated time serie

Zixun Fang, Wei Zhai, Aimin Su, Hongliang Song

Video virtual try-on aims to transfer a clothing item onto the video of a target person. Directly applying the technique of image-based try-on to the video domain in a frame-wise manner will cause temporal-inconsistent outcomes while previous video-based try-on solutions can only generate low visual quality and blurring results. In this work, we present ViVi

Ruiqi Wu, Chenran Zhang, Jianle Zhang, Yi Zhou

Current fundus image analysis models are predominantly built for specific tasks relying on individual datasets. The learning process is usually based on data-driven paradigm without prior knowledge, resulting in poor transferability and generalizability. To address this issue, we propose MM-Retinal, a multi-modal dataset that encompasses high-quality image-t

Wei-Ting Lai, Lachlan Birnie, Xingyu Chen, Amy Bastine

We propose an advance Steered Response Power (SRP) method for localizing multiple sources. While conventional SRP performs well in adverse conditions, it remains to struggle in scenarios with closely neighboring sources, resulting in ambiguous SRP maps. We address this issue by applying sparsity optimization in SRP to obtain high-resolution maps. Our approac

Yanran Tang, Ruihong Qiu, Yilun Liu, Xue Li

Legal case retrieval (LCR) is a specialised information retrieval task aimed at identifying relevant cases given a query case. LCR holds pivotal significance in facilitating legal practitioners to locate legal precedents. Existing LCR methods predominantly rely on traditional lexical models or language models; however, they typically overlook the domain-spec

Jen-Tsung Hsiang, Hing-Tong Cho, Bei-Lok Hu

The detection of gravitational waves in 2015 ushered in a new era of gravitational wave astronomy capable of probing into the strong field dynamics of black holes and neutron stars. It has opened up an exciting new window for laboratory and space tests of Einstein's theory of classical general relativity. In recent years there are two interesting proposals a

Michelle Espinoza

In April 2024, an 81-year-old Ohio man was charged with murder, assault, and kidnapping. The man believed that he was protecting his family from scammers threatening harm. What he did not realize was that the 61-year-old Uber driver he killed, was also a victim of the same scammers. This case study examines some common variants of the Grandparent Scam from a

Junlong Jia, Ying Hu, Xi Weng, Yiming Shi

We present TinyLLaVA Factory, an open-source modular codebase for small-scale large multimodal models (LMMs) with a focus on simplicity of code implementations, extensibility of new features, and reproducibility of training results. Following the design philosophy of the factory pattern in software engineering, TinyLLaVA Factory modularizes the entire system

Gaofeng Wang

We investigate the nonlinear stability problem for the two-dimensional Boussinesq system around the Poiseuille flow in a finite channel. The system has the characteristic of Navier-slip boundary condition for the velocity and Dirichlet boundary condition for the temperature, with a small viscosity $\nu$ and small thermal diffusion $\mu,$ respectively. More p

Jai-chan Hwang, Hyerim Noh

We study graviton-photon conversions in an environment of the uniform and constant magnetic field considering Euler-K\"ockel-Heisenberg-type nonlinear corrections in electrodynamics. We take the transverse-tracefree gauge for gravitons and employ both the field potential and the electric and magnetic (EM) fields for photons. The nonlinear correction causes p

Amit Kadan, Kevin Ryczko, Erika Lloyd, Adrian Roitberg

Generative AI has the potential to revolutionize drug discovery. Yet, despite recent advances in deep learning, existing models cannot generate molecules that satisfy all desired physicochemical properties. Herein, we describe IDOLpro, a generative chemistry AI combining diffusion with multi-objective optimization for structure-based drug design. Differentia

Jiexin Wang, Eiji Uchibe

We introduce the ``soft Deep MaxPain'' (softDMP) algorithm, which integrates the optimization of long-term policy entropy into reward-punishment reinforcement learning objectives. Our motivation is to facilitate a smoother variation of operators utilized in the updating of action values beyond traditional ``max'' and ``min'' operators, where the goal is enha

Shinyoung Kang, Jihan Kim

In this study, we explore the potential of using quantum natural language processing (QNLP) to inverse design metal-organic frameworks (MOFs) with targeted properties. Specifically, by analyzing 450 hypothetical MOF structures consisting of 3 topologies, 10 metal nodes and 15 organic ligands, we categorize these structures into four distinct classes for pore

Takuro Matsuta, Ryo Furue

Recent advancements in quantum computing suggest the potential to revolutionize computational algorithms across various scientific domains including oceanography and atmospheric science. The field is still relatively young and quantum computation is so different from classical computation that suitable frameworks to represent oceanic and atmospheric dynamics

Zach Shahn, Paul Zivich, Audrey Renson

Despite the common occurrence of interference in Difference-in-Differences (DiD) applications, standard DiD methods rely on an assumption that interference is absent, and comparatively little work has considered how to accommodate and learn about spillover effects within a DiD framework. Here, we extend the `DiD-SNMMs' of Shahn et al (2022) to accommodate in

Trevor Campbell

Bayesian coresets speed up posterior inference in the large-scale data regime by approximating the full-data log-likelihood function with a surrogate log-likelihood based on a small, weighted subset of the data. But while Bayesian coresets and methods for construction are applicable in a wide range of models, existing theoretical analysis of the posterior in

Rosemary Zielinski, Cedric Simenel, Patrick McGlynn

Currently there is no general theory of quantum tunnelling of a particle through a potential barrier which is compatible with QFT. We present a complete calculation of tunnelling amplitudes for a scalar field for some simple potentials using quantum field-theoretic methods. Using the perturbative S-matrix formalism, starting with the Klein-Gordon Lagrangian,

Qihan Liu, Jianing Ye, Xiaoteng Ma, Jun Yang

Multi-agent reinforcement learning (MARL) algorithms have accomplished remarkable breakthroughs in solving large-scale decision-making tasks. Nonetheless, most existing MARL algorithms are model-free, limiting sample efficiency and hindering their applicability in more challenging scenarios. In contrast, model-based reinforcement learning (MBRL), particularl

Benjamin Quici, Ross J. Turner, Nicholas Seymour, Natasha Hurley-Walker

The energy coupling efficiency of active galactic nucleus (AGN) outbursts is known to {vary} significantly with factors including the jet kinetic power, duration of the outburst, and properties of the host galaxy group or cluster. As such, constraints on their jet power and lifetime functions are crucial to quantify the role of kinetic-mode AGN feedback on t

Jake Ketchum, Ahalya Prabhakar, Todd D. Murphey

Tactile perception is important for robotic systems that interact with the world through touch. Touch is an active sense in which tactile measurements depend on the contact properties of an interaction--e.g., velocity, force, acceleration--as well as properties of the sensor and object under test. These dependencies make training tactile perceptual models ch

Siva Rajesh Kasa, Aniket Goel, Karan Gupta, Sumegh Roychowdhury

Ordinal Classification (OC) is a widely encountered challenge in Natural Language Processing (NLP), with applications in various domains such as sentiment analysis, rating prediction, and more. Previous approaches to tackle OC have primarily focused on modifying existing or creating novel loss functions that \textbf{explicitly} account for the ordinal nature

William W. Zheng, Jamison R. Ebert, Stefano Rini, Jean-Francois Chamberland

In [1], the linked loop code (LLC) is presented as a promising code for the unsourced A-channel with erasures (UACE). The UACE is an unsourced multiple access channel in which active users' transmitted symbols are erased with a given probability and the channel output is obtained as the union of the non-erased symbols. In this paper, we extend the UACE chann

Hoang Hai Ha, Ky Ho

We establish a Lions-type concentration-compactness principle and its variant at infinity for Musielak-Orlicz-Sobolev spaces associated with a double phase operator with variable exponents. Based on these principles, we demonstrate the existence and concentration of solutions for a class of critical double phase equations of Schr\"odinger type in $\mathbb{R}

Zhicheng He, Jiayang Wu, Jingwen Zhang, Shibowen Zhang

Performing acrobatic maneuvers like dynamic jumping in bipedal robots presents significant challenges in terms of actuation, motion planning, and control. Traditional approaches to these tasks often simplify dynamics to enhance computational efficiency, potentially overlooking critical factors such as the control of centroidal angular momentum (CAM) and the

Josh Andres, Rodolfo Ocampo, Hannah R. Feldman, Louisa Shen

We present the design and user study of an installation for office workers, enabling moments of temporal diversity and connection to nature. The installation is a form of creative computing experience that departs from the traditional focus on office technologies for productivity. Drawing on neuroscience insights and the slowing effect of nature sounds on ti

Josef F. Dorfmeister, Roland Hildebrand, Shimpei Kobayashi

In this paper we study isometric immersions $f:M^n \to {\mathbb {C}^{\prime}}\!P^n$ of an $n$-dimensional pseudo-Riemannian manifold $M^n$ into the $n$-dimensional para-complex projective space ${\mathbb {C}^{\prime}}\!P^n$. We study the immersion $f$ by means of a lift $\mathfrak f$ of $f$ into a quadric hypersurface in ${S^{2n+1}_{n+1}}$. We find the frame

Yuanwu Xu, Feifan Song, Haofeng Zhang

Few-shot object counting aims to count the number of objects in a query image that belong to the same class as the given exemplar images. Existing methods compute the similarity between the query image and exemplars in the 2D spatial domain and perform regression to obtain the counting number. However, these methods overlook the rich information about the sp

Mohammad Farzanullah, Han Zhang, Akram Bin Sediq, Ali Afana

Integrated sensing and communications is a key enabler for the 6G wireless communication systems. The multiple sensing modalities will allow the base station to have a more accurate representation of the environment, leading to context-aware communications. Some widely equipped sensors such as cameras and RADAR sensors can provide some environmental percepti

Ravi Trivedi, Seetha Lakshmy, Heera T. Nair, Alok Shukla

We present a detailed theoretical study of Pt3Sn2S2 a layered kagome type material inspired by recent investigation of Co3Sn2S2 reported in [Nature Communication 11, 3985 (2020) whose physical properties remain largely unexplored. Thermodynamic stability was confirmed via formation energy calculations while mechanical stability was evaluated using Voigt Reus

Eric Alcaide, Zhifeng Gao, Guolin Ke, Yaqi Li

In recent years, machine learning (ML) methods have emerged as promising alternatives for molecular docking, offering the potential for high accuracy without incurring prohibitive computational costs. However, recent studies have indicated that these ML models may overfit to quantitative metrics while neglecting the physical constraints inherent in the probl

Ashlesha Patil, Saikat Guha

All-photonic quantum repeaters use multi-qubit photonic graph states, called repeater graph states (RGS), instead of matter-based quantum memories, for protection against predominantly loss errors. The RGS comprises tree-graph-encoded logical qubits for error correction at the repeaters and physical {\em link} qubits to create entanglement between neighborin

Wen-Chin Huang, Yi-Chiao Wu, Tomoki Toda

The trend of scaling up speech generation models poses a threat of biometric information leakage of the identities of the voices in the training data, raising privacy and security concerns. In this paper, we investigate training multi-speaker text-to-speech (TTS) models using data that underwent speaker anonymization (SA), a process that tends to hide the sp

Olivier Letoffe, Xuanxiang Huang, Nicholas Asher, Joao Marques-Silva

A central goal of eXplainable Artificial Intelligence (XAI) is to assign relative importance to the features of a Machine Learning (ML) model given some prediction. The importance of this task of explainability by feature attribution is illustrated by the ubiquitous recent use of tools such as SHAP and LIME. Unfortunately, the exact computation of feature at

Jianhong Han, Liang Chen, Yupei Wang

Object detectors frequently encounter significant performance degradation when confronted with domain gaps between collected data (source domain) and data from real-world applications (target domain). To address this task, numerous unsupervised domain adaptive detectors have been proposed, leveraging carefully designed feature alignment techniques. However,

Nian Li, Xin Ban, Cheng Ling, Chen Gao

Recommender systems filter out information that meets user interests. However, users may be tired of the recommendations that are too similar to the content they have been exposed to in a short historical period, which is the so-called user fatigue. Despite the significance for a better user experience, user fatigue is seldom explored by existing recommender

Scipio Cuccagna, Masaya Maeda

Assuming as hypotheses the results proved numerically by Chang et al. \cite{Chang} for the exponent $p\in (3,5)$, we prove that some of the ground states of the nonlinear Schr\"odinger equation (NLS) with pure power nonlinearity of exponent $p$ in the line are asymptotically stable for a certain set of values of the exponent $p$ where the FGR occurs by means

Cheng Chen, Lei Fan

Landslide susceptibility mapping (LSM) is crucial for identifying high-risk areas and informing prevention strategies. This study investigates the interpretability of statistical, machine learning (ML), and deep learning (DL) models in predicting landslide susceptibility. This is achieved by incorporating various relevant interpretation methods and two types

Qingxin Li, Yiwei Chen, LingNan Wei, Hong Chen

Excitons, the bosonic quasiparticle emerging from Coulomb interaction between electrons and holes, will undergo a Bose-Einstein condensation(BEC) and transition into a superfluid state with global phase coherence at low temperatures. An important platform to study such excitonic physics is built on double-layer quantum wells or recent two-dimensional materia

Akihiro Mizutani, Masanori Terashita, Junya Matsubayashi, Shogo Mori

Differential-phase-shift (DPS) quantum key distribution stands as a promising protocol due to its simple implementation, which can be realized with a train of coherent pulses and a passive measurement unit. To implement the DPS protocol, it is crucial to establish security proofs incorporating practical imperfections in users' devices, however, existing secu

Douglas L. Miller, Francesca Molinari, Jörg Stoye

We test the null hypothesis that two parameters $(\mu_1,\mu_2)$ have the same sign, assuming that (asymptotically) normal estimators $(\hat{\mu}_1,\hat{\mu}_2)$ are available. Examples of this problem include the analysis of heterogeneous treatment effects, causal interpretation of reduced-form estimands, meta-studies, and mediation analysis. A number of tes

Jiayan Chen, Zhirong Qian, Tianhui Meng, Xitong Gao

Aiming at privacy preservation, Federated Learning (FL) is an emerging machine learning approach enabling model training on decentralized devices or data sources. The learning mechanism of FL relies on aggregating parameter updates from individual clients. However, this process may pose a potential security risk due to the presence of malicious devices. Exis

Jiawei Wang, Kai Hu, Qiang Huo

Document layout analysis (DLA) is crucial for understanding the physical layout and logical structure of documents, serving information retrieval, document summarization, knowledge extraction, etc. However, previous studies have typically used separate models to address individual sub-tasks within DLA, including table/figure detection, text region detection,

Kai Gan, Tong Wei

Semi-supervised learning (SSL) has witnessed remarkable progress, resulting in the emergence of numerous method variations. However, practitioners often encounter challenges when attempting to deploy these methods due to their subpar performance. In this paper, we present a novel SSL approach named FineSSL that significantly addresses this limitation by adap

Qiuyue Liang, Ippei Obata, Misao Sasaki

The positive evidence of a nano-hertz gravitational wave background recently found by several pulsar timing array (PTA) collaborations opened up a window to test modified gravity theories in a unique frequency band in parallel to other gravitational wave detection experiments. In particular, the overlap reduction function (ORF) in PTA observation is sensitiv

Runou Yang, Tian Tian, Jinwen Tian

Addressing the challenge of domain shift between datasets is vital in maintaining model performance. In the context of cross-domain object detection, the teacher-student framework, a widely-used semi-supervised model, has shown significant accuracy improvements. However, existing methods often overlook class differences, treating all classes equally, resulti

Doan-An Trieu, Trong-Thanh D. Nguyen, Thanh-Duy D. Nguyen, Thanh Tran

Precise control of the electron phase accumulated during its sub-cycle motion within intense laser fields is essential in strong-field physics, yet remains mostly indirect and complicated so far. In this Letter, we develop a novel approach to control this sub-cycle electron phase by tuning a low-frequency electric field applied on a centrosymmetric gaseous t

Zihao Wang, Zhe Wu

Developing accurate models for chemical reactors is often challenging due to the complexity of reaction kinetics and process dynamics. Traditional approaches require retraining models for each new system, limiting generalizability and efficiency. In this work, we take a step toward foundation models for chemical reactor modeling by introducing a neural netwo

Lydia Dehbi, Zhengfeng Yang, Chao Peng, Yaochen Xu

Algebraic curve interpolation is described by specifying the location of N points in the plane and constructing an algebraic curve of a function f that should pass through them. In this paper, we propose a novel approach to construct the algebraic curve that interpolates a set of data (points or neighborhoods). This approach aims to search the polynomial wit

Yue M. Lu, Mary I. Letey, Jacob A. Zavatone-Veth, Anindita Maiti

Transformers have a remarkable ability to learn and execute tasks based on examples provided within the input itself, without explicit prior training. It has been argued that this capability, known as in-context learning (ICL), is a cornerstone of Transformers' success, yet questions about the necessary sample complexity, pretraining task diversity, and cont

Wenwen Zuo, Hengxiao Guo, Jingbo Sun, Qi Yuan

To investigate the short-term variability and determine the size of the optical continuum emitting region of intermediate-mass black holes (IMBHs), we carried out high-cadence, multiband photometric monitoring of a Seyfert 1 galaxy J0249-0815 across two nights, together with a one-night single-band preliminary test. The presence of the broad Ha component in

Duncan A. Forbes, Daniel Lyon, Jonah Gannon, Aaron J. Romanowsky

A number of nearby dwarf galaxies have globular cluster (GC) candidates that require spectroscopic confirmation. Here we present Keck telescope spectra for 15 known GCs and GC candidates that may be associated with a host dwarf galaxy, and an additional 3 GCs in the halo of M31 that are candidates for accretion from a now disrupted dwarf galaxy. We confirm 6

Kenichiro Aoki, Takahisa Mitsui

The relation between the expectation values computed in the random walk theory, and the heat kernel method for the diffusion equation is explained concretely. The random walk is also realized by simulations and their statistical uncertainties are analyzed.

Hao Zeng, Wei Zhong, Xingbai Xu

It is important to incorporate spatial geographic information into U.S. presidential election analysis, especially for swing states. The state-level analysis also faces significant challenges of limited spatial data availability. To address the challenges of spatial dependence and small sample sizes in predicting U.S. presidential election results using spat

Pengdeng Li, Shuxin Li, Chang Yang, Xinrun Wang

Decision-making problems, categorized as single-agent, e.g., Atari, cooperative multi-agent, e.g., Hanabi, competitive multi-agent, e.g., Hold'em poker, and mixed cooperative and competitive, e.g., football, are ubiquitous in the real world. Various methods are proposed to address the specific decision-making problems. Despite the successes in specific categ

Young Ho Kim

In this paper, we study interior estimates for solutions to linearized Monge-Amp\`ere equations in divergence form with drift terms and the right-hand side containing the divergence of a bounded vector field. Equations of this type appear in the study of semigeostrophic equations in meteorology and the solvability of singular Abreu equations in the calculus

Xinjie Dai, Diancong Jin

This paper focuses on studying the convergence rate of the density function of the Euler--Maruyama (EM) method, when applied to the overdamped generalized Langevin equation with fractional noise which serves as an important model in many fields. Firstly, we give an improved upper bound estimate for the total variation distance between random variables by the