December 2023 arXiv papers — page 6

Yu-Chieh Ho, Te-Sheng Lin, She-Chih Wang, Chen-Shi Chang

We investigated the beat-to-beat fluctuation of the photoplethysmography (PPG) waveform. The motivation is that morphology variability extracted from the arterial blood pressure (ABP) has been found to correlate with baseline condition and short-term surgical outcome of the patients undergoing liver transplant surgery. Numerous interactions of physiological

Zhiyuan Huang, Henry Lam, Zhenyuan Liu

We consider the estimation of small probabilities or other risk quantities associated with rare but catastrophic events. In the model-based literature, much of the focus has been devoted to efficient Monte Carlo computation or analytical approximation assuming the model is accurately specified. In this paper, we study a distinct direction on the propagation

Fabio V. Difonzo, Sabrina F. Pellegrino

We study the implementation of a Chebyshev spectral method with forward Euler integrator to investigate a peridynamic nonlocal formulation of Richards' equation. We prove the convergence of the fully-discretization of the model showing the existence and uniqueness of a solution to the weak formulation of the method by using the compactness properties of the

Harsh Chaudhari, Anuja Patil, Dhanashree Lavekar, Pranav Khairnar

This work introduces the L3Cube-MahaSocialNER dataset, the first and largest social media dataset specifically designed for Named Entity Recognition (NER) in the Marathi language. The dataset comprises 18,000 manually labeled sentences covering eight entity classes, addressing challenges posed by social media data, including non-standard language and informa

Gideon Chiusole, Peter K. Friz

Wiener spaces are in many ways the decisive setting for fundamental results on Gaussian measures: large deviations (Schilder), quasi-invariance (Cameron--Martin), differential calculus (Malliavin), support description (Stroock--Varadhan), concentration of measure (Fernique), etc. Analogues of these classical results have been derived in the "enhanced" contex

Yaqing Hou, Mingyang Sun, Abhishek Gupta, Yaochu Jin

In this paper, we scale evolutionary algorithms to high-dimensional optimization problems that deceptively possess a low effective dimensionality (certain dimensions do not significantly affect the objective function). To this end, an instantiation of the multiform optimization paradigm is presented, where multiple low-dimensional counterparts of a target hi

Xin Yu, Rongye Shi, Pu Feng, Yongkai Tian

Incorporating symmetry as an inductive bias into multi-agent reinforcement learning (MARL) has led to improvements in generalization, data efficiency, and physical consistency. While prior research has succeeded in using perfect symmetry prior, the realm of partial symmetry in the multi-agent domain remains unexplored. To fill in this gap, we introduce the p

Junwen Yang, Ang Li, Xuewen Liao, Christos Masouros

This paper investigates block-level interference exploitation (IE) precoding for multi-user multiple-input single-output (MU-MISO) downlink systems. To overcome the need for symbol-level IE precoding to frequently update the precoding matrix, we propose to jointly optimize all the precoders or transmit signals within a transmission block. The resultant preco

Shiqi Wang, Yeqin Zhang, Cam-Tu Nguyen

In open-domain Question Answering (QA), dense retrieval is crucial for finding relevant passages for answer generation. Typically, contrastive learning is used to train a retrieval model that maps passages and queries to the same semantic space. The objective is to make similar ones closer and dissimilar ones further apart. However, training such a system is

Harald Ruess

We study solutions to systems of stream inclusions of the form 'f in T(f)', where the nondeterministic transformer 'T' on omega-infinite streams is assumed to be causal in the sense that elements in output streams are determined by a finite prefix of inputs. We first establish a correspondence between logic-based causality and metric-based contraction. Based

Xiaogang Xing, Ming Xu, Yujing Bai, Dongdong Yang

Backdoor attacks in the traditional graph neural networks (GNNs) field are easily detectable due to the dilemma of confusing labels. To explore the backdoor vulnerability of GNNs and create a more stealthy backdoor attack method, a clean-label graph backdoor attack method(CGBA) in the node classification task is proposed in this paper. Differently from exist

Ian von Hegner

Although many solar systems have been discovered, only one example of life is known. Thus, terrestrial life represents merely one data point. Consequently, extrapolating from terrestrial life to life elsewhere in the galaxy and beyond is often seen as a limitation in the search for different forms of life. Essentially, attempting to extrapolate from terrestr

Guojian Wang, Faguo Wu, Xiao Zhang, Tianyuan Chen

The sparsity of reward feedback remains a challenging problem in online deep reinforcement learning (DRL). Previous approaches have utilized offline demonstrations to achieve impressive results in multiple hard tasks. However, these approaches place high demands on demonstration quality, and obtaining expert-like actions is often costly and unrealistic. To t

Deepak Akhare, Tengfei Luo, Jian-Xun Wang

The hybrid neural differentiable models mark a significant advancement in the field of scientific machine learning. These models, integrating numerical representations of known physics into deep neural networks, offer enhanced predictive capabilities and show great potential for data-driven modeling of complex physical systems. However, a critical and yet un

Jiacheng Wang, Hongyang Du, Dusit Niyato, Mu Zhou

As indoor applications grow in diversity, wireless sensing, vital in areas like localization and activity recognition, is attracting renewed interest. Indoor wireless sensing relies on signal processing, particularly channel state information (CSI) based signal parameter estimation. Nonetheless, regarding reflected signals induced by dynamic human targets, n

Masachika Masuda, Mazen Soufi, Yoshito Otake, Keisuke Uemura

Progression of hip osteoarthritis (hip OA) leads to pain and disability, likely leading to surgical treatment such as hip arthroplasty at the terminal stage. The severity of hip OA is often classified using the Crowe and Kellgren-Lawrence (KL) classifications. However, as the classification is subjective, we aimed to develop an automated approach to classify

Jinhao Jiang, Kun Zhou, Wayne Xin Zhao, Yaliang Li

Question Answering over Knowledge Graph (KGQA) aims to seek answer entities for the natural language question from a large-scale Knowledge Graph~(KG). To better perform reasoning on KG, recent work typically adopts a pre-trained language model~(PLM) to model the question, and a graph neural network~(GNN) based module to perform multi-hop reasoning on the KG.

Yuan-De Jin, Chu-Dan Qiu, Wen-Long Ma

Metastability in open system dynamics describes the phenomena of initial relaxation to longlived metastable states before decaying to the asymptotic stable states. It has been predicted in continuous-time stochastic dynamics of both classical and quantum systems. Here we present a general theory of metastability in discrete-time open quantum dynamics, descri

Zhicheng Feng, Jun Yu, Jiping Zhang

Radical subgroups play an important role in both finite group theory and representation theory. This is the first of a series of papers of ours in classifying radical $p$-subgroups of finite reductive groups and in verifying the inductive blockwise Alperin weight condition for them, contributing to the program of proving the Alperin weight conjecture by veri

Linhao Xu, Lin Zhao, Xinxin Sun, Di Wang

Occlusion presents a significant challenge in human pose estimation. The challenges posed by occlusion can be attributed to the following factors: 1) Data: The collection and annotation of occluded human pose samples are relatively challenging. 2) Feature: Occlusion can cause feature confusion due to the high similarity between the target person and interfer

Falih Gozi Febrinanto, Mujie Liu, Feng Xia

Analyzing connections between brain regions of interest (ROI) is vital to detect neurological disorders such as autism or schizophrenia. Recent advancements employ graph neural networks (GNNs) to utilize graph structures in brains, improving detection performances. Current methods use correlation measures between ROI's blood-oxygen-level-dependent (BOLD) sig

Biswajit Maitya, Abdul Alima, Popuri Sree Rama Charana, Amlan Chakrabartib

Recent studies emphasize that vehicular honking contributes to over 50% of noise pollution in developing urban and suburban areas. Frequent honking negatively impacts health, road safety, and the environment. Recognizing and classifying different vehicle honks could offer valuable insights into environmental noise pollution. Existing research on outdoor soun

Jing Jiao, Jin Zhou, Xiaokang Li, Menghua Xia

Inadequate generality across different organs and tasks constrains the application of ultrasound (US) image analysis methods in smart healthcare. Building a universal US foundation model holds the potential to address these issues. Nevertheless, the development of such foundational models encounters intrinsic challenges in US analysis, i.e., insufficient dat

Kent A. Peacock

The assumption that the system Hamiltonian for entangled states is additive is widely used in orthodox quantum no-signalling arguments. It is shown that additivity implies a contradiction with the assumption that the system being studied is entangled.

Yaping Zhao, Edmund Y. Lam

The ability of snapshot compressive imaging (SCI) systems to efficiently capture high-dimensional (HD) data depends on the advent of novel optical designs to sample the HD data as two-dimensional (2D) compressed measurements. Nonetheless, the traditional SCI scheme is fundamentally limited, due to the complete disregard for high-level information in the samp

Wenjun Zhu, Yuan Sun, Jiani Liu, Yushi Cheng

The proliferation of images captured from millions of cameras and the advancement of facial recognition (FR) technology have made the abuse of FR a severe privacy threat. Existing works typically rely on obfuscation, synthesis, or adversarial examples to modify faces in images to achieve anti-facial recognition (AFR). However, the unmodified images captured

P. Ashokkumar, R. Kabilan, M. Sathish Aravindh, A. Venkatesan

We report the occurrence of vibrational resonance (VR) and the underlying mechanism in a simple piecewise linear electronic circuit, namely the Murali-Lakshmanan-Chua (MLC) circuit, driven by an additional biharmonic signal with widely different frequency. When the amplitude of the high-frequency force is tuned, the resultant vibrational resonance is used to

Cheng Zhang, Rui-Jiao Miao, Xiao-Qiu Qi

We construct a class of nonlinear coherent states (NLCSs) by introducing a more general nonlinear function and study their non-classical properties, specifically the second-order correlation function $g^{(2)}(0)$, Mandel parameter $Q$, squeezing, amplitude squared squeezing and Wigner function of the optical field. The results indicate that the non-classical

Wenjun Zhu, Xiaoyu Ji, Yushi Cheng, Shibo Zhang

Autonomous vehicles increasingly utilize the vision-based perception module to acquire information about driving environments and detect obstacles. Correct detection and classification are important to ensure safe driving decisions. Existing works have demonstrated the feasibility of fooling the perception models such as object detectors and image classifier

Subhajit Chakraborty, Ravi Tomar

Suppose $G$ is a finitely generated infinite group, and $\mathcal G$ is a graph of groups decomposition of $G$ such that the edge groups are finite. This paper establishes that the topology of the Floyd boundary of $G$ is uniquely determined by the topology of the Floyd boundary of each vertex group of $\mathcal G$.

Satish Kumar, Priya Malpani, Britant, Sandeep Mishra

Quantum cryptography is now considered as a promising technology due to its promise of unconditional security. In recent years, rigorous work is being done for the experimental realization of quantum key distribution (QKD) protocols to realize secure networks. Among various QKD protocols, coherent one way and differential phase shift QKD protocols have under

Subhanka Mal, Hiranmaya Mishra, Prasanta K. Panigrahi, Bimalendu Deb

We investigate the temperature effects in an imbalanced superfluid atomic Fermi gas. We consider a bilayer system of two-component dipolar fermionic atoms with one layer containing atoms of one component and the other layer the atoms of other component with an imbalance between the populations of the two components. This imbalance results in uniform and nonu

Ali Saffarini

The use of artificial intelligence models has recently grown common; we may use them to write lines of code for us, summarize readings, draft emails, or even illustrate images. But when it comes to important decisions we need to make, such as choosing between job offers or implementing certain economic policies, our level of confidence and trust in AI falls.

Weimin Wang

Miniaturized two-dimensional scanning mirror based on microelectromechanical systems (MEMS) technology has great potential in automotive industry, consumer electronics, and biomedicine, etc. Due to its high frequency and large angle, resonant scanning is the mainstream in all MEMS actuation mechanisms, such as harmonic resonant electromagnetic scanner and pa

Ali Al-Lawati, Elsayed Eshra, Prasenjit Mitra

Trajectory generation is an important concern in pedestrian, vehicle, and wildlife movement studies. Generated trajectories help enrich the training corpus in relation to deep learning applications, and may be used to facilitate simulation tasks. This is especially significant in the wildlife domain, where the cost of obtaining additional real data can be pr

Kun-Hui Fan, Yun Soo Myung, De-Cheng Zou, Meng-Yun Lai

We investigate the tachyonic instability of Kerr-Newman (KN) black hole with a rotation parameter $a$ in the Einstein-Chern-Simons-scalar theory coupled with a quadratic massive scalar field. This instability analysis corresponds to exploring the onset of spontaneous scalarization for KN black holes. First, we find no $a$-bound for $\alpha<0$ case by conside

Behrooz Mirafzal, Fariba Fateh

Numerous systems require the capability to switch their operational modes seamlessly without any disruptions. The "Synced Parallel Control Paths" method is an innovative control system architecture designed for seamless mode switching. It features multiple parallel control paths: the primary path for essential operational references, and the auxiliary paths

Michael DeBellevue, Claudia Miller

In recent work, Dao and Eisenbud define the notion of a Burch index, expanding the notion of Burch rings of Dao, Kobayashi, and Takahashi, and show that for any module over a ring of Burch index at least 2, its $n$th syzygy contains direct summands of the residue field for $n=4$ or $5$ and all $n\geq 7$. We investigate how this behavior is explained by the b

Song Guo, Minzhao Lyu, Hassan Habibi Gharakheili

With rising concerns about the security of IoT devices, network operators need better ways to handle potential risks. Luckily, IoT devices show consistent patterns in how they communicate. But despite previous efforts, it remains unclear how knowledge of these patterns can be made available. As data marketplaces become popular in different domains, this pape

Ziling Cheng

We study supercritical age-structured branching models starting from a single particle with a random lifetime, where the reproduction law depends on the remaining lifetime of the parent. The lifespan of an individual is decided at its birth and its remaining lifetime decreases at the unit speed. A necessary and sufficient condition is provided for the conver

Emanuele Sansone, Robin Manhaeve

Self-supervised learning excels at learning representations from large amounts of data. At the same time, generative models offer the complementary property of learning information about the underlying data generation process. In this study, we aim at establishing a principled connection between these two paradigms and highlight the benefits of their complem

Yue Shi, Zhaoyu Liu, Logan A. Burnett, Seokhyeong Lee

The antiferromagnetic layered compound EuCd$_2$As$_2$ is widely considered as a leading candidate of ideal Weyl semimetal, featuring a single pair of Weyl nodes in its field-induced ferromagnetic (FM) state. Nevertheless, this view has recently been challenged by an optical spectroscopy study, which suggests that it is a magnetic semiconductor. In this study

Qiannan Wang, Changchun Yin, Lu Zhou, Liming Fang

The extensive adoption of Self-supervised learning(SSL) has led to an increased security threat from backdoor attacks. While existing research has mainly focused on backdoor attacks in image classification, there has been limited exploration of their implications for object detection. Object detection plays a critical role in security-sensitive applications,

Jack C. Straton

We extend prior work to derive three additional M-1-dimensional integral representations--over the interval $[0,1]$ --for products of M Slater orbitals that allows their magnitudes of coordinate vector differences (square roots of polynomials) $|{\bf x}_{1}-{\bf x}_{2}|=\sqrt{x_{1}^{2}-2x_{1}x_{2}\cos\theta+x_{2}^{2}}$ to be moved from disjoint products of f

Shuo Xu, Yucheng Zhang, Gang Chen, Xincheng Xiang

Although sparse-view computed tomography (CT) has significantly reduced radiation dose, it also introduces severe artifacts which degrade the image quality. In recent years, deep learning-based methods for inverse problems have made remarkable progress and have become increasingly popular in CT reconstruction. However, most of these methods suffer several li

Tao He, Xue Li, Zhibin Wang, Kun Qian

Training large-scale language models is increasingly critical in various domains, but it is hindered by frequent failures, leading to significant time and economic costs. Current failure recovery methods in cloud-based settings inadequately address the diverse and complex scenarios that arise, focusing narrowly on erasing downtime for individual tasks withou

Indranil Ghosh

In fitting a continuous bounded data, the generalized beta (and several variants of this distribution) and the two-parameter Kumaraswamy (KW) distributions are the two most prominent univariate continuous distributions that come to our mind. There are some common features between these two rival probability models and to select one of them in a practical sit

Yuki Yada, Tsuneo Matsumoto, Fuyuko Kido, Hayato Yamana

Dark patterns are deceptive user interface designs for online services that make users behave in unintended ways. Dark patterns, such as privacy invasion, financial loss, and emotional distress, can harm users. These issues have been the subject of considerable debate in recent years. In this paper, we study interpretable dark pattern auto-detection, that is

Zheng Chen, Qingan Yan, Huangying Zhan, Changjiang Cai

Identifying spatially complete planar primitives from visual data is a crucial task in computer vision. Prior methods are largely restricted to either 2D segment recovery or simplifying 3D structures, even with extensive plane annotations. We present PlanarNeRF, a novel framework capable of detecting dense 3D planes through online learning. Drawing upon the

Feng Ji

In this paper, we present a signal processing framework for directed graphs. Unlike undirected graphs, a graph shift operator such as the adjacency matrix associated with a directed graph usually does not admit an orthogonal eigenbasis. This makes it challenging to define the Fourier transform. Our methodology leverages the polar decomposition to define two

Wenhao Ma, Yu-Cheng Chang, Jie Yang, Yu-Kai Wang

Multi-agent systems often require agents to collaborate with or compete against other agents with diverse goals, behaviors, or strategies. Agent modeling is essential when designing adaptive policies for intelligent machine agents in multiagent systems, as this is the means by which the ego agent understands other agents' behavior and extracts their meaningf

Hao Chen, Yu-Min Hu, Wucheng Zhang, Michael Alexander Kurniawan

In this article, we investigate periodically driven open quantum systems within the framework of Floquet-Lindblad master equations. Specifically, we discuss Lindblad master equations in the presence of a coherent, time-periodic driving and establish their general spectral features. We also clarify the notions of transient and non-decaying solutions from this

Yu Han, Aaron Ceross, Jeroen H. M. Bergmann

This study investigates the complexity of regulatory affairs in the medical device industry, a critical factor influencing market access and patient care. Through qualitative research, we sought expert insights to understand the factors contributing to this complexity. The study involved semi-structured interviews with 28 professionals from medical device co

Rudradip Biswas, Hongxing Chen, Kabeer Manali Rahul, Chris J. Parker

Recently, Amnon Neeman settled a bold conjecture by Antieau, Gepner, and Heller regarding the relationship between the regularity of finite-dimensional noetherian schemes and the existence of bounded $t$-structures on their derived categories of perfect complexes. In this paper, using different methods, we prove some very general results about the existence

Jiaqi Chen, Bo Feng

We generalize Integration-By-Parts (IBP) and differential equations methods to de Sitter correlators related to inflation. While massive correlators in de Sitter spacetime are usually regarded as highly intricate, we find they have remarkably hidden concise structures from the perspective of IBP. We find the factorization of the IBP relations of each vertex

Debamita Ghosh, Manjesh Kumar Hanawal, Nikola Zlatanova

Holographic Metasurface Transceivers (HMTs) are emerging as cost-effective substitutes to large antenna arrays for beamforming in Millimeter and TeraHertz wave communication. However, to achieve desired channel gains through beamforming in HMT, phase-shifts of a large number of elements need to be appropriately set, which is challenging. Also, these optimal

Lujia Wang, Hairong Wang, Fulvio D'Angelo, Lee Curtin

Glioblastoma (GBM) is one of the most aggressive and lethal human cancers. Intra-tumoral genetic heterogeneity poses a significant challenge for treatment. Biopsy is invasive, which motivates the development of non-invasive, MRI-based machine learning (ML) models to quantify intra-tumoral genetic heterogeneity for each patient. This capability holds great pr

Ashhadul Islam, Md. Rafiul Biswas, Wajdi Zaghouani, Samir Brahim Belhaouari

$ $The synergy of language and vision models has given rise to Large Language and Vision Assistant models (LLVAs), designed to engage users in rich conversational experiences intertwined with image-based queries. These comprehensive multimodal models seamlessly integrate vision encoders with Large Language Models (LLMs), expanding their applications in gener

Yosuke Sato, Haruto Ishikawa, Satoshi Takada

In this paper, we present a comprehensive investigation of stress propagation in a two-dimensional elastic circular disk. To accurately describe the displacements and stress fields within the disk, we employ a scalar and vector potential approach, representing them as sums of Bessel functions. The determination of the coefficients for these expansions is acc

Kaitlyn Wang, Yufang Jin

Effective monitoring of walnut water status and stress level across the whole orchard is an essential step towards precision irrigation management of walnuts, a significant crop in California. This study presents a machine learning approach using Random Forest (RF) models to map stem water potential (SWP) by integrating high-resolution multispectral remote s

S P Sharan, Francesco Pittaluga, Vijay Kumar B G, Manmohan Chandraker

Although planning is a crucial component of the autonomous driving stack, researchers have yet to develop robust planning algorithms that are capable of safely handling the diverse range of possible driving scenarios. Learning-based planners suffer from overfitting and poor long-tail performance. On the other hand, rule-based planners generalize well, but mi

Chengsi Liang, Hongyang Du, Yao Sun, Dusit Niyato

Generative artificial intelligence (GAI) has emerged as a rapidly burgeoning field demonstrating significant potential in creating diverse contents intelligently and automatically. To support such artificial intelligence-generated content (AIGC) services, future communication systems should fulfill much more stringent requirements (including data rate, throu

M. B. Vovchanskyi

This paper is an extended and reworked version of a short course given by the author at ''Uzbekistan-Ukrainian readings in stochastic processes'', Tashkent-Kyiv, 2022, and was prepared for a special issue of ''Theory of stochastic processes'', devoted to publishing lecture notes from the aforementioned workshop. The survey is devoted to operator splitting me

Ali Eshragh, Luke Yerbury, Asef Nazari, Fred Roosta

We develop a new efficient sequential approximate leverage score algorithm, SALSA, using methods from randomized numerical linear algebra (RandNLA) for large matrices. We demonstrate that, with high probability, the accuracy of SALSA's approximations is within $(1 + O({\varepsilon}))$ of the true leverage scores. In addition, we show that the theoretical com

Yuqi Liu, Xinyu Shan, Meiyue Shao

We propose a contour integral-based algorithm for computing a few singular values of a matrix or a few generalized singular values of a matrix pair. Mathematically, the generalized singular values of a matrix pair are the eigenvalues of an equivalent Hermitian-definite matrix pencil, known as the Jordan-Wielandt matrix pencil. However, direct application of

Yongwen Zhang, Maoxin Liu, Gaoke Hu, Teng Liu

We employ the eigen microstate approach to explore the self-organized criticality (SOC) in two celebrated sandpile models, namely, the BTW model and the Manna model. In both models, phase transitions from the absorbing-state to the critical state can be understood by the emergence of dominant eigen microstates with significantly increased weights. Spatial ei

Mieczysław Mastyło, Gord Sinnamon

A Christ-Kiselev maximal theorem is proved for linear operators between quasi-Banach function lattices satisfying certain lattice geometrical conditions. The result is further explored for weighted Lorentz spaces, classical Lorentz spaces, and Wiener amalgams of Lebesgue function and sequence spaces. Extensions are made to K\"othe dual operators and to opera

Jianhua Yang, Tao Li

Understanding the origin of electron incoherence is the first step toward a theoretical description of the non-Fermi liquid behavior of the high-T$_{c}$ cuprate superconductors. Such electron incoherence manifests itself most evidently in the non-Drude behavior of the optical response of the system and the anomalous density fluctuation behavior in the long w

Md Habibor Rahman, Rocco Cassandro, Thorsten Wuest, Mohammed Shafae

An attack taxonomy offers a consistent and structured classification scheme to systematically understand, identify, and classify cybersecurity threat attributes. However, existing taxonomies only focus on a narrow range of attacks and limited threat attributes, lacking a comprehensive characterization of manufacturing cybersecurity threats. There is little t

Subhajeet Karmakar, Jeewan C. Pandey, Nikita Rawat, Gurpreet Singh

We present an X-ray and UV investigation of five X-ray flares detected on two active systems, CC Eri and AB Dor, using the AstroSat observatory. The peak X-ray luminosities of the flares in the 0.3$-$7.0 keV band are found to be within 10$^{31-33}$ erg s$^{-1}$. Preliminary spectral analysis indicates the presence of three and four-temperature corona for CC

YeongKyu Lee, JunBeom Cho, Yongkyu Lee, Won Bo Lee

Ionic liquids (ILs) are appealing electrolytes for their favorable physicochemical properties. However, despite their longstanding use, understanding the capacitive behavior of ILs remains challenging. This is largely due to the formation of a non-conventional electric double layer (EDL) at the electrode-electrolyte interface. This study shows that the short

Shi-Yuan Li, Yan-Rui Liu, Zi-Long Man, Zong-Guo Si

Treating the $X(4140)$ as a compact $J^{PC}=1^{++}$ $cs\bar{c}\bar{s}$ state and using its mass as a reference scale, we systematically estimate the masses of doubly heavy tetraquark states $QQ\bar{q}\bar{q}$ where $Q=c,b$ and $q=u,d,s$. Their decay properties are studied with a simple rearrangement scheme. Based on our results, the lowest $I(J^P)=0(1^+)$ $b

Thierry Njougouo, Andreagiovanni Reina, Elio Tuci, Timoteo Carletti

Both humans and social animals live in groups and are frequently faced to choose between options with different qualities. When no leader agents are controlling the group decision, consensus can be achieved through repeated interactions among group members. Various studies on CDM illustrate how the dynamics of opinions are determined by the structure of the

Subhajeet Karmakar, Avrajit Bandyopadhyay, Wm. Bruce Weaver, Riddhi Shedge

Late-type stars are the most abundant in the galactic stellar population. These stars, with a similar internal structure to the Sun, are expected to have solar-like atmospheres. Investigating the stellar parameters and chemical abundances on late-type stars is essential to provide valuable constraints about stellar age, chemical evolution, and atmosphere of

Mahshid Helali Moghadam, Mateusz Rzymowski, Lukasz Kulas

This study presents an industry experience showcasing a vessel operational anomaly detection approach that utilizes semi-supervised deep learning models augmented with lightweight interpretable surrogate models, applied to an industrial sensorized vessel, called TUCANA. We leverage standard and Long Short-Term Memory (LSTM) autoencoders trained on normal ope

Yusef Maleki

Here, we review some quantum architectures designed for the engineering of the N00N state, a bipartite maximally entangled state crucial in quantum metrology applications. The fundamental concept underlying these schemes is the transformation of the initial state $|N\rangle \otimes |0\rangle$ to the N00N state $\frac{1}{\sqrt{2}} (|N\rangle \otimes|0\rangle

Peihua Mai, Youjia Yang, Ran Yan, Rui Ye

State-of-the-art large language models (LLMs) are typically deployed as online services, requiring users to transmit detailed prompts to cloud servers. This raises significant privacy concerns. In response, we introduce ConfusionPrompt, a novel framework for private LLM inference that protects user privacy by: (i) decomposing the original prompt into smaller

Shanchuan Lin, Xiao Yang

Diffusion models without guidance generate very unrealistic samples. Guidance is used ubiquitously, and previous research has attributed its effect to low-temperature sampling that improves quality by trading off diversity. However, this perspective is incomplete. Our research shows that the choice of the loss objective is the underlying reason raw diffusion

Xinye Chen

As time-series applications grow larger, there is increasing demand for symbolic representations that are compact, accurate, and scalable across many signals and computing resources. Current ABBA-based symbolic approximation methods produce high-quality, shape-preserving representations, but they handle each time series separately and sequentially. This mean

Ilyas Fatkhullin, Niao He, Yifan Hu

In this work, we consider constrained stochastic optimization problems under hidden convexity, i.e., those that admit a convex reformulation via non-linear (but invertible) map $c(\cdot)$. A number of non-convex problems ranging from optimal control, revenue and inventory management, to convex reinforcement learning all admit such a hidden convex structure.

Anderson Taurence, Sebastian König

Simulations of quantum systems in finite volume have proven to be a useful tool for calculating physical observables. Such studies to date have focused primarily on understanding the volume dependence of binding energies, from which it is possible to extract asymptotic properties of the corresponding bound state, as well as on extracting scattering informati

Zhi Li, Guoxin Wei

It is our purpose to study complete space-like self-expanders in the Minkovski space. By use of maximum principle of Omori-Yau type, we can obtain the rigidity theorems on $n$-dimensional complete space-like self-expanders in the Minkovski space $\mathbb R^{n+1}_{1}$. For complete space-like self-expanders of dimension $2$, we give a classification of them u

Jakob Wierzbowski, Bernd Bitnar, Siegfried Hold

In this paper, we elaborate on correctly predicting \'Echelle spectrograms by employing the fully three-dimensional representation of Snell's law to model the effects of prisms as cross-dispersers in \'Echelle spectrographs. We find that it is not sufficient to simply apply the frequently used trigonometric prism dispersion equation to describe recorded spec

Bin Lei, le Chen, Caiwen Ding

In the evolving field of machine learning, video generation has witnessed significant advancements with autoregressive-based transformer models and diffusion models, known for synthesizing dynamic and realistic scenes. However, these models often face challenges with prolonged inference times, even for generating short video clips such as GIFs. This paper in

Wenhao Lu, Xufeng Zhao, Thilo Fryen, Jae Hee Lee

Reinforcement learning (RL) is a powerful technique for training intelligent agents, but understanding why these agents make specific decisions can be quite challenging. This lack of transparency in RL models has been a long-standing problem, making it difficult for users to grasp the reasons behind an agent's behaviour. Various approaches have been explored

Gechun Liang, Yifan Sun, Thaleia Zariphopoulou

We extend the notion of forward performance criteria to settings with random endowment in incomplete markets. Building on these results, we introduce and develop the novel concept of \textit{forward optimized certainty equivalent (forward OCE)}, which offers a genuinely dynamic valuation mechanism that accommodates progressively adaptive market model updates

Wenda Guo

$\mathrm{J}/\psi$ production in high-energy hadronic collisions is sensitive to both perturbative and non-perturbative aspects of quantum chromodynamics (QCD) calculations. The production of a heavy-quark pair is well-described by perturbative QCD, whereas the formation of the bound state involves non-perturbative processes, treated in different ways by vari

Phuc Nguyen, Rohit Arora, Elliot D. Hill, Jasper Braun

Machine-learning datasets are typically characterized by measuring their size and class balance. However, there exists a richer and potentially more useful set of measures, termed S-entropy (similarity-sensitive entropy), that incorporate elements' frequencies and between-element similarities. Although these have been available in the R and Julia programming

Haina Wang, Salvatore Torquato

Knowledge of exact analytical functional forms for the pair correlation function $g_2(r)$ and its corresponding structure factor $S(k)$ of disordered many-particle systems is limited. For fundamental and practical reasons, it is highly desirable to add to the existing data base of analytical functional forms for such pair statistics. Here, we design a pletho

Gorapada Bera

The classical Seiberg-Witten equations in dimensions three and four admit a natural generalization within a unified framework known as the generalized Seiberg-Witten (GSW) equations, which encompasses many important equations in gauge theory. This article proves that the averaged $L^2$-norm of any spinor with non-constant pointwise norm in the GSW equations

Mitsuko Murakami, G. P. Zhang

Demagnetization in ferromagnetic transition metals driven by a femtosecond laser pulse is a fundamental problem in solid state physics, and its understanding is essential to the development of spintronics devices. Ab initio calculation of time-dependent magnetic moment in the velocity gauge so far has not been successful in reproducing the large amount of de

A. A. Araújo Filho, J. A. A. S. Reis, L. Lisboa-Santos

In this work, we generalize the spacetime induced by a rotating cosmic string, taking into account anisotropic effects due the breaking of the Lorentz violation. In particular, we explore the energy levels of a massive spinless particle that is covariantly coupled to a uniform magnetic field aligned with the string. Subsequently, we introduce a scalar potent

Bernard Vau, Tudor-Bogdan Airimitoaie

This paper presents a regularized recursive identification algorithm with simultaneous on-line estimation of both the model parameters and the algorithms hyperparameters. A new kernel is proposed to facilitate the algorithm development. The performance of this novel scheme is compared with that of the recursive least squares algorithm in simulation.

Ilyes Batatia, Philipp Benner, Yuan Chiang, Alin M. Elena

Atomistic simulations of matter, especially those that leverage first-principles (ab initio) electronic structure theory, provide a microscopic view of the world, underpinning much of our understanding of chemistry and materials science. Over the last decade or so, machine-learned force fields have transformed atomistic modeling by enabling simulations of ab

Felipe Akio Matsuoka

This paper presents a novel Automatic Essay Scoring (AES) algorithm tailored for the Portuguese-language essays of Brazil's Exame Nacional do Ensino M\'edio (ENEM), addressing the challenges in traditional human grading systems. Our approach leverages advanced deep learning techniques to align closely with human grading criteria, targeting efficiency and sca

Shiyu Zhao, Long Zhao, Vijay Kumar B. G, Yumin Suh

The recent progress in language-based open-vocabulary object detection can be largely attributed to finding better ways of leveraging large-scale data with free-form text annotations. Training such models with a discriminative objective function has proven successful, but requires good positive and negative samples. However, the free-form nature and the open

Xiaotong Guo, Hanyong Xu, Dingyi Zhuang, Yunhan Zheng

The rapid growth of the ride-hailing industry has revolutionized urban transportation worldwide. Despite its benefits, equity concerns arise as underserved communities face limited accessibility to affordable ride-hailing services. A key issue in this context is the vehicle rebalancing problem, where idle vehicles are moved to areas with anticipated demand.

Mrinal Sarkar, Tilman Enss, Nicolò Defenu

We investigate the role of the spectral dimension $d_s$ in determining the universality of phase transitions on a complex network. Due to its structural heterogeneity, a complex network generally acts as a disordered system. Specifically, we study the synchronization and entrainment transitions in the nonequilibrium dynamics of the Kuramoto model and the pha

Borja Aizpurua, Samuel Palmer, Roman Orus

In this paper we show how tensor networks help in developing explainability of machine learning algorithms. Specifically, we develop an unsupervised clustering algorithm based on Matrix Product States (MPS) and apply it in the context of a real use-case of adversary-generated threat intelligence. Our investigation proves that MPS rival traditional deep learn

Valerio Faraoni, Carla Zeyn

Disformal transformations of Friedmann-Lema\^itre-Robertson-Walker and Bianchi geometries are analyzed in the context of scalar-tensor gravity. Novel aspects discussed explicitly are the $3+1$ splitting, the effective fluid equivalent of the gravitational scalar, Bianchi models, stealth solutions, and de Sitter solutions with non-constant scalar field (which