December 2023 arXiv papers — page 21

Fangyikang Wang, Huminhao Zhu, Chao Zhang, Hanbin Zhao

Particle-based Variational Inference (ParVI) methods approximate the target distribution by iteratively evolving finite weighted particle systems. Recent advances of ParVI methods reveal the benefits of accelerated position update strategies and dynamic weight adjustment approaches. In this paper, we propose the first ParVI framework that possesses both acce

Yuhua Jiang, Feifei Gao, Shi Jin

Integrated sensing and communication (ISAC) has opened up numerous game-changing opportunities for future wireless systems. In this paper, we develop a novel scheme that utilizes orthogonal frequency division multiplexing (OFDM) pilot signals in ISAC systems to sense the electromagnetic (EM) property of the target and thus also identify the material of the t

Seunghan Lee, Taeyoung Park, Kibok Lee

Masked time series modeling has recently gained much attention as a self-supervised representation learning strategy for time series. Inspired by masked image modeling in computer vision, recent works first patchify and partially mask out time series, and then train Transformers to capture the dependencies between patches by predicting masked patches from un

Tinggang Zhao, Zhenyu Zhao, Changpin Li, Dongxia Li

Fractional calculus with respect to function $\psi$, also named as $\psi$-fractional calculus, generalizes the Hadamard and the Riemann-Liouville fractional calculi, which causes challenge in numerical treatment. In this paper we study spectral-type methods using mapped Jacobi functions (MJFs) as basis functions and obtain efficient algorithms to solve $\psi

Shijian Jiang, Qi Ye, Rengan Xie, Yuchi Huo

Our work aims to reconstruct a 3D object that is held and rotated by a hand in front of a static RGB camera. Previous methods that use implicit neural representations to recover the geometry of a generic hand-held object from multi-view images achieved compelling results in the visible part of the object. However, these methods falter in accurately capturing

Seunghan Lee, Taeyoung Park, Kibok Lee

Contrastive learning has shown to be effective to learn representations from time series in a self-supervised way. However, contrasting similar time series instances or values from adjacent timestamps within a time series leads to ignore their inherent correlations, which results in deteriorating the quality of learned representations. To address this issue,

Anqi Li, Congying Han, Tiande Guo, Haoran Li

Existing methods provide varying algorithms for different types of Boolean satisfiability problems (SAT), lacking a general solution framework. Accordingly, this study proposes a unified framework DCSAT based on integer programming and reinforcement learning (RL) algorithm to solve different types of SAT problems such as MaxSAT, Weighted MaxSAT, PMS, WPMS. S

Di Chen, Hao Li, Zhicheng Jin, Huizhao Tu

Autonomous vehicles (AVs) have the potential to prevent accidents caused by drivers errors and reduce road traffic risks. Due to the nature of heavy vehicles, whose collisions cause more serious crashes, the weights of vehicles need to be considered when making driving strategies aimed at reducing the potential risks and their consequences in the context of

Jinbo Hu, Yin Cao, Ming Wu, Qiuqiang Kong

Environment shifts and conflicts present significant challenges for learning-based sound event localization and detection (SELD) methods. SELD systems, when trained in particular acoustic settings, often show restricted generalization capabilities for diverse acoustic environments. Furthermore, obtaining annotated samples for spatial sound events is notably

Wenjing Chen, Zexi Wang

Let $(\mathcal{M},g)$ and $(\mathcal{K},\kappa)$ be two Riemannian manifolds of dimensions $N$ and $m$, respectively. Let $\omega\in C^2(\mathcal{M})$, $\omega>0$. The warped product $\mathcal{M}\times_\omega \mathcal{K}$ is the $(N+m)$-dimensional product manifold $\mathcal{M}\times \mathcal{K}$ furnished with metric $g+\omega^2\kappa$. We are concerned wit

Wenhui Yu, Zhaosheng Li, Yongqi Lu, Yuanyue Pan

Type I X-ray bursts in the ultracompact X-ray binary 4U 1820$-$30 are powered by the unstable thermonuclear burning of hydrogen-deficient material. We report the detection of 15 type I X-ray bursts from 4U 1820$-$30 observed by NICER in between 2017 and 2023. All these bursts occurred in the low state for the persistent flux in the range of $2.5-8\times10^{-

Shengmin Zhang, Zhencai Shen

A subgroup $A$ of a finite group $G$ is said to be a $CAP$-subgroup of $G$, if for any chief factor $H/K$ of $G$, either $A H= AK$ or $A\cap H = A \cap K$. Let $p$ be a prime, $S$ be a $p$-group and $\mathcal{F}$ be a saturated fusion system over $S$. Then $\mathcal{F}$ is said to be supersolvable, if there exists a series of $S$, namely $1 = S_0 \leq S_1 \l

Zhaisheng Ding, Haiyan Li, Ruichao Hou, Yanyu Liu

Developing robust multi-modal feature representations is crucial for enhancing object tracking performance. In pursuit of this objective, a novel X Modality Assisting Network (X-Net) is introduced, which explores the impact of the fusion paradigm by decoupling visual object tracking into three distinct levels, thereby facilitating subsequent processing. Init

Jiangkun Gong, Jun Yan, Deyong Kong, Deren Li

In this study, we showcased the detection of the wake vortex produced by a medium aircraft at distances exceeding 10 km using an X-band pulse-Doppler radar. We analyzed radar signals within the range profiles behind a Boeing 737 aircraft on February 7, 2021, within the airspace of the Runway Protection Zone (RPZ) at Tianhe Airport, Wuhan, China. The findings

Chenyang Qiu, Guoshun Nan, Tianyu Xiong, Wendi Deng

Graph convolution networks (GCNs) are extensively utilized in various graph tasks to mine knowledge from spatial data. Our study marks the pioneering attempt to quantitatively investigate the GCN robustness over omnipresent heterophilic graphs for node classification. We uncover that the predominant vulnerability is caused by the structural out-of-distributi

Jie Jiang, Ming Zhang

Accelerating black holes have been widely studied in the context of black hole thermodynamics, holographic gravity theories, and in the description of black holes at the center of galaxies. As a fundamental assumption to ensure spacetime causality, we investigated the weak cosmic censorship conjecture (WCCC) in the accelerating Reissner-Nordstr\"{o}m-Anti-de

Cai Heng Li, Yan Zhou Zhu

Building upon previous results, a classification is given of finite $p$-groups of which subgroups of order $p$ are all fused. This completes the classification problem dated back to Higman 1963 on the so-called Suzuki $2$-groups, and confirms a conjecture of Gross proposed in 1974. As a consequence, two open problems on AT-groups and FIF-groups are solved.

Matthew Ding, Jason Li

We devise a deterministic algorithm for minimum Steiner cut, which uses $(\log n)^{O(1)}$ maximum flow calls and additional near-linear time. This algorithm improves on Li and Panigrahi's (FOCS 2020) algorithm, which uses $(\log n)^{O(1/\epsilon^4)}$ maximum flow calls and additional $O(m^{1+\epsilon})$ time, for $\epsilon > 0$. Our algorithm thus shows that

Bao Nguyen, Binh Nguyen, Viet Anh Nguyen

Flow matching is a powerful framework for generating high-quality samples in various applications, especially image synthesis. However, the intensive computational demands of these models, especially during the finetuning process and sampling processes, pose significant challenges for low-resource scenarios. This paper introduces Bellman Optimal Stepsize Str

Chi-Yeh Chen

Coflow is a network abstraction used to represent communication patterns in data centers. The coflow scheduling problem encountered in large data centers is a challenging $\mathcal{NP}$-hard problem. This paper tackles the scheduling problem of coflows with release times in heterogeneous parallel networks, which feature an architecture consisting of multiple

Peter Vouras

In this paper we present a novel beamforming technique that can be used with an array of quantum sensors. The transmit waveform is a short-duration frequency comb constructed using a finite number of sinusoidal tones separated by a fixed offset. Each element in the array is tuned to one of the tones. When the radiated signal is received by the aperture, each

Minhyun Kim, Ki-Ahm Lee, Se-Chan Lee

We prove the Wolff potential estimates for nonlocal equations with Orlicz growth. As an application, we obtain the Wiener criterion in this framework, which provides a necessary and sufficient condition for boundary points to be regular. Our approach relies on the fine analysis of superharmonic functions in view of nonlocal nonlinear potential theory.

Xiaoliang Tan, Guanzhou Chen, Tong Wang, Jiaqi Wang

The field of Remote Sensing (RS) widely employs Change Detection (CD) on very-high-resolution (VHR) images. A majority of extant deep-learning-based methods hinge on annotated samples to complete the CD process. Recently, the emergence of Vision Foundation Model (VFM) enables zero-shot predictions in particular vision tasks. In this work, we propose an unsup

Yan Fan, Yu Wang, Pengfei Zhu, Qinghua Hu

Continual learning (CL) has shown promising results and comparable performance to learning at once in a fully supervised manner. However, CL strategies typically require a large number of labeled samples, making their real-life deployment challenging. In this work, we focus on semi-supervised continual learning (SSCL), where the model progressively learns fr

Anjie Gao, Hai Tao Li, Ian Moult, Hua Xing Zhu

We present an operator based factorization formula for the transverse energy-energy correlator in the back-to-back (dijet) region, and uncover its remarkable perturbative simplicity and relation to transverse momentum dynamics. This simplicity enables us to achieve next-to-next-to-next-to leading logarithmic (N$^3$LL) accuracy for a hadron collider dijet eve

Sayantani Bhattacharyya, Sukanya Mitra, Shuvayu Roy

In this work, a connection has been indicated between the different existing formulations of relativistic hydrodynamic theories, which, in order to be causal and stable, (i) either requires `non-fluid' variables apart from velocity and temperature to be promoted to new degrees of freedom, or, (ii) needs to be in a generalized hydrodynamic frame other than th

Alexander Tovbis, Fudong Wang

In this paper, we study the spectral theory of soliton condensates - a special limit of soliton gases - for the focusing NLS (fNLS). In particular, we analyze the kinetic equation for the fNLS circular condensate, which represents the first example of an explicitly solvable fNLS condensate with nontrivial large scale space-time dynamics. Solution of the kine

BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson

By analyzing $(27.12\pm0.14)\times10^8$ $\psi(3686)$ events collected with the BESIII detector operating at the BEPCII collider, the decay processes $\chi_{cJ} \to 3(K^+K^-)$ ($J=0,1,2$) are observed for the first time with statistical significances of 8.2$\sigma$, 8.1$\sigma$, and 12.4$\sigma$, respectively. The product branching fractions of $\psi(3686)\to

Zhenghua Xu, Ting Yu, Qinghai Huo

Recently, it is proven that positive harmonic functions defined in the unit disc or the upper half-plane in $\mathbb{C}$ are contractions in hyperbolic metrics \cite{Markovic}. Furthermore, the same result does not hold in higher dimensions as shown by given counterexamples \cite{Melentijevic-P}. In this paper, we shall show that positive (or bounded) harmon

Minbo Ma, Jilin Hu, Christian S. Jensen, Fei Teng

Spatio-temporal forecasting of future values of spatially correlated time series is important across many cyber-physical systems (CPS). Recent studies offer evidence that the use of graph neural networks to capture latent correlations between time series holds a potential for enhanced forecasting. However, most existing methods rely on pre-defined or self-le

Xueyuan Yang, Chao Yao, Xiaojuan Ban

Leveraging wearable devices for motion reconstruction has emerged as an economical and viable technique. Certain methodologies employ sparse Inertial Measurement Units (IMUs) on the human body and harness data-driven strategies to model human poses. However, the reconstruction of motion based solely on sparse IMUs data is inherently fraught with ambiguity, a

Jean-Baptiste Morée, Youhei Yamaji, Masatoshi Imada

The superconducting (SC) cuprate HgBa$_2$Ca$_2$Cu$_3$O$_8$ (Hg1223) has the highest $T_{c}^{\rm opt}\simeq 138$ K (the experimental SC transition temperature at optimal hole doping) among cuprates at ambient pressure $P_{\rm amb}$. $T_{c}^{\rm opt}$ increases under pressure $P$ and reaches $164$ K at $P_{\rm opt}\simeq 30$ GPa, then decreases with increasing

Makiko Konoshima, Hirotaka Tamura, Yoshiyuki Kabashima

The 0/1 matrix factorization defines matrix products using logical AND and OR as product-sum operators, revealing the factors influencing various decision processes. Instances and their characteristics are arranged in rows and columns. Formulating matrix factorization as an energy minimization problem and exploring it with Simulated Annealing (SA) theoretica

Xianyi Chen, Fazhan Liu, Dong Jiang, Kai Yan

Recently, some research show that deep neural networks are vulnerable to the adversarial attacks, the well-trainned samples or patches could be used to trick the neural network detector or human visual perception. However, these adversarial patches, with their conspicuous and unusual patterns, lack camouflage and can easily raise suspicion in the real world.

Zhiqiang Guo, Jianjun Li, Guohui Li, Chaoyang Wang

The multimodal recommendation has gradually become the infrastructure of online media platforms, enabling them to provide personalized service to users through a joint modeling of user historical behaviors (e.g., purchases, clicks) and item various modalities (e.g., visual and textual). The majority of existing studies typically focus on utilizing modal feat

Medha Dhurandhar

A hereditary class H of graphs is $\chi$-bounded if there is a $\chi$-binding function f such that for every $G$ in $H$, $\chi(G)$ less than or equal to $f(\omega(G))$. Here we prove that if a graph $G$ is free of 1. {Chair; P$_4$+K$_1$} or 2. {Chair; HVN}, then $\chi(G)$ is linearly bounded by maximum clique size of G. We further prove that if $G$ is free o

Raushan Buzyakova

We study groups of homeomorphic bijections on spaces that are finite unions of compact connected linearly ordered subsets. We prove that all such groups when endowed with the topology of point-wise convergence are topological groups. }

Kyungjin Cho, Jihun Shin, Eunjin Oh

In this paper, we present approximate distance and shortest-path oracles for fault-tolerant Euclidean spanners motivated by the routing problem in real-world road networks. An $f$-fault-tolerant Euclidean $t$-spanner for a set $V$ of $n$ points in $\mathbb{R}^d$ is a graph $G=(V,E)$ where, for any two points $p$ and $q$ in $V$ and a set $F$ of $f$ vertices o

Mohammad Allouche, Elie Bou-Zeid, Juho Iipponen

Unsteady land-sea breezes (LSBs) resulting from time-varying surface thermal contrasts are explored in the presence of a constant synoptic pressure forcing, Mg, when the latter is oriented from sea to land versus land to sea. Large eddy simulations reveal the development of four distinctive regimes depending on the joint interaction between (Mg, orientation)

Ryoga Matsumoto

We construct special idempotents in $\mathrm{End}_{U_q(\mathfrak{sl}_2)}(M(\mu_1)\otimes\cdots \otimes M(\mu_n))$ like the Jones Wenzl projector where $M(\mu_i)$ is Verma module whose highest weight is $\mu_i$ and is complex number except non-negative integer.

Dmitri Bykov, Anton Pribytok

We prove that the supersymmetric deformed $ \mathbb{CP}^{1} $ sigma model (the generalization of the Fateev-Onofri-Zamolodchikov model) admits an equivalent description as a generalized Gross-Neveu model. This formalism is useful for the study of renormalization properties and particularly for calculation of the one- and two-loop $ \beta $-function. We show

Igor Kukavica, Linfeng Li, Amjad Tuffaha

We address a system of equations modeling an incompressible fluid interacting with an elastic body. We prove the local existence when the initial velocity belongs to the space $H^{1.5+\epsilon}$ and the initial structure velocity is in $H^{1+\epsilon}$, where $\epsilon \in (0, 1/20)$.

Zenghui Zhang

A sharp inequality for $\ell_p$ quasi-norm with $0<p\leq 1$ and $\ell_q$-norm with $q>1$ is derived, which shows that the difference between $\|\textbf{\textit{x}}\|_p$ and $\|\textbf{\textit{x}}\|_q$ of an $n$-dimensional signal $\textbf{\textit{x}}$ is upper bounded by the difference between the maximum and minimum absolute value in $\textbf{\textit{x}}$.

Miyu Suzuki

Prasad and Takloo-Bighash proposed a conjecture which predicts a necessary condition in terms of epsilon factors for representations of $\mathrm{GL}_n(F)$ and its inner forms to have linear periods. In this rather expository article, we reformulate their conjecture in the following form: The distinguished members in each generic $L$-packet $\Pi_\phi$ are det

Songlin Yang, Liang Du, Li Huang

The Bayesian reconstruction entropy is considered an alternative to the Shannon-Jaynes entropy, as it does not exhibit the asymptotic flatness characteristic of the Shannon-Jaynes entropy and obeys the scale invariance. It is commonly utilized in conjunction with the maximum entropy method to derive spectral functions from Euclidean time correlators produced

Woochul Kang, Hyungseop Lee

Predictable adaptation of network depths can be an effective way to control inference latency and meet the resource condition of various devices. However, previous adaptive depth networks do not provide general principles and a formal explanation on why and which layers can be skipped, and, hence, their approaches are hard to be generalized and require long

Fuqiang Zhao, Kehan Zhang, Qian Liu, Zhuoyi Lyu

With the development of VR technology, especially the emergence of the metaverse concept, the integration of visual and tactile perception has become an expected experience in human-machine interaction. Therefore, achieving spatial-temporal consistency of visual and tactile information in VR applications has become a necessary factor for realizing this exper

Anima Ghosh, R. Thangavel

Quaternary chalcogenide compositions have been broadly explored due to their promising potential for various optoelectronic applications. The band structure, density of states and optical properties of CuZn2InS4 and CuZn2GaS4 for kesterite and stannite structures were studied with full potential augmented plane wave method (FP-LAPW) via Wien2k code. The tota

Jason Gavriel, Daniel Herr, Alexis Shaw, Michael J. Bremner

The development of quantum computing systems for large scale algorithms requires targeted error rates unachievable through hardware advancements alone. Quantum Error Correction (QEC) allows us to use systems with a large number of physical qubits to form a fault tolerant system with a lower number of logical qubits and a favourable logical error rate. While

Sunoh Kim, Jungchan Cho, Joonsang Yu, YoungJoon Yoo

In the weakly supervised temporal video grounding study, previous methods use predetermined single Gaussian proposals which lack the ability to express diverse events described by the sentence query. To enhance the expression ability of a proposal, we propose a Gaussian mixture proposal (GMP) that can depict arbitrary shapes by learning importance, centroid,

Han Li, Ruixin Yang, Min Zhang, Runze Mao

The application of deep neural networks (DNNs) holds considerable promise as a substitute for the direct integration of chemical source terms in combustion simulations. However, challenges persist in ensuring high precision and generalisation across various different fuels and flow conditions. In this study, we propose and validate a consistent DNN approach

Wei Huang, Jun Wang, Xiaoping Li, Qihang Peng

Orthogonal frequency division multiplexing (OFDM) is a widely adopted wireless communication technique but is sensitive to the carrier frequency offset (CFO). For high-mobility environments, severe Doppler shifts cause the CFO to extend well beyond the subcarrier spacing. Traditional algorithms generally estimate the integer and fractional parts of the CFO s

Hao Xu, Yuanbin Man, Mingyang Yang, Jichao Wu

The rapid accumulation of Earth observation data presents a formidable challenge for the processing capabilities of traditional remote sensing desktop software, particularly when it comes to analyzing expansive geographical areas and prolonged temporal sequences. Cloud computing has emerged as a transformative solution, surmounting the barriers traditionally

Ryo Nemoto, Masaki Shigemori

Supertubes are supersymmetric configurations in string theory in which branes are extending along a closed curve. For a supertube of codimension two, its dipole charge is characterized by the duality monodromy around the closed curve. When multiple codimension-2 supertubes are present, the monodromies around different supertubes can be non-commuting, namely

Qifei Li, Yingming Gao, Cong Wang, Yayue Deng

Speech emotion recognition (SER) systems aim to recognize human emotional state during human-computer interaction. Most existing SER systems are trained based on utterance-level labels. However, not all frames in an audio have affective states consistent with utterance-level label, which makes it difficult for the model to distinguish the true emotion of the

Gyeong-Geon Lee, Ehsan Latif, Lehong Shi, Xiaoming Zhai

This study compared the classification performance of Gemini Pro and GPT-4V in educational settings. Employing visual question answering (VQA) techniques, the study examined both models' abilities to read text-based rubrics and then automatically score student-drawn models in science education. We employed both quantitative and qualitative analyses using a d

Zeyad Abdelhay, Yahuza Bello, Ahmed Refaey

The upcoming Sixth Generation (6G) network is projected to grapple with a range of security concerns, encompassing access control, authentication, secure connections among 6G Core (6GC) entities, and trustworthiness. Classical Virtual Private Networks (VPNs), extensively deployed in Evolved Packet Core (EPC) network infrastructure, are notoriously susceptibl

C. Y. Kuo, F. Gao, J. A. Braatz, D. W. Pesce

High precision mapping of H2O megamaser emission from active galaxies has revealed more than a dozen Keplerian H2O maser disks, which enable a ~4% uncertainty estimate of the Hubble constant as well as providing accurate masses for the central black holes. These disks often have well-defined inner and outer boundaries of maser emission on sub-parsec scales.

Yunxin Li, Fan Liu, Zhen Du, Weijie Yuan

The emergence of the fifth-generation (5G) New Radio (NR) technology has provided unprecedented opportunities for vehicle-to-everything (V2X) networks, enabling enhanced quality of services. However, high-mobility V2X networks require frequent handovers and acquiring accurate channel state information (CSI) necessitates the utilization of pilot signals, lead

Sebastian M. Dawid

One has to study multivariable scattering amplitudes to extract properties of the three-body states from the generalizations of the L\"uscher finite-volume formalism. In particular, a three-body amplitude obtained from a Lattice QCD calculation must be analytically continued to unphysical Riemann sheets of the complex energy plane, where resonances of intere

Asel Sagingalieva, Stefan Komornyik, Arsenii Senokosov, Ayush Joshi

Accurate forecasting of photovoltaic power is essential for reliable grid integration, yet remains difficult due to highly variable irradiance, complex meteorological drivers, site geography, and device-specific behavior. Although contemporary machine learning has achieved successes, it is not clear that these approaches are optimal: new model classes may fu

Sanjay Oruganti, Sergei Nirenburg, Jesse English, Marjorie McShane

The paper describes a system that uses large language model (LLM) technology to support the automatic learning of new entries in an intelligent agent's semantic lexicon. The process is bootstrapped by an existing non-toy lexicon and a natural language generator that converts formal, ontologically-grounded representations of meaning into natural language sent

Runhan Xie, Isaac Grosof, Ziv Scully

Dispatching systems, where arriving jobs are immediately assigned to one of multiple queues, are ubiquitous in computer systems and service systems. A natural and practically relevant model is one in which each queue serves jobs in FCFS (First-Come First-Served) order. We consider the case where the dispatcher is size-aware, meaning it learns the size (i.e.

Siddharth Rout, Khai Phan, Chao-An Lin

Flow generated noise are caused shear flows and, hence, they can be used as feedback to control the flow. Existing flow control uses state variables like velocity, pressure, or vorticity, none use acoustic observables as the primary control signal. It is tough to model a classical control algorithm using sound level but data-driven approaches are not as do n

Yusuke Ino, Misaki Yonekawa, Hideto Yuzawa, Yuichiro Minato

Quantum computers are expected to perform the full-configuration interaction calculations with less computational resources compared to classical ones, thanks to the use of the quantum phase estimation (QPE) algorithms. However, only a limited number of the QPE-based quantum chemical calculations have been reported even for numerical simulations on a classic

Jinwen He, Yujia Gong, Kai Chen, Zijin Lin

Large Language Models (LLMs) have revolutionized various domains with extensive knowledge and creative capabilities. However, a critical issue with LLMs is their tendency to produce outputs that diverge from factual reality. This phenomenon is particularly concerning in sensitive applications such as medical consultation and legal advice, where accuracy is p

Yanqing Yin, Wang Zhou

In this paper, we introduce a joint central limit theorem (CLT) for specific bilinear forms, encompassing the resolvent of the sample covariance matrix under an elliptical distribution. Through an exhaustive exploration of our theoretical findings, we unveil a phase transition in the limiting parameters that relies on the moments of the random radius in our

William Gyory, Naoki Yamamoto

We study the convergence of the Ginzburg-Landau (GL) expansion in the context of the Bardeen-Cooper-Schrieffer (BCS) theory for superconductivity and the Nambu-Jona-Lasinio (NJL) model for chiral symmetry breaking at finite temperature $T$ and chemical potential $\mu$. We present derivations of the all-order formulas for the coefficients of the GL expansions

Sofia Ortega Castillo, Isidro Humberto Munive Lima

We reduce the polynomial cluster value problem for the algebra of bounded analytic functions, $H^{\infty}$, on the ball of Banach spaces $X$ to the same polynomial cluster value problem for $H^{\infty}$ but on the ball of those spaces which are $\ell_1$-sums of finite dimensional spaces.

Mina Dalirrooyfard, Slobodan Mitrović, Yuriy Nevmyvaka

Finding min $s$-$t$ cuts in graphs is a basic algorithmic tool with applications in image segmentation, community detection, reinforcement learning, and data clustering. In this problem, we are given two nodes as terminals, and the goal is to remove the smallest number of edges from the graph so that these two terminals are disconnected. We study the complex

Shikui Shang

Let $k$ be a field of characteristic $0$. We introduce a pair of adjoint functors, Allison-Benkart-Gao functor $\AG$ and Berman-Moody functor $\BM$, between the category of non-unital alternative algebras over $k$ and the category $\LieR$ of Lie algebras with compatible $sl_3(k)$-actions. Surprisingly, when $A$ is an alternative algebra without a unit, the A

Aviral Srivastava, Dhyan Thakkar, Sharda Valiveti, Pooja Shah

Machine learning and data mining techniques are utiized for enhancement of the security of any network. Researchers used machine learning for pattern detection, anomaly detection, dynamic policy setting, etc. The methods allow the program to learn from data and make decisions without human intervention, consuming a huge training period and computation power.

Klaus W. Hodapp, Eric Gaidos, Matthew A. Kenworthy, Michael Tucker

A previously unremarkable star near the Canis Major OB1/R1 association underwent an episode of multiple deep brightness minima. Light curves based on archival Gaia, ZTF, NEOWISE data and additional observations from LCO and UKIRT show that the star was not variable prior to 2019 Aug 18 (MJD 58700), and on that date started showing brightness dips of up to 3

Haitao Lin, Xiangru Li

Pulsar detection has become an active research topic in radio astronomy recently. One of the essential procedures for pulsar detection is pulsar candidate sifting (PCS), a procedure of finding out the potential pulsar signals in a survey. However, pulsar candidates are always class-imbalanced, as most candidates are non-pulsars such as RFI and only a tiny pa

Timo Klein, Susanna Weinberger, Adish Singla, Sebastian Tschiatschek

We consider the problem of third-person imitation learning with the additional challenge that the learner must select the perspective from which they observe the expert. In our setting, each perspective provides only limited information about the expert's behavior, and the learning agent must carefully select and combine information from different perspectiv

Xia Wang, Anda Liang, Jonathan Sprinkle, Taylor T. Johnson

Many decision-making scenarios in modern life benefit from the decision support of artificial intelligence algorithms, which focus on a data-driven philosophy and automated programs or systems. However, crucial decision issues related to security, fairness, and privacy should consider more human knowledge and principles to supervise such AI algorithms to rea

Alfredo Ferreira, Manuel J. Fonseca, Joaquim A. Jorge

Detecting polygons defined by a set of line segments in a plane is an important step in analyzing vector drawings. This paper presents an approach combining several algorithms to detect basic polygons from arbitrary line segments. The resulting algorithm runs in polynomial time and space, with complexities of $O\bigl((N + M)^4\bigr)$ and $O\bigl((N + M)^2\bi

Aditya Panwar, Ashwin T S, Ramkumar Rajendran, Kavi Arya

Online learning and MOOCs have become increasingly popular in recent years, and the trend will continue, given the technology boom. There is a dire need to observe learners' behavior in these online courses, similar to what instructors do in a face-to-face classroom. Learners' strategies and activities become crucial to understanding their behavior. One majo

Lihui Liu, Blaine Hill, Boxin Du, Fei Wang

Conversational question answering (convQA) over knowledge graphs (KGs) involves answering multi-turn natural language questions about information contained in a KG. State-of-the-art methods of ConvQA often struggle with inexplicit question-answer pairs. These inputs are easy for human beings to understand given a conversation history, but hard for a machine

Nisarg Bhatt, Subroto Mukerjee, Sriram Ramaswamy

The Hamiltonian nature of the precessional dynamics of the classical Heisenberg model leads to reciprocal interactions amongst the spins. Heisenberg spins are reciprocal in nature. In this work, we study the dynamics of a nonequilibrium classical spin chain in which the neighbours interact through a purely non-reciprocal exchange coupling [EPL 60, 418 (2002)

Nicole Fontana, Laura Savaré, Francesca Ieva

Globally, the incidence of heart failure is increasing, and its principal treatment involves drug therapy. However, widespread non-adherence to therapies is prevalent among heart failure patients and often results in worsening health conditions and an increase in hospital admissions. This study aims to develop an innovative approach, the State-Sequence analy

Henry H. Mattingly

We study bacterial diffusion in disordered porous media. Interactions with obstacles, at unknown locations, make this problem challenging. We approach it by abstracting the environment to cell states with memoryless transitions. With this, we derive an effective diffusivity that agrees well with simulations in explicit geometries. The diffusivity is non-mono

T S Ashwin, Shaikh Danish Shafi, Rajendran Ramkumar

Adaptive intelligent educational systems are gaining popularity, offering personalized learning experiences to students based on their individual needs and styles. One crucial feature of such systems is real-time personalized feedback. However, identifying real-time learning processes impacting student performance remains challenging due to data volume const

Qiang Fu, Ashia Wilson

We propose a new method called the N-particle underdamped Langevin algorithm for optimizing a special class of non-linear functionals defined over the space of probability measures. Examples of problems with this formulation include training mean-field neural networks, maximum mean discrepancy minimization and kernel Stein discrepancy minimization. Our algor

Subhajit Ghosh, Sergey Rumyantsev, Alexander A. Balandin

Low-frequency electronic noise in charge-density-wave van der Waals materials has been an important characteristic, providing information about the material quality, phase transitions, and collective current transport. However, the noise sources and mechanisms have not been completely understood, particularly for the materials with a non-fully gapped Fermi s

Bijita Sarma, Michael J. Hartmann

Fast quantum gates are crucial not only for the contemporary era of noisy intermediate-scale quantum devices but also for the prospective development of practical fault-tolerant quantum computing. Leakage errors, which arise from data qubits jumping beyond the confines of the computational subspace, are the main challenges in realizing non-adiabatically driv

Anna Malinovskaya, Rebecca Killick, Kathryn Leeming, Philipp Otto

Conventional modelling of networks evolving in time focuses on capturing variations in the network structure. However, the network might be static from the origin or experience only deterministic, regulated changes in its structure, providing either a physical infrastructure or a specified connection arrangement for some other processes. Thus, to detect chan

Fabio Federici, Bruce Lipschultz, Gijs R. A. Akkermans, Kevin Verhaegh

The linear plasma machine Magnum-PSI can replicate similar conditions to those found in a tokamak at the end of the divertor leg. A dedicated capacitor bank, in parallel to the plasma source, can release a sudden burst of energy, leading to a rapid increase in plasma temperature and density, resulting in a transient heat flux increase of half of an order of

Guanli Liu, Lars Kulik, Christian S. Jensen, Tianyi Li

A space-filling curve (SFC) maps points in a multi-dimensional space to one-dimensional points by discretizing the multi-dimensional space into cells and imposing a linear order on the cells. This way, an SFC enables the indexing of multi-dimensional data using a one-dimensional index such as a B+-tree. Choosing an appropriate SFC is crucial, as different SF

Victor Liu, Abderahmen Zoghbi, Jon M. Miller

Iron K$\alpha$ (Fe K$\alpha$) emission is observed ubiquitously in AGN, and it is a powerful probe of their circumnuclear environment. Examinations of the emission line play a pivotal role in understanding the disk geometry surrounding the black hole. It has been suggested that the torus and the broad line region (BLR) are the origins of emission. However, t

Sergi Elizalde, Alejandro B. Galván

A triangular partition is a partition whose Ferrers diagram can be separated from its complement (as a subset of $\mathbb{N}^2$) by a straight line. Having their origins in combinatorial number theory and computer vision, triangular partitions have been studied from a combinatorial perspective by Onn and Sturmfels, and by Corteel et al. under the name plane

Dongfang Zhao

Addressing the challenge of balancing security and efficiency when deploying machine learning systems in untrusted environments, such as federated learning, remains a critical concern. A promising strategy to tackle this issue involves optimizing the performance of fully homomorphic encryption (HE). Recent research highlights the efficacy of advanced caching

Luyi Ma, Nikhil Thakurdesai, Jiao Chen, Jianpeng Xu

Data processing is one of the fundamental steps in machine learning pipelines to ensure data quality. Majority of the applications consider the user-defined function (UDF) design pattern for data processing in databases. Although the UDF design pattern introduces flexibility, reusability and scalability, the increasing demand on machine learning pipelines br

Adam Koranyi

A simple new proof of the Harish-Chandra condition, preceded by an expository part on Hermitian symmetric spaces, holomorphic induction, and on some analytic tools.

Peter Potaptchik, Daniel M. Roy, David Schrittesser

We show the following generalizations of the de Finetti--Hewitt--Savage theorem: Given an exchangeable sequence of random elements, the sequence is conditionally i.i.d. if and only if each random element admits a regular conditional distribution given the exchangeable $\sigma$-algebra (equivalently, the shift invariant or the tail algebra). We use this resul

Kyle W. Martin, Nader Zaki, Matthew S. Bigelow, Benjamin K. Stuhl

Multi-node optical clock networks will enable future studies of fundamental physics and enable applications in quantum and classical communications as well as navigation and geodesy. We implement the first ever multi-node optical clock network with real-time, relative synchronization over free-space communication channels and precision on the order of 10 fs,

Patrick Concha, Fernando Izaurieta, Evelyn Rodríguez, Sebastián Salgado

In this work, we propose a four-dimensional gauged Wess-Zumino-Witten model, obtained as a dimensional reduction from a transgression field theory invariant under the $\mathcal{N}=1$ Poincar\'{e} supergroup. For this purpose, we consider that the two gauge connections on which the transgression action principle depends are given by linear and non-linear real

Huy Dang, Marzia Cremona, Nicole Lazar, Francesca Chiaromonte

Functional magnetic resonance imaging (fMRI) is a neuroimaging technique known for its ability to capture brain activity non-invasively and at fine spatial resolution (2-3mm). Cortical surface fMRI (cs-fMRI) is a recent development of fMRI that focuses on signals from tissues that have neuronal activities, as opposed to the whole brain. cs-fMRI data is plagu

Renzo Guido, Luis G. Sarasua, Arturo C. Marti

Transient dynamics in viscoelastic fluids exhibits notable difference with their Newtonian counterpart. In this work we study the changes in the formation and decay of vortex of viscoelastic fluids due to degradation caused by shear stress. With this aim we performed long duration experiments with solutions of polyacrylamide confined between coaxial cylinder

José A. Carrillo, Jakub Skrzeczkowski

There has been recently a lot of interest in the analysis of the Stein gradient descent method, a deterministic sampling algorithm. It is based on a particle system moving along the gradient flow of the Kullback-Leibler divergence towards the asymptotic state corresponding to the desired distribution. Mathematically, the method can be formulated as a joint l