March 2024 arXiv papers — page 118

Vanmathi A, Parangama Sarkar

We study the asymptotic behaviour of $v$-number and local $v$-numbers of Noetherian generalized symbolic power filtrations $\mathcal I=\{I_n\}$ in a Noetherian $\mathbb N$-graded domain and show that they are quasi-linear type. We provide sufficient conditions for the existence of the limits $\lim\limits_{n\to\infty}\frac{v(I_n)}{n}$ and $\lim\limits_{n\to\i

Hao Mo, Yan-Mei Chen, Zhi-Yun Zhang, Alexei Moiseev

We identify a fading AGN SDSS J220141.64+115124.3 from the internal Product Launch-11 (MPL-11) in Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey. The central region with a projected radius of $\sim$2.4 kpc is characterized as LINER-like line ratios while the outskirts extended to $\sim$15 kpc show Seyfert-like line ratios. The [OIII]$\lam

Yusheng Zhao, Hui Zhong, Xinyue Zhang, Yuqing Li

While quantum computing has strong potential in data-driven fields, the privacy issue of sensitive or valuable information involved in the quantum algorithm should be considered. Differential privacy (DP), which is a fundamental privacy tool widely used in the classical scenario, has been extended to the quantum domain, i.e., quantum differential privacy (QD

Zhuohang Jiang, Bingkui Tong, Xia Du, Ahmed Alhammadi

With the rise of social platforms, protecting privacy has become an important issue. Privacy object detection aims to accurately locate private objects in images. It is the foundation of safeguarding individuals' privacy rights and ensuring responsible data handling practices in the digital age. Since privacy of object is not shift-invariant, the essence of

Zhaoliang Chen, Zhihao Wu, Ylli Sadikaj, Claudia Plant

Although Graph Neural Networks (GNNs) have exhibited the powerful ability to gather graph-structured information from neighborhood nodes via various message-passing mechanisms, the performance of GNNs is limited by poor generalization and fragile robustness caused by noisy and redundant graph data. As a prominent solution, Graph Augmentation Learning (GAL) h

Ke Wang

We present a comprehensive analysis of singular vector and singular subspace perturbations in the signal-plus-noise matrix model with random Gaussian noise. Assuming a low-rank signal matrix, we extend the Davis-Kahan-Wedin theorem in a fully generalized manner, applicable to any unitarily invariant matrix norm, building on previous results by O'Rourke, Vu,

Rasmus Jouttijärvi

If we want to deform a compact Riemannian manifold with boundary using Ricci flow, we first need to decide on appropriate boundary conditions. We would like these conditions to reflect the geometric nature of the flow and allow for a variety of initial data. Importantly, the conditions should be compatible with the expected evolution of Einstein metrics. We

Seulgi Choi, Hyewon Lee, Yoonjoo Lee, Juho Kim

The lengthy monologue-style online lectures cause learners to lose engagement easily. Designing lectures in a "vicarious dialogue" format can foster learners' cognitive activities more than monologue-style. However, designing online lectures in a dialogue style catered to the diverse needs of learners is laborious for instructors. We conducted a design works

Jianwei Sun, Chaoyang Mei, Linlin Wei, Kaiyu Zheng

The efficacy of large language models (LLMs) is heavily dependent on the quality of the underlying data, particularly within specialized domains. A common challenge when fine-tuning LLMs for domain-specific applications is the potential degradation of the model's generalization capabilities. To address these issues, we propose a two-stage approach for the co

Yan-Han Yang, Xin-Zhu Liu, Xing-Zhou Zheng, Shao-Ming Fei

Quantum nonlocality as a witness of entanglement plays a crucial role in various fields. Existing quantum monogamy relations rule out the possibility of simultaneous violations of any Bell inequalities with partial statistics generated from one Bell experiment on any multipartite entanglement or post-quantum sources. In this paper, we report an efficient met

Cesar Gomez

The Page curve describing the process of black hole evaporation is derived in terms of a family, parametrized in terms of the evaporation time, of finite type II_1 factors, associated, respectively, to the entanglement wedges of the black hole and the radiation. The so defined Page curve measures the relative continuous dimension of the black hole and the ra

Li Yizhen, Huang Shaohan, Qi Jiaxing, Quan Lei

No previous work has studied the performance of Large Language Models (LLMs) in the context of Traditional Chinese Medicine (TCM), an essential and distinct branch of medical knowledge with a rich history. To bridge this gap, we present a TCM question dataset named TCM-QA, which comprises three question types: single choice, multiple choice, and true or fals

Eliza Mik

The current fascination with large language models, or LLMs, derives from the fact that many users lack the expertise to evaluate the quality of the generated text. LLMs may therefore appear more capable than they actually are. The dangerous combination of fluency and superficial plausibility leads to the temptation to trust the generated text and creates th

Haoran Yang, Yumeng Zhang, Jiaqi Xu, Hongyuan Lu

While Large Language Models (LLMs) have demonstrated exceptional multitasking abilities, fine-tuning these models on downstream, domain-specific datasets is often necessary to yield superior performance on test sets compared to their counterparts without fine-tuning. However, the comprehensive effects of fine-tuning on the LLMs' generalization ability are no

Haruto Takahashi, Rihito Sakurai, Hiroshi Shinaoka

Space-time dependence of imaginary-time propagators, vital for \textit{ab initio} and many-body calculations based on quantum field theories, has been revealed to be compressible using Quantum Tensor Trains (QTTs) [Phys. Rev. X {\bf 13}, 021015 (2023)]. However, the impact of system parameters, like temperature, on data size remains underexplored. This paper

Kai Pfeiffer, Abderrahmane Kheddar

In this work, we present several tools for efficient sequential hierarchical least-squares programming (S-HLSP) for lexicographical optimization tailored to robot control and planning. As its main step, S-HLSP relies on approximations of the original non-linear hierarchical least-squares programming (NL-HLSP) to a hierarchical least-squares programming (HLSP

Jaione Bengoetxea, Yi-Ling Chung, Marco Guerini, Rodrigo Agerri

Counter Narratives (CNs) are non-negative textual responses to Hate Speech (HS) aiming at defusing online hatred and mitigating its spreading across media. Despite the recent increase in HS content posted online, research on automatic CN generation has been relatively scarce and predominantly focused on English. In this paper, we present CONAN-EUS, a new Bas

Rodrigo Santos-Güemes, Christophe J. Ortiz, Javier Segurado

Object kinetic Montecarlo (OkMC) is a fundamental tool for modeling defect evolution in volumes and times far beyond atomistic models. The elastic interaction between defects is classically considered using a dipolar approximation but this approach is limited to simple cases and can be inaccurate for large and close interacting defects. In this work a novel

Mingya Zhang, Yue Yu, Limei Gu, Tingsheng Lin

In the field of medical image segmentation, models based on both CNN and Transformer have been thoroughly investigated. However, CNNs have limited modeling capabilities for long-range dependencies, making it challenging to exploit the semantic information within images fully. On the other hand, the quadratic computational complexity poses a challenge for Tra

Michael Cuntz, Thorsten Holm, Peter Jorgensen

Based on Berenstein and Retakh's notion of noncommutative polygons we introduce and study noncommutative frieze patterns. We generalize several notions and fundamental properties from the classic (commutative) frieze patterns to noncommutative frieze patterns, e.g. propagation formulae and $\mu$-matrices, quiddity cycles and reduction formulae, and we show t

Kfir Eliaz, Ran Spiegler

We present a model of news media that shape consumer beliefs by providing information (signals about an exogenous state) and narratives (models of what determines outcomes). To amplify consumers' engagement, media maximize consumers' anticipatory utility. Focusing on a class of separable consumer preferences, we show that a monopolistic media platform facing

Sachin Sonkar, Ramandeep S. Johal

A three-level quantum system having two energy gaps presents a nontrivial working medium for a quantum heat engine. Our focus lies in understanding the constraints on the ability to modulate these gaps relative to the changes in probability distributions at the two given heat reservoirs. It is seen that an Otto engine in the quasistatic limit is feasible if

Yulan Gao, Chao Ren, Han Yu

In the rapidly advancing field of federated learning (FL), ensuring efficient FL task delegation while incentivising FL client participation poses significant challenges, especially in wireless networks where FL participants' coverage is limited. Existing Contract Theory-based methods are designed under the assumption that there is only one FL server in the

Giorgio Gubbiotti, Bert van Geemen, Pierandrea Vergallo

We demonstrate that a pair consisting of a second-order homogeneous Hamiltonian structure in $N$ components and its associated system of conservation laws is in bijective correspondence with an alternating three-form on a $N+2$-dimensional vector space. Additionally, we show that the three-form offers $N+2$ linear equations in the Pl\"ucker coordinates that

Willem Esterhuizen, Philipp Sauerteig, Stefan Streif, Karl Worthmann

We consider the SEIR compartmental epidemic model subject to state and input constraints (a cap on the proportion of infectious individuals and limits on the allowed social distancing and quarantining measures, respectively). We present a tailored model predictive control (MPC) scheme without terminal conditions. We rigorously show recursive feasibility and

Motohiko Ezawa

In order to detect the N\'{e}el vector of an altermagnet, we investigate topological phases in a bilayer system composed of an altermagnet and a two-dimensional topological insulator described by the Bernevig-Hughes-Zhang model. A topological phase transition occurs from a first-order topological insulator to a trivial insulator at a certain critical alterma

Tony J. Puthenpurakal

Let $(A,\mathfrak{m})$ be a regular local ring of dimension $d \geq 1$. Let $\mathcal{D}^2_{fg}(A)$ denote the derived category of $2$-periodic complexes with finitely generated cohomology modules. Let $\mathcal{K}^2(\proj A) $ denote the homotopy category of $2$-periodic complexes of finitely generated free $A$-modules. We show the natural map $\mathcal{K}^

Lauren Rhue, Sofie Goethals, Arun Sundararajan

This study examines the use of Large Language Models (LLMs) for retrieving factual information, addressing concerns over their propensity to produce factually incorrect "hallucinated" responses or to altogether decline to even answer prompt at all. Specifically, it investigates the presence of gender-based biases in LLMs' responses to factual inquiries. This

Leyla Majidi, Abolhassan Vaezi, Mehdi Kargarian

Recent discovery of correlated electronic phases in twisted heterostructures raised a surge of interests in studying models and materials with flat bands where the electronic excitations are nearly dispersionless in momentum space. As such, the kinetic energy is quenched and the correlations are enhanced, giving rise to a plethora of unusual magnetic, superc

Gunter Malle

We discuss computational results on field extensions $K/{\mathbb Q}$ of degree $n\le11$ with Galois group of the Galois closure isomorphic to the full symmetric group ${\mathfrak S}_n$. More precisely, we present statistics on the number of such extensions as a function of the field discriminant and compare them to the known predictions by Bhargava and the a

Tomoyuki Yamakami

We study the computational complexity of counting constraint satisfaction problems (#CSPs) whose constraints assign complex numbers to Boolean inputs when the corresponding constraint hypergraphs are acyclic. These problems are called acyclic #CSPs or succinctly, #ACSPs. We wish to determine the computational complexity of all such #ACSPs when arbitrary unar

A. Irles, J. P. Márquez, R. Pöschl, F. Richard

The International Linear Collider (ILC) will allow the precise study of $e^{-}e^{+}\rightarrow q\bar{q}$ interactions at different center-of-mass energies from the $Z$-pole to 1 TeV. In this paper, we discuss the experimental prospects for measuring differential observables in $e^{-}e^{+}\rightarrow b\bar{b}$ and $e^{-}e^{+}\rightarrow c\bar{c}$ at the ILC b

Qiyuan Feng, Gengchen Cao, Haoxiang Chen, Tai-Jiang Mu

3D Gaussian splatting models, as a novel explicit 3D representation, have been applied in many domains recently, such as explicit geometric editing and geometry generation. Progress has been rapid. However, due to their mixed scales and cluttered shapes, 3D Gaussian splatting models can produce a blurred or needle-like effect near the surface. At the same ti

Erhan Zhang, Xingzhu Wang, Peiyuan Gong, Yankai Lin

Due to the advantages in the cost-efficiency and reproducibility, user simulation has become a promising solution to the user-centric evaluation of information retrieval systems. Nonetheless, accurately simulating user search behaviors has long been a challenge, because users' actions in search are highly complex and driven by intricate cognitive processes s

Gleb Radchenko, Victoria Andrea Fill

Initially considered as low-power units with limited autonomous processing, Edge IoT devices have seen a paradigm shift with the introduction of FPGAs and AI accelerators. This advancement has vastly amplified their computational capabilities, emphasizing the practicality of edge AI. Such progress introduces new challenges of optimizing AI tasks for the limi

Cheng Chen, Xiaofeng Yang, Fan Yang, Chengzeng Feng

Recent works on text-to-3d generation show that using only 2D diffusion supervision for 3D generation tends to produce results with inconsistent appearances (e.g., faces on the back view) and inaccurate shapes (e.g., animals with extra legs). Existing methods mainly address this issue by retraining diffusion models with images rendered from 3D data to ensure

Geng Chen, Qingyue Wang, Islem Rekik

A connectional brain template (CBT) is a holistic representation of a population of multi-view brain connectivity graphs, encoding shared patterns and normalizing typical variations across individuals. The federation of CBT learning allows for an inclusive estimation of the representative center of multi-domain brain connectivity datasets in a fully data-pre

Aprodhita Anindya Putri, Akhmad Yunani

Work time standardization helps to find and reduce wasteful movements and time in the workplace, such as chatting, mobile phone use, insufficient rest, or unproductive tasks. This study aims to map the process of displaying products from the warehouse to the shelves and calculate and determine the standard working time of employees of the Operations Division

Daniil Kozhemiachenko

In this paper, we devise non-distributive relatives of Exactly True Logic (ETL) by Pietz and Riveccio and its dual (NFL) Non-Falsity Logic by Shramko, Zaitsev and Belikov. We consider two pre-orders which are algebraic counterparts of the ETL's and NFL's entailment relations on the De Morgan lattice $\mathbf{4}$. We generalise these pre-orders and determine

Lipei Zhang, Yanqi Cheng, Lihao Liu, Carola-Bibiane Schönlieb

Recent advances in deep learning have significantly improved brain tumour segmentation techniques; however, the results still lack confidence and robustness as they solely consider image data without biophysical priors or pathological information. Integrating biophysics-informed regularisation is one effective way to change this situation, as it provides an

Huifang Du, Xuejing Feng, Jun Ma, Meng Wang

Research demonstrates that the proactivity of in-vehicle conversational assistants (IVCAs) can help to reduce distractions and enhance driving safety, better meeting users' cognitive needs. However, existing IVCAs struggle with user intent recognition and context awareness, which leads to suboptimal proactive interactions. Large language models (LLMs) have s

Mohit Gurumukhani, Ramamohan Paturi, Pavel Pudlák, Michael Saks

Depth-3 circuit lower bounds and $k$-SAT algorithms are intimately related; the state-of-the-art $\Sigma^k_3$-circuit lower bound and the $k$-SAT algorithm are based on the same combinatorial theorem. In this paper we define a problem which reveals new interactions between the two. Define Enum($k$, $t$) problem as: given an $n$-variable $k$-CNF and an initia

Qiushi Han, Chenxi Li, Zhenwei Lin, Caihua Chen

We introduce a new first-order method for solving general semidefinite programming problems, based on the alternating direction method of multipliers (ADMM) and a matrix-splitting technique. Our algorithm has an advantage over the Burer-Monteiro approach as it only involves much easier quadratically regularized subproblems in each iteration. For a linear obj

Ao Cai, Huihui Lv, Zhiguo Wang

This paper establishes an extreme $C^k$ reducibility theorem of quasi-periodic $SL(2, \mathbb{R})$ cocycles in the local perturbative region, revealing both the essence of Eliasson [Commun.Math.Phys.1992] and Hou-You [Invent.Math.2012] in respectively the non-resonant and resonant cases. By paralleling further the reducibility process with the almost reducib

Bo Shu, Yiting Zhang, Saisai Hu, Dong Shu

This report investigates the history and impact of Generative Models and Connected and Automated Vehicles (CAVs), two groundbreaking forces pushing progress in technology and transportation. By focusing on the application of generative models within the context of CAVs, the study aims to unravel how this integration could enhance predictive modeling, simulat

Chang Zong, Yuyan Chen, Weiming Lu, Jian Shao

Large Language Models (LLMs) have demonstrated efficacy in various linguistic applications, including question answering and controlled text generation. However, studies into their ability to switch between opposite styles of responses in professional domains remain underexplored. This study introduces a novel approach, named ProSwitch, which enables a langu

Mansoor Sheikh, David Saad

Medical diagnostic testing can be made significantly more efficient using pooled testing protocols. These typically require a sparse infection signal and use either binary or real-valued entries of O(1). However, existing methods do not allow for inferring viral loads which span many orders of magnitude. We develop a message passing algorithm coupled with a

Hai Xue, Yun Xia, Di Zhang, Honghua Wei

Pricing is an important issue in mobile edge computing. How to appropriately determine the bid of end user (EU) is an incentive factor for edge cloud (EC) to offer service. In this letter, we propose an equilibrium pricing scheme based on the all-pay auction model in end-to-end collaboration environment, wherein all EUs can acquire the service at a lower pri

Xiangtian Xue, Jiasong Wu, Youyong Kong, Lotfi Senhadji

Referring object removal refers to removing the specific object in an image referred by natural language expressions and filling the missing region with reasonable semantics. To address this task, we construct the ComCOCO, a synthetic dataset consisting of 136,495 referring expressions for 34,615 objects in 23,951 image pairs. Each pair contains an image wit

Aandrew Baggio S, Rachel Kalpana Kalaimani

In this paper, we address the finite time synchronization of a network of dynamical systems with time-varying interactions modeled using temporal networks. We synchronize a few nodes initially using external control inputs. These nodes are termed as pinning nodes. The other nodes are synchronized by interacting with the pinning nodes and with each other. We

Asanosuke Jinno, Yuki Kamiya, Tetsuo Hyodo, Akira Ohnishi

We examine the $\Lambda$-${}^4\mathrm{He}$ ($\alpha$) momentum correlation in high-energy collisions to elucidate the interaction between Lambdas ($\Lambda$) and nucleons ($N$). We compare phenomenological $\Lambda\alpha$ potentials with different strengths at short range. In addition to the conventional Gaussian-type potentials, we construct the $\Lambda\al

Subir Majumder, Lin Dong, Fatemeh Doudi, Yuting Cai

Large Language Models (LLMs) as chatbots have drawn remarkable attention thanks to their versatile capability in natural language processing as well as in a wide range of tasks. While there has been great enthusiasm towards adopting such foundational model-based artificial intelligence tools in all sectors possible, the capabilities and limitations of such L

Zhuoxuan Peng, S. -H. Gary Chan

Due to its promising results, density map regression has been widely employed for image-based crowd counting. The approach, however, often suffers from severe performance degradation when tested on data from unseen scenarios, the so-called "domain shift" problem. To address the problem, we investigate in this work single domain generalization (SDG) for crowd

Agniv Bandyopadhyay, Sandeep Juneja, Shubhada Agrawal

Top-$2$ methods have become popular in solving the best arm identification (BAI) problem. The best arm, or the arm with the largest mean amongst finitely many, is identified through an algorithm that at any sequential step independently pulls the empirical best arm, with a fixed probability $\beta$, and pulls the best challenger arm otherwise. The probabilit

Florent Foucaud, Clara Marcille, Zin Mar Myint, R. B. Sandeep

A monitoring edge-geodetic set, or simply an MEG-set, of a graph $G$ is a vertex subset $M \subseteq V(G)$ such that given any edge $e$ of $G$, $e$ lies on every shortest $u$-$v$ path of $G$, for some $u,v \in M$. The monitoring edge-geodetic number of $G$, denoted by $meg(G)$, is the minimum cardinality of such an MEG-set. This notion provides a graph theor

Fengjie Wang, Yanna Lin, Leni Yang, Haotian Li

Computational notebooks are widely utilized for exploration and analysis. However, creating slides to communicate analysis results from these notebooks is quite tedious and time-consuming. Researchers have proposed automatic systems for generating slides from notebooks, which, however, often do not consider the process of users conceiving and organizing thei

Tomoyuki Hisamoto

Based on the recent work of K.~Zhang, we discuss the Miyaoka-Yau type inequality for projective manifolds with nef anti-canonical line bundle, assuming the lower bound of the delta-invariant introduced by Fujita and Odaka.

Daniil Kozhemiachenko

In this paper, we present a~generalisation of proof simulation procedures for Frege systems by Bonet and Buss to some logics for which the deduction theorem does not hold. In particular, we study the case of finite-valued \L{}ukasiewicz logics. To this end, we provide proof systems that augment Avron's Frege system for \L{}ukasiewicz three-valued logic with

Arvin Hekmati, Bhaskar Krishnamachari

This study introduces a robust solution for the detection of Distributed Denial of Service (DDoS) attacks in Internet of Things (IoT) systems, leveraging the capabilities of Graph Convolutional Networks (GCN). By conceptualizing IoT devices as nodes within a graph structure, we present a detection mechanism capable of operating efficiently even in lossy netw

Mustafa Ustuner

The high-dimensional feature space of the hyperspectral imagery poses major challenges to the processing and analysis of the hyperspectral data sets. In such a case, dimensionality reduction is necessary to decrease the computational complexity. The random projections open up new ways of dimensionality reduction, especially for large data sets. In this paper

Preethi Gopalakrishnan, Shovan Dutta

Consider equal antiferromagnetic Heisenberg interactions between qubits forming a complex, nonbipartite network. We ask the question: How does the network topology determine the net magnetization of the ground state and to what extent is it tunable? By examining over 75000 networks of different families with tunable structural properties, we demonstrate that

Jun-Rong Zhang, Sheng-Yu Wang, Minrui Wei, Qiang Fu

X-ray absorption spectroscopy (XAS) is widely employed for structure characterization of graphitic carbon nitride (g-C$_3$N$_4$) and its composites. Nevertheless, even for pure g-C$_3$N$_4$, discrepancies in energy and profile exist across different experiments, which can be attributed to variations in structures arising from diverse synthesis conditions and

Young-Pil Choi, Kyungkeun Kang, Woojae Lee

This paper deals with the Toner-Tu (TT) model, which is a hydrodynamic model describing the collective motion of numerous self-propelled agents. We analytically study the global-in-time well-posedness of the TT model near the steady-state solution in the ordered phase. We also show the large-time behavior of solutions showing that the steady-state solution i

Zhuoqun Li, Hongyu Lin, Yaojie Lu, Hao Xiang

Declarative knowledge and procedural knowledge are two key parts in meta-cognitive theory, and these two hold significant importance in pre-training and inference of LLMs. However, a comprehensive analysis comparing these two types of knowledge is lacking, primarily due to challenges in definition, probing and quantitative assessment. In this paper, we explo

Ruiyi Zhang, Rushi Qiang, Sai Ashish Somayajula, Pengtao Xie

Large-scale pretraining followed by task-specific finetuning has achieved great success in various NLP tasks. Since finetuning all parameters of large pretrained models poses substantial computational and memory challenges, several efficient finetuning methods have been developed. Among them, low-rank adaptation (LoRA), which finetunes low-rank incremental u

Yi-Feng Zhao, Seong-Hoon Jang, Yukitoshi Motome

Competition and cooperation between electron correlation and relativistic spin-orbit coupling give rise to diverse exotic quantum phenomena in solids. An illustrative example is spin-orbit entangled quantum liquids, which exhibit remarkable features such as topological orders and fractional excitations. The Kitaev honeycomb model realizes such interesting st

Olamide Oladeji, Pedro Ciller Cutillas, Fernando de Cuadra, Ignacio Perez-Arriaga

In many developing countries, access to electricity remains a significant challenge. Electrification planners in these countries often have to make important decisions on the mode of electrification and the planning of electrical networks for those without access, while under resource constraints. An integrated approach to electrification planning in which t

Minrui Wei, Junxiang Zuo, Guangjun Tian, Weijie Hua

Purines and pyrimidines, crucial building blocks in biological systems, have attracted significant interest across molecular physics, biochemistry, pharmacology, and chemistry. Extensive spectroscopies have been employed for characterization, while the temperature and potential tautomeric effects can complicate the interpretation of underlying physics and ch

Jihyeon Seong, Jungmin Kim, Jaesik Choi

In Time Series Classification (TSC), temporal pooling methods that consider sequential information have been proposed. However, we found that each temporal pooling has a distinct mechanism, and can perform better or worse depending on time series data. We term this fixed pooling mechanism a single perspective of temporal poolings. In this paper, we propose a

Hyunkyung Han, Jihyeon Seong, Jaesik Choi

Capsule Neural Networks (CapsNets) is a novel architecture that utilizes vector-wise representations formed by multiple neurons. Specifically, the Dynamic Routing CapsNets (DR-CapsNets) employ an affine matrix and dynamic routing mechanism to train capsules and acquire translation-equivariance properties, enhancing its robustness compared to traditional Conv

Zhen Long, Qiyuan Wang, Yazhou Ren, Yipeng Liu

Anchor-based large-scale multi-view clustering has attracted considerable attention for its effectiveness in handling massive datasets. However, current methods mainly seek the consensus embedding feature for clustering by exploring global correlations between anchor graphs or projection matrices.In this paper, we propose a simple yet efficient scalable mult

Shota Amano, Yoshihiro Aritomo, Masahisa Ohta

We clarified that the fusion hindrance in heavy ion collisions is caused by the expansion of the neck bridge at the early stage of collision [Phys. Rev. C 108, 014612 (2023)]; however, our discussion was limited to the trajectory analysis. To get a reliable fusion cross section, it is important to understand the fusion process connecting with multinucleon tr

Papia Panda, Dinesh Kumar Singha, Monojit Ghosh, Rukmani Mohanta

In this work we investigate the effect of curved spacetime on neutrino oscillation. In a curved spacetime, the effect of curvature on fermionic fields is represented by spin connection. The spin connection consists of a non-universal ``contorsion" part which is expressed in terms of vector and axial current density of fermions. The contraction of contorsion

James Nianias, Jeremy Lim, Michael Yeung

Galactic-scale outflows of molecular gas from star-forming galaxies constitute the most direct evidence for regulation of star formation. In the early universe ($ z > 4 $), such outflows have recently been inferred from gravitationally-lensed dusty star-forming galaxies (DSFGs) based on ubiquitous detections of OH absorption extending to more blueshifted vel

Saima Samchuck-Schnarch

We define the affine Frobenius Brauer categories associated to each symmetric involutive Frobenius superalgebra $A$. We then define an action of these categories on the categories of finite-dimensional supermodules for orthosymplectic Lie superalgebras defined over $A$. When $A$ is the base field, we recover the previously-studied affine Brauer category; for

Yong Rui Poh, Dmitry Morozov, Nathanael P. Kazmierczak, Ryan G. Hadt

High-spin molecules allow for bottom-up qubit design and are promising platforms for magnetic sensing and quantum information science. Optical addressability of molecular electron spins has also been proposed in first-row transition metal complexes via optically-detected magnetic resonance (ODMR) mechanisms analogous to the diamond-NV colour centre. However,

Daiwei Yu, Zhuorong Li, Lina Wei, Canghong Jin

Adversarial training (AT) is currently one of the most effective ways to obtain the robustness of deep neural networks against adversarial attacks. However, most AT methods suffer from robust overfitting, i.e., a significant generalization gap in adversarial robustness between the training and testing curves. In this paper, we first identify a connection bet

Xilin Yang, Bijie Bai, Yijie Zhang, Musa Aydin

Systemic amyloidosis is a group of diseases characterized by the deposition of misfolded proteins in various organs and tissues, leading to progressive organ dysfunction and failure. Congo red stain is the gold standard chemical stain for the visualization of amyloid deposits in tissue sections, as it forms complexes with the misfolded proteins and shows a b

Jeonghan Lee, Haiyuan Wang, Keun-Yeol Park, Soonsang Huh

Quantum emitters in solid-state materials are highly promising building blocks for quantum information processing and communication science. Recently, single-photon emission from van der Waals materials has been reported in transition metal dichalcogenides and hexagonal boron nitride, exhibiting the potential to realize photonic quantum technologies in two-d

Po-Shen Hsin, David T. Stephen, Arpit Dua, Dominic J. Williamson

Topological quantum matter exhibits a range of exotic phenomena when enriched by subdimensional symmetries. This includes new features beyond those that appear in the conventional setting of global symmetry enrichment. A recently discovered example is a type of subsystem symmetry fractionalization that occurs through a different mechanism to global symmetry

Autumn Toney-Wails, Christian Schoeberl, James Dunham

Identifying scientific publications that are within a dynamic field of research often requires costly annotation by subject-matter experts. Resources like widely-accepted classification criteria or field taxonomies are unavailable for a domain like artificial intelligence (AI), which spans emerging topics and technologies. We address these challenges by infe

Yuan Fang, Yipeng Liu, Jie Chen, Zhen Long

In recent years, the fusion of high spatial resolution multispectral image (HR-MSI) and low spatial resolution hyperspectral image (LR-HSI) has been recognized as an effective method for HSI super-resolution (HSI-SR). However, both HSI and MSI may be acquired under extreme conditions such as night or poorly illuminating scenarios, which may cause different e

Yong-Yi Wang, Yun-Hao Shi, Zheng-Hang Sun, Chi-Tong Chen

Isolated interacting quantum systems generally thermalize, yet there are several examples for the breakdown of ergodicity, such as many-body localization and quantum scars. Recently, ergodicity breaking has been observed in systems subjected to linear potentials, termed Stark many-body localization. This phenomenon is closely associated with Hilbert-space fr

Zheng-Jun Shang, Yong Yu, Liang-Liang Wang, Mei-Ting Yang

From the mid-19th century to the end of the 20th century, photographic plates served as the primary detectors for astronomical observations. Astronomical photographic observations in China began in 1901, and over a century, a total of approximately 30,000 astronomical photographic plates have been captured. These historical plates play an irreplaceable role

Haohan Weng, Danqing Huang, Yu Qiao, Zheng Hu

Templates serve as a good starting point to implement a design (e.g., banner, slide) but it takes great effort from designers to manually create. In this paper, we present Desigen, an automatic template creation pipeline which generates background images as well as harmonious layout elements over the background. Different from natural images, a background im

Yupeng Li, Haorui He, Jin Bai, Dacheng Wen

The prevalence of fake news across various online sources has had a significant influence on the public. Existing Chinese fake news detection datasets are limited to news sourced solely from Weibo. However, fake news originating from multiple sources exhibits diversity in various aspects, including its content and social context. Methods trained on purely on

Victor Batyrev, Megumi Harada, Johannes Hofscheier, Kiumars Kaveh

Let $G$ be a connected reductive algebraic group over $\mathbb{C}$ with a maximal compact subgroup $K$. Let $G/H$ be a (quasi-affine) spherical homogeneous space. In the first part of the paper, following Akhiezer's definition of spherical functions, we introduce a $K$-invariant map $sLog_{\Gamma, t}: G/H \to \mathbb{R}^s$ which depends on a choice of a fini

Tianqi Zheng, Nicolas Loizou, Pengcheng You, Enrique Mallada

Gradient Descent Ascent (GDA) methods for min-max optimization problems typically produce oscillatory behavior that can lead to instability, e.g., in bilinear settings. To address this problem, we introduce a dissipation term into the GDA updates to dampen these oscillations. The proposed Dissipative GDA (DGDA) method can be seen as performing standard GDA o

Ruopeng Zhang, Sibo Zheng

We forecast high-frequency gravitational wave (GW) from preheating hosting gravitational dark matter (GDM) as the indirect probe of such GDM. We use proper lattice simulations to handle resonance, and to solve GW equation of motion with the resonance induced scalar field excitations as source term. Our numerical results show that Higgs scalar excitations in

Xu Zhang, Dingrong Xiong, Quangui Gao, Guiqin Yang

We examine the fundamental plane of 91 Blazars which include FSRQs and BL Lacs with known X-ray luminosity ($L_{R}$), radio luminosity ($L_X$), and black hole mass measurements ($M$) to reflect the relationship between jet and accretion for blazars. The fundamental plane of Blazars are log$L_{R}$=${0.273}_{+0.059}^{-0.059}$log$L_X$+${0.695}_{+0.191}^{-0.191}

Michele Cotrufo, Sedigheh Esfahani, Dmitriy Korobkin, Andrea Alù

Nonlocal metasurfaces have recently enabled an ultra-compact, low-power and high-speed platform to perform analog image processing. While several computational tasks have been demonstrated based on this platform, most of the previous studies have focused only on spatial operations, such as spatial differentiation and edge detection. Here, we demonstrate that

Andrew Hard, Antonious M. Girgis, Ehsan Amid, Sean Augenstein

How well do existing federated learning algorithms learn from client devices that return model updates with a significant time delay? Is it even possible to learn effectively from clients that report back minutes, hours, or days after being scheduled? We answer these questions by developing Monte Carlo simulations of client latency that are guided by real-wo

Kai Xiong, Xiao Ding, Ting Liu, Bing Qin

Large language models (LLMs) have developed impressive performance and strong explainability across various reasoning scenarios, marking a significant stride towards mimicking human-like intelligence. Despite this, when tasked with several simple questions supported by a generic fact, LLMs often struggle to abstract and apply the generic fact to provide cons

Jia-Qi Cai, Yu-Rong Shu, Xue-Qing Rao, Shuai Yin

We study the imaginary-time relaxation critical dynamics of the Neel-paramagnetic quantum phase transition in the two-dimensional (2D) dimerized S = 1/2 Heisenberg model. We focus on the scaling correction in the short-time region. A unified scaling form including both short-time and finite-size corrections is proposed. According to this full scaling form, i

Jeongjae Lee, Songnam Hong

Hybrid beamforming is an emerging technology for massive multiple-input multiple-output (MIMO) systems due to the advantages of lower complexity, cost, and power consumption. Recently, intelligent reflection surface (IRS) has been proposed as the cost-effective technique for robust millimeter-wave (mmWave) MIMO systems. Thus, it is required to jointly optimi

Ruonan Li, Ruhui Lu, Xueli Su, Shenggui Zhang

An edge-colored graph $G$ is called properly colored if every two adjacent edges are assigned different colors. A monochromatic triangle is a cycle of length 3 with all the edges having the same color. Given a tree $T_0$, let $\mathcal{T}(n,T_0)$ be the collection of $n$-vertex trees that are subdivisions of $T_0$. It is conjectured that for each fixed tree

Min Tsao

For regression model selection via maximum likelihood estimation, we adopt a vector representation of candidate models and study the likelihood ratio confidence region for the regression parameter vector of a full model. We show that when its confidence level increases with the sample size at a certain speed, with probability tending to one, the confidence r

Shyam Murthy, Santosh Kumar Upadhyaya, Srinivas Vivek

The rise of cloud computing has spurred a trend of transferring data storage and computational tasks to the cloud. To protect confidential information such as customer data and business details, it is essential to encrypt this sensitive data before cloud storage. Implementing encryption can prevent unauthorized access, data breaches, and the resultant financ

Tianyuan Yuan, Yucheng Mao, Jiawei Yang, Yicheng Liu

Autonomous vehicles rely extensively on perception systems to navigate and interpret their surroundings. Despite significant advancements in these systems recently, challenges persist under conditions like occlusion, extreme lighting, or in unfamiliar urban areas. Unlike these systems, humans do not solely depend on immediate observations to perceive the env

D. L. Mickelsen, Ruqian Wu, Clare C. Yu

Superconducting quantum interference devices (SQUIDs) show great promise as quantum bits (qubits) but continue to be hindered by flux noise. The flux noise power spectra of SQUIDs go as $1/f^\alpha$, where $\alpha$ is the temperature-dependent noise exponent. Experiments find $0.5 \lesssim \alpha \lesssim 1$. Furthermore, experiments find that the noise powe