April 2023 arXiv papers — page 80

Maxwell J. D. Ramstead, Mahault Albarracin, Alex Kiefer, Brennan Klein

This paper presents a model of consciousness that follows directly from the free-energy principle (FEP). We first rehearse the classical and quantum formulations of the FEP. In particular, we consider the inner screen hypothesis that follows from the quantum information theoretic version of the FEP. We then review applications of the FEP to the known sparse

Daniel Sternheimer

We briefly recount the long friendship that developed between Ludwig and us (Moshe Flato and I), since we first met at ICM 1966 in Moscow. That friendship extended to his school and family, and persists to this day. Its strong personal impact and main scientific components are sketched, including reflexions on what mathematical physics is (or should be).

Mruganka Kashyap, Laurent Lessard

It is well-known that linear quadratic regulators (LQR) enjoy guaranteed stability margins, whereas linear quadratic Gaussian regulators (LQG) do not. In this letter, we consider systems and compensators defined over directed acyclic graphs. In particular, there are multiple decision-makers, each with access to a different part of the global state. In this s

Shuzheng Gao, Xin-Cheng Wen, Cuiyun Gao, Wenxuan Wang

Pre-trained models of source code have gained widespread popularity in many code intelligence tasks. Recently, with the scaling of the model and corpus size, large language models have shown the ability of in-context learning (ICL). ICL employs task instructions and a few examples as demonstrations, and then inputs the demonstrations to the language models f

Yunqing Zhao, Chao Du, Milad Abdollahzadeh, Tianyu Pang

Few-shot image generation (FSIG) learns to generate diverse and high-fidelity images from a target domain using a few (e.g., 10) reference samples. Existing FSIG methods select, preserve and transfer prior knowledge from a source generator (pretrained on a related domain) to learn the target generator. In this work, we investigate an underexplored issue in F

Kai Liang, Songze Li, Ming Ding, Youlong Wu

In many distributed learning setups such as federated learning (FL), client nodes at the edge use individually collected data to compute local gradients and send them to a central master server. The master server then aggregates the received gradients and broadcasts the aggregation to all clients, with which the clients can update the global model. In this p

Huixin Dong, Yirong Xie, Xianan Zhang, Wei Wang

Despite the prevalence of GPS services, they still suffer from intermittent positioning with poor accuracy in partially shadowed regions like urban canyons, flyover shadows, and factories' indoor areas. Existing wisdom relies on hardware modifications of GPS receivers or power-hungry infrastructures requiring continuous plug-in power supply which is hard to

Hui Li, Ming Li, Alexander Petrov, Eite Tiesinga

Conical intersections are crossing points or lines between two or more adiabatic electronic potential energy surfaces in the multi-dimensional coordinate space of colliding atoms and molecules. Conical intersections and corresponding non-adiabatic coupling can greatly affect molecular dynamics and chemical properties. In this paper, we predict significant or

Walter Boscheri, Andrea Chiozzi, Michele Giuliano Carlino, Giulia Bertaglia

We introduce a new family of high order accurate semi-implicit schemes for the solution of non-linear hyperbolic partial differential equations on unstructured polygonal meshes. The time discretization is based on a splitting between explicit and implicit terms that may arise either from the multi-scale nature of the governing equations, which involve both s

Rui Yao

We numerically demonstrate a highly efficient optica huygens metagrating with unprecedentedly simple structure (only one meta-atom per period) designed via aggressively discretized method which initially originated from discretized metasurface design, performing nearly lossless anomalous refraction under TM-polarized incident light. A 2D full-wave floquet si

O. A. Malafeyev, N. D. Redinskikh, V. F. Bogachev

In this paper, the interaction of geopolitical actors in the production and sale of military equipment is studied. In section 2 the production of military equipment is considered as the two person zero-sum game. In such game, the strategies of the players are defined by the information state of the actors. The optimal strategy of geopolitical actors is found

Yang Yang, Zhongtian Fu, Xiangyu Wu, Wenjie Li

Current vision-language retrieval aims to perform cross-modal instance search, in which the core idea is to learn the consistent visionlanguage representations. Although the performance of cross-modal retrieval has greatly improved with the development of deep models, we unfortunately find that traditional hard consistency may destroy the original relationsh

Daria Pankratova, Khabib Yusupov, Alberto Vomiero, Sanath Kumar Honnali

Nanostructured materials and nanocomposites have shown great promise for improving the efficiency of thermoelectric materials. Herein, Fe nanoparticles were imbedded into a CrN matrix by combining two physical vapor deposition approaches, namely high-power impulse magnetron sputtering and a nanoparticle gun. The combination of these techniques allowed the fo

Masaki Izumi

For all Frobenius groups and a large class of finite multiply transitive permutation groups, we show that the corresponding group-subgroup subfactors are completely characterized by their principal graphs. The class includes all the sharply $k$-transitive permutation groups for $k=2,3,4$, and in particular the Mathieu group $M_{11}$ of degree 11.

Suyoung Choi, Younghan Yoon, Seonghyeon Yu

We compute the rational Betti numbers of the real toric varieties associated to Weyl chambers of types $E_7$ and $E_8$, completing the computations for all types of root systems.

Qifeng Zhang, Jiyuan Zhang, Zhimin Zhang

A rotation-two-component Camassa-Holm (R2CH) system was proposed recently to describe the motion of shallow water waves under the influence of gravity. This is a highly nonlinear and strongly coupled system of partial differential equations. A crucial issue in designing numerical schemes is to preserve invariants as many as possible at the discrete level. In

Xing Huang, Panpan Ren, Feng-Yu Wang

The $L^k$-Wasserstein distance $\mathbb{W}_k (k\ge 1)$ and the probability distance $\mathbb{W}_\psi$ induced by a concave function $\psi$, are estimated between different diffusion processes with singular coefficients. As applications, the well-posedness, probability distance estimates and the log-Harnack inequality are derived for McKean-Vlasov SDEs with m

Matteo Allaix, Yuxiang Lu, Yuhang Yao, Tefjol Pllaha

Linear computations over quantum many-to-one communication networks offer opportunities for communication cost improvements through schemes that exploit quantum entanglement among transmitters to achieve superdense coding gains, combined with classical techniques such as interference alignment. The problem becomes much more broadly accessible if suitable abs

Prasanna B, Sunandini Sanyal, R. Venkatesh Babu

In this paper, we propose to develop a method to address unsupervised domain adaptation (UDA) in a practical setting of continual learning (CL). The goal is to update the model on continually changing domains while preserving domain-specific knowledge to prevent catastrophic forgetting of past-seen domains. To this end, we build a framework for preserving do

Sreerup Raychaudhuri

The Higgs boson plays a central role in the Standard Model, as well as in theories which go beyond it. This article is therefore divided into two parts. The first takes s historical approach and shows how the mass problem entered weak interaction theory from the beginning and how it was solved by invoking the Higgs boson. This is followed by a construction o

Ella Gale

Dataset augmentation is a common way to deal with small datasets; Chemistry datasets are often small. Spherical convolutional neural networks (SphNNs) and Icosahedral neural networks (IcoNNs) are a type of geometric machine learning algorithm that maintains rotational symmetry. Molecular structure has rotational invariance and is inherently 3-D, and thus we

Anna Dmitrieva, Francesco Gallinaro, Mark Kamsma

We give definitions of the properties OP, IP, $k$-TP, TP$_1$, $k$-TP$_2$, SOP$_1$, SOP$_2$ and SOP$_3$ in positive logic, and prove various implications and equivalences between them. We also provide a characterisation of stability in positive logic in analogy with the one in full first-order logic, both on the level of formulas and on the level of theories.

David N. Reynolds, Francesco Tudisco

The modeling of opinion dynamics has seen much study in varying academic disciplines. Understanding the complex ways information can be disseminated is a complicated problem for mathematicians as well as social scientists. We present a nonlinear model of opinion dynamics that utilizes an environmental averaging protocol similar to the DeGroot and Freidkin-Jo

Anuran Roy, Sridhar Raj S

Distributed computation is always a tricky topic to deal with, especially in context of various requirements in various scenarios. A popular solution is to use Apache Spark with a setup of multiple systems forming a cluster. However, the prerequisite setup for a Spark cluster often induces an additional overhead, often limiting usage in constrained scenarios

Ella Gale

3-D shape is important to chemistry, but how important? Machine learning works best when the inputs are simple and match the problem well. Chemistry datasets tend to be very small compared to those generally used in machine learning so we need to get the most from each datapoint. Persistent homology measures the topological shape properties of point clouds a

Alexander V. Evako

This book provides an introduction to the theory of digital (molecular) spaces (TDS). Digital spaces are combinatorial models of continuous spaces. TDS is one of alternative branches of digital topology that studies constructing and modifying 2, 3 and n-dimensional digital image arrays in a computer and its memory. Demands for mathematical theory of multidim

Andrzej A. Zdziarski, Alexandra Veledina, Michal Szanecki, David A. Green

Recently, the accretion geometry of the black-hole X-ray binary Cyg X-1 was probed with the X-ray polarization. The position angle of the X-ray emitting flow was found to be aligned with the position angle of the radio jet in the plane of the sky. At the same time, the observed high polarization degree could be obtained only for a high inclination of the X-r

Ilias Kaperonis, Dimitra-Dionysia Stergiopoulou

Projectively coresolved Gorenstein flat modules were introduced recently by Saroch and Stovicek and were shown to be Gorenstein projective. While the relation between Gorenstein projective and Gorenstein flat modules is not well understood, the class of projectively coresolved Gorenstein flat modules is contained in the class of Gorenstein flat modules. This

Wouter Dessein, Alex Frankel, Navin Kartik

Many U.S. colleges now use test-optional admissions. A frequent claim is that by not seeing standardized test scores, a college can admit a student body it prefers, say with more diversity. But how can observing less information improve decisions? This paper proposes that test-optional policies are a response to social pressure on admission decisions. We mod

Peter Albers, Philipp Aretz, Irene Seifert

We construct and analyze families of periodic delay orbits for a class of delay differential equations in two dimensions depending on two real-valued functions. These families are parametrized by the delay parameter. It is possible to represent the dependency of these periodic delay orbits on the delay parameter by a curve in the plane, without loss of infor

Ajian Liu, Yanyan Liang

The existing multi-modal face anti-spoofing (FAS) frameworks are designed based on two strategies: halfway and late fusion. However, the former requires test modalities consistent with the training input, which seriously limits its deployment scenarios. And the latter is built on multiple branches to process different modalities independently, which limits t

Congying Xu, Valerio Terragni, Hengcheng Zhu, Jiarong Wu

Metamorphic Testing (MT) alleviates the oracle problem by defining oracles based on metamorphic relations (MRs), that govern multiple related inputs and their outputs. However, designing MRs is challenging, as it requires domain-specific knowledge. This hinders the widespread adoption of MT. We observe that developer-written test cases can embed domain knowl

Jingyao Li, Pengguang Chen, Shengju Qian, Shu Liu

Contrastive Language-Image Pre-training (CLIP) has recently shown great promise in pixel-level zero-shot learning tasks. However, existing approaches utilizing CLIP's text and patch embeddings to generate semantic masks often misidentify input pixels from unseen classes, leading to confusion between novel classes and semantically similar ones. In this work,

Hongwei Shi, Bowen Sun, Weichao Yang, Xu Guo

In this paper, we consider tests for ultrahigh-dimensional partially linear regression models. The presence of ultrahigh-dimensional nuisance covariates and unknown nuisance function makes the inference problem very challenging. We adopt machine learning methods to estimate the unknown nuisance function and introduce quadratic-form test statistics. Interesti

Josué Corujo, Vlada Limic

The Erd\H{o}s-R\'enyi random graph is the fundamental random graph model. In this paper we consider its continuous-time version, where multi-edges and self-loops are also allowed. It is well-known that the sizes of its connected components evolve according to the multiplicative coalescent dynamics. Moreover, with the additional information on the number of s

David Beyer, Peter Košovan, Christian Holm

Recent experiments on weak polyelectrolyte brushes found marked shifts in the effective p$K_\mathrm{A}$ that are linear in the logarithm of the salt concentration. Comparing explicit-particle simulations with mean-field calculations we show that for high grafting densities the salt concentration effect can be explained using the ideal Donnan theory, but for

A. Rios-Navarro, S. Guo, G Abarajithan, K. Vijayakumar

In-camera event denoising reduces the data rate of event cameras by filtering out noise at the source. A lightweight multilayer perceptron denoising filter (MLPF) provides state-of-the-art low-cost denoising accuracy. It processes a small neighborhood of pixels from the timestamp image around each event to discriminate signal and noise events. This paper pro

Absalom E. Ezugwu, Olaide N. Oyelade, Abiodun M. Ikotun, Jeffery O. Agushaka

In this paper, a critical bibliometric analysis study is conducted, coupled with an extensive literature survey on recent developments and associated applications in machine learning research with a perspective on Africa. The presented bibliometric analysis study consists of 2761 machine learning-related documents, of which 98% were articles with at least 48

Théa de Seauve, Sophie Bosonnet, Olivier Grauby, Alexandre Semerok

Lead white is the most ancient and common white pigment used in mural paintings. However, it tends to blacken with time due to its oxidation to plattnerite (\b{eta}-PbO2). Chemical treatments were used but they can put the pictorial layers supports at risks. Hereby we address the possibility of thermally reconverting black plattnerite to white lead carbonate

Naoki Kitazawa

Previously, we have systematically constructed explicit real algebraic functions which are represented as the compositions of smooth real algebraic maps whose images are domains surrounded by hypersurfaces of degree 1 or 2 with canonical projections. Here we give new examples with the hypersurfaces each of which is the product of a connected component of a h

Joshua Wrigley

As several different formal systems with inequivalent syntax may describe equivalent semantics, it is possible to find `completions' to more expressive syntaxes that are semantically invariant. Doctrine theory, in the sense of Lawvere, is the natural categorical framework in which to express completions for first-order logic. We study the suitability of a fi

Binnur Görer, Fatma Başak Aydemir

[Context] Interviewing stakeholders is the most popular requirements elicitation technique among multiple methods. The success of an interview depends on the collaboration of the interviewee which can be fostered through the interviewer's preparedness and communication skills. Mastering these skills requires experience and practicing interviews. [Problem] Pr

Chenyang Ma, Xinchi Qiu, Daniel J. Beutel, Nicholas D. Lane

The privacy-sensitive nature of decentralized datasets and the robustness of eXtreme Gradient Boosting (XGBoost) on tabular data raise the needs to train XGBoost in the context of federated learning (FL). Existing works on federated XGBoost in the horizontal setting rely on the sharing of gradients, which induce per-node level communication frequency and ser

Atanu Paul, Anthony Ruffino, Stefan Masiuk, Jonathan Spanier

The application of molecular dynamics (MD) simulations to the interpretation of Raman scattering spectra is hindered by inability of atomistic simulations to account for the dynamic evolution of electronic polarizability, requiring the use of either ab initio method or parameterization of machine learning models. More broadly, the dynamic evolution of electr

Edoardo Di Paolo, Marinella Petrocchi, Angelo Spognardi

Online Social Networks have revolutionized how we consume and share information, but they have also led to a proliferation of content not always reliable and accurate. One particular type of social accounts is known to promote unreputable content, hyperpartisan, and propagandistic information. They are automated accounts, commonly called bots. Focusing on Tw

Stefan Borozan, Spyros Giannelos, Paola Falugi, Alexandre Moreira

The necessary decarbonization efforts in energy sectors entail the integration of flexibility assets, as well as increased levels of uncertainty for the planning and operation of power systems. To cope with this in a cost-effective manner, transmission expansion planning (TEP) models need to incorporate progressively more details to represent potential long-

Amrollah Seifoddini, Koen Vernooij, Timon Künzle, Alessandro Canopoli

Accurately estimating human body shape from photos can enable innovative applications in fashion, from mass customization, to size and fit recommendations and virtual try-on. Body silhouettes calculated from user pictures are effective representations of the body shape for downstream tasks. Smartphones provide a convenient way for users to capture images of

Mayank Ratan Bhardwaj, Jaydeep Pawar, Abhijnya Bhat, Deepanshu

Accurate prediction of agricultural crop prices is a crucial input for decision-making by various stakeholders in agriculture: farmers, consumers, retailers, wholesalers, and the Government. These decisions have significant implications including, most importantly, the economic well-being of the farmers. In this paper, our objective is to accurately predict

Na Yuan, ShuaiLing Wang

We calculate the Hausdorff dimension of the fractal set \begin{equation*} \Big\{\mathtt{x}\in \mathbb{T}^d: \prod_{1\leq i\leq d}|T_{\beta_i}^n(x_i)-x_i| < \psi(n) \text{ for infinitely many } n\in \mathbb{N}\Big\}, \end{equation*} where the $T_{\beta_i}$ is the standard $\beta_i$-transformation with $\beta_i>1$, $\psi$ is a positive function on $\mathbb{N}$

Daniel Feitosa, Matei-Tudor Penca, Massimiliano Berardi, Rares-Dorian Boza

Context: The popularity of cloud computing as the primary platform for developing, deploying, and delivering software is largely driven by the promise of cost savings. Therefore, it is surprising that no empirical evidence has been collected to determine whether cost awareness permeates the development process and how it manifests in practice. Objective: Thi

Igor E. Protsenko, Alexander V. Uskov

We investigate the bistability in a small Fabry-Perot interferometer (FPI) with the optical wavelength size cavity, the nonlinear Kerr medium and only a few photons, on average, excited by the external quantum field. Analytical expressions for the stationary mean photon number, the bistability domain, the field and the photon number fluctuation spectra are o

Astha Agrawal, R. K. Sharma

The applications of additive codes mainly lie in quantum error correction and quantum computing. Due to their applications in quantum codes, additive codes have grown in importance. In addition to this, additive codes allow the implementation of a variety of dualities. The article begins by developing the properties of Additive Complementary Dual (ACD) codes

M. G. Ryskin

The QCD instanton can be observed at relatively low energies at Nica collider by studying the spin-spin correlations between the incoming proton and the produced hyperons .

Zhi Cai, Songtao Liu, Guodong Wang, Zheng Ge

DETR has set up a simple end-to-end pipeline for object detection by formulating this task as a set prediction problem, showing promising potential. Despite its notable advancements, this paper identifies two key forms of misalignment within the model: classification-regression misalignment and cross-layer target misalignment. Both issues impede DETR's conve

Anne Bernheim-Groswasser, Gefen Livne, Paola Nardinocchi, Filippo Recrosi

Active gels play an important role in biology and in inspiring biomimetic active materials, due to their ability to change shape, size and create their own morphology; the relevant mechanics behind these changes is driven by self-contraction and liquid flow. Here, we couple contraction and liquid flow within a nonlinear mechanical model of an active gel disc

Dylan Johnston

In this paper we will investigate contramodules for algebraic groups. Namely, we give contra-analogs to two 20th century results about comodules. Firstly, we show that induction of contramodules over coordinate rings of algebraic groups is exact if and only if the associated quotient variety is affine. Secondly, we give an inverse limit theorem for construct

Folkert Kuipers

We construct an explicit one-to-one correspondence between non-relativistic stochastic processes and solutions of the Schrodinger equation and between relativistic stochastic processes and solutions of the Klein-Gordon equation. The existence of this equivalence suggests that the Lorentzian path integral can be defined as an Ito integral, similar to the defi

Amrita Dey, Sudip Kumar Acharyya, Sagarmoy Bag, Dhananjoy Mandal

Let $\mathcal{P}$ be an ideal of closed subsets of a topological space $X$. Consider the ring, $C(X)_\mathcal{P}$ of real valued functions on $X$ whose closure of discontinuity set is a member of $\mathcal{P}$. We investigate the ring properties of $C(X)_\mathcal{P}$ for different choices of $\mathcal{P}$, such as the $\aleph_0$-self injectivity and regulari

Mingrui Li, William A. P. Smith, Patrik Huber

Embedding a face image to a descriptor vector using a deep CNN is a widely used technique in face recognition. Via several possible training strategies, such embeddings are supposed to capture only identity information. Information about the environment (such as background and lighting) or changeable aspects of the face (such as pose, expression, presence of

Lik-Hang Lee, Pengyuan Zhou, Chaoning Zhang, Simo Hosio

The global metaverse development is facing a "cooldown moment", while the academia and industry attention moves drastically from the Metaverse to AI Generated Content (AIGC) in 2023. Nonetheless, the current discussion rarely considers the connection between AIGCs and the Metaverse. We can imagine the Metaverse, i.e., immersive cyberspace, is the black void

Sirui Chen, Zhaowei Zhang, Yaodong Yang, Yali Du

Centralized Training with Decentralized Execution (CTDE) has been proven to be an effective paradigm in cooperative multi-agent reinforcement learning (MARL). One of the major challenges is credit assignment, which aims to credit agents by their contributions. While prior studies have shown great success, their methods typically fail to work in episodic rein

Huimin Wu, Xiaomeng Li, Yiqun Lin, Kwang-Ting Cheng

This study investigates barely-supervised medical image segmentation where only few labeled data, i.e., single-digit cases are available. We observe the key limitation of the existing state-of-the-art semi-supervised solution cross pseudo supervision is the unsatisfactory precision of foreground classes, leading to a degenerated result under barely-supervise

Paola Loreti, Daniela Sforza, Masahiro Yamamoto

We consider an initial boundary value problem in a bounded domain $\Omega$ over a time interval $(0, T)$ for a time-fractional wave equation where the order of the fractional time derivative is between $1$ and $2$ and the spatial elliptic operator has time-independent coefficients and is not necessarily symmetric. We prove that if for arbitrarily chosen subd

Géza Csima

The history of the isoptic curves goes back to the 19th century, but nowadays the topic is experiencing a renaissance, providing numerous new results and new applications. First, we define the notion of isoptic curve and outline some of the well-known results for strictly convex, closed curves. Overviewing the types of centered trochoids, we will be able to

Yijia Chen, Yi Feng, Bundit Laekhanukit, Yanlin Liu

In the parameterized $k$-clique problem, or $k$-Clique for short, we are given a graph $G$ and a parameter $k\ge 1$. The goal is to decide whether there exist $k$ vertices in $G$ that induce a complete subgraph (i.e., a $k$-clique). This problem plays a central role in the theory of parameterized intractability as one of the first W[1]-complete problems. Exi

Biao Zhang, Li-Ke Liu, Hua Pei, Shusu Shi

The Zero Degree Calorimeter (ZDC) plays a crucial role in determining centrality at the Cooling-Storage-Ring External-target Experiment (CEE) in the Heavy Ion Research Facility in Lanzhou (HIRFL). A Boosted Decision Trees (BDT) multi-classification algorithm is employed to classify the centrality of the collision events based on the raw features from ZDC suc

Lennart Bastian, Alexander Baumann, Emily Hoppe, Vincent Bürgin

Statistical shape models (SSMs) are an established way to represent the anatomy of a population with various clinically relevant applications. However, they typically require domain expertise, and labor-intensive landmark annotations to construct. We address these shortcomings by proposing an unsupervised method that leverages deep geometric features and fun

Ahmad Faraz Khan, Xinran Wang, Qi Le, Zain ul Abdeen

Existing incentive solutions for traditional Federated Learning (FL) focus on individual contributions to a single global objective, neglecting the nuances of clustered personalization with multiple cluster-level models and the non-monetary incentives such as personalized model appeal for clients. In this paper, we first propose to treat incentivization and

Kalinath Katuri, Ioannis Zografopoulos, Ha Thi Nguyen, Charalambos Konstantinou

Smart grid advancements and the increased integration of digital devices have transformed the existing power grid into a cyber-physical energy system. This reshaping of the current power system can make it vulnerable to cyberattacks, which could cause irreversible damage to the energy infrastructure resulting in the loss of power, equipment damage, etc. Cons

Linfeng Feng, Yijun Gong, Xiao-Lei Zhang

Recently, an end-to-end two-dimensional sound source localization algorithm with ad-hoc microphone arrays formulates the sound source localization problem as a classification problem. The algorithm divides the target indoor space into a set of local areas, and predicts the local area where the speaker locates. However, the local areas are encoded by one-hot

Rongxuan Mu, Yuhe Nie, Kent Cao, Ruoxin You

Virtual reality (VR) supports audiences to engage with cultural heritage proactively. We designed an easy-to-access and guided Pilgrimage To Pureland VR reconstruction of Dunhuang Mogao Grottoes to offer the general public an accessible and engaging way to explore the Dunhuang murals. We put forward an immersive VR reconstruction paradigm that can efficientl

Dani Kaufman

We give a precise definition of folded quivers and folded cluster algebras. We give many examples of including some with finite mutation structure that do not have analogues in the unfolded cases. We relate these examples to the finite mutation type quivers $X_6$ and $X_7$. We also construct a folded cluster algebra associated to triangulations of punctured

Bei Lin, You Li, Ning Gui, Zhuopeng Xu

Unsupervised graph representation learning(GRL) aims to distill diverse graph information into task-agnostic embeddings without label supervision. Due to a lack of support from labels, recent representation learning methods usually adopt self-supervised learning, and embeddings are learned by solving a handcrafted auxiliary task(so-called pretext task). Howe

Oliver Wipfli, Henry Fernandes Passagem, Christoph Fischer, Matt Grau

We report on the realization of a hemispherical optical cavity with a finesse of F = 13000 sustaining inter-cavity powers of 10 kW, which we operate in a closed-cycle cryostat vacuum system close to 4 Kelvin. This was designed and built with an integrated radio-frequency Paul trap, in order to combine optical and radio-frequency trapping. The cavity provides

Asahi Takaoka

The notion of $12$-representable graphs was introduced as a variant of a well-known class of word-representable graphs. Recently, these graphs were shown to be equivalent to the complements of simple-triangle graphs. This indicates that a $12$-representant of a graph (i.e., a word representing the graph) can be obtained in polynomial time if it exists. Howev

Bingchao Wu, Yangyuxuan Kang, Daoguang Zan, Bei Guan

Incorporating knowledge graph into recommendation is an effective way to alleviate data sparsity. Most existing knowledge-aware methods usually perform recursive embedding propagation by enumerating graph neighbors. However, the number of nodes' neighbors grows exponentially as the hop number increases, forcing the nodes to be aware of vast neighbors under t

Dachao Lin, Yuze Han, Haishan Ye, Zhihua Zhang

We study finite-sum distributed optimization problems involving a master node and $n-1$ local nodes under the popular $\delta$-similarity and $\mu$-strong convexity conditions. We propose two new algorithms, SVRS and AccSVRS, motivated by previous works. The non-accelerated SVRS method combines the techniques of gradient sliding and variance reduction and ac

Tongya Zheng, Xinchao Wang, Zunlei Feng, Jie Song

Temporal graphs exhibit dynamic interactions between nodes over continuous time, whose topologies evolve with time elapsing. The whole temporal neighborhood of nodes reveals the varying preferences of nodes. However, previous works usually generate dynamic representation with limited neighbors for simplicity, which results in both inferior performance and hi

Ruoyou Wu, Cheng Li, Juan Zou, Qiegen Liu

Deep learning-based methods have achieved encouraging performances in the field of magnetic resonance (MR) image reconstruction. Nevertheless, to properly learn a powerful and robust model, these methods generally require large quantities of data, the collection of which from multiple centers may cause ethical and data privacy violation issues. Lately, feder

Tongya Zheng, Zunlei Feng, Tianli Zhang, Yunzhi Hao

Researchers of temporal networks (e.g., social networks and transaction networks) have been interested in mining dynamic patterns of nodes from their diverse interactions. Inspired by recently powerful graph mining methods like skip-gram models and Graph Neural Networks (GNNs), existing approaches focus on generating temporal node embeddings sequentially wit

Milind Naphade, Shuo Wang, David C. Anastasiu, Zheng Tang

The AI City Challenge's seventh edition emphasizes two domains at the intersection of computer vision and artificial intelligence - retail business and Intelligent Traffic Systems (ITS) - that have considerable untapped potential. The 2023 challenge had five tracks, which drew a record-breaking number of participation requests from 508 teams across 46 countr

Jionghao Lin, Wei Tan, Ngoc Dang Nguyen, David Lang

Dialogue acts (DAs) can represent conversational actions of tutors or students that take place during tutoring dialogues. Automating the identification of DAs in tutoring dialogues is significant to the design of dialogue-based intelligent tutoring systems. Many prior studies employ machine learning models to classify DAs in tutoring dialogues and invest muc

Panagiotis Kourtesis, Evangelia-Chrysanthi Kouklari, Petros Roussos, Vasileios Mantas

Poor social skills in autism spectrum disorder (ASD) are associated with reduced independence in daily life. Current interventions for improving the social skills of individuals with ASD fail to represent the complexity of real-life social settings and situations. Virtual reality (VR) may facilitate social skills training in social environments and situation

Xidong Wang, Zhan Li, Zhen He

This brief aims at the issue of globally composite-learning-based neural fast finite-time (F-FnT) tracking control for a class of uncertain systems in strict-feedback form subject to nonlinearly periodic disturbances. First, uncertain dynamics with periodic parameters are identified by incorporating Fourier series expansion (FSE) into an intelligent estimato

Yujie Duan, Siyan Lin, Junji Jia

We study the deflection and gravitational lensing (GL) of both timelike and null signals in the equatorial plane of arbitrary stationary and axisymmetric spacetimes in the strong deflection limit. Our approach employs a perturbative method to show that both the deflection angle and the total travel time take quasi-series forms $\displaystyle \sum_{n=0}\left[

Li-Xing Lin, Jie Cao, Qun Hao

Illumination patterns of computational ghost imaging (CGI) systems suffer from reduced contrast when passing through a scattering medium, which causes the effective information in the reconstruction result to be drowned out by noise. A two-dimensional (2D) Gaussian filter performs linear smoothing operation on the whole image for image denoising. It can be c

Yibo Zhou, Dongfei Cui, Xiangming Dong, Zongkai Wu

Guiding robots can not only detect close-range obstacles like other guiding tools, but also extend its range to perceive the environment when making decisions. However, most existing works over-simplified the interaction between human agents and robots, ignoring the differences between individuals, resulting in poor experiences for different users. To solve

Cong Guo, Jiaming Tang, Weiming Hu, Jingwen Leng

Transformer-based large language models (LLMs) have achieved great success with the growing model size. LLMs' size grows by $240\times$ every two years, which outpaces the hardware progress and makes model inference increasingly costly. Model quantization is a promising approach to mitigate the widening gap between LLM size and hardware capacity. However, th

Shaoyou Ao, Yong Niu, Zhu Han, Bo Ai

In recent years, with the explosive growth of data traffic, communication base stations (BSs) need to serve more and more users. Offloading traffic from BSs has become an efficient way to reduce the burden on BSs. Device-to-Device (D2D) communications have emerged to improve spectrum utilization by reusing the frequency spectrum of the cellular frequency ban

Ayane Ito, Takefumi Kasai, Akira Terui

We demonstrate computer-assisted proofs of "Kariya's theorem," a theorem in elementary geometry, with computer algebra. In the proof of geometry theorem with computer algebra, vertices of geometric figures that are subjects for the proof are expressed as variables. The variables are classified into two classes: arbitrarily given points and the points defined

Zhipeng Sun, Hai-Qing Lin

The enigma of unconventional superconductivity in doped cuprates presents a formidable challenge in the realm of condensed matter physics. Recent findings of strong near-neighbor attractions in one-dimensional cuprate chains suggest a new avenue for investigating cuprate superconductors. Consequently, we revisited the superconductivity in the extended Hubbar

Raimund Bürger, Julio Careaga, Stefan Diehl, Romel Pineda

Sequencing batch reactors (SBRs) are devices widely used in wastewater treatment, chemical engineering, and other areas. They allow for the sedimentation and compression of solid particles of biomass simultaneously with biochemical reactions with nutrients dissolved in the liquid. The kinetics of these reactions may be given by one of the established activat

Riyasat Ohib, Bishal Thapaliya, Pratyush Gaggenapalli, Jingyu Liu

Federated learning (FL) enables the training of a model leveraging decentralized data in client sites while preserving privacy by not collecting data. However, one of the significant challenges of FL is limited computation and low communication bandwidth in resource limited edge client nodes. To address this, several solutions have been proposed in recent ti

Yueqing Xuan, Kacper Sokol, Mark Sanderson, Jeffrey Chan

Recommender systems trained on implicit feedback data rely on negative sampling to distinguish positive items from negative items for each user. Since the majority of positive interactions come from a small group of active users, negative samplers are often impacted by data imbalance, leading them to choose more informative negatives for prominent users whil

Xin Kang, Chaoqun Wang, Xuejin Chen

Semantic segmentation in complex scenes relies not only on object appearance but also on object location and the surrounding environment. Nonetheless, it is difficult to model long-range context in the format of pairwise point correlations due to the huge computational cost for large-scale point clouds. In this paper, we propose using regions as the intermed

Ce Zhang, Kailiang Wu, Zhihai He

Given an unknown dynamical system, what is the minimum number of samples needed for effective learning of its governing laws and accurate prediction of its future evolution behavior, and how to select these critical samples? In this work, we propose to explore this problem based on a design approach. Starting from a small initial set of samples, we adaptivel

Mitsunori Ogawa, Yui Tomo

In logistic regression modeling, Firth's modified estimator is widely used to address the issue of data separation, which results in the nonexistence of the maximum likelihood estimate. Firth's modified estimator can be formulated as a penalized maximum likelihood estimator in which Jeffreys' prior is adopted as the penalty term. Despite its widespread use i

Hsin-Ping Huang, Yu-Chuan Su, Ming-Hsuan Yang

We tackle the long video generation problem, i.e.~generating videos beyond the output length of video generation models. Due to the computation resource constraints, video generation models can only generate video clips that are relatively short compared with the length of real videos. Existing works apply a sliding window approach to generate long videos at

Md Athikul Islam, Rizbanul Hasan, Nasir U. Eisty

Context: Agile development methodologies in the software industry have increased significantly over the past decade. Although one of the main aspects of agile software development (ASD) is less documentation, there have always been conflicting opinions about what to document in ASD. Objective: This study aims to systematically identify what to document in AS

Alexey Chernyshev, Vladimir Saleev

In the article, we study the associated production of prompt $J/\psi$ mesons and $Z(W)$ bosons in the improved color evaporation model using the high-energy factorization as it is formulated in the parton Reggeization approach. The last one is based on the modified Kimber-Martin-Ryskin-Watt model for unintegrated parton distribution functions and the effecti