October 2023 arXiv papers — page 145

Yucheng Jiang, Han Peng, Yan Song, Jie Yu

The recursive intra-frame block partitioning decision process, a crucial component of the next-generation video coding standards, exerts significant influence over the encoding time. In this paper, we propose an encoder-decoder neural network (NN) to accelerate this process. Specifically, a CNN is utilized to compress the pixel data of the largest coding uni

Kazuyuki Sekizawa, Kenta Yoshimura

In this contribution, current status and future prospects of our ongoing project is summarized. In the inner crust of neutron stars, a variety of crystalline structures may emerge, as a result of competition of Coulomb and nuclear interactions, which are immersed in a sea of superfluid neutrons. The best quantum mechanical approach to study properties of dri

Anton Arnold, Gayrat Toshpulatov

This paper is concerned with a modified entropy method to establish the large-time convergence towards the (unique) steady state, for kinetic Fokker-Planck equations with non-quadratic confinement potentials in whole space. We extend previous approaches by analyzing Lyapunov functionals with non-constant weight matrices in the dissipation functional (a gener

T. Santhosh, P. C. Rout, S. Santra, A. Shrivastava

The collective enhancement of nuclear level density and its fade out with excitation energy in deformed $^{171}$Yb nucleus has been inferred through an exclusive measurement of neutron spectra.The statistical model analysis of neutron spectra demonstrated a large collective enhancement factor of 40$\pm$3 for the first time, which corroborates with the recent

Aditya R. Vaidya, Javier Turek, Alexander G. Huth

Language models that are trained on the next-word prediction task have been shown to accurately model human behavior in word prediction and reading speed. In contrast with these findings, we present a scenario in which the performance of humans and LMs diverges. We collected a dataset of human next-word predictions for five stimuli that are formed by repeati

Peng Peng, Tianlong Fan, Xiao-Long Ren, Linyuan Lü

Edges, binding together nodes within networks, have the potential to induce dramatic transitions when specific collective failure behaviors emerge. These changes, initially unfolding covertly and then erupting abruptly, pose substantial, unforeseeable threats to networked systems, and are termed explosive vulnerability. Thus, identifying influential edges ca

Haijie Ren, Weiqiang Wang, Wentao Tang, Rui Zhang

Topological defects play a key role in the structures and dynamics of liquid crystals (LCs) and other ordered systems. There is a recent interest in studying defects in different biological systems with distinct textures. However, a robust method to directly recognize defects and extract their structural features from various traditional and nontraditional n

Callum W. Duncan

We study the single-particle properties of two-dimensional quasicrystals where the underlying geometry of the tight-binding lattice is crystalline but the on-site potential is quasicrystalline. We will focus on the 2D generalised Aubry-Andr\'e model which has a varying form to its quasiperiodic potential, through a deformation parameter and varied irrational

Daejin Jo, Daniel Wontae Nam, Gunsoo Han, Kyoung-Woon On

A common practice in knowledge-grounded dialogue generation is to explicitly utilize intermediate steps (e.g., web-search, memory retrieval) with modular approaches. However, data for such steps are often inaccessible compared to those of dialogue responses as they are unobservable in an ordinary dialogue. To fill in the absence of these data, we develop a s

Diego Vallarino

Using as a central instrument a new database, resulting from a compilation of historical administrative records, which covers the period 1974-2010, we can have new evidence on how industrial companies used tax benefits, and claim that these are decisive for the investment decision of the Uruguayan industrial companies during that period. The aforementioned f

Zhenying Fang, Jun Yu, Richang Hong

Temporal action detection aims to recognize the action category and determine each action instance's starting and ending time in untrimmed videos. The mixed methods have achieved remarkable performance by seamlessly merging anchor-based and anchor-free approaches. Nonetheless, there are still two crucial issues within the mixed framework: (1) Brute-force mer

Emilie Chouzenoux, Jean-Christophe Pesquet, Fernando Roldán

In this article, we study the convergence of algorithms for solving monotone inclusions in the presence of adjoint mismatch. The adjoint mismatch arises when the adjoint of a linear operator is replaced by an approximation, due to computational or physical issues. This occurs in inverse problems, particularly in computed tomography. In real Hilbert spaces, m

Bohao Lu, Zhiqing Wei, Lin Wang, Ruiyun Zhang

Integrated Sensing and Communication(ISAC) has become a key technology for the 5th generation (5G) and 6th generation (6G) wireless communications due to its high spectrum utilization efficiency. Utilizing infrastructure such as 5G Base Stations (BS) to realize environmental imaging and reconstruction is important for promoting the construction of smart citi

S. Realini, R. Hesper, J. Barkhof, A. Baryshev

We present the results of a study on the feasibility of upgrading the existing ALMA Band 9 receivers (602-720 GHz). In the current configuration, each receiver is a dual channel heterodyne system capable of detecting orthogonally polarized signals through the use of a wire grid and a compact arrangement of mirrors. The main goals of the study are the upgrade

Simon Steshin

Finding new drugs is getting harder and harder. One of the hopes of drug discovery is to use machine learning models to predict molecular properties. That is why models for molecular property prediction are being developed and tested on benchmarks such as MoleculeNet. However, existing benchmarks are unrealistic and are too different from applying the models

Margot Boughelilba, Anita Reimer

Although Fanaroff-Riley (FR) type 0 radio galaxies are known to be the most numerous jet population in the local Universe, they are much less explored than the well-established class of FR I and FR II galaxies due to their intrinsic weakness. Observationally, their nuclear radio, optical and X-ray properties are comparable to the nuclear environment of FR Is

Kaiming Huang, Mathias Payer, Zhiyun Qian, Jack Sampson

Heap memory errors remain a major source of software vulnerabilities. Existing memory safety defenses aim at protecting all objects, resulting in high performance cost and incomplete protection. Instead, we propose an approach that accurately identifies objects that are inexpensive to protect, and design a method to protect such objects comprehensively from

Kai Zhao, Qiyu Kang, Yang Song, Rui She

Graph neural networks (GNNs) are vulnerable to adversarial perturbations, including those that affect both node features and graph topology. This paper investigates GNNs derived from diverse neural flows, concentrating on their connection to various stability notions such as BIBO stability, Lyapunov stability, structural stability, and conservative stability

Lakhan Bainsla, Bing Zhao, Anamul Md. Hoque, Lars Sjöström

Topological quantum materials, with novel spin textures and broken crystal symmetries are suitable candidates for spintronic memory technologies. Their unique electronic properties, such as protected surface states and exotic quasiparticles, can provide an out-of-plane spin polarized current needed for external field free magnetization switching of magnets w

Takumi Sakabe, Suguru Shimomura, Yusuke Ogura, Ken-ichi Okubo

This paper proposes a space-division multiplexed spatial-photonic Ising machine (SDM-SPIM) that physically calculates the weighted sum of the Ising Hamiltonians for individual components in a multi-component model. Space-division multiplexing enables tuning a set of weight coefficients as an optical parameter and obtaining the desired Ising Hamiltonian at a

Maja Schneider, Marco Körner

With leaps in machine learning techniques and their applicationon Earth observation challenges has unlocked unprecedented performance across the domain. While the further development of these methods was previously limited by the availability and volume of sensor data and computing resources, the lack of adequate reference data is now constituting new bottle

Pallabi Manna, Santanu Mandal, Manideepa Saha

In this communication, the co-maximal subgroup graph $\Gamma(G)$ of a finite group $G$ is examined when $G$ is a finite nilpotent group, finite abelian group, dihedral group $D_n$, dicyclic group $Q_{2^n}$, and $p$-group. We derive the necessary and sufficient conditions for $\Gamma(G)$ to be a cluster graph, triangle-free graph, claw-free graph, cograph, ch

Stefano De Paoli

This draft paper presents a workflow for creating User Personas with Large Language Models, using the results of a Thematic Analysis of qualitative interviews. The proposed workflow uses improved prompting and a larger pool of Themes, compared to previous work conducted by the author for the same task. This is possible due to the capabilities of a recently r

Joosung Lee, Minsik Oh, Donghun Lee

The use of persona-grounded retrieval-based chatbots is crucial for personalized conversations, but there are several challenges that need to be addressed. 1) In general, collecting persona-grounded corpus is very expensive. 2) The chatbot system does not always respond in consideration of persona at real applications. To address these challenges, we propose

Huangjie Zheng, Zhendong Wang, Jianbo Yuan, Guanghan Ning

Diffusion models excel at generating photo-realistic images but come with significant computational costs in both training and sampling. While various techniques address these computational challenges, a less-explored issue is designing an efficient and adaptable network backbone for iterative refinement. Current options like U-Net and Vision Transformer oft

M. Martini, M. Ericson, G. Chanfray

We discuss the electron and muon neutrino and antineutrino double differential cross sections on carbon in the quasielastic as well as in the multinucleon and one pion production channels. By projecting them in the transferred momentum - transferred energy plane and in the neutrino energy - lepton scattering angle plane, as well as by performing simple consi

Zeming Wei, Yifei Wang, Ang Li, Yichuan Mo

Large Language Models (LLMs) have shown remarkable success in various tasks, yet their safety and the risk of generating harmful content remain pressing concerns. In this paper, we delve into the potential of In-Context Learning (ICL) to modulate the alignment of LLMs. Specifically, we propose the In-Context Attack (ICA) which employs harmful demonstrations

Ghanta Sai Krishna, Kundrapu Supriya, Sabur Baidya

The existence of variable factors within the environment can cause a decline in camera localization accuracy, as it violates the fundamental assumption of a static environment in Simultaneous Localization and Mapping (SLAM) algorithms. Recent semantic SLAM systems towards dynamic environments either rely solely on 2D semantic information, or solely on geomet

Nikolaos Stathoulopoulos, Emanuele Pagliari, Luca Davoli, George Nikolakopoulos

This paper presents a framework addressing the challenge of global localization in autonomous mobile robotics by integrating LiDAR-based descriptors and Wi-Fi fingerprinting in a pre-mapped environment. This is motivated by the increasing demand for reliable localization in complex scenarios, such as urban areas or underground mines, requiring robust systems

Siting Li, Chenzhuang Du, Yue Zhao, Yu Huang

With the growing success of multi-modal learning, research on the robustness of multi-modal models, especially when facing situations with missing modalities, is receiving increased attention. Nevertheless, previous studies in this domain exhibit certain limitations, as they often lack theoretical insights or their methodologies are tied to specific network

Jinghui Piao, Zhiqing Wei, Xin Yuan, Xiaoyu Yang

As the uplink sensing has the advantage of easy implementation, it attracts great attention in integrated sensing and communication (ISAC) system. This paper presents an uplink ISAC system based on multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) technology. The mutual information (MI) is introduced as a unified metric to evalu

Belle II Collaboration, I. Adachi, L. Aggarwal, H. Ahmed

We report measurements of the branching fractions and direct $\it{CP}$ asymmetries of the decays $B^0 \to K^+ \pi^-$, $B^+ \to K^+ \pi^0$, $B^+ \to K^0 \pi^+$, and $B^0 \to K^0 \pi^0$, and use these for testing the standard model through an isospin-based sum rule. In addition, we measure the branching fraction and direct $\it{CP}$ asymmetry of the decay $B^+

Minwook Kim, Juseong Kim, Ki Beom Kim, Giltae Song

Tabular data is one of the most widely used data modalities, encompassing numerous datasets with substantial amounts of unlabeled data. Despite this prevalence, there is a notable lack of simple and versatile methods for utilizing unlabeled data in the tabular domain, where both gradient-boosting decision trees and neural networks are employed. In this conte

Takeshi Koshizuka, Masahiro Fujisawa, Yusuke Tanaka, Issei Sato

In this paper, we explores the expressivity and trainability of the Fourier Neural Operator (FNO). We establish a mean-field theory for the FNO, analyzing the behavior of the random FNO from an edge of chaos perspective. Our investigation into the expressivity of a random FNO involves examining the ordered-chaos phase transition of the network based on the w

Fei Shi, Yu Ning, Qi Zhao, Xiande Zhang

Do $N$-partite $k$-uniform states always exist when $k\leq \lfloor\frac{N}{2}\rfloor-1$? In this work, we provide new upper bounds on the parameter $k$ for the existence of $k$-uniform states in $(\mathbb{C}^{d})^{\otimes N}$ when $d=3,4,5$, which extend Rains' bound in 1999 and improve Scott's bound in 2004. Since a $k$-uniform state in $(\mathbb{C}^{d})^{\

Wei Qiang Chen, Majid Sedighi, Florent Curvalle, Andrey P Jivkov

Montmorillonite-based barriers are key elements of the engineered barrier systems (EBS) in geological disposal facilities (GDF). Their performance at temperatures above 100 {\deg}C is not sufficiently understood to assess the possibility of raising the temperature limits in GDF designs that could reduce construction costs and CO2 footprint. The present work

Arthur Adjedj, Meven Lennon-Bertrand, Kenji Maillard, Pierre-Marie Pédrot

We present an extensive mechanization of the meta-theory of Martin-L\"of Type Theory (MLTT) in the Coq proof assistant. Our development builds on pre-existing work in Agda to show not only the decidability of conversion, but also the decidability of type checking, using an approach guided by bidirectional type checking. From our proof of decidability, we obt

Hyeondo Oh, Daniel K. Park

Anomaly detection is a critical problem in data analysis and pattern recognition, finding applications in various domains. We introduce quantum support vector data description (QSVDD), an unsupervised learning algorithm designed for anomaly detection. QSVDD utilizes a shallow-depth quantum circuit to learn a minimum-volume hypersphere that tightly encloses n

Di Wu, Wasi Uddin Ahmad, Kai-Wei Chang

Keyphrase Generation (KPG) is a longstanding task in NLP with widespread applications. The advent of sequence-to-sequence (seq2seq) pre-trained language models (PLMs) has ushered in a transformative era for KPG, yielding promising performance improvements. However, many design decisions remain unexplored and are often made arbitrarily. This paper undertakes

R. Adam, M. Ricci, D. Eckert, P. Ade

Three galaxy clusters selected from the XXL X-ray survey at high redshift and low mass ($z\sim1$ and $M_{500} \sim 1-2 \times 10^{14}$ M$_{\odot}$) were observed with NIKA2 to image their Sunyaev-Zel'dovich effect (SZ) signal. They all present an SZ morphology, together with the comparison with X-ray and optical data, that indicates dynamical activity relate

Lukas Struppek, Martin B. Hentschel, Clifton Poth, Dominik Hintersdorf

Backdoor attacks pose a serious security threat for training neural networks as they surreptitiously introduce hidden functionalities into a model. Such backdoors remain silent during inference on clean inputs, evading detection due to inconspicuous behavior. However, once a specific trigger pattern appears in the input data, the backdoor activates, causing

Meifan Zhang, Dihang Deng, Lihua Yin

Private synthetic data sharing is preferred as it keeps the distribution and nuances of original data compared to summary statistics. The state-of-the-art methods adopt a select-measure-generate paradigm, but measuring large domain marginals still results in much error and allocating privacy budget iteratively is still difficult. To address these issues, our

Munawar Ali, Baoqun Yin, Hazrat Bilal, Aakash Kumar

Several deep learning algorithms have shown amazing performance for existing object detection tasks, but recognizing darker objects is the largest challenge. Moreover, those techniques struggled to detect or had a slow recognition rate, resulting in significant performance losses. As a result, an improved and accurate detection approach is required to addres

Sung Moon Ko, Sumin Lee, Dae-Woong Jeong, Woohyung Lim

Transfer learning is a crucial technique for handling a small amount of data that is potentially related to other abundant data. However, most of the existing methods are focused on classification tasks using images and language datasets. Therefore, in order to expand the transfer learning scheme to regression tasks, we propose a novel transfer technique bas

Zekang Zhang, Guangyu Gao, Jianbo Jiao, Chi Harold Liu

Class incremental semantic segmentation aims to strike a balance between the model's stability and plasticity by maintaining old knowledge while adapting to new concepts. However, most state-of-the-art methods use the freeze strategy for stability, which compromises the model's plasticity.In contrast, releasing parameter training for plasticity could lead to

Bowen Gao, Bo Qiang, Haichuan Tan, Minsi Ren

Virtual screening, which identifies potential drugs from vast compound databases to bind with a particular protein pocket, is a critical step in AI-assisted drug discovery. Traditional docking methods are highly time-consuming, and can only work with a restricted search library in real-life applications. Recent supervised learning approaches using scoring fu

Yujie Qin, Mustafa A. Kishk, Mohamed-Slim Alouini

In this paper, we analyze the peak age of information (PAoI) in UAV-assisted internet of thing (IoT) networks, in which the locations of IoT devices are modeled by a Mat\'{e}rn cluster process (MCP) and UAVs are deployed at the cluster centers to collect the status updates from the devices. Specifically, we consider that IoT devices can either monitor the sa

Qian Li, Cheng Ji, Shu Guo, Zhaoji Liang

Multi-Modal Entity Alignment (MMEA) is a critical task that aims to identify equivalent entity pairs across multi-modal knowledge graphs (MMKGs). However, this task faces challenges due to the presence of different types of information, including neighboring entities, multi-modal attributes, and entity types. Directly incorporating the above information (e.g

Soonhyeon Choi, Jung-Woo Choi

Unsupervised anomalous sound detection (ASD) aims to identify anomalous sounds by learning the features of normal operational sounds and sensing their deviations. Recent approaches have focused on the self-supervised task utilizing the classification of normal data, and advanced models have shown that securing representation space for anomalous data is impor

Nefton Pali, Bruno Salvy

This article is the continuation of the first named author work "On maximal totally real embeddings". For real analytic compact manifolds equipped with a covariant derivative operator acting on the real analytic sections of its tangent bundle, a construction of canonical maximal totally real embeddings is known from previous works by Guillemin-Stenzel, Lempe

Yifan Song, Peiyi Wang, Weimin Xiong, Dawei Zhu

Continual learning (CL) aims to constantly learn new knowledge over time while avoiding catastrophic forgetting on old tasks. We focus on continual text classification under the class-incremental setting. Recent CL studies have identified the severe performance decrease on analogous classes as a key factor for catastrophic forgetting. In this paper, through

Kimon Kieslich, Nicholas Diakopoulos, Natali Helberger

The tremendous rise of generative AI has reached every part of society - including the news environment. There are many concerns about the individual and societal impact of the increasing use of generative AI, including issues such as disinformation and misinformation, discrimination, and the promotion of social tensions. However, research on anticipating th

Davide Polimeni, Susanna Terracini

We deal, for the classical $N$-body problem, with the existence of action minimizing half entire expansive solutions with prescribed asymptotic direction and initial configuration of the bodies. We tackle the cases of hyperbolic, hyperbolic-parabolic and parabolic arcs in a unitary manner. Our approach is based on the minimization of a renormalized Lagrangia

O. R. Rubinas, V. V. Soshenko, I. S. Cojocaru, S. V. Bolshedvorskii

The nitrogen-vacancy center in diamond attracts a lot of attention in sensing applications, mainly for temperature, magnetic field, and rotation measurements. Nuclear spins of carbon-13 surrounding the nitrogen-vacancy center can be used as a memory or sensing element. In the current work, a diamond plate with a relatively large concentration of carbon-13 wa

Natarajan Meghanathan

We perform factor analysis on the raw data of the four major neighborhood and shortest paths-based centrality metrics (Degree, Eigenvector, Betweeenness and Closeness) and propose a novel quantitative measure called the Core-Intermediate-Peripheral (CIP) Index to capture the extent with which a node could play the role of a core node (nodes at the center of

Zijun Gao

False discovery rate (FDR) is a commonly used criterion in multiple testing and the Benjamini-Hochberg (BH) procedure is arguably the most popular approach with FDR guarantee. To improve power, the adaptive BH procedure has been proposed by incorporating various null proportion estimators, among which Storey's estimator has gained substantial popularity. The

Aiwei Liu, Leyi Pan, Xuming Hu, Shiao Meng

Watermark algorithms for large language models (LLMs) have achieved extremely high accuracy in detecting text generated by LLMs. Such algorithms typically involve adding extra watermark logits to the LLM's logits at each generation step. However, prior algorithms face a trade-off between attack robustness and security robustness. This is because the watermar

Siyao Liu, Yong Wang

In [5], [6] and [8], the authors gave some modular forms over $\Gamma^0(2)$. In this note, we proceed with the study of cancellation formulas relating to the modular forms.

Ethan Y. H. Li, Luyi Li, Ping Li

Let $\mathcal{S}$ be a fixed family of graphs on vertex set $V$ and $\mathcal{G}$ be a collection of elements in $\mathcal{S}$. We investigated the transversal problem of finding the maximum value of $|\mathcal{G}|$ when $\mathcal{G}$ contains no rainbow elements in $\mathcal{S}$. Specifically, we determine the exact values when $\mathcal{S}$ is a family of

Joshua Méndez Harper, Josef Dufek, Larry Hartman

Large wildfires are becoming more frequent at temperate latitudes. Often, fires generate massive convective columns that can carry solid particles (ash) into the stratosphere. Like their meteorological counterparts, pyrocumulonimbus (pyroCb) clouds can generate intense lightning storms. Recently, pyrogenic lightning has garnered renewed interest for its capa

Xue Zhang, Tao Li, Xu Wang, Hui Dong

The low energy of the isomeric state of the radionuclide thorium-229 (229Th) makes it highly promising for applications in fundamental physics, precision metrology, and quantum technologies. However, directly accessing the isomeric state from its ground state remains a challenge. We propose here a tabletop approach utilizing the scanning tunneling microscope

Arafat Islam, Md. Imtiaz Habib

For the detection of fire-like targets in indoor, outdoor and forest fire images, as well as fire detection under different natural lights, an improved YOLOv5 fire detection deep learning algorithm is proposed. The YOLOv5 detection model expands the feature extraction network from three dimensions, which enhances feature propagation of fire small targets ide

Niyazi Ulas Dinc, Christophe Moser, Demetri Psaltis

We demonstrate the fabrication of volume holograms using 2-photon polymerization with dynamic control of light exposure. We refer to our method as (3+1)D printing. Volume holograms that are recorded by interfering reference and signal beams have a diffraction efficiency relation that is inversely proportional with the square of the number of superimposed hol

Giacomo Alliata, Sarah Kenderdine, Lily Hibberd, Ingrid Mason

This article proposes an innovative framework to explore large audiovisual archives using Immersive Environments to place users inside a dataset and create an embodied experience. It starts by outlining the need for such a novel interface to meet the needs of archival scholars and the GLAM sector, and discusses issues in the current modes of access, mostly r

Wen Sun

We construct a compound Poisson process conditioned on its random summation that represents the sizes of the connected components in the sparse Erd\H{o}s-R\'enyi random graph $G(n,c/n)$. This new representation depicts a connection between the phase transition in the sparse random graph and the condensation transition in the zero-range model. Under this fram

Jingyang Zhang, Shiwei Li, Yuanxun Lu, Tian Fang

We introduce JointNet, a novel neural network architecture for modeling the joint distribution of images and an additional dense modality (e.g., depth maps). JointNet is extended from a pre-trained text-to-image diffusion model, where a copy of the original network is created for the new dense modality branch and is densely connected with the RGB branch. The

Francesco Giacosa, Vanamali Shastry

We present the so-called Sill distribution, both in the nonrelativistic and relativistic cases, as a natural and simple way to include the effect of threshold(s) on the energy line shapes of resonances. The Sill is correctly normalized (even for broad states), is continuous at threshold(s), does not require any modification to the `mass part', is easily exte

Ikuya Kaneko

We prove strong estimates for averages of shifted convolution sums consisting of quadratic twists of $\mathrm{GL}_{2}$ $L$-functions. The key input involves the circle method together with standard tools such as Vorono\u{\i}, quadratic reciprocity, amplification, and divisor switching.

Bojue Wang, Chunmei Ma, Bin Liu, Nianbo Liu

Structured network pruning excels non-structured methods because they can take advantage of the thriving developed parallel computing techniques. In this paper, we propose a new structured pruning method. Firstly, to create more structured redundancy, we present a data-driven loss function term calculated from the correlation coefficient matrix of different

Yuhui Chen, Haoran Li, Dongbin Zhao

Due to its training stability and strong expression, the diffusion model has attracted considerable attention in offline reinforcement learning. However, several challenges have also come with it: 1) The demand for a large number of diffusion steps makes the diffusion-model-based methods time inefficient and limits their applications in real-time control; 2)

Yubo Zhang, Yanfang Liu, Xinxin Fan, Yunfeng Lu

Code search aims to retrieve the code snippet that highly matches the given query described in natural language. Recently, many code pre-training approaches have demonstrated impressive performance on code search. However, existing code search methods still suffer from two performance constraints: inadequate semantic representation and the semantic gap betwe

Tongxin Yin, Xuwei Tan, Xueru Zhang, Mohammad Mahdi Khalili

Federated learning (FL) is a distributed learning paradigm that allows multiple decentralized clients to collaboratively learn a common model without sharing local data. Although local data is not exposed directly, privacy concerns nonetheless exist as clients' sensitive information can be inferred from intermediate computations. Moreover, such information l

Alexander Zimmermann

For a Dedekind domain $R$ with field of fractions $K$ a classical $R$-order in a semisimple $K$-algebra $A$ is an $R$-projective $R$-subalgebra $\Lambda$ of $A$ such that $K\Lambda=A$. We study differential graded $K$-algebras which are semisimple as $K$-algebras and define differential graded $R$-orders as a differential graded $R$-subalgebras, which are in

Siyuan Jiang, Yan Ding, Yuling Wang, Lei Xu

Ultrasound is a vital diagnostic technique in health screening, with the advantages of non-invasive, cost-effective, and radiation free, and therefore is widely applied in the diagnosis of nodules. However, it relies heavily on the expertise and clinical experience of the sonographer. In ultrasound images, a single nodule might present heterogeneous appearan

Srivatsan Sridhar, Dionysis Zindros, David Tse

The security of blockchain protocols is a combination of two properties: safety and liveness. It is well known that no blockchain protocol can provide both to sleepy (intermittently online) clients under adversarial majority. However, safety is more critical in that a single safety violation can cause users to lose money. At the same time, liveness must not

Daichi Haraguchi, Seiichi Uchida

When we compare fonts, we often pay attention to styles of local parts, such as serifs and curvatures. This paper proposes an attention mechanism to find important local parts. The local parts with larger attention are then considered important. The proposed mechanism can be trained in a quasi-self-supervised manner that requires no manual annotation other t

Jingzhi Hu, Zhe Chen, Tianyue Zheng, Robert Schober

Positioning is an essential service for various applications and is expected to be integrated with existing communication infrastructures in 5G and 6G. Though current Wi-Fi and cellular base stations (BSs) can be used to support this integration, the resulting precision is unsatisfactory due to the lack of precise control of the wireless signals. Recently, B

Dahlia Malkhi, Chrysoula Stathakopoulou, Maofan Yin

This paper presents a partially synchronous BFT consensus protocol powered by BBCA, a lightly modified Byzantine Consistent Broadcast (BCB) primitive. BBCA provides a Complete-Adopt semantic through an added probing interface to allow either aborting the broadcast by correct nodes or exclusively, adopting the message consistently in case of a potential deliv

Mandira Das, Subhradip Ghosh

Surface Engineering in two-dimensional(2D) materials has turned out to be an useful technique to improve their functional properties. By designing Janus compounds MM$^{\prime}$C in MXene family of compounds M$_{2}$C where the two surfaces are constituted by two different transition metal M and M$^{\prime}$, we have explored their potentials as electrodes in

Davin Choo, Joy Qiping Yang, Arnab Bhattacharyya, Clément L. Canonne

We establish finite-sample guarantees for efficient proper learning of bounded-degree polytrees, a rich class of high-dimensional probability distributions and a subclass of Bayesian networks, a widely-studied type of graphical model. Recently, Bhattacharyya et al. (2021) obtained finite-sample guarantees for recovering tree-structured Bayesian networks, i.e

Buzhen Huang, Jingyi Ju, Yangang Wang

This is a technical report for the GigaCrowd challenge. Reconstructing 3D crowds from monocular images is a challenging problem due to mutual occlusions, server depth ambiguity, and complex spatial distribution. Since no large-scale 3D crowd dataset can be used to train a robust model, the current multi-person mesh recovery methods can hardly achieve satisfa

Muhammad Tausif

Students' migration from public to private schools, due to lack of school performance of public schools, is one of the major issues faced by the Government of Punjab to provide compulsory and quality education at low cost. Due to complex adaptive nature of educational system, interdependencies with society, constant feedback loops conventional linear regress

Martin Miglioli

This article presents a new proof of a theorem concerning bounds of the spectrum of the product of unitary operators and a generalization for differentiable curves of this theorem. The proofs involve metric geometric arguments in the group of unitary operators and the sphere where these operators act.

Hyebin Song, Stephen Berg

Markov chain Monte Carlo (MCMC) is a commonly used method for approximating expectations with respect to probability distributions. Uncertainty assessment for MCMC estimators is essential in practical applications. Moreover, for multivariate functions of a Markov chain, it is important to estimate not only the auto-correlation for each component but also to

Aditya Vadduri, Anagh Benjwal, Abhishek Pai, Elkan Quadros

Recent years have seen tremendous advancements in the area of autonomous payload delivery via unmanned aerial vehicles, or drones. However, most of these works involve delivering the payload at a predetermined location using its GPS coordinates. By relying on GPS coordinates for navigation, the precision of payload delivery is restricted to the accuracy of t

Lu Jiang, Ross. K. W. Marceau, Thomas Dorin

Recycled aluminum alloys are pivotal for sustainable manufacturing, offering strength, durability, and environmental advantages. However, the presence of iron (Fe) impurities poses a major challenge, undermining their properties and recyclability. Conventional manufacturing processes result in coarse Fe-rich intermetallic compounds that limit the tolerance o

Yusheng Huang, Zhouhan Lin

Multimodal information extraction is attracting research attention nowadays, which requires aggregating representations from different modalities. In this paper, we present the Intra- and Inter-Sample Relationship Modeling (I2SRM) method for this task, which contains two modules. Firstly, the intra-sample relationship modeling module operates on a single sam

Jiabin Zuo, Juliana Honda Lopes, Vicentiu D. Rădulescu

We consider a Kirchhoff-type diffusion problem driven by the magnetic fractional Laplace operator. The main result in this paper establishes that infinite time blow-up cannot occur for the problem. The proof is based on the potential well method, in relationship with energy and Nehari functionals.

Dinh Si Tiep, Nhan Nguyen

In this paper, we give a simple proof that the density at infinity of fibers of a definable function is locally Lipschitz outside the set of asymptotic critical values.

Neha Gupta, Suhith K N

It has been studied by Curto et al. (SIAM J. on App. Alg. and Geom., 1(1) : 222 $\unicode{x2013}$ 238, 2017) that a neural code that has an open convex realization does not have any local obstruction relative to the neural code. Further, a neural code $ \mathcal{C} $ has no local obstructions if and only if it contains the set of mandatory codewords, $ \math

Wen Tao Mo, Jonathan H. Chan

Freezing of gait is a Parkinson's Disease symptom that episodically inflicts a patient with the inability to step or turn while walking. While medical experts have discovered various triggers and alleviating actions for freezing of gait, the underlying causes and prediction models are still being explored today. Current freezing of gait prediction models tha

Ruiting Mao, Jeong Eun Lee, Ollie Burke, Alvin J. K. Chua

Approximations are commonly employed in realistic applications of scientific Bayesian inference, often due to convenience if not necessity. In the field of gravitational-wave (GW) data analysis, fast-to-evaluate but approximate waveform models of astrophysical GW signals are sometimes used in lieu of more accurate models to infer properties of a true GW sign

Laura Plein, Wendkûuni C. Ouédraogo, Jacques Klein, Tegawendé F. Bissyandé

Software testing is a core discipline in software engineering where a large array of research results has been produced, notably in the area of automatic test generation. Because existing approaches produce test cases that either can be qualified as simple (e.g. unit tests) or that require precise specifications, most testing procedures still rely on test ca

Jungang Chen, Eduardo Gildin, John E. Killough

A physics-informed convolutional neural network is proposed to simulate two phase flow in porous media with time-varying well controls. While most of PICNNs in existing literatures worked on parameter-to-state mapping, our proposed network parameterizes the solution with time-varying controls to establish a control-to-state regression. Firstly, finite volume

Yubiao Yue, Zhenzhang Li

Due to the lack of efficient mpox diagnostic technology, mpox cases continue to increase. Recently, the great potential of deep learning models in detecting mpox and non-mpox has been proven. However, existing models learn image representations via image classification, which results in they may be easily susceptible to interference from real-world noise, re

Xian-Hui Ge, Shuta Ishigaki, Sang-Jin Sin, Taewon Yuk

We consider the D3-D7 model and use the spectral function of a probe fermion on D7 to analyze the first order phase transition from the black-hole embedding phase to another black-hole embedding phase in the presence of the finite density and temperature. From the fermionic spectral functions, we study the temperature dependence of the decay rate, and we obs

John Joseph M. Carrasco, Nicolas H. Pavao

From the perspective of effective field theory (EFT), Wilson coefficients of the low energy theory are determined by integrating out modes of the full ultraviolet (UV) theory. The spectrum can be in principle resummed if one has access to all available infrared (IR) coefficients at low energies. In this work we show that there exists a general class of consi

Ismaïla Baldé, Debashis Ghosh

Radiomics is an emerging area of medical imaging data analysis particularly for cancer. It involves the conversion of digital medical images into mineable ultra-high dimensional data. Machine learning algorithms are widely used in radiomics data analysis to develop powerful decision support model to improve precision in diagnosis, assessment of prognosis and

Chen-Bo Jia, Rong-Hua Wang, Michael X. X. Zhong

In this note, we apply the power-partible reduction to show the following arithmetic properties of large Schr\"oder polynomials $S_n(z)$ and little Schr\"oder polynomials $s_n(z)$: for any odd prime $p$, nonnegative integer $r\in\mathbb{N}$, $\varepsilon\in\{-1,1\}$ and $z\in\mathbb{Z}$ with $\gcd(p,z(z+1))=1$, we have \[ \sum_{k=0}^{p-1}(2k+1)^{2r+1}\vareps

Fei Shen, Hu Ye, Jun Zhang, Cong Wang

Recent work has showcased the significant potential of diffusion models in pose-guided person image synthesis. However, owing to the inconsistency in pose between the source and target images, synthesizing an image with a distinct pose, relying exclusively on the source image and target pose information, remains a formidable challenge. This paper presents Pr