April 2023 arXiv papers — page 66

Han Yao Choong, Suryansh Kumar, Luc Van Gool

This paper proposes a quantum computing-based algorithm to solve the single image super-resolution (SISR) problem. One of the well-known classical approaches for SISR relies on the well-established patch-wise sparse modeling of the problem. Yet, this field's current state of affairs is that deep neural networks (DNNs) have demonstrated far superior results t

Diletta Abbonato, Stefano Bianchini, Floriana Gargiulo, Tommaso Venturini

Artificial intelligence (AI) has emerged as one of the most promising technologies to support COVID-19 research, with interdisciplinary collaborations between medical professionals and AI specialists being actively encouraged since the early stages of the pandemic. Yet, our analysis of more than 10,000 papers at the intersection of COVID-19 and AI suggest th

Joonhyun La, Jean-Michel Roquejoffre, Lenya Ryzhik

We obtain uniform in time $L^\infty$-bounds for the solutions to a class of thermo-diffusive systems. The nonlinearity is assumed to be at most sub-exponentially growing at infinity and have a linear behavior near zero.

Koji Azuma

A quantum internet holds promise for achieving distributed quantum sensing and large-scale quantum computer networks, as well as quantum communication among arbitrary clients all over the globe. The main building block is efficient distribution of entanglement, entangled bits (ebits), between clients with fixed error, irrespective of their distance. In pract

Silvio C. Patricio, Trifon I. Missov

The Makeham term is a crucial element in mortality modeling, representing a constant additive hazard that addresses background mortality factors unrelated to aging. Widely used in mortality analysis, this term enables the capture of risks not linked to age-related decline. This paper aims to explore the relationship between Makeham mortality models and compe

David Criens

We establish a convergence theorem for Crandall-Lions viscosity solutions to path-dependent Hamilton-Jacobi-Bellman PDEs. Our proof is based on a novel convergence theorem for dynamic sublinear expectations and the stochastic representation of viscosity solutions as value functions.

Kwalombota Ilwale

On an associative algebra, we introduce the concept of symmetric $(\sigma,\tau)$-derivations together with a regularity condition and prove that strongly regular symmetric $(\sigma,\tau)$-derivations are inner. Symmetric $(\sigma,\tau)$-derivations are $(\sigma,\tau)$-derivations that are simultaneously $(\sigma,\tau)$-derivations as well as $(\tau,\sigma)$-

Haval M. Mohammed Salih

Let $\mathbb{F}_qG$ be a finite group algebra. We denote by $P(\mathbb{F}_qG)$ the probability that the product of two elements of $\mathbb{F}_qG$ be zero. In this paper, the general formula for computing the $P(\mathbb{F}_qG)$ are established for the cyclic groups $C_n$, the Quaternion group $Q_8$ and the symmetric group $S_3$, for some cases.

A. R. Balasubramanian

Threshold automata are a formalism for modeling fault-tolerant distributed algorithms. The main feature of threshold automata is the notion of a threshold guard, which allows us to compare the number of received messages with the total number of different types of processes. In this paper, we consider the coefficient synthesis problem for threshold automata,

Zeeshan Khan Suri

Self-supervised monocular depth estimation approaches suffer not only from scale ambiguity but also infer temporally inconsistent depth maps w.r.t. scale. While disambiguating scale during training is not possible without some kind of ground truth supervision, having scale consistent depth predictions would make it possible to calculate scale once during inf

Peng Zeng, Xiaotian Song, Andrew Lensen, Yuwei Ou

Symbolic regression (SR) is the process of discovering hidden relationships from data with mathematical expressions, which is considered an effective way to reach interpretable machine learning (ML). Genetic programming (GP) has been the dominator in solving SR problems. However, as the scale of SR problems increases, GP often poorly demonstrates and cannot

Peifeng Gao, Qianqian Xu, Peisong Wen, Huiyang Shao

In this paper, we extend original Neural Collapse Phenomenon by proving Generalized Neural Collapse hypothesis. We obtain Grassmannian Frame structure from the optimization and generalization of classification. This structure maximally separates features of every two classes on a sphere and does not require a larger feature dimension than the number of class

Jakub Kudela, Martin Juricek

The GKLS generator is one of the most used testbeds for benchmarking global optimization algorithms. In this paper, we conduct both a computational analysis and the Exploratory Landscape Analysis (ELA) of the GKLS generator. We utilize both canonically used and newly generated classes of GKLS-generated problems and show their use in benchmarking three state-

Yen-Chieh Lien, Hamed Zamani, W. Bruce Croft

Neural ranking models (NRMs) have demonstrated effective performance in several information retrieval (IR) tasks. However, training NRMs often requires large-scale training data, which is difficult and expensive to obtain. To address this issue, one can train NRMs via weak supervision, where a large dataset is automatically generated using an existing rankin

Haolan Zhan, Xuming Lin, Shaobo Cui, Zhongzhou Zhao

Existing neural methods have shown great potentials towards generating informative text from structured tabular data as well as maintaining high content fidelity. However, few of them shed light on generating personalized expressions, which often requires well-aligned persona-table-text datasets that are difficult to obtain. To overcome these obstacles, we e

Sébastien Lleo, Wolfgang J. Runggaldier

A typical approach to tackle stochastic control problems with partial observation is to separate the control and estimation tasks. However, it is well known that this separation generally fails to deliver an actual optimal solution for risk-sensitive control problems. This paper investigates the separability of a general class of risk-sensitive investment ma

Marcel Makovník, Pavel Chalmoviansky

We introduce the novel method for estimation of mean and Gaussian curvature and several related quantities for polygonal meshes. The algebraic quadric fitting curvature (AQFC) is based on local approximation of the mesh vertices and associated normals by a quadratic surface. The quadric is computed as an implicit surface, so it minimizes algebraic distances

Mario A. V. Saucedo, Akash Patel, Rucha Sawlekar, Akshit Saradagi

In this article, we propose a novel LiDAR and event camera fusion modality for subterranean (SubT) environments for fast and precise object and human detection in a wide variety of adverse lighting conditions, such as low or no light, high-contrast zones and in the presence of blinding light sources. In the proposed approach, information from the event camer

A. Alessi, O. Cavani, R. Grasset, H. -J. Drouhin

In this article, we report some examples of how high-energy electron irradiation can be used as a tool for shaping material properties turning the generation of point-defects into an advantage beyond the presumed degradation of the properties. Such an approach is radically different from what often occurs when irradiation is used as a test for radiation hard

Manuel Cuerno, Luis Guijarro, Rosa María Arnaldo Valdés, Fernando Gómez Comendador

Analyzing flight trajectory data sets poses challenges due to the intricate interconnections among various factors and the high dimensionality of the data. Topological Data Analysis (TDA) is a way of analyzing big data sets focusing on the topological features this data sets have as point clouds in some metric space. Techniques as the ones that TDA provides

Amit Goft, Yuval Abulafia, Nadav Orion, Claude L. Schochet

Specific types of spatial defects or potentials can turn monolayer graphene into a topological material. These topological defects are classified by a spatial dimension $D$ and they are systematically obtained from the Hamiltonian by means of its symbol $\mathcal{H} (\boldsymbol{k}, \boldsymbol{r}) $, an operator which generalises the Bloch Hamiltonian and c

Tanu Raghav, Stefano Boccaletti, Sarika Jalan

Most real-world networks are endowed with the small-world property, by means of which the maximal distance between any two of their nodes scales logarithmically rather than linearly with their size. The evidence sparkled a wealth of studies trying to reveal possible mechanisms through which the pairwise interactions amongst the units of a network are structu

Raquel Couto

In order to obtain functional limit theorems for heavy tailed stationary processes arising from dynamical systems, one needs to understand the clustering patterns of the tail observations of the process. These patterns are well described by means of a structure called the pilling process introduced recently in the context of dynamical systems. So far, the pi

Mamdouh Alenezi

In today's world, many cities are embracing cutting-edge technology and transforming into "smart cities". These emerging innovations are revolutionizing the standard of living for people, and as a result, smart city infrastructure development has become a major focus for city planners and policymakers worldwide. The goal is to create more livable, sustainabl

Nick James, Max Menzies

Since its conception, the cryptocurrency market has been frequently described as an immature market, characterized by significant swings in volatility and occasionally described as lacking rhyme or reason. There has been great speculation as to what role it plays in a diversified portfolio. For instance, is cryptocurrency exposure an inflationary hedge or a

Alpay Sabuncuoglu, T. Metin Sezgin

We collected a new dataset that includes approximately eight hours of audiovisual recordings of a group of students and their self-evaluation scores for classroom engagement. The dataset and data analysis scripts are available on our open-source repository. We developed baseline face-based and group-activity-based image and video recognition models. Our imag

Xiaoding Lu, Aleksey Korshuk, Zongyi Liu, William Beauchamp

This work explores the impact of moderation on users' enjoyment of conversational AI systems. While recent advancements in Large Language Models (LLMs) have led to highly capable conversational AIs that are increasingly deployed in real-world settings, there is a growing concern over AI safety and the need to moderate systems to encourage safe language and p

Pedro Cosme, Diogo Simões

Graphene devices are known to have the potential to operate THz signals. In particular, graphene field-effect transistors have been proposed as devices to host plasmonic instabilities in the THz realm; for instance, Dyakonov-Shur instability which relies upon dc excitation. In this work, starting from a hydrodynamical description of the charge carriers, we e

Anjali Nambudiripad, J. Bharathi Kannan, M. S. Santhanam

Despite the periodic kicks, a linear kicked rotor (LKR) is an integrable and exactly solvable model in which the kinetic energy term is linear in momentum. It was recently shown that spatially interacting LKRs are also integrable, and results in dynamical localization in the corresponding quantum regime. Similar localized phases exist in other non-integrable

Ana Contreras-Santos, Alexander Knebe, Weiguang Cui, Roan Haggar

Using the data set of The Three Hundred project, i.e. 324 hydrodynamical resimulations of cluster-sized haloes and the regions of radius 15 $h^{-1}$Mpc around them, we study galaxy pairs in high-density environments. By projecting the galaxies' 3D coordinates onto a 2D plane, we apply observational techniques to find galaxy pairs. Based on a previous theoret

Glenn Ceusters, Muhammad Andy Putratama, Rüdiger Franke, Ann Nowé

Safe reinforcement learning (RL) with hard constraint guarantees is a promising optimal control direction for multi-energy management systems. It only requires the environment-specific constraint functions itself a priori and not a complete model. The project-specific upfront and ongoing engineering efforts are therefore still reduced, better representations

Alan C. Santos, Christian Schneider, Romain Bachelard, Ana Predojević

We propose a general scheme to generate entanglement encoded in the photon-number basis, via a sequential resonant two-photon excitation of a three-level system. We apply it to the specific case of a quantum dot three-level system, which can emit a photon pair through a biexciton-exciton cascade. The state generated in our scheme constitutes a tool for secur

Yang Yang, Meng Fei Zhang, Lailai Zhu, Tian Hui Zhang

Memory can remarkably modify the collective behaviors of active particles. We show that in a micellar fluid, Quincke particles driven by a square-wave electric field exhibit a frequency-dependent memory. Upon increasing the frequency, a memory of directions emerges whereas the activity of particles decreases. As the activity is dominated by interaction, Quin

Zihan Wang, Gang wu, Haotong Wang

Limited intra-session information is the performance bottleneck of the early GNN based SBR models. Therefore, some GNN based SBR models have evolved to introduce additional inter-session information to facilitate the next-item prediction. However, we found that the introduction of inter-session information may bring interference to these models. The possible

Aswin Iyer, Santosh Narayan, Naren M, Manoj kumar Rajagopal

Drones are a promising technology for autonomous data collection and indoor sensing. In situations when human-controlled UAVs may not be practical or dependable, such as in uncharted or dangerous locations, the usage of autonomous UAVs offers flexibility, cost savings, and reduced risk. The system creates a simulated quadcopter capable of autonomously travel

Bernhard Haeupler, D Ellis Hershkowitz, Zihan Tan

A $t$-spanner of a graph is a subgraph that $t$-approximates pairwise distances. The greedy algorithm is one of the simplest and most well-studied algorithms for constructing a sparse spanner: it computes a $t$-spanner with $n^{1+O(1/t)}$ edges by repeatedly choosing any edge which does not close a cycle of chosen edges with $t+1$ or fewer edges. We demonstr

Javad Pourmostafa Roshan Sharami, Dimitar Shterionov, Frédéric Blain, Eva Vanmassenhove

While quality estimation (QE) can play an important role in the translation process, its effectiveness relies on the availability and quality of training data. For QE in particular, high-quality labeled data is often lacking due to the high cost and effort associated with labeling such data. Aside from the data scarcity challenge, QE models should also be ge

Sergii Parchenko, Antoni Frej, Hiroki Ueda, Robert Carley

Resonant absorption of a photon by bound electrons in a solid can promote an electron to another orbital state or transfer it to a neighboring atomic site. Such a transition in a magnetically ordered material could affect the magnetic order. While this process is an obvious road map for optical control of magnetization, experimental demonstration of such a p

Stéphane Drobot, Matteo Tacchi, Carmen Cardozo, Colin N. Jones

This paper presents a new Matlab toolbox, aimed at facilitating the use of polynomial optimization for stability analysis of nonlinear systems. In the past decade several decisive contributions made it possible to recast this type of problems as convex optimization ones that are tractable in modest dimensions. However, available software requires their user

Christine Bauer, Ben Carterette, Nicola Ferro, Norbert Fuhr

This report documents the program and the outcomes of Dagstuhl Seminar 23031 ``Frontiers of Information Access Experimentation for Research and Education'', which brought together 37 participants from 12 countries. The seminar addressed technology-enhanced information access (information retrieval, recommender systems, natural language processing) and specif

Debasish Borah, Arnab Dasgupta, Indrajit Saha

We study the possibility of constraining a scenario with high scale first order phase transition (FOPT) responsible for the cogenesis of baryon and dark matter using gravitational wave (GW) (non)-observations. While the FOPT at high scale is responsible for generating baryon asymmetry through leptogenesis and dark matter via the \textit{mass-gain} mechanism,

Yicheng Hsu, Mingsian R. Bai

Personal voice activity detection has received increased attention due to the growing popularity of personal mobile devices and smart speakers. PVAD is often an integral element to speech enhancement and recognition for these applications in which lightweight signal processing is only enabled for the target user. However, in real-world scenarios, the detecti

M. Natividad Gomez-Cerezo, Nataliya Perevoshchikova, Rui Ruan, Kevin M. Moerman

The regeneration of the ruptured scapholunate interosseous ligament (SLIL) represents a clinical challenge. Here, we propose the use of a Bone-Ligament-Bone (BLB) 3D-printed polyethylene terephthalate (PET) scaffold for achieving mechanical stabilisation of the scaphoid and lunate following SLIL rupture. The BLB scaffold featured two bone compartments bridge

Eshtiak Ahmed, Laura Cosio, Juho Hamari, Oğuz 'Oz' Buruk

Socially Assistive Robots (SARs) are becoming very popular every day because of their effectiveness in handling social situations. However, social robots are perceived as intelligent, and thus their decision-making process might have a significant effect on how they are perceived and how effective they are. In this paper, we present the findings from a parti

Hoang Ngoc Tuan, Yongdo Lim, Nguyen Dong Yen

This paper gives some results related to the research problem about infinite-dimensional affine variational inequalities raised by N.D. Yen and X. Yang [Affine variational inequalities on normed spaces, J. Optim. Theory Appl., 178 (2018), 36--55]. Namely, we obtain local error bounds for affine variational inequalities on Hilbert spaces. To do so, we revisit

Muhammad Zakir Khan, Jawad Ahmad, Wadii Boulila, Matthew Broadbent

Ambient computing is gaining popularity as a major technological advancement for the future. The modern era has witnessed a surge in the advancement in healthcare systems, with viable radio frequency solutions proposed for remote and unobtrusive human activity recognition (HAR). Specifically, this study investigates the use of Wi-Fi channel state information

Daniel Oeltz, Jan Hamaekers, Kay F. Pilz

We discuss and analyze a neural network architecture, that enables learning a model class for a set of different data samples rather than just learning a single model for a specific data sample. In this sense, it may help to reduce the overfitting problem, since, after learning the model class over a larger data sample consisting of such different data sets,

Lauren Olson, Emitzá Guzmán, Florian Kunneman

In this paper, we identified marginalized communities' ethical concerns about social platforms. We performed this identification because recent platform malfeasance indicates that software teams prioritize shareholder concerns over user concerns. Additionally, these platform shortcomings often have devastating effects on marginalized populations. We first sc

Ragnhild Holden Helland, Alexandros Ferles, André Pedersen, Ivar Kommers

Extent of resection after surgery is one of the main prognostic factors for patients diagnosed with glioblastoma. To achieve this, accurate segmentation and classification of residual tumor from post-operative MR images is essential. The current standard method for estimating it is subject to high inter- and intra-rater variability, and an automated method f

Yuanwei Fang, Zihao Liu, Yanheng Lu, Jiawei Liu

With the end of Moore's Law, there is a growing demand for rapid architectural innovations in modern processors, such as RISC-V custom extensions, to continue performance scaling. Program sampling is a crucial step in microprocessor design, as it selects representative simulation points for workload simulation. While SimPoint has been the de-facto approach f

Pepe Ojeda, Javier Monroy, Javier Gonzalez-Jimenez

Gas source localization (GSL) with an autonomous robot is a problem with many prospective applications, from finding pipe leaks to emergency-response scenarios. In this work, we present a new method to perform GSL in realistic indoor environments, featuring obstacles and turbulent flow. Given the highly complex relationship between the source position and th

Jihyeon Seo, Kyusam Oh, Chanho Min, Yongkeun Yun

Many existing studies on knowledge distillation have focused on methods in which a student model mimics a teacher model well. Simply imitating the teacher's knowledge, however, is not sufficient for the student to surpass that of the teacher. We explore a method to harness the knowledge of other students to complement the knowledge of the teacher. We propose

Subhra Bhattacharya, Subhasis Nalui

It is known that static traversable wormhole in Einstein gravity is supported by matter that violates null energy conditions (NEC). Essentially such wormhole will be characterised by a central throat with anisotropic matter lining the throat that violates NEC. This in turn provides viable geometry for the wormhole to sustain. In 2018, L. Herrera introduced a

Chang Xu, Jian Ding, Jinwang Wang, Wen Yang

Detecting arbitrarily oriented tiny objects poses intense challenges to existing detectors, especially for label assignment. Despite the exploration of adaptive label assignment in recent oriented object detectors, the extreme geometry shape and limited feature of oriented tiny objects still induce severe mismatch and imbalance issues. Specifically, the posi

Yuntao Wang, Zhou Su, Qichao Xu, Tom H. Luan

In order to save computing power yet enhance safety, there is a strong intention for autonomous vehicles (AVs) in future to drive collaboratively by sharing sensory data and computing results among neighbors. However, the intense collaborative computing and data transmissions among unknown others will inevitably introduce severe security concerns. Aiming at

Tuan Q. Do, W. F. Kao

Cosmological implication of a generalized model of two scalar and two vector fields, in which both scalar fields are non-minimally coupled to each vector field, is studied in this paper. In particular, we will seek a set of new anisotropic power-law inflationary solutions to this model. Additionally, the stability of the obtained solutions will be examined b

Zihan Wang, Gang Wu, Haotong Wang

Session-based recommendation systems(SBRS) are more suitable for the current e-commerce and streaming media recommendation scenarios and thus have become a hot topic. The data encountered by SBRS is typically highly sparse, which also serves as one of the bottlenecks limiting the accuracy of recommendations. So Contrastive Learning(CL) is applied in SBRS owi

Javier Esparza, Ruben Rubio, Salomon Sickert

In the mid 80s, Lichtenstein, Pnueli, and Zuck showed that every formula of Past LTL (the extension of Linear Temporal Logic with past operators) is equivalent to a conjunction of formulas of the form $\mathbf{G}\mathbf{F} \varphi \vee \mathbf{F}\mathbf{G} \psi$, where $\varphi$ and $\psi$ contain only past operators. Some years later, Chang, Manna, and Pnue

Yusuke Miyajima, Masahito Mochizuki

We propose two machine-learning methods based on neural networks, which we respectively call the phase-classification method and the temperature-identification method, for detecting different types of phase transitions in the XXZ models without prior knowledge of their critical temperatures. The XXZ models have exchange couplings which are anisotropic in the

Soon Yau Cheong, Armin Mustafa, Andrew Gilbert

Text-to-image models (T2I) such as StableDiffusion have been used to generate high quality images of people. However, due to the random nature of the generation process, the person has a different appearance e.g. pose, face, and clothing, despite using the same text prompt. The appearance inconsistency makes T2I unsuitable for pose transfer. We address this

Soumen Senapati, Mourad Sini, Haibing Wang

Dealing with the inverse source problem for the scalar wave equation, we have shown recently that we can reconstruct the space-time dependent source function from the measurement of the wave, collected at a single point $x$ for a large enough interval of time, generated by a small scaled droplets, enjoying large contrasts of its bulk modulus, injected inside

Dmitry Artemasov, Kirill Andreev, Pavel Rybin, Alexey Frolov

Deep neural network (DNN)-based channel decoding is widely considered in the literature. The existing solutions are investigated for the case of hard output, i.e. when the decoder returns the estimated information word. At the same time, soft-output decoding is of critical importance for iterative receivers and decoders. In this paper, we focus on the soft-o

Matteo Fornoni

In this paper, we address an optimal distributed control problem for a non-local model of phase-field type, describing the evolution of tumour cells in presence of a nutrient. The model couples a non-local and viscous Cahn-Hilliard equation for the phase parameter with a reaction-diffusion equation for the nutrient. The optimal control problem aims at findin

Qi Liu, Tian-Wei Mao, Ming Xue, Ling-Na Wu

We propose an efficient nonlinear readout scheme for entangled non-Gaussian spin states (ENGSs) based on the intrinsic quasi-cyclic dynamics of interacting spin-1/2 systems. We focus on two well-known spin models of twist-and-turn (TNT) and two-axis-counter-twisting (TACT), where ENGS can be generated by spin dynamics starting from unstable fixed points. In

Sukannya Purkayastha, Sebastian Ruder, Jonas Pfeiffer, Iryna Gurevych

Large multilingual pretrained language models (mPLMs) have become the de facto state of the art for cross-lingual transfer in NLP. However, their large-scale deployment to many languages, besides pretraining data scarcity, is also hindered by the increase in vocabulary size and limitations in their parameter budget. In order to boost the capacity of mPLMs to

Kushagra Chatterjee, Biswadeep Sen, Yuhao Wang

The division of goods in the online realm poses opportunities and challenges. While innovative mechanisms can be developed, uncertainty about the future may hinder effective solutions. This project aims to explore fair distribution models for goods among agents with arbitrary entitlements, specifically addressing food charity challenges in the real world. Bu

Dongmei Han, Fengxiao Sun, Na Wang, Yu Xiang

Remote state preparation enables one to prepare and manipulate quantum state non-locally. As an essential quantum resource, optical cat state is usually prepared locally by subtracting photons from a squeezed vacuum state. For remote quantum information processing, it is essential to prepare and manipulate optical cat states remotely based on Gaussian entang

Maurits Bleeker, Pawel Swietojanski, Stefan Braun, Xiaodan Zhuang

This paper presents an extension to train end-to-end Context-Aware Transformer Transducer ( CATT ) models by using a simple, yet efficient method of mining hard negative phrases from the latent space of the context encoder. During training, given a reference query, we mine a number of similar phrases using approximate nearest neighbour search. These sampled

Lukas-Valentin Herm

While the emerging research field of explainable artificial intelligence (XAI) claims to address the lack of explainability in high-performance machine learning models, in practice, XAI targets developers rather than actual end-users. Unsurprisingly, end-users are often unwilling to use XAI-based decision support systems. Similarly, there is limited interdis

Ramiro Martínez, Paz Morillo

This work formalizes efficient Fast Fourier-based multiplication algorithms for polynomials in quotient rings such as $\mathbb{Z}_{m}[x]/\left<x^{n}-a\right>$, with $n$ a power of 2 and $m$ a non necessarily prime integer. We also present a meticulous study on the necessary and/or sufficient conditions required for the applicability of these multiplication a

Majid Mohammadi, Damian A. Tamburri, Jafar Rezaei

Priorities in multi-criteria decision-making (MCDM) convey the relevance preference of one criterion over another, which is usually reflected by imposing the non-negativity and unit-sum constraints. The processing of such priorities is different than other unconstrained data, but this point is often neglected by researchers, which results in fallacious stati

Kaiyuan Zhou, Xiang Zhan, Zishuang Li, Haotian Li

The interlayer antiferromagnetic coupling rare-earth/transition-metal bilayer ferrimagnet systems have attracted much attention because they present variously unusual temperature-and field-dependent nontrivial magnetic states and dynamics. These properties and the implementation of their applications in spintronics highly depend on the significant temperatur

Yury A. Kutoyants

The model of partially observed linear stochastic differential equations depending on some unknown parameters is considered. An proximation of the unobserved component is proposed. This approximation is realized in three steps. First an estimator of the thod of moments of unknown parameter is constructed. Then this estimator is used for defining the One-step

Haotian Li, Liyuan Li, Kaiyuan Zhou, Chunjie Yan

Neuromorphic hardware as a non-Von Neumann architecture has better energy efficiency and parallelism than the conventional computer. Here, with numerical modeling spin-orbit torque (SOT) device using current-induced SOT and Joule heating effects, we acquire its magnetization switching probability as a function of the input current pulses and use it to mimic

Firas Bayram, Bestoun S. Ahmed

Most machine learning methods assume that the input data distribution is the same in the training and testing phases. However, in practice, this stationarity is usually not met and the distribution of inputs differs, leading to unexpected performance of the learned model in deployment. The issue in which the training and test data inputs follow different pro

Huiling Li, Zhilin Zhang, Shenglin Zhou

In this paper we show that a flag-transitive automorphism group $G$ of a non-trivial $2$-$(v,k,\lambda)$ design with $\lambda\geq (r, \lambda)^2$ is not of product action type. In conclusion, $G$ is a primitive group of affine or almost simple type.

Pierre Popineau, Seva Shneer

In this paper, we present a condition to obtain instability for a class of queueing networks where the arrival rates in each server are constant and the departure rate in each server is a decreasing function of the queue lengths of other servers. Under a stronger assumption, that the departure rates are proportional to the queue length in each server, we obt

Hassan Imani, Md Baharul Islam, Lai-Kuan Wong

Stereo video retargeting aims to resize an image to a desired aspect ratio. The quality of retargeted videos can be significantly impacted by the stereo videos spatial, temporal, and disparity coherence, all of which can be impacted by the retargeting process. Due to the lack of a publicly accessible annotated dataset, there is little research on deep learni

Guangze Ye, Wen Wu, Liye Shi, Wenxin Hu

Ephemeral group recommendation (EGR) aims to suggest items for a group of users who come together for the first time. Existing work typically consider individual preferences as the sole factor in aggregating group preferences. However, they neglect to take into account the importance of the individual inherent factors, such as personality, and thus fail to a

V. E. Didenko, A. V. Korybut

We show that the recently proposed equations for holomorphic sector of higher-spin theory in $d=4$, also known as chiral, can be naturally extended to describe interacting symmetric higher-spin gauge fields in any dimension. This is achieved with the aid of Vasiliev's off shell higher-spin algebra. The latter contains ideal associated to traces that has to b

Jhen-Dong Lin, Yueh-Nan Chen

Many-body localization (MBL) can occur when strong disorders prevent an interacting system from thermalization. To study the dynamics of such systems, it is typically necessary to perform an ensemble average over many different disorder configurations. Previous works have utilized an algorithm in which different disorder profiles are mapped into a quantum an

Andreas Lindner, Karl Palmskog, Scott Constable, Mads Dam

Control flow in unstructured programs can be complex and dynamic, which makes static analysis difficult. Yet, automated reasoning about unstructured control flow is important when certifying properties of binary (machine) code in trustworthy systems, e.g., cryptographic routines. We present a theory of forward symbolic execution for unstructured programs sui

Mohammad Naseri, Yufei Han, Emiliano De Cristofaro

Federated learning (FL) enables multiple parties to collaboratively train a machine learning model without sharing their data; rather, they train their own model locally and send updates to a central server for aggregation. Depending on how the data is distributed among the participants, FL can be classified into Horizontal (HFL) and Vertical (VFL). In VFL,

Sizhong Zhou, Jiancheng Wu

Let $k\geq2$ be an integer. A tree $T$ is called a $k$-tree if $d_T(v)\leq k$ for each $v\in V(T)$, that is, the maximum degree of a $k$-tree is at most $k$. Let $\lambda_1(D(G))$ denote the distance spectral radius in $G$, where $D(G)$ denotes the distance matrix of $G$. In this paper, we verify a upper bound for $\lambda_1(D(G))$ in a connected graph $G$ t

Yujie Yang, Zhilong Zheng, Shengbo Eben Li, Wei Xu

Safety is the priority concern when applying reinforcement learning (RL) algorithms to real-world control problems. While policy iteration provides a fundamental algorithm for standard RL, an analogous theoretical algorithm for safe RL remains absent. In this paper, we propose feasible policy iteration (FPI), the first foundational dynamic programming algori

K. Rajaswathi, S. Bhuvaneswari, R. Radha, P. Muruganandam

In this paper, we bring out several potential signatures of negative mass regimes while investigating an expanding spin-orbit (SO) coupled spinor $F=1$ Bose-Einstein condensates by analyzing the dispersion relation of the single-particle quantum system. In SO-coupled spinor condensates, a negative mass parameter generates a wave packet that propagates in the

Takashi Inagaki, Akari Kato, Koichi Takahashi, Haruka Ozaki

The use of laboratory automation by all researchers may substantially accelerate scientific activities by humans, including those in the life sciences. However, computer programs to operate robots should be written to implement laboratory automation, which requires technical knowledge and skills that may not be part of a researcher's training or expertise. I

Rutwig Campoamor-Stursberg, Eduardo Fernandez-Saiz, Francisco J. Herranz

Using the theory of Lie-Hamilton systems, formal generalized time-dependent Hamiltonian systems that enlarge a recently proposed SIS epidemic model with a variable infection rate are considered. It is shown that, independently on the particular interpretation of the time-dependent coefficients, these systems generally admit an exact solution, up to the case

Sicen Guo, Jiahang Li, Yi Feng, Dacheng Zhou

In the nascent domain of urban digital twins (UDT), the prospects for leveraging cutting-edge deep learning techniques are vast and compelling. Particularly within the specialized area of intelligent road inspection (IRI), a noticeable gap exists, underscored by the current dearth of dedicated research efforts and the lack of large-scale well-annotated datas

Bosong Huang, Weihao Yu, Ruzhong Xie, Jing Xiao

Source localization is the inverse problem of graph information dissemination and has broad practical applications. However, the inherent intricacy and uncertainty in information dissemination pose significant challenges, and the ill-posed nature of the source localization problem further exacerbates these challenges. Recently, deep generative models, partic

Zheyu Zhuang, Yizhak Ben-Shabat, Jiahao Zhang, Stephen Gould

The current transformation towards smart manufacturing has led to a growing demand for human-robot collaboration (HRC) in the manufacturing process. Perceiving and understanding the human co-worker's behaviour introduces challenges for collaborative robots to efficiently and effectively perform tasks in unstructured and dynamic environments. Integrating rece

Luhan Li, Dongdong Liu, Bo Wang

Type Ia supernovae (SNe Ia) are among the most energetic events in the Universe. They are excellent cosmological distance indicators due to the remarkable homogeneity of their light curves. However, the nature of the progenitors of SNe Ia is still not well understood. In the single-degenerate model, a carbon-oxygen white dwarf (CO WD) could grow its mass by

Tiantian Liu, Huan Li, Hua Lu, Muhammad Aamir Cheema

Pandemics often cause dramatic losses of human lives and impact our societies in many aspects such as public health, tourism, and economy. To contain the spread of an epidemic like COVID-19, efficient and effective contact tracing is important, especially in indoor venues where the risk of infection is higher. In this work, we formulate and study a novel que

John Cao, Muhammad Umar B. Niazi, Matthieu Barreau, Karl Henrik Johansson

This paper presents a novel observer-based approach to detect and isolate faulty sensors in nonlinear systems. The proposed sensor fault detection and isolation (s-FDI) method applies to a general class of nonlinear systems. Our focus is on s-FDI for two types of faults: complete failure and sensor degradation. The key aspect of this approach lies in the uti

Laurent Cantier

We define a categorical framework in which we build a systematic construction that provides generic invariants for C*-algebras. The benefit is significant as we show that any invariant arising this way automatically enjoys nice properties such as continuity, metric on morphisms and a theory of ideals and quotients which naturally encapsulates compatibility d

Bahram Mashhoon, Masoud Molaei, Yuri N. Obukhov

The coupling of intrinsic spin with the nonlinear gravitomagnetic fields of Goedel-type spacetimes is studied. We work with Goedel-type universes in order to show that the main features of spin-gravity coupling are independent of causality problems of the Goedel universe. The connection between the spin-gravitomagnetic field coupling and Mathisson's spin-cur

Lucianno Defaveri, Maike A. F. dos Santos, David A. Kessler, Eli Barkai

We study non-normalizable quasi-equilibrium states (NNQE) arising from anomalous diffusion. Initially, particles in contact with a thermal bath are released from an asymptotically flat potential well, with dynamics that is described by fractional calculus. For temperatures that are sufficiently low compared to the potential depth, the properties of the syste

Mayuko Yamashita

This is the proceeding of a talk given at Stringmath 2022. We introduce a Cheeger-Simons type model for the differential extension of Anderson dual to generalized homology theory with physical interpretations. This construction generalizes the construction of the differential Anderson dual to bordism homology theories, given in a previous work of Yonekura an

Ellin Q. Zhao, Alexander Vilesov, Pradyumna Chari, Laleh Jalilian

Widely deployed for fever detection, infrared thermometers (IRTs) enable rapid non-contact measurement of core body temperature but are inaccurate in unconstrained environments when skin temperature is transient. In this work, we present the first study on the effect of solar loading--solar radiation-induced elevation of skin but not core temperature--on IRT

Zhen Li, Xiao-Kan Guo, Faqiang Yuan

Recently, it has been demonstrated by Comisso and Asenjo that magnetic reconnection processes in the ergosphere of a Kerr black hole can provide us with a promising mechanism for extracting the rotational energy from it. In this paper, we study the energy extraction from the the newly proposed rotating regular black holes via this Comisso-Asenjo mechanism. T