March 2023 arXiv papers — page 60

Qiliang Fang, Keiichi Maeda

The relation between the progenitor mass and the kinetic energy of the explosion is a key toward revealing the explosion mechanism of stripped-envelope (SE) core-collapse (CC) supernovae (SNe). Here, we present a method to derive this relation using the nebular spectra of SESNe, based on the correlation between the [O~I]/[Ca~II], which is an indicator of the

Emanuele Loffredo, Elisabetta Vesconi, Rostam Razban, Orit Peleg

Understanding how a stressor applied on a biological system shapes its evolution is key to achieving targeted evolutionary control. Here we present a toy model of two interacting lattice proteins to quantify the response to the selective pressure defined by the binding energy. We generate sequence data of proteins and study how the sequence and structural pr

Parul Gupta, Yashpreet Kaur, Anupam Singh

Differential central simple algebras are the main object of study in this survey article. We recall some crucial notions such as differential subfields, differential splitting fields, tensor products etc. Our main focus is on differential splitting fields which connects these objects to the classical differential Galois theory. We mention several known resul

Constantinos Patsakis, Nikolaos Lykousas

The collection and use of personal data are becoming more common in today's data-driven culture. While there are many advantages to this, including better decision-making and service delivery, it also poses significant ethical issues around confidentiality and privacy. Text anonymisation tries to prune and/or mask identifiable information from a text while k

Ray Ganardi, Ekta Panwar, Mahasweta Pandit, Bianka Wołoncewicz

In plethora of physical situations one can distinguish a mediator -- a system that couples other, non-interacting systems. Often the mediator itself is not directly accessible to experimentation, yet it is interesting and sometimes crucial to understand if it admits non-classical properties. An example of this sort that recently enjoys considerable attention

Wen Chen

A transformation on homogeneous polynomials is proposed, which is further applied to parametric Feynman integrals. The two representations related through this transformation are dual to each other. It naturally leads to dualities of Landau equations and linear integral relations between the two representations. For integrals with momentum-space corresponden

Florian Bemmann, Carmen Mayer, Sven Mayer

A pro-environmental attitude in the general population is essential to combat climate change. Society as a whole has the power to change economic processes through market demands and to exert pressure on policymakers - both are key social factors that currently undermine the goals of decarbonization. Creating long-lasting, sustainable attitudes is challengin

Axel Brandenburg, Detlef Elstner, Youhei Masada, Valery Pipin

Mean-field dynamo theory has important applications in solar physics and galactic magnetism. We discuss some of the many turbulence effects relevant to the generation of large-scale magnetic fields in the solar convection zone. The mean-field description is then used to illustrate the physics of the $\alpha$ effect, turbulent pumping, turbulent magnetic diff

Dayuan Jian, Mohammad Rostami

Event-based cameras offer reliable measurements for preforming computer vision tasks in high-dynamic range environments and during fast motion maneuvers. However, adopting deep learning in event-based vision faces the challenge of annotated data scarcity due to recency of event cameras. Transferring the knowledge that can be obtained from conventional camera

Xin Gu, Guang Chen, Yufei Wang, Libo Zhang

Video captioning aims to describe the content of videos using natural language. Although significant progress has been made, there is still much room to improve the performance for real-world applications, mainly due to the long-tail words challenge. In this paper, we propose a text with knowledge graph augmented transformer (TextKG) for video captioning. No

Soumangsu Chakraborty, Amit Giveon, David Kutasov

We explore the holographic duality between string theory in backgrounds that interpolate between asymptotically linear dilaton spacetime in the UV and $AdS_3$ in the IR, and single-trace $T\bar T$ deformed CFT. In particular, we explain how the deformation of states in the boundary theory is reflected in the bulk geometry, and show that the coupling above wh

Shenghai Liao, Xuya Liu, Ruyi Han, Shujun Fu

Digital image inpainting is an interpolation problem, inferring the content in the missing (unknown) region to agree with the known region data such that the interpolated result fulfills some prior knowledge. Low-rank and nonlocal self-similarity are two important priors for image inpainting. Based on the nonlocal self-similarity assumption, an image is divi

D. Cho

We review a few ideas and experiments that our laboratory at Korea University has proposed and carried out to use vector polarizability \beta to manipulate alkali-metal atoms. \beta comes from spin-orbit coupling, and it produces an ac Stark shift that resembles a Zeeman shift. When a circularly polarized laser field is properly detuned between the D1 and D2

Shuo Yang, Zhaopan Xu, Kai Wang, Yang You

As one of the most fundamental techniques in multimodal learning, cross-modal matching aims to project various sensory modalities into a shared feature space. To achieve this, massive and correctly aligned data pairs are required for model training. However, unlike unimodal datasets, multimodal datasets are extremely harder to collect and annotate precisely.

Huixu Dong, Ziyi Zheng, Haotian Guo, Sihao Yang

Force-aware grasping is an essential capability for most robots in practical applications. Especially for compliant grippers, such as Fin-Ray grippers, it still remains challenging to build a bidirectional mathematical model that mutually maps the shape deformation and contact force. Part I of this article has constructed the force-displacement relationship

Yihan Zeng, Chenhan Jiang, Jiageng Mao, Jianhua Han

Contrastive Language-Image Pre-training, benefiting from large-scale unlabeled text-image pairs, has demonstrated great performance in open-world vision understanding tasks. However, due to the limited Text-3D data pairs, adapting the success of 2D Vision-Language Models (VLM) to the 3D space remains an open problem. Existing works that leverage VLM for 3D u

Leonor Cui Domingo Centeno, Víctor Puente García

During the propagation of a tsunami, gravity and sound waves can be produced, spreading from its source to the ionosphere's upper layers, thus generating perturbed electron densities in its E and F regions. These ionospheric disturbances can be studied in detail using measurements of the ionosphere's Total Electron Content (TEC), registered by permanent GNSS

Itsugun Cho, Ryota Takahashi, Yusaku Yanase, Hiroaki Saito

Traditionally, approximate dynamic programming is employed in dialogue generation with greedy policy improvement through action sampling, as the natural language action space is vast. However, this practice is inefficient for reinforcement learning (RL) due to the sparsity of eligible responses with high action values, which leads to weak improvement sustain

Nabamita Banerjee, Muktajyoti Saha

Computing the 4D Euclidean path integral to one-loop order we find the large quantum corrections that govern the behavior of a spherically symmetric non-supersymmetric near-extremal black hole at very low temperature. These corrections appear from the near-horizon geometry of the near-extremal black hole. Using first-order perturbation theory we find that su

Frank Po-Chen Lin, Seyyedali Hosseinalipour, Nicolò Michelusi, Christopher Brinton

Federated learning has gained popularity as a means of training models distributed across the wireless edge. The paper introduces delay-aware hierarchical federated learning (DFL) to improve the efficiency of distributed machine learning (ML) model training by accounting for communication delays between edge and cloud. Different from traditional federated le

Mladen Bestvina, Federica Fanoni, Jing Tao

We introduce and study tempered mapping classes of surfaces of infinite type. These are maps for which curves under iteration do not accumulate onto geodesic laminations with non-proper leaves, but only on unions of possibly intersecting curves or proper lines. Assuming an additional finiteness condition on the accumulation set, we prove a Nielsen-Thurston-t

Andrea Brini, Antonio Teolis

In this paper, we consider a special class of Capelli bitableaux, namely the Capelli-Deruyts bitableaux. The main results we prove are the hook coefficient lemma and the expansion theorem. Capelli-Deruyts bitableaux of rectangular shape are of particular interest since they are central elements in the enveloping algebra. The expansion theorem implies that th

Ece Kilerci, Tetsuya Hashimoto, Tomotsugu Goto, Ersin Göğüş

We report on 167 infrared (IR) galaxies selected by AKARI and IRAS and detected in the Atacama Cosmology Telescope (ACT) Data Release 5 (DR5) sky maps at the 98, 150 and 220 GHz frequency bands. Of these detections, 134 (80%) of the millimeter counterparts are first-time identifications with ACT. We expand the previous ACT extragalactic source catalogs, by i

Johann Brehmer, Joey Bose, Pim de Haan, Taco Cohen

Embodied agents operate in a structured world, often solving tasks with spatial, temporal, and permutation symmetries. Most algorithms for planning and model-based reinforcement learning (MBRL) do not take this rich geometric structure into account, leading to sample inefficiency and poor generalization. We introduce the Equivariant Diffuser for Generating I

Guang-Yao Xiao, Yu-Juan Liu, Huan-Yu Teng, Wei Wang

Being one of the most fundamental physical parameter of astronomical objects, mass plays a vital role in the study of exoplanets, including their temperature structure, chemical composition, formation, and evolution. However, nearly a quarter of the known confirmed exoplanets lack measurements of their masses. This is particularly severe for those discovered

Changwoon Choi, Sang Min Kim, Young Min Kim

We present EgoNeRF, a practical solution to reconstruct large-scale real-world environments for VR assets. Given a few seconds of casually captured 360 video, EgoNeRF can efficiently build neural radiance fields which enable high-quality rendering from novel viewpoints. Motivated by the recent acceleration of NeRF using feature grids, we adopt spherical coor

Shogo Nakakita

We study Langevin-type algorithms for sampling from Gibbs distributions such that the potentials are dissipative and their weak gradients have finite moduli of continuity not necessarily convergent to zero. Our main result is a non-asymptotic upper bound of the 2-Wasserstein distance between a Gibbs distribution and the law of general Langevin-type algorithm

Xinjie Han, Yong Geng, Haocheng Ke, Kun Qiu

We demonstrate an all-optical phase regeneration technique based on Kerr soliton combs, which can realize degraded quaternary phase shift keying (QPSK) signal regeneration through phase-sensitive amplification. A Kerr soliton comb is generated at the receiver side of optical communication systems based on a carrier recovery scheme and is used as coherent dua

Hajime Fukushima, Hidenobu Yajima

We study the formation of globular clusters in massive compact clouds with the low-metallicity of $Z=10^{-3}~Z_{\odot}$ by performing three-dimensional radiative-hydrodynamics simulations. Considering the uncertainty of the initial mass function (IMF) of stars formed in low-metallicity and high-density clouds, we investigate the impacts of the IMF on the clo

Antoine Chambert-Loir

Proof assistants are computer softwares that allow us to write mathematical proofs so as to assess their correctness. In November 2021, I started the project of checking the simplicity of the alternating groups within the Lean theorem prover and its mathlib library. This text aims at reviewing this experiment. -- (French) Les assistants de preuves sont des l

Malte Granderath, Jürgen Schönwälder

In recent years, the open source operating system OpenWrt has become a popular option for replacing proprietary firmware on networking devices such as home routers or access points. In order to configure an OpenWrt system, like setting up firewall rules, the user has to either sign in to the web interface or use SSH to manually change configuration files on

Kübra Tanınmış, Necati Aras, Evren Güney, Markus Sinnl

The COVID-19 pandemic has been a recent example for the spread of a harmful contagion in large populations. Moreover, the spread of harmful contagions is not only restricted to an infectious disease, but is also relevant to computer viruses and malware in computer networks. Furthermore, the spread of fake news and propaganda in online social networks is also

Chinmay Divekar, Soudeep Deb, Rishideep Roy

This paper employs a Bayesian methodology to predict the results of soccer matches in real-time. Using sequential data of various events throughout the match, we utilize a multinomial probit regression in a novel framework to estimate the time-varying impact of covariates and to forecast the outcome. English Premier League data from eight seasons are used to

Tianhang Wang, Guang Chen, Kai Chen, Zhengfa Liu

Multi-agent collaborative perception (MCP) has recently attracted much attention. It includes three key processes: communication for sharing, collaboration for integration, and reconstruction for different downstream tasks. Existing methods pursue designing the collaboration process alone, ignoring their intrinsic interactions and resulting in suboptimal per

Chien-Hua Chen

In this paper, we give an explicit bound on the irreducibility of mod-$\mathfrak{l}$ Galois representation for Drinfeld modules of arbitrary rank without complex multiplication. This is a function field analogue of Masser-W\"ustholz bound on irreducibility of mod-$\ell$ Galois representation for elliptic curves over number field.

Anahita Nekoozadeh, Mohammad Reza Ahmadzadeh, Zahra Mardani

Transformers have achieved widespread success in computer vision. At their heart, there is a Self-Attention (SA) mechanism, an inductive bias that associates each token in the input with every other token through a weighted basis. The standard SA mechanism has quadratic complexity with the sequence length, which impedes its utility to long sequences appearin

L. Monaco, D. Sertore, M. Baylac, L. B. Jones

The European Workshop on Photocathodes for (particle) Accelerator Applications (EWPAA) brings together experts in the field of photocathode based electron sources for use in particle accelerators, with the aim of sharing their knowledge and latest research and development progress in this crucial field of particle accelerator science. The workshop is convene

Jinyin Chen, Haibin Zheng, Tao Liu, Rongchang Li

With the development of deep learning processors and accelerators, deep learning models have been widely deployed on edge devices as part of the Internet of Things. Edge device models are generally considered as valuable intellectual properties that are worth for careful protection. Unfortunately, these models have a great risk of being stolen or illegally c

Yang Hai, Rui Song, Jiaojiao Li, Mathieu Salzmann

Most recent 6D object pose estimation methods first use object detection to obtain 2D bounding boxes before actually regressing the pose. However, the general object detection methods they use are ill-suited to handle cluttered scenes, thus producing poor initialization to the subsequent pose network. To address this, we propose a rigidity-aware detection me

Arham Muslim, Mohamed Amine Chatti, Mouadh Guesmi

Open Learning Analytics (OLA) is an emerging research area that aims at improving learning efficiency and effectiveness in lifelong learning environments. OLA employs multiple methods to draw value from a wide range of educational data coming from various learning environments and contexts in order to gain insight into the learning processes of different sta

Qianxiong Xu, Cheng Long, Liang Yu, Chen Zhang

Road extraction is a process of automatically generating road maps mainly from satellite images. Existing models all target to generate roads from the scratch despite that a large quantity of road maps, though incomplete, are publicly available (e.g. those from OpenStreetMap) and can help with road extraction. In this paper, we propose to conduct road extrac

Andrei Khrennikov, Irina Basieva

This paper is devoted to clarification of the notion of entanglement through decoupling it from the tensor product structure and treating as a constraint posed by probabilistic dependence of quantum observable A and B. In our framework, it is meaningless to speak about entanglement without pointing to the fixed observables A and B, so this is AB-entanglement

Mohamed Amine Chatti, Arham Muslim, Manpriya Guliani, Mouadh Guesmi

Human-Centered learning analytics (HCLA) is an approach that emphasizes the human factors in learning analytics and truly meets user needs. User involvement in all stages of the design, analysis, and evaluation of learning analytics is the key to increase value and drive forward the acceptance and adoption of learning analytics. Visual analytics is a multidi

Matilde Maccan

We extend to characteristic $2$ and $3$ the classification of projective homogeneous varieties of Picard group isomorphic to $\mathbf{Z}$, corresponding to parabolic subgroup schemes with maximal reduced subgroup. The latter are all obtained as product of a maximal reduced parabolic with the kernel of a purely inseparable isogeny. This fails in type $G_2$ an

William Hunt, Jack Ryan, Ayodeji O. Abioye, Sarvapali D. Ramchurn

Autonomous swarms of robots can bring robustness, scalability and adaptability to safety-critical tasks such as search and rescue but their application is still very limited. Using semi-autonomous swarms with human control can bring robot swarms to real-world applications. Human operators can define goals for the swarm, monitor their performance and interfer

Eloi Martinet

This paper deals with the numerical optimization of the first three eigenvalues of the Laplace-Beltrami operator of domain in the Euclidean sphere in $\mathbb{R}^3$ with Neumann boundary conditions. We address two approaches : the first one is a generalization of the initial problem leading to a density method and the other one is a shape optimization proced

Mohamed Amine Chatti, Volkan Yücepur, Arham Muslim, Mouadh Guesmi

There is an increased interest in the application of learning analytics (LA) to promote self-regulated learning (SRL). A variety of LA dashboards and indicators were proposed to support different crucial SRL processes, such as planning, awareness, self-reflection, self-monitoring, and feedback. However, the design of these dashboards and indicators is often

Masataka Satoh

In this paper, we introduce the notion of generalized quasi-shuffle products and give a criterion for their associativity. These extend the quasi-shuffle products introduced by Hoffman, which are often used to describe the stuffle and shuffle product for multiple zeta values. For $q$-analogues of multiple zeta values, the description of an analogue for the s

Soudeep Deb, Shubhabrata Das

The success of a football team depends on various individual skills and performances of the selected players as well as how cohesively they perform. We propose a two-stage process for selecting optimal playing eleven of a football team from its pool of available players. In the first stage a LASSO-induced modified multinomial logistic regression model is der

Jiuming Liu, Guangming Wang, Zhe Liu, Chaokang Jiang

Although point cloud registration has achieved remarkable advances in object-level and indoor scenes, large-scale registration methods are rarely explored. Challenges mainly arise from the huge point number, complex distribution, and outliers of outdoor LiDAR scans. In addition, most existing registration works generally adopt a two-stage paradigm: They firs

Chico Sundermann, Heiko Raab, Tobias Heß, Thomas Thüm

Feature models are commonly used to specify the valid configurations of a product line. In industry, feature models are often complex due to a large number of features and constraints. Thus, a multitude of automated analyses have been proposed. Many of those rely on computing the number of valid configurations which typically depends on solving a #SAT proble

Youri Duchene, Gérome C Gauchard, Guillaume Mornieux

The aims of this study were twofold: firstly, to compare core stability and knee joint loading between sidestepping experts and nonexperts; secondly, to determine core predictors of knee joint loading. Thirteen handball male players (experts) and fourteen karatekas (nonexperts) performed six unanticipated 45{\textdegree} sidestepping manoeuvers, while trunk

Zhimin He, Maijie Deng, Shenggen Zheng, Lvzhou Li

Quantum Architecture Search (QAS) is a promising approach to designing quantum circuits for variational quantum algorithms (VQAs). However, existing QAS algorithms require to evaluate a large number of quantum circuits during the search process, which makes them computationally demanding and limits their applications to large-scale quantum circuits. Recently

Sonia Haddad, Takeo Kato, Jihang Zhu, Lassaad Mandhour

The salient property of the electronic band structure of twisted bilayer graphene (TBG), at the so-called magic angle (MA), is the emergence of flat bands around the charge neutrality point. These bands are associated with the observed superconducting phases and the correlated insulating states. Scanning tunneling microscopy combined with angle resolved phot

Yi-Shan Lee, Wei-Cheng Tseng, Fu-En Wang, Min Sun

We propose a content-based system for matching video and background music. The system aims to address the challenges in music recommendation for new users or new music give short-form videos. To this end, we propose a cross-modal framework VMCML that finds a shared embedding space between video and music representations. To ensure the embedding space can be

Camille Frévent

We propose a new approach to the autoregressive spatial functional model, based on the notion of signature, which represents a function as an infinite series of its iterated integrals. It presents the advantage of being applicable to a wide range of processes. After having provided theoretical guarantees to the proposed model, we have shown in a simulation s

Niharika Bhootna, Monika Singh Dhull, Arun Kumar, Nikolai Leonenko

In this article, we use the generating functions of the Humbert polynomials to define two types of Humbert generalized fractional differenced ARMA processes. We present stationarity and invertibility conditions for the introduced models. The singularities for the spectral densities of the introduced models are obtained. In particular, Pincherle ARMA, Horadam

Malcolm Crowe, Fritz Laux

Recent work on database application development platforms has sought to include a declarative formulation of a conceptual data model in the application code, using annotations or attributes. Some recent work has used metadata to include the details of such formulations in the physical database, and this approach brings significant advantages in that the mode

Hirotaka Tahara, Hikaru Sasaki, Hanbit Oh, Edgar Anarossi

Partial Automation (PA) with intelligent support systems has been introduced in industrial machinery and advanced automobiles to reduce the burden of long hours of human operation. Under PA, operators perform manual operations (providing actions) and operations that switch to automatic/manual mode (mode-switching). Since PA reduces the total duration of manu

Stijn Heldens, Ben van Werkhoven

Graphic Processing Units (GPUs) have become ubiquitous in scientific computing. However, writing efficient GPU kernels can be challenging due to the need for careful code tuning. To automatically explore the kernel optimization space, several auto-tuning tools - like Kernel Tuner - have been proposed. Unfortunately, these existing auto-tuning tools often do

Paweł J. Szabłowski

Our focus is on the set of lower-triangular, infinite matrices that have natural operations like addition, multiplication by a number, and matrix multiplication. With respect to addition this set forms and abelian group while with respect to matrix multiplication, the invertivle elements of the set form a group. The set becomes an algebra (non-commutative in

László Jenkovszky, Matthew J. Lake, Vladimir Soloviev

Foreward to the Bolyai-Gauss-Lobachevsky 2022 special issue, published in ${\it Symmetry}$, in lieu of the 12${}^{\rm th}$ International Conference on Non-Euclidean Geometry, ``BGL-2022''.

Florent Noisette

We establish the uniqueness of solutions of the Camassa-Holm equation on a finite interval with non-homogeneous boundary conditions in the case of bounded momentum. A similar result for the higher-order Camassa-Holm system is also given. Our proofs rely on energy-type methods, with some multipliers given as solutions of some auxiliary elliptic systems.

Perla Doubinsky, Nicolas Audebert, Michel Crucianu, Hervé Le Borgne

The latent space of GANs contains rich semantics reflecting the training data. Different methods propose to learn edits in latent space corresponding to semantic attributes, thus allowing to modify generated images. Most supervised methods rely on the guidance of classifiers to produce such edits. However, classifiers can lead to out-of-distribution regions

Guoliang You, Xiaomeng Chu, Yifan Duan, Jie Peng

It is important for deep reinforcement learning (DRL) algorithms to transfer their learned policies to new environments that have different visual inputs. In this paper, we introduce Prompt based Proximal Policy Optimization ($P^{3}O$), a three-stage DRL algorithm that transfers visual representations from a target to a source environment by applying prompti

Sixun Dong, Huazhang Hu, Dongze Lian, Weixin Luo

Sequential video understanding, as an emerging video understanding task, has driven lots of researchers' attention because of its goal-oriented nature. This paper studies weakly supervised sequential video understanding where the accurate time-stamp level text-video alignment is not provided. We solve this task by borrowing ideas from CLIP. Specifically, we

Hui Lv, Zhongqi Yue, Qianru Sun, Bin Luo

Weakly Supervised Video Anomaly Detection (WSVAD) is challenging because the binary anomaly label is only given on the video level, but the output requires snippet-level predictions. So, Multiple Instance Learning (MIL) is prevailing in WSVAD. However, MIL is notoriously known to suffer from many false alarms because the snippet-level detector is easily bias

JunYong Choi, SeokYeong Lee, Haesol Park, Seung-Won Jung

We propose a scene-level inverse rendering framework that uses multi-view images to decompose the scene into geometry, a SVBRDF, and 3D spatially-varying lighting. Because multi-view images provide a variety of information about the scene, multi-view images in object-level inverse rendering have been taken for granted. However, owing to the absence of multi-

Volviane Saphir Mfogo, Alain Zemkoho, Laurent Njilla, Marcellin Nkenlifack

The proliferation of the Internet of Things (IoT) has raised concerns about the security of connected devices. There is a need to develop suitable and cost-efficient methods to identify vulnerabilities in IoT devices in order to address them before attackers seize opportunities to compromise them. The deception technique is a prominent approach to improving

Stefan Schwede

We introduce Chern classes in $U(m)$-equivariant homotopical bordism that refine the Conner-Floyd-Chern classes in the $MU$-cohomology of $B U(m)$. For products of unitary groups, our Chern classes form regular sequences that generate the augmentation ideal of the equivariant bordism rings. Consequently, the Greenlees-May local homology spectral sequence col

Leon Eifler, Ambros Gleixner

This paper is concerned with the exact solution of mixed-integer programs (MIPs) over the rational numbers, i.e., without any roundoff errors and error tolerances. Here, one computational bottleneck that should be avoided whenever possible is to employ large-scale symbolic computations. Instead it is often possible to use safe directed rounding methods, e.g.

Maurice Rupp, Oriane Peter, Thirupathi Pattipaka

In this study, we introduce ExBEHRT, an extended version of BEHRT (BERT applied to electronic health records), and apply different algorithms to interpret its results. While BEHRT considers only diagnoses and patient age, we extend the feature space to several multimodal records, namely demographics, clinical characteristics, vital signs, smoking status, dia

Hao Wang, Chen Li, Jinzhe Jiang, Xin Zhang

Recently, the robustness of deep learning models has received widespread attention, and various methods for improving model robustness have been proposed, including adversarial training, model architecture modification, design of loss functions, certified defenses, and so on. However, the principle of the robustness to attacks is still not fully understood,

U. Shrikant, Prabha Mandayam

In the current era of noisy intermediate-scale quantum (NISQ) devices, research in the theory of open system dynamics has a crucial role to play. In particular, understanding and quantifying memory effects in quantum systems is critical to gain a better handle on the effects of noise in quantum devices. The main focus of this review is to address the fundame

Vincent Unsel, Stephan Wiefling, Nils Gruschka, Luigi Lo Iacono

Online services have difficulties to replace passwords with more secure user authentication mechanisms, such as Two-Factor Authentication (2FA). This is partly due to the fact that users tend to reject such mechanisms in use cases outside of online banking. Relying on password authentication alone, however, is not an option in light of recent attack patterns

Zhilong Liang, Zhenzhi Tan, Ruixin Hong, Wanli Ouyang

Many material properties are manifested in the morphological appearance and characterized with microscopic image, such as scanning electron microscopy (SEM). Polymer miscibility is a key physical quantity of polymer material and commonly and intuitively judged by SEM images. However, human observation and judgement for the images is time-consuming, labor-int

Hartmut Schmidt, José Montes, Andreas Grübl, Maurice Güttler

The first-generation of BrainScaleS, also referred to as BrainScaleS-1, is a neuromorphic system for emulating large-scale networks of spiking neurons. Following a "physical modeling" principle, its VLSI circuits are designed to emulate the dynamics of biological examples: analog circuits implement neurons and synapses with time constants that arise from the

Panagiotis Lingos, George Perrakis, Odysseas Tsilipakos, George D. Tsibidis

Formation of nanoscale laser-induced periodic surface structures on thin metal films (of the size of the optical penetration depth) is a yet unexplored area that is expected to open new routes for laser patterning and a wealth of exciting applications in optics, photonics, and sensing. In contrast to the common belief that excitation of Surface Plasmon Polar

Wenjie Wang, Li Xiong, Jian Lou

Adversarial examples are crafted by adding indistinguishable perturbations to normal examples in order to fool a well-trained deep learning model to misclassify. In the context of computer vision, this notion of indistinguishability is typically bounded by $L_{\infty}$ or other norms. However, these norms are not appropriate for measuring indistinguishiabili

Jiahui Song, Aiguo Xu, Long Miao, Feng Chen

A discrete Boltzmann model (DBM) for plasma kinetics is proposed. The constructing of DBM mainly considers two aspects. The first is to build a physical model with sufficient physical functions before simulation. The second is to present schemes for extracting more valuable information from massive data after simulation. For the first aspect, the model is eq

Qingquan Peng, Binwu Gao, Konstantin Zaitsev, Dongyang Wang

Unauthorized light injection has always been a vital threat to the practical security of a quantum key distribution (QKD) system. An optical power limiter (OPL) based on the thermo-optical defocusing effect has been proposed and implemented, limiting the injected hacking light. As a hardware countermeasure, the performance of the OPL under various light-inje

Yuan Chen, Quecheng Qiu, Xiangyu Liu, Guangda Chen

Reliable localization is crucial for autonomous robots to navigate efficiently and safely. Some navigation methods can plan paths with high localizability (which describes the capability of acquiring reliable localization). By following these paths, the robot can access the sensor streams that facilitate more accurate location estimation results by the local

Yu-Lin Cheng, Fei Xiang, Heng Yu, Shu-Mei Jia

The analysis of light variation of M87 can help us understand the disc evolution. In the past decade, M87 has experienced several short-term light variabilities related to flares. We also find there are year-scale X-ray variations in the core of M87. Their light variability properties are similar to clumpy-ADAF. By re-analyzing 56 $\it Chandra$ observations

Frances Fengyi Yang, Michele Sasdelli, Tat-Jun Chin

A successful application of quantum annealing to machine learning is training restricted Boltzmann machines (RBM). However, many neural networks for vision applications are feedforward structures, such as multilayer perceptrons (MLP). Backpropagation is currently the most effective technique to train MLPs for supervised learning. This paper aims to be forwar

Satoshi Masaki, Ryusei Tsukuda

In this paper, we consider the scattering problem for a class of $N$-coupled systems of the cubic nonlinear Schr\"odinger equations in three space dimensions. We prove the scattering of solutions that have a mass-energy quantity less than that for the ground states. This result is previously obtained by Duyckaerts-Holmer-Roudenko for the single cubic nonline

Borui Cai, Yong Xiang, Longxiang Gao, Di Wu

Knowledge graph embedding (KGE) that maps entities and relations into vector representations is essential for downstream applications. Conventional KGE methods require high-dimensional representations to learn the complex structure of knowledge graph, but lead to oversized model parameters. Recent advances reduce parameters by low-dimensional entity represen

Qian Qi

This paper explores the capacity of artificial intelligence (AI) algorithms to autonomously design incentive-compatible contracts in dual-principal-agent settings, a relatively unexplored aspect of algorithmic mechanism design. We develop a dynamic model where two principals, each equipped with independent Q-learning algorithms, interact with a single agent.

Aliasghar Sarizadeh

In the present work, we study the attractors of iterated function systems (IFSs) on connected and compact metric spaces. We prove that the whole of the phase space of a forward minimal IFS, for which some map admits an attracting fixed point, is an attractor.

Cheng Peng, D. N. Sheng, Hong-Chen Jiang

We have performed large-scale density-matrix renormalization group studies of the lightly doped Hubbard model on the honeycomb lattice on long three and four-leg cylinders. We find that the ground state of the system upon lightly doping is consistent with that of a superconducting state with coexisting quasi-long-range superconducting and charge density wave

Wei Zhang

In this paper, we can show that \begin{align*} S_{\Lambda}(x)=\sum_{1\leq n\leq x}\Lambda \left(\left[\frac{x}{n}\right]\right)= \sum_{n=1}^{\infty} \frac{\Lambda(n)}{n(n+1)}x +O\left(x^{7/15+1/195+\varepsilon}\right), \end{align*} where $\Lambda(n)$ is the von Mangdolt function. Moreover, we can also give a similar result related to the divisor function, wh

Shengming Yin, Chenfei Wu, Huan Yang, Jianfeng Wang

In this paper, we propose NUWA-XL, a novel Diffusion over Diffusion architecture for eXtremely Long video generation. Most current work generates long videos segment by segment sequentially, which normally leads to the gap between training on short videos and inferring long videos, and the sequential generation is inefficient. Instead, our approach adopts a

Jun-Qian Jiang, Gen Ye, Yun-Song Piao

The injection of early dark energy (EDE) before the recombination, a possible resolution of the Hubble tension, will not only shift the scalar spectral index $n_s$ towards $n_s=1$, but also be likely to tighten the current upper limit on tensor-to-scalar ratio $r$. In this work, with the latest CMB datasets (Planck PR4, ACT, SPT and BICEP/Keck), as well as B

Gerán Molpeceres, Víctor. M. Rivilla, Kenji Furuya, Johannes Kästner

Hydroxylamine, NH2OH, is one of the already detected interstellar molecules with the highest prebiotic potential. Yet, the abundance of this molecule found by astronomical observations is rather low for a relatively simple molecule, $\sim$ 10$^{-10}$ relative to H2. This seemingly low abundance can be rationalized by destruction routes operating on interstel

Koutilya Pnvr, Bharat Singh, Pallabi Ghosh, Behjat Siddiquie

Large-scale pre-training tasks like image classification, captioning, or self-supervised techniques do not incentivize learning the semantic boundaries of objects. However, recent generative foundation models built using text-based latent diffusion techniques may learn semantic boundaries. This is because they have to synthesize intricate details about all o

Ali Kashefi, Leonidas J. Guibas, Tapan Mukerji

Regular physics-informed neural networks (PINNs) predict the solution of partial differential equations using sparse labeled data but only over a single domain. On the other hand, fully supervised learning models are first trained usually over a few thousand domains with known solutions (i.e., labeled data) and then predict the solution over a few hundred un

Jingtao Li, Xinyu Wang, Shaoyu Wang, Hengwei Zhao

Hyperspectral anomaly detection (HAD) involves identifying the targets that deviate spectrally from their surroundings, without prior knowledge. Recently, deep learning based methods have become the mainstream HAD methods, due to their powerful spatial-spectral feature extraction ability. However, the current deep detection models are optimized to complete a

Chao Chen, Haoyu Geng, Nianzu Yang, Xiaokang Yang

Dynamic graphs arise in various real-world applications, and it is often welcomed to model the dynamics directly in continuous time domain for its flexibility. This paper aims to design an easy-to-use pipeline (termed as EasyDGL which is also due to its implementation by DGL toolkit) composed of three key modules with both strong fitting ability and interpre

Jos Wetzels, Daniel dos Santos, Mohammad Ghafari

We inspected 45 actively deployed Operational Technology (OT) product families from ten major vendors and found that every system suffers from at least one trivial vulnerability. We reported a total of 53 weaknesses, stemming from insecure by design practices or basic security design failures. They enable attackers to take a device offline, manipulate its op

Zhen Hua, John Turek, Mike Childress, David Nolte

Enrolling patients in clinical trials to obtain fresh tumor biopsies to profile anticancer agents can be slow and expensive. However, if flash-frozen biopsies can be thawed to produce viable living tissue with relevant biodynamic profiles, then a large reservoir of tissue-banked samples could become available for phenotypic library building. Here, we report

Peng Kian Tan, Xi Jie Yeo, Alvin Zhen Wei Leow, Lijiong Shen

Many quantum sensing suggestions rely on temporal correlations found in photon pairs generated by parametric down-conversion. In this work, we show that the temporal correlations in light with a thermal photon statistics can be equally useful for such applications. Using a sub-threshold laser diode as an ultrabright source of thermal light, we demonstrate op