July 2022 arXiv papers — page 75

Marc Herrmann, Martin Obaidi, Larissa Chazette, Jil Klünder

Social aspects of software projects become increasingly important for research and practice. Different approaches analyze the sentiment of a development team, ranging from simply asking the team to so-called sentiment analysis on text-based communication. These sentiment analysis tools are trained using pre-labeled data sets from different sources, including

Matthieu Zins, Gilles Simon, Marie-Odile Berger

In this paper, we propose an object-based camera pose estimation from a single RGB image and a pre-built map of objects, represented with ellipsoidal models. We show that contrary to point correspondences, the definition of a cost function characterizing the projection of a 3D object onto a 2D object detection is not straightforward. We develop an ellipse-el

Daniele Bartolucci, Yeyao Hu, Aleks Jevnikar, Wen Yang

In this paper we prove that generically, in the sense of domain variations, the unbounded Rabinowitz continuum of solutions to a nonlinear eigenvalue problem is a simple analytic curve. The global bifurcation diagram resembles the classic model case of the Gel'fand problem in dimension two.

Minghan Chu, Xiaohua Wu, David E. Rival

It is well known that Boussinesq turbulent-viscosity hypothesis can introduce uncertainty in predictions for complex flow features such as separation, reattachment, and laminar-turbulent transition. This study adopts a recent physics-based uncertainty quantification (UQ) approach to address such model form uncertainty in Reynolds-averaged Naiver- Stokes (RAN

Oliver Attie, Sylvain Cappell

In this paper, we set up two surgery theories and two kinds of Whitehead torsion for foliations. First, we construct a bounded surgery theory and bounded Whitehead torsion for foliations, which correspond to the Connes' foliation algebra in the K-theory of operator algebras, in the sense that there is an analogy between surgery theory and index theory, and a

Christoph Grunau, Ahmet Alper Özüdoğru, Václav Rozhoň, Jakub Tětek

The famous $k$-means++ algorithm of Arthur and Vassilvitskii [SODA 2007] is the most popular way of solving the $k$-means problem in practice. The algorithm is very simple: it samples the first center uniformly at random and each of the following $k-1$ centers is then always sampled proportional to its squared distance to the closest center so far. Afterward

Justice Darko, Hyoshin Park

Traditionally, traffic incident management (TIM) programs coordinate the deployment of emergency resources to immediate incident requests without accommodating the interdependencies on incident evolutions in the environment. However, ignoring inherent interdependencies on the evolution of incidents in the environment while making current deployment decisions

Sudeep Salgia, Sattar Vakili, Qing Zhao

We study collaborative learning among distributed clients facilitated by a central server. Each client is interested in maximizing a personalized objective function that is a weighted sum of its local objective and a global objective. Each client has direct access to random bandit feedback on its local objective, but only has a partial view of the global obj

Dietmar Ferger

The supremum of the standardized empirical process is a promising statistic for testing whether the distribution function $F$ of i.i.d. real random variables is either equal to a given distribution function $F_0$ (hypothesis) or $F \ge F_0$ (one-sided alternative). Since \cite{r5} it is well-known that an affine-linear transformation of the suprema converge

Taihei Oki, Tasuku Soma

Matrix representations are a powerful tool for designing efficient algorithms for combinatorial optimization problems such as matching, and linear matroid intersection and parity. In this paper, we initiate the study of matrix representations using the concept of non-commutative rank (nc-rank), which has recently attracted attention in the research of Edmond

Hanbyel Cho, Yekang Lee, Jaemyung Yu, Junmo Kim

When a high-resolution (HR) image is degraded into a low-resolution (LR) image, the image loses some of the existing information. Consequently, multiple HR images can correspond to the LR image. Most of the existing methods do not consider the uncertainty caused by the stochastic attribute, which can only be probabilistically inferred. Therefore, the predict

Taehyeong Kim, Jaemin Park

Let $w=(w_1,\dots,w_d)$ be a $d$-tuple of positive real numbers such that $\sum_{i}w_i =1$ and $w_1\geq \cdots \geq w_d$. A $d$-dimensional vector $x=(x_1,\dots,x_d)\in\mathbb{R}^d$ is said to be $w$-singular if for every $\epsilon>0$ there exists $T_0>1$ such that for all $T>T_0$ the system of inequalities \[ \max_{1\leq i\leq d}|qx_i - p_i|^{\frac{1}{w_i}}

J. Blümlein, P. Marquard, C. Schneider, K. Schönwald

We report on the calculation of the three-loop polarized and unpolarized flavor non-singlet and the polarized singlet anomalous dimensions using massless off-shell operator matrix elements in a gauge-variant framework. We also reconsider the unpolarized two-loop singlet anomalous dimensions and correct errors in the foregoing literature.

HaoKe Xu, Mingqiang Gu, Fucong Fei, YiSheng Gu

Breaking time reversal symmetry in a topological insulator may lead to quantum anomalous Hall effect and axion insulator phase. MnBi4Te7 is a recently discovered antiferromagnetic topological insulator with TN ~12.5 K, which is constituted of alternatively stacked magnetic layer (MnBi2Te4) and non-magnetic layer (Bi2Te3). By means of scanning tunneling spect

Ali Ramezani-Kebrya, Iman Tabrizian, Fartash Faghri, Petar Popovski

Implementations of SGD on distributed systems create new vulnerabilities, which can be identified and misused by one or more adversarial agents. Recently, it has been shown that well-known Byzantine-resilient gradient aggregation schemes are indeed vulnerable to informed attackers that can tailor the attacks (Fang et al., 2020; Xie et al., 2020b). We introdu

Wei Wu, Junlin He, Yu Qiao, Guoheng Fu

The in-memory approximate nearest neighbor search (ANNS) algorithms have achieved great success for fast high-recall query processing, but are extremely inefficient when handling hybrid queries with unstructured (i.e., feature vectors) and structured (i.e., related attributes) constraints. In this paper, we present HQANN, a simple yet highly efficient hybrid

Niels Benedikter, Davide Desio

We revisit the derivation of the time-dependent Hartree-Fock equation for interacting fermions in a regime coupling a mean-field and a semiclassical scaling, contributing two comments to the result obtained in 2014 by Benedikter, Porta, and Schlein. First, the derivation holds in arbitrary space dimension. Second, by using an explicit formula for the unitary

Sophie Grivaux, Etienne Matheron, Quentin Menet

If $X$ is a separable reflexive Banach space, there are several natural Polish topologies on $\mathcal{B}(X)$, the set of contraction operators on $X$ (none of which being clearly ``more natural'' than the others), and hence several a priori different notions of genericity -- in the Baire category sense -- for properties of contraction operators. So it makes

Adya Danaditya, Lynnette Hui Xian Ng, Kathleen M. Carley

Coordinated campaigns in the digital realm have become an increasingly important area of study due to their potential to cause political polarization and threats to security through real-world protests and riots. In this paper, we introduce a methodology to profile two case studies of coordinated actions in Indonesian Twitter discourse. Combining network and

I. S. Krasil'shchik, O. I. Morozov

We study a Lagrangian extension of the 5d Mart\'inez Alonso--Shabat equation $\mathcal{E}$ \begin{equation*} u_{yz}=u_{tx}+u_y\,u_{xs}-u_x\,u_{ys} \end{equation*} that coincides with the cotangent equation $\mathcal{T^*E}$ to the latter. We describe the Lie algebra structure of its symmetries (which happens to be quite nontrivial and is described in terms of

Amir Shirian, Krishna Somandepalli, Victor Sanchez, Tanaya Guha

Perception of auditory events is inherently multimodal relying on both audio and visual cues. A large number of existing multimodal approaches process each modality using modality-specific models and then fuse the embeddings to encode the joint information. In contrast, we employ heterogeneous graphs to explicitly capture the spatial and temporal relationshi

Xiaolin Chen, Xuemeng Song, Liqiang Jing, Shuo Li

Text response generation for multimodal task-oriented dialog systems, which aims to generate the proper text response given the multimodal context, is an essential yet challenging task. Although existing efforts have achieved compelling success, they still suffer from two pivotal limitations: 1) overlook the benefit of generative pre-training, and 2) ignore

Qian Ye, Ling Jiang, Wang Zhen, Yuyang Du

Low-cost autonomous agents including autonomous driving vehicles chiefly adopt monocular 3D object detection to perceive surrounding environment. This paper studies 3D intermediate representation methods which generate intermediate 3D features for subsequent tasks. For example, the 3D features can be taken as input for not only detection, but also end-to-end

Jiazhen Liu, Xirong Li, Qijie Wei, Jie Xu

For retinal image matching (RIM), we propose SuperRetina, the first end-to-end method with jointly trainable keypoint detector and descriptor. SuperRetina is trained in a novel semi-supervised manner. A small set of (nearly 100) images are incompletely labeled and used to supervise the network to detect keypoints on the vascular tree. To attack the incomplet

Yu-Shan Tai, Cheng-Yang Chang, Chieh-Fang Teng, AnYeu

Recently, deep convolutional neural networks (CNNs) have achieved many eye-catching results. However, deploying CNNs on resource-constrained edge devices is constrained by limited memory bandwidth for transmitting large intermediated data during inference, i.e., activation. Existing research utilizes mixed-precision and dimension reduction to reduce computat

Yan-Liang Shi, Roxana Zeraati, Anna Levina, Tatiana A. Engel

Correlated fluctuations in the activity of neural populations reflect the network's dynamics and connectivity. The temporal and spatial dimensions of neural correlations are interdependent. However, prior theoretical work mainly analyzed correlations in either spatial or temporal domains, oblivious to their interplay. We show that the network dynamics and co

Yong Guo, Mingkui Tan, Zeshuai Deng, Jingdong Wang

Deep neural networks have exhibited remarkable performance in image super-resolution (SR) tasks by learning a mapping from low-resolution (LR) images to high-resolution (HR) images. However, the SR problem is typically an ill-posed problem and existing methods would come with several limitations. First, the possible mapping space of SR can be extremely large

Yafu Tian, Alexander Carballo, Ruifeng Li, Kazuya Takeda

Behavioral and semantic relationships play a vital role on intelligent self-driving vehicles and ADAS systems. Different from other research focused on trajectory, position, and bounding boxes, relationship data provides a human understandable description of the object's behavior, and it could describe an object's past and future status in an amazingly brief

Uddalok Sen, Detlef Lohse, Maziyar Jalaal

The retraction of an impacting droplet on a non-wetting substrate is often associated with the formation of a Worthington jet, which is fed by the retracting liquid. A non-Newtonian rheology of the liquid is known to affect the retraction of the impacting droplet. Here we present a novel phenomenon related to the impact of viscoelastic droplets on non-wettab

Finn Box, Lucie Domino, Tiago Outerelo Corvo, Mokhtar Adda-Bedia

Everyday experience confirms the tendency of adhesive films to detach from spheroidal regions of rigid substrates -- what is a petty frustration when placing a sticky bandage onto an elbow or knee is a more serious matter in the coating and painting industries. Irrespective of their resistance to bending, a key driver of such phenomena is Gauss' \textit{Theo

Muhammad Redo Ramadhan, Budi Adiperdana, Irwan Ramli, Dita Puspita Sari

The on-site Coulomb potential, U, and the covalent state of electronic orbitals play key roles for the Cooper pair symmetry and exotic electromagnetic properties of high-Tc superconducting cuprates. In this paper, we demonstrate a way to determine the value of U and present the whole picture of the covalent state of Cu spins in the mother system of the La-ba

Gerardo Urrutia, Fabio De Colle, Diego López-Cámara

After the detection of GRB 170817A, the first unambiguous off-axis gamma-ray burst (GRB), several studies tried to understand the structure of GRB jets. The initial jet structure (directly produced by the central engine) can be partially preserved, or can be completely modified by the interaction with the environment. In this study, we perform three-dimensio

Tomoyuki Kubota, Yudai Suzuki, Shumpei Kobayashi, Quoc Hoan Tran

Quantum computing has been moving from a theoretical phase to practical one, presenting daunting challenges in implementing physical qubits, which are subjected to noises from the surrounding environment. These quantum noises are ubiquitous in quantum devices and generate adverse effects in the quantum computational model, leading to extensive research on th

K. A. Villegas Rosales, P. T. Madathil, Y. J. Chung, L. N. Pfeiffer

Composite fermions (CFs), exotic quasi-particles formed by pairing an electron and an even number of magnetic flux quanta emerge at high magnetic fields in an interacting electron system, and can explain phenomena such as the fractional quantum Hall state (FQHS) and other many-body phases. CFs possess an effective mass ($m_{CF}$) whose magnitude is inversely

Yong Liu, Ran Yu, Fei Yin, Xinyuan Zhao

Recently, several spatial-temporal memory-based methods have verified that storing intermediate frames and their masks as memory are helpful to segment target objects in videos. However, they mainly focus on better matching between the current frame and the memory frames without explicitly paying attention to the quality of the memory. Therefore, frames with

Xin Feng, Haobo Ji, Wenjie Pei, Fanglin Chen

While the research on image background restoration from regular size of degraded images has achieved remarkable progress, restoring ultra high-resolution (e.g., 4K) images remains an extremely challenging task due to the explosion of computational complexity and memory usage, as well as the deficiency of annotated data. In this paper we present a novel model

Karl Schrader, Tobias Alt, Joachim Weickert, Michael Ertel

Euler's elastica constitute an appealing variational image inpainting model. It minimises an energy that involves the total variation as well as the level line curvature. These components are transparent and make it attractive for shape completion tasks. However, its gradient flow is a singular, anisotropic, and nonlinear PDE of fourth order, which is numeri

Taekyung Kim, Hojin Lee, Wonsuk Lee

Non-holonomic vehicle motion has been studied extensively using physics-based models. Common approaches when using these models interpret the wheel/ground interactions using a linear tire model and thus may not fully capture the nonlinear and complex dynamics under various environments. On the other hand, neural network models have been widely employed in th

Poornima Singh Thakur, Pritee Khanna, Tanuja Sheorey, Aparajita Ojha

Plant diseases are the primary cause of crop losses globally, with an impact on the world economy. To deal with these issues, smart agriculture solutions are evolving that combine the Internet of Things and machine learning for early disease detection and control. Many such systems use vision-based machine learning methods for real-time disease detection and

Amit Bhati, Neha Gour, Pritee Khanna, Aparajita Ojha

It is feasible to recognize the presence and seriousness of eye disease by investigating the progressions in retinal biological structure. Fundus examination is a diagnostic procedure to examine the biological structure and anomaly of the eye. Ophthalmic diseases like glaucoma, diabetic retinopathy, and cataract are the main reason for visual impairment arou

Wen Qi Wei, An He, Bo Yang, Jing-Zhi Huang

Silicon photonic integration has gained great success in many application fields owing to the excellent optical device properties and complementary metal-oxide semiconductor (CMOS) compatibility. Realizing monolithic integration of III-V lasers and silicon photonic components on single silicon wafer is recognized as a long-standing obstacle for ultra-dense p

Chaofan Zheng, Lianli Gao, Xinyu Lyu, Pengpeng Zeng

The current studies of Scene Graph Generation (SGG) focus on solving the long-tailed problem for generating unbiased scene graphs. However, most de-biasing methods overemphasize the tail predicates and underestimate head ones throughout training, thereby wrecking the representation ability of head predicate features. Furthermore, these impaired features from

Sreenath K. Manikandan

Here we explore the possibility of precise time-keeping in quantum systems using athermal resources. We show that quantum measurement engineered reservoirs can be used as athermal resources to drive the ticks of a quantum clock. Two and three level quantum systems act as transducers in our model, converting the quantum measurement induced noise to produce a

Gabriele D'Acunto, Paolo Di Lorenzo, Sergio Barbarossa

The inference of causal structures from observed data plays a key role in unveiling the underlying dynamics of the system. This paper exposes a novel method, named Multiscale-Causal Structure Learning (MS-CASTLE), to estimate the structure of linear causal relationships occurring at different time scales. Differently from existing approaches, MS-CASTLE takes

Muhamad Darwis Umar, Katsuhiko Ishida, Rie Murayama, Dita Puspita Sari

The muon spin relaxation method ({\mu}SR) is a powerful microscopic tool to probe electronic states of materials observing local magnetic field distributions on the muon. It often happens that a distribution of local magnetic fields shows intermediate state between Gaussian and Lorentzian shapes. In order to generally describe intermediate field distribution

Carlos Lopez-Coba, Sebastian F. Sanchez, Lihwai Lin, Joseph P. Anderson

We present MUSE integral field stellar and ionized velocity maps for a sample of 14 barred galaxies. Most of these objects exhibit "S"-shape iso-velocities in the bar region indicative of the presence of streaming motions in the velocity fields. % By applying circular rotation models we observe that bars leave symmetric structures in the residual maps of the

Shin'ichi Nojiri, Sergei D. Odintsov, Valerio Faraoni

While the Bekenstein-Hawking entropy is the unique notion of entropy that makes classical black hole thermodynamics consistent, alternative entropy notions (R\'enyi, Tsallis, and generalized constructs) abound in the literature. We explore conditions under which they are part of a consistent horizon thermodynamics for certain classes of modified gravity blac

Niccolò Zagli, Grigorios A. Pavliotis, Valerio Lucarini, Alexander Alecio

We consider a class of models describing an ensemble of identical interacting agents subject to multiplicative noise. In the thermodynamic limit, these systems exhibit continuous and discontinuous phase transitions in a, generally, nonequilibrium setting. We provide a systematic dimension reduction methodology for constructing low dimensional, reduced-order

Mir Shahnawaz Ahmed, Shahid Mehraj Shah

The Internet of Things (IoT) has altered living by controlling devices/things over the Internet. IoT has specified many smart solutions for daily problems, transforming cyber-physical systems (CPS) and other classical fields into smart regions. Most of the edge devices that make up the Internet of Things have very minimal processing power. To bring down the

Allen Juntao Fang

In this paper, we prove that the slowly-rotating Kerr-de Sitter family of black holes are linearly stable as a family of solutions to the Einstein vacuum equations with $\Lambda>0$ in harmonic (wave) gauge. This article is part of a series that provides a novel proof of the full nonlinear stability of the slowly-rotating Kerr-de Sitter family. This paper and

Mahyuddin K. M. Nasution, Rahmat Hidayat, Rahmad Syah

Possible for science itself, conceptually, to have and will understand differently, let alone science also seen as technology, such as computer science. After all, science and technology are viewpoints diverse by either individual, community, or social. Generally, it depends on socioeconomic capabilities. So it is with computer science has become a phenomeno

Juze Zhang, Jingya Wang, Ye Shi, Fei Gao

Inter-person occlusion and depth ambiguity make estimating the 3D poses of monocular multiple persons as camera-centric coordinates a challenging problem. Typical top-down frameworks suffer from high computational redundancy with an additional detection stage. By contrast, the bottom-up methods enjoy low computational costs as they are less affected by the n

Kuantay Boshkayev, Gulmira Nurbakyt, Hernando Quevedo, Gulnara Suliyeva

We present the main aspects of the adiabatic theory and show that it can be used to study the motion of test particles in general relativity. The theory is based upon the use of vector elements of the orbits and adiabatic invariants. To prove the applicability of the adiabatic theory in Einstein's gravity, we derive a particular representation of the Kerr me

Minhyeok Lee, Chaewon Park, Suhwan Cho, Sangyoun Lee

RGB-D salient object detection (SOD) has been in the spotlight recently because it is an important preprocessing operation for various vision tasks. However, despite advances in deep learning-based methods, RGB-D SOD is still challenging due to the large domain gap between an RGB image and the depth map and low-quality depth maps. To solve this problem, we p

Mikhail Tsitsvero, Mingoo Jin, Andrey Lyalin

Uncertainty control and scalability to large datasets are the two main issues for the deployment of Gaussian process (GP) models within the autonomous machine learning-based prediction pipelines in material science and chemistry. One way to address both of these issues is by introducing the latent inducing point variables and choosing the right approximation

Yarden Rotem, Nathaniel Shimoni, Lior Rokach, Bracha Shapira

In this paper, we propose an innovative Transfer learning for Time series classification method. Instead of using an existing dataset from the UCR archive as the source dataset, we generated a 15,000,000 synthetic univariate time series dataset that was created using our unique synthetic time series generator algorithm which can generate data with diverse pa

Dongjiang Cao, Ruofeng Liu, Hao Li, Shuai Wang

Human identification is a key requirement for many applications in everyday life, such as personalized services, automatic surveillance, continuous authentication, and contact tracing during pandemics, etc. This work studies the problem of cross-modal human re-identification (ReID), in response to the regular human movements across camera-allowed regions (e.

Haimei Zhao, Jing Zhang, Sen Zhang, Dacheng Tao

Depth estimation, visual odometry (VO), and bird's-eye-view (BEV) scene layout estimation present three critical tasks for driving scene perception, which is fundamental for motion planning and navigation in autonomous driving. Though they are complementary to each other, prior works usually focus on each individual task and rarely deal with all three tasks

Muhammad Abdullah Jamal, Omid Mohareri

Data-driven approaches to assist operating room (OR) workflow analysis depend on large curated datasets that are time consuming and expensive to collect. On the other hand, we see a recent paradigm shift from supervised learning to self-supervised and/or unsupervised learning approaches that can learn representations from unlabeled datasets. In this paper, w

Haris Fawad, Pål Ryalen, Vasiliki Tsarpali, Kristian Heldal

Patients with end-stage kidney disease can expect to wait for several years before they receive a transplant, all the while their health deteriorates. How would the survival change if we managed to reduce these waiting times? To provide an answer, we present a continuous-time marginal structural model (MSM) of hypothetical scenarios in which the time until t

Sneha Mavi, Anuj Bishnoi

In this paper, for a valued field $(K, v)$ of arbitrary rank and an extension $w$ of $v$ to $K(X),$ a relation between induced complete sequences of abstract key polynomials and MacLane-Vaqui\'e chains is given.

Kenneth Duru, Christopher Williams, Frederick Fung

In this paper we derive and analyse efficient and stable numerical methods for accurate numerical simulations of nonlinear dynamic shear ruptures on non-planar faults embedded in 3D elastic solids using dual-paring (DP) summation by parts (SBP) finite difference (FD) methods. Specifically, for nonlinear dynamic earthquake ruptures, we demonstrate that the DP

Chia-Rui Chang, Yue Song, Fan Li, Rui Wang

When analyzing data from randomized clinical trials, covariate adjustment can be used to account for chance imbalance in baseline covariates and to increase precision of the treatment effect estimate. A practical barrier to covariate adjustment is the presence of missing data. In this paper, in the light of recent theoretical advancement, we first review sev

Zhenchao Jin, Dongdong Yu, Luchuan Song, Zehuan Yuan

Feature pyramid network (FPN) is one of the key components for object detectors. However, there is a long-standing puzzle for researchers that the detection performance of large-scale objects are usually suppressed after introducing FPN. To this end, this paper first revisits FPN in the detection framework and reveals the nature of the success of FPN from th

Davide Buffelli, Pietro Liò, Fabio Vandin

In the past few years, graph neural networks (GNNs) have become the de facto model of choice for graph classification. While, from the theoretical viewpoint, most GNNs can operate on graphs of any size, it is empirically observed that their classification performance degrades when they are applied on graphs with sizes that differ from those in the training d

Adrian Langer

We prove some strong results on approximation of strongly semistable bundles with vanishing numerical Chern classes by filtrations, whose quotients are line bundles of similar slope. This generalizes some earlier results of Parameswaran-Subramanian in the curve case and Koley-Parameswaran in the surface case and it confirms the conjecture posed by Koley and

Juan Gómez-Luna, Yuxin Guo, Sylvan Brocard, Julien Legriel

Training machine learning (ML) algorithms is a computationally intensive process, which is frequently memory-bound due to repeatedly accessing large training datasets. As a result, processor-centric systems (e.g., CPU, GPU) suffer from costly data movement between memory units and processing units, which consumes large amounts of energy and execution cycles.

Jingjia Huang, Yinan Li, Jiashi Feng, Xinglong Wu

Building a universal Video-Language model for solving various video understanding tasks (\emph{e.g.}, text-video retrieval, video question answering) is an open challenge to the machine learning field. Towards this goal, most recent works build the model by stacking uni-modal and cross-modal feature encoders and train it with pair-wise contrastive pre-text t

Deacon Linkhorn

Let I be a dense linear order with a left endpoint but no right endpoint. We consider the lattice L(I) of finite unions of closed intervals of I. This lattice arises naturally in the setting of o-minimality, as these are precisely the closed definable sets in any o-minimal expansion of I. Our main result says that L(I), the expansion of the lattice by consta

Zhilu Lai, Wei Liu, Xudong Jian, Kiran Bacsa

The order/dimension of models derived on the basis of data is commonly restricted by the number of observations, or in the context of monitored systems, sensing nodes. This is particularly true for structural systems (e.g., civil or mechanical structures), which are typically high-dimensional in nature. In the scope of physics-informed machine learning, this

Ertuğrul Ekiz, Oguzhan Kasikci, Mehmet Ozkan, Cemal Berfu Senisik

We present a method of contraction that can be applied to re-construct the recent extended non-relativistic and ultra-relativistic algebras as well as corresponding action principles. The methodology involves the use of multiple copies of Poincar\'e algebra. Consequently, the contraction defines non-relativistic or ultra-relativistic limits of multimetric th

Jianzhu Huai, Yukai Lin, Yujia Zhang

Nonlinear systems of affine control inputs overarch many sensor fusion instances. Analyzing whether a state variable in such a nonlinear system can be estimated (i.e., observability) informs better estimator design. Among the research on local observability of nonlinear systems, approaches based on differential geometry have attracted much attention for the

Lei Zhang, Alessandro Ridolfi, Harsha Blumer, Paulo Freire

We report the discovery of a new 5.78 ms-period millisecond pulsar (MSP), PSR J1740-5340B (NGC 6397B), in an eclipsing binary system discovered with the Parkes radio telescope (now also known as Murriyang), Australia, and confirmed with the MeerKAT radio telescope in South Africa. The measured orbital period, 1.97 days, is the longest among all eclipsing bin

Rahul Mukerjee

Let a stick be broken at random at n-1 points to form n pieces. We consider three problems on forming k-gons with k out of these n pieces, and show how a statistical approach, through a linear transformation of variables, yields simple solutions that also allow fast computation.

Saurabh Sharma, Devendra K. Ojha, Arpan Ghosh, Joe P. Ninan

We present the design and performance of the TANSPEC, a medium-resolution $0.55-2.5~\mu$m cryogenic spectrometer and imager, now in operation at the 3.6-m Devasthal Optical Telescope (DOT), Nainital, India. The TANSPEC provides three modes of operation which include, photometry with broad- and narrow-band filters, spectroscopy with short slits of 20$^{\prime

Chongze Wang, Shuyuan Liu, Hyunsoo Jeon, Yu Jia

Using first-principles density functional theory calculations, we investigate the pressure-induced quantum phase transition (QPT) from the charge density wave (CDW) to the pristine phase in the layered kagome metal CsV$_3$Sb$_5$ consisting of three-atom-thick Sb$-$V$_3$Sb$-$Sb and one-atom-thick Cs layers. The CDW structure having the formation of trimeric a

Gianluca Francica, Luca Dell'Anna

When a system is driven across a second-order quantum phase transition, the number of defects which are produced scales with the speed of the variation of the tuning parameter according to a universal law described by the Kibble-Zurek mechanism. We study a possible breakdown of this prediction proving that the number of defects can exhibit another universal

Diane Wagner, Fabio Ferreira, Danny Stoll, Robin Tibor Schirrmeister

Self-Supervised Learning (SSL) has become a very active area of Deep Learning research where it is heavily used as a pre-training method for classification and other tasks. However, the rapid pace of advancements in this area comes at a price: training pipelines vary significantly across papers, which presents a potentially crucial confounding factor. Here,

Zizheng Huang, Haoxing Chen, Ziqi Wen, Chao Zhang

Contrastive learning (CL) continuously achieves significant breakthroughs across multiple domains. However, the most common InfoNCE-based methods suffer from some dilemmas, such as \textit{uniformity-tolerance dilemma} (UTD) and \textit{gradient reduction}, both of which are related to a $\mathcal{P}_{ij}$ term. It has been identified that UTD can lead to un

Zhen-Ting Ong, Bhan Lam, Kenneth Ooi, Karn N. Watcharasupat

Studies involving soundscape perception often exclude participants with hearing loss to prevent impaired perception from affecting experimental results. Participants are typically screened with pure tone audiometry, the "gold standard" for identifying and quantifying hearing loss at specific frequencies, and excluded if a study-dependent threshold is not met

Xin-zhe Zhang, Lei-hua Liu, Taotao Qiu

In this paper, we investigate the primordial perturbations of inflation model induced from the multi-field mimetic gravity, where there are two field during inflation, and thus both adiabatic and isocurvature perturbation modes are generated. We show that although it is true that the original adiabatic perturbation mode loses the kinetic term due to the cons

Luca Cavalli, Marc Pollefeys, Daniel Barath

Since RANSAC, a great deal of research has been devoted to improving both its accuracy and run-time. Still, only a few methods aim at recognizing invalid minimal samples early, before the often expensive model estimation and quality calculation are done. To this end, we propose NeFSAC, an efficient algorithm for neural filtering of motion-inconsistent and po

Sushant Dutta, Veeresh Singh, C. H. Ishwara Chandra, Yogesh Wadadekar

The cessation of AGN activity in radio galaxies leads to a remnant phase during which jets are no longer sustained, but lobes can be detected for a period of time before they fade away due to radiative and dynamical energy losses. The time-scale of the remnant phase and AGN duty cycle are vital to understanding the evolution of radio galaxies. In this paper,

Yuhong Deng, Qie Sima, Di Guo, Huaping Liu

The ability to handle objects in cluttered environment has been long anticipated by robotic community. However, most of works merely focus on manipulation instead of rendering hidden semantic information in cluttered objects. In this work, we introduce the scene graph for embodied exploration in cluttered scenarios to solve this problem. To validate our meth

Shunli Wang, Shuaibing Wang, Bo Jiao, Dingkang Yang

Reliable and stable 6D pose estimation of uncooperative space objects plays an essential role in on-orbit servicing and debris removal missions. Considering that the pose estimator is sensitive to background interference, this paper proposes a counterfactual analysis framework named CASpaceNet to complete robust 6D pose estimation of the spaceborne targets u

Zixuan Zhou, Xuefei Ning, Yi Cai, Jiashu Han

One-shot Neural Architecture Search (NAS) has been widely used to discover architectures due to its efficiency. However, previous studies reveal that one-shot performance estimations of architectures might not be well correlated with their performances in stand-alone training because of the excessive sharing of operation parameters (i.e., large sharing exten

Xiaotian Lin, Leiyang Xu, Qiang Wang

In the past decade, object detection tasks are defined mostly by large public datasets. However, building object detection datasets is not scalable due to inefficient image collecting and labeling. Furthermore, most labels are still in the form of bounding boxes, which provide much less information than the real human visual system. In this paper, we present

S. Adhikari, Y. Wang, P. Spaeth, F. Scalerandi

Magnetic nanomaterials record information as fast as picoseconds in computer memories but retain it for millions of years in ancient rocks. This exceedingly broad range of times is covered by hopping over a potential energy barrier through temperature, ultrafast optical excitation for demagnetization or magnetization manipulation, mechanical stress, or micro

Emily Frost, Dylan Helliwell, Suki Shergill

We explore taxicab conic sections from the perspective of slicing taxicab cones by planes, as opposed to the more well-studied approach from the perspective of distance formulations. After establishing a significant amount of structural framework, a complete characterization of the resulting taxicab conic sections is established, and a number of special case

Andrzej A. Zdziarski, Elise Egron

We calculate the electron-positron pair production rate at the base of the jet of Cyg X-1 by collisions of photons from its hot accretion flow using the measurement of its average soft gamma-ray spectra by the Compton Gamma Ray Observatory and INTEGRAL satellites. We have found that this rate approximately equals the flow rate of the leptons emitting the obs

Jonathan Pan

Financial forensics has an important role in the field of finance to detect and investigate the occurrence of finance related crimes like money laundering. However, as with other forms of criminal activities, the forensics analysis of such activities is a complex undertaking with attempts by the adversaries to constantly upgrade their ability to evade detect

Minki Jeong, Wanyeong Jung

DRAM-based in-situ accelerators have shown their potential in addressing the memory wall challenge of the traditional von Neumann architecture. Such accelerators exploit charge sharing or logic circuits for simple logic operations at the DRAM subarray level. However, their throughput is limited due to low array utilization, as only a few row cells in a DRAM

Hongtao Wen, Jianhang Yan, Wanli Peng, Yi Sun

Grasp pose estimation is an important issue for robots to interact with the real world. However, most of existing methods require exact 3D object models available beforehand or a large amount of grasp annotations for training. To avoid these problems, we propose TransGrasp, a category-level grasp pose estimation method that predicts grasp poses of a category

Matthias Maier, John N. Shadid, Ignacio Tomas

We discuss structure-preserving numerical discretizations for repulsive and attractive Euler-Poisson equations that find applications in fluid-plasma and self-gravitation modeling. The scheme is fully discrete and structure preserving in the sense that it maintains a discrete energy law, as well as hyperbolic invariant domain properties, such as positivity o

Jianfei Yang, Xinyan Chen, Dazhuo Wang, Han Zou

WiFi sensing has been evolving rapidly in recent years. Empowered by propagation models and deep learning methods, many challenging applications are realized such as WiFi-based human activity recognition and gesture recognition. However, in contrast to deep learning for visual recognition and natural language processing, no sufficiently comprehensive public

Zhongzhan Huang, Senwei Liang, Mingfu Liang, Wei He

Recently many plug-and-play self-attention modules (SAMs) are proposed to enhance the model generalization by exploiting the internal information of deep convolutional neural networks (CNNs). In general, previous works ignore where to plug in the SAMs since they connect the SAMs individually with each block of the entire CNN backbone for granted, leading to

Purba Mukherjee

The present thesis is devoted to the non-parametric reconstruction of some cosmological parameters using diverse observational datasets. The Universe is assumed to be spatially homogeneous and isotropic, thus described by the FLRW metric. The first chapter provides a brief introduction to cosmology and focuses on the reconstruction methods. An assessment of

Iskander A. Taimanov

This article surveys the Weierstrass representation of surfaces in the three- and four-dimensional spaces, with an emphasis on its relation to the Willmore functional. We also describe an application of this representation to constructing a new type of solutions to the Davey-Stewartson II equation. They have regular initial data, gain one-point singularities

Alexander Fradkov

In the paper a simple discrete dynamical model for dynamics of two antagonistic agents (opponents) under iterated sanctions and counter-sanctions is proposed. The model is inspired with Osipov-Lanchester model for combats. Simple stability criteria are derived both for the full information case and for stochastic uncertainty case. The results provide some im

Xing-Long Zhu, Wei-Yuan Liu, Min Chen, Su-Ming Weng

Laser-driven ion acceleration is an attractive technique for compact high-energy ion sources. Currently, among various physical and technical issues to be solved, the boost of ion energy and the reduction of energy spread represent the key challenges with this technique. Here we present a scheme to tackle these challenges by using a hundred-terawatt-class la