October 2022 arXiv papers — page 137

Juan Yepes, Daniel Raviv

Looming, traditionally defined as the relative expansion of objects in the observer's retina, is a fundamental visual cue for perception of threat and can be used to accomplish collision free navigation. In this paper we derive novel solutions for obtaining visual looming quantitatively from the 2D motion field resulting from a six-degree-of-freedom motion o

Yan V. Sym, João Gabriel M. Campos, Marcos M. José, Fabio G. Cozman

The majority of NLG systems have been designed following either a template-based or a pipeline-based architecture. Recent neural models for data-to-text generation have been proposed with an end-to-end deep learning flavor, which handles non-linguistic input in natural language without explicit intermediary representations. This study compares the most often

Steven Squires, Elaine F. Harkness, D. Gareth Evans, Susan M. Astley

Purpose: High breast density is associated with reduced efficacy of mammographic screening and increased risk of developing breast cancer. Accurate and reliable automated density estimates can be used for direct risk prediction and passing density related information to further predictive models. Expert reader assessments of density show a strong relationshi

Shangbin Feng, Zhaoxuan Tan, Wenqian Zhang, Zhenyu Lei

With the advent of pretrained language models (LMs), increasing research efforts have been focusing on infusing commonsense and domain-specific knowledge to prepare LMs for downstream tasks. These works attempt to leverage knowledge graphs, the de facto standard of symbolic knowledge representation, along with pretrained LMs. While existing approaches have l

Vincent Grondin, François Pomerleau, Philippe Giguère

Vision-based segmentation in forested environments is a key functionality for autonomous forestry operations such as tree felling and forwarding. Deep learning algorithms demonstrate promising results to perform visual tasks such as object detection. However, the supervised learning process of these algorithms requires annotations from a large diversity of i

Hugo P. Maia, Silvio C. Ferreira, Marcelo L. Martins

Rumors have ignited revolutions, undermined the trust in political parties, or threatened the stability of human societies. Such destructive potential has been significantly enhanced by the development of on-line social networks. Several theoretical and computational studies have been devoted to understanding the dynamics and to control rumor spreading. In t

Josef Taalbi

A long-standing discussion is to what extent patents can be used to monitor trends in innovation activity. This study quantifies the amount and quality of information about actual innovation contained in the patent system, based on 4,460 Swedish innovations (1970-2015) that have been matched to international patents. The results show that most innovations we

Patrick Concha, Evelyn Rodríguez, Gustavo Rubio

In this work we present a non-relativistic gravity theory defined in four spacetime dimensions using the MacDowell-Mansouri geometrical formulation. We obtain a Newtonian gravity action which is constructed from the curvature of a Newton-Hooke version of the so-called Newtonian algebra. We show that the non-relativistic gravity theory presented here contains

Chao Cheng, Liangyuan Hu, Fan Li

The marginal structure quantile model (MSQM) provides a unique lens to understand the causal effect of a time-varying treatment on the full distribution of potential outcomes. Under the semiparametric framework, we derive the efficiency influence function for the MSQM, from which a new doubly robust estimator is proposed for point estimation and inference. W

Wagner A. A. de Moraes, Joel E. Restrepo, Michael Ruzhansky

We prove existence, uniqueness and give the analytical solution of heat and wave type equations on a compact Lie group $G$ by using a non-local (in time) differential operator and a positive left invariant operator (maybe unbounded) acting on the group. For heat type equations, solutions are given in $L^q(G)$ for data in $L^p(G)$ with $1<p\leqslant 2\leqslan

Victor Ceballos Inza, Florian Rist, Johannes Wallner, Helmut Pottmann

The property of a surface being developable can be expressed in different equivalent ways, by vanishing Gauss curvature, or by the existence of isometric mappings to planar domains. Computational contributions to this topic range from special parametrizations to discrete-isometric mappings. However, so far a local criterion expressing developability of gener

Nathan Dahlin, Subhonmesh Bose, Venugopal V. Veeravalli

We consider the control of a Markov decision process (MDP) that undergoes an abrupt change in its transition kernel (mode). We formulate the problem of minimizing regret under control-switching based on mode change detection, compared to a mode-observing controller, as an optimal stopping problem. Using a sequence of approximations, we reduce it to a quickes

Susmita Sadhu, Saikat Chakraborty Thakur

In this paper, we develop a method of analyzing long transient dynamics in a class of predator-prey models with two species of predators competing explicitly for their common prey, where the prey evolves on a faster timescale than the predators. In a parameter regime near a {\em{singular zero-Hopf bifurcation}} of the coexistence equilibrium state, we assume

Ji Won Park, Samuel Stanton, Saeed Saremi, Andrew Watkins

Bayesian optimization offers a sample-efficient framework for navigating the exploration-exploitation trade-off in the vast design space of biological sequences. Whereas it is possible to optimize the various properties of interest jointly using a multi-objective acquisition function, such as the expected hypervolume improvement (EHVI), this approach does no

Nina Lebedeva, Anton Petrunin

A metric space is said to be all-set-homogeneous if any of its partial isometries can be extended to a genuine isometry. We give a classification of a certain subclass of all-set-homogeneous length spaces.

Xiao Liu, Lijun Zhang, Hui Guan

Message passing neural networks (MPNNs) learn the representation of graph-structured data based on graph original information, including node features and graph structures, and have shown astonishing improvement in node classification tasks. However, the expressive power of MPNNs is upper bounded by the first-order Weisfeiler-Leman test and its accuracy stil

Ye Hong, Henry Martin, Martin Raubal

Predicting the next visited location of an individual is a key problem in human mobility analysis, as it is required for the personalization and optimization of sustainable transport options. Here, we propose a transformer decoder-based neural network to predict the next location an individual will visit based on historical locations, time, and travel modes,

Ali Waqar Azim, Ahmad Bazzi, Mahrukh Fatima, Raed Shubair

We propose a dual-mode (DM) time domain multiplexed (TDM) chirp spread spectrum (CSS) modulation for spectral and energy-efficient low-power wide-area networks (LPWANs). DM-CSS modulation that uses both the even and odd cyclic time shifts has been proposed for LPWANs to achieve noteworthy performance improvement over classical counterparts. However, its spec

Kohei Kawabata, Anish Kulkarni, Jiachen Li, Tokiro Numasawa

We study the open quantum dynamics of the Sachdev-Ye-Kitaev (SYK) model described by the Lindblad master equation, where the SYK model is coupled to Markovian reservoirs with jump operators that are either linear or quadratic in the Majorana fermion operators. Of particular interest for us is the time evolution of the dissipative form factor, which quantifie

Yihua Zhang, Yuguang Yao, Parikshit Ram, Pu Zhao

The deployment constraints in practical applications necessitate the pruning of large-scale deep learning models, i.e., promoting their weight sparsity. As illustrated by the Lottery Ticket Hypothesis (LTH), pruning also has the potential of improving their generalization ability. At the core of LTH, iterative magnitude pruning (IMP) is the predominant pruni

Anh Tung Nguyen, Sribalaji C. Anand, André M. H. Teixeira

This paper proposes a game-theoretic approach to address the problem of optimal sensor placement against an adversary in uncertain networked control systems. The problem is formulated as a zero-sum game with two players, namely a malicious adversary and a detector. Given a protected performance vertex, we consider a detector, with uncertain system knowledge,

Deniz Ağaoğlu Çağırıcı, Onur Çağırıcı

A unit disk graph is the intersection graph of a set of disk of unit radius in the Euclidean plane. In 1998, Breu and Kirkpatrick showed that the recognition problem for unit disk graphs is NP-hard. Given $k$ horizontal and $m$ vertical lines, an APUD($k,m$) is a unit disk graph such that each unit disk is centered either on a given horizontal or vertical li

Tzula B. Propp

Quantum mechanics cements the intimate relationship between the nature of light and its detection. Historically, quantum theories of photodetection have generally fallen into two categories: the first tries to determine what quantum field observable is measured when photoelectrons are detected, laying the theoretical groundwork for photodetection being possi

Mohammad Ismail Daud

Current content filtering and blocking methods are susceptible to various circumvention techniques and are relatively slow in dealing with new threats. This is due to these methods using shallow pattern recognition that is based on regular expression rules found in crowdsourced block lists. We propose a novel system that aims to remedy the aforementioned iss

Blerta Lindqvist

This paper uses symmetry to make Convolutional Neural Network classifiers (CNNs) robust against adversarial perturbation attacks. Such attacks add perturbation to original images to generate adversarial images that fool classifiers such as road sign classifiers of autonomous vehicles. Although symmetry is a pervasive aspect of the natural world, CNNs are una

Md. Mohaimenul Islam Sourav, Mohammed Russedul Islam, H M Imran Kays, Md. Hadiuzzaman

This research aims at building a multivariate statistical model for assessing users' perceptions of acceptance of ride-sharing services in Dhaka City. A structured questionnaire is developed based on the users' reported attitudes and perceived risks. A total of 350 normally distributed responses are collected from ride-sharing service users and stakeholders

Shijie Li, Ming-Ming Cheng, Juergen Gall

The goal of semantic image synthesis is to generate photo-realistic images from semantic label maps. It is highly relevant for tasks like content generation and image editing. Current state-of-the-art approaches, however, still struggle to generate realistic objects in images at various scales. In particular, small objects tend to fade away and large objects

Lois Orosa, Ulrich Rührmair, A. Giray Yaglikci, Haocong Luo

RowHammer is a DRAM vulnerability that can cause bit errors in a victim DRAM row solely by accessing its neighboring DRAM rows at a high-enough rate. Recent studies demonstrate that new DRAM devices are becoming increasingly vulnerable to RowHammer, and many works demonstrate system-level attacks for privilege escalation or information leakage. In this work,

Sumegha Premchandar, Sandeep Madireddy, Sanket Jantre, Prasanna Balaprakash

Robust machine learning models with accurately calibrated uncertainties are crucial for safety-critical applications. Probabilistic machine learning and especially the Bayesian formalism provide a systematic framework to incorporate robustness through the distributional estimates and reason about uncertainty. Recent works have shown that approximate inferenc

Jeremy Hoskins, Manas Rachh, John C Schotland

We consider the quantum electrodynamics of a binary array of chiral waveguides, each containing many atoms. We show that the one-photon amplitude of a single-excitation state obeys a two-dimensional Dirac equation. Using this result, we develop the scattering theory for the Dirac equation in this setting and illustrate our results with numerical simulations.

Jaemin Yoo, Meng-Chieh Lee, Shubhranshu Shekhar, Christos Faloutsos

How can we solve semi-supervised node classification in various graphs possibly with noisy features and structures? Graph neural networks (GNNs) have succeeded in many graph mining tasks, but their generalizability to various graph scenarios is limited due to the difficulty of training, hyperparameter tuning, and the selection of a model itself. Einstein sai

Jared Coleman, Evangelos Kranakis, Danny Krizanc, Oscar Morales-Ponce

Two cooperating, autonomous mobile robots with arbitrary nonzero max speeds are placed at arbitrary initial positions in the plane. A remotely detonated bomb is discovered at some source location and must be moved to a safe distance away from its initial location as quickly as possible. In the Bomb Squad problem, the robots cooperate by communicating face-to

Mengxi Liu, Sizhen Bian, Bo Zhou, Agnes Grünerbl

This paper presents a novel beverage intake monitoring system that can accurately recognize beverage kinds and freshness. By mounting carbon electrodes on the commercial cup, the system measures the electrochemical impedance spectrum of the fluid in the cup. We studied the frequency sensitivity of the electrochemical impedance spectrum regarding distinct bev

Jing Wang, HaiYing Wang, Shifeng Xiong

To tackle massive data, subsampling is a practical approach to select the more informative data points. However, when responses are expensive to measure, developing efficient subsampling schemes is challenging, and an optimal sampling approach under measurement constraints was developed to meet this challenge. This method uses the inverses of optimal samplin

Alfredo M. Ozorio de Almeida

Oscillations in the probability density of quantum transitions of the eigenstates of a chaotic Hamiltonian within classically narrow energy ranges have been shown to depend on closed compound orbits. These are formed by a pair of orbit segments, one in the energy shell of the original Hamiltonian and the other in the energy shell of the driven Hamiltonian, w

Luigi Ambrosio, Shayan Aziznejad, Camillo Brena, Michael Unser

In this paper, we characterize the class of extremal points of the unit ball of the Hessian-Schatten total variation (HTV) functional. The underlying motivation for our work stems from a general representer theorem that characterizes the solution set of regularized linear inverse problems in terms of the extremal points of the regularization ball. Our analys

Aleksandar Petrov, Marta Kwiatkowska

Unsupervised representation learning leverages large unlabeled datasets and is competitive with supervised learning. But non-robust encoders may affect downstream task robustness. Recently, robust representation encoders have become of interest. Still, all prior work evaluates robustness using a downstream classification task. Instead, we propose a family of

M. J. Mehdipour, A. Rejali

In this paper, we study the weak amenability of weighted measure algebras and prove that $M(G, \omega)$ is weakly amenable if and only if $G$ is discrete and every bounded quasi-additive function is inner. We also study the weak amenability of $L^1(G, \omega)^{**}$ and $M(G, \omega)^{**}$ and show that the weak amenability of theses Banach algebras are equiv

Haoyu Wang, Hongming Zhang, Yueguan Wang, Yuqian Deng

Natural language expresses events with varying granularities, where coarse-grained events (goals) can be broken down into finer-grained event sequences (steps). A critical yet overlooked aspect of understanding event processes is recognizing that not all step events hold equal importance toward the completion of a goal. In this paper, we address this gap by

Tzu-Hsiang Lin, Ta-Chung Chi, Anna Rumshisky

Recent advancements in dialogue response selection (DRS) are based on the \textit{task-adaptive pre-training (TAP)} approach, by first initializing their model with BERT~\cite{devlin-etal-2019-bert}, and adapt to dialogue data with dialogue-specific or fine-grained pre-training tasks. However, it is uncertain whether BERT is the best initialization choice, o

Yao Wei, George Vosselman, Michael Ying Yang

Generating a 3D point cloud from a single 2D image is of great importance for 3D scene understanding applications. To reconstruct the whole 3D shape of the object shown in the image, the existing deep learning based approaches use either explicit or implicit generative modeling of point clouds, which, however, suffer from limited quality. In this work, we ai

Carlos A. Bertulani, Francis W. Hall, Benjamin I. Santoyo

The Big Bang Nucleosynthesis (BBN) model is a cornerstone for the understanding of the evolution of the early universe, making seminal predictions that are in outstanding agreement with the present observation of light element abundances in the universe. Perhaps, the only remaining issue to be solved by theory is the so-called "lithium abundance problem". De

Liam Armstrong, Bryan Ducasse, Thomas Meyer, Holly Swisher

In 2020, Kang and Park conjectured a "level $2$" Alder-type partition inequality which encompasses the second Rogers-Ramanujan Identity. Duncan, Khunger, the fourth author, and Tamura proved Kang and Park's conjecture for all but finitely many cases utilizing a "shift" inequality and conjectured a further, weaker generalization that would extend both Alder's

Prabha Chuphal, Vladimir V. Palyulin

We study conformations and dynamics of active star polymers. The analysis shows that active star polymers stretching behaviour is quite different from that of active linear chains. The visual inspection of conformations and bond-bond correlations reveal a better coordination for the alignment and coordination of bonds for the star polymers than for the linea

Prashant Pandey, Michael A. Bender, Alex Conway, Martín Farach-Colton

Modern hash table designs strive to minimize space while maximizing speed. The most important factor in speed is the number of cache lines accessed during updates and queries. This is especially important on PMEM, which is slower than DRAM and in which writes are more expensive than reads. This paper proposes two stronger design objectives: stability and low

Julius Jankowski, Lara Brudermüller, Nick Hawes, Sylvain Calinon

Achieving reactive robot behavior in complex dynamic environments is still challenging as it relies on being able to solve trajectory optimization problems quickly enough, such that we can replan the future motion at frequencies which are sufficiently high for the task at hand. We argue that current limitations in Model Predictive Control (MPC) for robot man

Sonia Díaz-Santos, Pino Caballero-Gil

The main objective of this work is to detect early if a driver shows symptoms of sleepiness that indicate that he/she is falling asleep and, in that case, generate an alert to wake him/her up. To solve this problem, an application has been designed that collects various parameters, through a smartwatch while driving. First, the application detects the drivin

Kalpesh Ghag, Anil Raghav, Zubair Shaikh, Georgios Nicolaou

Interplanetary Coronal Mass Ejections (ICMEs) and High Speed Streams (HSSs) are noteworthy drivers of disturbance of interplanetary space. Interaction between them can cause several phenomena, such as; generation of waves, enhanced geo-effectiveness, particle acceleration, etc. However, how does thermodynamic properties vary during the ICME-HSS interaction r

Javier Correa-Marichal, Pino Caballero-Gil, Carlos Rosa-Remedios, Rames Sarwat-Shaker

The National Identity Document is a fundamental piece of documentation for the identification of citizens throughout the world. That is precisely the case of the DNI (Documento Nacional de Identidad) of Spain. Its importance has been enhanced in recent years with the addition of a chip for the authentication of users within telematic administrative services.

Konstantin Osetrin, Evgeny Osetrin, Elena Osetrina

For gravitational-wave spacetimes of Shapovalov type III, exact general solutions of geodesic deviation equations and equations of motion of test particles are obtained. Solutions are found in a privileged coordinate system, where the metric of the considered spacetime models depends on the wave variable. The exact form of tidal accelerations of the gravitat

Chutong Meng, Junyi Ao, Tom Ko, Mingxuan Wang

Speech is the surface form of a finite set of phonetic units, which can be represented by discrete codes. We propose the Code BERT (CoBERT) approach for self-supervised speech representation learning. The idea is to convert an utterance to a sequence of discrete codes, and perform code representation learning, where we predict the code representations based

Furkan Ercan, Kevin Galligan, Ken R. Duffy, Muriel Medard

Malicious attacks such as jamming can cause significant disruption or complete denial of service (DoS) to wireless communication protocols. Moreover, jamming devices are getting smarter, making them difficult to detect. Forward error correction, which adds redundancy to data, is commonly deployed to protect communications against the deleterious effects of c

Ioana Bica, Mihaela van der Schaar

Consider the problem of improving the estimation of conditional average treatment effects (CATE) for a target domain of interest by leveraging related information from a source domain with a different feature space. This heterogeneous transfer learning problem for CATE estimation is ubiquitous in areas such as healthcare where we may wish to evaluate the eff

Lutz Mattner

The classical Berry-Esseen error bound, for the normal approximation to the law of a sum of independent and identically distributed random variables, is here improved by replacing the standardised third absolute moment by a weak norm distance to normality. We thus sharpen and simplify two results of Ulyanov (1976) and of Senatov (1998), each of them previous

Manuel E. Rodrigues, Marcos V. de S. Silva

A string is the one-dimensional generalization of a point particle. In this sense, the analog of a cloud of dust would be a cloud of strings. In this work, we consider a cloud of strings around a regular solution in general gelativity. We consider the Bardeen solution and the Simpson--Visser solution, analyzing the consequences of the cloud in the regularity

Hui Xie, Weiyu Xu, Xiaodong Wu

Multiple-surface segmentation in Optical Coherence Tomography (OCT) images is a challenge problem, further complicated by the frequent presence of weak image boundaries. Recently, many deep learning (DL) based methods have been developed for this task and yield remarkable performance. Unfortunately, due to the scarcity of training data in medical imaging, it

Boris Epstein, Will Ma

Motivated by hiring pipelines, we study three selection and ordering problems in which applicants for a finite set of positions must be interviewed or sent offers. There is a finite time budget for interviewing/sending offers, and every interview/offer is followed by a stochastic realization of discovering the applicant's quality or acceptance decision, lead

Agnieszka Kuźmicz, Arti Goyal, Stanisław Zola, Marek Jamrozy

We characterize the optical variability properties of eight lobe-dominated radio quasars (QSOs): B2 0709$+$37, FBQS J095206.3$+$235245, PG 1004$+$130, [HB89] 1156$+$631, [HB89] 1425$+$267, [HB89] 1503$+$691, [HB89] 1721$+$343, 4C $+$74.26, systematically monitored for a duration of 13 years since 2009. The quasars are radio-loud objects with extended radio l

Anurag Banerjee, Tapas Bar

We study the off-equilibrium critical phenomena across a hysteretic first-order transition in disordered athermal systems. The study focuses on the zero temperature random field Ising model (ZTRFIM) above the critical disorder for spatial dimensions $d=2,3,$ and $4$. We use Monte Carlo simulations to show that disorder suppresses critical slowing down in pha

Xing Wu, Chaochen Gao, Zijia Lin, Jizhong Han

Contrastive learning has been extensively studied in sentence embedding learning, which assumes that the embeddings of different views of the same sentence are closer. The constraint brought by this assumption is weak, and a good sentence representation should also be able to reconstruct the original sentence fragments. Therefore, this paper proposes an info

Gaurang Ramakant Kane, Bibhas Ranjan Majhi

Recent investigations revealed that the near horizon Hamiltonian of a massless, chargeless outgoing particle, for its particular motion in static as well as stationary black holes, is effectively $\sim xp$ kind. This is unstable by nature and has the potential to explain a few interesting physical phenomena. From the path integral kernel, we first calculate

Albert Reuther, Peter Michaleas, Michael Jones, Vijay Gadepally

This paper updates the survey of AI accelerators and processors from past three years. This paper collects and summarizes the current commercial accelerators that have been publicly announced with peak performance and power consumption numbers. The performance and power values are plotted on a scatter graph, and a number of dimensions and observations from t

Yasuhiro Terakado, Chia-Fu Yu

In this article we compute the mass associated to any unimodular lattice in a Hermitian space over an arbitrary CM field under a condition at 2. We study the geometry and arithmetic of the basic locus of the GU(r,s)-Shimura variety associated to an imaginary quadratic field modulo a good prime p>2. We give explicit formulas for the numbers of irreducible and

L. Montejano, J. Ramirez Alfonsin, I. Rasskin

In this paper, we present necessary and sufficient combinatorial conditions for a link to be projective, that is, a link in $RP^3$. This characterization is closely related to the notions of antipodally self-dual and antipodally symmetric maps. We also discuss the notion of symmetric cycle, an interesting issue arising in projective links leading us to an ea

Jiahui Chen, Yi Zhao, Qi Li, Xuewei Feng

Deep learning (DL) methods have been widely applied to anomaly-based network intrusion detection system (NIDS) to detect malicious traffic. To expand the usage scenarios of DL-based methods, federated learning (FL) allows multiple users to train a global model on the basis of respecting individual data privacy. However, it has not yet been systematically eva

Mingyu Yan, Fei Teng

This paper, for the first time, proposes a joint electricity and data trading mechanism based on cooperative game theory. All prosumers first submit the parameters associated with both electricity and data to the market operator. The operator utilizes the public and prosumers' private data to forecast the distributed renewable generators (DRGs) and quantify

Yuxiang Guo, Cheng Peng, Chun Pong Lau, Rama Chellappa

Whole-body-based human authentication is a promising approach for remote biometrics scenarios. Current literature focuses on either body recognition based on RGB images or gait recognition based on body shapes and walking patterns; both have their advantages and drawbacks. In this work, we propose Dual-Modal Ensemble (DME), which combines both RGB and silhou

Alexandre Magno de Melo Faria, Helde A. D. Hdom

This article aims to launch light on the limitations of the Coase and Pigou approach in the solution of externalities. After contextualizing the need for integration of ecological and economic approaches, we are introducing a new conceptual proposal complementary to conventional economic approaches. Whose process is guaranteed by a set of diffuse agents in t

Lun Quan, Longji Yin, Tingrui Zhang, Mingyang Wang

Formation flight has a vast potential for aerial robot swarms in various applications. However, existing methods lack the capability to achieve fully autonomous large-scale formation flight in dense environments. To bridge the gap, we present a complete formation flight system that effectively integrates real-world constraints into aerial formation navigatio

M Seetha Ramaiah

In this thesis, we propose an alternative characterization of the notion of Configuration Space, which we call Visual Configuration Space (VCS). This new characterization allows an embodied agent (e.g., a robot) to discover its own body structure and plan obstacle-free motions in its peripersonal space using a set of its own images in random poses. Here, we

Andrew Knight

Because closed timelike curves are consistent with general relativity, many have asserted that time travel into the past is physically possible if not technologically infeasible. However, the possibility of time travel into the past rests on the unstated and false assumption that zero change to the past implies zero change to the present. I show that this as

C. Pépin, H. Freire

In this paper, we present our studies of the phase diagram of the cuprate superconductors performed in recent years. We describe how a few field-theoretical concepts can be used to account for the puzzling properties of these compounds. Starting with a short exposition of recent experimental developments, we then introduce the concept of an emergent SU(2) sy

Manuel Kauers, Jakob Moosbauer

In response to a recent Nature article which announced an algorithm for multiplying $5\times5$-matrices over $\mathbb{Z}_2$ with only 96 multiplications, two fewer than the previous record, we present an algorithm that does the job with only 95 multiplications.

Tetsuya Abe, Keiji Tagami

Agol proved that ribbon concordance forms a partial ordering on the set of knots in the $3$-sphere. In this paper, we prove that all tight fibered knots are minimal in this partially ordered set. We also give the table of prime minimal knots up to $8$-crossings except for $8_{15}$.

Faraz Ahmad

We define the notions of a compact perception pair, compactification of a perception pair, and compactification of a space of group equivariant non-expansive operators. We prove that every perception pair with totally bounded space of measurements, which is also rich enough to endow the common domain with a metric structure, can be isometrically embedded in

Eric Simonnet

Predicting the occurence of rare and extreme events in complex systems is a well-known problem in non-equilibrium physics. These events can have huge impacts on human societies. New approaches have emerged in the last ten years, which better estimate tail distributions. They often use large deviation concepts without the need to perform heavy direct ensemble

Minh Thanh Vu

In this work, we establish an asymptotic limit of almost-lossless compression of a random, finite alphabet tensor which admits a low-rank canonical polyadic decomposition.

Tim Maurice Julitz, Antoine Tordeux, Manuel Löwer

Automated driving functions at high levels of autonomy operate without driver supervision. The system itself must provide suitable responses in case of hardware element failures. This requires fault-tolerant approaches using domain ECUs and multicore processors operating in lockstep mode. The selection of a suitable architecture for fault-tolerant vehicle sy

Najirul Islam, Shyamal Biswas

We have numerically obtained theoretical results for the collapse and the revival of the quantum Rabi oscillations for low average number of coherent photons injected on a two-level system in a lossy resonant cavity. We have adopted the multimode Jaynes-Cummings model for the same and especially treated the ``Ohmic" loss to the walls of the cavity, the leaka

Thomas Wennink

We use Pixton's relations to prove a reconstruction theorem for genus 2 Gromov-Witten invariants in the style of Kontsevich-Manin (genus 0) and Getzler (genus 1). We also calculate genus 2 (descendant) Gromov-Witten invariants of $\mathbb{P}^2$ blown up at a finite number of points in general position.

Tim Wallis, Philip T. Barton, Nikolaos Nikiforakis

This work outlines a diffuse interface method for the study of fracture and fragmentation in ductile metals at high strain-rates in Eulerian finite volume simulations. The work is based on an existing diffuse interface method capable of simulating a broad range of different multi-physics applications, including multi-material interaction, damage and void ope

Dmitry Ponomarev

We prove the flat space analogue of the Flato-Fronsdal theorem. It features the flat space singleton representation suggested recently. We do that by deriving a kernel that intertwines a pair of singleton representations with massless higher-spin fields in flat space. Next, we derive two-point functions of flat space singletons, which are then used to constr

Dmitry Ponomarev

We study the chiral flat space higher-spin algebra, which is the global symmetry algebra of the chiral higher-spin theory in the 4d Minkowski space. We find that it can be constructed as the universal enveloping algebra of a certain chiral deformation of the Poincare algebra quotiented by a set of quadratic identities. These identities allow us to identify a

Yue Li, Baocheng Zhang, Li You

The influence of gravitational field on entanglement of bipartite states is investigated based on the recent idea of superposition states of gravitational field. Different from earlier considerations, we study the case where the gravitational field cannot be separated unitarily from the bipartite system in the final stage of the interaction. When the differe

Lorenzo Valzania, Sylvain Gigan

Thanks to the latest advancements in wavefront shaping, optical methods have proven crucial to achieve imaging and control light in multiply scattering media, like biological tissues. However, the stability times of living biological specimens often prevent such methods from gaining insights into relevant functioning mechanisms in cellular and organ systems.

Najirul Islam, Shyamal Biswas

We have generalized Einstein coefficients and rate equations from quantum field theoretic point of view by bringing the fundamental processes and the quantum Rabi oscillation in a single footing for the light-matter interactions for nonzero Rabi frequency. We have analytically obtained multimode Jaynes-Cummings model results for the quantum Rabi oscillations

Farhad Samadi Gazijahani, Rasoul Esmaeilzadeh

This research proposes an incremental welfare consensus method based on flexible alternating current transmission systems (FACTS) and demand response (DR) programs to control transmission network congestion in order to increase the penetration of wind power. The locational marginal prices are used as input by the suggested model to control the FACTS device a

Lin Chen, Mustafa A. Kishk, Mohamed-Slim Alouini

A new generation of aerial vehicles is hopeful to be the next frontier for the transportation of people and goods, becoming even as important as ground users in the communication systems. To enhance the coverage of aerial users, appropriate adjustments should be made to the existing cellular networks that mainly provide services for ground users by the down-

Yuping Wu, Ching-Hsun Tseng, Jiayu Shang, Shengzhong Mao

Extractive models usually formulate text summarization as extracting fixed top-$k$ salient sentences from the document as a summary. Few works exploited extracting finer-grained Elementary Discourse Unit (EDU) with little analysis and justification for the extractive unit selection. Further, the selection strategy of the fixed top-$k$ salient sentences fits

Francesco Mazzoncini, Vasco Cavina, Gian Marcello Andolina, Paolo Andrea Erdman

We investigate the optimal charging processes for several models of quantum batteries, finding how to maximize the energy stored in a given battery with a finite-time modulation of a set of external fields. We approach the problem using advanced tools of optimal control theory, highlighting the universality of some features of the optimal solutions, for inst

Subakti Subakti, Mohammadreza Daqiqshirazi, Daniel Wolf, Martin Linck

Here, by making use of medium and high resolution autocorrected off-axis electron holography, we directly probe the electrostatic potential as well as in-plane and out-of-plane charge delocalization at edges and steps in multilayer hexagonal boron nitride. In combination with ab-initio calculations, the data allows to directly reveal the formation of out-of-

Yun Liu, Xiaomeng Xu, Weihang Chen, Haocheng Yuan

When manipulating an object to accomplish complex tasks, humans rely on both vision and touch to keep track of the object's 6D pose. However, most existing object pose tracking systems in robotics rely exclusively on visual signals, which hinder a robot's ability to manipulate objects effectively. To address this limitation, we introduce TEG-Track, a tactile

Yong Xu, Pikai Zhang, Qingyang Meng, Shangpeng Li

Transmission of hydrogen detonation wave (DW) in an inert particle curtain is simulated using the Eulerian-Lagrangian approach with gas-particle two-way coupling. A detailed chemical mechanism is used for hydrogen detonative combustion and parametric studies are conducted based on a two-dimensional computational domain. A detonation map of propagation and ex

Mingoo Ji, Saehanseul Yi, Changjin Koo, Sol Ahn

When executing a deep neural network (DNN), its model parameters are loaded into GPU memory before execution, incurring a significant GPU memory burden. There are studies that reduce GPU memory usage by exploiting CPU memory as a swap device. However, this approach is not applicable in most embedded systems with integrated GPUs where CPU and GPU share a comm

Alex Bird, Christopher K. I. Williams, Christopher Hawthorne

Time series datasets are often composed of a variety of sequences from the same domain, but from different entities, such as individuals, products, or organizations. We are interested in how time series models can be specialized to individual sequences (capturing the specific characteristics) while still retaining statistical power by sharing commonalities a

Pasquale Avella, Alice Calamita, Laura Palagi

This paper addresses the optimal design of wireless networks through the site and power assignment problem. Given a set of candidate transmitters, this problem involves choosing optimal transmitter locations and powers to provide service coverage over a target area. In the modern context of increasing traffic, establishing suitable locations and power emissi

Ivan Toftul, Gleb Fedorovich, Denis Kislov, Kristina Frizyuk

Optically-induced mechanical torque leading to the rotation of small objects requires the presence of absorption or breaking cylindrical symmetry of a scatterer. A spherical non-absorbing particle cannot rotate due to the conservation of the angular momentum of light upon scattering. Here, we suggest a novel physical mechanism for the angular momentum transf

Tao Yang, Haokui Zhang, Wenze Hu, Changwen Chen

Transformer models have made tremendous progress in various fields in recent years. In the field of computer vision, vision transformers (ViTs) also become strong alternatives to convolutional neural networks (ConvNets), yet they have not been able to replace ConvNets since both have their own merits. For instance, ViTs are good at extracting global features

Sung-Soo Byun, Meng Yang

We consider determinantal Coulomb gas ensembles with a class of discrete rotational symmetric potentials whose droplets consist of several disconnected components. Under the insertion of a point charge at the origin, we derive the asymptotic behaviour of the correlation kernels both in the macro- and microscopic scales. In the macroscopic scale, this particu

Jayoung Kim, Chaejeong Lee, Noseong Park

Tabular data synthesis is a long-standing research topic in machine learning. Many different methods have been proposed over the past decades, ranging from statistical methods to deep generative methods. However, it has not always been successful due to the complicated nature of real-world tabular data. In this paper, we present a new model named Score-based