November 2022 arXiv papers — page 36

Hatem Helal, Jesun Firoz, Jenna Bilbrey, Mario Michael Krell

Molecular property calculations are the bedrock of chemical physics. High-fidelity \textit{ab initio} modeling techniques for computing the molecular properties can be prohibitively expensive, and motivate the development of machine-learning models that make the same predictions more efficiently. Training graph neural networks over large molecular databases

Heegon Jin, Jongwon Choi

Although transformer networks are recently employed in various vision tasks with outperforming performance, extensive training data and a lengthy training time are required to train a model to disregard an inductive bias. Using trainable links between the channel-wise spatial attention of a pre-trained Convolutional Neural Network (CNN) and the attention hea

Qi Huang, Jingtao Shi

This paper is devoted to a high-dimensional mixed leadership stochastic differential game on a finite horizon in feedback information mode, where the control variables enter into the diffusion term of state equation. A verification theorem for the feedback Stackelberg-Nash equilibrium is obtained by using a system of coupled and fully nonlinear parabolic par

Chi-Kwong Fok

In this paper, we compute explicitly both the $K$-theory and integral cohomology rings of the space of commuting elements in $SU(2)$ via the $K$-theory of its desingularization. We also briefly discuss the different behavior of its cohomology with complex and $\mathbb{Z}_2$ coefficients in the context of representation stability and FI-modules.

Bill P. Truong, Kartiek Agarwal, T. Pereg-Barnea

Topological quantum computing is based on the notion of braiding non-Abelian anyons, such as Majorana zero modes (MZMs), to perform gate operations. A crucial building block of these protocols is the adiabatic shuttling of MZMs through topological superconductors. Here, we consider the "piano key" approach, where MZMs are transported using local electric gat

Christopher M. Baker, Howard Bondell, Nathaniel Bloomfield, Elena Tartaglia

National governments use border information to efficiently manage the biosecurity risk presented by travel and commerce. In the Australian border biosecurity system, data about cargo consignments are collected from records of directions: that is, the records of actions taken by the biosecurity regulator. This data collection is complicated by the way directi

Denizalp Goktas, Jiayi Zhao, Amy Greenwald

Zero-sum stochastic games have found important applications in a variety of fields, from machine learning to economics. Work on this model has primarily focused on the computation of Nash equilibrium due to its effectiveness in solving adversarial board and video games. Unfortunately, a Nash equilibrium is not guaranteed to exist in zero-sum stochastic games

Daniel A. Williams, Airlie Chapman, Chris Manzie

With the increasing ubiquity of networked control systems, various strategies for sampling constituent subsystems' outputs have emerged. In contrast with periodic sampling, event-triggered control provides a way to efficiently sample a subsystem and conserve network resource usage, by triggering an update only when a state-dependent error threshold is satisf

Abodh Poudyal, Charlotte Wertz, Amy Mi Nguyen, Sajjad Uddin Mahmud

This paper develops a spatiotemporal probabilistic impact assessment framework to analyze and quantify the compounding effect of hurricanes and storm surges on the bulk power grid. The probabilistic synthetic hurricane tracks are generated using historical hurricane data, and storm surge scenarios are generated based on observed hurricane parameters. The sys

Nachiketa Adhikari

We define a derived enhancement of the classical quot functor of quotients associated to a coherent sheaf on a nonsingular quasiprojective variety. We prove its representability and show that it has the expected tangent complex. The derived quot scheme of points can be covered by affine charts obtained as spectra of commutative graded differential algebras (

Ryota Yoshihashi, Shuhei Nishimura, Dai Yonebayashi, Yuya Otsuka

Siamese-network-based self-supervised learning (SSL) suffers from slow convergence and instability in training. To alleviate this, we propose a framework to exploit intermediate self-supervisions in each stage of deep nets, called the Ladder Siamese Network. Our self-supervised losses encourage the intermediate layers to be consistent with different data aug

Josh Pinskier, Prabhat Kumar, Matthijs Langelaar, David Howard

Soft robotic grasping has rapidly spread through the academic robotics community in recent years and pushed into industrial applications. At the same time, multimaterial 3D printing has become widely available, enabling the monolithic manufacture of devices containing rigid and elastic sections. We propose a novel design technique that leverages both technol

Lin Ge, Yuzi Zhang, Kevin C. Ward, Timothy L. Lash

Surveillance research is of great importance for effective and efficient epidemiological monitoring of case counts and disease prevalence. Taking specific motivation from ongoing efforts to identify recurrent cases based on the Georgia Cancer Registry, we extend recently proposed "anchor stream" sampling design and estimation methodology. Our approach offers

Cassio L. Barbosa, Felipe Navarete, Robert D. Blum, Augusto Damineli

W51A is the most active star formation region of the Giant \ion{H}{2} region W51. It harbors the two massive proto-clusters W51e and W51\,IRS2, which are very rare in the Galaxy. We aim to identify the new born massive stars and UCHII regions to derive its distance and age. We performed IFU observations with NIFS+ALTAIR of nine targets in the W51A sub-region

Ryosuke Yamamoto

In this paper, we give a sharp sparse domination of pseudodifferential operators associated with symbols belonging to the H\"{o}rmander class, and fundamental solutions of dispersive equations. Furthermore, we give boundedness results of these operators on weighted Besov spaces by using the sparse domination.

Roberto Vila, Narayanaswamy Balakrishnan, Helton Saulo, Ana Protazio

The bivariate Gaussian distribution has been a key model for many developments in statistics. However, many real-world phenomena generate data that follow asymmetric distributions, and consequently bivariate normal model is inappropriate in such situations. Bidimensional log-symmetric models have attractive properties and can be considered as good alternativ

Sachit Kuhar, Alexey Tumanov, Judy Hoffman

Efficient inference of Deep Neural Networks (DNNs) is essential to making AI ubiquitous. Two important algorithmic techniques have shown promise for enabling efficient inference - sparsity and binarization. These techniques translate into weight sparsity and weight repetition at the hardware-software level enabling the deployment of DNNs with critically low

Lingkai Kong, Jiaming Cui, Yuchen Zhuang, Rui Feng

Decision-focused learning (DFL) was recently proposed for stochastic optimization problems that involve unknown parameters. By integrating predictive modeling with an implicitly differentiable optimization layer, DFL has shown superior performance to the standard two-stage predict-then-optimize pipeline. However, most existing DFL methods are only applicable

G. Manolakos, G. Patellis, G. Zoupanos

We examine the vacua of the scalar potential of the effective 4-d action, obtained after the dimensional reduction of the 10-d $\mathcal{N}=1$ heterotic supergravity coupled to an $\mathcal{N}=1$ Yang-Mills sector. The (Coset Space) dimensional reduction takes place over the three 6-d nearly-K\"ahler manifolds, namely the homogeneous 6-d non-symmetric coset

Sota Ikebe, Kazuhiro Takefuji, Toshio Terasawa, Sujin Eie

We present a detection of a bright burst from FRB 20201124A, which is one of the most active repeating FRBs, based on S-band observations with the 64-m radio telescope at the Usuda Deep Space Center/JAXA. This is the first FRB observed by using a Japanese facility. Our detection at 2 GHz in February 2022 is the highest frequency for this FRB and the fluence

Alex Buchel

We use ${\cal N}=2^*$ and cascading gauge theory holographic models to extract the general features of the gravitational susceptibility $\kappa$ of strongly coupled nonconformal quark-gluon plasma. We show that in theories with a relevant coupling constant the gravitational susceptibility is renormalization scheme dependent. We propose to use its temperature

M. A. Gali Labarias, E. E. Mitchell

Superconducting quantum interference device arrays have been extensively studied for their high magnetic field sensitivity. The performance of these devices strongly depends on the characteristic parameters of their Josephson junctions, i.e. their critical currents and shunt resistances. Using a resistively shunted junction model and including thermal noise,

Marlon Brenes, Giacomo Guarnieri, Archak Purkayastha, Jens Eisert

We propose a highly-scalable method to compute the statistics of charge transfer in driven conductors. The framework can be applied in situations of non-zero temperature, strong coupling to terminals and in the presence of non-periodic light-matter interactions, away from equilibrium. The approach combines the so-called mesoscopic leads formalism with full c

Álvaro Nodar, Ruben Esteban, Carlos Maciel-Escudero, Jon Lasa-Alonso

The optical resonances supported by nanostructures offer the possibility to enhance the interaction between matter and the quantum states of light. In this work, we provide a framework to study the scattering of quantum states of light with information encoded in their helicity by a nanostructure. We analyze the purity of the scattered output quantum state,

Jucai Zhai, Pengcheng Zeng, Chihao Ma, Yong Zhao

Recent research showed that the dual-pixel sensor has made great progress in defocus map estimation and image defocus deblurring. However, extracting real-time dual-pixel views is troublesome and complex in algorithm deployment. Moreover, the deblurred image generated by the defocus deblurring network lacks high-frequency details, which is unsatisfactory in

Ka Kit Kelvin Ho, Geng-Bo Wu, Bao-Jie Chen, Ka Fai Chan

Focus scanning is critically important in many terahertz (THz) imaging and sensing applications. A traditional single focusing transmitarray can achieve a good focus when the source is on-axis but moving the source off-axis produces a significant aberration. This paper presents a novel approach to reducing coma in off-axis scanning in the THz region. Here, a

Poly H. da Silva, Arash Jamshidpey, Simon Tavaré

We explore the cycle types of a class of biased random derangements, described as a random game played by some children labeled $1,\cdots,n$. Children join the game one by one, in a random order, and randomly form some circles of size at least $2$, so that no child is left alone. The game gives rise to the cyclic decomposition of a random derangement, induci

Alain Hecq, Daniel Velasquez-Gaviria

This paper investigates new ways of estimating and identifying causal, noncausal, and mixed causal-noncausal autoregressive models driven by a non-Gaussian error sequence. We do not assume any parametric distribution function for the innovations. Instead, we use the information of higher-order cumulants, combining the spectrum and the bispectrum in a minimum

Kong Yao Chee, M. Ani Hsieh, Nikolai Matni

Nonlinear model predictive control (MPC) is a flexible and increasingly popular framework used to synthesize feedback control strategies that can satisfy both state and control input constraints. In this framework, an optimization problem, subjected to a set of dynamics constraints characterized by a nonlinear dynamics model, is solved at each time step. Des

Xiang Chen, Yan Xia, Nishant Ravikumar, Alejandro F Frangi

Medical image registration is a challenging task involving the estimation of spatial transformations to establish anatomical correspondence between pairs or groups of images. Recently, deep learning-based image registration methods have been widely explored, and demonstrated to enable fast and accurate image registration in a variety of applications. However

Bruna Ferreira, Silvio Marques, Marcos Kalinowski, Helio Lopes

Context: User-Centered Design and Agile methodologies focus on human issues. Nevertheless, agile methodologies focus on contact with contracting customers and generating value for them. Usually, the communication between end users and the agile team is mediated by customers. However, they do not know the problems end users face in their routines. Hence, UX i

Jack Allsop, Ian M. Wanless

A \emph{Latin square} is a matrix of symbols such that each symbol occurs exactly once in each row and column. A Latin square $L$ is \emph{row-Hamiltonian} if the permutation induced by each pair of distinct rows of $L$ is a full cycle permutation. Row-Hamiltonian Latin squares are equivalent to perfect $1$-factorisations of complete bipartite graphs. For th

Federico Compagnin, Stefano Profumo, Nicolao Fornengo

We consider the cosmology and phenomenology of a dark photon portal to a simple dark sector consisting of a single, light, fermionic dark matter particle species with mass in the MeV range. We entertain three possible kinetic mixing structures of a new Abelian gauge group U(1)$_{\text{dark}}$ with the visible sector through U(1)$_{\text{e.m.}}$, U(1)$_{\text

Ödül Tetik

We introduce a tangential theory for linked smooth manifolds of depth $1$, i.e., for spans $\mathfrak{S}=(M\overset{\pi}{\twoheadleftarrow} L\overset{\iota}{\hookrightarrow}N)$ of smooth manifolds where $\pi$ is a fibre bundle and $\iota$ is a closed embedding. The tangent classifier of $\mathfrak{S}$ is given as a topological span map $\mathfrak{S}\to B\mat

Jian Jiang, Oya Celiktutan

Task incremental learning aims to enable a system to maintain its performance on previously learned tasks while learning new tasks, solving the problem of catastrophic forgetting. One promising approach is to build an individual network or sub-network for future tasks. However, this leads to an ever-growing memory due to saving extra weights for new tasks an

Deepesh Singhal, Yuxin Lin

Denote the set of algebraic numbers as $\overline{\mathbb{Q}}$ and the set of algebraic integers as $\overline{\mathbb{Z}}$. For $\gamma\in\overline{\mathbb{Q}}$, consider its irreducible polynomial in $\mathbb{Z}[x]$, $F_{\gamma}(x)=a_nx^n+\dots+a_0$. Denote $e(\gamma)=\gcd(a_{n},a_{n-1},\dots,a_1)$. Drungilas, Dubickas and Jankauskas show in a recent paper

Kamil Urbaś

The aim of this paper is to prove the existence of inductive and inverse limits of direct and inverse systems in a certain category of compact metric spaces as well as of compact metric groups. Some applications are presented.

Julien Wolf, Kirpal Nandra, Mara Salvato, Johannes Buchner

This study aims at identifying luminous quasars at $z>5.7$ among X-ray-selected sources in the eROSITA Final Equatorial-Depth Survey (eFEDS) in order to place a lower limit on black hole accretion well into the epoch of re-ionisation. We confirm the low significance detection with eROSITA of a previously known, optically faint $z=6.56$ quasar from the Subaru

Xiang Dai, Sarvnaz Karimi

Information Extraction from scientific literature can be challenging due to the highly specialised nature of such text. We describe our entity recognition methods developed as part of the DEAL (Detecting Entities in the Astrophysics Literature) shared task. The aim of the task is to build a system that can identify Named Entities in a dataset composed by sch

Mushu Li, Jie Gao, Conghao Zhou, Xuemin

This paper presents a novel approach for computing resource management of edge servers in vehicular networks based on digital twins and artificial intelligence (AI). Specifically, we construct two-tier digital twins tailored for vehicular networks to capture networking-related features of vehicles and edge servers. By exploiting such features, we propose a t

Danilo Almeida Felipe, Marcos Kalinowski, Daniel Graziotin, Jean Carlos Natividade

Context: Although software development is a human activity, Software Engineering (SE) research has focused mostly on processes and tools, making human factors underrepresented. This kind of research may be improved using knowledge from human-focused disciplines. An example of missed opportunities is how SE employs psychometric instruments. Objective: Provide

Maximilian Zipfl, Moritz Jarosch, J. Marius Zöllner

Examining graphs for similarity is a well-known challenge, but one that is mandatory for grouping graphs together. We present a data-driven method to cluster traffic scenes that is self-supervised, i.e. without manual labelling. We leverage the semantic scene graph model to create a generic graph embedding of the traffic scene, which is then mapped to a low-

Anna M. Piekarska, Tadeusz K. Kopeć

We study a system of strongly correlated bosons with off-diagonal disorder, i.e., randomness in the kinetic energy, and find a family of reentrant phase transitions that occur as a function of the on-site interaction. We model the system using the paradigmatic Bose-Hubbard Hamiltonian with a random hopping term and solve it employing the replica trick and Tr

Tanish Lad, Himanshu Maheshwari, Shreyas Kottukkal, Radhika Mamidi

Pre-training a language model and then fine-tuning it for downstream tasks has demonstrated state-of-the-art results for various NLP tasks. Pre-training is usually independent of the downstream task, and previous works have shown that this pre-training alone might not be sufficient to capture the task-specific nuances. We propose a way to tailor a pre-traine

Moumita Dasgupta, Sougata Guha, Leon Armbruster, Dibyendu Das

Intuition suggests that passage times across a region increases with the number of barriers along the path. Can this fail depending on the nature of the barrier? To probe this fundamental question, we exactly solve for the first passage time in general d-dimensions for diffusive transport through a spatially patterned array of obstacles - either entropic or

Zhichao Yang, Sunjae Kwon, Zonghai Yao, Hong Yu

Automatic International Classification of Diseases (ICD) coding aims to assign multiple ICD codes to a medical note with an average of 3,000+ tokens. This task is challenging due to the high-dimensional space of multi-label assignment (155,000+ ICD code candidates) and the long-tail challenge - Many ICD codes are infrequently assigned yet infrequent ICD code

Ali Sekhavati, Won-Sook Lee

High computational power and significant time are usually needed to train a deep learning based tracker on large datasets. Depending on many factors, training might not always be an option. In this paper, we propose a framework with two ideas on Siamese-based trackers. (i) Extending number of templates in a way that removes the need to retrain the network an

R. Dokara, Y. Gong, W. Reich, M. Rugel

Context. While over 1000 supernova remnants (SNRs) are estimated to exist in the Milky Way, only less than 400 have been found to date. In the context of this apparent deficiency, more than 150 SNR candidates were recently identified in the D-configuration Very Large Array (VLA-D) continuum images of the 4--8 GHz global view on star formation (GLOSTAR) surve

Tanja Auge, Andreas Heuer

Long-term data-driven studies have become indispensable in many areas of science. Often, the data formats, structures and semantics of data change over time, the data sets evolve. Therefore, studies over several decades in particular have to consider changing database schemas. The evolution of these databases lead at some point to a large number of schemas,

R. Arcidiacono, G. Borghi, M. Boscardin, N. Cartiglia

The basic principle of operation of silicon sensors with resistive read-out is built-in charge sharing. Resistive Silicon Detectors (RSD, also known as AC-LGAD), exploiting the signals seen on the electrodes surrounding the impact point, achieve excellent space and time resolutions even with very large pixels. In this paper, a TCT system using a 1064 nm pico

Rushikesh Zawar, Krupa Bhayani, Neelanjan Bhowmik, Kamlesh Tiwari

Automatic detection of anomalies such as weapons or threat objects in baggage security, or detecting impaired items in industrial production is an important computer vision task demanding high efficiency and accuracy. Most of the available data in the anomaly detection task is imbalanced as the number of positive/anomalous instances is sparse. Inadequate ava

Daniel Arkushin, Bar Cohen, Shmuel Peleg, Ohad Fried

In the Clothes-Changing Re-Identification (CC-ReID) problem, given a query sample of a person, the goal is to determine the correct identity based on a labeled gallery in which the person appears in different clothes. Several models tackle this challenge by extracting clothes-independent features. However, the performance of these models is still lower for t

Libor Šmejkal, Alberto Marmodoro, Kyo-Hoon Ahn, Rafael Gonzalez-Hernandez

Magnons in ferromagnets have one chirality, and typically are in the GHz range and have a quadratic dispersion near the zero wavevector. In contrast, magnons in antiferromagnets are commonly considered to have bands with both chiralities that are degenerate across the entire Brillouin zone, and to be in the THz range and to have a linear dispersion near the

Okan K. Orhan, Mewael Isiet, Mauricio Ponga, David D. O'Regan

Multi-principal element materials (MPEMs) have been attracting a rapidly growing interest due to their exceptional performance under extreme conditions, from cryogenic conditions to extreme-high temperatures and pressures. Despite the simple conceptual premise behind their formation, computational high-throughput first-principles design of such materials is

João Ranhel, Cacilda Vilela de Lima

In this study, conversations between humans and avatars are linguistically, organizationally, and structurally analyzed, focusing on what is necessary for creating face-to-face multimodal interfaces for machines. We videorecorded thirty-four human-avatar interactions, performed complete linguistic microanalysis on video excerpts, and marked all the occurrenc

M. Magnaterra, M. Moretti Sala, G. Monaco, P. Becker

Motivated by several claims of spin-orbit driven spin-liquid physics in hexagonal Ba$_3$Ti$_{3-x}$Ir$_x$O$_9$ hosting Ir2O9 dimers, we report on resonant inelastic x-ray scattering (RIXS) at the Ir L3 edge for different x. We demonstrate that magnetism in Ba$_3$Ti$_{3-x}$Ir$_x$O$_9$ is governed by an unconventional realization of strong disorder, where catio

M. Nikhil Krishnan, MohammadReza Ebrahimi, Ashish Khisti

In distributed computing, slower nodes (stragglers) usually become a bottleneck. Gradient Coding (GC), introduced by Tandon et al., is an efficient technique that uses principles of error-correcting codes to distribute gradient computation in the presence of stragglers. In this paper, we consider the distributed computation of a sequence of gradients $\{g(1)

Tobias Friedrich, Timo Kötzing, Frank Neumann, Aishwarya Radhakrishnan

Estimation of distribution algorithms (EDAs) provide a distribution - based approach for optimization which adapts its probability distribution during the run of the algorithm. We contribute to the theoretical understanding of EDAs and point out that their distribution approach makes them more suitable to deal with rugged fitness landscapes than classical lo

Aref Einizade, Sepideh Hajipour Sardouie

Numerous approaches have been proposed to discover causal dependencies in machine learning and data mining; among them, the state-of-the-art VAR-LiNGAM (short for Vector Auto-Regressive Linear Non-Gaussian Acyclic Model) is a desirable approach to reveal both the instantaneous and time-lagged relationships. However, all the obtained VAR matrices need to be a

A. Datas

Thermophotovoltaic (TPV) energy conversion efficiency has recently surpassed 30%. The key behind such high efficiency is the inclusion of a highly efficient mirror in the rear of the TPV cell that turns back to the thermal emitter the outband energy photons. Efficiencies over 50% could be theoretically attainable by approaching a mirror reflectance of 100%.

Nikita Klemyatin, Shuang Liang, Chuwen Wang

We obtain a priori $L^\infty$ estimate for a general class of $(n-1)-$form fully nonlinear partial differential equations on compact Hermitian manifolds. Our method relies on the local version of comparison with auxiliary Monge-Amp\`ere equations, developed earlier by B. Guo and D. H. Phong. The key is to find the appropriate elliptic operator such that the

Sihong He, Zhili Zhang, Shuo Han, Lynn Pepin

Electric vehicles (EVs) are being rapidly adopted due to their economic and societal benefits. Autonomous mobility-on-demand (AMoD) systems also embrace this trend. However, the long charging time and high recharging frequency of EVs pose challenges to efficiently managing EV AMoD systems. The complicated dynamic charging and mobility process of EV AMoD syst

Mehmet Tosun, Burak Bilki, Kutlu Kagan Sahbaz

Resistive Plate Chambers (RPCs) are essential active media of large-scale experiments as part of the muon systems and (semi-)digital hadron calorimeters. Among the several outstanding issues associated with the RPCs, the loss of efficiency for the detection of particles when subjected to high particle fluxes, and the limitations associated with the common RP

Ritu R. Raj, C. Wyatt Shields, Ankur Gupta

In this work, we numerically investigate the impact of two-dimensional solute gradients on the distribution of colloidal particles, i.e., colloidal banding, induced via diffusiophoresis. The solute gradients are generated by spatially arranged sources and sinks that emit/absorb a time-dependent solute flux. First we study a dipole system, i.e., one source an

Ilmari Kylliäinen, Roman Yangarber

Recent advances in the field of language modeling have improved the state-of-the-art in question answering (QA) and question generation (QG). However, the development of modern neural models, their benchmarks, and datasets for training them has mainly focused on English. Finnish, like many other languages, faces a shortage of large QA/QG model training resou

Teja Gupta, Neeraj Wagh, Samarth Rawal, Brent Berry

Identifying abnormal patterns in electroencephalography (EEG) remains the cornerstone of diagnosing several neurological diseases. The current clinical EEG review process relies heavily on expert visual review, which is unscalable and error-prone. In an effort to augment the expert review process, there is a significant interest in mining population-level EE

Alexandre Dumont, Pierre Février, Christian Lupien, Bertrand Reulet

We propose a new approach to detailed balance violation in electrical circuits by relying on the scattering matrix formalism commonly used in microwave electronics. This allows to include retardation effects which are paramount at high frequencies. We define the spectral densities of phase space angular momentum, heat transfer and cross power, which can serv

Bjorn K. Berntson, Edwin Langmann, Jonatan Lenells

We construct elliptic multi-soliton solutions of the spin non-chiral intermediate long-wave (sncILW) equation with periodic boundary conditions. These solutions are obtained by a spin-pole ansatz including a dynamical background term; we show that this ansatz solves the periodic sncILW equation provided the spins and poles satisfy the elliptic $A$-type spin

Ferenc Bencs, Jeroen Huijben, Guus Regts

We show that for any non-real algebraic number $q$ such that $|q-1|>1$ or $\Re(q)>\frac{3}{2}$ it is \textsc{\#P}-hard to compute a multiplicative (resp. additive) approximation to the absolute value (resp. argument) of the chromatic polynomial evaluated at $q$ on planar graphs. This implies \textsc{\#P}-hardness for all non-real algebraic $q$ on the family

Wathela Alhassan, Tomasz Bulik, Mariusz Suchenek

The expected volume of data from the third-generation gravitational waves (GWs) Einstein Telescope (ET) detector would make traditional GWs search methods such as match filtering impractical. This is due to the large template bank required and the difficulties in waveforms modelling. In contrast, machine learning (ML) algorithms have shown a promising altern

Victor Vidyapin, Yingwen Zhang, Duncan England, Benjamin Sussman

We show a simple yet effective method that can be used to characterize the per pixel quantum efficiency and temporal resolution of a single photon event camera for quantum imaging applications. Utilizing photon pairs generated through spontaneous parametric down-conversion, the detection efficiency of each pixel, and the temporal resolution of the system, ar

AKM Bahalul Haque, A. K. M. Najmul Islam, Patrick Mikalef

The final search query for the Systematic Literature Review (SLR) was conducted on 15th July 2022. Initially, we extracted 1707 journal and conference articles from the Scopus and Web of Science databases. Inclusion and exclusion criteria were then applied, and 58 articles were selected for the SLR. The findings show four dimensions that shape the AI explana

Andrea Cavagna, Nan Li, Alexandros Iosifidis, Qi Zhang

This paper aims to design robust Edge Intelligence using semantic communication for time-critical IoT applications. We systematically analyze the effect of image DCT coefficients on inference accuracy and propose the channel-agnostic effectiveness encoding for offloading by transmitting the most meaningful task data first. This scheme can well utilize all av

Shubhanshu Mishra, Jana Diesner

Online data streams make training machine learning models hard because of distribution shift and new patterns emerging over time. For natural language processing (NLP) tasks that utilize a collection of features based on lexicons and rules, it is important to adapt these features to the changing data. To address this challenge we introduce PyTAIL, a python l

Edoardo Niccolai

This paper investigates the concept of harmonic functions of bounded mean oscillation, starting from John-Nirenberg's pioneering studies, under a renewed formalism, suitable for bringing out some fundamental properties inherent in it. In more detail: after a quick introduction, the second Section presents the main theorem, plus complete proof, relating to th

Sepidehsadat Hosseini, Mohammad Amin Shabani, Saghar Irandoust, Yasutaka Furukawa

This paper presents an end-to-end neural architecture based on Diffusion Models for spatial puzzle solving, particularly jigsaw puzzle and room arrangement tasks. In the latter task, for instance, the proposed system "PuzzleFusion" takes a set of room layouts as polygonal curves in the top-down view and aligns the room layout pieces by estimating their 2D tr

Marcus Riesmeier, Frank Woittennek

Infinite-dimensional linear systems with unbounded input and output operators are considered. For the purpose of finite-dimensional observer-based state feedback, an observer approximation scheme will be developed which can be directly combined with existing late-lumping controllers and observer output injection gains. It relies on a decomposition of the fee

Fabiano F. Santos, Bruno G. da Costa, Ignacio S. Gomez

In this work, we study the effect of $\kappa$-deformed space on the thermodynamic quantities, this are find through the holographic renormalization that provide the free energy, which is fundamental to derive the another thermodynamic quantities. For this scenario we consider an charged AdS$_{4}$ black hole for an Einstein-Maxwell model where the derivative

Francisco J. González, Diego Tancara, Hossein T. Dinani, Raúl Coto

Understanding defect-phonon interactions in solid-state devices is crucial for improving our current knowledge of quantum platforms. In this work, we develop first-principles calculations for a defect composed of two spin-$1/2$ particles that interact with phonon modes in a one-dimensional lattice. We follow a bottom-up approach that begins with a dipolar ma

Haina Wang, Frank H. Stillinger, Salvatore Torquato

An outstanding problem in statistical mechanics is the determination of whether prescribed functional forms of the pair correlation function $g_2(r)$ [or equivalently, structure factor $S(k)$] at some number density $\rho$ can be achieved by $d$-dimensional many-body systems. The Zhang-Torquato conjecture states that any realizable set of pair statistics, wh

Mengxin Zheng, Qian Lou, Fan Chen, Lei Jiang

Fully homomorphic encryption (FHE) protects data privacy in cloud computing by enabling computations to directly occur on ciphertexts. To improve the time-consuming FHE operations, we present an electro-optical (EO) FHE accelerator, CryptoLight. Compared to prior FHE accelerators, on average, CryptoLight reduces the latency of various FHE applications by >94

Franco Flandoli, Fausto Corvino, Marta Leocata, Giulia Livieri

This paper develops two mathematical models to understand subjects' behavior in response to the urgency of a change and inputs from governments e.g., (subsides) in the context of the diffusion of the solar photovoltaic in Italy. The first model is a Markovian model of interacting particle systems. The second one, instead, is a Mean Field Game model. In both

Xinjie Liu, Lasse Peters, Javier Alonso-Mora

Many autonomous agents, such as intelligent vehicles, are inherently required to interact with one another. Game theory provides a natural mathematical tool for robot motion planning in such interactive settings. However, tractable algorithms for such problems usually rely on a strong assumption, namely that the objectives of all players in the scene are kno

Zhongtian Dong, Nan Li, Alexandros Iosifidis, Qi Zhang

For time-critical IoT applications using deep learning, inference acceleration through distributed computing is a promising approach to meet a stringent deadline. In this paper, we implement a working prototype of a new distributed inference acceleration method HALP using three raspberry Pi 4. HALP accelerates inference by designing a seamless collaboration

Lorenzo Lucchese, Mikko Pakkanen, Almut Veraart

In this paper, we conduct a systematic large-scale analysis of order book-driven predictability in high-frequency returns by leveraging deep learning techniques. First, we introduce a new and robust representation of the order book, the volume representation. Next, we carry out an extensive empirical experiment to address various questions regarding predicta

Dojun Park, Seohyun Park

In this study, we experimented to examine the effect of adding the most frequent n phoneme bigrams to the basic vocabulary on the German phoneme recognition model using the text-to-phoneme data augmentation strategy. As a result, compared to the baseline model, the vowel30 model and the const20 model showed an increased BLEU score of more than 1 point, and t

Tomer Stolik, Itai Lang, Shai Avidan

A triangular mesh is one of the most popular 3D data representations. As such, the deployment of deep neural networks for mesh processing is widely spread and is increasingly attracting more attention. However, neural networks are prone to adversarial attacks, where carefully crafted inputs impair the model's functionality. The need to explore these vulnerab

Takashi Hirotsu

In a given hypercube, draw grid lines parallel to the edges, and consider all hypercuboids (or hypercubes) whose edges are lying on the grid lines or the boundary. We find the limit of the value of the ratio of the arithmetic mean of the volumes of those hypercuboids (or hypercubes) to the entire volume as the grid spacing becomes smaller.

R. A. Phillipson, M. S. Vogeley, P. T. Boyd

We present results of recurrence analysis of 46 active galactic nuclei (AGN) using light curves from the 157-month catalog of the Swift Burst Alert Telescope (BAT) in the 14-150 keV band. We generate recurrence plots and compute recurrence plot metrics for each object. We use the surrogate data method to compare all derived recurrence-based quantities to thr

Z. Ding, R. Schober, H. V. Poor

The aim of this paper is to characterize the impact of non-orthogonal multiple access (NOMA) on the age of information (AoI) of grant-free transmission. In particular, a low-complexity form of NOMA, termed NOMA-assisted random access, is applied to grant-free transmission in order to illustrate the two benefits of NOMA for AoI reduction, namely increasing ch

Ecenaz Erdemir, Tze-Yang Tung, Pier Luigi Dragotti, Deniz Gunduz

Recent works have shown that joint source-channel coding (JSCC) schemes using deep neural networks (DNNs), called DeepJSCC, provide promising results in wireless image transmission. However, these methods mostly focus on the distortion of the reconstructed signals with respect to the input image, rather than their perception by humans. However, focusing on t

Euclides Carlos Pinto Neto, Derick Moreira Baum, Jorge Rady de Almeida, Joao Batista Camargo

Air transportation is essential for society, and it is increasing gradually due to its importance. To improve the airspace operation, new technologies are under development, such as Unmanned Aircraft Systems (UAS). In fact, in the past few years, there has been a growth in UAS numbers in segregated airspace. However, there is an interest in integrating these

Alexandra Senderovich, Ekaterina Bulatova, Anton Obukhov, Maxim Rakhuba

In general, convolutional neural networks (CNNs) are easy to train, but their essential properties, such as generalization error and adversarial robustness, are hard to control. Recent research demonstrated that singular values of convolutional layers significantly affect such elusive properties and offered several methods for controlling them. Nevertheless,

Cesar A. Bernardes

The study of femtoscopic correlations in high-energy collisions is a powerful tool to investigate the space-time structure of the particle emitting region formed in such collisions, as well as to probe interactions that the involved particles may undergo after being emitted. An overview of the recent results from the CMS experiment at the LHC on the two-part

Saksham Aggarwal, Taneesh Gupta, Pawan Kumar Sahu, Arnav Chavan

Object trackers deployed on low-power devices need to be light-weight, however, most of the current state-of-the-art (SOTA) methods rely on using compute-heavy backbones built using CNNs or transformers. Large sizes of such models do not allow their deployment in low-power conditions and designing compressed variants of large tracking models is of great impo

Daneng Yang, Ethan O. Nadler, Hai-Bo Yu

We perform a high-resolution cosmological zoom-in simulation of a Milky Way (MW)--like system, which includes a realistic Large Magellanic Cloud analog, using a large differential elastic dark matter self-interaction cross section that reaches $\approx 100~\mathrm{cm}^2\ \mathrm{g}^{-1}$ at relative velocities of $\approx 10~\mathrm{km\ s}^{-1}$, motivated b

Cem M. Unsal, Lucas T. Brady

One of the leading candidates for near-term quantum advantage is the class of Variational Quantum Algorithms, but these algorithms suffer from classical difficulty in optimizing the variational parameters as the number of parameters increases. Therefore, it is important to understand the expressibility and power of various ans\"atze to produce target states

C. A. Potts, Y. Huang, V. A. S. V Bittencourt, S. Viola Kusminskiy

The interaction between magnons and mechanical vibrations dynamically modify the properties of the mechanical oscillator, such as its frequency and decay rate. Known as dynamical backaction, this effect is the basis for many theoretical protocols, such as entanglement generation or mechanical ground-state cooling. However, dynamical backaction is also detrim

Shahnawaz Ahmed, Nathan Killoran, Juan Felipe Carrasquilla Álvarez

Several quantities important in condensed matter physics, quantum information, and quantum chemistry, as well as quantities required in meta-optimization of machine learning algorithms, can be expressed as gradients of implicitly defined functions of the parameters characterizing the system. Here, we show how to leverage implicit differentiation for gradient

Riccardo Middei, Ioannis Liodakis, Matteo Perri, Simonetta Puccetti

Blazars are a class of jet-dominated active galactic nuclei with a typical double-humped spectral energy distribution. It is of common consensus the Synchrotron emission to be responsible for the low frequency peak, while the origin of the high frequency hump is still debated. The analysis of X-rays and their polarization can provide a valuable tool to under