May 2022 arXiv papers — page 120

Li-ping Han, Jian Zou, Hai Li, Bin Shao

In this paper we investigate the dynamics of a spin chain whose two end spins interact with two independent non-Markovian baths by using the non-Markovian quantum state diffusion (QSD) equation approach. Specifically two issues about quantum information scrambling in open quantum system are addressed. The first issue is that tripartite mutual information (TM

Alberto Carrasco-Casado, Koichi Shiratama, Phuc V. Trinh, Dimitar Kolev

With the goal of meeting the diverse requirements of many different types of platforms, ranging from small drones to big satellites, and being applied in a variety of diverse scenarios, ranging from fixed terrestrial links to moving platforms in general, and operating within a wide range of conditions and distances, the Japanese National Institute of Informa

Hervé Déjean, Stéphane Clinchant, Jean-Luc Meunier

This paper investigates the Relation Extraction task in documents by benchmarking two different neural network models: a multi-modal language model (LayoutXLM) and a Graph Neural Network: Edge Convolution Network (ECN). For this benchmark, we use the XFUND dataset, released along with LayoutXLM. While both models reach similar results, they both exhibit very

Junwen Ding, Liangcai Song, Siyuan Li, Chen Wu

Computing workflows in heterogeneous multiprocessor systems are frequently modeled as directed acyclic graphs of tasks and data blocks, which represent computational modules and their dependencies in the form of data produced by a task and used by others. However, for some workflows, such as the task schedule in a digital signal processor may run out of memo

Greg Baker, Diego Molla-Aliod

The LEAFTOP (language extracted automatically from thousands of passages) dataset consists of nouns that appear in multiple places in the four gospels of the New Testament. We use a naive approach -- probabilistic inference -- to identify likely translations in 1480 other languages. We evaluate this process and find that it provides lexiconaries with accurac

Devlin Gualtieri

I present a method for text analysis based on an analogy with the dynamic friction of sliding surfaces. One surface is an array of points with a 'friction coefficient' derived from the distribution frequency of a text's alphabetic characters. The other surface is a test patch having points with this friction coefficient equal to a median value. E

Muhammad Ali Chaudhry, Mutlu Cukurova, Rose Luckin

Numerous AI ethics checklists and frameworks have been proposed focusing on different dimensions of ethical AI such as fairness, explainability, and safety. Yet, no such work has been done on developing transparent AI systems for real-world educational scenarios. This paper presents a Transparency Index framework that has been iteratively co-designed with di

Jakub Fil, Neil Dalchau, Dominique Chu

Hebbian theory seeks to explain how the neurons in the brain adapt to stimuli, to enable learning. An interesting feature of Hebbian learning is that it is an unsupervised method and as such, does not require feedback, making it suitable in contexts where systems have to learn autonomously. This paper explores how molecular systems can be designed to show su

Daniel Domínguez-Vázquez, Bjoern F. Klose, Gustaaf B. Jacobs

A closed and predictive particle cloud tracer method is presented. The tracer builds upon the Subgrid Particle Averaged Reynolds Stress Equivalent (SPARSE) formulation first introduced in [Davis et al., Proceedings of the Royal Society A, 473(2199), 2017] for the tracing of particle clouds. It was later extended to a Cloud-In-Cell (CIC) formulation in [Taver

Jinming Zhao, Tenggan Zhang, Jingwen Hu, Yuchen Liu

The emotional state of a speaker can be influenced by many different factors in dialogues, such as dialogue scene, dialogue topic, and interlocutor stimulus. The currently available data resources to support such multimodal affective analysis in dialogues are however limited in scale and diversity. In this work, we propose a Multi-modal Multi-scene Multi-lab

Sam Greydanus

Structural optimization is a useful and interesting tool. Unfortunately, it can be hard for new researchers to get started on the topic because existing tutorials assume the reader has substantial domain knowledge. They obscure the fact that structural optimization is really quite simple, elegant, and easy to implement. With that in mind, let's write our

Lachlan J. Gunn, Andrew Allison, Derek Abbott

Nonlinearity in many systems is heavily dependent on component variation and environmental factors such as temperature. This is often overcome by keeping signals close enough to the device's operating point that it appears approximately linear. But as the signal being measured becomes larger, the deviation from linearity increases, and the device's n

Nic Fishman, Leif Hancox-Li

"Attention is all you need" has become a fundamental precept in machine learning research. Originally designed for machine translation, transformers and the attention mechanisms that underpin them now find success across many problem domains. With the apparent domain-agnostic success of transformers, many researchers are excited that similar model ar

Katia Meziani, Karim Lounici, Benjamin Riu

The capacity of a ML model refers to the range of functions this model can approximate. It impacts both the complexity of the patterns a model can learn but also memorization, the ability of a model to fit arbitrary labels. We propose Adaptive Capacity (AdaCap), a training scheme for Feed-Forward Neural Networks (FFNN). AdaCap optimizes the capacity of FFNN

Dvij Kalaria

This is Btech thesis report on detection and purification of adverserially attacked images. A deep learning model is trained on certain training examples for various tasks such as classification, regression etc. By training, weights are adjusted such that the model performs the task well not only on training examples judged by a certain metric but has an exc

Andrea M. Storås, Anders Åsberg, Pål Halvorsen, Michael A. Riegler

Tacrolimus is one of the cornerstone immunosuppressive drugs in most transplantation centers worldwide following solid organ transplantation. Therapeutic drug monitoring of tacrolimus is necessary in order to avoid rejection of the transplanted organ or severe side effects. However, finding the right dose for a given patient is challenging, even for experien

Alberto Carrasco-Casado, Koichi Shiratama, Dimitar Kolev, Phuc V. Trinh

Being able to dynamically control the transmitted-beam divergence can bring important advantages in free-space optical communications. Specifically, this technique can help to optimize the overall communications performance when the optimum laser-beam divergence is not fixed or known. This is the case in most realistic space laser communication systems, sinc

Maryam Zamani, Sander Paekivi, Philipp Meyer, Holger Kantz

We analyze the interaction between stock prices of big companies in the USA and Germany using Granger Causality. We claim that the increase in pair-wise Granger causality interaction between prices in the times of crisis is the consequence of simultaneous response of the markets to the outside events or external stimulus that is considered as a common driver

Chenrui Zhang

This paper discusses how to crawl the data of financial forums such as stock bar, and conduct emotional analysis combined with the in-depth learning model. This paper will use the Bert model to train the financial corpus and predict the Shenzhen stock index. Through the comparative study of the maximal information coefficient (MIC), it is found that the emot

Manfred Bucher

The rotated stripes are a consequence of the orthorhombic crystal lattice and the isotropy of Coulomb repulsion between pairs of doped holes, residing at oxygen lattice sites. With stripe slanting, the doped-hole pairs come closer to equidistance than without. The slant ratio depends on the orthorhombicity $o$ as $s = \sqrt{o/2}$.

Federico Laudisa

The history of the debates on the foundational implications of the Bell non-locality theorem displayed very soon a tendency to put the theorem in a perspective that was not entirely motivated by its very assumptions, in particular in term of a 'local-realistic' narrative, according to which a major target of the theorem would be the very possibility

P. V. Gorskyi

In the paper, the thermoEMF of powder-based thermoelectric materials (TEM) is calculated. The calculation is made on the assumption of power dependence of mean free path on energy. The thermoEMF decreases with increasing the average radius of powder particles, however, it drastically increases with an increase in power exponent in the law of dependence of th

Christopher Csíkszentmihályi

A critical and re-configured HRI might look to the arts, where another history of robots has been unfolding since the Czech artist Karel Capek's critical robotic labor parable of 1921, in which the word robot was coined in its modern usage. This paper explores several vectors by which artist-created robots, both physical and imaginary, have offered prono

A. Max Reppen, H. Mete Soner, Valentin Tissot-Daguette

This paper outlines, and through stylized examples evaluates a novel and highly effective computational technique in quantitative finance. Empirical Risk Minimization (ERM) and neural networks are key to this approach. Powerful open source optimization libraries allow for efficient implementations of this algorithm making it viable in high-dimensional struct

Ashesh Chattopadhyay, Jaideep Pathak, Ebrahim Nabizadeh, Wahid Bhimji

Recent years have seen a surge in interest in building deep learning-based fully data-driven models for weather prediction. Such deep learning models if trained on observations can mitigate certain biases in current state-of-the-art weather models, some of which stem from inaccurate representation of subgrid-scale processes. However, these data-driven models

Brendan V Christensen, Mark Owkes

Understanding the process of primary and secondary atomization in liquid jets is crucial in describing spray distribution and droplet geometry for industrial applications and is essential in the development of physics-based low-fidelity atomization models that can quickly predict these sprays. Significant advances in numerical modelling and computational res

Miguel Couceiro, Erkko Lehtonen

Analogical proportions are 4-ary relations that read "A is to B as C is to D". Recent works have highlighted the fact that such relations can support a specific form of inference, called analogical inference. This inference mechanism was empirically proved to be efficient in several reasoning and classification tasks. In the latter case, it relies on

Domenic P. J. Germano, Stuart T. Johnston, Edmund J. Crampin, James M. Osborne

The maintenance of tissue and organ structures during dynamic homeostasis is often not well understood. In order for a system to be stable, cell renewal, cell migration and cell death must be finely balanced. Moreover, a tissue's shape must remain relatively unchanged. Simple epithelial tissues occur in various structures throughout the body, such as the

Hassan H. Alnasser, Claudia Czado

In aviation safety, runway overruns are of great importance because they are the most frequent type of landing accidents. Identification of factors which contribute to the occurrence of runway overruns can help mitigate the risk and prevent such accidents. Methods such as physics-based and statistical-based models were proposed in the past to estimate runway

S. J. Curran, J. P. Moss, Y. C. Perrott

With the aim of using machine learning techniques to obtain photometric redshifts based upon a source's radio spectrum alone, we have extracted the radio sources from the Million Quasars Catalogue. Of these, 44,119 have a spectroscopic redshift, required for model validation, and for which photometry could be obtained. Using the radio spectral properties

Ian Frederick Vigogne Goodbody Hunter, Alessandro Palla, Sebastian Eusebiu Nagy, Richard Richmond

Calculating the most efficient schedule of work in a neural network compiler is a difficult task. There are many parameters to be accounted for that can positively or adversely affect that schedule depending on their configuration - How work is shared between distributed targets, the subdivision of tensors to fit in memory, toggling the enablement of optimiz

Rabindra Basnet, K. Kotur, M. Rybak, Cory Stephenson

Recent discoveries in two-dimensional (2D) magnetism have intensified the investigation of van der Waals (vdW) magnetic materials and further improved our ability to tune their magnetic properties. Tunable magnetism has been widely studied in antiferromagnetic metal thiophosphates MPX3. Substitution of metal ions M has been adopted as an important technique

Alejandro Tolcachier

In this article we study the relation between flat solvmanifolds and $G_2$-geometry. First, we give a classification of 7-dimensional flat splittable solvmanifolds using the classification of finite subgroups of $\mathsf{GL}(n,\mathbb{Z})$ for $n=5$ and $n=6$. Then, we look for closed, coclosed and divergence-free $G_2$-structures compatible with the flat me

Joyce Byun, Elisabeth Krause

We extend the modal decomposition method, previously applied to compress the information in the real-space bispectrum, to the anisotropic redshift-space galaxy bispectrum. In the modal method approach, the bispectrum is expanded on a basis of smooth functions of triangles and their orientations, such that a set of modal expansion coefficients can capture the

Go Ogiya, Daisuke Nagai

The rotation curves of some star forming massive galaxies at redshift two decline over the radial range of a few times the effective radius, indicating a significant deficit of dark matter (DM) mass in the galaxy centre. The DM mass deficit is interpreted as the existence of a DM density core rather than the cuspy structure predicted by the standard cosmolog

Yicheng Gao, Giuliano Casale

With constrained resources, what, where, and how to cache at the edge is one of the key challenges for edge computing systems. The cached items include not only the application data contents but also the local caching of edge services that handle incoming requests. However, current systems separate the contents and services without considering the latency in

Xiaoyu Cheng

When sample data are governed by an unknown sequence of independent but possibly non-identical distributions, the data-generating process (DGP) in general cannot be perfectly identified from the data. For making decisions facing such uncertainty, this paper presents a novel approach by studying how the data can best be used to robustly improve decisions. Tha

Kouki Nakata, Yuichi Ohnuma

The developments in the field of quantum optics raise expectations that laser-matter coupling is a promising building block for magnonics. Here, we propose a method for the generation of direct and alternating spin currents of magnons across the junction interface irradiated by linearly polarized laser. In a junction of ferromagnetic insulators with a large

Wai Kin Lai, Xiaohui Liu, Manman Wang, Hongxi Xing

We reinterpret jet clustering as an axis-finding procedure which, along with the proton beam, defines the virtual-photon transverse momentum $q_T$ in deep inelastic scattering (DIS). In this way, we are able to probe the nucleon intrinsic structure using jet axes in a fully inclusive manner, similar to the Drell-Yan process. We present the complete list of a

Yongda Zhu, George D. Becker, Sarah E. I. Bosman, Laura C. Keating

We present a new investigation of the intergalactic medium (IGM) near reionization using dark gaps in the Lyman-$β$ (Ly$β$) forest. With its lower optical depth, Ly$β$ offers a potentially more sensitive probe to any remaining neutral gas compared to commonly used Ly$α$ line. We identify dark gaps in the Ly$β$ forest using spectra of 42 QSOs at $z_{\rm em}>5

Belén Acosta-Tripailao, Walter Max-Moerbeck, Denisse Pastén, Pablo S. Moya

{We focus on characterizing the high-energy emission mechanisms of blazars by analyzing the variability in the radio band of the light curves of more than a thousand sources. We are interested in assigning complexity parameters to these sources, modeling the time series of the light curves with the method of the Horizontal Visibility Graph (HVG), which allow

Michael Dikshtein, Nir Weinberger, Shlomo Shamai

We formulate and analyze the compound information bottleneck programming. In this problem, a Markov chain $ \mathsf{X} \rightarrow \mathsf{Y} \rightarrow \mathsf{Z} $ is assumed with fixed marginal distributions $\mathsf{P}_{\mathsf{X}}$ and $\mathsf{P}_{\mathsf{Y}}$, and the mutual information between $ \mathsf{X} $ and $ \mathsf{Z} $ is sought to be maximi

Chih-Wei Wang, Xiaohan Liu, Tian Qiao, Mohit Khurana

Hot electrons play a crucial role in enhancing the efficiency of photon-to-current conversion or photocatalytic reactions. In semiconductor nanocrystals, energetic hot electrons capable of photoemission can be generated via the upconversion process involving the dopant-originated intermediate state, currently known only in Mn-doped cadmium chalcogenide quant

HyunJun Jung, Patrick Ruhkamp, Guangyao Zhai, Nikolas Brasch

Depth estimation is a core task in 3D computer vision. Recent methods investigate the task of monocular depth trained with various depth sensor modalities. Every sensor has its advantages and drawbacks caused by the nature of estimates. In the literature, mostly mean average error of the depth is investigated and sensor capabilities are typically not discuss

Michael Neely, Stefan F. Schouten, Maurits Bleeker, Ana Lucic

There has been significant debate in the NLP community about whether or not attention weights can be used as an explanation - a mechanism for interpreting how important each input token is for a particular prediction. The validity of "attention as explanation" has so far been evaluated by computing the rank correlation between attention-based explana

Claudio A. Buzzi, Yagor Romano Carvalho, Jaume Llibre

These last years an increasing interest appeared for studying the planar discontinuous piecewise differential systems motivated by the rich applications in modelling real phenomena. One of the difficulties for understanding the dynamics of these systems is the study their limit cycles. In this paper we study the maximum number of crossing limit cycles of som

Loris Di Cairano

We develop a geometric theory of phase transitions (PTs) for Hamiltonian systems in the microcanonical ensemble. This theory allows to reformulate Bachmann's classification of PTs for finite-size systems in terms of geometric properties of the energy level sets (ELSs) associated to the Hamiltonian function. Specifically, by defining the microcanonical en

Nikhil Chandak, Kenny Chour, Sivakumar Rathinam, R. Ravi

We interleave sampling based motion planning methods with pruning ideas from minimum spanning tree algorithms to develop a new approach for solving a Multi-Goal Path Finding (MGPF) problem in high dimensional spaces. The approach alternates between sampling points from selected regions in the search space and de-emphasizing regions that may not lead to good

Jens Helge Reelfs, Oliver Hohlfeld, Markus Strohmaier, Niklas Henckell

Social media is subject to constant growth and evolution, yet little is known about their early phases of adoption. To shed light on this aspect, this paper empirically characterizes the initial and country-wide adoption of a new type of social media in Saudi Arabia that happened in 2017. Unlike established social media, the studied network Jodel is anonymou

Simon Marynissen, Jesse Heyninck, Bart Bogaerts, Marc Denecker

Justification theory is a general framework for the definition of semantics of rule-based languages that has a high explanatory potential. Nested justification systems, first introduced by Denecker et al. (2015), allow for the composition of justification systems. This notion of nesting thus enables the modular definition of semantics of rule-based languages

Ramis Movassagh, Mario Szegedy, Guanyang Wang

Sourav Chatterjee, Persi Diaconis, Allan Sly and Lingfu Zhang, prompted by a question of Ramis Movassagh, renewed the study of a process proposed in the early 1980s by Jean Bourgain. A state vector $v \in \mathbb R^n$, labeled with the vertices of a connected graph, $G$, changes in discrete time steps following the simple rule that at each step a random edge

Alvin Chan, Yew-Soon Ong, Clement Tan

Model robustness is vital for the reliable deployment of machine learning models in real-world applications. Recent studies have shown that data augmentation can result in model over-relying on features in the low-frequency domain, sacrificing performance against low-frequency corruptions, highlighting a connection between frequency and robustness. Here, we

Stefano Lanza, Fernando Marchesano, Luca Martucci, Irene Valenzuela

In any consistent effective field theory of quantum gravity limits of infinite field distance are expected to lead to the EFT breakdown due to the appearance of an infinite tower of light states, as predicted by the Distance Conjecture. We review the Distant Axionic String Conjecture, which proposes that any 4d EFT infinite-field-distance limit can be realiz

Sergei A. Plotnikov

The problem of synchronization in heterogeneous networks of linear systems with nonlinear delayed diffusive coupling is considered. The network is presented in new coordinates mean-field dynamics and synchronization errors. Thus the problem of network synchronization is reduced to the studying of synchronization-error system stability. The circle criterion f

Claudia Roberts, Maria Dimakopoulou, Qifeng Qiao, Ashok Chandrashekhar

Contextual bandits are widely used in industrial personalization systems. These online learning frameworks learn a treatment assignment policy in the presence of treatment effects that vary with the observed contextual features of the users. While personalization creates a rich user experience that reflect individual interests, there are benefits of a shared

Theodore Steele, Kinwah Wu

Fronts are regions of transition from one state to another in a medium. They are present in many areas of science and applied mathematics, and modelling them and their evolution is often an effective way of treating the underlying phenomena responsible for them. In this paper, we propose a new approach to modelling front propagation, which characterises the

Zhijian Yang, Junhao Wen, Christos Davatzikos

A plethora of machine learning methods have been applied to imaging data, enabling the construction of clinically relevant imaging signatures of neurological and neuropsychiatric diseases. Oftentimes, such methods don't explicitly model the heterogeneity of disease effects, or approach it via nonlinear models that are not interpretable. Moreover, unsuper

Dixon Domfeh, Arpita Chatterjee, Matthew Dixon

Catastrophe (CAT) bond markets are incomplete and hence carry uncertainty in instrument pricing. As such various pricing approaches have been proposed, but none treat the uncertainty in catastrophe occurrences and interest rates in a sufficiently flexible and statistically reliable way within a unifying asset pricing framework. Consequently, little is known

Jurijs Nazarovs, Zhichun Huang

Generating smooth animations from a limited number of sequential observations has a number of applications in vision. For example, it can be used to increase number of frames per second, or generating a new trajectory only based on first and last frames, e.g. a motion of face emotions. Despite the discrete observed data (frames), the problem of generating a

Md. Mashih Ibn Yasin Adan, Md. Kamrujjaman, Md. Mamun Molla, Muhammad Mohebujjaman

This paper investigates the competition of two species in a heterogeneous environment subject to the effect of harvesting. The most realistic harvesting case is connected with the intrinsic growth rate, and the harvesting functions are developed based on this clause instead of random choice. We prove the existence and uniqueness of the solution to the model

Dian Yu, Mingqiu Wang, Yuan Cao, Izhak Shafran

Carefully-designed schemas describing how to collect and annotate dialog corpora are a prerequisite towards building task-oriented dialog systems. In practical applications, manually designing schemas can be error-prone, laborious, iterative, and slow, especially when the schema is complicated. To alleviate this expensive and time consuming process, we propo

S. Mironov, An. Morozov

We study the Mana and Magic for quantum states. They have a standard definition through the Clifford group, which is finite and thus classically computable. We introduce a modified Mana and Magic, which keep their main property of classical computability, while making other states classically computable. We also apply these new definitions to the studies of

Nikil Pancha, Andrew Zhai, Jure Leskovec, Charles Rosenberg

Sequential models have become increasingly popular in powering personalized recommendation systems over the past several years. These approaches traditionally model a user's actions on a website as a sequence to predict the user's next action. While theoretically simplistic, these models are quite challenging to deploy in production, commonly requiri

Courtney Mansfield, Amandalynne Paullada, Kristen Howell

Many datasets contain personally identifiable information, or PII, which poses privacy risks to individuals. PII masking is commonly used to redact personal information such as names, addresses, and phone numbers from text data. Most modern PII masking pipelines involve machine learning algorithms. However, these systems may vary in performance, such that in

Allard Oelen, Markus Stocker, Sören Auer

As the number of published scholarly articles grows steadily each year, new methods are needed to organize scholarly knowledge so that it can be more efficiently discovered and used. Natural Language Processing (NLP) techniques are able to autonomously process scholarly articles at scale and to create machine readable representations of the article content.

Ted C. Rogers

I propose a method for tracking and assessing scientific progress using a prediction consensus algorithm designed for the purpose. The protocol obviates the need for centralized referees to generate scientific questions, gather predictions, and assess the accuracy or success of those predictions. It relies instead on crowd wisdom and a system of checks and b

Zeyu Ma, Zachary Teed, Jia Deng

We address multiview stereo (MVS), an important 3D vision task that reconstructs a 3D model such as a dense point cloud from multiple calibrated images. We propose CER-MVS (Cascaded Epipolar RAFT Multiview Stereo), a new approach based on the RAFT (Recurrent All-Pairs Field Transforms) architecture developed for optical flow. CER-MVS introduces five new chan

Janet Y Sheung, Jonathan Garamella, Stella K Kahl, Brian Y Lee

The cytoskeleton -- a composite network of biopolymers, molecular motors, and associated binding proteins -- is a paradigmatic example of active matter. Particle transport through the cytoskeleton can range from anomalous and heterogeneous subdiffusion to superdiffusion and advection. Yet, recapitulating and understanding these properties -- ubiquitous to th

Iman Askari, Shen Zeng, Huazhen Fang

Nonlinear model predictive control (NMPC) has gained widespread use in many applications. Its formulation traditionally involves repetitively solving a nonlinear constrained optimization problem online. In this paper, we investigate NMPC through the lens of Bayesian estimation and highlight that the Monte Carlo sampling method can offer a favorable way to im

Xiaoqi Huang, Christopher D. Sogge, Michael E. Taylor

We show that if $Y$ is a compact Riemannian manifold with improved $L^q$ eigenfunction estimates then, at least for large enough exponents, one always obtains improved $L^q$ bounds on the product manifold $X\times Y$ if $X$ is another compact manifold. Similarly, improved Weyl remainder term bounds on the spectral counting function of $Y$ lead to correspondi

Trystyn A. M. Berg, Guido Cupani, Pedro Figueira, Andrea Mehner

As of October 2021 (Period 108), the European Southern Observatory (ESO) offers a new mode of the ESPRESSO spectrograph designed to use the High Resolution grating with 4x2 binning (spatial by spectral; HR42 mode) with the specific objective of observing faint targets with a single Unit Telescope at Paranal. We validated the new HR42 mode using four hours of

Joakim Argillander, Alvaro Alarcón, Guilherme B. Xavier

Quantum random number generators (QRNG) are based on the naturally random measurement results performed on individual quantum systems. Here, we demonstrate a branching-path photonic QRNG implemented with a Sagnac interferometer with a tunable splitting ratio. The fine-tuning of the splitting ratio allows us to maximize the entropy of the generated sequence o

Gopal-Krishna, Ravi Joshi, Dusmanta Patra, Xiaolong Yang

We report observations of a bright dumb-bell system of galaxies ($z =0.162$) with the upgraded Giant Metrewave Radio Telescope ($u$GMRT), which show that each member of this gravitationally bound pair of galaxies hosts bipolar radio jets extended on 100 kiloparsec scales. Only two cases of such radio morphology have been reported previously, both being dumb-

M. Beneke, M. Garny, S. Jaskiewicz, J. Strohm

The lack of convergence of the convolution integrals appearing in next-to-leading-power (NLP) factorization theorems prevents the applications of existing methods to resum power-suppressed large logarithmic corrections in collider physics. We consider thrust distribution in the two-jet region for the flavour-nonsinglet off-diagonal contribution, where a gluo

Junhyun Baek, Aeree Chung, Alastair Edge, Tom Rose

We present the circumnuclear multi-phase gas properties of the brightest cluster galaxy (BCG) in the center of Abell 1644-South. A1644-S is the main cluster in a merging system, which is well known for X-ray hot gas sloshing in its core. The sharply peaked X-ray profile of A1644-S implies the presence of a strongly cooling gas core. In this study, we analyze

M. Armas Padilla, P. Rodríguez-Gil, T. Muñoz-Darias, M. A. P. Torres

We present a detailed time-resolved photometric study of the ultra-compact X-ray binary candidate 4U 1812-12. The multicolor light curves obtained with HiPERCAM on the 10.4-m Gran Telescopio Canarias show an aprox 114 min modulation similar to a superhump. Under this interpretation, this period should lie very close to the orbital period of the system. Contr

Martin Chileshe, Mayumbo Nyirenda

In Zambia, there is a serious shortage of medical staff where each practitioner attends to about 17000 patients in a given district while still, other patients travel over 10 km to access the basic medical services. In this research, we implement a deep learning model that can perform the clinical diagnosis process. The study will prove whether image analysi

Sreekrishnan Venkateswaran, Santonu Sarkar

The hybrid cloud idea is increasingly gaining momentum because it brings distinct advantages as a hosting platform for complex software systems. However, there are several challenges that need to be surmounted before hybrid hosting can become pervasive and penetrative. One main problem is to architecturally partition workloads across permutations of feasible

M. R. Setare, M. Koohgard

Recently a general prescription for determining the vacuum modular flow generator and the corresponding modular Hamiltonian in the BMS-invariant field theories (BMSFTs) have provided by Apolo et. al \cite{Apolo}. According to this paper Einstein gravity in asymptotically flat three-dimensional spacetimes is dual to a BMSFT. In the present paper we extend thi

Ryan McCloy, Lai Wei, Jie Bao

Many chemical processes exhibit diverse timescale dynamics with a strong coupling between timescale sensitive variables. Model predictive control with a non-uniformly spaced optimisation horizon is an effective approach to multi-timescale control and offers opportunities for reduced computational complexity. In such an approach the fast, moderate and slow dy

Cameron Darwin

Over an algebraically closed field k, there are 16 lines on a degree 4 del Pezzo surface, but for other fields the situation is more subtle. In order to improve enumerative results over perfect fields, Kass and Wickelgren introduce a method analogous to counting zeroes of sections of smooth vector bundles using the Poincare-Hopf theorem. However, the techniq

Jayadev Vijayan, Zhao Zhang, Johannes Piotrowski, Dominik Windey

The field of levitodynamics has made significant progress towards controlling and studying the motion of a levitated nanoparticle. Motional control relies on either autonomous feedback via a cavity or measurement-based feedback via external forces. Recent demonstrations of measurement-based ground-state cooling of a single nanoparticle employ linear velocity

Payaswini Saikia, David M. Russell, M. C. Baglio, D. M. Bramich

We present a detailed study of the evolution of the Galactic black hole transient GRS 1716-249 during its 2016-2017 outburst at optical (Las Cumbres Observatory), mid-infrared (Very Large Telescope), near-infrared (Rapid Eye Mount telescope), and ultraviolet (the Neil Gehrels Swift Observatory Ultraviolet/Optical Telescope) wavelengths, along with archival r

Liliana Borcea, Josselin Garnier, Knut Solna

We study the paraxial wave equation with a randomly perturbed index of refraction, which can model the propagation of a wave beam in a turbulent medium. The random perturbation is a stationary and isotropic process with a general form of the covariance that may be integrable or not. We focus attention mostly on the non-integrable case, which corresponds to a

Weijie Zhang, Yulong Xing, Eirik Endeve

This paper presents high-order Runge-Kutta (RK) discontinuous Galerkin methods for the Euler-Poisson equations in spherical symmetry. The scheme can preserve a general polytropic equilibrium state and achieve total energy conservation up to machine precision with carefully designed spatial and temporal discretizations. To achieve the well-balanced property,

Silas Alben

We optimize three-dimensional snake kinematics for locomotor efficiency. We assume a general space-curve representation of the snake backbone with small-to-moderate lifting off the ground and negligible body inertia. The cost of locomotion includes work against friction and internal viscous dissipation. When restricted to planar kinematics, our population-ba

Zexi Niu, Haibo Yuan, Yilun Wang, Jifeng Liu

The metallicity dependence of the wide binary fraction (WBF) is critical for studying the formation of wide binaries. While controversial results have been found in recent years. Here we combine the wide binary catalog recognized from Gaia EDR3 and stellar parameters from LAMOST to investigate this topic. Taking bias of the stellar temperature at given separ

Yamile Godoy, Michael Harrison, Marcos Salvai

Let $v$ be a unit vector field on a complete, umbilic (but not totally geodesic) hypersurface $N$ in a space form; for example on the unit sphere $S^{2k-1} \subset \mathbb{R}^{2k}$, or on a horosphere in hyperbolic space. We give necessary and sufficient conditions on $v$ for the rays with initial velocities $v$ (and $-v$) to foliate the exterior $U$ of $N$.

Andreas Psaroudakis, Dimitrios Kollias

Automatic Facial Expression Recognition (FER) has attracted increasing attention in the last 20 years since facial expressions play a central role in human communication. Most FER methodologies utilize Deep Neural Networks (DNNs) that are powerful tools when it comes to data analysis. However, despite their power, these networks are prone to overfitting, as

S. B. L. Chislett-McDonald, E. Surrey, J. Naish, A. Turner

All high field superconductors producing magnetic fields above 12 T are brittle. Nevertheless, they will probably be the materials of choice in commercial tokamaks because the fusion power density in a tokamak scales as the fourth power of magnetic field. Here we propose using robust, ductile superconductors during the reactor commissioning phase in order to

Md Tahmid Rahman Laskar, Cheng Chen, Aliaksandr Martsinovich, Jonathan Johnston

An Entity Linking system aligns the textual mentions of entities in a text to their corresponding entries in a knowledge base. However, deploying a neural entity linking system for efficient real-time inference in production environments is a challenging task. In this work, we present a neural entity linking system that connects the product and organization

M. Leticia Rubio Puzzo, Ernesto S. Loscar, Andres De Virgiliis, Tomas S. Grigera

We study the short-time dynamics (STD) of the Vicsek model with vector noise. The study of STD has proved to be very useful in the determination of the critical point, critical exponents, and spinodal points in equilibrium phase transitions. Here we aim to test its applicability in active systems. We find that, despite the essential non-equilibrium character

Sean J. Weinberg, Fabio Sanches, Takanori Ide, Kazumitzu Kamiya

Noisy intermediate-scale quantum (NISQ) hardware is almost universally incompatible with full-scale optimization problems of practical importance which can have many variables and unwieldy objective functions. As a consequence, there is a growing body of literature that tests quantum algorithms on miniaturized versions of problems that arise in an operations

Dimitrios Tyrovolas, Sotiris A Tegos, Emmanouela C Dimitriadou Panidou, Panagiotis D Diamantoulakis

Reconfigurable intelligent surfaces (RIS) have been presented as a solution to realize the concept of smart radio environments, wherein uninterrupted coverage and extremely high quality of service can be ensured. In this paper, assuming that multiple RIS are deployed in the propagation environment, the performance of a cascaded RIS network affected by imperf

Catalin Zorila, Rama Doddipatla

Improving the accuracy of single-channel automatic speech recognition (ASR) in noisy conditions is challenging. Strong speech enhancement front-ends are available, however, they typically require that the ASR model is retrained to cope with the processing artifacts. In this paper we explore a speaker reinforcement strategy for improving recognition performan

Rajdeep Mukherjee, Omer Tripp, Ben Liblit, Michael Wilson

Amazon Web Services (AWS) is a comprehensive and broadly adopted cloud provider, offering over 200 fully featured services, including compute, database, storage, networking and content delivery, machine learning, Internet of Things and many others. AWS SDKs provide access to AWS services through API endpoints. However, incorrect use of these APIs can lead to

Gage W. Harmon, Jarrod T. Reilly, Murray J. Holland, Simon B. Jäger

We present a theoretical description for a lasing scheme for atoms with three internal levels in a $V$-configuration and interacting with an optical cavity. The use of a $V$-level system allows for an efficient closed lasing cycle to be sustained on a dipole-forbidden transition without the need for incoherent repumping. This is made possible by utilizing an

Pavel Vashchenko, Alexei Verenikin, Anna Verenikina

The article is devoted to the competitiveness analysis of Russian institutions of higher education in international and local markets. The methodology of research is based on generalized modified principal component analysis. Principal components analysis has proven its efficiency in business performance assessment. We apply a modification of this methodolog

Gaia Saveri, Luca Bortolussi

Graph Neural Networks (GNNs) have been recently leveraged to solve several logical reasoning tasks. Nevertheless, counting problems such as propositional model counting (#SAT) are still mostly approached with traditional solvers. Here we tackle this gap by presenting an architecture based on the GNN framework for belief propagation (BP) of Kuch et al., exten

Tobia Marcucci, Mark Petersen, David von Wrangel, Russ Tedrake

Trajectory optimization offers mature tools for motion planning in high-dimensional spaces under dynamic constraints. However, when facing complex configuration spaces, cluttered with obstacles, roboticists typically fall back to sampling-based planners that struggle in very high dimensions and with continuous differential constraints. Indeed, obstacles are