October 2022 arXiv papers — page 49

Yuta Takada

This article extends Bayer-Fluckiger's theorem on characteristic polynomials of isometries on an even unimodular lattice to the case where the isometries have determinant $-1$. As an application, we show that the logarithm of every Salem number of degree $20$ is realized as the topological entropy of an automorphism of a nonprojective K3 surface.

Xili Dai, Mingyang Li, Pengyuan Zhai, Shengbang Tong

Despite strong empirical performance for image classification, deep neural networks are often regarded as ``black boxes'' and they are difficult to interpret. On the other hand, sparse convolutional models, which assume that a signal can be expressed by a linear combination of a few elements from a convolutional dictionary, are powerful tools for analyzing n

Prabath Abeysekara, Hai Dong, A. K. Qin

With the breakthroughs in Deep Learning, recent years have witnessed a massive surge in Artificial Intelligence applications and services. Meanwhile, the rapid advances in Mobile Computing and Internet of Things has also given rise to billions of mobile and smart sensing devices connected to the Internet, generating zettabytes of data at the network edge. Th

Simon Grall, Shuo Li, Laurent Jalabert, Soo-Hyeon Kim

Redox monolayers are the base for a wide variety of devices including high-frequency molecular diodes or biomolecular sensors. We introduce a formalism to describe the electrochemical shot-noise of such monolayer, confirmed experimentally at room temperature in liquid. The proposed method, carried out at equilibrium, avoids parasitic capacitance, increases t

Zhiqiang Hu, Roy Kaa-Wei Lee, Nancy F. Chen

Task-oriented dialogue (TOD) systems have assisted users on many tasks, including ticket booking and service inquiries. While existing TOD systems have shown promising performance in serving customer needs, these systems mostly assume that users would interact with the dialogue agent politely. This assumption is unrealistic as impatient or frustrated custome

Yihong Huang, Liping Wang, Fan Zhang, Xuemin Lin

A large number of studies on Graph Outlier Detection (GOD) have emerged in recent years due to its wide applications, in which Unsupervised Node Outlier Detection (UNOD) on attributed networks is an important area. UNOD focuses on detecting two kinds of typical outliers in graphs: the structural outlier and the contextual outlier. Most existing works conduct

Xiaolin Zheng, Rui Wu, Zhongxuan Han, Chaochao Chen

Recommender systems are fundamental information filtering techniques to recommend content or items that meet users' personalities and potential needs. As a crucial solution to address the difficulty of user identification and unavailability of historical information, session-based recommender systems provide recommendation services that only rely on users' b

B. Sykes, B. Mueller, I. Cordero-Carrión, P. Cerdá-Durán

Fallback supernovae and the collapsar scenario for long-gamma ray burst and hypernovae have received considerable interest as pathways to black-hole formation and extreme transient events. Consistent simulations of these scenarios require a general relativistic treatment and need to deal appropriately with the formation of a singularity. Free evolution schem

Tan Nhu Nhat Doan, Kyungeun Kim, Boram Song, Jin Tae Kwak

An automated segmentation and classification of nuclei is an essential task in digital pathology. The current deep learning-based approaches require a vast amount of annotated datasets by pathologists. However, the existing datasets are imbalanced among different types of nuclei in general, leading to a substantial performance degradation. In this paper, we

Peilong Dong, Yali Chen

In this paper, we study the relationship between isoparametric hypersurfaces and hypersurfaces with constant principal curvatures in Finsler spaces. We give some examples of isoparametric hypersurfaces with (non)constant principal curvatures on Randers manifolds with nonconstant flag curvatures. Furthermore, we construct an example of a conformally flat Rand

Yanzhao Wu, Ling Liu

The choice of learning rate (LR) functions and policies has evolved from a simple fixed LR to the decaying LR and the cyclic LR, aiming to improve the accuracy and reduce the training time of Deep Neural Networks (DNNs). This paper presents a systematic approach to selecting and composing an LR policy for effective DNN training to meet desired target accurac

Bolivar Solarte, Chin-Hsuan Wu, Yueh-Cheng Liu, Yi-Hsuan Tsai

We present 360-MLC, a self-training method based on multi-view layout consistency for finetuning monocular room-layout models using unlabeled 360-images only. This can be valuable in practical scenarios where a pre-trained model needs to be adapted to a new data domain without using any ground truth annotations. Our simple yet effective assumption is that mu

Shulun Han, Chi Sin Tang, Linyang Li, Yi Liu

Owing to its inherent non-trivial geometry, the unique structural motif of the recently discovered Kagome topological superconductor AV3Sb5 is an ideal host of diverse topologically non-trivial phenomena, including giant anomalous Hall conductivity, topological charge order, charge density wave, and unconventional superconductivity. Despite possessing a norm

Yuhao Jiang, Kunjie Zhang, Qimai Li, Jiaxin Chen

In recent years, Multi-Agent Path Finding (MAPF) has attracted attention from the fields of both Operations Research (OR) and Reinforcement Learning (RL). However, in the 2021 Flatland3 Challenge, a competition on MAPF, the best RL method scored only 27.9, far less than the best OR method. This paper proposes a new RL solution to Flatland3 Challenge, which s

Pramod Padmanabhan, Abhishek Chowdhury

Loop braid groups characterize the exchange of extended objects, namely loops, in three dimensional space generalizing the notion of braid groups that describe the exchange of point particles in two dimensional space. Their interest in physics stems from the fact that they capture anyonic statistics in three dimensions which is otherwise known to only exist

Allen Chang, Mary Knapp, James LaBelle, John Swoboda

Radio frequency data in astronomy enable scientists to analyze astrophysical phenomena. However, these data can be corrupted by radio frequency interference (RFI) that limits the observation of underlying natural processes. In this study, we extend recent developments in deep learning algorithms to astronomy data. We remove RFI from time-frequency spectrogra

Qing Li, Songtao Li, Yanling Zhang, Xiaojie Chen

A vast body of experiments share the view that social norms are major factors for the emergence of fairness in a population of individuals playing the dictator game (DG). Recently, to explore which social norms are conducive to sustaining cooperation has obtained considerable concern. However, thus far few studies have investigated how social norms influence

Vikas Raunak, Arul Menezes

Memorization presents a challenge for several constrained Natural Language Generation (NLG) tasks such as Neural Machine Translation (NMT), wherein the proclivity of neural models to memorize noisy and atypical samples reacts adversely with the noisy (web crawled) datasets. However, previous studies of memorization in constrained NLG tasks have only focused

Dianbo Liu, Moksh Jain, Bonaventure Dossou, Qianli Shen

Bayesian Inference offers principled tools to tackle many critical problems with modern neural networks such as poor calibration and generalization, and data inefficiency. However, scaling Bayesian inference to large architectures is challenging and requires restrictive approximations. Monte Carlo Dropout has been widely used as a relatively cheap way for ap

Enyu Zhao, Chanjuan Liu, Houfu Su, Yang Liu

Intelligence agents and multi-agent systems play important roles in scenes like the control system of grouped drones, and multi-agent navigation and obstacle avoidance which is the foundational function of advanced application has great importance. In multi-agent navigation and obstacle avoidance tasks, the decision-making interactions and dynamic changes of

Sedrick Scott Keh

This work builds upon the Euphemism Detection Shared Task proposed in the EMNLP 2022 FigLang Workshop, and extends it to few-shot and zero-shot settings. We demonstrate a few-shot and zero-shot formulation using the dataset from the shared task, and we conduct experiments in these settings using RoBERTa and GPT-3. Our results show that language models are ab

Yiheng Shu, Zhiwei Yu, Yuhan Li, Börje F. Karlsson

Pre-trained language models (PLMs) have shown their effectiveness in multiple scenarios. However, KBQA remains challenging, especially regarding coverage and generalization settings. This is due to two main factors: i) understanding the semantics of both questions and relevant knowledge from the KB; ii) generating executable logical forms with both semantic

Benoit Steiner, Mostafa Elhoushi, Jacob Kahn, James Hegarty

The size of deep neural networks has grown exponentially in recent years. Unfortunately, hardware devices have not kept pace with the rapidly increasing memory requirements. To cope with this, researchers have turned to techniques such as spilling and recomputation, which increase training time, or reduced precision and model pruning, which can affect model

Sho Ejiri

In this note, we prove the coherence of Frobenius stable direct images in a new case. We also show a generation theorem regarding to it. Furthermore, we prove a corresponding theorem in characteristic zero.

Wenhui Chen, Zhijiang Zhang, Liang Yu, Yichun Tai

Airport runway segmentation can effectively reduce the accident rate during the landing phase, which has the largest risk of flight accidents. With the rapid development of deep learning (DL), related methods achieve good performance on segmentation tasks and can be well adapted to complex scenes. However, the lack of large-scale, publicly available datasets

Ahnaf Mozib Samin, M. Humayon Kobir, Md. Mushtaq Shahriyar Rafee, M. Firoz Ahmed

Despite huge improvements in automatic speech recognition (ASR) employing neural networks, ASR systems still suffer from a lack of robustness and generalizability issues due to domain shifting. This is mainly because principal corpus design criteria are often not identified and examined adequately while compiling ASR datasets. In this study, we investigate t

Lina Zhu, Lin Bai, Lin Zhou, Jinho Choi

Due to increasing demands of seamless connection and massive information exchange across the world, the integrated satellite-terrestrial communication systems develop rapidly. To shed lights on the design of this system, we consider an uplink communication model consisting of a single satellite, a single terrestrial station and multiple ground users. The ter

C. R. Constante-Amores, L. Kahouadji, S. Shin, J. Chergui

We study the effect of insoluble surfactants on the impact of surfactant-free droplets on surfactant-laden thin liquid films via a fully three-dimensional direct numerical simulations approach that employs a hybrid interface-tracking/level-set method, and by taking into account surfactant-induced Marangoni stresses due to gradients in interfacial surfactant

Alireza Nasiri, Tristan Bepler

In many imaging modalities, objects of interest can occur in a variety of locations and poses (i.e. are subject to translations and rotations in 2d or 3d), but the location and pose of an object does not change its semantics (i.e. the object's essence). That is, the specific location and rotation of an airplane in satellite imagery, or the 3d rotation of a c

Wesley dos Reis Bezerra, Fernando Koch, Carlos Becker Westphall

We analyze the utilization of publish-subscribe protocols in IoT and Fog Computing and challenges around security configuration, performance, and qualitative characteristics. Such problems with security configuration lead to significant disruptions and high operation costs. Yet, These issues can be prevented by selecting the appropriate transmission technolo

Natasha Fernandes, Annabelle McIver, Parastoo Sadeghi

The study of leakage measures for privacy has been a subject of intensive research and is an important aspect of understanding how privacy leaks occur in computer systems. Differential privacy has been a focal point in the privacy community for some years and yet its leakage characteristics are not completely understood. In this paper we bring together two a

Wei Wang, Gang Wang, Chenlong Hu, K. C. Ho

The presence of outliers can significantly degrade the performance of ellipse fitting methods. We develop an ellipse fitting method that is robust to outliers based on the maximum correntropy criterion with variable center (MCC-VC), where a Laplacian kernel is used. For single ellipse fitting, we formulate a non-convex optimization problem to estimate the ke

Jia Guo, Haifeng Wang, Chenping Hou

Physics-Informed Neural Networks (PINNs) have become a kind of attractive machine learning method for obtaining solutions of partial differential equations (PDEs). Training PINNs can be seen as a semi-supervised learning task, in which only exact values of initial and boundary points can be obtained in solving forward problems, and in the whole spatio-tempor

Martín Mereb

In this note we prove an assertion made by M. Levin in 1999: the Pascal matrix modulo 2 has the property that each of the square sub-matrices laying on the upper border or on the left border has determinants, computed in $\mathbb{Z}$, equal to 1 or -1.

Rene Carmona, Quentin Cormier, H. Mete Soner

The classical Kuramoto model is studied in the setting of an infinite horizon mean field game. The system is shown to exhibit both synchronization and phase transition. Incoherence below a critical value of the interaction parameter is demonstrated by the stability of the uniform distribution. Above this value, the game bifurcates and develops self-organizin

Jian Zhang, Jianjun Zhang, Xuexiu Zhong

In this paper, we are concerned with normalized solutions of the Kirchhoff type equation \begin{equation*} -M\left(\int_{\R^N}|\nabla u|^2\mathrm{d} x\right)\Delta u = \lambda u +f(u) \ \ \mathrm{in} \ \ \mathbb{R}^N \end{equation*} with $u \in S_c:=\left\{u \in H^1(\R^N): \int_{\R^N}u^2 \mathrm{d}x=c^2\right\}$. When $N=2$ and $f$ has exponential critical g

Jiyoung Lee, Hantae Kim, Hyunchang Cho, Edward Choi

Multi-domain Neural Machine Translation (NMT) trains a single model with multiple domains. It is appealing because of its efficacy in handling multiple domains within one model. An ideal multi-domain NMT should learn distinctive domain characteristics simultaneously, however, grasping the domain peculiarity is a non-trivial task. In this paper, we investigat

Meng-Long Song, Li-Juan Li, Xue-Ke Song, Liu Ye

We studied the dynamics of entropic uncertainty in Markovian and non-Markovian systems during the charging of open quantum batteries (QBs) mediated by a common dissipation environment. In the non-Markovian regime, the battery is almost fully charged efficiently, and the strong non-Markovian property is beneficial for improving the charging power. In addition

William Croft, Jörg-Rüdiger Sack

Prediction of the future performance of academic journals is a task that can benefit a variety of stakeholders including editorial staff, publishers, indexing services, researchers, university administrators and granting agencies. Using historical data on journal performance, this can be framed as a machine learning regression problem. In this work, we study

Meris Sipp, Patrick LaChance, Rupert Croft, Yueying Ni

AI super-resolution, combining deep learning and N-body simulations has been shown to successfully reproduce the large scale structure and halo abundances in the Lambda Cold Dark Matter cosmological model. Here, we extend its use to models with a different dark matter content, in this case Fuzzy Dark Matter (FDM), in the approximation that the difference is

T. Ssettumba, R. Di Renna, L. Landau, R. C. de Lamare

This paper proposes an iterative detection and decoding (IDD) scheme for a cell free massive multiple input multiple output (CF-mMIMO) system. Users send coded data to the access points (APs), which is jointly detected at central processing unit (CPU). The symbols are exchanged iteratively in the form of log likelihood ratios (LLRs) between the detector and

Yue Yang, Artemis Panagopoulou, Marianna Apidianaki, Mark Yatskar

Neural language models encode rich knowledge about entities and their relationships which can be extracted from their representations using probing. Common properties of nouns (e.g., red strawberries, small ant) are, however, more challenging to extract compared to other types of knowledge because they are rarely explicitly stated in texts. We hypothesize th

A. C. Caligula do E. S. Pedreira, N. Fraija, S. Dichiara, P. Veres

The GRB 190829A has been widely studied due to its nature and the high energy emission presented. Due to the detection of a very-high-energy component by the High Energy Stereoscopic System and the event's atypically middling luminosity, it has been categorized in a select, limited group of bursts bordering classic GRBs and nearby sub-energetic events. Given

Lucas Jaffe, Avideh Zakhor

In person search, we aim to localize a query person from one scene in other gallery scenes. The cost of this search operation is dependent on the number of gallery scenes, making it beneficial to reduce the pool of likely scenes. We describe and demonstrate the Gallery Filter Network (GFN), a novel module which can efficiently discard gallery scenes from the

Junru Lu, Xingwei Tan, Gabriele Pergola, Lin Gui

Human reading comprehension often requires reasoning of event semantic relations in narratives, represented by Event-centric Question-Answering (QA). To address event-centric QA, we propose a novel QA model with contrastive learning and invertible event transformation, call TranCLR. Our proposed model utilizes an invertible transformation matrix to project s

Yongliang Wang

Josephson junction circuits, such as superconducting quantum interference devices (SQUIDs) and single-flux-quantum (SFQ) circuits, have been applied in both analog and digital systems for their ultralow-noise, high-speed, and power-efficient features. However, their analyses are not well supported by the conventional circuit diagrams and the charge-based ana

Marilyn Jimenez, Andrei Smolyakov, Oleksandr Chapurin, Peter Yushmanov

Kinetic effects in plasma flow due to a finite ion temperature and ion reflections in a converging-diverging magnetic nozzle are investigated with collisionless quasineutral hybrid simulations with kinetic ions and isothermal Boltzmann electrons. It is shown that in the cold ions limit the velocity profile of the particles agrees well with the analytical the

Abhishek Moitra, Abhiroop Bhattacharjee, Runcong Kuang, Gokul Krishnan

SNNs are an active research domain towards energy efficient machine intelligence. Compared to conventional ANNs, SNNs use temporal spike data and bio-plausible neuronal activation functions such as Leaky-Integrate Fire/Integrate Fire (LIF/IF) for data processing. However, SNNs incur significant dot-product operations causing high memory and computation overh

S P Sharan, Wenqing Zheng, Kuo-Feng Hsu, Jiarong Xing

Recent advances in TCP congestion control (CC) have achieved tremendous success with deep reinforcement learning (RL) approaches, which use feedforward neural networks (NN) to learn complex environment conditions and make better decisions. However, such "black-box" policies lack interpretability and reliability, and often, they need to operate outside the tr

Javier Peraza, Martín Reiris, Omar E. Ortiz

In recent years black hole configurations with non standard topology or with non-standard asymptotic have gained considerable attention. In this article we carry out numerical investigations aimed to find periodic coaxial configurations of co-rotating 3+1 vacuum black holes, for which existence and uniqueness has not yet been theoretically proven. The aimed

Chongxiao Zhao, Wenjie Dou

A stochastic resolution of identity approach (sRI) is applied to the second-order coupled cluster singles and doubles (CC2) model to calculate the ground-state energy. Utilizing a set of stochastic orbitals to optimize the expensive tensor contraction steps in CC2, we greatly reduce the overall computational cost. Compared with the RI-CC2 model, the sRI-CC2

Shijie Han, Siyuan Li, Bo An, Wei Zhao

Multi-agent reinforcement learning (MARL) is a prevalent learning paradigm for solving stochastic games. In most MARL studies, agents in a game are defined as teammates or enemies beforehand, and the relationships among the agents remain fixed throughout the game. However, in real-world problems, the agent relationships are commonly unknown in advance or dyn

Paula Egging, George Avalos

This work presents qualitative and numerical results on a system of partial differential equations (PDEs) which models certain fluid-fluid interaction dynamics. This system models a compressible fluid in a domain $\Omega^+ \subset \mathbb{R}^2$, coupled to an incompressible fluid modeled by Stokes flow in domain $\Omega^- \subset \mathbb{R}^2$, with the stro

Conrad J. Burden, Robert C. Griffiths

Consider the diffusion process defined by the forward equation $u_t(t, x) = \tfrac{1}{2}\{x u(t, x)\}_{xx} - \alpha \{x u(t, x)\}_{x}$ for $t, x \ge 0$ and $-\infty < \alpha < \infty$, with an initial condition $u(0, x) = \delta(x - x_0)$. This equation was introduced and solved by Feller to model the growth of a population of independently reproducing indiv

Farrokh Vatan

In combinatory logic it is known that the set of two combinators K and S are universal; in the sense that any other combinator can be expressed in terms of these two. K combinator can not be expressed only in terms of the S combinator. This will answer a question raised by Stephen Wolfram as ``Is the S combinator on its own computation universal?''

Adarsh Sehgal, Muskan Sehgal, Hung Manh La

Actor learning and critic learning are two components of the outstanding and mostly used Deep Deterministic Policy Gradient (DDPG) reinforcement learning method. Since actor and critic learning plays a significant role in the overall robot's learning, the performance of the DDPG approach is relatively sensitive and unstable as a result. We propose a multi-ac

Igor Mezic

The original intent of the Koopman-von Neumann formalism was to put classical and quantum mechanics on the same footing by introducing an operator formalism into classical mechanics. Here we pursue their path the opposite way and examine what transfer operators can say about quantum mechanical evolution. To that end, we introduce a physically motivated scala

Arvind Seshan

Determining neuronal identity in imaging data is an essential task in neuroscience, facilitating the comparison of neural activity across organisms. Cross-organism comparison, in turn, enables a wide variety of research including whole-brain analysis of functional networks and linking the activity of specific neurons to behavior or environmental stimuli. The

Chul Min Kim, Sang Pyo Kim

We present a 3+1 formulation of the light modes in nonlinear electrodynamics described by Plebanski-type Lagrangians, which include Post-Maxwellian, Born-Infeld, ModMax, and Heisenberg-Euler-Schwinger QED Lagrangians. In nonlinear electrodynamics, strong electromagnetic fields modify the vacuum to acquire optical properties. Such a field-modified vacuum can

Cara Bennett, Lucy Martinez, Ava Mock, Gordon Rojas Kirby

Given a simple graph $G$, one can define a hyperplane arrangement called the $G$-Shi arrangement. The Pak-Stanley algorithm labels the regions of this arrangement with $G_\bullet$-parking functions. When $G$ is a complete graph, we recover the Shi arrangement, and the Pak-Stanley labels give a bijection with ordinary parking functions. However, for proper su

Jonathan A. Hillman

We extend work of Turaev and Bleile to relax the $π_1$-injectivity hypothesis in the characterization of the fundamental triples of $PD_3$-pairs with aspherical boundary components. This is further extended to pairs $(P,\partial{P})$ which also have spherical boundary components and with $c.d.π_1(P)\leq2$.

Evelina Leivada, Elliot Murphy, Gary Marcus

Machine intelligence is increasingly being linked to claims about sentience, language processing, and an ability to comprehend and transform natural language into a range of stimuli. We systematically analyze the ability of DALL-E 2 to capture 8 grammatical phenomena pertaining to compositionality that are widely discussed in linguistics and pervasive in hum

Nitya Mani, Edward Yu

We investigate natural Tur\'an problems for mixed graphs, generalizations of graphs where edges can be either directed or undirected. We study a natural \textit{Tur\'an density coefficient} that measures how large a fraction of directed edges an $F$-free mixed graph can have; we establish an analogue of the Erd\H{o}s-Stone-Simonovits theorem and give a varia

Wenhao Yu, Chenguang Zhu, Zhihan Zhang, Shuohang Wang

A common thread of retrieval-augmented methods in the existing literature focuses on retrieving encyclopedic knowledge, such as Wikipedia, which facilitates well-defined entity and relation spaces that can be modeled. However, applying such methods to commonsense reasoning tasks faces two unique challenges, i.e., the lack of a general large-scale corpus for

Aditi Vijayan, Bocheng Zhu, Miao Li, Feng Yuan

Interstellar (ISM) and circumgalactic mediums (CGM) around galaxies are linked to several physical processes that drive galaxy evolution. For example, the X-ray emission from the CGM gas around ellipticals has been linked to the AGN feedback occurring in the host. Upcoming telescopes, such as HUBS with ~2 eV resolution, can provide us with deep insights abou

Marcel Dengler

In this work our main objective is to establish various (high frequency-) uniqueness criteria. Initially, we consider $p-$Dirichlet type functionals on a suitable class of measure preserving maps $u: B\subset \mathbb{R}^2 \mapsto \mathbb{R}^2,$ $B$ being the unit disk, and subject to suitable boundary conditions. In the second part we focus on a very similar

Sarah Gregory, Fernando Piñero-González, Doel Rivera-Laboy, Lani Southern

The polar orthogonal Grassmann code $C(\mathbb{O}_{3,6})$ is the linear code associated to the Grassmann embedding of the Dual Polar space of $Q^+(5,q)$. In this manuscript we study the minimum distance of this embedding. We prove that the minimum distance of the polar orthogonal Grassmann code $C(\mathbb{O}_{3,6})$ is $q^3-q^3$ for $q$ odd and $q^3$ for $q$

Konstantina Dritsa, Kaiti Thoma, John Pavlopoulos, Panos Louridas

Large, diachronic datasets of political discourse are hard to come across, especially for resource-lean languages such as Greek. In this paper, we introduce a curated dataset of the Greek Parliament Proceedings that extends chronologically from 1989 up to 2020. It consists of more than 1 million speeches with extensive metadata, extracted from 5,355 parliame

Sasila Ilandarideva, Yannis Bekri, Anatoli Juditsky, Vianney Perchet

In this paper we discuss an application of Stochastic Approximation to statistical estimation of high-dimensional sparse parameters. The proposed solution reduces to resolving a penalized stochastic optimization problem on each stage of a multistage algorithm; each problem being solved to a prescribed accuracy by the non-Euclidean Composite Stochastic Mirror

Oguzhan Akcin, Robert P. Streit, Benjamin Oommen, Sriram Vishwanath

There are a multitude of Blockchain-based physical infrastructure systems, operating on a crypto-currency enabled token economy, where infrastructure suppliers are rewarded with tokens for enabling, validating, managing and/or securing the system. However, today's token economies are largely designed without infrastructure systems in mind, and often operate

Zhao Guo, Gordon I. Ogilvie, Adrian J. Barker

We simulate the propagation and dissipation of tidally induced nonlinear gravity waves in the cores of solar-type stars. We perform hydrodynamical simulations of a previously developed Boussinesq model using a spectral-element code to study the stellar core as a wave cavity that is periodically forced at the outer boundary with a given azimuthal wavenumber a

Paloma L. Ocola, Ivana Dimitrova, Brandon Grinkemeyer, Elmer Guardado-Sanchez

Rydberg atom arrays constitute a promising quantum information platform, where control over several hundred qubits has been demonstrated. Further scaling could significantly benefit from coupling to integrated optical or electronic devices, enabling quantum networking and new control tools, but this integration is challenging due to Rydberg sensitivity to th

Shubham Dokania, A. H. Abdul Hafez, Anbumani Subramanian, Manmohan Chandraker

Autonomous driving and assistance systems rely on annotated data from traffic and road scenarios to model and learn the various object relations in complex real-world scenarios. Preparation and training of deploy-able deep learning architectures require the models to be suited to different traffic scenarios and adapt to different situations. Currently, exist

Chidera Biringa, Gökhan Kul

Software weaknesses that create attack surfaces for adversarial exploits, such as lateral SQL injection (LSQLi) attacks, are usually introduced during the design phase of software development. Security design patterns are sometimes applied to tackle these weaknesses. However, due to the stealthy nature of lateral-based attacks, employing traditional security

Igor Nesterenko

An unambiguous accordance between an objective lens for Ayscough's telescope 'Adapted to Use at Sea' and another compound lens which was described by William Eastland in well-known Quere during the trial in October of 1765 was revealed. Preliminary analysis of small fragments of Quere from Eastland', Stedman' and Champneys' testimonies was carried.

David J. Thompson, Colleen A. Wilson-Hodge

The Fermi Gamma-ray Space Telescope, a key mission in multiwavelength and multimessenger studies, has been surveying the gamma-ray sky from its low-Earth orbit since 2008. Its two scientific instruments, the Gamma-ray Burst Monitor (GBM) and the Large Area Telescope (LAT), cover 8 orders of magnitude in photon energy. The GBM consists of 12 Sodium Iodide det

Vin Sachidananda, Ziyi Yang, Chenguang Zhu

Contrastive Learning has recently achieved state-of-the-art performance in a wide range of tasks. Many contrastive learning approaches use mined hard negatives to make batches more informative during training but these approaches are inefficient as they increase epoch length proportional to the number of mined negatives and require frequent updates of neares

Kaiyuan Zhang, Guanhong Tao, Qiuling Xu, Siyuan Cheng

Federated Learning (FL) is a distributed learning paradigm that enables different parties to train a model together for high quality and strong privacy protection. In this scenario, individual participants may get compromised and perform backdoor attacks by poisoning the data (or gradients). Existing work on robust aggregation and certified FL robustness doe

Piotr Kipinski, Hubert Guzowski, Aleksandra Urbanczyk, Maciej Smolka

Metaheuristics are universal optimization algorithms which should be used for solving difficult problems, unsolvable by classic approaches. In this paper we aim at constructing novel socio-cognitive metaheuristic based on castes, and apply several versions of this algorithm to optimization of time-delay system model. Besides giving the background and the det

Elazar Cohen, Yizhak Yisrael Elboher, Clark Barrett, Guy Katz

Neural networks have become critical components of reactive systems in various domains within computer science. Despite their excellent performance, using neural networks entails numerous risks that stem from our lack of ability to understand and reason about their behavior. Due to these risks, various formal methods have been proposed for verifying neural n

Md Manjurul Ahsan, Md Shahin Ali, Zahed Siddique

Class imbalance in a dataset is one of the major challenges that can significantly impact the performance of machine learning models resulting in biased predictions. Numerous techniques have been proposed to address class imbalanced problems, including, but not limited to, Oversampling, Undersampling, and cost-sensitive approaches. Due to its ability to gene

Akshayaa Magesh, Zhongchang Sun, Venugopal V. Veeravalli, Shaofeng Zou

The problem of robust binary hypothesis testing is studied. Under both hypotheses, the data-generating distributions are assumed to belong to uncertainty sets constructed through moments; in particular, the sets contain distributions whose moments are centered around the empirical moments obtained from training samples. The goal is to design a test that perf

Asilata Bapat, Robyn Brooks, Celia Hacker, Claudia Landi

The exact computation of the matching distance for multi-parameter persistence modules is an active area of research in computational topology. Achieving an easily obtainable exact computation of this distance would permit multi-parameter persistent homology to be a viable option for data analysis. For this purpose, two approaches are currently available, li

Ashwini Pokle, Zhengyang Geng, Zico Kolter

Diffusion-based generative models are extremely effective in generating high-quality images, with generated samples often surpassing the quality of those produced by other models under several metrics. One distinguishing feature of these models, however, is that they typically require long sampling chains to produce high-fidelity images. This presents a chal

Oleksiy Bystrenko, Jingxian Zhang, Dong Fangdong, Xiaoguang Li

We examine the effects produced by replacing B-B interlayer bonds by C-C bonds in AlMgB14-related boron network on its mechanical properties. The elastic constants, Vickers hardness and shear strength are evaluated by means of first principle computer simulations on the basis of density functional theory. The results of simulations suggest a possibility of e

Matteo Gabburo, Rik Koncel-Kedziorski, Siddhant Garg, Luca Soldaini

Recent studies show that Question Answering (QA) based on Answer Sentence Selection (AS2) can be improved by generating an improved answer from the top-k ranked answer sentences (termed GenQA). This allows for synthesizing the information from multiple candidates into a concise, natural-sounding answer. However, creating large-scale supervised training data

Renkun Ni, Ping-yeh Chiang, Jonas Geiping, Micah Goldblum

Sharpness-Aware Minimization (SAM) has recently emerged as a robust technique for improving the accuracy of deep neural networks. However, SAM incurs a high computational cost in practice, requiring up to twice as much computation as vanilla SGD. The computational challenge posed by SAM arises because each iteration requires both ascent and descent steps and

Takashi Minato, Ryuichiro Higashinaka, Kurima Sakai, Tomo Funayama

Although many competitions have been held on dialogue systems in the past, no competition has been organized specifically for dialogue with humanoid robots. As the first such attempt in the world, we held a dialogue robot competition in 2020 to compare the performances of interactive robots using an android that closely resembles a human. Dialogue Robot Comp

Xin Bing, Marten Wegkamp

In high-dimensional classification problems, a commonly used approach is to first project the high-dimensional features into a lower dimensional space, and base the classification on the resulting lower dimensional projections. In this paper, we formulate a latent-variable model with a hidden low-dimensional structure to justify this two-step procedure and t

Martin Mattos, Ricardo Grando, André Kelbouscas

Commercial drones are not yet dimensioned to perform indoor autonomous tasks, since they use GPS for their location in the environment. When it comes to a space with physical obstacles (walls, metal, etc.) between the communication of the drone and the satellites that allow the precise location of the same, there is great difficulty in finding the satellites

Agustina Marion de Freitas Vidal, Anthony Rodriguez, Richard Suarez, André Kelbouscas

Currently, research in robotics, artificial intelligence and drones are advancing exponentially, they are directly or indirectly related to various areas of the economy, from agriculture to industry. With this context, this project covers these topics guiding them, seeking to provide a framework that is capable of helping to develop new future researchers. F

Mark Huber

Given a stream of Bernoulli random variables, consider the problem of estimating the mean of the random variable within a specified relative error with a specified probability of failure. Until now, the Gamma Bernoulli Approximation Scheme (GBAS) was the method that accomplished this goal using the smallest number of average samples. In this work, a new meth

Ingo Wald

We present an algorithm that allows for find-closest-point and kNN-style traversals of left-balanced k-d trees, without the need for either recursion or software-managed stacks; instead using only current and last previously traversed node to compute which node to traverse next.

Yunqi Zhang, Shaileshh Bojja Venkatakrishnan

Blockchains have become the catalyst for a growing movement to create a more decentralized Internet. A fundamental operation of applications in a decentralized Internet is data storage and retrieval. As today's blockchains are limited in their storage functionalities, in recent years a number of peer-to-peer data storage networks have emerged based on the Ka

Jian Zhu, Zuoyu Tian, Yadong Liu, Cong Zhang

Inducing semantic representations directly from speech signals is a highly challenging task but has many useful applications in speech mining and spoken language understanding. This study tackles the unsupervised learning of semantic representations for spoken utterances. Through converting speech signals into hidden units generated from acoustic unit discov

Sevgi Altun, Mustafa Cem Gunes, Yusuf H. Sahin, Alican Mertan

This study integrates artificial intelligence and computational design tools to extract information from architectural heritage. Photogrammetry-based point cloud models of brick walls from the Anatolian Seljuk period are analysed in terms of the interrelated units of construction, simultaneously considering both the inherent symmetries and irregularities. Th

Rafael Alves Batista

Recently an extraordinarily bright gamma-ray burst, GRB 221009A, was observed by several facilities covering the whole electromagnetic spectrum. Gamma rays with energies up to 18 TeV were detected, as well as a possible photon with 251 TeV. Such energetic events are not expected because they would be attenuated by pair-production interactions with the extrag

Junfei Xiao, Zhichao Xu, Shiyi Lan, Zhiding Yu

This report describes the winning solution to the Robust Vision Challenge (RVC) semantic segmentation track at ECCV 2022. Our method adopts the FAN-B-Hybrid model as the encoder and uses SegFormer as the segmentation framework. The model is trained on a composite dataset consisting of images from 9 datasets (ADE20K, Cityscapes, Mapillary Vistas, ScanNet, VIP

Jaein Lim, Mahdi Ghanei, R. Connor Lawson, Siddhartha Srinivasa

We present a lazy incremental search algorithm, Lifelong-GLS (L-GLS), along with its bounded suboptimal version, Bounded L-GLS (B-LGLS) that combine the search efficiency of incremental search algorithms with the evaluation efficiency of lazy search algorithms for fast replanning in problem domains where edge-evaluations are more expensive than vertex-expans

Corey Bregman, Daniel Groves, Kejia Zhu

We prove that for $n\geq 2$, a non-uniform lattice in $\text{PU}(n,1)$ does not admit a relatively geometric action on a $\mathrm{CAT}(0)$ cube complex, in the sense of Einstein and Groves. As a consequence, if $\Gamma$ is a non-uniform lattice in a non-compact semisimple Lie group $G$ without compact factors that admits a relatively geometric action on a $\