October 2022 arXiv papers — page 52

Diego Ortega, Elka Korutcheva

Segregation affects millions of urban dwellers. The main expression of this reality is the creation of ghettos which are city parts characterized by a combination of features: low income, poor cultural level... Segregation models have been usually defined over regular lattices. However, in recent years, the focus has shifted from these unrealistic frameworks

Ziyu Huang, Shanjian Tang

In this paper, we consider a mean field game (MFG) with a major and $N$ minor agents. We first consider the limiting problem and allow the coefficients to vary with the conditional distribution in a nonlinear way. We use the stochastic maximum principle to transform the limiting control problem into a system of two coupled conditional distribution dependent

Hoang Thang Ta, Alexander Gelbukha, Grigori Sidorov

Acknowledged as one of the most successful online cooperative projects in human society, Wikipedia has obtained rapid growth in recent years and desires continuously to expand content and disseminate knowledge values for everyone globally. The shortage of volunteers brings to Wikipedia many issues, including developing content for over 300 languages at the p

Ahmad Al-Badawi

We investigate the Dirac and Klein-Gordon equations, as well as greybody radiation, for the Hayward black hole (BH) spacetime. We first consider the Dirac equation using a null tetrad in the Newman- Penrose (NP) formalism. The equations are then separated into angular and radial parts. A pair of one-dimensional Schr\"odinger like wave equations with effectiv

Panzhong Lu, Xin Zhang, Meishan Zhang, Min Zhang

Conventional phrase grounding aims to localize noun phrases mentioned in a given caption to their corresponding image regions, which has achieved great success recently. Apparently, sole noun phrase grounding is not enough for cross-modal visual language understanding. Here we extend the task by considering pronouns as well. First, we construct a dataset of

Chang Xu

Our sense of touch is essential and permeates in interactions involving natural explorations and affective communications. For instance, we routinely judge the ripeness of fruit at the grocery store, caress the arm of a spouse to offer comfort, and stroke textiles to gauge their softness. Meanwhile, interactive displays that provide tactile feedback are beco

Jonas O. Wolff, Daniele Liprandi, Federico Bosia, Anna-Christin Joel

Living systems are built of multiscale-composites: materials formed of components with different properties that are assembled in complex micro- and nano-structures. Such biological multiscale-composites often show outstanding physical properties that are unachieved by artificial materials. A major scientific goal is thus to understand the assembly processes

A. D. Alhaidari, H. Bahlouli

The interaction of an electron with a local static charge distribution (e.g., an atom or molecule) is dominated at large distances by the radial 1/r Coulomb potential. The second order effect comes from the non-central electric dipole contribution cos(theta)/r^2. Moreover, the third order effect is due to the electric quadrupole potential, [3*cos^2(theta)-1]

Minghui Xu, Yihao Guo, Qin Hu, Zehui Xiong

Metaverse has rekindled human beings' desire to further break space-time barriers by fusing the virtual and real worlds. However, security and privacy threats hinder us from building a utopia. A metaverse embraces various techniques, while at the same time inheriting their pitfalls and thus exposing large attack surfaces. Blockchain, proposed in 2008, was re

Alon Eirew, Avi Caciularu, Ido Dagan

The task of Cross-document Coreference Resolution has been traditionally formulated as requiring to identify all coreference links across a given set of documents. We propose an appealing, and often more applicable, complementary set up for the task - Cross-document Coreference Search, focusing in this paper on event coreference. Concretely, given a mention

Hui Chen, Wei Han, Soujanya Poria

Self-training methods have been explored in recent years and have exhibited great performance in improving semi-supervised learning. This work presents a Simple instance-Adaptive self-Training method (SAT) for semi-supervised text classification. SAT first generates two augmented views for each unlabeled data and then trains a meta-learner to automatically i

Bhanu Prakash Pant, Sunanda, Reetanjali Moharana, Sarathykanan S

Photon-axion-like particle (ALP) oscillations result in the survival of gamma rays from distant sources above TeV energies. Studies of events observed by CAST, Fermi-LAT, and IACT have constrained the ALP parameters. We investigate the effect of photon-ALP oscillations on the gamma-ray spectra of the first extragalactic neutrino source, TXS 0506+056, for obs

Guangtao Zeng, Wei Lu

Training a good deep learning model requires substantial data and computing resources, which makes the resulting neural model a valuable intellectual property. To prevent the neural network from being undesirably exploited, non-transferable learning has been proposed to reduce the model generalization ability in specific target domains. However, existing app

Maryam Morvaridi, Giorgio Carta, Federico Bosia, Antonio S. Gliozzi

We propose a novel two-dimensional hierarchical auxetic structure consisting of a porous medium in which a homogeneous matrix includes a rank-two set of cuts characterised by different scales. The six-fold symmetry of the perforations makes the medium isotropic in the plane. Remarkably, the mesoscale interaction between the first- and second-level cuts enabl

Zeyun Zhong, David Schneider, Michael Voit, Rainer Stiefelhagen

Although human action anticipation is a task which is inherently multi-modal, state-of-the-art methods on well known action anticipation datasets leverage this data by applying ensemble methods and averaging scores of unimodal anticipation networks. In this work we introduce transformer based modality fusion techniques, which unify multi-modal data at an ear

Kshitij Alwadhi, Rohan Sharma, Siddhant Sharma

A MIDI based approach for music recognition is proposed and implemented in this paper. Our Clarinet music retrieval system is designed to search piano MIDI files with high recall and speed. We design a novel melody extraction algorithm that improves recall results by more than 10%. We also implement 3 algorithms for retrieval-two self designed (RSA Note and

Lingfei Jin, Liming Ma, Chaoping Xing

Sequences with a low correlation have very important applications in communications, cryptography, and compressed sensing. In the literature, many efforts have been made to construct good sequences with various lengths where binary sequences attracts great attention. As a result, various constructions of good binary sequences have been proposed. However, mos

Liyuan Ma, Hongxia Wang, Ningyi Leng, Ziyang Yuan

Fourier phase retrieval (FPR) is an inverse problem that recovers the signal from its Fourier magnitude measurement, it's ill-posed especially when the sampling rates are low. In this paper, an untrained generative prior is introduced to attack the ill-posedness. Based on the alternating direction method of multipliers (ADMM), an algorithm utilizing the untr

Kuang-Ru Wu

We show that if $E$ is an ample vector bundle of rank at least two with some curvature bound on $O_{P(E^*)}(1)$, then $E^*\otimes \det E$ is Kobayashi positive. The proof relies on comparing the curvature of $(\det E^*)^k$ and $S^kE$ for large $k$ and using duality of convex Finsler metrics. Following the same thread of thought, we show if $E$ is ample with

Guanzhong Li, Lvzhou Li

Grover's algorithm provides a quadratic speedup over classical algorithms to search for marked elements in an unstructured database. The original algorithm is probabilistic, returning a marked element with bounded error. There are several schemes to achieve the deterministic version, by using the generalized Grover's iteration $G(\alpha,\beta):=S_r(\beta)\,

Zhijie Deng, Feng Zhou, Jun Zhu

Laplace approximation (LA) and its linearized variant (LLA) enable effortless adaptation of pretrained deep neural networks to Bayesian neural networks. The generalized Gauss-Newton (GGN) approximation is typically introduced to improve their tractability. However, LA and LLA are still confronted with non-trivial inefficiency issues and should rely on Kronec

Yi Yang, Dong Liu, Zhaoyi Xu, Zheng-Wen Long

In the string theory, the fundamental blocks of nature are not particles but one-dimensional strings. Therefore, a generalization of this idea is to think of it as a cloud of strings. Rodrigues et al. embedded the black bounces spacetime into the string cloud, which demonstrates that the existence of the string cloud makes the Bardeen black hole singular, wh

Xiaohan Xu, Xuying Meng, Yequan Wang

Emotional support conversation (ESC) task can utilize various support strategies to help people relieve emotional distress and overcome the problem they face, which has attracted much attention in these years. However, most state-of-the-art works rely heavily on external commonsense knowledge to infer the mental state of the user in every dialogue round. Alt

A. C. A. Boogert, K. Brewer, A. Brittain, K. S Emerson

An important tracer of the origin and evolution of cometary ices is the comparison with ices found in dense clouds and towards Young Stellar Objects (YSOs). We present a survey of ices in the 2-5 micron spectra of 23 massive YSOs, taken with the NASA InfraRed Telescope Facility SpeX spectrometer. The 4.90 micron absorption band of OCS ice is detected in 20 s

Yingcong Lu, Yipeng Liu, Zhen Long, Zhangxin Chen

With powerful ability to exploit latent structure of self-representation information, different tensor decompositions have been employed into low rank multi-view clustering (LRMVC) models for achieving significant performance. However, current approaches suffer from a series of problems related to those tensor decomposition, such as the unbalanced matricizat

Zhijie Deng, Jiaxin Shi, Hao Zhang, Peng Cui

This paper introduces a structured, adaptive-length deep representation called Neural Eigenmap. Unlike prior spectral methods such as Laplacian Eigenmap that operate in a nonparametric manner, Neural Eigenmap leverages NeuralEF to parametrically model eigenfunctions using a neural network. We show that, when the eigenfunction is derived from positive relatio

Farida Mohsen, Hazrat Ali, Nady El Hajj, Zubair Shah

Healthcare data are inherently multimodal, including electronic health records (EHR), medical images, and multi-omics data. Combining these multimodal data sources contributes to a better understanding of human health and provides optimal personalized healthcare. Advances in artificial intelligence (AI) technologies, particularly machine learning (ML), enabl

Juhyeok Lee, Moosung Lee, YongKeun Park, Colin Ophus

Electron tomography offers important three-dimensional (3D) structural information which cannot be observed by two-dimensional imaging. By combining annular dark field scanning transmission electron microscopy (ADF-STEM) with aberration correction, the resolution of electron tomography has reached atomic resolution. However, tomography based on ADF-STEM inhe

Xiaoyu Liu, Xu Li, Joan Serrà

Single channel target speaker separation (TSS) aims at extracting a speaker's voice from a mixture of multiple talkers given an enrollment utterance of that speaker. A typical deep learning TSS framework consists of an upstream model that obtains enrollment speaker embeddings and a downstream model that performs the separation conditioned on the embeddings.

Yang Zhan, Zhitong Xiong, Yuan Yuan

In this paper, we introduce the task of visual grounding for remote sensing data (RSVG). RSVG aims to localize the referred objects in remote sensing (RS) images with the guidance of natural language. To retrieve rich information from RS imagery using natural language, many research tasks, like RS image visual question answering, RS image captioning, and RS

Ali Hosseinalipour Jazi, S. Mohammad Razavizadeh, Tommy Svensson

One of the major challenges with cell-free (CF) massive multiple-input multiple-output (MIMO) networks is providing backhaul links for a large number of distributed access points (APs). In general, providing fiber optics backhaul for these APs is not cost-effective and also reduces network scalability. Wireless backhauling can be a promising solution that ca

Haizhong Li, Botong Xu

In this paper, we prove a class of weighted isoperimetric inequalities for bounded domains in hyperbolic space by using the isoperimetric inequality with log-convex density in Euclidean space. As a consequence, we remove the horo-convex assumption of domains in a weighted isoperimetric inequality proved by Scheuer-Xia. Furthermore, we prove weighted isoperim

Shuo Cheng, Danfei Xu

To assist with everyday human activities, robots must solve complex long-horizon tasks and generalize to new settings. Recent deep reinforcement learning (RL) methods show promise in fully autonomous learning, but they struggle to reach long-term goals in large environments. On the other hand, Task and Motion Planning (TAMP) approaches excel at solving and g

Haiyan Gao, Lianfu Wei

Nanomechanical resonators (NMRs), as the quantum mechanical sensing probers, have played the important roles for various high-precision quantum measurements. Differing from the previous emission spectral probes (i.e., the NMR modified the atomic emission), in this paper we propose an alternative approach, i.e., by probing the scattering spectra of the quantu

Xuming He, Xiaoou Pan, Kean Ming Tan, Wen-Xin Zhou

Censored quantile regression (CQR) has become a valuable tool to study the heterogeneous association between a possibly censored outcome and a set of covariates, yet computation and statistical inference for CQR have remained a challenge for large-scale data with many covariates. In this paper, we focus on a smoothed martingale-based sequential estimating eq

Weirui Ye, Pieter Abbeel, Yang Gao

One of the most important AI research questions is to trade off computation versus performance since ``perfect rationality" exists in theory but is impossible to achieve in practice. Recently, Monte-Carlo tree search (MCTS) has attracted considerable attention due to the significant performance improvement in various challenging domains. However, the expensi

Baadr Suleman M Alwheepy, Leandros Maglaras, Nick Ayres

Due to the high number of users on social media and the massive amounts of queries requested every second to share a new video, picture, or message, social platforms struggle to manage this humungous amount of data that is endlessly coming in. HFTCT relies on wordlists to classify opinions. It can carry out its tasks reasonably well; however, sometimes, the

Fangjun Lu, Long Yuan, Jian Zhang, Boqiang Li

The search for the experimental evidence of quantum spin liquid (QSL) states is critical but extremely challenging, as the quenched interaction randomness introduced by structural imperfection is usually inevitable in real materials. YCu$_3$(OH)$_{6.5}$Br$_{2.5}$ (YCOB) is a spin-1/2 kagome Heisenberg antiferromagnet (KHA) with strong coupling of $\langle J_

Jack Borthwick, Xiaojun Chang, Louis Jeanjean, Nicola Soave

In this paper, we study, for functionals having a mountain pass geometry on a constraint, the existence of bounded Palais-Smale sequences carrying Morse index type information.

Yi Wei, Zixin Zhong, Vincent Y. F. Tan

The beam alignment (BA) problem consists in accurately aligning the transmitter and receiver beams to establish a reliable communication link in wireless communication systems. Existing BA methods search the entire beam space to identify the optimal transmit-receive beam pair. This incurs a significant latency when the number of antennas is large. In this wo

Heshan Fernando, Han Shen, Miao Liu, Subhajit Chaudhury

Machine learning problems with multiple objective functions appear either in learning with multiple criteria where learning has to make a trade-off between multiple performance metrics such as fairness, safety and accuracy; or, in multi-task learning where multiple tasks are optimized jointly, sharing inductive bias between them. This problems are often tack

Rajesh P. N. Rao, Dimitrios C. Gklezakos, Vishwas Sathish

Predictive coding has emerged as a prominent model of how the brain learns through predictions, anticipating the importance accorded to predictive learning in recent AI architectures such as transformers. Here we propose a new framework for predictive coding called active predictive coding which can learn hierarchical world models and solve two radically dif

Iker García-Ferrero, Rodrigo Agerri, German Rigau

Zero-resource cross-lingual transfer approaches aim to apply supervised models from a source language to unlabelled target languages. In this paper we perform an in-depth study of the two main techniques employed so far for cross-lingual zero-resource sequence labelling, based either on data or model transfer. Although previous research has proposed translat

Surabhi Gupta, Ashwath Shetty, Avinash Sharma

To see what is not in the image is one of the broader missions of computer vision. Technology to inpaint images has made significant progress with the coming of deep learning. This paper proposes a method to tackle occlusion specific to human faces. Virtual presence is a promising direction in communication and recreation for the future. However, Virtual Rea

Changjun Hu, Quan Shi, Xin Li, Xiaoxian Guo

The harsh ocean environment and complex operating condition require high dynamic positioning (DP) capability of offshore vessel. The design, development and performance evaluation of DP system are generally carried out by numerical simulations or scale model experiments. Compared with the time-consuming and laborious experiment, the simulation is convenient

Md Imran Khan, Md. Mamun Billah, Mohammed Mizanur Rahman

In this thesis, a variable speed heat conducting cylinder is positioned in the middle of a rectangular channel with an active flow modification system to demonstrate a numerical study of steady two-dimensional mixed convention heat transfer phenomena. In the current study, the lower wall has a discrete isoflux heater installed while the upper wall is kept at

Prafulla Kumar Choubey, Yu Bai, Chien-Sheng Wu, Wenhao Liu

Pre-trained language models (PLMs) have been shown effective for zero-shot (0shot) text classification. 0shot models based on natural language inference (NLI) and next sentence prediction (NSP) employ cross-encoder architecture and infer by making a forward pass through the model for each label-text pair separately. This increases the computational cost to m

Minjoon Jung, Seongho Choi, Joochan Kim, Jin-Hwa Kim

Video corpus moment retrieval (VCMR) is the task to retrieve the most relevant video moment from a large video corpus using a natural language query. For narrative videos, e.g., dramas or movies, the holistic understanding of temporal dynamics and multimodal reasoning is crucial. Previous works have shown promising results; however, they relied on the expens

A. V. Guglielmi

Phenomenology is the unity of principles and methods of studying the essence of phenomena. This paper is a concise review of recent works in which the phenomenological ideas of physics are used to analyze earthquakes. An example of a phenomenological theory is thermodynamics. Maxwell's electrodynamics is also a perfect example of a phenomenological theory. T

Zijin Li, Xinghong Pan, Jiaqi Yang

In this paper, we will solve the Leray's problem for the stationary Navier-Stokes system in a 2D infinite distorted strip with the Navier-slip boundary condition. The existence, uniqueness, regularity and asymptotic behavior of the solution will be investigated. Moreover, we discuss how the friction coefficient affects the well-posedness of the solution. Due

Rafael I. Cabral Muchacho, Riddhiman Laha, Luis F. C. Figueredo, Sami Haddadin

This paper is about fast slosh free fluid transportation. Existing approaches are either computationally heavy or only suitable for specific robots and container shapes. We model the end effector as a point mass suspended by a spherical pendulum and study the requirements for slosh free motion and the validity of the point mass model. In this approach, slosh

Gourav Datta, Haoqin Deng, Robert Aviles, Peter A. Beerel

Spiking Neural Networks (SNNs) have emerged as an attractive spatio-temporal computing paradigm for complex vision tasks. However, most existing works yield models that require many time steps and do not leverage the inherent temporal dynamics of spiking neural networks, even for sequential tasks. Motivated by this observation, we propose an \rev{optimized s

Theshani Nuradha, Ziv Goldfeld

Pufferfish privacy (PP) is a generalization of differential privacy (DP), that offers flexibility in specifying sensitive information and integrates domain knowledge into the privacy definition. Inspired by the illuminating formulation of DP in terms of mutual information due to Cuff and Yu, this work explores PP through the lens of information theory. We pr

Christopher Dodd

The purpose of this paper is to develop a new theory of gauges in mixed characteristic. Namely, let $k$ be a perfect field of characteristic $p>0$ and $W(k)$ the $p$-typical Witt vectors. Making use of Berthelot's arithmetic differential operators, we define for a smooth formal scheme $\mathfrak{X}$ over $W(k)$, a new sheaf of algebras $\widehat{\mathcal{D}}

Constantin Adam, Abdulhamid Adebayo, Hubertus Franke, Edward Snible

Zero Trust is a novel cybersecurity model that focuses on continually evaluating trust to prevent the initiation and horizontal spreading of attacks. A cloud-native Service Mesh is an example of Zero Trust Architecture that can filter out external threats. However, the Service Mesh does not shield the Application Owner from internal threats, such as a rogue

Tahmid Hasan Pranto, Kazi Tamzid Akhter Md Hasib, Tahsinur Rahman, AKM Bahalul Haque

Financial fraud cases are on the rise even with the current technological advancements. Due to the lack of inter-organization synergy and because of privacy concerns, authentic financial transaction data is rarely available. On the other hand, data-driven technologies like machine learning need authentic data to perform precisely in real-world systems. This

Mahfujur Rahaman, Emanuele Marino, Alan G. Joly, Seunguk Song

Observation of interlayer, charge-transfer (CT) excitons in van der Waals heterostructures (vdWHs) based on 2D-2D systems has been well investigated. While conceptually interesting, these charge transfer excitons are highly delocalized and spatially localizing them requires twisting layers at very specific angles. This issue of localizing the CT excitons can

Victor S. Bursztyn, David Demeter, Doug Downey, Larry Birnbaum

How to usefully encode compositional task structure has long been a core challenge in AI. Recent work in chain of thought prompting has shown that for very large neural language models (LMs), explicitly demonstrating the inferential steps involved in a target task may improve performance over end-to-end learning that focuses on the target task alone. However

Maria-Florina Balcan, Rattana Pukdee, Pradeep Ravikumar, Hongyang Zhang

Adversarial training is a standard technique for training adversarially robust models. In this paper, we study adversarial training as an alternating best-response strategy in a 2-player zero-sum game. We prove that even in a simple scenario of a linear classifier and a statistical model that abstracts robust vs. non-robust features, the alternating best res

Shadaj Laddad, Conor Power, Mae Milano, Alvin Cheung

Despite decades of research and practical experience, developers have few tools for programming reliable distributed applications without resorting to expensive coordination techniques. Conflict-free replicated datatypes (CRDTs) are a promising line of work that enable coordination-free replication and offer certain eventual consistency guarantees in a relat

Peng Luo, Kefan Pan, Shuangjie Peng

We are concerned with the Moser-Trudinger problem \begin{equation*} \begin{cases} -\Delta u=\lambda ue^{u^2}~~&\mbox{in}~\Omega,\\[0.5mm] u>0 ~~ &{\text{in}~\Omega},\\[0.5mm] u=0~~&\mbox{on}~\partial \Omega, \end{cases} \end{equation*} where $\Omega \subset \mathbb{R}^2$ is a smooth bounded domain and $\lambda>0$ is sufficiently small. Qualitative analysis f

Efrem Braun, Chris Baluta, Trisha F. Doyle, Patricia L. Hall

We present XSLIDE (X-Ray Spectral Line IDentifier and Explorer), a graphical user interface that has been designed as a quick-look tool for the upcoming X-Ray Imaging and Spectroscopy Mission (XRISM). XSLIDE is a simple and user-friendly application that allows for the interactive plotting of spectra from XRISM's Resolve instrument without requiring the sele

Nima Tatari

Secondary radiation in a radiation therapy environment is considered a cause for post-treatment side effects including secondary carcinogenesis. In a proton-therapy setup, neutrons deliver a considerable extra dose to the patient. Various measurements and calculations of neutron dose suggest a wide range of values for this quantity. This paper provides a com

Pan Peng, Yuichi Yoshida

We show sublinear-time algorithms for Max Cut and Max E2Lin$(q)$ on expanders in the adjacency list model that distinguishes instances with the optimal value more than $1-\varepsilon$ from those with the optimal value less than $1-\rho$ for $\rho \gg \varepsilon$. The time complexities for Max Cut and Max $2$Lin$(q)$ are $\widetilde{O}(\frac{1}{\phi^2\rho} \

Y. Watanabe, D. Matsumoto, Y. Urakami, A. Masuda

We find novel polar orders that yield electron (e-) and hole (h+) gas and depend on surface terminations, using density functional theory (DFT) that, unlike existing reports, relaxed all the ion positions of ATiO3 having spontaneous polarization Ps (A: alkali earth metal). By the experiments of atomic-oxygen cleaned surfaces of BaTiO3, we find both e- and h+

Francesco Luzi, Aneesh Gupta, Leslie Collins, Kyle Bradbury

There is evidence that transformers offer state-of-the-art recognition performance on tasks involving overhead imagery (e.g., satellite imagery). However, it is difficult to make unbiased empirical comparisons between competing deep learning models, making it unclear whether, and to what extent, transformer-based models are beneficial. In this paper we syste

Lanqing Du, Michelle Kim, Jinwook Lee

Non-Fungible Tokens (NFTs) are crypto assets with a unique digital identifier for ownership, powered by blockchain technology. Technically speaking, anything digital could be minted and sold as an NFT, which provides proof of ownership and authenticity of a digital file. For this reason, it helps us distinguish between the originals and their copies, making

Junyuan Fang, Haixian Wen, Jiajing Wu, Qi Xuan

Graph neural networks (GNNs) have found successful applications in various graph-related tasks. However, recent studies have shown that many GNNs are vulnerable to adversarial attacks. In a vast majority of existing studies, adversarial attacks on GNNs are launched via direct modification of the original graph such as adding/removing links, which may not be

Massimiliano Alvioli, Mark Strikman

We argue that the Drell-Yan process in the $x_A \ge 0.15$ kinematics recently studied at FNAL by the E906/SeaQuest experiment may allow to observe an analogous of the EMC effect for antiquarks. The effects of Fermi motion and energy loss are considered. The preliminary E906/SeaQuest data are inconsistent with the growth of the $\sigma_A/A\sigma_N$ ratio expe

Pedram Agand, Michael Chang, Mo Chen

Utilizing a single camera for measuring object distances is a cost-effective alternative to stereo-vision and LiDAR. Although monocular distance estimation has been explored in the literature, most existing techniques rely on object class knowledge to achieve high performance. Without this contextual data, monocular distance estimation becomes more challengi

Pedram Agand, Mo Chen, Hamid D. Taghirad

Although the Bayesian paradigm offers a formal framework for estimating the entire probability distribution over uncertain parameters, its online implementation can be challenging due to high computational costs. We suggest the Adaptive Recursive Markov Chain Monte Carlo (ARMCMC) method, which eliminates the shortcomings of conventional online techniques whi

Sunaina Rajora, Mansi Butola, Kedar Khare

We investigate the problem of 3D complex field reconstruction corresponding to unstained red blood cells (RBCs) with a single defocused off-axis digital hologram. We employ recently introduced mean gradient descent (MGD) optimization framework, to solve the 3D recovery problem. While investigating volume recovery problem for a continuous phase object like RB

Quan H. Nguyen, William J. Beksi

In this paper, we introduce a novel implicit neural network for the task of single image super-resolution at arbitrary scale factors. To do this, we represent an image as a decoding function that maps locations in the image along with their associated features to their reciprocal pixel attributes. Since the pixel locations are continuous in this representati

Motohiko Ezawa, Natsuko Ishida, Yasutomo Ota, Satoshi Iwamoto

We investigate laser emission at the interface of a topological and trivial phases with loss and gain. The system is described by a Su-Schrieffer-Heeger model with site-dependent hopping parameters. We study numerically and analytically the interface states. The ground state is described by the Jackiw-Rebbi mode with a pure imaginary energy, reflecting the n

Mohammad Wali Ur Rahman, Sicong Shao, Pratik Satam, Salim Hariri

Social media has become an essential part of the modern lifestyle, with its usage being highly prevalent. This has resulted in unprecedented amounts of data generated from users in social media, such as users' attitudes, opinions, interests, purchases, and activities across various aspects of their lives. Therefore, in a world of social media, where its powe

Dennis Mancl, Steven D. Fraser

An agile organization adapts what they are building to match their customer's evolving needs. Agile teams also adapt to changes in their organization's work environment. The latest change is the evolving environment of "hybrid" work - a mix of in-person and virtual staff. Team members might sometimes work together in the office, work from home, or work in ot

Huiliang Zhang, Di Wu, Arnaud Zinflou, Benoit Boulet

The building sector is one of the largest contributors to global energy consumption. Improving its energy efficiency is essential for reducing operational costs and greenhouse gas emissions. Energy management systems (EMS) play a key role in monitoring and controlling building appliances efficiently and reliably. With the increasing integration of renewable

Andrés Ramírez-Hassan, David T. Frazier

We present a procedure to diagnose model misspecification in situations where inference is performed using approximate Bayesian computation. We demonstrate theoretically, and empirically that this procedure can consistently detect the presence of model misspecification. Our examples demonstrates that this approach delivers good finite-sample performance and

X. H. Sun, X. Y. Gao, W. Reich, P. Jiang

We present total-intensity and polarized-intensity images of the Cygnus Loop supernova remnant (SNR) observed by the Five-hundred-meter Aperture Spherical radio Telescope (FAST). The high angular-resolution and high-sensitivity images enable us to thoroughly compare the properties of the northern part with the southern part of the SNR. The central filament i

Xiangyu Peng, Chen Xing, Prafulla Kumar Choubey, Chien-Sheng Wu

Prompt tuning approaches, which learn task-specific soft prompts for a downstream task conditioning on frozen pre-trained models, have attracted growing interest due to its parameter efficiency. With large language models and sufficient training data, prompt tuning performs comparably to full-model tuning. However, with limited training samples in few-shot s

Nawaf Nazir, Thiagarajan Ramachandaran, Soumya Kundu, Veronica Adetola

Critical energy infrastructure are constantly understress due to the ever increasing disruptions caused by wildfires, hurricanes, other weather related extreme events and cyber-attacks. Hence it becomes important to make critical infrastructure resilient to threats from such cyber-physical events. Such events are however hard to predict and numerous in natur

Simone Franchini, Riccardo Balzan

An influential theory of increasing returns has been proposed by the economist W. B. Arthur in the '80s to explain the lock-in phenomenon between two competing commercial products. In the most simplified situation there are two competing products that gain customers according to a majority mechanism: each new customer arrives and asks which product they boug

Xinling Yu, José E. C. Serrallés, Ilias I. Giannakopoulos, Ziyue Liu

Electrical properties (EP), namely permittivity and electric conductivity, dictate the interactions between electromagnetic waves and biological tissue. EP can be potential biomarkers for pathology characterization, such as cancer, and improve therapeutic modalities, such radiofrequency hyperthermia and ablation. MR-based electrical properties tomography (MR

Alessandro Saviolo, Jonathan Frey, Abhishek Rathod, Moritz Diehl

Model-based control requires an accurate model of the system dynamics for precisely and safely controlling the robot in complex and dynamic environments. Moreover, in the presence of variations in the operating conditions, the model should be continuously refined to compensate for dynamics changes. In this paper, we present a self-supervised learning approac

Liliang Ren, Zixuan Zhang, Han Wang, Clare R. Voss

Modern large-scale Pre-trained Language Models (PLMs) have achieved tremendous success on a wide range of downstream tasks. However, most of the LM pre-training objectives only focus on text reconstruction, but have not sought to learn latent-level interpretable representations of sentences. In this paper, we manage to push the language models to obtain a de

Pei Liu, Xiaoyu Sun, Yanjie Zhao, Yonghui Liu

Continuous Integration (CI) and Continuous Delivery (CD) have been demonstrated to be effective in facilitating software building, testing, and deployment. Many research studies have investigated and subsequently improved their working processes. Unfortunately, such research efforts have largely not touched on the usage of CI/CD in the development of Android

Yohji Akama, Atina Husnaqilati

We consider a $p$-dimensional, centered normal population such that all variables have a positive variance $\sigma^2$ and any correlation coefficient between different variables is a given nonnegative constant $\rho<1$. Suppose that both the sample size $n$ and population dimension $p$ tend to infinity with $p/n \to c>0$. We prove that the limiting spectral

Nishant Yadav, Nicholas Monath, Rico Angell, Manzil Zaheer

Efficient k-nearest neighbor search is a fundamental task, foundational for many problems in NLP. When the similarity is measured by dot-product between dual-encoder vectors or $\ell_2$-distance, there already exist many scalable and efficient search methods. But not so when similarity is measured by more accurate and expensive black-box neural similarity mo

Takumi Hase, Megumi Nakao, Mitsuhiro Nakamura, Tetsuya Matsuda

Unsupervised image translation using adversarial learning has been attracting attention to improve the image quality of medical images. However, adversarial training based on the global evaluation values of discriminators does not provide sufficient translation performance for locally different image features. We propose adversarial learning with a feedback

Marc Distel, David R. Wood

A "tree-partition" of a graph $G$ is a partition of $V(G)$ such that identifying the vertices in each part gives a tree. It is known that every graph with treewidth $k$ and maximum degree $\Delta$ has a tree-partition with parts of size $O(k\Delta)$. We prove the same result with the extra property that the underlying tree has maximum degree $O(\Delta)$ and

Pingzhi Yuan, Jiagui Luo, Alain Togbé

In the present paper we introduce old and new results related to St\"ormer theorem about Pell equations. Moreover we give four types of applications of these results.

Junyuan Hong, Lingjuan Lyu, Jiayu Zhou, Michael Spranger

As deep learning blooms with growing demand for computation and data resources, outsourcing model training to a powerful cloud server becomes an attractive alternative to training at a low-power and cost-effective end device. Traditional outsourcing requires uploading device data to the cloud server, which can be infeasible in many real-world applications du

Erfan Salavati

A non-linear differential equation arising from a stochastic process known as branching Brownian motion is considered. We find an explicit solution and show the uniqueness of the solution under some boundedness conditions using probabilistic ideas. We discuss non-negative solutions. We also generalize this idea to a class of non-linear parabolic differential

Koustuv Sinha, Amirhossein Kazemnejad, Siva Reddy, Joelle Pineau

Transformer language models encode the notion of word order using positional information. Most commonly, this positional information is represented by absolute position embeddings (APEs), that are learned from the pretraining data. However, in natural language, it is not absolute position that matters, but relative position, and the extent to which APEs can

Huan He, Shifan Zhao, Ziyuan Tang, Joyce C Ho

Nonlinear acceleration methods are powerful techniques to speed up fixed-point iterations. However, many acceleration methods require storing a large number of previous iterates and this can become impractical if computational resources are limited. In this paper, we propose a nonlinear Truncated Generalized Conjugate Residual method (nlTGCR) whose goal is t

Laurence Davies, Robert Salomone, Matthew Sutton, Christopher Drovandi

Reversible jump Markov chain Monte Carlo (RJMCMC) proposals that achieve reasonable acceptance rates and mixing are notoriously difficult to design in most applications. Inspired by recent advances in deep neural network-based normalizing flows and density estimation, we demonstrate an approach to enhance the efficiency of RJMCMC sampling by performing trans

Mehrin Kiani, Javier Andreu-Perez, Hani Hagras

Explainable Artificial Intelligence (XAI) is a paradigm that delivers transparent models and decisions, which are easy to understand, analyze, and augment by a non-technical audience. Fuzzy Logic Systems (FLS) based XAI can provide an explainable framework, while also modeling uncertainties present in real-world environments, which renders it suitable for ap

Filomena Barbosa Rodrigues Mendes, Fredy M. Sobrado Suárez, Richard S. W. Sanguino Bejarano

In this work, we study the stability and regularity of the system formed by the third-order vibration equation in Moore-Gilson-Thompson time coupled with the classical heat equation with Fourier's law. We consider fractional couplings. He the fractional coupling is given by: $\eta A^\phi\theta, \alpha\eta A^\phi u_{tt}$ and $\eta A^\phi u_t$, where the opera

Eugene Gorsky, Mikhail Mazin, Alexei Oblomkov

We compute the Poincar\'e polynomials of the compactified Jacobians for plane curve singularities with Puiseaux exponents $(nd,md,md+1)$, and relate them to the combinatorics of $q,t$-Catalan numbers in the non-coprime case. We also confirm a conjecture of Cherednik and Danilenko for such curves.

Suraj Ravindran, Vatsa Gandhi, Akshay Joshi, Guruswami Ravichandran

Shock compression plate impact experiments conventionally rely on point-wise velocimetry measurements based on laser-based interferometric techniques. This study presents an experimental methodology to measure the free surface full-field particle velocity in shock compression experiments using high-speed imaging and three-dimensional (3D) digital image corre