May 2023 arXiv papers — page 9

Koji Shimizu, Ryuji Otsuka, Masahiro Hara, Emi Minamitani

Understanding ionic behaviour under external electric fields is crucial to develop electronic and energy-related devices using ion transport. In this study, we propose a neural network (NN) model to predict the Born effective charges of ions along an axis parallel to an applied electric field from atomic structures. The proposed NN model is applied to Li$_3$

Bowen Deng

The integration of artificial intelligence and science has resulted in substantial progress in computational chemistry methods for the design and discovery of novel catalysts. Nonetheless, the challenges of electrocatalytic reactions and developing a large-scale language model in catalysis persist, and the recent success of ChatGPT's (Chat Generative Pre-tra

Dongyan Zhang, Wuyue Yang, Wanqi Wen, Liangrong Peng

We make a retrospective review on various control measures taken by 127 countries/territories during the first wave of COVID-19 pandemic until July 7, 2020, and evaluate their impacts on the epidemic dynamics quantitatively. The SEIR-QD model, as a representative for general compartment models, is used to fit the epidemic data, enabling the extraction of cru

Haisong Ding, Bozhi Luan, Dongnan Gui, Kai Chen

Constructing a highly accurate handwritten OCR system requires large amounts of representative training data, which is both time-consuming and expensive to collect. To mitigate the issue, we propose a denoising diffusion probabilistic model (DDPM) to generate training samples. This model conditions on a printed glyph image and creates mappings between printe

Imran Khan Tutul, Sara Karimi, Mohammadreza Soltaninia, Junpeng Zhan

Large integer factorization is a prominent research challenge, particularly in the context of quantum computing. This holds significant importance, especially in information security that relies on public key cryptosystems. The classical computation of prime factors for an integer has exponential time complexity. Quantum computing offers the potential for si

Shuja Ud Din, Muhammad Zeeshan Baig, Muhammad Khateeb Khan

Serious games are simulation software designed to assist people in learning the practical concepts of various application fields such as Health, wellness, Education and Culture. People improve their individual knowledge, skills and attitude through training. This study identified the changing trends with existing studied applications, approaches and methods.

Yanxiong Li, Hao Chen, Wenchang Cao, Qisheng Huang

Existing methods for few-shot speaker identification (FSSI) obtain high accuracy, but their computational complexities and model sizes need to be reduced for lightweight applications. In this work, we propose a FSSI method using a lightweight prototypical network with the final goal to implement the FSSI on intelligent terminals with limited resources, such

Sheikh Mohammad Jubaer, Nazifa Tabassum, Md. Ataur Rahman, Mohammad Khairul Islam

Handwriting recognition remains challenging for some of the most spoken languages, like Bangla, due to the complexity of line and word segmentation brought by the curvilinear nature of writing and lack of quality datasets. This paper solves the segmentation problem by introducing a state-of-the-art method (BN-DRISHTI) that combines a deep learning-based obje

Jun-Yu Huang, Hsiao-Chun Hung, Kung-Chi Hsu, Chia-Hsun Chen

In this study, we develop a steady state and time-dependent exciton diffusion model including singlet and triplet excitons coupled with a modified Poisson and drift-diffusion solver to explain the mechanism of hyper triplet-triplet fusion (TTF) organic light-emitting diodes (OLEDs). Using this modified simulator, we demonstrate various characteristics of OLE

Yanxiong Li, Wenchang Cao, Wei Xie, Jialong Li

Most existing methods for audio classification assume that the vocabulary of audio classes to be classified is fixed. When novel (unseen) audio classes appear, audio classification systems need to be retrained with abundant labeled samples of all audio classes for recognizing base (initial) and novel audio classes. If novel audio classes continue to appear,

Joan Étude Arrow, Sara E. Marsh, Josephine C. Meyer

This paper first provides an overview of the growing subfield of quantum ethics, including a working definition; research to date into social, economic, and political implications of quantum technologies; and directions for future research. Second, it introduces the Quantum Ethics Project (QEP), its activities to date, and its organizing philosophy. The thir

Nariman Habili, Jeremy Oorloff, Lars Petersson

We develop a deep learning network to estimate the illumination spectrum of hyperspectral images under various lighting conditions. To this end, a dataset, IllumNet, was created. Images were captured using a Specim IQ camera under various illumination conditions, both indoor and outdoor. Outdoor images were captured in sunny, overcast, and shady conditions a

Xuelong Gu, Yushun Wang, Wenjun Cai

The energy dissipation law and maximum bound principle are significant characteristics of the Allen-Chan equation. To preserve discrete counterpart of these properties, the linear part of the target system is usually discretized implicitly, resulting in a large linear or nonlinear system of equations. The Fast Fourier Transform (FFT) algorithm is commonly us

Yizhi Han, Salvatore Calabrese, Huihua Du, Jun Yin

The nonlinear Penman and Penman-Monteith equations, widely used for estimating surface evapotranspiration at regional and global scales, were derived from turbulent transport of heat fluxes and thus apply to subhourly scale. However, these equations have been directly applied with hydrometeorological variables averaged at longer time intervals, leading to bi

Peter G. Chang, Gerardo Durán-Martín, Alexander Y Shestopaloff, Matt Jones

We propose an efficient online approximate Bayesian inference algorithm for estimating the parameters of a nonlinear function from a potentially non-stationary data stream. The method is based on the extended Kalman filter (EKF), but uses a novel low-rank plus diagonal decomposition of the posterior precision matrix, which gives a cost per step which is line

Kojiro Iizuka, Yoshifumi Seki, Makoto P. Kato

Practical news feed platforms generate a hybrid list of news articles and advertising items (e.g., products, services, or information) and many platforms optimize the position of news articles and advertisements independently. However, they should be arranged with careful consideration of each other, as we show in this study, since user behaviors toward adve

Mohammad Mahmudul Alam, Edward Raff, Stella Biderman, Tim Oates

In recent years, self-attention has become the dominant paradigm for sequence modeling in a variety of domains. However, in domains with very long sequence lengths the $\mathcal{O}(T^2)$ memory and $\mathcal{O}(T^2 H)$ compute costs can make using transformers infeasible. Motivated by problems in malware detection, where sequence lengths of $T \geq 100,000$

Hanqing Zhu, Jiaqi Gu, Hanrui Wang, Zixuan Jiang

The wide adoption and significant computing resource of attention-based transformers, e.g., Vision Transformers and large language models (LLM), have driven the demand for efficient hardware accelerators. There is a growing interest in exploring photonics as an alternative technology to digital electronics due to its high energy efficiency and ultra-fast pro

Chris Reese

A simplified, pseudoplastic rheology characterized by constant viscosity plateaus above and below a transition strain rate is applied to axisymmetric, gravitationally driven spreading of a thin fluid film with constant volume flux source in planar and spherical geometries. The model admits analytical solutions for flow velocity and volume flux. Shear thinnin

Zhaocheng Liu, Zhongxiang Fan, Jian Liang, Dongying Kong

The one-epoch overfitting phenomenon has been widely observed in industrial Click-Through Rate (CTR) applications, where the model performance experiences a significant degradation at the beginning of the second epoch. Recent advances try to understand the underlying factors behind this phenomenon through extensive experiments. However, it is still unknown w

Eduardo Espindola, Yu Tang

This paper presents a generalization of conventional sliding mode control designs for systems in Euclidean spaces to fully actuated simple mechanical systems whose configuration space is a Lie group for the trajectory-tracking problem. A generic kinematic control is first devised in the underlying Lie algebra, which enables the construction of a Lie group on

Jianhao Wang, Jin Zhang, Haozhe Jiang, Junyu Zhang

Recent offline meta-reinforcement learning (meta-RL) methods typically utilize task-dependent behavior policies (e.g., training RL agents on each individual task) to collect a multi-task dataset. However, these methods always require extra information for fast adaptation, such as offline context for testing tasks. To address this problem, we first formally c

Kojiro Iizuka, Yoshifumi Seki, Makoto P. Kato

In this study, we propose an efficient method for comparing the post-click metric (e.g., dwell time and conversion rate) of multiple rankings in online experiments. The proposed method involves (1) the decomposition of the post-click metric measurement of a ranking into a click model estimation and a post-click metric measurement of each item in the ranking,

Dinh-Nho H`ao, Thuy T. Le, Loc H. Nguyen

The objective of this article is to introduce a novel technique for computing numerical solutions to the nonlinear inverse heat conduction problem. This involves solving nonlinear parabolic equations with Cauchy data provided on one side $\Gamma$ of the boundary of the computational domain $\Omega$. The key step of our proposed method is the truncation of th

Anup Biswas, Erwin Topp

We study the existence-uniqueness of solution $(u, \lambda)$ to the ergodic Hamilton-Jacobi equation $$(-\Delta)^s u + H(x, \nabla u) = f-\lambda\quad \text{in}\; \mathbb{R}^d,$$ and $u\geq 0$, where $s\in (\frac{1}{2}, 1)$. We show that the critical $\lambda=\lambda^*$, defined as the infimum of all $\lambda$ attaining a non-negative supersolution, attains

Laila El-Hamamsy, María Zapata-Cáceres, Estefanía Martín-Barroso, Francesco Mondada

The introduction of computing education into curricula worldwide requires multi-year assessments to evaluate the long-term impact on learning. However, no single Computational Thinking (CT) assessment spans primary school, and no group of CT assessments provides a means of transitioning between instruments. This study therefore investigated whether the compe

Ziming Liu, Patrick Obin Sturm, Saketh Bharadwaj, Sam Silva

Discovering conservation laws for a given dynamical system is important but challenging. In a theorist setup (differential equations and basis functions are both known), we propose the Sparse Invariant Detector (SID), an algorithm that auto-discovers conservation laws from differential equations. Its algorithmic simplicity allows robustness and interpretabil

Xiao Liu, Chongchun Zeng

We consider the spectra of the 2-dim gravity waves of finite depth linearized at a uniform monotonic shear flow $U(x_2)$, $x_2 \in (-h, 0)$, where the wave numbers $k$ of the horizontal variable $x_1$ is treated as a parameter. Our main results include a.) a complete branch of non-singular neutral modes $c^+(k)$ strictly decreasing in $k\ge 0$ and converging

Xiaoxin He, Xavier Bresson, Thomas Laurent, Adam Perold

Representation learning on text-attributed graphs (TAGs) has become a critical research problem in recent years. A typical example of a TAG is a paper citation graph, where the text of each paper serves as node attributes. Initial graph neural network (GNN) pipelines handled these text attributes by transforming them into shallow or hand-crafted features, su

Guanghou Liu, Yongmao Zhang, Yi Lei, Yunlin Chen

Style transfer TTS has shown impressive performance in recent years. However, style control is often restricted to systems built on expressive speech recordings with discrete style categories. In practical situations, users may be interested in transferring style by typing text descriptions of desired styles, without the reference speech in the target style.

Shubham Ugare, Tarun Suresh, Debangshu Banerjee, Gagandeep Singh

Randomized smoothing-based certification is an effective approach for obtaining robustness certificates of deep neural networks (DNNs) against adversarial attacks. This method constructs a smoothed DNN model and certifies its robustness through statistical sampling, but it is computationally expensive, especially when certifying with a large number of sample

Pavel Castro-Villarreal, César O. Solano-Cabrera, Ramón Castañeda-Priego

Brownian motion is a universal characteristic of colloidal particles embedded in a host medium, and it is the fingerprint of molecular transport or diffusion, a generic feature of relevance not only in Physics but also in several branches of Science and Engineering. Since its discovery, Brownian motion or colloid dynamics has been important in elucidating th

Kojiro Iizuka, Hajime Morita, Makoto P. Kato

This study presents a theoretical analysis on the efficiency of interleaving, an efficient online evaluation method for rankings. Although interleaving has already been applied to production systems, the source of its high efficiency has not been clarified in the literature. Therefore, this study presents a theoretical analysis on the efficiency of interleav

Kohei Yoshimura, Sosuke Ito

Recently, the theoretical framework of stochastic thermodynamics has been revealed to be useful for macroscopic systems. However, despite its conceptual and practical importance, the connection to hydrodynamics has yet to be explored. In this Letter, we reformulate the thermodynamics of compressible and incompressible Newtonian fluids so that it becomes comp

Jian Chen, Ruiyi Zhang, Tong Yu, Rohan Sharma

Learning from noisy labels is an important and long-standing problem in machine learning for real applications. One of the main research lines focuses on learning a label corrector to purify potential noisy labels. However, these methods typically rely on strict assumptions and are limited to certain types of label noise. In this paper, we reformulate the la

Gabriel Menezes

Multi-messenger astronomy provides us with the possibility of discovering phenomenological signatures of quantum-gravity effects. This should be of paramount importance in the pursuit of an elusive quantum theory for the gravitational interactions. Here we discuss feasible explorations within the effective field theory treatment of general relativity. By exp

Yong Liu, Hao Wang, Linxin Guo, Zhanfeng Yan

We developed and validated an accurate inter-atomic potential for molecular dynamics simulation in cubic silicon carbide (3C-SiC) using a deep learning framework combined with smooth Ziegler-Biersack-Littmark (ZBL) screened nuclear repulsion potential interpolation. Comparisons of multiple important properties were made between the deep-learning potential an

Xuetao Yang, Quanxin Zhu

In this paper, we concentrate on the exponential stabilization of stochastic nonlinear systems. Different from the single event-triggering mechanism in traditional deterministic/stochastic control systems, based on two stopping time sequences, we put forward a double-event-triggering mechanism (DETM) to update control signals and make two different controls

Hyun Jeong, Hyemin Lee

As the significance of blockchain innovation grows and the focus on scalability intensifies, rollup technology has emerged as a promising approach to tackle these scalability concerns. Nonetheless, rollups encounter restrictions when interacting with other rollups, leading to diminished throughput, increased latency, higher fees, and a complex user experienc

Zhenglai Li, Chang Tang, Xinwang Liu, Xingchen Hu

Change detection (CD) is essential for various real-world applications, such as urban management and disaster assessment. Numerous CD methods have been proposed, and considerable results have been achieved recently. However, detecting changes in hard regions, i.e., the change boundary and irrelevant pseudo changes caused by background clutters, remains diffi

Yiwei Lyu, Tiange Luo, Jiacheng Shi, Todd C. Hollon

Diffusion probabilistic models have shown great success in generating high-quality images controllably, and researchers have tried to utilize this controllability into text generation domain. Previous works on diffusion-based language models have shown that they can be trained without external knowledge (such as pre-trained weights) and still achieve stable

Konstantinos Bakas, John Kornak, Hernando Ombao

Bayesian methods are commonly applied to solve image analysis problems such as noise-reduction, feature enhancement and object detection. A primary limitation of these approaches is the computational complexity due to the interdependence of neighboring pixels which limits the ability to perform full posterior sampling through Markov chain Monte Carlo (MCMC).

Kedar Karhadkar, Michael Murray, Hanna Tseran, Guido Montúfar

We study the loss landscape of both shallow and deep, mildly overparameterized ReLU neural networks on a generic finite input dataset for the squared error loss. We show both by count and volume that most activation patterns correspond to parameter regions with no bad local minima. Furthermore, for one-dimensional input data, we show most activation regions

Yao Yao, Liang He, Perla Maiolino

Soft pneumatic actuators (SPAs) produce motions for soft robots with simple pressure input, however they require to be appropriately designed to fit the target application. Available design methods employ kinematic models and optimization to estimate the actuator response and the optimal design parameters, to achieve a target actuator's shape. Within SPAs, B

Weiguo Li, Wendi Bao, Lili Xing, Zhiwei Guo

In this paper, several Kaczmarz-type numerical methods for solving the matrix equation $AX=B$ and $XA=C$ are proposed, where the coefficient matrix $A$ may be full rank or rank deficient. These methods are iterative methods without matrix multiplication. Theoretically, the convergence of these methods is proved. The numerical results show that these methods

Xingzhe Su, Changwen Zheng, Wenwen Qiang, Fengge Wu

Generative Adversarial Networks (GANs) have shown notable accomplishments in remote sensing domain. However, this paper reveals that their performance on remote sensing images falls short when compared to their impressive results with natural images. This study identifies a previously overlooked issue: GANs exhibit a heightened susceptibility to overfitting

D. W. Boukhvalov, D. A. Zatsepin, Yu. A. Kuznetsova, V. I. Pryakhina

In this article, we report the results of relatively facile fabrication of carbon nanodots from single-walled and multi-walled carbon nanotubes (SWCNT and MWCNT). The results of X-ray photoelectron spectroscopy (XPS) and Raman measurements show that the obtained carbon nanodots are quasi-two-dimensional objects with a diamond-like structure. Based on the cha

Jiaqi Gu, Hanqing Zhu, Chenghao Feng, Zixuan Jiang

Photonic computing shows promise for transformative advancements in machine learning (ML) acceleration, offering ultra-fast speed, massive parallelism, and high energy efficiency. However, current photonic tensor core (PTC) designs based on standard optical components hinder scalability and compute density due to their large spatial footprint. To address thi

Bo Yang

We have discovered a novel transition rule for binary cellular automata (CA) that yields self-replicating structures across two spatial and temporal scales from sparsely populated random initial conditions. Lower-level, shapeshifting clusters frequently follow a transient attractor trajectory, generating new clusters, some of which periodically self-duplicat

Shuxiang Feng, Yingbo Han, Kaige Jiang, Shihshu Walter Wei

In this paper, we motivate and extend the study of harmonic maps or $\Phi_{(1)}$-harmonic maps (cf [15], Remark 1.3 (iii)), $\Phi$-harmonic maps or $\Phi_{(2)}$-harmonic maps (cf. [24], Remark 1.3 (v)), and explore geometric properties of $\Phi_{(3)}$-harmonic maps by unified geometric analytic methods. We define the notion of $\Phi_{(3)}$-harmonic maps and

Yi Luo, Guangchun Luo, Ke Qin, Aiguo Chen

Node classifiers are required to comprehensively reduce prediction errors, training resources, and inference latency in the industry. However, most graph neural networks (GNN) concentrate only on one or two of them. The compromised aspects thus are the shortest boards on the bucket, hindering their practical deployments for industrial-level tasks. This work

Lijun Zhang, Haomin Bai, Wei-Wei Tu, Ping Yang

While traditional distributionally robust optimization (DRO) aims to minimize the maximal risk over a set of distributions, Agarwal and Zhang (2022) recently proposed a variant that replaces risk with excess risk. Compared to DRO, the new formulation$\unicode{x2013}$minimax excess risk optimization (MERO) has the advantage of suppressing the effect of hetero

Axel Potier, Zahed Wahhaj, Raphael Galicher, Johan Mazoyer

Direct imaging is the primary technique currently used to detect young and warm exoplanets and understand their formation scenarios. The extreme flux ratio between an exoplanet and its host star requires the use of coronagraphs to attenuate the starlight and create high contrast images. However, their performance is limited by wavefront aberrations that caus

Yulin Chen, Ning Ding, Xiaobin Wang, Shengding Hu

Consistently scaling pre-trained language models (PLMs) imposes substantial burdens on model adaptation, necessitating more efficient alternatives to conventional fine-tuning. Given the advantage of prompting in the zero-shot setting and the observed performance fluctuation among different prompts, we explore the instance-level prompt and their generalizabil

Shumin Ma, Zhiri Yuan, Qi Wu, Yiyan Huang

Classical Domain Adaptation methods acquire transferability by regularizing the overall distributional discrepancies between features in the source domain (labeled) and features in the target domain (unlabeled). They often do not differentiate whether the domain differences come from the marginals or the dependence structures. In many business and financial

Zhenghua Peng, Yu Luo, Tianshui Chen, Keke Xu

Deep sequence recognition (DSR) models receive increasing attention due to their superior application to various applications. Most DSR models use merely the target sequences as supervision without considering other related sequences, leading to over-confidence in their predictions. The DSR models trained with label smoothing regularize labels by equally and

Keisuke Shirai, Hirotaka Kameko, Shinsuke Mori

Machine comprehension of procedural texts is essential for reasoning about the steps and automating the procedures. However, this requires identifying entities within a text and resolving the relationships between the entities. Previous work focused on the cooking domain and proposed a framework to convert a recipe text into a flow graph (FG) representation.

William Brown, Jon Schneider, Kiran Vodrahalli

We consider a number of questions related to tradeoffs between reward and regret in repeated gameplay between two agents. To facilitate this, we introduce a notion of $\textit{generalized equilibrium}$ which allows for asymmetric regret constraints, and yields polytopes of feasible values for each agent and pair of regret constraints, where we show that any

Axel Potier, Camilo Mejia Prada, Garreth Ruane, Hong Tang

Space-based stellar coronagraph instruments aim to directly image exoplanets that are a fraction of an arcsecond separation and ten billion times fainter than their host star. To achieve this, one or more deformable mirrors (DMs) are used in concert with coronagraph masks to control the wavefront and minimize diffracted starlight in a region of the image kno

Meng Gao, Wen Ting Zhang, Yan Feng Luo

Recently, we have found a non-finitely based involution semigroup of order five. It is natural to question what is the smallest order of non-finitely based involution semigroups. It is known that every involution semigroup of order up to three is finitely based. In this paper, it is shown that every involution semigroup of order four is finitely based. There

Leibny Paola Garcia Perera, Y. H. Victoria Chua, Hexin Liu, Fei Ting Woon

This paper introduces the inaugural Multilingual Everyday Recordings- Language Identification on Code-Switched Child-Directed Speech (MERLIon CCS) Challenge, focused on developing robust language identification and language diarization systems that are reliable for non-standard, accented, spontaneous code-switched, child-directed speech collected via Zoom. A

Ruimin Gao, Hao Zou, Zhekai Duan

In computer vision, different basic blocks are created around different matrix operations, and models based on different basic blocks have achieved good results. Good results achieved in vision tasks grants them rationality. However, these experimental-based models also make deep learning long criticized for principle and interpretability. Deep learning orig

Lacey West, Kristen Garofali, Bret D. Lehmer, Andrea Prestwich

Luminous infrared galaxies (LIRGs), the most extreme star-forming galaxies in the nearby (D$<$30 Mpc) Universe, show a notable X-ray emission deficiency (up to a factor of $\sim$10) compared with predictions from scaling relations of galaxy-wide high mass X-ray binary (HMXB) luminosity with star-formation rate. In the nearby ($\approx$20 Mpc) LIRG NGC 7552,

Madhuresh Gupta, Soumyakanti Giri, Prabhakar Karthikeyan Shanmugam, Mahajan Sagar Bhaskar

Renewable energy has become a reality in the present and is being preferred by countries to become a considerable part of the central grid. With the increasing adoption of renewables it will soon become crucial to have a platform which would facilitate secure transaction of energy for consumers as well as producers. This paper discusses and implements a Bloc

Kenju Otsuka, Seiichi Sudo

This paper reexamines a 60 year old mystery of spiking behavior in ruby lasers with a cw 532 nm pump, paying special attention to mode matching between the pump and lasing beam within a ruby crystal placed in a semi-confocal laser cavity. Periodic spiking oscillations were observed in a limited pump power regime, where spikes obeying the generic asymmetric h

Yingshan Guo, Chuansheng Shen, Hanshuang Chen

We study the extinction of epidemics in a simplicial susceptible-infected-susceptible model, where each susceptible individual becomes infected either by two-body interactions ($S+I \to 2I$) with a rate $\beta$ or by three-body interactions ($S+2I \to 3I$) with a rate $\beta (1+\delta)$, and each infected individual spontaneously recovers ($I \to S$) with a

Houssem Jmal, Firas Ben Hmida, Nardine Basta, Muhammad Ikram

Attack paths are the potential chain of malicious activities an attacker performs to compromise network assets and acquire privileges through exploiting network vulnerabilities. Attack path analysis helps organizations to identify new/unknown chains of attack vectors that reach critical assets within the network, as opposed to individual attack vectors in si

Arpit Garg, Cuong Nguyen, Rafael Felix, Thanh-Toan Do

Deep learning faces a formidable challenge when handling noisy labels, as models tend to overfit samples affected by label noise. This challenge is further compounded by the presence of instance-dependent noise (IDN), a realistic form of label noise arising from ambiguous sample information. To address IDN, Label Noise Learning (LNL) incorporates a sample se

G. Jordan Maclay

In an atom, the interaction of a bound electron with the vacuum fluctuations of the electromagnetic field leads to complex shifts in the energy levels of the electron, with the real part of the shift corresponding to a shift in the energy level and the imaginary part to the width of the energy level. The most celebrated radiative shift is the Lamb shift betw

Mohsen Fathi, Martín Molina, J. R. Villanueva

In this paper, we apply the Carath\'eodory's method of geometrothermodynamics to investigate the behavior of the main thermodynamic parameters associated with a Schwarzschild black hole surrounded by quintessence. The corresponding Pfaffian form is constructed by means of the Schwarzschild radius $r_s$, and the quintessential radius $r_{\gamma}$ as independe

Kasper Johansson, Mehmet Giray Ogut, Markus Pelger, Thomas Schmelzer

We consider the well-studied problem of predicting the time-varying covariance matrix of a vector of financial returns. Popular methods range from simple predictors like rolling window or exponentially weighted moving average (EWMA) to more sophisticated predictors such as generalized autoregressive conditional heteroscedastic (GARCH) type methods. Building

Tenzin Rabga, Yangheon Lee, Dalmin Bae, Myeonghyeon Kim

We study the vortex nucleation dynamics in inhomogeneous atomic Bose gases quenched into a superfluid phase and investigate the dependence of the Kibble-Zurek (KZ) scaling exponent on the underlying trap configuration. For samples in a number of different inhomogeneous traps, we observe the characteristic power-law scaling of the vortex number with the therm

Xintao Xia, Zhanrui Cai

Differentially private multiple testing procedures can protect the information of individuals used in hypothesis tests while guaranteeing a small fraction of false discoveries. In this paper, we propose a differentially private adaptive FDR control method that can control the classic FDR metric exactly at a user-specified level $\alpha$ with privacy guarante

John Kornak, Karl Young, Eric Friedman

Bayesian image analysis has played a large role over the last 40+ years in solving problems in image noise-reduction, de-blurring, feature enhancement, and object detection. However, these problems can be complex and lead to computational difficulties, due to the modeled interdependence between spatial locations. The Bayesian image analysis in Fourier space

Quoc-Huy Tran, Muhammad Ahmed, Murad Popattia, M. Hassan Ahmed

This paper presents a self-supervised temporal video alignment framework which is useful for several fine-grained human activity understanding applications. In contrast with the state-of-the-art method of CASA, where sequences of 3D skeleton coordinates are taken directly as input, our key idea is to use sequences of 2D skeleton heatmaps as input. Unlike CAS

Ekaterina Landgren, Alice Nadeau, Nikole Lewis, Tiffany Kataria

Sub-Neptune type exoplanets are abundant in our galaxy yet have no solar system analogs. They exist in a broad range of stellar forcing and rotational regimes that are distinctly different from solar system planets and more commonly studied hot Jupiters. Here we present simulations that explore global atmospheric circulation of sub-Neptunes generated with a

Quoc-Huy Tran, Ahmed Mehmood, Muhammad Ahmed, Muhammad Naufil

This paper presents an unsupervised transformer-based framework for temporal activity segmentation which leverages not only frame-level cues but also segment-level cues. This is in contrast with previous methods which often rely on frame-level information only. Our approach begins with a frame-level prediction module which estimates framewise action classes

Olusesi Balogun, Daniel Takabi

Insider Threat is a significant and potentially dangerous security issue in corporate settings. It is difficult to mitigate because, unlike external threats, insiders have knowledge of an organization's access policies, access hierarchy, access protocols, and access scheduling. Several approaches to reducing insider threat have been proposed in the literatur

Dongyoung Kim, Jinwoo Shin, Pieter Abbeel, Younggyo Seo

A promising technique for exploration is to maximize the entropy of visited state distribution, i.e., state entropy, by encouraging uniform coverage of visited state space. While it has been effective for an unsupervised setup, it tends to struggle in a supervised setup with a task reward, where an agent prefers to visit high-value states to exploit the task

John Dickerson, Seyed A. Esmaeili, Jamie Morgenstern, Claire Jie Zhang

The remarkable attention which fair clustering has received in the last few years has resulted in a significant number of different notions of fairness. Despite the fact that these notions are well-justified, they are often motivated and studied in a disjoint manner where one fairness desideratum is considered exclusively in isolation from the others. This l

Manuel Mager, Elisabeth Mager, Katharina Kann, Ngoc Thang Vu

In recent years machine translation has become very successful for high-resource language pairs. This has also sparked new interest in research on the automatic translation of low-resource languages, including Indigenous languages. However, the latter are deeply related to the ethnic and cultural groups that speak (or used to speak) them. The data collection

Saeed Saremi, Ji Won Park, Francis Bach

We introduce a theoretical framework for sampling from unnormalized densities based on a smoothing scheme that uses an isotropic Gaussian kernel with a single fixed noise scale. We prove one can decompose sampling from a density (minimal assumptions made on the density) into a sequence of sampling from log-concave conditional densities via accumulation of no

Faeze Brahman, Chandra Bhagavatula, Valentina Pyatkin, Jena D. Hwang

Procedural planning, which entails decomposing a high-level goal into a sequence of temporally ordered steps, is an important yet intricate task for machines. It involves integrating common-sense knowledge to reason about complex and often contextualized situations, e.g. ``scheduling a doctor's appointment without a phone''. While current approaches show enc

Il-Seung Jang, Kyu-Hwan Lee, Se-jin Oh

As a continuation of \cite{JLO1}, we investigate the quantum virtual Grothendieck ring $\frakK_q(\g)$ associated with a finite dimensional simple Lie algebra $\g$, especially of non-simply-laced type. We establish an isomorphism $\Uppsi_Q$ between the heart subring $\frakK_{q,Q}(\g)$ of $\frakK_q(\g)$ associated with a Dynkin quiver $Q$ of type $\g$ and the

Jianxin Zhang, Clayton Scott

Label embedding is a framework for multiclass classification problems where each label is represented by a distinct vector of some fixed dimension, and training involves matching model output to the vector representing the correct label. While label embedding has been successfully applied in extreme classification and zero-shot learning, and offers both comp

Xu Liu, Dong Wu, Jie Zhang

In the double-cone ignition inertial confinement fusion scheme, high density DT fuel is rapidly heated with high-flux fast electrons, which are generated by short and intense laser pulses. Gold cone target is usually used to shorten the distance between the critical surface and the compressed high density DT core. The material properties of solid gold may af

Ali Mehrabi, Yeshwanth Bethi, André van Schaik, Andrew Wabnitz

This paper presents an efficient hardware implementation of the recently proposed Optimized Deep Event-driven Spiking Neural Network Architecture (ODESA). ODESA is the first network to have end-to-end multi-layer online local supervised training without using gradients and has the combined adaptation of weights and thresholds in an efficient hierarchical str

Shaoyan Pan, Elham Abouei, Jacob Wynne, Tonghe Wang

Magnetic resonance imaging (MRI)-based synthetic computed tomography (sCT) simplifies radiation therapy treatment planning by eliminating the need for CT simulation and error-prone image registration, ultimately reducing patient radiation dose and setup uncertainty. We propose an MRI-to-CT transformer-based denoising diffusion probabilistic model (MC-DDPM) t

Amirhossein Kazemnejad, Inkit Padhi, Karthikeyan Natesan Ramamurthy, Payel Das

Length generalization, the ability to generalize from small training context sizes to larger ones, is a critical challenge in the development of Transformer-based language models. Positional encoding (PE) has been identified as a major factor influencing length generalization, but the exact impact of different PE schemes on extrapolation in downstream tasks

Nicholas Piaquadio, N. Eva Wu, Morteza Sarailoo, Jianzhuang Huang

A Pulse-Compression Probing (PCP) method is applied in time-domain to identify an equivalent circuit model of a distribution network as seen from the transmission grid. A Pseudo-Random Binary Pulse Train (PRBPT) is injected as a voltage signal at the input of the feeder and processed to recover the impulse response. A transfer function and circuit model is f

Jooyoung Shin

Let $R$ be a $\mathbb{Q}$-algebra and $d$ be a locally nilpotent derivation on $R$. We will show that the Jacobson radical of a differential polynomial ring $R[x;d]$ equals $I[x;d]$ where $I$ is a nil ideal of $R$. This answers a question posed by Agata Smoktunowicz.

Ian Charlesworth, Rolando de Santiago, Ben Hayes, David Jekel

We show that graph products of non trivial finite dimensional von Neumann algebras are strongly 1-bounded when the underlying *-algebra has vanishing first L2-Betti number. The proof uses a combination of the following two key ideas to obtain lower bounds on the Fuglede-Kadison determinant of matrix polynomials in a generating set: a notion called ''algebrai

Luca Sardellitti, Glen Takahara, Fady Alajaji

Data Fusion of wireless sensors is a common technique employed in many communication systems. This work focuses on incorporating the principles of non-orthogonal-multiple-access (NOMA) to optimize error performance directly in the choice of constellation design. More specifically, the problem of two sensor data fusion of a binary uniform source sent over a G

Yuichi Goto, Xuze Zhang, Benjamin Kedem, Shuo Chen

Coherence is a widely used measure to assess linear relationships between time series. However, it fails to capture nonlinear dependencies. To overcome this limitation, this paper introduces the notion of residual spectral density as a higher-order extension of the squared coherence. The method is based on an orthogonal decomposition of time series regressio

César Muñoz, Antonela Monachesi, David L. Nidever, Steven R. Majewski

We present the first detailed chemical analysis from APOGEE-2S observations of stars in six regions of recently discovered substructures in the outskirts of the Magellanic Clouds extending to 20 degrees from the LMC center. We also present, for the first time, the metallicity and alpha-abundance radial gradients of the LMC and SMC out to 11 degrees and 6 deg

Yue Li, Shijie Shang, Tusheng Zhang

In this paper, we established quadratic transportation cost inequalities for solutions of stochastic reaction diffusion equations driven by multiplicative space-time white noise on the whole line $\mathbb{R}$. Since the space variable is defined on the unbounded domain $\mathbb{R}$, the inequalities are proved under a weighted $L^2$-norm and a weighted unifo

Shuhei Watanabe, Noor Awad, Masaki Onishi, Frank Hutter

Hyperparameter optimization (HPO) is a vital step in improving performance in deep learning (DL). Practitioners are often faced with the trade-off between multiple criteria, such as accuracy and latency. Given the high computational needs of DL and the growing demand for efficient HPO, the acceleration of multi-objective (MO) optimization becomes ever more i

Joaquin Quiñonero-Candela, Yuwen Wu, Brian Hsu, Sakshi Jain

Operationalizing AI fairness at LinkedIn's scale is challenging not only because there are multiple mutually incompatible definitions of fairness but also because determining what is fair depends on the specifics and context of the product where AI is deployed. Moreover, AI practitioners need clarity on what fairness expectations need to be addressed at the

Lautaro Cilenti, Maria Cameron, Balakumar Balachandran

An experimental arrangement and a set of experiments are developed to generate empirical evidence of the effect of noise on a rotating, macro-scale cantilever structure. The experiment is a controlled representation of a rotating machinery blade. Due to the nature of the nonlinear restoring forces acting on the cantilever structure, the structure's response

Shentong Mo, Pedro Morgado

The ability to accurately recognize, localize and separate sound sources is fundamental to any audio-visual perception task. Historically, these abilities were tackled separately, with several methods developed independently for each task. However, given the interconnected nature of source localization, separation, and recognition, independent models are lik