October 2023 arXiv papers — page 20

Fei Zhang, Tianfei Zhou, Boyang Li, Hao He

This paper studies the problem of weakly open-vocabulary semantic segmentation (WOVSS), which learns to segment objects of arbitrary classes using mere image-text pairs. Existing works turn to enhance the vanilla vision transformer by introducing explicit grouping recognition, i.e., employing several group tokens/centroids to cluster the image tokens and per

Johann Jakob Abel, Jonathan Apell, Felix Wiesner, Julius Reinhard

Non-destructive cross-sectional characterization of materials systems with a resolution in the nanometer range and the ability to allow for time-resolved in-situ studies is of great importance in material science. Here, we present such a measurements method, extreme ultraviolet coherence tomography (XCT). The method is non-destructive during sample preparati

Daniel Grange, Ricardo Baptista, Amirhossein Taghvaei, Allen Tannenbaum

The distributed filtering problem sequentially estimates a global state variable using observations from a network of local sensors with different measurement models. In this work, we introduce a novel methodology for distributed nonlinear filtering by combining techniques from transportation of measures, dimensionality reduction, and consensus algorithms. W

Shakshi Sharma, Anwitaman Datta, Rajesh Sharma

Misinformation has emerged as a major societal threat in recent years in general; specifically in the context of the COVID-19 pandemic, it has wrecked havoc, for instance, by fuelling vaccine hesitancy. Cost-effective, scalable solutions for combating misinformation are the need of the hour. This work explored how existing information obtained from social me

Narges Mehran, Arman Haghighi, Pedram Aminharati, Nikolay Nikolov

Today, many users deploy their microservice-based applications with various interconnections on a cluster of Cloud machines, subject to stochastic changes due to dynamic user requirements. To address this problem, we compare three machine learning (ML) models for predicting the microservice call rates based on the microservice times and aiming at estimating

Victor Volfson

The paper considers a universal approach that allows one to quite simply obtain nonlinear asymptotic estimates of various summation functions. It is shown the application of this approach to the asymptotic estimation of divergent Dirichlet series. Several assertions have been proven and numerous examples have been considered.

Kaichen Zhou, Jia-Xing Zhong, Sangyun Shin, Kai Lu

The introduction of neural radiance fields has greatly improved the effectiveness of view synthesis for monocular videos. However, existing algorithms face difficulties when dealing with uncontrolled or lengthy scenarios, and require extensive training time specific to each new scenario. To tackle these limitations, we propose DynPoint, an algorithm designed

Yasemin Engur, Mahsa Shoaran

This paper presents an output capacitor-less low-dropout regulator (LDO) with a bias switching scheme for biomedical applications with dual-range load currents. Power optimization is crucial for systems with multiple activation modes such as neural interfaces, IoT and edge devices with varying load currents. To enable rapid switching between low and high cur

Hong-Bo Huang, Geng Li, Hui Dong

Landauer's principle shows that the minimum energy cost to reset a classical bit in a bath with temperature $T$ is $k_{B}T\ln2$ in the infinite time. However, the task to reset the bit in finite time has posted a new challenge, especially for quantum bit (qubit) where both the operation time and controllability are limited. We design a shortcut-to-isothermal

Thomas Etourneau, Jean-Marc Le Goff, James Rich, Ting Tan

We present a publicly-available code to generate sets of mock Lyman-$\alpha$ (\lya) forest data that have realistic large-scale correlations including those due to the Baryonic Acoustic Oscillations (BAO). The primary purpose of these mocks is to test the analysis procedures of the Extended Baryon Oscillation Survey (eBOSS) and the Dark Energy Spectroscopy I

Hani Barhum, Cormac McDonne, Oleksii O. Peltek, Rudhvi Jain

Biocompatible fluorescent agents, such as phenylenediamine carbon dots (CDs), are key contributors to the theragnostic paradigm, enabling real-time in vivo imaging of drug delivery cargoes. This study explores the optical properties of these CDs, demonstrating their potential for two-photon fluorescence imaging in brain vessels. Using an open aperture z-scan

H. Lohani, P. Mishra, B. R. Sekhar

Correlation effects are observed strong in Iron chalcogenides superconductors by experimental and theoretical investigations. We present a comparative study of the influence of Coulomb interaction and Hund's coupling in the electronic structure of FeSe and FeTe. The calculation is based on density functional theory (DFT) with local density approximation(LDA+

Ahmad Rouintan

Motivic homotopy theory is meant to play the role of algebraic topology, in particular homotopy theory, in the context of algebraic geometry. As proved by Oliver Rondigs and Paul Arne Ostvaer, this theory is closely connected to Voevodsky's triangulated category of motives. A connection that is the motivic analogue of the connection between algebraic topolog

Qianren Mao, Shaobo Zhao, Jiarui Li, Xiaolei Gu

Pre-trained sentence representations are crucial for identifying significant sentences in unsupervised document extractive summarization. However, the traditional two-step paradigm of pre-training and sentence-ranking, creates a gap due to differing optimization objectives. To address this issue, we argue that utilizing pre-trained embeddings derived from a

Yasuaki Nakayama, Akihiro Miyata, Tomonori Ugajin

We study properties of the Petz recovery map in chaotic systems, such as the Hayden-Preskill setup for evaporating black holes and the SYK model. Since these systems exhibit the phenomenon called scrambling, we expect that the expression of the recovery channel $\mathcal{R}$ gets simplified, given by just the adjoint $\mathcal{N}^{\dagger}$ of the original c

Łukasz Bibrzycki, Nadine Hammoud, Vincent Mathieu, Robert J. Perry

A calculation of the angular moments of two-pion photoproduction is presented. The underlying theoretical model encodes the prominent $\rho(770)$ resonance and the expected leading background contribution coming from the Deck mechanism. The model contains a number of free parameters which are fit to experimental data. A good description of the angular moment

Marjan Petreski, Stefan Tanevski, Irena Stojmenovska

This paper examines how investment in environmentally sustainable practices impacts employment and labor productivity growth of firms in transition economies. The study considers labor skill composition and geographical differences, shedding light on sustainability dynamics. The empirical analysis relies on the World Bank-s Enterprise Survey 2019 for 24 tran

Alicia Curth, Alan Jeffares, Mihaela van der Schaar

Conventional statistical wisdom established a well-understood relationship between model complexity and prediction error, typically presented as a U-shaped curve reflecting a transition between under- and overfitting regimes. However, motivated by the success of overparametrized neural networks, recent influential work has suggested this theory to be general

Soroush Hashemifar, Saeed Parsa, Akram Kalaee

Deep learning has revolutionized numerous fields, yet the reliability of Deep Neural Networks (DNNs) remains a concern due to their complexity and data dependency. Traditional software fault localization methods, such as Spectrum-based Fault Localization (SBFL), have been adapted for DNNs but often fall short in effectiveness. These methods typically overloo

Nhat Le, Tuong Do, Khoa Do, Hien Nguyen

Music-driven group choreography poses a considerable challenge but holds significant potential for a wide range of industrial applications. The ability to generate synchronized and visually appealing group dance motions that are aligned with music opens up opportunities in many fields such as entertainment, advertising, and virtual performances. However, mos

Nan Pan

The prediction of RNA-small molecule binding sites is crucial for the discovery of effective drugs. Various computational methods have been developed to address this challenge, using information about the structure and sequence of RNA. In this study, we introduce CplxCavity, a combination of a new algorithm and a machine learning model specifically designed

Yutaka Yoshii

In the hyperalgebra $\mathcal{U}_r$ of the $r$-th Frobenius kernel $({\rm SL}_2)_r$ of the algebraic group ${\rm SL}_2$, we construct a basis of the $\mathcal{U}_r$-module generated by a certain element which was given by the author before. As its applications, we also prove some results on the $\mathcal{U}_r$-modules and the algebra $\mathcal{U}_r$.

Anran Wu, Luwei Xiao, Xingjiao Wu, Shuwen Yang

Visually-situated languages such as charts and plots are omnipresent in real-world documents. These graphical depictions are human-readable and are often analyzed in visually-rich documents to address a variety of questions that necessitate complex reasoning and common-sense responses. Despite the growing number of datasets that aim to answer questions over

Wenxin Ding, Rong Yu

In this work, we present an analytical framework for studying antiferromagnetic (AFM) Mott insulating states in the Hubbard model. We first derive an analytical solution for the single-particle Green's functions in the atomic limit. Within a second-order perturbation approach, we compute the ground state energy and show that the ground state is antiferromagn

Maria Albareda-Sambola, Elena Fernández, Francisco Saldanha-da-Gama

This paper focuses on the Facility Location Problem with Bernoulli Demand, a discrete facility location problem with uncertainty where the joint distribution of the customers' demands is expressed by means of a set of possible scenarios. A two-stage stochastic program with recourse is used to select the facility locations and the a priori assignments of cust

Yulin Chang, Jie Han, Lin Sun

We investigate the occurrence of powers of tight Hamilton cycles in random hypergraphs. For every $r\ge 3$ and $k\ge 1$, we show that there exists a constant $C > 0$ such that if $p=p(n) \ge Cn^{-1/\binom{k+r-2}{r-1}}$ then asymptotically almost surely the random hypergraph $H^{(r)}(n,p)$ contains the $k$th power of a tight Hamilton cycle. This improves on a

Jan Mendling, Henrik Leopold, Henning Meyerhenke, Benoît Depaire

Research on algorithms has drastically increased in recent years. Various sub-disciplines of computer science investigate algorithms according to different objectives and standards. This plurality of the field has led to various methodological advances that have not yet been transferred to neighboring sub-disciplines. The central roadblock for a better knowl

Wen Liu, Liwei Duan

We investigate a generalized Dicke model by introducing two interacting spin ensembles coupled with a single-mode bosonic field. Apart from the normal to superradiant phase transition induced by the strong spin-boson coupling, interactions between the two spin ensembles enrich the phase diagram by introducing ferromagnetic, antiferromagnetic and paramagnetic

Jun Nian, Wei-Tou Ni

For space gravitational wave (GW) detection, arm locking is a proposal useful in decreasing the frequency noise of the laser sources for current developing space missions LISA and Taiji/TianQin. In this paper, we study the application of arm locking to the Astrodynamical Middle-frequency Interferometric Gravitational wave Observatory (AMIGO) to decrease the

Jessica S. Purcell, Corbin Reid, John Stewart

Classical fully augmented links have explicit hyperbolic geometry, and have diagrams on the 2-sphere in the 3-sphere. We generalise to construct fully augmented links projected to the reflection surface of any 3-manifold obtained by doubling a compact 3-manifold and show that the results of the classical setting extend to these links. When the resulting mani

Mahdi Salmani, Alireza Dehghanpour Farashah, Mohammad Azizmalayeri, Mahdi Amiri

Despite the remarkable success achieved by deep learning algorithms in various domains, such as computer vision, they remain vulnerable to adversarial perturbations. Adversarial Training (AT) stands out as one of the most effective solutions to address this issue; however, single-step AT can lead to Catastrophic Overfitting (CO). This scenario occurs when th

Ashutosh Dwivedi, Pradhyumna Lavania, Ashutosh Modi

Etiquettes are an essential ingredient of day-to-day interactions among people. Moreover, etiquettes are region-specific, and etiquettes in one region might contradict those in other regions. In this paper, we propose EtiCor, an Etiquettes Corpus, having texts about social norms from five different regions across the globe. The corpus provides a test bed for

Prasanna K. Dhani, Germán Rodrigo, German F. R. Sborlini

The singularities associated with QCD factorization in the collinear limit are key ingredients for high-precision theoretical predictions in particle physics. They govern the collinear behaviour of scattering amplitudes, as well as the perturbative energy evolution of parton densities (PDFs) and fragmentation functions (FFs). In this talk, we present the com

Jiehua Mai, Enhui Shi, Kesong Yan, Fanping Zeng

Let $G$ be a graph and $f: G\rightarrow G$ be a continuous map. We establish a structure theorem which describes the structures of the set $R(f)-\overline{P(f)}$, where $R(f)$ and $P(f)$ are the recurrent point set and the periodic point set of $f$ respectively. Roughly speaking, the set $R(f)-\overline{P(f)}$ is covered by finitely many pairwise disjoint $f

Nabeel Seedat, Jonathan Crabbé, Zhaozhi Qian, Mihaela van der Schaar

Data quality is crucial for robust machine learning algorithms, with the recent interest in data-centric AI emphasizing the importance of training data characterization. However, current data characterization methods are largely focused on classification settings, with regression settings largely understudied. To address this, we introduce TRIAGE, a novel da

Martina G. Vilas, Timothy Schaumlöffel, Gemma Roig

Despite the growing use of transformer models in computer vision, a mechanistic understanding of these networks is still needed. This work introduces a method to reverse-engineer Vision Transformers trained to solve image classification tasks. Inspired by previous research in NLP, we demonstrate how the inner representations at any level of the hierarchy can

Yu Zhang, Zhuo Jin, Jiaqin Wei, George Yin

This paper develops a new deep learning algorithm to solve a class of finite-horizon mean-field games. The proposed hybrid algorithm uses Markov chain approximation method combined with a stochastic approximation-based iterative deep learning algorithm. Under the framework of finite-horizon mean-field games, the induced measure and Monte-Carlo algorithm are

Lev Ivan Bulyk, Taras Demkiv, Oleh Antonyak, Yaroslav M. Chornodolskyy

This study investigates the effect of hydrostatic pressure on the luminescence properties of CsPbBr3 single crystals at 12 K. The luminescence at the edge of the band gap reveals a structure attributed to free excitons, phonon replica of the free excitons, and Rashba excitons. Changes in the relative intensity of the free and Rashba excitons were observed wi

Nicolas Bourriez, Adrien Loizeau, Adam F. Abdin

The space environment around the Earth is becoming increasingly populated by both active spacecraft and space debris. To avoid potential collision events, significant improvements in Space Situational Awareness (SSA) activities and Collision Avoidance (CA) technologies are allowing the tracking and maneuvering of spacecraft with increasing accuracy and relia

Tomoyuki Yamakami

Lately, there have been intensive studies on strengths and limitations of nonuniform families of promise decision problems solvable by various types of polynomial-size finite automata families, where ``polynomial-size'' refers to the polynomially-bounded state complexity of a finite automata family. In this line of study, we further expand the scope of these

Ahmad Nasir, Aadish Sharma, Kokil Jaidka, Saifuddin Ahmed

In the evolving landscape of online communication, hate speech detection remains a formidable challenge, further compounded by the diversity of digital platforms. This study investigates the effectiveness and adaptability of pre-trained and fine-tuned Large Language Models (LLMs) in identifying hate speech, to address two central questions: (1) To what exten

Qian Xiong, Zuoxiang Peng

Expectile, as the minimizer of an asymmetric quadratic loss function, is a coherent risk measure and is helpful to use more information about the distribution of the considered risk. In this paper, we propose a new risk measure by replacing quantiles by expectiles, called expectile-based conditional tail moment, and focus on the estimation of this new risk m

Shuaitongze Zhao, Shafqat Riaz, Cosimo Bambi

In the past decade, we have seen an unprecedented progress in our ability of testing general relativity in the strong field regime with black hole observations. Most studies have focused on the so-called tests of the Kerr hypothesis: they have tried to verify whether the spacetime geometry around black holes is described by the Kerr solution as expected in g

Qihang Zhou, Guansong Pang, Yu Tian, Shibo He

Zero-shot anomaly detection (ZSAD) requires detection models trained using auxiliary data to detect anomalies without any training sample in a target dataset. It is a crucial task when training data is not accessible due to various concerns, eg, data privacy, yet it is challenging since the models need to generalize to anomalies across different domains wher

Imre Bárány, Gábor Domokos

Given a polytope $P\subset R^3$ and a non-zero vector $z \in R^3$, the plane $\{x\in R^3:zx=t\}$ intersects $P$ in convex polygon $P(z,t)$ for $t \in [t^-,t^+]$ where $t^-=\min \{zx: x \in P\}$ and $t^+=\max \{zx: x\in P\}$, $zx$ is the scalar product of $z,x \in R^3$. Let $A(P,z)$ denote the average number of vertices of $P(z,t)$ on the interval $[t^-,t^+]$

Ekrem Taha Güldeste, Ceyhun Bulutay

Nitrogen-vacancy (NV) centers in diamond constitute a solid-state nanosensing paradigm. Specifically for high-precision magnetometry, the so-called Ramsey interferometry is the prevalent choice where the sensing signal is extracted from time-resolved spin-state-dependent photoluminescence (PL) data. Its sensitivity is ultimately limited by the photon shot no

Stefan Deaconu

We prove, using a theorem of Northcott, that if a number field K with s real embeddings and 2t complex ones has a group of units U such that all elements in U have all its complex conjugates of same absolute value, then one necessarily has t = 1. This fact has an interesting implication in complex hermitian geometry, namely it describes all Oeljeklaus-Toma m

Peter Balazs, Rosario Corso, Diana Stoeva

In this paper we ask when it is possible to transform a given sequence into a frame or a lower semi frame by multiplying the elements by numbers. In other words, we ask when a given sequence is a weighted frame or a weighted lower semi frame and for each case we formulate a conjecture. We determine several conditions under which these conjectures are true. F

Benjamin Towle, Ke Zhou

Reply suggestion systems represent a staple component of many instant messaging and email systems. However, the requirement to produce sets of replies, rather than individual replies, makes the task poorly suited for out-of-the-box retrieval architectures, which only consider individual message-reply similarity. As a result, these system often rely on additi

Abhishek Sinha, Rahul Vaze

A well-studied generalization of the standard online convex optimization (OCO) framework is constrained online convex optimization (COCO). In COCO, on every round, a convex cost function and a convex constraint function are revealed to the learner after it chooses the action for that round. The objective is to design an online learning policy that simultaneo

Yuetian Weng, Mingfei Han, Haoyu He, Mingjie Li

Video Semantic Segmentation (VSS) involves assigning a semantic label to each pixel in a video sequence. Prior work in this field has demonstrated promising results by extending image semantic segmentation models to exploit temporal relationships across video frames; however, these approaches often incur significant computational costs. In this paper, we pro

Megh Shukla, Mathieu Salzmann, Alexandre Alahi

Deep heteroscedastic regression involves jointly optimizing the mean and covariance of the predicted distribution using the negative log-likelihood. However, recent works show that this may result in sub-optimal convergence due to the challenges associated with covariance estimation. While the literature addresses this by proposing alternate formulations to

Ricardo Fariello, Marcus A. M. de Aguiar

The multidimensional Kuramoto model describes the synchronization dynamics of particles moving on the surface of D-dimensional spheres, generalizing the original model where particles were characterized by a single phase. In this setup, particles are more easily represented by $D$-dimensional unit vectors than by $D-1$ spherical angles, allowing for the coup

Zhihang Yu, Shu Wang, Yunqiang Zhu, Zhiqiang Zou

The Ecological Civilization Pattern Recommendation System (ECPRS) aims to recommend suitable ecological civilization patterns for target regions, promoting sustainable development and reducing regional disparities. However, the current representative recommendation methods are not suitable for recommending ecological civilization patterns in a geographical c

Francesco D'Andrea

The aim of this book is to provide an elementary introduction, complete with detailed proofs, to the celebrated tilings of the plane discovered by Sir Roger Penrose in the `70s. The book covers many aspects of Penrose tilings, including the study of the space parameterizing Penrose tilings from the point of view of Connes' Noncommutative Geometry.

Shaocong Zhang

Generating images from human sketches typically requires dedicated networks trained from scratch. In contrast, the emergence of the pre-trained Vision-Language models (e.g., CLIP) has propelled generative applications based on controlling the output imagery of existing StyleGAN models with text inputs or reference images. Parallelly, our work proposes a fram

Boris Z. Malkin, Roman V. Yusupov, Ildar F. Gilmutdinov, Ruslan G. Batulin

Experimental and theoretical studies of the dynamic magnetization in swept magnetic fields of the orthorhombic SrY$_2$O$_4$ single-crystals doped with the Dy$^{3+}$ Kramers ions (0.01 and 0.5 at.%) with natural abundances of even and odd Dy isotopes are presented. Impurity ions substitute for Y$^{3+}$ ions at two nonequivalent crystallographic sites with the

Ping Hu, Simon Niklaus, Lu Zhang, Stan Sclaroff

In this work, we first propose a fully differentiable Many-to-Many (M2M) splatting framework to interpolate frames efficiently. Given a frame pair, we estimate multiple bidirectional flows to directly forward warp the pixels to the desired time step before fusing overlapping pixels. In doing so, each source pixel renders multiple target pixels and each targe

Dmitri I. Panyushev

Let $\mathfrak g$ be a complex simple Lie algebra and $\mathfrak n$ the nilradical of a parabolic subalgebra of $\mathfrak g$. We consider some properties of the coadjoint representation of $\mathfrak n$ and related algebras of invariants. This includes (i) the problem of existence of generic stabilisers, (ii) a description of the Frobenius semiradical of $\

Yongxiu Xu, Heyan Huang, Yue Hu

Named Entity Recognition (NER) remains challenging due to the complex entities, like nested, overlapping, and discontinuous entities. Existing approaches, such as sequence-to-sequence (Seq2Seq) generation and span-based classification, have shown impressive performance on various NER subtasks, but they are difficult to scale to datasets with longer input tex

Yashvir Tibrewal, Nishchal Dwivedi

Studying water droplets is a rich lesson in fields of fluid dynamics, nonlinear systems, and differential equations. Understanding various physical aspects of raindrops can help us in understanding drop dynamics, rainfall density estimation, size distributions which can be grant insights in the fields of meteorology, hydrology, and climate science. This work

Fei Sun, Shuang Li, Rui Wen, Anping Huang

In this study, we investigate the impact of rotation on the thermodynamic characteristics of QCD matter using the three-flavor NJL model. We examine the temperature, quark chemical potential, and angular velocity dependencies of key thermodynamic quantities, such as the trace anomaly, specific heat, speed of sound, angular momentum, and moment of inertia. As

Igor Leite Freire

Pseudospherical surfaces determined by Cauchy problems involving the Camassa-Holm equation are considered herein. We study how global solutions influence the corresponding surface, as well as we investigate two sorts of singularities of the metric: the first one is just when the co-frame of dual form is not linearly independent. The second sort of singularit

Zelai Xu, Chao Yu, Fei Fang, Yu Wang

Agents built with large language models (LLMs) have shown great potential across a wide range of domains. However, in complex decision-making tasks, pure LLM-based agents tend to exhibit intrinsic bias in their choice of actions, which is inherited from the model's training data and results in suboptimal performance. To develop strategic language agents, i.e

Tian Yao, Dehai Liu, Kaishun Wang

Let $V$ be a finite dimensional vector space over a finite field. Suppose that $\mathscr{F}_1$, $\mathscr{F}_2$, $\dots$, $\mathscr{F}_r$ are $r$-cross $t$-intersecting families of $k$-subspaces of $V$. In this paper, we determine the extremal structure when $\prod_{i=1}^r|\mathscr{F}_i|$ is maximum under the condition that $\dim(\bigcap_{F\in\mathscr{F}_i}F

Shreyan Deo, Nishchal Dwivedi

This work focuses on the analysis of Chess 960, also known as Fischer Random Chess, a variant of traditional chess where the starting positions of the pieces are randomized. The study aims to predict the game outcome using machine learning techniques and develop an opening theme for each starting position. The first part of the analysis utilizes machine lear

Eoin M. Kenny, Weipeng Huang

When users receive either a positive or negative outcome from an automated system, Explainable AI (XAI) has almost exclusively focused on how to mutate negative outcomes into positive ones by crossing a decision boundary using counterfactuals (e.g., \textit{"If you earn 2k more, we will accept your loan application"}). Here, we instead focus on \textit{posit

Ang Li, Yifei Wang, Yiwen Guo, Yisen Wang

The existence of adversarial examples has been a mystery for years and attracted much interest. A well-known theory by \citet{ilyas2019adversarial} explains adversarial vulnerability from a data perspective by showing that one can extract non-robust features from adversarial examples and these features alone are useful for classification. However, the explan

Yiwen Kou, Zixiang Chen, Quanquan Gu

The implicit bias towards solutions with favorable properties is believed to be a key reason why neural networks trained by gradient-based optimization can generalize well. While the implicit bias of gradient flow has been widely studied for homogeneous neural networks (including ReLU and leaky ReLU networks), the implicit bias of gradient descent is current

Siqi He, Jie Liu

In this article, we study the Hitchin morphism over a smooth projective variety $X$. The Hitchin morphism is a map from the moduli space of Higgs bundles to the Hitchin base, which in general not surjective when the dimension of X is greater than one. Chen-Ng\^{o} introduced the spectral base, which is a closed subvariety of the Hitchin base. They conjecture

Rishi D. Jha, Jonathan Hayase, Sewoong Oh

In a backdoor attack, an adversary injects corrupted data into a model's training dataset in order to gain control over its predictions on images with a specific attacker-defined trigger. A typical corrupted training example requires altering both the image, by applying the trigger, and the label. Models trained on clean images, therefore, were considered sa

Kyung Geun Kim, Byeong Tak Lee

Many diverse phenomena in nature often inherently encode both short- and long-term temporal dependencies, which especially result from the direction of the flow of time. In this respect, we discovered experimental evidence suggesting that interrelations of these events are higher for closer time stamps. However, to be able for attention-based models to learn

Bryan S. Hernandez, Patrick Vincent N. Lubenia, Eduardo R. Mendoza

The Wnt signaling pathway plays a critical role in various biochemical processes, including embryonic development, tissue homeostasis, and cancer progression. In this paper, we conduct a comparative analysis of $\beta$-catenin-dependent Wnt signaling reaction networks, which we refer to as the Feinberg, Schmitz, and MacLean models, based on the previous stud

Sapan Shah, Sreedhar Reddy, Pushpak Bhattacharyya

We present a novel retrofitting method to induce emotion aspects into pre-trained language models (PLMs) such as BERT and RoBERTa. Our method updates pre-trained network weights using contrastive learning so that the text fragments exhibiting similar emotions are encoded nearby in the representation space, and the fragments with different emotion content are

Mona Abdel-Keream, Daniel Beßler, Ayden Janssen, Sascha Jongebloed

The notion of preferences plays an important role in many disciplines including service robotics which is concerned with scenarios in which robots interact with humans. These interactions can be favored by robots taking human preferences into account. This raises the issue of how preferences should be represented to support such preference-aware decision mak

Abolfazl Younesi, Reza Afrouzian, Yousef Seyfari

Due to the epidemic of the coronavirus (Covid-19) and its rapid spread around the world, the world has faced an enormous crisis. To prevent the spread of the coronavirus, the World Health Organization (WHO) has introduced the use of masks and keeping social distance as the best preventive method. So, developing an automatic monitoring system for detecting fa

Giuseppe Castagnoli

We show that the sheer existence of a quantum computational speedup logically implies the mutually exclusive or of well-defined causal loops. In each of them, it is as if the problem-solver knew in advance one of the possible halves of the information about the solution she will produce and measure in the future and could use this knowledge to produce the so

Rukai Wei, Yu Liu, Jingkuan Song, Heng Cui

Compressing videos into binary codes can improve retrieval speed and reduce storage overhead. However, learning accurate hash codes for video retrieval can be challenging due to high local redundancy and complex global dependencies between video frames, especially in the absence of labels. Existing self-supervised video hashing methods have been effective in

Xuhua He, Connor Simpson, Kaitao Xie

We define the totally nonnegative matroid Schubert variety $\mathcal Y_V$ of a linear subspace $V \subset \mathbb R^n$. We show that $\mathcal Y_V$ is a regular CW complex homeomorphic to a closed ball, with strata indexed by pairs of acyclic flats of the oriented matroid of $V$. This closely resembles the regularity theorem for totally nonnegative generaliz

Sungho Suh, Dhruv Aditya Mittal, Hymalai Bello, Bo Zhou

Lithium-ion batteries are widely used in various applications, including electric vehicles and renewable energy storage. The prediction of the remaining useful life (RUL) of batteries is crucial for ensuring reliable and efficient operation, as well as reducing maintenance costs. However, determining the life cycle of batteries in real-world scenarios is cha

Frédérique Bassino, Cyril Nicaud, Pascal Weil

We show how to efficiently count and generate uniformly at random finitely generated subgroups of the modular group $\textsf{PSL}(2,\mathbb{Z})$ of a given isomorphism type. The method to achieve these results relies on a natural map of independent interest, which associates with any finitely generated subgroup of $\textsf{PSL}(2,\mathbb{Z})$ a graph which w

H. Lohani, P. Mishra, A. Banerjee, K. Majhi

We present our angle resolved photoelectron spectroscopy (ARPES) and density functional theory results on quaternary topological insulator (TI) BiSbTe1.25Se1.75 (BSTS) confirming the non-trivial topology of the surface state bands (SSBs) in this compound. We find that the SSBs, which are are sensitive to the atomic composition of the terminating surface have

Parikshit Singh Rathore

In this paper, we present an efficient solution for weed classification in agriculture. We focus on optimizing model performance at inference while respecting the constraints of the agricultural domain. We propose a Quantized Deep Neural Network model that classifies a dataset of 9 weed classes using 8-bit integer (int8) quantization, a departure from standa

A. V. Kotikov, I. A. Zemlyakov

We present a brief overview of fractional analytic QCD.

Zehua Fu, Wenhang Zuo, Zhenghui Hu, Qingjie Liu

Human pose estimation and tracking are fundamental tasks for understanding human behaviors in videos. Existing top-down framework-based methods usually perform three-stage tasks: human detection, pose estimation and tracking. Although promising results have been achieved, these methods rely heavily on high-performance detectors and may fail to track persons

Nikki Lijing Kuang, Ming Yin, Mengdi Wang, Yu-Xiang Wang

Recent studies in reinforcement learning (RL) have made significant progress by leveraging function approximation to alleviate the sample complexity hurdle for better performance. Despite the success, existing provably efficient algorithms typically rely on the accessibility of immediate feedback upon taking actions. The failure to account for the impact of

Nurendra Choudhary, Nikhil Rao, Chandan K. Reddy

The progress in hyperbolic neural networks (HNNs) research is hindered by their absence of inductive bias mechanisms, which are essential for generalizing to new tasks and facilitating scalable learning over large datasets. In this paper, we aim to alleviate these issues by learning generalizable inductive biases from the nodes' local subgraph and transfer t

Chengyao Duan, Zhiliu Yang

Previous attempts to integrate Neural Radiance Fields (NeRF) into the Simultaneous Localization and Mapping (SLAM) framework either rely on the assumption of static scenes or require the ground truth camera poses, which impedes their application in real-world scenarios. This paper proposes a time-varying representation to track and reconstruct the dynamic sc

Yanbing Zhang, Jianan Zeng, Ruifeng Yuan, Wei Liu

Recently, the general synthetic iterative scheme (GSIS) has been proposed to find the steady-state solution of the Boltzmann equation in the whole range of gas rarefaction, where its fast-converging and asymptotic-preserving properties lead to the significant reduction of iteration numbers and spatial cells in the near-continuum flow regime. However, the eff

Shuaiyu Zhou, Yiheng Wei, Jinde Cao, Yang Liu

Prescribed-time convergence mechanism has become a prominent research focus in the current field of optimization and control due to its ability to precisely control the target completion time. The recently arisen prescribed-time algorithms for distributed optimization, currently necessitate multi-stage structures to achieve global convergence. This paper int

Andrei Vasin

Given a bounded Lipschitz domain $D\subset \mathbb{R}^d,$ a convolution Calder\'{o}n-Zygmund operator $T$ and a growth function $\omega(x)$ of type $n$, we study what conditions on the boundary of the domain are sufficient for boundedness of the restricted even operator $T_D$ on the generalized Zygmund space $C^{\omega}_*(D)$. Based on a recent T(P) theorem,

Tomasz Limisiewicz, David Mareček, Tomáš Musil

Large language models are becoming the go-to solution for the ever-growing number of tasks. However, with growing capacity, models are prone to rely on spurious correlations stemming from biases and stereotypes present in the training data. This work proposes a novel method for detecting and mitigating gender bias in language models. We perform causal analys

Hao Zhang, Yang Liu, Xiaoyan Liu, Tianming Liang

We introduce a novel graph-based framework for alleviating key challenges in distantly-supervised relation extraction and demonstrate its effectiveness in the challenging and important domain of biomedical data. Specifically, we propose a graph view of sentence bags referring to an entity pair, which enables message-passing based aggregation of information r

Brisha Jain, Mainack Mondal

Gender bias in political discourse is a significant problem on today's social media. Previous studies found that the gender of politicians indeed influences the content directed towards them by the general public. However, these works are particularly focused on the global north, which represents individualistic culture. Furthermore, they did not address whe

Muyang Li, Runze Wu, Haoyu Liu, Jun Yu

Semi-supervised learning (SSL) has been a fundamental challenge in machine learning for decades. The primary family of SSL algorithms, known as pseudo-labeling, involves assigning pseudo-labels to confident unlabeled instances and incorporating them into the training set. Therefore, the selection criteria of confident instances are crucial to the success of

Captain R. Singh, Mohammad Yousuf Jamal, Raghunath Sahoo

Quarkonium production in ultra-relativistic collisions plays a crucial role in probing the existence of hot QCD matter. This study explores quarkonia states dissociation and regeneration in the hot QCD medium while considering momentum anisotropy. The net quarkonia decay width ($\Gamma_{D}$) arises from two essential processes: collisional damping and gluoni

Yibo Yang, Stephan Eckstein, Marcel Nutz, Stephan Mandt

In the theory of lossy compression, the rate-distortion (R-D) function $R(D)$ describes how much a data source can be compressed (in bit-rate) at any given level of fidelity (distortion). Obtaining $R(D)$ for a given data source establishes the fundamental performance limit for all compression algorithms. We propose a new method to estimate $R(D)$ from the p

Ashiqur Rasul, Md Shafayat Hossain, Ankan Ghosh Dastider, Himaddri Roy

Prediction and discovery of new materials with desired properties are at the forefront of quantum science and technology research. A major bottleneck in this field is the computational resources and time complexity related to finding new materials from ab initio calculations. In this work, an effective and robust deep learning-based model is proposed by inco

Duke Nguyen, Khaing Myat Noe Naing, Aditya Joshi

This paper reports our submission under the team name `SynthDetectives' to the ALTA 2023 Shared Task. We use a stacking ensemble of Transformers for the task of AI-generated text detection. Our approach is novel in terms of its choice of models in that we use accessible and lightweight models in the ensemble. We show that ensembling the models results in an

Xueqing Liu, Tianchen Qian, Lauren Bell, Bibhas Chakraborty

In mobile health, tailoring interventions for real-time delivery is of paramount importance. Micro-randomized trials have emerged as the "gold-standard" methodology for developing such interventions. Analyzing data from these trials provides insights into the efficacy of interventions and the potential moderation by specific covariates. The "causal excursion