October 2023 arXiv papers — page 113

Victor Miguel Banda Guzmán, Adnan Bashir, Luis Albino, Dania Rodríguez-Tzintzun

We carry out a detailed study of the three-point fermion-photon interaction vertex at one loop order for massive fermions in reduced quantum electrodynamics. This calculation is carried out in arbitrary covariant gauges and space-time dimensions within a recently proposed innovative approach based upon an efficient combination of the first and second order f

Ki-Hwan Oh, Leonardo Borgioli, Miloš Žefran, Liaohai Chen

Robotic surgery promises enhanced precision and adaptability over traditional surgical methods. It also offers the possibility of automating surgical interventions, resulting in reduced stress on the surgeon, better surgical outcomes, and lower costs. Cholecystectomy, the removal of the gallbladder, serves as an ideal model procedure for automation due to it

Chenyang Yang, Rishabh Rustogi, Rachel Brower-Sinning, Grace A. Lewis

Current model testing work has mostly focused on creating test cases. Identifying what to test is a step that is largely ignored and poorly supported. We propose Weaver, an interactive tool that supports requirements elicitation for guiding model testing. Weaver uses large language models to generate knowledge bases and recommends concepts from them interact

Zhepeng Wang, Isaacshubhanand Putla, Weiwen Jiang, Youzuo Lin

Seismic full waveform inversion (FWI) is a widely used technique in geophysics for inferring subsurface structures from seismic data. And InversionNet is one of the most successful data-driven machine learning models that is applied to seismic FWI. However, the high computing costs to run InversionNet have made it challenging to be efficiently deployed on ed

Jan Vogelsang, Lukas Wittenbecher, Sara Mikaelsson, Chen Guo

Electrons photoemitted by extreme ultraviolet attosecond pulses derive spatially from the first few atomic surface layers and energetically from the valence band and highest atomic orbitals. As a result, it is possible to probe the emission dynamics from a narrow two-dimensional region in the presence of optical fields as well as obtain elemental specific in

Xiao Li, Weili Wu

Federated learning (FL) is emerging as a sought-after distributed machine learning architecture, offering the advantage of model training without direct exposure of raw data. With advancements in network infrastructure, FL has been seamlessly integrated into edge computing. However, the limited resources on edge devices introduce security vulnerabilities to

Yoshiharu Asaki, Luke T. Maud, Harold Francke, Hiroshi Nagai

The Atacama Large Millimeter/submillimeter Array (ALMA) was used in 2021 to image the carbon-rich evolved star R Lep in Bands 8-10 (397-908 GHz) with baselines up to 16 km. The goal was to validate the calibration, using band-to-band (B2B) phase referencing with a close phase calibrator J0504-1512, 1.2 deg from R Lep in this case, and the imaging procedures

Mohammad M. Jalalzai, Chen Feng, Victoria Lemieux

Low latency is one of the most desirable features of partially synchronous Byzantine consensus protocols. Existing low-latency protocols have achieved consensus with just two communication steps by reducing the maximum number of faults the protocol can tolerate (from $f = \frac{n-1}{3}$ to $f = \frac{n+1}{5}$), \textcolor{black}{by relaxing protocol safety g

F. S. Nascimento, L. B. de Oliveira, D. G. Duarte, C. I. L. de Araujo

Magnetic structure factor (MSF) is employed to investigate the ground state degeneracy in rectangular-like artificial spin ices. Our analysis considers the importance of nanoislands size via dumbbell model approximation. Pinch points in MSF and residual entropy are found for rectangular lattices with disconnected nanoislands, signalizing an emergent gauge fi

Olumide E. Ojo, Olaronke O. Adebanji, Hiram Calvo, Damian O. Dieke

In this paper, we share our best performing submission to the Arabic AI Tasks Evaluation Challenge (ArAIEval) at ArabicNLP 2023. Our focus was on Task 1, which involves identifying persuasion techniques in excerpts from tweets and news articles. The persuasion technique in Arabic texts was detected using a training loop with XLM-RoBERTa, a language-agnostic

Liang Zhao, Xiongfei Wang, Zheming Jin

This paper presents an analytical approach to explore the damping effect of inner loops on grid-forming converters. First, an impedance model is proposed to characterize the behaviors of inner loops, thereby illustrating their influence on output impedance shaping. Then, based on the impedance representation, the complex torque coefficient method is employed

Zhengying Lou, Baha Eddine Youcef Belmekki, Mohamed-Slim Alouini

High altitude platform stations (HAPS) have recently emerged as a new key stratospheric player in non-terrestrial networks (NTN) alongside satellites and low-altitude platforms. In this paper, we present the main communication links between HAPS and other NTN platforms, their advantages, and their challenges. Then, prospective network architectures in which

Tim P. Schulze

We introduce a simple extensive-form algorithm for finding equilibria of two-player, zero-sum games. The algorithm is realization equivalent to a generalized form of Fictitious Play. We compare its performance to that of a similar extensive-form fictitious play algorithm and a counter-factual regret minimization algorithm. All three algorithms share the same

Khaled Mohammed Saifuddin, Mehmet Emin Aktas, Esra Akbas

Hypergraphs, with their capacity to depict high-order relationships, have emerged as a significant extension of traditional graphs. Although Graph Neural Networks (GNNs) have remarkable performance in graph representation learning, their extension to hypergraphs encounters challenges due to their intricate structures. Furthermore, current hypergraph transfor

Hengrui Zhang, Jiani Zhang, Balasubramaniam Srinivasan, Zhengyuan Shen

Recent advances in tabular data generation have greatly enhanced synthetic data quality. However, extending diffusion models to tabular data is challenging due to the intricately varied distributions and a blend of data types of tabular data. This paper introduces Tabsyn, a methodology that synthesizes tabular data by leveraging a diffusion model within a va

Marco Perin, Massimiliano Bertoni, Giulia Michieletto, Roberto Oboe

Tilted hexarotors embody a technology that remains partially unexploited in terms of its potential, especially concerning precise and concurrent position and attitude control. Focusing on these aerial platforms, we propose two control architectures that can tackle the trajectory tracking task, ensuring also the attitude regulation: one is designed resting on

Elias Hess-Childs, Keefer Rowan

In this paper, we study diffusions with bounded pairwise interaction. We show for the first time propagation of chaos on arbitrary time horizons in a stronger $L^2$-based distance, as opposed to the usual Wasserstein or relative entropy distances. The estimate is based on iterating inequalities derived from the BBGKY hierarchy and does not follow directly fr

Paarth Neekhara, Shehzeen Hussain, Rafael Valle, Boris Ginsburg

We propose SelfVC, a training strategy to iteratively improve a voice conversion model with self-synthesized examples. Previous efforts on voice conversion focus on factorizing speech into explicitly disentangled representations that separately encode speaker characteristics and linguistic content. However, disentangling speech representations to capture suc

Wenjie Lv, Zhen Wang, Yitao Zheng, Zhehua Zhong

Machine learning security has recently become a prominent topic in the natural language processing (NLP) area. The existing black-box adversarial attack suffers prohibitively from the high model querying complexity, resulting in easily being captured by anti-attack monitors. Meanwhile, how to eliminate redundant model queries is rarely explored. In this pape

Zhengxiang Shi, Procheta Sen, Aldo Lipani

Conversational agents have become ubiquitous in assisting with daily tasks, and are expected to possess human-like features. One such feature is lexical entrainment (LE), a phenomenon in which speakers in human-human conversations tend to naturally and subconsciously align their lexical choices with those of their interlocutors, leading to more successful an

Jacob Thrasher, Alina Devkota, Prasiddha Siwakotai, Rohit Chivukula

Recent advancements in multimodal machine learning have empowered the development of accurate and robust AI systems in the medical domain, especially within centralized database systems. Simultaneously, Federated Learning (FL) has progressed, providing a decentralized mechanism where data need not be consolidated, thereby enhancing the privacy and security o

Ferdinand Ihringer, Paulien Jansen, Linde Lambrecht, Yannick Neyt

Given a finite Lie incidence geometry which is either a polar space of rank at least $3$ or a strong parapolar space of symplectic rank at least $4$ and diameter at most $4$, or the parapolar space arising from the line Grassmannian of a projective space of dimension at least $4$, we show that its point graph is determined by its local structure. This follow

Frieder Conradt, Vincent Bezold, Volker Wiechert, Steffen Huber

Spectral diffusion (SD) represents a substantial obstacle towards implementation of solid-state quantum emitters as a source of indistinguishable photons. By performing high-resolution emission spectroscopy for individual colloidal quantum dots at cryogenic temperatures, we prove the causal link between the quantum-confined Stark effect and SD. Statistically

Byeongjun Park, Changick Kim

Dynamic radiance fields have emerged as a promising approach for generating novel views from a monocular video. However, previous methods enforce the geometric consistency to dynamic radiance fields only between adjacent input frames, making it difficult to represent the global scene geometry and degenerates at the viewpoint that is spatio-temporally distant

Sameera Hewage, Yongli Sang

The categorical Gini correlation, $\rho_g$, was proposed by Dang et al. to measure the dependence between a categorical variable, $Y$ , and a numerical variable, $X$. It has been shown that $\rho_g$ has more appealing properties than current existing dependence measurements. In this paper, we develop the jackknife empirical likelihood (JEL) method for $\rho_

V. G. M. Duarte, D. R. da Costa, N. M. R. Peres, L. K. Teles

Using the tight-binding model, we report a gap opening in the energy spectrum of the twisted bilayer graphene under the application of pressure, that can be further amplified by the presence of a perpendicular bias voltage. The valley edges are located along the K-Gamma path of the superlattice Brillouin Zone, with the bandgap reaching values up to 200 meV i

Yu Wang, Wei Cui

Classical shadow tomography, harnessing randomized informationally complete (IC) measurements, provides an effective avenue for predicting many properties of unknown quantum states with sample-efficient precision. Projections onto $2^n+1$ mutually unbiased bases (MUBs) are widely recognized as minimal and optimal IC measurements for full-state tomography. We

Andreas Blass, Dhruv Kulshreshtha

We consider several notions of well-foundedness of cardinals in the absence of the Axiom of Choice. Some of these have been conflated by some authors, but we separate them carefully. We then consider implications among these, and also between these and other consequences of Choice. For instance, we show that the Partition Principle implies that all of our ve

Venkat Surya Teja Chereddy

This paper presents an attempt to replicate the robot imitation work conducted by Sermanet et al., with a specific focus on the experiments involving robot joint position prediction. While the original study utilized human poses to predict robot joint positions, this project aimed to achieve robot-to-robot imitation due to the challenges of obtaining human-t

Zohar Nussinov

When liquids are cooled sufficiently rapidly below their melting temperature, they may bypass crystalization and, instead, enter a long-lived metastable supercooled state that has long been the focus of intense research. Although they exhibit strikingly different properties, both the (i) long-lived supercooled liquid state and (ii) truly equilibrated (i.e.,

Suvam Maharana, Tripurari Srivastava

We propose a minimal clockwork model to illustrate the possibility of baryogenesis via leptogenesis in a theory space setting. The standard lepton sector is augmented with three copies of a clockwork lattice made of SM neutral fermions. The two boundaries of these one-dimensional lattices are endowed with couplings to the SM leptons and three dark sector fer

Liang Zhang, Bingcong Li, Kiran Koshy Thekumparampil, Sewoong Oh

The widespread practice of fine-tuning large language models (LLMs) on domain-specific data faces two major challenges in memory and privacy. First, as the size of LLMs continues to grow, the memory demands of gradient-based training methods via backpropagation become prohibitively high. Second, given the tendency of LLMs to memorize training data, it is imp

Mojtaba Ostovari, Alireza Zarei

We present combinatorial approximation algorithms for the weighted correlation clustering problem. In this problem, we have a set of vertices and two weight values for each pair of vertices, denoting their difference and similarity. The goal is to cluster the vertices with minimum total intra-cluster difference weights plus inter-cluster similarity weights.

Rohith Arumugam S, Angel Deborah S

This report focuses on enhancing a binary code comment quality classification model by integrating generated code and comment pairs, to improve model accuracy. The dataset comprises 9048 pairs of code and comments written in the C programming language, each annotated as "Useful" or "Not Useful." Additionally, code and comment pairs are generated using a Larg

Maxim Grigoriev, Mikhail Markov

We propose a framework to study local gauge theories on manifolds with boundaries and asymptotic symmetries, which is based on representing them as so-called gauge PDEs. These objects extend the conventional BV-AKSZ sigma-models to the case of not necessarily topological and diffeomorphism invariant systems and are known to behave well with respect to restri

Tiberiu Boros, Stefan Daniel Dumitrescu, Ionut Mironica, Radu Chivereanu

We describe an end-to-end speech synthesis system that uses generative adversarial training. We train our Vocoder for raw phoneme-to-audio conversion, using explicit phonetic, pitch and duration modeling. We experiment with several pre-trained models for contextualized and decontextualized word embeddings and we introduce a new method for highly expressive c

Steven Duplij, Raimund Vogl

We first reconsider the mathematical background of superqubit theory and describe important peculiarities of superspaces and supermatrices which are usually out of attention. Then we study states in super Hilbert spaces using super-bra/super-ket formalism in details. The qubit (qudit) and superqubit (superqudit) are defined as linear spans in the correspondi

Eyüp Kaan Akdeniz, Selma Tekir, Malik Nizar Asad Al Hinnawi

Increased reproducibility of machine learning research has been a driving force for dramatic improvements in learning performances. The scientific community further fosters this effort by including reproducibility ratings in reviewer forms and considering them as a crucial factor for the overall evaluation of papers. Accompanying source code is not sufficien

Wojciech Kozłowski, Michał Szachniewicz, Michał Stypułkowski, Maciej Zięba

Enhancing low-light images while maintaining natural colors is a challenging problem due to camera processing variations and limited access to photos with ground-truth lighting conditions. The latter is a crucial factor for supervised methods that achieve good results on paired datasets but do not handle out-of-domain data well. On the other hand, unsupervis

Juan D. Yepes, Daniel Raviv

This paper focuses on visual motion-based invariants that result in a representation of 3D points in which the stationary environment remains invariant, ensuring shape constancy. This is achieved even as the images undergo constant change due to camera motion. Nonlinear functions of measurable optical flow, which are related to geometric 3D invariants, are u

Kamal Rana, Kushanav Bhuyan, Joaquin Vicente Ferrer, Fabrice Cotton

The death toll and monetary damages from landslides continue to rise despite advancements in predictive modeling. The predictive capability of these models is limited as landslide databases used in training and assessing the models often have crucial information missing, such as underlying failure types. Here, we present an approach for identifying failure t

Li Chen, Jonathan Rubin, Jiahong Ouyang, Naveen Balaraju

Self-supervised learning (SSL) methods have shown promise for medical imaging applications by learning meaningful visual representations, even when the amount of labeled data is limited. Here, we extend state-of-the-art contrastive learning SSL methods to 2D+time medical ultrasound video data by introducing a modified encoder and augmentation method capable

Harish Loghashankar, Hieu Nguyen

This research project aims to develop a real-time traffic sign detection system using the YOLOv5 architecture and deploy it for efficient traffic sign recognition during a drive in a suburban neighborhood. The project's primary objectives are to train the YOLOv5 model on a diverse dataset of traffic sign images and deploy the model on a suitable hardware pla

Siyuan Zhou, Yilun Du, Shun Zhang, Mengdi Xu

Diffusion models have risen as a promising approach to data-driven planning, and have demonstrated impressive robotic control, reinforcement learning, and video planning performance. Given an effective planner, an important question to consider is replanning -- when given plans should be regenerated due to both action execution error and external environment

Nur Banu Altinpulluk, Deniz Altinpulluk, Paritosh Ramanan, Noah Paulson

Battery diagnosis, prognosis and health management models play a critical role in the integration of battery systems in energy and mobility fields. However, large-scale deployment of these models is hindered by a myriad of challenges centered around data ownership, privacy, communication, and processing. State-of-the-art battery diagnosis and prognosis metho

Daniel Raviv, Juan D. Yepes, Ayush Gowda

This paper focuses on a novel approach for detecting moving objects during camera motion. We present an optical-flow-based transformation that yields a consistent 2D invariant image output regardless of time instants, range of points in 3D, and the speed of the camera. In other words, this transformation generates a lookup image that remains invariant despit

Maike Paetzel-Prüsmann, Alessandra Rossi, Merel Keijsers

The goal of RoboCup is to make research in the area of robotics measurable over time, and grow a community that works together to solve increasingly difficult challenges over the years. The most ambitious of these challenges it to be able to play against the human world champions in soccer in 2050. To better understand what members of the RoboCup community b

Sylvio R. Bistafa

The year 2022 marked the 200th anniversary of the first appearance of the Navier-Stokes equation, a landmark in Fluid Dynamics introduced by Claude-Louis Navier in 1822. This equation revolutionized the understanding of fluid motion by incorporating viscosity and friction into the equations, expanding their applicability beyond idealized fluids. In this manu

Lixuan Chen, Xuanyu Tian, Jiangjie Wu, Ruimin Feng

Magnetic Resonance Imaging (MRI) stands as a powerful modality in clinical diagnosis. However, it is known that MRI faces challenges such as long acquisition time and vulnerability to motion-induced artifacts. Despite the success of many existing motion correction algorithms, there has been limited research focused on correcting motion artifacts on the estim

Alex Mei, Sharon Levy, William Yang Wang

As large language models are integrated into society, robustness toward a suite of prompts is increasingly important to maintain reliability in a high-variance environment.Robustness evaluations must comprehensively encapsulate the various settings in which a user may invoke an intelligent system. This paper proposes ASSERT, Automated Safety Scenario Red Tea

Dimitris Gkoumas, Adam Tsakalidis, Maria Liakata

The use of spontaneous language to derive appropriate digital markers has become an emergent, promising and non-intrusive method to diagnose and monitor dementia. Here we propose methods to capture language coherence as a cost-effective, human-interpretable digital marker for monitoring cognitive changes in people with dementia. We introduce a novel task to

Edson Pindza, Jules Clement Mba, Sutene Mwambi, Nneka Umeorah

Cryptocurrencies and Bitcoin, in particular, are prone to wild swings resulting in frequent jumps in prices, making them historically popular for traders to speculate. A better understanding of these fluctuations can greatly benefit crypto investors by allowing them to make informed decisions. It is claimed in recent literature that Bitcoin price is influenc

Antigoni Polychroniadou, Gilad Asharov, Benjamin Diamond, Tucker Balch

Inventory matching is a standard mechanism/auction for trading financial stocks by which buyers and sellers can be paired. In the financial world, banks often undertake the task of finding such matches between their clients. The related stocks can be traded without adversely impacting the market price for either client. If matches between clients are found,

Jindong Han, Weijia Zhang, Hao Liu, Hui Xiong

The increasing air pollution poses an urgent global concern with far-reaching consequences, such as premature mortality and reduced crop yield, which significantly impact various aspects of our daily lives. Accurate and timely analysis of air pollution is crucial for understanding its underlying mechanisms and implementing necessary precautions to mitigate p

Abhimanyu Das, Weihao Kong, Rajat Sen, Yichen Zhou

Motivated by recent advances in large language models for Natural Language Processing (NLP), we design a time-series foundation model for forecasting whose out-of-the-box zero-shot performance on a variety of public datasets comes close to the accuracy of state-of-the-art supervised forecasting models for each individual dataset. Our model is based on pretra

Wenqi Zhang, Yongliang Shen, Qingpeng Nong, Zeqi Tan

Generating mathematical equations from natural language requires an accurate understanding of the relations among math expressions. Existing approaches can be broadly categorized into token-level and expression-level generation. The former treats equations as a mathematical language, sequentially generating math tokens. Expression-level methods generate each

Nakwon Rim, Marc G. Berman, Yuan Chang Leong

Polarization has increased substantially in political discourse, contributing to a widening partisan divide. In this paper, we analyzed large-scale, real-world language use in Reddit communities (294,476,146 comments) and in news outlets (6,749,781 articles) to uncover psychological dimensions along which partisan language is divided. Using word embedding mo

Nam Wook Kim, Yongsu Ahn, Grace Myers, Benjamin Bach

Data visualization creators often lack formal training, resulting in a knowledge gap in design practice. Large language models such as ChatGPT, with their vast internet-scale training data, offer transformative potential to address this gap. In this study, we used both qualitative and quantitative methods to investigate how well ChatGPT can address visualiza

J. M. Duran-Pinilla, Aldo H. Romero, A. C. Garcia-Castro

Antiferromagnetic antiperovskites, where magnetically active 3$d$ metal cations are placed in the octahedral corners of a perovskite structure, are in the spotlight due to their intertwined magnetic structure and topological properties. Especially their anomalous Hall conductivity, which can be controlled by applied strain and/or electric field, makes them h

Weipu Zhang, Gang Wang, Jian Sun, Yetian Yuan

Recently, model-based reinforcement learning algorithms have demonstrated remarkable efficacy in visual input environments. These approaches begin by constructing a parameterized simulation world model of the real environment through self-supervised learning. By leveraging the imagination of the world model, the agent's policy is enhanced without the constra

Nam Wook Kim, Grace Myers, Jinhan Choi, Yoonsuh Cho

Although empirical research often underpins practical visualization guidelines, it remains unclear how well these research-driven insights are reflected in the guidelines practitioners actually use. In this paper, we investigate the research-practice gap in visualization design guidelines through a mixed-methods approach. We collected 390 design guidelines f

Venkata Gandikota, Nikita Polyanskii, Haodong Yang

The study in group testing aims to develop strategies to identify a small set of defective items among a large population using a few pooled tests. The established techniques have been highly beneficial in a broad spectrum of applications ranging from channel communication to identifying COVID-19-infected individuals efficiently. Despite significant research

Alexa R. Tartaglini, Sheridan Feucht, Michael A. Lepori, Wai Keen Vong

Although deep neural networks can achieve human-level performance on many object recognition benchmarks, prior work suggests that these same models fail to learn simple abstract relations, such as determining whether two objects are the same or different. Much of this prior work focuses on training convolutional neural networks to classify images of two same

Md. Imtiaz Habib, Abdullah Al Maruf, Md. Jobair Ahmed Nabil

The most common attacks against web sessions are reviewed in this paper, for example, some attacks against web browsers' honest users attempting to create session with trusted web browser application legally. We have assessed with four different ways to judge the viability of a certain solution by reviewing existing security solutions which prevent or halt t

Joshua Gorniak, Yoon Kim, Donglai Wei, Nam Wook Kim

Traditional accessibility methods like alternative text and data tables typically underrepresent data visualization's full potential. Keyboard-based chart navigation has emerged as a potential solution, yet efficient data exploration remains challenging. We present VizAbility, a novel system that enriches chart content navigation with conversational interact

Kirill Riabtsev

Some fraction of the diffuse photon background is supposed to be linked to high-energy neutrinos by astrophysical mechanisms of production and electromagnetic cascades. This article presents a simulation study of axion-like particles (ALPs) implications for that component, exploiting transport equations. Alternations of that spectrum due to ALP-photon conver

Khuong N. Nguyen, Abhishek Sehgal, Yuming Zhu, Junsu Choi

As the complexity and scale of modern computer networks continue to increase, there has emerged an urgent need for precise traffic analysis, which plays a pivotal role in cutting-edge wireless connectivity technologies. This study focuses on leveraging Machine Learning methodologies to create an advanced network traffic classification system. We introduce a

Arthur Vereijken

Glueballs remain an experimentally undiscovered prediction of QCD. Lattice QCD predicts a spectrum of glueballs, with the tensor $(J^{PC}=2^{++})$ glueball being the second lightest, behind the scalar glueball. From an effective hadronic model based on spontaneous and explicit chiral symmetry breaking, we compute decay ratios of the tensor glueball into vari

N. Annalakshmi, S. Umarani

New wireless mobile technology has been released every ten years, improving previous generations' facilities. Even though 5G supports many services on demand nowadays, its higher radiation increases the queries about the safety of humans and other living things. The health effects related to EMF of 5G is still under in discussion. Number of health organizati

Enrico Trotti, Shahriyar Jafarzade

In this note, we present our recent analyses of the thermodynamic properties of the glueball resonance gas. We observe that the dominant contribution to the thermodynamic quantities, such as pressure, trace anomaly, and entropy, is coming from the free glueball gas with the states of positive charge conjugation (i.e., pomeron). A comparison of pomeron states

Huatao Xu, Liying Han, Qirui Yang, Mo Li

Recent developments in Large Language Models (LLMs) have demonstrated their remarkable capabilities across a range of tasks. Questions, however, persist about the nature of LLMs and their potential to integrate common-sense human knowledge when performing tasks involving information about the real physical world. This paper delves into these questions by exp

Jiali Cui, Ying Nian Wu, Tian Han

This paper studies the fundamental problem of multi-layer generator models in learning hierarchical representations. The multi-layer generator model that consists of multiple layers of latent variables organized in a top-down architecture tends to learn multiple levels of data abstraction. However, such multi-layer latent variables are typically parameterize

Hao Wang, Qiang Song, Ruofeng Yin, Rui Ma

Spinal curvature estimation is important to the diagnosis and treatment of the scoliosis. Existing methods face several issues such as the need of expensive annotations on the vertebral landmarks and being sensitive to the image quality. It is challenging to achieve robust estimation and obtain interpretable results, especially for low-quality images which a

Chen-Wei Tong, Bin-Hao Wang, Jia-Rui Sun

In this paper, we investigate the topological numbers of the four-dimensional Schwarzschild black hole, $d$-dimensional Reissner-Nordstr\"om (RN) black hole, $d$-dimensional singly rotating Kerr black hole and five-dimensional Gauss-Bonnet black hole via the R\'enyi statistics. We find that the topological number calculated via the R\'enyi statistics is diff

Alessandro Marinoni, John Chris Rost, Miklos Porkolab

The PCI diagnostic is an internal reference interferometer that creates an image of absolutely calibrated electron density fluctuations integrated along the line of sight of the probing light beam. While conventional PCI diagnostics installed on fusion experiments worldwide employ light of wavelength equal to 10.59 $\mu$~m, the same system using light at 1.5

Jiabei He, Yang Shen, Xiu-Shen Wei, Ye Wu

Fine-Grained Image Recognition (FGIR) is a fundamental and challenging task in computer vision and multimedia that plays a crucial role in Intellectual Economy and Industrial Internet applications. However, the absence of a unified open-source software library covering various paradigms in FGIR poses a significant challenge for researchers and practitioners

Bingyin Zhang, Hongfei Fu

Due to the lack of corresponding analysis on appropriate mapping operator between two grids, high-order two-grid difference algorithms are rarely studied. In this paper, we firstly discuss the boundedness of a local bi-cubic Lagrange interpolation operator. And then, taking the semilinear parabolic equation as an example, we first construct a variable-step h

Paolo Tomasini

We define $k$-rationalized $G$-equivariant elliptic cohomology, for a field of characteristic zero $k$ and a compact Lie group $G$, via adelic descent. We also give adelic descriptions of rationalized $G$-equivariant singular cohomology and K-theory. This completes a program first proposed by Ro\c{s}u. These descriptions are then used to obtain comparison re

Nicolo Cesa-Bianchi, Roberto Colomboni, Maximilian Kasy

We consider the problem of repeatedly choosing policies to maximize social welfare. Welfare is a weighted sum of private utility and public revenue. Earlier outcomes inform later policies. Utility is not observed, but indirectly inferred. Response functions are learned through experimentation. We derive a lower bound on regret, and a matching adversarial upp

Junjie Ye, Jie Zhou, Junfeng Tian, Rui Wang

Recently, Target-oriented Multimodal Sentiment Classification (TMSC) has gained significant attention among scholars. However, current multimodal models have reached a performance bottleneck. To investigate the causes of this problem, we perform extensive empirical evaluation and in-depth analysis of the datasets to answer the following questions: Q1: Are th

Szabolcs Kelemen, Máté Józsa, Tibor Hartel, György Csóka

The diameter distribution of a given species of deciduous trees in mature, temperate zone forests is well approximated by a Gamma distribution. Here we give new experimental evidence for this conjecture by analyzing deciduous tree size data in mature semi-natural forest and ancient, traditionally managed wood-pasture from Central Europe. These distribution f

Siddhartha Ganguly, Nakul Randad, Rihan Aaron D'Silva, Mukesh S Raj

We introduce the MATLAB-based software QuITO (Quasi-Interpolation based Trajectory Optimization) to numerically solve a wide class of constrained nonlinear optimal control problems (OCP). The solver is based on the QuITO (the same abbreviation) algorithm, which is a direct multiple shooting (DMS) technique that leverages a particular type of quasi-interpolat

Zhihui Zhang, JianXiang Yu, Xiang Li

Session-based recommendation (SBR) is a task that aims to predict items based on anonymous sequences of user behaviors in a session. While there are methods that leverage rich context information in sessions for SBR, most of them have the following limitations: 1) they fail to distinguish the item-item edge types when constructing the global graph for exploi

Yifan Gao, Xinyi Li, Petr Panov, Daisuke Shiraishi

We consider the occupation measure of the cut points of a simple random walk on a $d$-dimensional cubic lattice for $d = 2, 3$, and we show that the scaling limit of the occupation measure in weak topology is the natural fractal measure on the Brownian cut points defined via its Minkowski content.

Ivan Dimitrov, Charles Paquette, David Wehlau, Tianyuan Xu

We prove that over an algebraically closed field $\mathbb{K}$ of characteristic different from $2$, the group algebra $R=\mathbb{K} D_\infty$ of the infinite dihedral group $D_\infty$ has exactly six conjugacy classes of involutions (equivalently, of idempotents). This allows us to recover the fact that $R$ admits exactly four non-isomorphic indecomposable p

Yi Bin, Wenhao Shi, Yujuan Ding, Yang Yang

Math word problem (MWP) solving aims to understand the descriptive math problem and calculate the result, for which previous efforts are mostly devoted to upgrade different technical modules. This paper brings a different perspective of \textit{reexamination process} during training by introducing a pseudo-dual task to enhance the MWP solving. We propose a p

Zheyu Zhang, Zhuorui Ye, Yikang Shen, Chuang Gan

Large Language Models have excelled in remarkable reasoning capabilities with advanced prompting techniques, but they fall short on tasks that require exploration, strategic foresight, and sequential decision-making. Recent works propose to utilize external programs to define search logic, such that LLMs can perform passive tree search to solve more challeng

Manduhu Manduhu, Alexander Dow, Petar Trslic, Gerard Dooly

The safe operation of drone swarms beyond visual line of sight requires multiple safeguards to mitigate the risk of collision between drones flying in close-proximity scenarios. Cooperative navigation and flight coordination strategies that rely on pre-planned trajectories, constant %{satellite and network connectivity and reliable Global Navigation Satellit

Gerald Schweiger

The pursuit of excellence seems to be the True North of academia. What is meant by excellence? Can excellence be measured? This article discusses the concept of excellence in the context of research and competition.

Prithvi Kewalramani

This paper presents a comprehensive investigation into the modeling of rotor wake velocities, in a simplistic manner, using the Viscous Vortex Particle Method (VVPM). The study aims to accurately simulate wind velocities in the wake of helicopter rotors while comparing the VVPM with other established methods such as Momentum theory, Blade element theory, and

Guoxin Chen, Yongqing Wang, Fangda Guo, Qinglang Guo

Most existing methods that address out-of-distribution (OOD) generalization for node classification on graphs primarily focus on a specific type of data biases, such as label selection bias or structural bias. However, anticipating the type of bias in advance is extremely challenging, and designing models solely for one specific type may not necessarily impr

Amirhossein Azarbahram, Onel L. A. Lopez, Petar Popovski, Matti Latva-aho

One of the primary goals of future wireless systems is to foster sustainability, for which, radio frequency (RF) wireless power transfer (WPT) is considered a key technology enabler. The key challenge of RF-WPT systems is the extremely low end-to-end efficiency, mainly due to the losses introduced by the wireless channel. Distributed antenna systems are undo

Tomasz Schoen

We prove that every subset of $\{1,\dots, N\}$ which does not contain any solutions to the equation $x+y+z=3w$ has at most $\exp(-c(\log N)^{1/5+o(1)})N$ elements, for some $c>0$. This theorem improves upon previous estimates. Additionally, our method has the potential to yield an optimal estimate for this problem that matches the known Behrend's lower estim

Shutong Ding, Tianyu Cui, Jingya Wang, Ye Shi

Deep Equilibrium Models (DEQs) and Neural Ordinary Differential Equations (Neural ODEs) are two branches of implicit models that have achieved remarkable success owing to their superior performance and low memory consumption. While both are implicit models, DEQs and Neural ODEs are derived from different mathematical formulations. Inspired by homotopy contin

Fabrizio G. Oliviero, Weslei B. Fontana, Rodrigo G. Pereira

We use junctions of critical spin-1 chains as the basic elements to construct a honeycomb network that harbors a gapless chiral spin liquid phase. The low-energy modes are described by spin-1 Majorana fermions that form a two-dimensional Fermi surface when the interactions at the junctions are tuned to the vicinity of chiral fixed points with staggered chira

Steven Dale Cutkosky

This article discusses ramification and the structure of relative K\"ahler differentials of extensions of valued fields. We begin by surveying the theory developed in recent work with Franz-Viktor Kuhlmann and Anna Rzepka constructing the relative K\"ahler differentials of extensions of valuation rings in Artin-Schreier and Kummer extensions. We then show ho

Emmanuel Ndidi Osegi

The recent developments in soft computing cannot be complete without noting the contributions of artificial neural machine learning systems that draw inspiration from real cortical tissue or processes that occur in human brain. The universal approximability of such neural systems has led to its wide spread use, and novel developments in this evolving technol

Julian Heinovski, Falko Dressler

Platooning is a promising cooperative driving application for future intelligent transportation systems. In order to assign vehicles to platoons, some algorithm for platoon formation is required. Such vehicle-to-platoon assignments have to be computed on-demand, e.g., when vehicles join or leave the freeways. In order to get best results from platooning, ind

Xiang Li, Jiguang Bao

In this paper, we give some existence and nonexistence results for nonradial entire large solutions of the Hessian equation $S_k\left(D^2 u\right)=b(x) u^\gamma$ in the sublinear case $0<\gamma<k$. The exact asymptotic behavior of large solutions at infinity is also studied when $b(x)$ is the oscillation of a radial function $|x|^{-l}$ at infinity for $l\leq

Anubha Goel, Puneet Pasricha, Juho Kanniainen

In this research, we introduce a novel methodology for the index tracking problem with sparse portfolios by leveraging topological data analysis (TDA). Utilizing persistence homology to measure the riskiness of assets, we introduce a topological method for data-driven learning of the parameters for regularization terms. Specifically, the Vietoris-Rips filtra