March 2024 arXiv papers — page 49

Albert J. Miao, Shan Lin, Jingpei Lu, Florian Richter

Hemorrhaging occurs in surgeries of all types, forcing surgeons to quickly adapt to the visual interference that results from blood rapidly filling the surgical field. Introducing automation into the crucial surgical task of hemostasis management would offload mental and physical tasks from the surgeon and surgical assistants while simultaneously increasing

Y. F. Adans, A. R. Aguirre, J. F. Gomes, G. V. Lobo

The construction of Integrable Hierarchies in terms of zero curvature representation provides a systematic construction for a series of integrable non-linear evolution equations (flows) which shares a common affine Lie algebraic structure. The integrable hierarchies are then classified in terms of a decomposition of the underlying affine Lie algebra $\hat {\

Sophie Huczynska, Sophie Hume

Classical strong external difference families (SEDFs) are much-studied combinatorial structures motivated by information security applications; it is conjectured that only one classical abelian SEDF exists with more than two sets. Recently, non-disjoint SEDFs were introduced; it was shown that families of these exist with arbitrarily many sets. We present co

Jingyue Huang, Changbao Wu, Leilei Zeng

Causal inference is crucial for understanding the true impact of interventions, policies, or actions, enabling informed decision-making and providing insights into the underlying mechanisms that shape our world. In this paper, we establish a framework for the estimation and inference of average treatment effects using a two-sample empirical likelihood functi

Purnachandra Mandadapu

This paper explores the significant history of professional football and the betting industry, tracing its evolution from clandestine beginnings to a lucrative multi-million-pound enterprise. Initiated by the legalization of gambling in 1960 and complemented by advancements in football data gathering pioneered by Thorold Charles Reep, the symbiotic relations

Hideki Nishizawa, Giacomo Borraccini, Takeo Sasai, Yue-Kai Huang

We propose methods and an architecture to conduct measurements and optimize newly installed optical fiber line systems semi-automatically using integrated physics-aware technologies in a data center interconnection (DCI) transmission scenario. We demonstrate, for the first time, digital longitudinal monitoring (DLM) and optical line system (OLS) physical par

Jordan Talbot, James A. McLaughlin, Gert J. J. Botha, Mark Hancock

The oscillatory reconnection mechanism is investigated for a parameter study of eight orders of magnitude of resistivity, with a particular interest in the evolution of the oscillating current density at the null point and its associated periodicity. The resistive, nonlinear MHD simulations are solved in 2.5D for different levels of resistivity. Three method

Amlan Datta, Kamal R. Joshi, Giulia Berti, Sunil Ghimire

The effect of annealing on the superconducting properties of niobium single crystals cut from the same master boule was studied by local and global magnetic measurements, as well as scanning tunneling microscopy (STM). The formation of large hydride precipitates was observed in unannealed samples. The variation in structural and magnetic properties was studi

Erik Carlsson, Raymond Chou

We assign to each Young diagram $\lambda$ a subset $\mathcal{B}_{\lambda'}$ of the collection of Garsia-Stanton descent monomials, and prove that it determines a basis of the Garsia-Procesi module $R_\lambda$, whose graded character is the Hall-Littlewood polynomial $\tilde{H}_{\lambda}[X;t]$. This basis is a major index analogue of the basis $\mathcal{B}_\l

Magí Dalmau-Moreno, Néstor García, Vicenç Gómez, Héctor Geffner

The challenge in combined task and motion planning (TAMP) is the effective integration of a search over a combinatorial space, usually carried out by a task planner, and a search over a continuous configuration space, carried out by a motion planner. Using motion planners for testing the feasibility of task plans and filling out the details is not effective

Yolo Yunlong Tang, Daiki Shimada, Jing Bi, Mingqian Feng

Large language models (LLMs) have demonstrated remarkable capabilities in natural language and multimodal domains. By fine-tuning multimodal LLMs with temporal annotations from well-annotated datasets, e.g., dense video captioning datasets, their temporal understanding capacity in video-language tasks can be obtained. However, there is a notable lack of untr

Ishaan Mehta, Junseo Kim, Sharareh Taghipour, Sajad Saeedi

Task execution quality significantly impacts multi-robot missions, yet existing task allocation frameworks rarely consider quality of service as a decision variable, despite its importance in applications like robotic disinfection and cleaning. We introduce the multi-robot, multi-objective, and multi-mode routing and scheduling (M3RS) problem, designed for t

Niklas Dömer, Julian Koelmann, Mira Hesselink, Tobias Hehemann

Femto-/nanosecond pulse-induced, red and near-infrared absorption is studied in $\mathrm{LiNb}_{1-x}\mathrm{Ta}_{x}\mathrm{O}_3$ (LNT) solid solutions with the goal to probe the intrinsic defect structure via the formation, transport and recombination of optically generated small bound electron polarons with strong coupling to the lattice. As a result, long-

Simon Müller, Ravit Helled

Updated formation and structure models of Jupiter predict a metal-poor envelope. This is at odds with the two to three times solar metallicity measured by the Galileo probe. Additionally, Juno data imply that water and ammonia are enriched. Here we explore whether Jupiter can have a deep radiative layer separating the atmosphere from the deeper interior. The

Rachid Zeghlache, Pierre-Henri Conze, Mostafa El Habib Daho, Yihao Li

Pre-training strategies based on self-supervised learning (SSL) have proven to be effective pretext tasks for many downstream tasks in computer vision. Due to the significant disparity between medical and natural images, the application of typical SSL is not straightforward in medical imaging. Additionally, those pretext tasks often lack context, which is cr

Siyuan Liang, Wei Wang, Ruoyu Chen, Aishan Liu

With the emergence of foundation models, deep learning-based object detectors have shown practical usability in closed set scenarios. However, for real-world tasks, object detectors often operate in open environments, where crucial factors (e.g., data distribution, objective) that influence model learning are often changing. The dynamic and intricate nature

Xiangpeng Li, Ali Mostafavi

There is a limitation in the literature of data-driven analyses for the ex-post evaluation of community risk and resilience, particularly using features related to the performance of coupled human-infrastructure systems. To address this gap, in this study we created a machine learning-based method for the ex-post assessment of community risk and resilience a

Marcella Astrid, Muhammad Zaigham Zaheer, Seung-Ik Lee

In order to devise an anomaly detection model using only normal training data, an autoencoder (AE) is typically trained to reconstruct the data. As a result, the AE can extract normal representations in its latent space. During test time, since AE is not trained using real anomalies, it is expected to poorly reconstruct the anomalous data. However, several r

Jonathan Gorard

This article serves as a preliminary introduction to the design of a new, open-source applied and computational category theory framework, named Categorica, built on top of the Wolfram Language. Categorica allows one to configure and manipulate abstract quivers, categories, groupoids, diagrams, functors and natural transformations, and to perform a vast arra

Márton Balázs, Sudeshna Bhattacharjee, Karambir Das, David Harper

We propose a road layout and traffic model, based on last passage percolation (LPP). An easy naive argument shows that coalescence of traffic trajectories is essential to be considered when observing traffic networks around us. This is a fundamental feature in first passage percolation (FPP) models where nearby geodesics naturally coalesce in search of the e

Andrew Snowden

Knop constructed a tensor category associated to a finitely-powered regular category equipped with a degree function. In recent work with Harman, we constructed a tensor category associated to an oligomorphic group equipped with a measure. In this paper, we explain how Knop's approach fits into our theory. The first, and most important, step describes finite

Pronoy Das, Li-Ping Yang, Zubin Jacob

The total angular momentum of light has received attention for its application in a variety of phenomena such as optical communication, optical forces and sensing. However, the quantum behavior including the commutation relations have been relatively less explored. Here, we derive the correct commutation relation for the total angular momentum of light using

Zhuoyi Peng, Yi Yang

We study the patent phrase similarity inference task, which measures the semantic similarity between two patent phrases. As patent documents employ legal and highly technical language, existing semantic textual similarity methods that use localized contextual information do not perform satisfactorily in inferring patent phrase similarity. To address this, we

Vyacheslav P. Spiridonov

A unifying scheme of classical special functions of hypergeometric type obeying orthogonality or biorthogonality relations is described. It expands the Askey scheme of classical orthogonal polynomials and its $q$-analogue based on the Askey--Wilson polynomials. On the top, it has two-index biorthogonal functions formed from elliptic hypergeometric series wit

Savinay Nagendra, Prapti Panigrahi

Applications of an efficient emotion recognition system can be found in several domains such as medicine, driver fatigue surveillance, social robotics, and human-computer interaction. Appraising human emotional states, behaviors, and reactions displayed in real-world settings can be accomplished using latent continuous dimensions. Continuous dimensional mode

E. J. Ferrer, V. de la Incera

This paper proposes an alternative mechanism to solve the so-called missing pulsar problem, a standing paradox between the theoretical expectations about the number of pulsars that should exist in the galaxy center of the Milky Way and their absence in the observations. The mechanism is based on the transformation of incident $\gamma$ rays into hybridized mo

Amir Iqbal, Sushant Veer, Christopher Niezrecki, Yan Gu

This paper presents a hierarchical control framework that enables robust quadrupedal locomotion on a dynamic rigid surface (DRS) with general and unknown vertical motions. The key novelty of the framework lies in its higher layer, which is a discrete-time, provably stabilizing footstep controller. The basis of the footstep controller is a new hybrid, time-va

Anil Kumar, Edmilson Roque dos Santos, Paul J. Laurienti, Erik Bollt

Experiments in the human brain reveal switching between different activity patterns and functional network organization over time. Recently, multilayer modeling has been employed across multiple neurobiological levels (from spiking networks to brain regions) to unveil novel insights into the emergence and time evolution of synchrony patterns. We consider two

Chenhui Xu, Fuxun Yu, Zirui Xu, Nathan Inkawhich

Recent research underscores the pivotal role of the Out-of-Distribution (OOD) feature representation field scale in determining the efficacy of models in OOD detection. Consequently, the adoption of model ensembles has emerged as a prominent strategy to augment this feature representation field, capitalizing on anticipated model diversity. However, our intro

Atefeh Khoshkhahtinat, Ali Zafari, Piyush M. Mehta, Nasser M. Nasrabadi

While replacing Gaussian decoders with a conditional diffusion model enhances the perceptual quality of reconstructions in neural image compression, their lack of inductive bias for image data restricts their ability to achieve state-of-the-art perceptual levels. To address this limitation, we adopt a non-isotropic diffusion model at the decoder side. This m

Siyuan Liang, Kuanrong Liu, Jiajun Gong, Jiawei Liang

Multimodal contrastive learning has emerged as a powerful paradigm for building high-quality features using the complementary strengths of various data modalities. However, the open nature of such systems inadvertently increases the possibility of backdoor attacks. These attacks subtly embed malicious behaviors within the model during training, which can be

Patrick van Hage, Saskia le Cessie, Marissa C. van Maaren, Hein Putter

Covariate imbalance between treatment groups makes it difficult to compare cumulative incidence curves in competing risk analyses. In this paper we discuss different methods to estimate adjusted cumulative incidence curves including inverse probability of treatment weighting and outcome regression modeling. For these methods to work, correct specification of

I. Chalendar, J. R. Partington

Let $f$ and $g$ be analytic functions on the open unit disc $\mathbb D$ such that $|f|=|g|$ on a set $A$. We give an alternative proof of the result of Perez that there exists $c$ in the unit circle $\mathbb T$ such that $f=cg$ when $A$ is the union of two lines in $\mathbb D$ intersecting at an angle that is an irrational multiple of $\pi$, and from this de

Giacomo Albi, Giacomo Dimarco, Federica Ferrarese, Lorenzo Pareschi

The principle behind magnetic fusion is to confine high temperature plasma inside a device in such a way that the nuclei of deuterium and tritium joining together can release energy. The high temperatures generated needs the plasma to be isolated from the wall of the device to avoid damages and the scope of external magnetic fields is to achieve this goal. I

Qiuhan He, James W. Nightingale, Aris Amvrosiadis, Andrew Robertson

In a strong gravitational lensing system, the distorted light from a source is analysed to infer the properties of the lens. However, light emitted by the lens itself can contaminate the image of the source, introducing systematic errors in the analysis. We present a simple and efficient lens light model based on the well-tested multi-Gaussian expansion (MGE

Zijian He, Sangli Teng, Tzu-Yuan Lin, Maani Ghaffari

This paper investigates the robot state estimation problem within a non-inertial environment. The proposed state estimation approach relaxes the common assumption of static ground in the system modeling. The process and measurement models explicitly treat the movement of the non-inertial environments without requiring knowledge of its motion in the inertial

Andrew H. Marcus, Spiridoula Matsika, Dylan Heussman, Mohammed I. Sorour

DNA regulation and repair processes require direct interactions between proteins and DNA at specific sites. Local fluctuations of the sugar-phosphate backbones and bases of DNA (a form of DNA "breathing") play a central role in such processes. Here we review the development and application of novel spectroscopic methods and analyses - both at the ensemble an

Jack T. Warfield, Joel C. Zinn, Jessica Schonhut-Stasik, James W. Johnson

We present stellar age determinations for 4661 red giant branch stars in the APO-K2 catalog, derived using mass estimates from K2 asteroseismology from the K2 Galactic Archaeology Program and elemental abundances from the Apache Point Galactic Evolution Experiment survey. Our sample includes 17 of the 19 fields observed by K2, making it one of the most compr

Angela Hicks, Samantha Miller-Brown

The Schur functions, a basis for the symmetric polynomials (Sym), encode the irreducible representations of the symmetric group, $\mathfrak{S}_n$, via the Frobenius characteristic map. In 1996, Krob and Thibon defined a quasisymmetric Frobenius map on the representations of $\mathcal{H}_n(0)$, mapping them to the quasisymmetric functions (QSym). Despite the

Andrea Bosco

The importance of considering contextual probabilities in shaping response patterns within psychological testing is underscored, despite the ubiquitous nature of order effects discussed extensively in methodological literature. Drawing from concepts such as path-dependency, first-order autocorrelation, state-dependency, and hysteresis, the present study is a

Yida Mu, Chun Dong, Kalina Bontcheva, Xingyi Song

Topic modelling, as a well-established unsupervised technique, has found extensive use in automatically detecting significant topics within a corpus of documents. However, classic topic modelling approaches (e.g., LDA) have certain drawbacks, such as the lack of semantic understanding and the presence of overlapping topics. In this work, we investigate the u

Aditya Saxena, Ashutosh Ranjan

As human society transitions into the information age, reduction in our attention span is a contingency, and people who spend time reading lengthy news articles are decreasing rapidly and the need for succinct information is higher than ever before. Therefore, it is essential to provide a quick overview of important news by concisely summarizing the top news

Subhodip Panda, Shashwat Sourav, Prathosh A. P

In order to adhere to regulatory standards governing individual data privacy and safety, machine learning models must systematically eliminate information derived from specific subsets of a user's training data that can no longer be utilized. The emerging discipline of Machine Unlearning has arisen as a pivotal area of research, facilitating the process of s

Minaoar Hossain Tanzil, Gias Uddin, Ann Barcomb

Modern-day software development is often facilitated by the reuse of third-party software libraries. Despite the significant effort to understand the factors contributing to library selection, it is relatively unknown how the libraries are selected and what tools are still needed to support the selection process. Using Straussian grounded theory, we conducte

Che-Jui Chang, Danrui Li, Seonghyeon Moon, Mubbasir Kapadia

We study, from an empirical standpoint, the efficacy of synthetic data in real-world scenarios. Leveraging synthetic data for training perception models has become a key strategy embraced by the community due to its efficiency, scalability, perfect annotations, and low costs. Despite proven advantages, few studies put their stress on how to efficiently gener

Gabriel Frieden, Florian Schreier-Aigner

We introduce a probabilistic generalization of the dual Robinson--Schensted--Knuth correspondence, called $qt$RSK${}^*$, depending on two parameters $q$ and $t$. This correspondence extends the $q$RS$t$ correspondence, recently introduced by the authors, and allows the first tableaux-theoretic proof of the dual Cauchy identity for Macdonald polynomials. By s

Xiaoyu Zhu, Junwei Liang, Po-Yao Huang, Alex Hauptmann

We study the problem of unsupervised domain adaptation for egocentric videos. We propose a transformer-based model to learn class-discriminative and domain-invariant feature representations. It consists of two novel designs. The first module is called Generative Adversarial Domain Alignment Network with the aim of learning domain-invariant representations. I

Abhishek Kumar, M. A. Shaz, N. K. Mukhopadhyay, Thakur Prasad Yadav

Recent theoretical and practical research has focused on multi-component High Entropy Alloys (HEAs), which have superior mechanical and functional properties than standard alloys based on a single major element, thereby establishing a new field. A multi-component HEA contains five or more primary elements at concentrations ranging from 5 to 35 atomic percent

Sean Rendell, Jinming Duan

Diffeomorphic image registration is a commonly used method to deform one image to resemble another. While warping a single image to another is useful, it can be advantageous to warp multiple images simultaneously, such as in tracking the motion of the heart across a sequence of images. In this paper, our objective is to propose a novel method capable of regi

Savinay Nagendra

In this project, we design a four-layer (Silicon|TIM|Silicon|TIM), 3D floor plan for NVIDIA GTX480 Fermi GPU architecture and compare heat dissipation and power trends for matrix multiplication and Needleman-Wunsch kernels. First, cuda kernels for the two algorithms are written. These kernels are compiled and executed with the GPGPU Simulator to extract powe

Abdelrahman Younes, Tamim Asfour

Despite the recent progress on 6D object pose estimation methods for robotic grasping, a substantial performance gap persists between the capabilities of these methods on existing datasets and their efficacy in real-world grasping and mobile manipulation tasks, particularly when robots rely solely on their monocular egocentric field of view (FOV). Existing r

Oscar Amador, Maria Calderon, Manuel Urueña, Ignacio Soto

This paper evaluates the performance of the two ETSI non-area forwarding algorithms in the GeoNetworking specification: Greedy Forwarding and Non-Area Contention-Based Forwarding (CBF). Non-area forwarding occurs when a packet is sent to a geographical Destination Area from a node located outside of this area, e.g., when a vehicle wants to alert of hazardous

Angelos Karakonstantakis, Debora Lančová, Włodek Kluźniak, Miljenko Čemeljić

We present two-dimensional general relativistic radiative magnetohydrodynamical simulations of accretion disks around non-rotating stellar-mass black hole. We study the evolution of an equilibrium accreting torus in different grid resolutions to determine an adequate resolution to produce a stable turbulent disk driven by magneto-rotational instability. We e

Michele Cantiello, John P. Blakeslee, Patrick Côté, Gabriella Raimondo

The surface brightness fluctuation (SBF) method is a robust and efficient way of measuring distances to galaxies containing evolved stellar populations. Although many recent applications of the method have used space-based imaging, SBF remains a powerful technique for ground-based telescopes. Deep, wide-field imaging surveys with subarsecond seeing enable SB

A. Zegarelli, D Guetta, S. Celli, S. Gagliardini

Choked jets (CJ) have attracted particular attention as potential sources of high-energy cosmic neutrinos. Testing this hypothesis is challenging because of the missing gamma-ray counterpart, hence the identification of other electromagnetic (EM) signatures is crucial. A CJ source is expected harbouring in core-collapse supernovae (CCSNe) with extended H env

Amit K. Chakraborty, Shan Gao, Reza Miry, Pouria Ramazi

The timely detection of disease outbreaks through reliable early warning signals (EWSs) is indispensable for effective public health mitigation strategies. Nevertheless, the intricate dynamics of real-world disease spread, often influenced by diverse sources of noise and limited data in the early stages of outbreaks, pose a significant challenge in developin

Leila Bassou, Mao Fabrice Djete, Nizar Touzi

We consider the mean field game of cross--holding introduced in \citeauthor*{DjeteTouzi} \cite{DjeteTouzi} in the context where the equity value dynamics are affected by a common noise. In contrast with \cite{DjeteTouzi}, the problem exhibits the standard paradigm of mean--variance trade off. Our crucial observation is to search for equilibrium solutions of

Monika Matuszková, Gabriel Török, Kateřina Klimovičová, Jiří Horák

The four characteristic oscillation frequencies of accretion flows are, in addition to the Keplerian orbital frequency, often discussed in the context of the time variability of the black hole and neutron star (NS) low-mass X-ray binaries (LMXBs). These are namely the frequencies of the axisymmetric radial and vertical epicyclic oscillations, and the frequen

Igor Sokolov

Atomic force microscopy (AFM or SPM) imaging is one of the best matches with machine learning (ML) analysis among microscopy techniques. The digital format of AFM images allows for direct utilization in ML algorithms without the need for additional processing. Additionally, AFM enables the simultaneous imaging of distributions of over a dozen different physi

Dawood Kothawala

One version of the principle of equivalence, as originally formulated by Einstein, states that ``gravity" can be mimicked locally by going to an ``accelerated frame of reference". As highlighted by Synge, the physical content of this principle remains obscure in so far as it does not refer to the Riemann tensor $R_{abcd}$, which encodes the true effects of g

Bahman Angoshtari, Shida Duan

Predictable forward performance processes (PFPPs) are stochastic optimal control frameworks for an agent who controls a randomly evolving system but can only prescribe the system dynamics for a short period ahead. This is a common scenario in which a controlling agent frequently re-calibrates her model. We introduce a new class of PFPPs based on rank-depende

Jing Li, Lu Bai, Bin Yang, Chang Li

Infrared and visible image fusion (IVF) plays an important role in intelligent transportation system (ITS). The early works predominantly focus on boosting the visual appeal of the fused result, and only several recent approaches have tried to combine the high-level vision task with IVF. However, they prioritize the design of cascaded structure to seek unifi

Monika Matuszková, Gabriel Török, Debora Lančová, Kateřina Klimovičová

We present a full general relativistic analytic solution for a radiation-pressure supported equilibrium fluid torus orbiting a rotating neutron star (NS). Previously developed analytical methods are thoroughly applied in the Hartle-Thorne geometry, including the effects of both the NS's angular momentum and quadrupole moment. The structure, size and shape of

Dilara Akturk, Burak Dagli, Saleh Sultansoy

Recently, the construction of an antimuon-electron collider, {\mu}TRISTAN, at KEK has been proposed. We argue that the construction of a similar muon ring tangential to FCC-ee and CEPC will give an opportunity to realize antimuon-electron collisions at higher center-of-mass energies. Moreover, the same ring may be used later to realize energy-frontier antimu

Longhao Yan, Jinhao Liang, Kaidi Yang

Connected and automated vehicles (CAVs) can be beneficial for improving the operation of highway bottlenecks such as weaving sections. This paper proposes a bi-level control approach based on an upper-level deep reinforcement learning controller and a lower-level model predictive controller to coordinate the lane-changings of a mixed fleet of CAVs and human-

Haoyuan Wang, Wenbo Hu, Lei Zhu, Rynson W. H. Lau

Inverse rendering aims at recovering both geometry and materials of objects. It provides a more compatible reconstruction for conventional rendering engines, compared with the neural radiance fields (NeRFs). On the other hand, existing NeRF-based inverse rendering methods cannot handle glossy objects with local light interactions well, as they typically over

Sarah H. Q. Li, Yue Yu, Florian Dörfler, John Lygeros

In competitive multi-player interactions, simultaneous optimality is a key requirement for establishing strategic equilibria. This property is explicit when the game-theoretic equilibrium is the simultaneously optimal solution of coupled optimization problems. However, no such optimization problems exist for the correlated equilibrium, a strategic equilibriu

Ryan Barron, Maksim E. Eren, Manish Bhattarai, Selma Wanna

Much of human knowledge in cybersecurity is encapsulated within the ever-growing volume of scientific papers. As this textual data continues to expand, the importance of document organization methods becomes increasingly crucial for extracting actionable insights hidden within large text datasets. Knowledge Graphs (KGs) serve as a means to store factual info

Libo Huang, Zhulin An, Yan Zeng, Chuanguang Yang

Exemplar-Free Class Incremental Learning (efCIL) aims to continuously incorporate the knowledge from new classes while retaining previously learned information, without storing any old-class exemplars (i.e., samples). For this purpose, various efCIL methods have been proposed over the past few years, generally with elaborately constructed old pseudo-features

Marin Bužančić, Pedro Hernandez-Llanos, Igor Velčić, Josip Žubrinić

In this paper, the starting point of our analysis is a coupled system of linear elasticity and Stokes equation. We consider two small parameters: the thickness $h$ of the thin plate and the pore scale $\varepsilon(h)$ which depends on $h$. We will focus specifically on the case when the pore size is comparatively small relative to the thickness of the plate.

Sergei M. Gorbunov

We consider a family of linear operators, diagonalized by the Hankel transform. The Fredholm determinants of these operators, restricted to $L_2[0, R]$, are expressed in a convenient form for asymptotic analysis as $R\to\infty$. The result is an identity, in which the determinant is equal to the leading asymptotic multiplied by an asymptotically small factor

Ayoub Ghorbel, Snežana Č. Živković-Zlatanović

This paper explores additional properties of some classes of Saphar type operators, namely left Drazin invertible, essentially left Drazin invertible, right Drazin invertible, and essentially right Drazin invertible operators on Banach spaces, building upon the groundwork laid in \cite{GM} and \cite{ZS}. Specifically, we propose alternative definitions for t

Juan Altmayer Pizzorno, Emery D. Berger

Testing is an essential part of software development. Test generation tools attempt to automate the otherwise labor-intensive task of test creation, but generating high-coverage tests remains challenging. This paper proposes CoverUp, a novel approach to driving the generation of high-coverage Python regression tests. CoverUp combines coverage analysis, code

Paul-Hermann Balduf, Kimia Shaban

We present efficient data-driven approaches to predict Feynman periods in $\phi^4$-theory from properties of the underlying Feynman graphs. We find that the numbers of cuts and cycles determines the period to approximately 2% accuracy. Hepp bound and Martin invariant allow to predict the period with accuracy much better than 1%. In most cases, the period is

Igor V. Netay

We construct geohashing procedure based on using of space-filling H-curve. This curve provides a way to construct geohash with less computations than the construction based on usage of Hilbert curve. At the same time, H-curve has better clustering properties.

Chinmay Datar, Adwait Datar, Felix Dietrich, Wil Schilders

Discovering a suitable neural network architecture for modeling complex dynamical systems poses a formidable challenge, often involving extensive trial and error and navigation through a high-dimensional hyper-parameter space. In this paper, we discuss a systematic approach to constructing neural architectures for modeling a subclass of dynamical systems, na

Akash Harapanahalli, Samuel Coogan

In this paper, we efficiently compute overapproximating reachable sets for control systems evolving on Lie groups, building off results from monotone systems theory and geometric integration theory. We consider intervals in the tangent space, which describe real sets on the Lie group through the exponential map. A local equivalence between the original syste

Mohammad Daher

Let X be a complex Banach space, in this work we characterize the property of Frechet differentiability for the dual space of X. In the following, we show that if the dual space of X is Gateaux differentiable, then the dual space of Lp(X) has the same property for all p betwwen one and infiniy

Shaojie Li, Haichen Qu, Xinqi Dong, Bo Dang

Exploring the application of deep learning technologies in the field of medical diagnostics, Magnetic Resonance Imaging (MRI) provides a unique perspective for observing and diagnosing complex neurodegenerative diseases such as Alzheimer Disease (AD). With advancements in deep learning, particularly in Convolutional Neural Networks (CNNs) and the Xception ne

Gourav Kumawat, Craig O. Heinke, Haldan N. Cohn, Phyllis M. Lugger

We analyse a Chandra observation of the rich globular cluster NGC 362, finding 33 X-ray sources within 1' (1.2 half-mass radii) of the cluster center. Spectral analysis of the brightest source (X1) shows blackbody-like emission, indicating it is likely a quiescent low-mass X-ray binary; we find a possible counterpart that falls in the sub-subgiant region. We

Han Yan, Yang Li, Zhennan Wu, Shenzhou Chen

We present Frankenstein, a diffusion-based framework that can generate semantic-compositional 3D scenes in a single pass. Unlike existing methods that output a single, unified 3D shape, Frankenstein simultaneously generates multiple separated shapes, each corresponding to a semantically meaningful part. The 3D scene information is encoded in one single tri-p

Tianrui Liu, Qi Cai, Changxin Xu, Bo Hong

Image captioning strives to generate pertinent captions for specified images, situating itself at the crossroads of Computer Vision (CV) and Natural Language Processing (NLP). This endeavor is of paramount importance with far-reaching applications in recommendation systems, news outlets, social media, and beyond. Particularly within the realm of news reporti

Yang Jing, Lei Li

Deep generative models aim to learn the underlying distribution of data and generate new ones. Despite the diversity of generative models and their high-quality generation performance in practice, most of them lack rigorous theoretical convergence proofs. In this work, we aim to establish some convergence results for OT-Flow, one of the deep generative model

Yongqing Liang, Congyi Zhang, Junli Zhao, Wenping Wang

Deducing the 3D face from a skull is a challenging task in forensic science and archaeology. This paper proposes an end-to-end 3D face reconstruction pipeline and an exploration method that can conveniently create textured, realistic faces that match the given skull. To this end, we propose a tissue-guided face creation and adaptation scheme. With the help o

Giorgio Nordo, Saeid Jafari, Arif Mehmood, Bhimraj Basumatary

In this paper we present an open source framework developed in Python and consisting of three distinct classes designed to manipulate in a simple and intuitive way both symbolic representations of neutrosophic sets over universes of various types as well as mappings between them. The capabilities offered by this framework extend and generalize previous attem

Tianrui Liu, Qi Cai, Changxin Xu, Bo Hong

The wide spread of rumors on social media has caused a negative impact on people's daily life, leading to potential panic, fear, and mental health problems for the public. How to debunk rumors as early as possible remains a challenging problem. Existing studies mainly leverage information propagation structure to detect rumors, while very few works focus on

Bang-Dang Pham, Phong Tran, Anh Tran, Cuong Pham

This paper presents an innovative framework designed to train an image deblurring algorithm tailored to a specific camera device. This algorithm works by transforming a blurry input image, which is challenging to deblur, into another blurry image that is more amenable to deblurring. The transformation process, from one blurry state to another, leverages unpa

Mohammadreza Pourreza, Davood Rafiei, Yuxi Feng, Raymond Li

Detecting structural similarity between queries is essential for selecting examples in in-context learning models. However, assessing structural similarity based solely on the natural language expressions of queries, without considering SQL queries, presents a significant challenge. This paper explores the significance of this similarity metric and proposes

Sándor P. Fekete, Phillip Keldenich, Dominik Krupke, Stefan Schirra

We give an overview of the 2024 Computational Geometry Challenge targeting the problem \textsc{Maximum Polygon Packing}: Given a convex region $P$ in the plane, and a collection of simple polygons $Q_1, \ldots, Q_n$, each $Q_i$ with a respective value $c_i$, find a subset $S \subseteq \{1, \ldots,n\}$ and a feasible packing within $P$ of the polygons $Q_i$ (

Geetanjali Sharma, Gaurav Jaswal, Aditya Nigam, Raghavendra Ramachandra

Biometric authentication, which utilizes contactless features, such as forehead patterns, has become increasingly important for identity verification and access management. The proposed method is based on learning a 3D spatio-spatial temporal convolution to create detailed pictures of forehead patterns. We introduce a new CNN model called the Forehead Spatio

Xiang Zhang

We consider the problem of deep fair clustering, which partitions data into clusters via the representations extracted by deep neural networks while hiding sensitive data attributes. To achieve fairness, existing methods present a variety of fairness-related objective functions based on the group fairness criterion. However, these works typically assume that

Ke Zhu, Wenlei Qin, Yaping Gan, Yizhe Huang

Iron-based molybdenum disulfide (Fe-MoS$_2$) has emerged as a Fenton-like catalyst for the highly efficient degradation of antibiotics, but the structure-activity relationship remains elusive. Herein, garland-like MIL-101(Fe)/MoS$_2$ nanosheets (MMS) with dual metal active sites (Fe and Mo) and rich sulfur vacancies were fabricated to directly activate perox

Ya Deng, Katsutoshi Yamanoi

Given a complex quasi-projective normal variety $X$ and a linear representation $\varrho:\pi_1(X)\to {\rm GL}_{N}(K)$ with $K$ any field of positive characteristic, we mainly establish the following results: 1. the construction of the Shafarevich morphism ${\rm sh}_\varrho:X\to {\rm Sh}_\varrho(X)$ associated with $\varrho$. 2. In cases where $X$ is projecti

Junqiao Fan, Jianfei Yang, Yuecong Xu, Lihua Xie

Human pose estimation (HPE) from Radio Frequency vision (RF-vision) performs human sensing using RF signals that penetrate obstacles without revealing privacy (e.g., facial information). Recently, mmWave radar has emerged as a promising RF-vision sensor, providing radar point clouds by processing RF signals. However, the mmWave radar has a limited resolution

Andrew Steane

If a vertex in a graph can be deleted without affecting distances among the other vertices, we shall say it is distance-redundant. Graphs with all, some or no such vertices are discussed. (The latter class was termed distance-critical by Erd\H{o}s and Howorka).

Mario Raciti, Giampaolo Bella

Criminal investigations are inherently complex as they typically involve interactions among various actors like investigators, prosecutors, and defendants. The pervasive integration of technology in daily life adds an extra layer of complexity, especially in crimes that involve a digital element. The establishment of digital forensics as a foundational disci

Hongshuang Liu, Jin Cao, Zeying Zhang, Jiashuo Liang

Realizing ideal Weyl semimetal state with a single pair of Weyl points has been a long-sought goal in the field of topological semimetals. Here, we reveal such a state in the Cr-based half-Heusler compounds XCrTe (X=K, Rb). We show that these materials have a half metal ground state, with Fermi level crossing only one spin channel. Importantly, the Fermi sur

Siddharth Tourani, Ahmed Alwheibi, Arif Mahmood, Muhammad Haris Khan

Unsupervised landmarks discovery (ULD) for an object category is a challenging computer vision problem. In pursuit of developing a robust ULD framework, we explore the potential of a recent paradigm of self-supervised learning algorithms, known as diffusion models. Some recent works have shown that these models implicitly contain important correspondence cue

Fanpeng Shi, Dong Li, Zongjun Ning, Jun Xu

Context. Solar jets play a role in the coronal heating and the supply of solar wind. Aims. This study calculated the energies of 23 small-scale jets emerging from a quiet-Sun region to investigate their contributions for coronal heating. Conclusions. Our observations suggest that although these jets cannot provide sufficient energy for the heating of the who

Eugeny Babichev, Christos Charmousis, Daniela D. Doneva, Galin N. Gyulchev

There is strong observational evidence that almost every large galaxy has a supermassive black hole at its center. It is of fundamental importance to know whether such black holes are described by the standard Kerr solution in General Relativity (GR) or by another black hole solution. An interesting alternative is the so-called disformal Kerr black holes whi