October 2023 arXiv papers — page 109

Jun Zhang, Lipeng Zhu, Chao Wang, Shutao Li

Integrating a low-spatial-resolution hyperspectral image (LR-HSI) with a high-spatial-resolution multispectral image (HR-MSI) is recognized as a valid method for acquiring HR-HSI. Among the current fusion approaches, the tensor ring (TR) decomposition-based method has received growing attention owing to its superior performance on preserving the spatial-spec

Wei Su, Ze-Bing Zhou, Yan Wang, Chen Zhou

TianQin (TQ) project plans to deploy three satellites in space around the Earth to measure the displacement change of test masses caused by gravitational waves via laser interferometry. The requirement of the acceleration noise of the test mass is on the order of $10^{-15}~\,{\rm m}\,{\rm s}^{-2}\,{\rm Hz}^{-1/2}$ in the sensitive frequency range of TQ, %the

Lorenzo Travaglini, Nga T. Lam, Artur Sawicki, Hee-Jeong Cha

Electronically conductive protein-based materials could enable the creation of bioelectronic components and devices from sustainable and nontoxic materials, while also being well-suited to interface with biological systems, such as living cells, for biosensor applications. In addition, protein materials have other desirable properties such as inherent self-a

Ling Liu, Junjie Ma

This paper studies a family of convolution quadratures, a numerical technique for efficient evaluation of convolution integrals. We employ the block generalized Adams method to discretize the underlying initial value problem, departing from the well-established approaches that rely on linear multistep formulas or Runge-Kutta methods. The convergence order of

Detian Liu, Haichou Li, Kit Ian Kou

We examine how the square-integrable function subspaces are transformed using the holomorphic Fourier transform. On account of this, the extended Paley-Wiener theorem over the Hardy-Sobolev spaces is produced. The theorem also asserts that the reproducing kernel of the Hardy-Sobolev spaces can be found. We discuss the relationship between the disc and the up

Jingkai Yan, Shiyu Wang, Xinyu Rain Wei, Jimmy Wang

In scientific and engineering scenarios, a recurring task is the detection of low-dimensional families of signals or patterns. A classic family of approaches, exemplified by template matching, aims to cover the search space with a dense template bank. While simple and highly interpretable, it suffers from poor computational efficiency due to unfavorable scal

Victor Adewopo, Nelly Elsayed

The dynamic and unpredictable nature of road traffic necessitates effective accident detection methods for enhancing safety and streamlining traffic management in smart cities. This paper offers a comprehensive exploration study of prevailing accident detection techniques, shedding light on the nuances of other state-of-the-art methodologies while providing

Tian-Ren Jin, Yun-Hao Shi, Zheng-An Wang, Tian-Ming Li

Quantum error mitigation aims to reduce errors in quantum systems and improve accuracy. Zero-noise extrapolation (ZNE) is a commonly used method, where noise is amplified, and the target expectation is extrapolated to a noise-free point. However, ZNE relies on assumptions about error rates based on the error model. In this study, a purity-assisted zero-noise

Long Zhuo, Shenghai Luo, Shunquan Tan, Han Chen

Anti-forensics seeks to eliminate or conceal traces of tampering artifacts. Typically, anti-forensic methods are designed to deceive binary detectors and persuade them to misjudge the authenticity of an image. However, to the best of our knowledge, no attempts have been made to deceive forgery detectors at the pixel level and mis-locate forged regions. Tradi

Tingyu Xie, Qi Li, Jian Zhang, Yan Zhang

Large language models (LLMs) exhibited powerful capability in various natural language processing tasks. This work focuses on exploring LLM performance on zero-shot information extraction, with a focus on the ChatGPT and named entity recognition (NER) task. Inspired by the remarkable reasoning capability of LLM on symbolic and arithmetic reasoning, we adapt

Yong Du

We perform a global fit to the electroweak vertices and 4-fermion operators of the standard model effective field theory in this work using $N_{\rm eff}$ from cosmological probes, as well as data sets from colliders and low-energy experiments. We find $N_{\rm eff}$, both its current measurement and future projections, can only marginally improve the fit in b

Wenxue Cui, Xiaopeng Fan, Jian Zhang, Debin Zhao

Inspired by certain optimization solvers, the deep unfolding network (DUN) has attracted much attention in recent years for image compressed sensing (CS). However, there still exist the following two issues: 1) In existing DUNs, most hyperparameters are usually content independent, which greatly limits their adaptability for different input contents. 2) In e

Aydin Cem Keser, Oleg Sushkov

It has been established that the Coulomb interactions can transform the electron gas into a viscous fluid. This fluid is realized in a number of platforms, including graphene and two-dimensional semiconductor heterostructures. The defining characteristic of the electron fluid is the formation of layers of charge carriers that are in local thermodynamic equil

Bobby Wilson

In this article, we examine the theorem of Mattila establishing rectifiability for regular sets in the setting of strictly convex, finite-dimensional Banach spaces.

Bo Li, Ali Mostafavi

The standard model of infrastructure resilience, the resilience triangle, has been the primary way of characterizing and quantifying infrastructure resilience. However, the theoretical model merely provides a one-size-fits-all framework for all infrastructure systems. Most of the existing studies examine the characteristics of infrastructure resilience curve

Xin Zhang, Pietro Balatti, Mattia Leonori, Arash Ajoudani

Supernumerary robotic arms (SRAs) can be used as the third arm to complement and augment the abilities of human users. The user carrying a SRA forms a connected kinodynamic chain, which can be viewed as a special class of floating-base robot systems. However, unlike general floating-base robot systems, human users are the bases of SRAs and they have their su

Swarnabha Chattaraj, Supratik Guha, Giulia Galli

We present a predictive and general approach to investigate near-field energy transfer processes between localized defects in semiconductors, which couples first principle electronic structure calculations and a nonrelativistic quantum electrodynamics description of photons in the weak-coupling regime. We apply our approach to investigate an exemplar point d

Giovanna Citti, Alessandro Sarti

In a joint paper, Jean Petitot together with the authors of the present paper described the functional geometry of the visual cortex as the symplectization of a contact form to describe the family of cells sensitive to position, orientation and scale. In the present paper, as a "homage" to the enormous contribution of Jean Petitot to neurogeometry, we will e

Yiqun Zhao, Zibo Zhao, Jing Li, Sixun Dong

Indoor scene generation aims at creating shape-compatible, style-consistent furniture arrangements within a spatially reasonable layout. However, most existing approaches primarily focus on generating plausible furniture layouts without incorporating specific details related to individual furniture pieces. To address this limitation, we propose a two-stage m

Kashyap Patel, Anton Kovalyov, Issa Panahi

This paper introduces a practical approach for leveraging a real-time deep learning model to alternate between speech enhancement and joint speech enhancement and separation depending on whether the input mixture contains one or two active speakers. Scale-invariant signal-to-distortion ratio (SI-SDR) has shown to be a highly effective training measure in tim

Liangliang Chen, Hongzhan Lin, Jinshan Ma, Guang Chen

In the sequential recommendation task, the recommender generally learns multiple embeddings from a user's historical behaviors, to catch the diverse interests of the user. Nevertheless, the existing approaches just extract each interest independently for the corresponding sub-sequence while ignoring the global correlation of the entire interaction sequence,

Yi Cao, Swetava Ganguli, Vipul Pandey

There exists a correlation between geospatial activity temporal patterns and type of land use. A novel self-supervised approach is proposed to stratify landscape based on mobility activity time series. First, the time series signal is transformed to the frequency domain and then compressed into task-agnostic temporal embeddings by a contractive autoencoder,

Arsen Sultanov, Jean-Claude Crivello, Tabea Rebafka, Nataliya Sokolovska

The discovery of new functional and stable materials is a big challenge due to its complexity. This work aims at the generation of new crystal structures with desired properties, such as chemical stability and specified chemical composition, by using machine learning generative models. Compared to the generation of molecules, crystal structures pose new diff

Koki Aoki, Kenji Koide, Shuji Oishi, Masashi Yokozuka

This paper presents an accurate and fast 3D global localization method, 3D-BBS, that extends the existing branch-and-bound (BnB)-based 2D scan matching (BBS) algorithm. To reduce memory consumption, we utilize a sparse hash table for storing hierarchical 3D voxel maps. To improve the processing cost of BBS in 3D space, we propose an efficient roto-translatio

Ling Zhou, Mingpei Wang, Xiaohua Huang, Wenming Zheng

Micro-expression recognition (MER) in low-resolution (LR) scenarios presents an important and complex challenge, particularly for practical applications such as group MER in crowded environments. Despite considerable advancements in super-resolution techniques for enhancing the quality of LR images and videos, few study has focused on investigate super-resol

Jesse Zhang, Jiahui Zhang, Karl Pertsch, Ziyi Liu

We propose BOSS, an approach that automatically learns to solve new long-horizon, complex, and meaningful tasks by growing a learned skill library with minimal supervision. Prior work in reinforcement learning require expert supervision, in the form of demonstrations or rich reward functions, to learn long-horizon tasks. Instead, our approach BOSS (BOotStrap

Jared K. Averitt, Sajedeh Pourianejad, Olubunmi Ayodele, Kirby Schmidt

In this study, we investigate both experimentally and computationally the molecular interactions of two distinct polymers with graphene. Our experimental findings indicate that the use of a polymer mixture reduces the transfer induced doping and strain in fabricated graphene devices as compared to conventional single polymer wet transfer. We found that such

Xiaoxuan Zhang, Xiao Jiang, Matthew Tivnan, J. Webster Stayman

Three-dimensional digital subtraction angiography (3D-DSA) is a widely adopted technique for clinical evaluation of contrast-enhanced vasculatures. The distribution of a contrast agent such as iodine is often estimated via temporal subtraction. Advancements in spectral imaging technologies such as photon counting detectors offer new opportunities to improve

Guillaume Barraquand, Ivan Corwin, Sayan Das

We consider the point-to-point log-gamma polymer of length $2N$ in a half-space with i.i.d. $\operatorname{Gamma}^{-1}(2\theta)$ distributed bulk weights and i.i.d. $\operatorname{Gamma}^{-1}(\alpha+\theta)$ distributed boundary weights for $\theta>0$ and $\alpha>-\theta$. We establish the KPZ exponents ($1/3$ fluctuation and $2/3$ transversal) for this mode

Mohit Bhardwaj, Daniele Michilli, Aida Yu. Kirichenko, Obinna Modilim

We present the host galaxies of four apparently non-repeating fast radio bursts (FRBs), FRBs 20181223C, 20190418A, 20191220A, and 20190425A, reported in the first Canadian Hydrogen Intensity Mapping Experiment (CHIME/FRB) catalog. Our selection of these FRBs is based on a planned hypothesis testing framework where we search all CHIME/FRB Catalog-1 events tha

Noriaki Kitazawa

The Hubble constant problem is that the values of Hubble constant from the observation of cosmic microwave background assuming the LambdaCDM model disagrees with the values from direct measurements. This problem suggests some new physics beyond the LambdaCDM model. Typically there are two ways of reconciliation: one is the realization of smaller value of sou

Joao Otavio Chervinski, Diego Kreutz, Jiangshan Yu

Blockchains were originally designed as closed execution environments and lack the ability to communicate directly with external systems. To overcome this limitation, many blockchains employ relayers, external applications capable of transporting data between different blockchains. Typically, the process of relaying data is permissionless and multiple indepe

Sungbok Shin, Andrea Batch, Peter W. S. Butcher, Panagiotis D. Ritsos

The advent of low cost, accessible, and high performance augmented reality (AR) has shed light on a situated form of analytics where in-situ visualizations embedded in the real world can facilitate sensemaking based on the user's physical location. In this work, we identify prior literature in this emerging field with a focus on situated analytics. After col

D. Q. Fang

Ternary nitride ZnSnN$_2$ is a promising photovoltaic absorber material. In this work, using first-principles calculations, we investigate the stability and electronic properties of "4-8"-type ZnSnN$_2$ thin films. We find that below a certain thickness "4-8"-type thin films have lower total energy than polar films. For 4-layer ZnSnN$_2$ thin film, the Pna2$

Isay Katsman, Eric Ming Chen, Sidhanth Holalkere, Anna Asch

Recent methods in geometric deep learning have introduced various neural networks to operate over data that lie on Riemannian manifolds. Such networks are often necessary to learn well over graphs with a hierarchical structure or to learn over manifold-valued data encountered in the natural sciences. These networks are often inspired by and directly generali

Yu-Lin Tsai, Chia-Yi Hsu, Chulin Xie, Chih-Hsun Lin

Diffusion models for text-to-image (T2I) synthesis, such as Stable Diffusion (SD), have recently demonstrated exceptional capabilities for generating high-quality content. However, this progress has raised several concerns of potential misuse, particularly in creating copyrighted, prohibited, and restricted content, or NSFW (not safe for work) images. While

Anjia Ying, Tian Liang, Zhigang Li, Lin Fu

A new resolvent-based method is developed to predict the space-time properties of the flow field. To overcome the deterioration of the prediction accuracy with the increasing distance between the measurements and predictions in the Resolvent-Based Estimation (RBE), the newly proposed method utilizes the RBE to estimate the relative energy distribution near t

Sheng Zheng, Chaoning Zhang, Xinhong Hao

Deep recognition models are widely vulnerable to adversarial examples, which change the model output by adding quasi-imperceptible perturbation to the image input. Recently, Segment Anything Model (SAM) has emerged to become a popular foundation model in computer vision due to its impressive generalization to unseen data and tasks. Realizing flexible attacks

Ellen Guan, Maury Goodman

In a 2015 study, particle physicists reported the first observed seasonal variation of multiple muon events in the MINOS Far and Near Detectors, where multiple muon events created by cosmic rays were observed to be more numerous in the winter than in the summer. This goes against the initial hypothesis of the researchers, because it has long been measured th

Yiyuan Zhang, Kaixiong Gong, Xiaohan Ding, Kaipeng Zhang

We propose $\textbf{UniDG}$, a novel and $\textbf{Uni}$fied framework for $\textbf{D}$omain $\textbf{G}$eneralization that is capable of significantly enhancing the out-of-distribution generalization performance of foundation models regardless of their architectures. The core idea of UniDG is to finetune models during the inference stage, which saves the cos

Iwashita Ryoji, Kataoka Jun, Sofue Yoshiaki

The North Polar Spur (NPS) is a giant structure that is clearly visible in both radio and X-ray all-sky maps. We analyzed broadband radio observations covering a range between 22 MHz and 70 GHz to systematically analyze the thermal/non-thermal emissions associated with the NPS. We demonstrate that the radio emission of the NPS comprises synchrotron, free-fre

Matthew J. Holland, Kosuke Nakatani

As a heuristic for improving test accuracy in classification, the "flooding" method proposed by Ishida et al. (2020) sets a threshold for the average surrogate loss at training time; above the threshold, gradient descent is run as usual, but below the threshold, a switch to gradient ascent is made. While setting the threshold is non-trivial and is usually do

Yoshiyuki Ohmura, Yasuo Kuniyoshi

Downward causation is self-causation, the causel effect from the whole to its parts, and is considered a promising theory for the problem of mental causation. However, it remains to be clarified how an irreducible but supervenient downward causal power can arise. Here, we argue that a feedback control of lower micro-level synaptic weights using higher macro-

Kang-Sin Choi

Using Wilsonian renormalization, we calculate the quantum correction to observable quantities, rather than the bare parameters, of the Higgs field. A physical parameter, such as a mass-squared or a quartic coupling, at an energy scale $\mu$ is obtained from that at a reference scale by integrating in the degrees of freedom in between. In this process, heavy

Yash Patel, Sahana Rayan, Ambuj Tewari

Data-driven approaches to predict-then-optimize decision-making problems seek to mitigate the risk of uncertainty region misspecification in safety-critical settings. Current approaches, however, suffer from considering overly conservative uncertainty regions, often resulting in suboptimal decisionmaking. To this end, we propose Conformal-Predict-Then-Optimi

Shisheng Zhang, Ramtin Gharleghi, Sonit Singh, Arcot Sowmya

Coronary artery diseases are among the leading causes of mortality worldwide. Timely and accurate diagnosis, facilitated by precise coronary artery segmentation, is pivotal in changing patient outcomes. In the realm of biomedical imaging, convolutional neural networks, especially the U-Net architecture, have revolutionised segmentation processes. However, on

Xuefeng Bao

The Schr\"odinger equation is universally accepted due to its excellent predictions aligning with observed results within its defined conditions. Nevertheless, it does not seem to possess the simplicity of fundamental laws, such as Newton's laws of motion. Various insightful attempts have been made to elucidate the rationale behind the Schr\"odinger equation

Dominik Bär, Francesco Pierri, Gianmarco De Francisci Morales, Stefan Feuerriegel

Political advertising on social media has become a central element in election campaigns. However, granular information about political advertising on social media was previously unavailable, thus raising concerns regarding fairness, accountability, and transparency in the electoral process. In this paper, we analyze targeted political advertising on social

Brienne Elisabeth Brown, Richard Evan Schwartz

We introduce the crisscross and the cup, both of which are immersed $3$-twist polygonal paper Moebius band of aspect ratio $3$. We explain why these two objects are limits of smooth embedded paper Moebius bands having knotted boundary. We conjecture that any smooth embedded paper Moebius band with knotted boundary has aspect ratio greater than $3$. The criss

Jirong Yi, Jingchao Gao, Tianming Wang, Xiaodong Wu

This paper considers the problem of recovering signals modeled by generative models from linear measurements contaminated with sparse outliers. We propose an outlier detection approach for reconstructing the ground-truth signals modeled by generative models under sparse outliers. We establish theoretical recovery guarantees for reconstruction of signals usin

Tan-Hanh Pham, Xianqi Li, Kim-Doang Nguyen

Automated medical image segmentation is becoming increasingly crucial to modern clinical practice, driven by the growing demand for precise diagnosis, the push towards personalized treatment plans, and the advancements in machine learning algorithms, especially the incorporation of deep learning methods. While convolutional neural networks (CNN) have been pr

Thomas Jiralerspong, Flemming Kondrup, Doina Precup, Khimya Khetarpal

The ability to plan at many different levels of abstraction enables agents to envision the long-term repercussions of their decisions and thus enables sample-efficient learning. This becomes particularly beneficial in complex environments from high-dimensional state space such as pixels, where the goal is distant and the reward sparse. We introduce Forecaste

Wenbo Lyu

In the post-epidemic era, consumption recovery has obvious time and space transmission laws, and there are different valuation criteria for consumption segments. Using the A-share data of the consumption recovery stage from January to April 2022, this paper quantitatively compares the rotation effect between different consumption sectors when the valuation r

Daosheng Tang

The topological effects of phonons have been extensively studied in various materials, particularly in the wide-bandgap semiconductor GaN, which has the potential to improve heat dissipation in power electronics due to its intrinsic, topologically-protected, non-dissipative phonon surface states. Nevertheless, the phase transition of the Weyl phonons in nitr

Wouter J. E. C. van Eekelen, Grani A. Hanasusanto, John J. Hasenbein, Johan S. H. van Leeuwaarden

Consider an M/M/$s$ queue with the additional feature that the arrival rate is a random variable of which only the mean, variance, and range are known. Using semi-infinite linear programming and duality theory for moment problems, we establish for this setting tight bounds for the expected waiting time. These bounds correspond to an arrival rate that takes o

Aryan Vats, Manan Suri

The use of Deep Learning techniques for classification in Hyperspectral Imaging (HSI) is rapidly growing and achieving improved performances. Due to the nature of the data captured by sensors that produce HSI images, a common issue is the dimensionality of the bands that may or may not contribute to the label class distinction. Due to the widespread nature o

Yu Lu Liu, Thomas Jiralerspong

In recent years, citizen science has become a larger and larger part of the scientific community. Its ability to crowd source data and expertise from thousands of citizen scientists makes it invaluable. Despite the field's growing popularity, the interactions and structure of citizen science projects are still poorly understood and under analyzed. We use the

Khalid Hasan Al Jasimee, Francisco Javier Blanco-Encomienda

This study re-examines the impact of participative budgeting on managerial performance and budgetary slack, addressing gaps in current research. A revised conceptual model is developed, considering the conditioning roles of leadership style and leader-member exchange. The sample includes 408 employees with managerial experience in manufacturing companies. Hy

Diego Fernando Díaz Padilla, Jesús Alonso Ochoa Arango

In this paper we continue the study of the nonvanishing minors property (NVM) initiated by Garcia, Karaali and Katz, for the compressed Fourier matrix attached to a subgroup $H$ of the multiplicative group of a finite field $\mathbb{F}_q$ and a character $\chi$ defined over $H$. Here we provide a characterization of this aforementioned property for \textit{s

Thanh Tung Khuat, Robert Bassett, Ellen Otte, Alistair Grevis-James

While machine learning (ML) has made significant contributions to the biopharmaceutical field, its applications are still in the early stages in terms of providing direct support for quality-by-design based development and manufacturing of biopharmaceuticals, hindering the enormous potential for bioprocesses automation from their development to manufacturing

Hannah Sleath, Bortolo Mognetti, Yuval Elani, Lorenzo Di Michele

Living cells are capable of interacting with their environments in a variety of ways, including cell signalling, adhesion, and directed motion. These behaviours are often mediated by receptor molecules embedded in the cell membrane, which bind specific ligands. Adhesion mediated by a large number of weakly binding moieties - multivalent binding - is prevalen

Zhongqiang Chen, Hongsong Qiu, Xinjuan Cheng, Jizhe Cui

Nonlinear transport enabled by symmetry breaking in quantum materials has aroused considerable interest in condensed matter physics and interdisciplinary electronics. However, the nonlinear optical response in centrosymmetric Dirac semimetals via the defect engineering has remained highly challenging. Here, we observe the helicity-dependent terahertz (THz) e

Zhihong Lei, Ernest Pusateri, Shiyi Han, Leo Liu

Recent advances in deep learning and automatic speech recognition have improved the accuracy of end-to-end speech recognition systems, but recognition of personal content such as contact names remains a challenge. In this work, we describe our personalization solution for an end-to-end speech recognition system based on connectionist temporal classification.

Mingshan Jia, Pasquale De Meo, Bogdan Gabrys, Katarzyna Musial

Network disruption is pivotal in understanding the robustness and vulnerability of complex networks, which is instrumental in devising strategies for infrastructure protection, epidemic control, cybersecurity, and combating crime. In this paper, with a particular focus on disrupting criminal networks, we proposed to impose a within-the-largest-connected-comp

Elizabeth Munch

The Euler characteristic transform (ECT) is a simple to define yet powerful representation of shape. The idea is to encode an embedded shape using sub-level sets of a a function defined based on a given direction, and then returning the Euler characteristics of these sublevel sets. Because the ECT has been shown to be injective on the space of embedded simpl

Valentino Tosatti

We survey some recent developments on various notions of semipositivity for (1,1)-classes on complex manifolds, and discuss a number of open questions.

Huizhen Yu

This paper concerns discrete-time infinite-horizon stochastic control systems with Borel state and action spaces and universally measurable policies. We study optimization problems on strategic measures induced by the policies in these systems. The results are then applied to risk-neutral and risk-sensitive Markov decision processes, as well as their partial

Andrew Naguib, Waleed A. Yousef, Issa Traoré, Mohammad Mamun

Recently, machine learning of the branch and bound algorithm has shown promise in approximating competent solutions to NP-hard problems. In this paper, we utilize and comprehensively compare the outcomes of three neural networks--graph convolutional neural network (GCNN), GraphSAGE, and graph attention network (GAT)--to solve the capacitated vehicle routing

Shm Garanganao Almeda, J. D. Zamfirescu-Pereira, Kyu Won Kim, Pradeep Mani Rathnam

Design space exploration (DSE) for Text-to-Image (TTI) models entails navigating a vast, opaque space of possible image outputs, through a commensurately vast input space of hyperparameters and prompt text. Minor adjustments to prompt input can surface unexpectedly disparate images. How can interfaces support end-users in reliably steering prompt-space explo

Sathya Rengaswami, José Torres Santaella

In this paper, we obtain the asymptotic expansion for the analogue of the bowl-soliton for a large `nondegenerate' class of fully nonlinear curvature flows. We use this to show the uniqueness of these bowl-type solitons in their asymptotic class. We also give examples to illustrate the situation for `degenerate' speeds and how they different they can be. Fin

Noveen Sachdeva, Zexue He, Wang-Cheng Kang, Jianmo Ni

We study data distillation for auto-regressive machine learning tasks, where the input and output have a strict left-to-right causal structure. More specifically, we propose Farzi, which summarizes an event sequence dataset into a small number of synthetic sequences -- Farzi Data -- which are optimized to maintain (if not improve) model performance compared

Jiaxin Wei, Stefan Leutenegger, Laurent Kneip

Perspective-$n$-Point (P$n$P) stands as a fundamental algorithm for pose estimation in various applications. In this paper, we present a new approach to the P$n$P problem with relaxed constraints, eliminating the need for precise 3D coordinates, which is especially suitable for object pose estimation where corresponding object models may not be available in

Kanan Mahammadli, Abdullah Burkan Bereketoglu, Ayse Gul Kabakci

Breast Cancer is the most common cancer among women, which is also visible in men, and accounts for more than 1 in 10 new cancer diagnoses each year. It is also the second most common cause of women who die from cancer. Hence, it necessitates early detection and tailored treatment. Early detection can provide appropriate and patient-based therapeutic schedul

David Stern, Mikuláš Zindulka

We study partitions of totally positive integers in real quadratic fields. We develop an algorithm for computing the number of partitions, prove a result about the parity of the partition function, and characterize the quadratic fields such that there exists an element with exactly 1-5, 7, and 11 partitions.

Kendric Schefers

Let $f: X \to \mathbb{A}^1$ be a regular function on a smooth complex algebraic variety $X$. We formulate and prove an equivalence between the algebraic formal twisted de Rham complex of $f$ and the vanishing cycles with respect to $f$ as objects in the category of sheaves valued in the derived $\infty$-category of modules over $\widehat{\mathscr{E}}_{\mathb

Weijian Ma, Yanyang Kong

Artistic style transfer aims to modify the style of the image while preserving its content. Style transfer using deep learning models has been widely studied since 2015, and most of the applications are focused on specific artists like Van Gogh, Monet, Cezanne. There are few researches and applications on traditional Chinese painting style transfer. In this

Ataberk Olgun, Yahya Can Tugrul, Nisa Bostanci, Ismail Emir Yuksel

We introduce ABACuS, a new low-cost hardware-counter-based RowHammer mitigation technique that performance-, energy-, and area-efficiently scales with worsening RowHammer vulnerability. We observe that both benign workloads and RowHammer attacks tend to access DRAM rows with the same row address in multiple DRAM banks at around the same time. Based on this o

Megha Yadav, Vanshika, Chamkor Singh

Homogeneous suspensions of red blood cells (RBCs or erythrocytes) in blood plasma are unstable in the absence of driving forces and form elongated stacks, called rouleau. These erythrocyte aggregates are often branched porous networks -- a feature that existing red blood cell aggregation models and simulations fail to predict exactly. Here we establish that

Tianshui Ma, Abdenacer Makhlouf

The aim of this paper is to investigate representation theory of infinitesimal (BiHom-)bialgebras of any weight $\l$ (abbr. $\l$-inf(BH)-bialgebras). Firstly, inspired by the well-known Majid-Radford's bosonization theory in Hopf algebra theory, we present a class of $\l$-inf(BH)-bialgebras, named $\l$-inf(BH)-biproduct bialgebras, consisting of an inf(BH)-p

Kiriaki Frangias, Andrew Lin, Ellen Vitercik, Manolis Zampetakis

Ranking is fundamental to many areas, such as search engine optimization, human feedback for language models, as well as peer grading. Crowdsourcing, which is often used for these tasks, requires proper incentivization to ensure accurate inputs. In this work, we draw on the field of \emph{contract theory} from Economics to propose a novel mechanism that enab

Pál Bärnkopf, Ervin Győri

We investigate the problem of extending partial edge colorings in Cartesian products of graphs, with a particular focus on cases where the precolored edges form a matching. Casselgren, Granholm, and Petros conjectured that any precolored distance-3 matching in $G = C^d_{2k}$ can be extended to a $2d$-edge coloring. In this paper, we prove a theorem that impl

Connor M. Depies, Jonathan D. H. Smith, Mitchell D. Ashburn

The associative Cayley-Dickson algebras over the field of real numbers are also Clifford algebras. The alternative but nonassociative real Cayley-Dickson algebras, notably the octonions and split octonions, share with Clifford algebras an involutary anti-automorphism and a set of mutually anticommutative generators. On the basis of these similarities, we int

Jake Grigsby, Linxi Fan, Yuke Zhu

We introduce AMAGO, an in-context Reinforcement Learning (RL) agent that uses sequence models to tackle the challenges of generalization, long-term memory, and meta-learning. Recent works have shown that off-policy learning can make in-context RL with recurrent policies viable. Nonetheless, these approaches require extensive tuning and limit scalability by c

Mahdi Shamsi, Farokh Marvasti

Distributed algorithms, particularly Diffusion Least Mean Square, are widely favored for their reliability, robustness, and fast convergence in various industries. However, limited observability of the target can compromise the integrity of the algorithm. To address this issue, this paper proposes a framework for analyzing combination strategies by drawing i

Abdulaziz Shamsah, Ye Zhao

Social navigation for bipedal robots remains relatively unexplored due to the highly complex, nonlinear dynamics of bipedal locomotion. This study presents a preliminary exploration of social navigation for bipedal robots in a human crowded environment. We propose a social path planner that ensures the locomotion safety of the bipedal robot while navigating

Yandi Wu

In this paper, we show that simple, thick negatively curved two-dimensional P-manifolds, a large class of surface amalgams, are marked length spectrum rigid. That is, if two piecewise negatively curved Riemannian metrics (satisfying certain smoothness conditions) on a simple, thick two-dimensional P-manifold assign the same lengths to all closed geodesics, t

Somnath Pradhan, Zachary Selk, Serdar Yüksel

In this article we show a robustness theorem for controlled stochastic differential equations driven by approximations of Brownian motion. Often, Brownian motion is used as an idealized model of a diffusion where approximations such as Wong-Zakai, Karhnen-Lo\`eve or fractional Brownian motion are often seen as more physical. However, there has been extensive

Najam Ul Abbas, Imran Ahmed, Ayesha Kiran

We first construct the total simplicial complex (TSC) of a finite simple graph $G$ in order to generalize the total graph $T(G)$. We show that $\Delta_T(G)$ is not Cohen-Macaulay (CM) in general. For a connected graph $G$, we prove that the TSC is Buchsbaum. We demonstrate that the vanishing of first homology group of TSC associated to a connected graph $G$

Binglun Wang, Niladri Shekhar Dutt, Niloy J. Mitra

Neural Radiance Fields (NeRFs) have recently emerged as a popular option for photo-realistic object capture due to their ability to faithfully capture high-fidelity volumetric content even from handheld video input. Although much research has been devoted to efficient optimization leading to real-time training and rendering, options for interactive editing N

Joshua Pickard

This document explores structural controllability of polynomial dynamical systems or polysystems. We extend Lin's concept of structural controllability for linear systems, offering hypergraph-theoretic methods to rapidly assess strong controllability. Our main result establishes that a polysytem is structurally controllable when its hypergraph representation

Ammar N. Abbas, Georgios C. Chasparis, John D. Kelleher

Traditional controllers have limitations as they rely on prior knowledge about the physics of the problem, require modeling of dynamics, and struggle to adapt to abnormal situations. Deep reinforcement learning has the potential to address these problems by learning optimal control policies through exploration in an environment. For safety-critical environme

Isiaka Lukman, Matthew D. McCluskey, Leah Bergman

$\beta$-Ga$_2$O$_3$ is a semiconductor with bandgap in the deep-UV ~ 5 eV. Due to its strong phonon-hole coupling, holes are self-trapped inhibiting bandgap luminescence at the deep-UV. In contrast, the self-trapped holes (STH) can exhibit a strong luminescence at ~ 3.5 eV. This research addresses the thermal response of the STH photoluminescence (PL), and t

B. F. L. Ward, S. Jadach, W. Placzek, M. Skrzypek

There is a continuing effort to support and prepare the precision physics programs for the present and planned future colliders such as HL-LHC, FCC, CLIC, CEPC, and CPPC. We discuss new results from IR-improved amplitude-based resummation in quantum field theory relevant to such support and preparation with some emphasis on the interplay between soft and col

Domokos M. Kelen, Mihály Petreczky, Péter Kersch, András A. Benczúr

In this work, we examine Asymmetric Shapley Values (ASV), a variant of the popular SHAP additive local explanation method. ASV proposes a way to improve model explanations incorporating known causal relations between variables, and is also considered as a way to test for unfair discrimination in model predictions. Unexplored in previous literature, relaxing

Hangbin Lee, Youngjo Lee

Stein's (1959) problem highlights the phenomenon called the probability dilution in high dimensional cases, which is known as a fundamental deficiency in probabilistic inference. The satellite conjunction problem also suffers from probability dilution that poor-quality data can lead to a dilution of collision probability. Though various methods have been pro

Xiaotong Chen, Zheming Zhou, Zhuo Deng, Omid Ghasemalizadeh

Reconstructing transparent objects using affordable RGB-D cameras is a persistent challenge in robotic perception due to inconsistent appearances across views in the RGB domain and inaccurate depth readings in each single-view. We introduce a two-stage pipeline for reconstructing transparent objects tailored for mobile platforms. In the first stage, off-the-

Hangbin Lee, Youngjo Lee

Fisher's likelihood is widely used for statistical inference for fixed unknowns. This paper aims to extend two important likelihood-based methods, namely the maximum likelihood procedure for point estimation and the confidence procedure for interval estimation, to embrace a broader class of statistical models with additional random unknowns. We propose the n

Asher Auel, Richard Haburcak, Hannah Larson

We study the restriction of Brill-Noether loci to the gonality stratification of the moduli space of curves of fixed genus. As an application, we give new proofs that Brill-Noether loci with $\rho=-1$ have distinct support, and for fixed $r$ give lower bounds on when one direction of the non-containments of the Maximal Brill-Noether Loci Conjecture hold for

Zamir Beleño, Marcelo F. Santos, Felipe Barra

The interaction of a three-level atom with the electromagnetic field of a quantum cavity in the presence of a laser field presents a rich behavior that we exploit to discuss two quantum batteries. In the first setup, we consider a single three-level atom interacting sequentially with many cavities, each in a thermal state. We show that under this process, th

Arshia Soltani Moakhar, Mohammad Azizmalayeri, Hossein Mirzaei, Mohammad Taghi Manzuri

Despite considerable theoretical progress in the training of neural networks viewed as a multi-agent system of neurons, particularly concerning biological plausibility and decentralized training, their applicability to real-world problems remains limited due to scalability issues. In contrast, error-backpropagation has demonstrated its effectiveness for trai