October 2023 arXiv papers — page 100

Adam Schmidt, Omid Mohareri, Simon DiMaio, Michael C. Yip

As computer vision algorithms increase in capability, their applications in clinical systems will become more pervasive. These applications include: diagnostics, such as colonoscopy and bronchoscopy; guiding biopsies, minimally invasive interventions, and surgery; automating instrument motion; and providing image guidance using pre-operative scans. Many of t

Christopher Fifty, Dennis Duan, Ronald G. Junkins, Ehsan Amid

Large Language Models like ChatGPT demonstrate a remarkable capacity to learn new concepts during inference without any fine-tuning. However, visual models trained to detect new objects during inference have been unable to replicate this ability, and instead either perform poorly or require meta-training and/or fine-tuning on similar objects. In this work, w

Ruixian Liu, Peter Gerstoft

The physics-informed neural network (PINN) is capable of recovering partial differential equation (PDE) coefficients that remain constant throughout the spatial domain directly from physical measurements. In this work, we propose a spatially dependent physics-informed neural network (SD-PINN), which enables the recovery of coefficients in spatially-dependent

Hannah Santa Cruz Baur, Vladimir Itskov

Hodge Laplacians have been previously proposed as a natural tool for understanding higher-order interactions in networks and directed graphs. Here we introduce a Hodge-theoretic approach to spectral theory and dimensionality reduction for probability distributions on sequences and simplicial complexes. We demonstrate that this Hodge theory has desirable prop

L. Lanosa, O. Santillan

In the present letter the infinite domain wall geometry in GR \cite{vilenkin1}-\cite{ipser} is reconsidered in Taub coordinates \cite{taub}. The use of these coordinates makes explicit that the regions between the horizons and the wall and the outer ones are flat. By use of these coordinates, it is suggested that points inside the horizon and outside never c

Hang Yin, Pinren Lu, Ziang Li, Bin Sun

The need for high-quality data has been a key issue hindering the research of dialogue tasks. Recent studies try to build datasets through manual, web crawling, and large pre-trained models. However, man-made data is expensive and data collected from the internet often includes generic responses, meaningless statements, and toxic dialogues. Automatic data ge

Federica Ferretti, Mehran Kardar

We introduce a novel, multi-scale model for affinity maturation, which aims to capture the intra-clonal, inter-clonal and epitope-specific organization of the B cell population in a germinal center. We describe the evolution of the B cell population via a quasispecies dynamics, with species corresponding to unique B cell receptors (BCRs), where the desired m

Dongmei Yu, Gehao Zhang, Cairong Chen, Deren Han

An inverse-free neural network model with mixed delays is proposed for solving the absolute value equation (AVE) $Ax -|x| - b =0$, which includes an inverse-free neural network model with discrete delay as a special case. By using the Lyapunov-Krasovskii theory and the linear matrix inequality (LMI) method, the developed neural network models are proved to b

Jiahui Feng, Zheng Shi, Yaru Fu, Hong Wang

A variable-length cross-packet hybrid automatic repeat request (VL-XP-HARQ) is proposed to boost the spectral efficiency (SE) and the energy efficiency (EE) of communications. The SE is firstly derived in terms of the outage probabilities, with which the SE is proved to be upper bounded by the ergodic capacity (EC). Moreover, to facilitate the maximization o

Seyedeh Mahya Mousavi, Mohammad Mostafavi

This paper presents a method based on a kernel dictionary learning algorithm for segmenting brain tumor regions in magnetic resonance images (MRI). A set of first-order and second-order statistical feature vectors are extracted from patches of size 3 * 3 around pixels in the brain MRI scans. These feature vectors are utilized to train two kernel dictionaries

Huiming Wang, Zhaodonghui Li, Liying Cheng, Soh De Wen

Recently, large language models (LLMs) have emerged as a groundbreaking technology and their unparalleled text generation capabilities have sparked interest in their application to the fundamental sentence representation learning task. Existing methods have explored utilizing LLMs as data annotators to generate synthesized data for training contrastive learn

Nikhil Angad Bakshi, Jeff Schneider

Stealthy multi-agent active search is the problem of making efficient sequential data-collection decisions to identify an unknown number of sparsely located targets while adapting to new sensing information and concealing the search agents' location from the targets. This problem is applicable to reconnaissance tasks wherein the safety of the search agents c

Sayan Das, Weitao Zhu

We consider the log-Gamma polymer in the half-space with bulk weights distributed as $\operatorname{Gamma}^{-1}(2\theta)$ and diagonal weights as $\operatorname{Gamma}^{-1}(\alpha+\theta)$ for $\theta>0$ and $\alpha>-\theta$. We show that in the bound phase, i.e., when $\alpha\in (-\theta,0)$, the endpoint of the polymer lies within an $O(1)$ stochastic wind

Shuai Liu, Sheeraz Athar, Michael Yu Wang

Origami offers a promising alternative for designing innovative soft robotic actuators. While features of origami, such as bi-directional motion and structural anisotropy, haven't been extensively explored in the past, this letter presents a novel design inspired by origami tubes for a bi-directional actuator. This actuator is capable of moving in two orthog

Hejie Ying, Mengmeng Song, Yaohong Tang, Shungen Xiao

Deep neural networks (DNN) have achieved remarkable success in various fields, including computer vision and natural language processing. However, training an effective DNN model still poses challenges. This paper aims to propose a method to optimize the training effectiveness of DNN, with the goal of improving model performance. Firstly, based on the observ

Long Zeng, Kaigui Wu

Transformers have achieved significant success in medical image segmentation, owing to its capability to capture long-range dependencies. Previous works incorporate convolutional layers into the encoder module of transformers, thereby enhancing their ability to learn local relationships among pixels. However, transformers may suffer from limited generalizati

Jules Deschamps, Quentin Hubert, Lucas Ryckelynck

In the context of a group project for the course COMSW4995 002 - Geometric Data Analysis, we bring our attention to the design of fast-typing keyboards. Leveraging some geometric tools in an optimization framework allowed us to propose novel keyboard layouts that offer a faster typing.

Esmat Sahak, Zining Zhu, Frank Rudzicz

The impressive success of recent deep neural network (DNN)-based systems is significantly influenced by the high-quality datasets used in training. However, the effects of the datasets, especially how they interact with each other, remain underexplored. We propose a state-vector framework to enable rigorous studies in this direction. This framework uses idea

Ratna Chakrabarty, Angshuman Khan

Quantum dot Cellular Automata (QCA) is the most promising nanotechnology in the field of microelectronics and VLSI systems. QCA-based circuits require less power with a high switching speed of operation compared to CMOS technology. QCA inverter is one of the basic building blocks of QCA circuit design. The conventional QCA inverter design requires many cells

Yeganeh Alimohammadi, Luana Ruiz, Amin Saberi

We propose a theoretical framework for training Graph Neural Networks (GNNs) on large input graphs via training on small, fixed-size sampled subgraphs. This framework is applicable to a wide range of models, including popular sampling-based GNNs, such as GraphSAGE and FastGCN. Leveraging the theory of graph local limits, we prove that, under mild assumptions

Jie Jian, Mu Zhu, Peijun Sang

The stochastic block model (SBM) is a widely used framework for community detection in networks, where the network structure is typically represented by an adjacency matrix. However, conventional SBMs are not directly applicable to an adjacency matrix that consists of non-negative zero-inflated continuous edge weights. To model the international trading netw

Zongyi Li, Hongbing Lyu, Jun Wang

In recent years, U-Net and its variants have been widely used in pathology image segmentation tasks. One of the key designs of U-Net is the use of skip connections between the encoder and decoder, which helps to recover detailed information after upsampling. While most variations of U-Net adopt the original skip connection design, there is semantic gap betwe

Jinghai Shao

This work concerns the optimal control problem for McKean-Vlasov SDEs. In order to characterize the value function, we develop the viscosity solution theory for Hamilton-Jacobi-Bellman (HJB) equations on the Wasserstein space using Mortensen's derivative. In particular, a comparison principle for viscosity solution is established. Our approach is based on Bo

Jinghai Shao

This work concerns the optimal control problem for McKean-Vlasov SDEs. We provide explicit conditions to ensure the existence of optimal Markovian feedback controls. Moreover, based on the flow property of the McKean-Vlasov SDE, the dynamic programming principle is established, which will enable to characterize the value function via the theory of Hamilton-J

Nanduni Nimalsiri, Elizabeth Ratnam

This paper proposes a distributed optimization-based algorithm for electric vehicle (EV) charging and discharging, incorporating EV customer economics and distribution network constraints enforced on an unbalanced distribution grid. Building on a consensus-based alternating direction method of multipliers (ADMM), the algorithm is designed such that EVs coord

Xianyue Peng, Shenyang Chen, Hang Gao, Hao Wang

Over the years, reinforcement learning has emerged as a popular approach to develop signal control and vehicle platooning strategies either independently or in a hierarchical way. However, jointly controlling both in real-time to alleviate traffic congestion presents new challenges, such as the inherent physical and behavioral heterogeneity between signal co

Tomas Basile, Jose Alfredo de Leon, Alejandro Fonseca, Francois Leyvraz

Quantum channels, a subset of quantum maps, describe the unitary and non-unitary evolution of quantum systems. We study a generalization of the concept of Pauli maps to the case of multipartite high dimensional quantum systems through the use of the Weyl operators. The condition for such maps to be valid quantum channels, i.e. complete positivity, is derived

Yasunari Hikima

This paper studies bandit convex optimization with constraints, where the learner aims to generate a sequence of decisions under partial information of loss functions such that the cumulative loss is reduced as well as the cumulative constraint violation is simultaneously reduced. We adopt the cumulative \textit{hard} constraint violation as the metric of co

Juzhan Xu, Minglun Gong, Hao Zhang, Hui Huang

We present a novel learning framework to solve the transport-and-packing (TAP) problem in 3D. It constitutes a full solution pipeline from partial observations of input objects via RGBD sensing and recognition to final box placement, via robotic motion planning, to arrive at a compact packing in a target container. The technical core of our method is a neura

Yijie Zhou, Robert Ciarla, Artittaya Boonkird, Thanh Nguyen

To push upper boundaries of effective thermal conductivity in polymer composites, a fundamental understanding of thermal transport mechanisms is crucial. Although there is intensive simulation research, systematic experimental investigation on thermal transport in polymer composites is limited. To better understand thermal transport processes, we design poly

Wenhua Cheng, Yiyang Cai, Kaokao Lv, Haihao Shen

As large language models (LLMs) become more prevalent, there is a growing need for new and improved quantization methods that can meet the computationalast layer demands of these modern architectures while maintaining the accuracy. In this paper, we present TEQ, a trainable equivalent transformation that preserves the FP32 precision of the model output while

Yunlong Song, Angel Romero, Matthias Mueller, Vladlen Koltun

A central question in robotics is how to design a control system for an agile mobile robot. This paper studies this question systematically, focusing on a challenging setting: autonomous drone racing. We show that a neural network controller trained with reinforcement learning (RL) outperformed optimal control (OC) methods in this setting. We then investigat

Yangyang Guo, Fangkai Jiao, Zhiqi Shen, Liqiang Nie

Teaching Visual Question Answering (VQA) models to refrain from answering unanswerable questions is necessary for building a trustworthy AI system. Existing studies, though have explored various aspects of VQA but somewhat ignored this particular attribute. This paper aims to bridge the research gap by contributing a comprehensive dataset, called UNK-VQA. Th

Fardin Ahsan Sakib, Ahnaf Atef Choudhury, Ozlem Uzuner

Depression is a mental health disorder that has a profound impact on people's lives. Recent research suggests that signs of depression can be detected in the way individuals communicate, both through spoken words and written texts. In particular, social media posts are a rich and convenient text source that we may examine for depressive symptoms. The Beck De

Michael Updike, Joshua Burby

We present a Hamiltonian method of constructing BBGKY-like hierarchies for quantum field theories. With suitable choices, our method creates a hierarchical system of evolution equations for the k-th order reduced density matrices. These equations can be closed at finite order using methods developed for the classical BBGKY hierarchy to give non-perturbative

Peter Macgregor

Spectral clustering is a popular and effective algorithm designed to find $k$ clusters in a graph $G$. In the classical spectral clustering algorithm, the vertices of $G$ are embedded into $\mathbb{R}^k$ using $k$ eigenvectors of the graph Laplacian matrix. However, computing this embedding is computationally expensive and dominates the running time of the a

Dmitri V. Alekseevsky, Masoud Ganji, Gerd Schmalz, Andrea Spiro

We explicitly derive the Christoffel symbols in terms of adapted frame fields for the Levi-Civita connection of a Lorentzian $n$-manifold $(M, g)$, equipped with a prescribed optical geometry of K\"ahler-Sasaki type. The formulas found in this paper have several important applications, such as determining the geometric invariants of Lorentzian manifolds with

I. A. Ivanov, A. S. Kheifets, Kyung Taec Kim

We study formation and evolution of the electron wave-packets in the process of strong field ionization of various atomic targets. Our study is based on reformulating the problem in terms of conditional amplitudes, i.e., the amplitudes describing outcomes of measurements of different observables provided that the electron is found in the ionized state after

Katsumi Ishikawa, Takayuki Morifuji, Masaaki Suzuki

Based on a vanishing theorem for non-fibered knots due to Friedl and Vidussi, we define the twisted Alexander vanishing order of a knot to be the order of the smallest finite group such that the corresponding twisted Alexander polynomial is zero. In this paper, we show its basic properties, and provide several explicit values for knots with $10$ or fewer cro

Fardin Ahsan Sakib, A H M Rezaul Karim, Saadat Hasan Khan, Md Mushfiqur Rahman

As voice assistants cement their place in our technologically advanced society, there remains a need to cater to the diverse linguistic landscape, including colloquial forms of low-resource languages. Our study introduces the first-ever comprehensive dataset for intent detection and slot filling in formal Bangla, colloquial Bangla, and Sylheti languages, tot

Denis Boyer, Gabriel Mercado-Vásquez, Satya N. Majumdar, Grégory Schehr

In many random search processes of interest in chemistry, biology or during rescue operations, an entity must find a specific target site before the latter becomes inactive, no longer available for reaction or lost. We present exact results on a minimal model system, a one-dimensional searcher performing a discrete time random walk or L\'evy flight. In contr

Yue Heng Liu, Qi Li

The challenge in building high-fidelity quantum gates lies in overcoming control errors and decoherence effects caused by the coupling between the quantum system and the external environment. Nonadiabatic holonomic quantum computation uses the topological protection of the cyclic evolution of the computational subspace to make holonomic gates highly robust t

Wayne A. Johnson

For a semisimple, simply-connected linear algebraic group, $G$, and parabolic subgroup, $P\subseteq G$, we use the fact that the Hilbert polynomial of the equivariant embedding of $G/P$ is equal to the Hilbert function to compute an explicit formula for the Hilbert series of $G/P$ in terms of the dimensions of finitely many irreducible representations of $G$

Yanyan Li, Zhao Guo, Ze Yang, Yanbiao Sun

This paper presents Open-Structure, a novel benchmark dataset for evaluating visual odometry and SLAM methods. Compared to existing public datasets that primarily offer raw images, Open-Structure provides direct access to point and line measurements, correspondences, structural associations, and co-visibility factor graphs, which can be fed to various stages

Woohyeon Moon, Taeyoung Kim, Bumgeun Park, Dongsoo Har

Transformer is a state-of-the-art model in the field of natural language processing (NLP). Current NLP models primarily increase the number of transformers to improve processing performance. However, this technique requires a lot of training resources such as computing capacity. In this paper, a novel structure of Transformer is proposed. It is featured by f

Wei Liu, Jingqi Chen, Wenjie Dou

We present a non-perturbative theory that describes how light regulates chiral-induced spin selectivity (CISS) from the perspective of strong light-matter interactions. The research results indicate that 1) light can have opposite effects on the CISS, 2) the difference in CISS is caused by the steady states of nuclei coupled to spin electrons and 3) this ste

Adam Valen Levinson, Abhay Goyal, Roger Ho Chun Man, Roy Ka-Wei Lee

Telehealth is a valuable tool for primary health care (PHC), where depression is a common condition. PHC is the first point of contact for most people with depression, but about 25% of diagnoses made by PHC physicians are inaccurate. Many other barriers also hinder depression detection and treatment in PHC. Artificial intelligence (AI) may help reduce depres

Huai-Ke Guo

We show that dissipative effects during cosmological first order phase transitions lead to a frequency-dependent suppression for the usually dominant gravitational wave production from sound waves, through an analytical modelling of the source based on the sound shell model. This damping effect is more pronounced for high frequencies or small scales, and mod

Shuang Chen, Jicai Huang

In this paper, we study the destabilization of synchronous periodic solutions for patch models. By applying perturbation theory for matrices, we derive asymptotic expressions of the Floquet spectra and provide a destabilization criterion for synchronous periodic solutions arising from closed orbits or degenerate Hopf bifurcations in terms of period functions

Dan Shen

Automated Vehicle Path Following Control (PFC) is an advanced control system that can regulate the vehicle into a collision-free region in the presence of other objects on the road. Common collision avoidance functions, such as forward collision warning and automatic emergency braking, have recently been developed and equipped on production vehicles. However

Surajit Sen, Tushar Kanti Dey, Bimalendu Deb

We theoretically study the resonance fluorescence spectra of the lambda ($\Lambda$), vee ($V$) and cascade ($\Xi$) type three-level configurations. It is shown that each system with two detuning frequencies can be modelled using the $SU(3)$ symmetry group to derive a generalized optical Bloch equation. For each configuration, this equation is solved to calcu

Jeremy Bourhill, Gwendal Cochet, Julien Haumant, Vincent Vlaminck

We report the first observation of a superconducting transition in a 3D printed, metallised-plastic device. A cylindrical cavity is 3D printed from a photosensitive polymer resin and then a 20 $\mu$m layer of tin deposited. A resonant TE microwave mode at 13.41 GHz is observed to reduce its losses by an order of magnitude once it is cooled below 3.72 K; the

Dongyoung Go, Tomasz Korbak, Germán Kruszewski, Jos Rozen

As language models (LMs) become more capable, it is increasingly important to align them with human preferences. However, the dominant paradigm for training Preference Models (PMs) for that purpose suffers from fundamental limitations, such as lack of transparency and scalability, along with susceptibility to overfitting the preference dataset. We propose Co

Antoni Dimitriadis, Siqi Pan, Vidhyasaharan Sethu, Beena Ahmed

Self-supervised learning has been used to leverage unlabelled data, improving accuracy and generalisation of speech systems through the training of representation models. While many recent works have sought to produce effective representations across a variety of acoustic domains, languages, modalities and even simultaneous speakers, these studies have all b

Nathan Cooper

Software has eaten the world with many of the necessities and quality of life services people use requiring software. Therefore, tools that improve the software development experience can have a significant impact on the world such as generating code and test cases, detecting bugs, question and answering, etc., The success of Deep Learning (DL) over the past

Anurag Acharya, Sai Munikoti, Aaron Hellinger, Sara Smith

As LLMs have become increasingly popular, they have been used in almost every field. But as the application for LLMs expands from generic fields to narrow, focused science domains, there exists an ever-increasing gap in ways to evaluate their efficacy in those fields. For the benchmarks that do exist, a lot of them focus on questions that don't require prope

C. Drago, J. E. Sipe

We develop a formalism to describe squeezed light with large spectral-temporal correlations. This description is valid in all regimes, but is especially applicable in the long pulse to continuous-wave limit where the photon density at any particular time is small, although the total number of photons can be quite large. Our method relies on the Whittaker-Sha

Ryan Stees

In his 1957 paper, John Milnor introduced a collection of invariants for links in $S^3$ detecting higher-order linking phenomena by studying lower central quotients of link groups and comparing them to those of the unlink. These invariants, now known as Milnor's $\overline{\mu}$-invariants, were later shown to be topological link concordance invariants and h

Boqun Zhao, Chongjun Ouyang, Yuanwei Liu, Xingqi Zhang

As the technical trends for the next-generation wireless network significantly extend the near-field region, a performance reevaluation of integrated sensing and communications (ISAC) with an appropriate channel model to account for the effects introduced by the near field becomes essential. In this paper, a near-field ISAC framework is proposed for both dow

L. Y. Shi, Zhenisbek Tagay, Jiahao Liang, Khoan Duong

We present time-domain THz spectroscopy of thin films of the heavy-fermion superconductor CeCoIn$_5$. Below the $\approx$ 40 K Kondo coherence temperature, a narrow Drude-like peak forms, as the result of the $f$ orbital - conduction electron hybridization and the formation of the heavy-fermion state. The complex optical conductivity is analyzed through a Dr

Andrew J. Womack, Daniel Taylor-Rodriguez, Gerasimos Fergadiotis, William D. Hula

Naming tests represent an essential tool in gauging the severity of aphasia and monitoring the trajectory of recovery for individuals afflicted with this debilitating condition. In these assessments, patients are presented with images corresponding to common nouns, and their responses are evaluated for accuracy. The Philadelphia Naming Test (PNT) stands as a

Yuanyuan Qiao, Yi Zhou

This paper investigates the stabilization effect of a background magnetic vorticity on electrically conducting fluids. By exploring the dissipation nature of the linearized equations, we prove the global existence of smooth solutions to the two-dimensional incompressible viscous and non-resistive MHD system.

Juan C. R. Luna, Mini Das

Photon counting detectors (PCDs) offer promising advancements in computed tomography (CT) imaging by enabling the quantification and 3D imaging of contrast agents and tissue types through multi-energy projections. However, the accuracy of these decomposition methods hinges on precise composite spectral attenuation values that one must reconstruct from spectr

Lv Tang, Peng-Tao Jiang, Hao-Ke Xiao, Bo Li

The realm of computer vision has witnessed a paradigm shift with the advent of foundational models, mirroring the transformative influence of large language models in the domain of natural language processing. This paper delves into the exploration of open-world segmentation, presenting a novel approach called Image Prompt Segmentation (IPSeg) that harnesses

Yangliu Wu, Deju Zhang, Yanning Zhang, Longjiang Deng

Multiferroic materials provide robust and efficient routes for the control of magnetism by electric fields, which has been diligently sought after for a long time. The two-dimensional (2D) vdW multiferroics is a more exciting endeavour. To date, the nonvolatile manipulation of magnetism through ferroelectric polarization still remains challenging in a 2D vdW

Davut Emre Tasar, Kutan Koruyan, Ceren Ocal Tasar

This work endeavors to juxtapose the efficacy of machine learning algorithms within classical and quantum computational paradigms. Particularly, by emphasizing on Support Vector Machines (SVM), we scrutinize the classification prowess of classical SVM and Quantum Support Vector Machines (QSVM) operational on quantum hardware over the Iris dataset. The method

Zihan Qiu, Zhen Liu, Shuicheng Yan, Shanghang Zhang

It has been shown that semi-parametric methods, which combine standard neural networks with non-parametric components such as external memory modules and data retrieval, are particularly helpful in data scarcity and out-of-distribution (OOD) scenarios. However, existing semi-parametric methods mostly depend on independent raw data points - this strategy is d

Zihan Qiu, Zeyu Huang, Jie Fu

Modular Neural Networks (MNNs) demonstrate various advantages over monolithic models. Existing MNNs are generally $\textit{explicit}$: their modular architectures are pre-defined, with individual modules expected to implement distinct functions. Recent works reveal that there exists $\textit{implicit}$ modularity in standard pre-trained transformers, namely

Nathan Wycoff

Surrogate modeling and active subspaces have emerged as powerful paradigms in computational science and engineering. Porting such techniques to computational models in the social sciences brings into sharp relief their limitations in dealing with discontinuous simulators, such as Agent-Based Models, which have discrete outputs. Nevertheless, prior applied wo

Zhigang Cai, Yi-Xiang Wang

Recent experiments reported that the magnetic field can drive the Lifshitz transition and one-dimensional (1D) Weyl nodes in the quantum limit of three-dimensional pentatellurides, as they own low carrier densities and can achieve the extreme quantum limit at a low magnetic field. In this paper, we will investigate the conditions for the existence of the 1D

Samuel R. Vizvary, Zachary J. Wall, Matthew J. Boguslawski, Michael Bareian

The $\textit{omg}$ protocol is a promising paradigm that uses multiple, application-specific qubit subspaces within the Hilbert space of each single atom during quantum information processing. A key assumption for $\textit{omg}$ operation is that a subspace can be accessed independently without deleterious effects on information stored in other subspaces. We

A. Emran, D. P. Cruikshank, C. J. Ahrens, J. M. Moore

In contrast with regional primarily methane composition, Kiladze and its surroundings exhibit a water-ice spectral signature that carries an ammoniated compound, similar to two other cryovolcanic sites on Pluto. The faulted structure of Kiladze, including shaping by numerous collapse pits and the distortion of the shape of the depression, are compatible with

Sharin Jacob, Tamara Tate, Mark Warschauer

The rapid proliferation of ChatGPT has incited debates regarding its impact on human writing. Amid concerns about declining writing standards, this study investigates the role of ChatGPT in facilitating academic writing, especially among language learners. Using a case study approach, this study examines the experiences of Kailing, a doctoral student, who in

Yue Niu, Rajgopal Kannan, Ajitesh Srivastava, Viktor Prasanna

Spectral-domain CNNs have been shown to be more efficient than traditional spatial CNNs in terms of reducing computation complexity. However they come with a `kernel explosion' problem that, even after compression (pruning), imposes a high memory burden and off-chip bandwidth requirement for kernel access. This creates a performance gap between the potential

Xiaoying Wang, Yuecai Han, Yong Li

The main focus of this paper is to explore how much similarity between two stochastic differential systems. Motivated by the conjugate theory of stochastic dynamic systems, we study the relationship between two systems by finding homeomorphic mappings $K$. Particularly, we use the minimizer $K^*$ to measure the degree of similarity. Under appropriate assumpt

Ery Arias-Castro, Siddharth Vishwanath

Sequential lateration is a class of methods for multidimensional scaling where a suitable subset of nodes is first embedded by some method, e.g., a clique embedded by classical scaling, and then the remaining nodes are recursively embedded by lateration. A graph is a lateration graph when it can be embedded by such a procedure. We provide a stability result

Enyan Zhang, Michael A. Lepori, Ellie Pavlick

Despite the recent success of artificial neural networks on a variety of tasks, we have little knowledge or control over the exact solutions these models implement. Instilling inductive biases -- preferences for some solutions over others -- into these models is one promising path toward understanding and controlling their behavior. Much work has been done t

Samin Aref, Mahdi Mostajabdaveh

Community detection, which involves partitioning nodes within a network, has widespread applications across computational sciences. Modularity-based algorithms identify communities by attempting to maximize the modularity function across network node partitions. Our study assesses the performance of various modularity-based algorithms in obtaining optimal pa

Tetsuya Shiromizu, Keisuke Izumi

Under certain conditions, it is shown that the positivity of the Geroch/Hawking quasi-local mass holds for the attractive gravity probe surfaces in any higher dimensions than three. We also comment on the Arnowitt-Deser-Misner mass.

Leo Yoshioka

Graph homologies are powerful tools to compute the rational homotopy group of the space of long embeddings. Two graph homologies have been invented from two approaches to study the space of long embeddings: the hairy graph homology from embedding calculus, and BCR graph homology from configuration space integral. In this paper, we construct a monomorphism fr

Luis A. Álvarez-García, Wolfram Liebermeister, Ian Leifer, Hernán A. Makse

Symmetry principles play an important role in geometry, and physics, allowing for the reduction of complicated systems to simpler, more comprehensible models that preserve the system's features of interest. Biological systems are often highly complex and may consist of a large number of interacting parts. Using symmetry fibrations, the relevant symmetries fo

Ognjen Milatovic

In analogy with the definition of ``extended Sobolev scale" on $\mathbb{R}^n$ by Mikhailets and Murach, working in the setting of the lattice $\mathbb{Z}^n$, we define the ``extended Sobolev scale" $H^{\varphi}(\mathbb{Z}^n)$, where $\varphi$ is a function which is $RO$-varying at infinity. Using the scale $H^{\varphi}(\mathbb{Z}^n)$, we describe all Hilbert

Kristian Hauser Villegas

We formulate a generalized pseudospin formalism for multiband superconductors in the presence of an external perturbing electromagnetic field. Our theory naturally captures the effects of quantum band geometric quantities and is valid even for flat-band superconductors. As an interesting consequence of our theory, we show that there is an interband pairing f

Rodrigo Gutiérrez-Cuevas, Mark R. Dennis, Miguel A. Alonso

The Ince-Gauss beams, separable in elliptic coordinates, are studied through a ray-optical approach. Their ray structure can be represented over a ray-Poincaré sphere by generalized Viviani curves (intersections of a cylinder and a sphere). This representation shows two topologically different regimes, in which the curve is composed of one or two loops. The

Kasturi Barkataki, Louis H. Kauffman, Eleni Panagiotou

The entanglement of open curves in 3-space appears in many physical systems and affects their material properties and function. A new framework in knot theory was introduced recently, that enables to characterize the complexity of collections of open curves in 3-space using the theory of knotoids and linkoids, which are equivalence classes of diagrams with o

Hyun-Young Park, Seung-Hyun Nam, Si-Hyeon Lee

In this paper, we propose a new class of local differential privacy (LDP) schemes based on combinatorial block designs for discrete distribution estimation. This class not only recovers many known LDP schemes in a unified framework of combinatorial block design, but also suggests a novel way of finding new schemes achieving the exactly optimal (or near-optim

Pengfei Zhang, Seojin Bang, Heewook Lee

T cell receptors (TCRs) are critical components of adaptive immune systems, responsible for responding to threats by recognizing epitope sequences presented on host cell surface. Computational prediction of binding affinity between TCRs and epitope sequences using machine/deep learning has attracted intense attention recently. However, its success is hindere

Sebastiaan Y. Haffert, Jared R. Males, Olivier Guyon

Uncorrected wavefront errors create speckle noise in high-contrast observations at small inner-working angles. These speckles can be sensed and controlled by using coronagraph integrated wavefront sensors. Here, we will present how the Phase Induced Amplitude Apodized Complex Mask Corongraph (PIAACMC) can be integrated with both a Self-Coherent Camera (SCC)

Xiangdi Huang, Wei Yan

In this paper, we prove the global existence of strong solutions to the two-dimensional compressible MHD equations with density dependent viscosity coefficients (known as Kazhikhov-Vaigant model) on 2D solid balls with arbitrary large initial smooth data where shear viscosity $\mu$ being constant and the bulk viscosity $\lambda$ be a polynomial of density up

Martin Bridgeman, Kenneth Bromberg

We bound the derivative of complex length of a geodesic under variation of the projective structure on a closed surface in terms of the norm of the Schwarzian in a neighborhood of the geodesic. One application is to cone-manifold deformations of acylindrical hyperbolic 3-manifolds.

Sebastiaan Y. Haffert, Jared R. Males, Olivier Guyon

In the last two decades many people have been searching for the optimal wavefront sensor as it can boost the performance of high-contrast imagining by orders of magnitude on the ELTs. According classical information theory, the optimal sensitivity of a wavefront sensor is 1/2 radian rms per photon. We show that classical limit is also the quantum metrology l

Maggie Kautz, Jared R. Males, Laird M. Close, Sebastiaan Y. Haffert

GMagAO-X is a visible to NIR extreme adaptive optics (ExAO) system that will be used at first light for the Giant Magellan Telescope (GMT). GMagAO-X is designed to deliver diffraction-limited performance at visible and NIR wavelengths (6 to 10 mas) and contrasts on the order of $10^{-7}$. The primary science case of GMagAO-X will be the characterization of m

Paul Leger, Felipe Ruiz, Nicolás Sepúlveda, Ismael Figueroa

Context. Package repositories for a programming language are increasingly common. A repository can keep a register of the evolution of its packages. In the programming language Haskell, with its defining characteristic monads, we can find the Stackage repository, which is a curated repository for stable Haskell packages in the Hackage repository. Despite the

Douglas S. Blank, James B. Marshall

Calysto Scheme is written in Scheme in Continuation-Passing Style, and converted through a series of correctness-preserving program transformations into Python. It has support for standard Scheme functionality, including call/cc, as well as syntactic extensions, a nondeterministic operator for automatic backtracking, and many extensions to allow Python inter

Azar C. Nakhl, Thomas Quella, Muhammad Usman

Entanglement is a key property of quantum computing that separates it from its classical counterpart, however, its exact role in the performance of quantum algorithms, especially variational quantum algorithms, is not well understood. In this work, we utilise tensor network methods to systematically probe the role of entanglement in the working of two variat

Gregor Posnjak, Xin Yin, Paul Butler, Oliver Bienek

Colloidal self-assembly allows rational design of structures on the micrometer and submicrometer scale. One architecture that can generate complete 3D photonic band gaps is the diamond cubic lattice, which has remained difficult to realize at length scales comparable to the wavelength of visible or ultraviolet light. Here, we demonstrate three-dimensional ph

Katherine Goldman

Shephard groups are common generalizations of Coxeter groups, Artin groups, and graph products of cyclic groups. Their definition is similar to that of a Coxeter group, but generators may have arbitrary order rather than strictly order 2. We extend a well known result that Coxeter groups are $\mathrm{CAT}(0)$ to a class of Shephard groups that have "enough"

Daniel Leiker

The Generative Education (GenEd) Framework explores the transition from Large Language Models (LLMs) to Large Multimodal Models (LMMs) in education, envisioning a harmonious relationship between AI and educators to enhance learning experiences. This paper delves into the potential of LMMs to create personalized, interactive, and emotionally-aware learning en

Timothy Proctor, Kevin Young

We present an algorithm that decomposes any $n$-qubit Clifford operator into a circuit consisting of three subcircuits containing only CNOT or CPHASE gates with layers of one-qubit gates before and after each of these subcircuits. As with other asymptotically optimal Clifford compilation algorithms, the resulting circuit contains $O(n^2/\log n)$ two-qubit ga

F. Demontis, S. Pennisi

We consider two possible ways, i.e., the Maxwellian Iteration and the Chapman-Enskog Method, to recover Relativistic Ordinary Thermodynamics from Relativistic Extended Thermodynamics of Polyatomic gases with N moments. Both of these methods give the Eckart equations which are the relativistic version of the Navier-Stokes and Fourier laws as a first iteration