April 2023 arXiv papers — page 11

Andrew Clark, Stuart Phinn, Peter Scarth

In this paper, we trialled different methods of data preparation for Convolutional Neural Network (CNN) training and semantic segmentation of land use land cover (LULC) features within aerial photography over the Wet Tropics and Atherton Tablelands, Queensland, Australia. This was conducted through trialling and ranking various training patch selection sampl

Seungjin Lee, Kyunghyun Baek, Jeongho Bang

We provide an angular parametrization of the special unitary group $\textrm{SU}(2^{n})$ generalizing Euler angles for $\textrm{SU}(2)$ by successively applying the KAK decomposition. We then determine constraint equations for the parametric curve of generalized Euler angles corresponding to the exponential curve of a given Hamiltonian. The constraint equatio

Lu Yu, Malvina Nikandrou, Jiali Jin, Verena Rieser

Automated image captioning has the potential to be a useful tool for people with vision impairments. Images taken by this user group are often noisy, which leads to incorrect and even unsafe model predictions. In this paper, we propose a quality-agnostic framework to improve the performance and robustness of image captioning models for visually impaired peop

Babar Shahzaad, Balsam Alkouz, Jermaine Janszen, Athman Bouguettaya

We propose a novel context-aware drone delivery framework for optimizing package delivery through skyway networks in smart cities. We reformulate the problem of finding an optimal drone service delivery pathway as a more congruent and elegant drone delivery service composition problem. In this respect, we propose a novel line-of-sight heuristic-based context

Chenqing Hua, Sitao Luan, Minkai Xu, Rex Ying

Molecule generation is a very important practical problem, with uses in drug discovery and material design, and AI methods promise to provide useful solutions. However, existing methods for molecule generation focus either on 2D graph structure or on 3D geometric structure, which is not sufficient to represent a complete molecule as 2D graph captures mainly

Takanori Anegawa, Norihiro Iizuka

We study the holographic complexity in de Sitter spacetime, especially how the hyperfast growth of holographic complexity in de Sitter spacetime is affected under a small and early perturbation. The perturbed geometry is de Sitter spacetime with shock waves. We find that the critical time, at which de Sitter holographic complexity diverges, becomes always gr

Dangxing Chen, Weicheng Ye

In this paper, we study the problem of establishing the accountability and fairness of transparent machine learning models through monotonicity. Although there have been numerous studies on individual monotonicity, pairwise monotonicity is often overlooked in the existing literature. This paper studies transparent neural networks in the presence of three typ

Ailing Pan, Chao Dai, Chen Pan, Dongping Zhang

The majority of current salient object detection (SOD) models are focused on designing a series of decoders based on fully convolutional networks (FCNs) or Transformer architectures and integrating them in a skillful manner. These models have achieved remarkable high performance and made significant contributions to the development of SOD. Their primary rese

Yilin Lyu, Xin Liu, Mingyang Song, Xinyue Wang

Information Bottlenecks (IBs) learn representations that generalize to unseen data by information compression. However, existing IBs are practically unable to guarantee generalization in real-world scenarios due to the vacuous generalization bound. The recent PAC-Bayes IB uses information complexity instead of information compression to establish a connectio

Akira Kofuji, Robert Peters

High harmonic generation(HHG) is one of the most commonly studied nonlinear optical phenomena, originating in the ultrafast dynamics of electrons in atomic gasses and semiconductors. It has attracted much attention because of its non-perturbative nature and potential for future attosecond laser pulse sources. On the theory side, a semi-classical picture base

Zhengqian Cheng, Chris A. Marianetti

The recently developed variational discrete action theory (VDAT) provides a systematic variational approach to the ground state of the quantum many-body problem, where the quality of the solution is controlled by an integer $\mathcal{N}$, and increasing $\mathcal{N}$ monotonically approaches the exact solution. VDAT can be exactly evaluated in the $d=\infty$

Zishi Chen, Xueyuan Hu

We present a resource theory to investigate the power of a multqubit system as a probe in the task of dephasing estimation. Our approach employs the quantum Fisher information about the dephasing parameter as the resource measure. Based on the monotonicity of quantum Fisher information, we propose two sets of free operations in our resource theory, the Hammi

Zhiyuan Cheng, Hongjun Choi, James Liang, Shiwei Feng

Multi-sensor fusion (MSF) is widely used in autonomous vehicles (AVs) for perception, particularly for 3D object detection with camera and LiDAR sensors. The purpose of fusion is to capitalize on the advantages of each modality while minimizing its weaknesses. Advanced deep neural network (DNN)-based fusion techniques have demonstrated the exceptional and in

Yuchen Sun, Tianpeng Liu, Panhe Hu, Qing Liao

Deep Neural Networks (DNNs), from AlexNet to ResNet to ChatGPT, have made revolutionary progress in recent years, and are widely used in various fields. The high performance of DNNs requires a huge amount of high-quality data, expensive computing hardware, and excellent DNN architectures that are costly to obtain. Therefore, trained DNNs are becoming valuabl

Mingsong Li, Yikun Liu, Tao Xiao, Yuwen Huang

Pan-sharpening aims to increase the spatial resolution of the low-resolution multispectral (LrMS) image with the guidance of the corresponding panchromatic (PAN) image. Although deep learning (DL)-based pan-sharpening methods have achieved promising performance, most of them have a two-fold deficiency. For one thing, the universally adopted black box princip

Chunhui Chen, Xueyan Niu, Wenhao Ye, Shitong Wu

The nascent field of Rate-Distortion-Perception (RDP) theory is seeing a surge of research interest due to the application of machine learning techniques in the area of lossy compression. The information RDP function characterizes the three-way trade-off between description rate, average distortion, and perceptual quality measured by discrepancy between prob

Ling Li, Dong Liang, Yuanhang Gao, Sheng-Jun Huang

Evaluating the performance of low-light image enhancement (LLE) is highly subjective, thus making integrating human preferences into image enhancement a necessity. Existing methods fail to consider this and present a series of potentially valid heuristic criteria for training enhancement models. In this paper, we propose a new paradigm, i.e., aesthetics-guid

Sha Jin, Xue Fan, Caleb Stamper, Richard A. Mole

We report neutron-scattering measurements of the density of states (DOS) of water and liquid Fomblin in a wide range of temperatures. In the liquid phase, we confirm the presence of a universal low-energy linear scaling of the experimental DOS as a function of the frequency, $g(\omega)= a(T) \omega$, which persists at all temperatures. The low-frequency scal

Wenlian Li, Xiaohui Liu, Wei Tian, Fuyudi Zhang

In September 2021, a site scouting mission known as the TRIDENT pathfinder experiment (TRIDENT EXplorer, T-REX for short) was conducted in the South China Sea with the goal of envisaging a next-generation multi-cubic-kilometer neutrino telescope. One of the main tasks is to measure the in-situ optical properties of seawater at depths between $2800~\mathrm{m}

H S Perlman

It is shown that quantum mechanics is, like thermodynamics, a phenomenological theory i.e., not a causal theory, ( not because it is a statistical theory - statistical theories with caused probability distributions can be regarded as causal) but because pure states, i.e., probability distributions of measurement values, cannot inhere in elementary particles

Noorul Ali

A reward algorithm is needed for games which rewards risk, i.e. early play, and extends the longevity of a reward pool. This would allow a higher number of players and greater engagement. I created a reward mechanism that rewards risk, lasts longer, and is more profitable than existing mechanisms. I also implemented an algorithm within the mechanism to self-

Dhinakaran D, Joe Prathap P. M

Data mining is the way toward mining fascinating patterns or information from an enormous level of the database. Data mining additionally opens another risk to privacy and data security.One of the maximum significant themes in the research fieldis privacy-preserving DM (PPDM). Along these lines, the investigation of ensuring delicate information and securing

Kentaro Kanamori, Takuya Takagi, Ken Kobayashi, Yuichi Ike

This paper proposes a new framework of algorithmic recourse (AR) that works even in the presence of missing values. AR aims to provide a recourse action for altering the undesired prediction result given by a classifier. Existing AR methods assume that we can access complete information on the features of an input instance. However, we often encounter missin

D. Dhinakaran, P. M. Joe Prathap

These days, investigations of information are becoming essential for various associations all over the globe. By and large, different associations need to perform information examinations on their joined data sets. Privacy and security have become a relentless concern wherein business experts do not desire to contribute their classified transaction data. The

Yuehaw Khoo, Sounak Paul, Nir Sharon

Orbit recovery problems are a class of problems that often arise in practice and various forms. In these problems, we aim to estimate an unknown function after being distorted by a group action and observed via a known operator. Typically, the observations are contaminated with a non-trivial level of noise. Two particular orbit recovery problems of interest

Vishakha Ramani, Jiachen Chen, Roy D. Yates

Time-critical cyber-physical applications demand the timely delivery of information. In this work, we employ a high-speed packet processing testbed to quantitatively analyze a packet forwarding application running on a shared memory multi-processor architecture, where efficient synchronization of concurrent access to a Forwarding Information Base is essentia

Hongxiang Yu, Dashun Guo, Zhongxiang Zhou, Yue Wang

This paper introduces a learning-based framework for robot adaptive manipulating the object with a revolute joint in unstructured environments. We concentrate our discussion on various cabinet door opening tasks. To improve the performance of Deep Reinforcement Learning in this scene, we analytically provide an efficient sampling manner utilizing the constra

Jinhao Duan, Quanfu Fan, Hao Cheng, Xiaoshuang Shi

Recent works reveal that adversarial augmentation benefits the generalization of neural networks (NNs) if used in an appropriate manner. In this paper, we introduce Temporal Adversarial Augmentation (TA), a novel video augmentation technique that utilizes temporal attention. Unlike conventional adversarial augmentation, TA is specifically designed to shift t

Peng Gao, Liangyi Zhao

We develop the $L$-functions ratios conjecture with one shift in the numerator and denominator in certain ranges for the family of quadratic twist of modular $L$-functions using multiple Dirichlet series under the generalized Riemann hypothesis.

Gunther Jikeli, Sameer Karali, Daniel Miehling, Katharina Soemer

One of the major challenges in automatic hate speech detection is the lack of datasets that cover a wide range of biased and unbiased messages and that are consistently labeled. We propose a labeling procedure that addresses some of the common weaknesses of labeled datasets. We focus on antisemitic speech on Twitter and create a labeled dataset of 6,941 twee

Yandi Cao, Haifan Yin, Ziao Qin, Weidong Li

Massive multi-input multi-output (MIMO) in Frequency Division Duplex (FDD) mode suffers from heavy feedback overhead for Channel State Information (CSI). In this paper, a novel manifold learning-based CSI feedback framework (MLCF) is proposed to reduce the feedback and improve the spectral efficiency for FDD massive MIMO. Manifold learning (ML) is an effecti

David Gros, Prem Devanbu, Zhou Yu

In this 4-page manuscript we discuss the problem of long-term AI Safety from a Software Engineering (SE) research viewpoint. We briefly summarize long-term AI Safety, and the challenge of avoiding harms from AI as systems meet or exceed human capabilities, including software engineering capabilities (and approach AGI / "HLMI"). We perform a quantified litera

Wei Fang, Yu-Cheng Zhu, Yi-Han Cheng, Yi-Ping Hao

Simulation of surface processes is a key part of computational chemistry that offers atomic-scale insights into mechanisms of heterogeneous catalysis, diffusion dynamics, as well as quantum tunneling phenomena. The most common theoretical approaches involve optimization of reaction pathways, including semiclassical tunneling pathways (called instantons). How

Antoine Abram, Yining Hu, Shuo Li

Let $m$ be a positive integer larger than $1$, let $w$ be a finite word over $\left\{0,1,...,m-1\right\}$ and let $a_{m;w}(n)$ be the number of occurrences of the word $w$ in the $m$-expansion of $n$ mod $p$ for any non-negative integer $n$. In this article, we first give a fast algorithm to generate all sequences of the form $(a_{m;w}(n))_{n \in \mathbf{N}}

Shaoyan Pan, Chih-Wei Chang, Marian Axente, Tonghe Wang

The advent of computed tomography significantly improves patient health regarding diagnosis, prognosis, and treatment planning and verification. However, tomographic imaging escalates concomitant radiation doses to patients, inducing potential secondary cancer. We demonstrate the feasibility of a data-driven approach to synthesize volumetric images using pat

Rui Nian, Guoyao Zhang, Yao Sui, Yuqi Qian

Magnetic resonance imaging (MRI) is critically important for brain mapping in both scientific research and clinical studies. Precise segmentation of brain tumors facilitates clinical diagnosis, evaluations, and surgical planning. Deep learning has recently emerged to improve brain tumor segmentation and achieved impressive results. Convolutional architecture

Yongcheng Jing, Chongbin Yuan, Li Ju, Yiding Yang

In this paper, we explore a novel model reusing task tailored for graph neural networks (GNNs), termed as "deep graph reprogramming". We strive to reprogram a pre-trained GNN, without amending raw node features nor model parameters, to handle a bunch of cross-level downstream tasks in various domains. To this end, we propose an innovative Data Reprogramming

Bhavya Sehgal, Gavin, Gui, Md Nahid sadik

This project developed a web application using VTK for ultrasound visualization. The images were enhanced using median and Gaussian filters, and two algorithms were utilized for data visualization: isosurface extraction and Delaunay triangulation. Results showed that both algorithms were effective at reducing Gaussian noise and high-frequency noise, such as

Chris Smeenk, James Owen Weatherall

We consider some of the epistemic benefits of exploring "theory space" in the context of modifications of general relativity with intended applications in cosmology. We show how studying modifications of general relativity can help in assessing the robustness of empirical inferences, particularly in inaccessible regimes. We also discuss challenges to sharply

Sean Deyo, Veit Elser

We introduce the logical grammar emdebbing (LGE), a model inspired by pregroup grammars and categorial grammars to enable unsupervised inference of lexical categories and syntactic rules from a corpus of text. LGE produces comprehensible output summarizing its inferences, has a completely transparent process for producing novel sentences, and can learn from

Ziheng Wang, Andrea Mariani, Arianna Menciassi, Elena De Momi

Objective technical skill assessment is crucial for effective training of new surgeons in robot-assisted surgery. With advancements in surgical training programs in both physical and virtual environments, it is imperative to develop generalizable methods for automatically assessing skills. In this paper, we propose a novel approach for skill assessment by tr

Wenqing Zheng, S P Sharan, Ajay Kumar Jaiswal, Kevin Wang

For a complicated algorithm, its implementation by a human programmer usually starts with outlining a rough control flow followed by iterative enrichments, eventually yielding carefully generated syntactic structures and variables in a hierarchy. However, state-of-the-art large language models generate codes in a single pass, without intermediate warm-ups to

Jiaxi Nie

Let $\mathcal{F}$ be a family of $r$-uniform hypergraphs. The random Tur\'an number $\mathrm{ex}(G^r_{n,p},\mathcal{F})$ is the maximum number of edges in an $\mathcal{F}$-free subgraph of $G^r_{n,p}$, where $G^r_{n,p}$ is the Erd\"os-R\'enyi random $r$-graph with parameter $p$. Let $C^r_{\ell}$ denote the $r$-uniform linear cycle of length $\ell$. For $p\ge

Kenshiro Oguri

This paper proposes a new indirect solution method for solving state-constrained optimal control problems by revisiting the well-established optimal control theory and addressing the long-standing issue of discontinuous control and costate due to pure state inequality constraints. It is well-known that imposing pure state path constraints in optimal control

James T. Wheeler

We find a large internal symmetry within 4-dimensional Poincare gauge theory. In the Riemann-Cartan geometry of Poincare gauge theory the field equation and geodesics are invariant under projective transformation, just as in affine geometry. However, in the Riemann-Cartan case the torsion and nonmetricity tensors change. By generalizing the Riemann-Cartan ge

Feng Xie, Xiang Zeng, Bin Zhou, Yusong Tan

Entity alignment (EA) which links equivalent entities across different knowledge graphs (KGs) plays a crucial role in knowledge fusion. In recent years, graph neural networks (GNNs) have been successfully applied in many embedding-based EA methods. However, existing GNN-based methods either suffer from the structural heterogeneity issue that especially appea

Chang Liu, Antwan D. Clark

Analysing the impact of bottom friction on shallow water waves over bottom terrains is important in areas including environmental and coastal engineering as well as the oceanic and atmospheric sciences. However, current theoretical developments rely on making certain limiting assumptions about these flows and thus more development is needed to be able to fur

Ishan Shivansh Bangroo, Samia Tahzeen

Epilepsy is a neurological illness that is characterised by continuous spasms of shaking, sometimes known as convulsions. Although there are effective treatments for epilepsy, such as drugs and surgery, there is still a group of individuals who have intractable epilepsy that fails to respond to standard methods. Intractable epilepsy is a severe neurological

Charles Cardot, Joshua Kas, Jared Abramson, John Rehr

Recent advances using Density Functional Theory (DFT) to augment Multiplet Ligand Field Theory (MLFT) have led to ab-initio calculations of many formerly empirical parameters. This development makes MLFT more predictive instead of interpretive, thus improving its value for understanding highly correlated 3d, 4d, and f-electron systems. Here, we explore a DFT

Ze Liu, Bo Li, Mao Yang, ZhongJiang Yan

Ad Hoc networks with multi-hop topology are widely used in military and civilian applications. One challenge for Ad Hoc networks is to design efficient Media Access Control (MAC) protocols to ensure the quality of service (QoS). In Ad Hoc networks, there is a kind of node called key node, which undertakes more forwarding traffic than other surrounding nodes.

Jia-Qi Li, Zhao-Min Gao, Wen-Xiao Liu, Xin Wang

The light-mater interactions for an emitter coupling to the bulk region of a Hofstadter lattice has recently investigated by De Bernardis \textit{et al.} [D. De Bernardis, Z.-P. Cian, I. Carusotto, M. Hafezi, and P. Rabl, \href{https://link.aps.org/doi/10.1103/PhysRevLett.126.103603}{Phys. Rev. Lett. 126, 103603 (2021)}]. We propose the light-mater interacti

Anton Trygub

For an edge-colored complete graph, we define the color degree of a node as the number of colors appearing on edges incident to it. In this paper, we consider colorings that don't contain tricolored triangles (also called rainbow triangles); these colorings are also called Gallai colorings. We give a complete characterization of all possible color degree seq

Preston Culbertson, Ryan K. Cosner, Maegan Tucker, Aaron D. Ames

Input-to-State Stability (ISS) is fundamental in mathematically quantifying how stability degrades in the presence of bounded disturbances. If a system is ISS, its trajectories will remain bounded, and will converge to a neighborhood of an equilibrium of the undisturbed system. This graceful degradation of stability in the presence of disturbances describes

Winnie Ma, Vincent Valton

In this paper we, an epistemologist and a machine learning scientist, argue that we need to pursue a novel area of philosophical research in AI - the ethics of belief for AI. Here we take the ethics of belief to refer to a field at the intersection of epistemology and ethics concerned with possible moral, practical, and other non-truth-related dimensions of

Pulak Mehta, Gauri Jagatap, Kevin Gallagher, Brian Timmerman

Recent advancements in machine learning and computer vision have led to the proliferation of Deepfakes. As technology democratizes over time, there is an increasing fear that novice users can create Deepfakes, to discredit others and undermine public discourse. In this paper, we conduct user studies to understand whether participants with advanced computer s

Ian Clingerman, Quan Luu

Czerwinski's paper "Separation of ${\rm PSPACE}$ and ${\rm EXP}$" [Cze21] claims to prove that ${\rm PSPACE} \neq {\rm EXP}$ by showing there is no length-increasing polynomial-time reduction from a given ${\rm EXP}$-complete set to a given ${\rm PSPACE}$-complete set. However, in this critique, we show that there are fundamental flaws within the paper's app

X. Zuo, S. Osher, W. Li

We propose an unconstrained optimization method based on the well-known primal-dual hybrid gradient (PDHG) algorithm. We first formulate the optimality condition of the unconstrained optimization problem as a saddle point problem. We then compute the minimizer by applying generalized primal-dual hybrid gradient algorithms. Theoretically, we demonstrate the c

Azade Farshad, Yousef Yeganeh, Yu Chi, Chengzhi Shen

Text-conditioned image generation has made significant progress in recent years with generative adversarial networks and more recently, diffusion models. While diffusion models conditioned on text prompts have produced impressive and high-quality images, accurately representing complex text prompts such as the number of instances of a specific object remains

Yousef Yeganeh, Azade Farshad, Goktug Guevercin, Amr Abu-zer

Although the preservation of shape continuity and physiological anatomy is a natural assumption in the segmentation of medical images, it is often neglected by deep learning methods that mostly aim for the statistical modeling of input data as pixels rather than interconnected structures. In biological structures, however, organs are not separate entities; f

Yousef Yeganeh, Azade Farshad, Peter Weinberger, Seyed-Ahmad Ahmadi

Although purely transformer-based architectures showed promising performance in many computer vision tasks, many hybrid models consisting of CNN and transformer blocks are introduced to fit more specialized tasks. Nevertheless, despite the performance gain of both pure and hybrid transformer-based architectures compared to CNNs in medical imaging segmentatio

Carlos Paradis, Rick Kazman

Background: Since Alitheia Core was proposed and subsequently retired, tools that support empirical studies of software projects continue to be proposed, such as Codeface, Codeface4Smells, GrimoireLab and SmartSHARK, but they all make different design choices and provide overlapping functionality. Aims: We seek to understand the design decisions adopted by t

Anna Wirth-Singh, Johannes E. Fröch, Zheyi Han, Luocheng Huang

A broad range of imaging and sensing technologies in the infrared require large Field-of-View (FoV) operation. To achieve this, traditional refractive systems often employ multiple elements to compensate for aberrations, which leads to excess size, weight, and cost. For many applications, including night vision eye-wear, air-borne surveillance, and autonomou

Furkan Sezer, Ceyhun Eksin

Information design in an incomplete information game includes a designer with the goal of influencing players' actions through signals generated from a designed probability distribution so that its objective function is optimized. We consider a setting in which the designer has partial knowledge on agents' utilities. We address the uncertainty about

Tobias Weich, Lasse Lennart Wolf

Given a geometrically finite hyperbolic surface of infinite volume it is a classical result of Patterson that the positive Laplace-Beltrami operator has no $L^2$-eigenvalues $\geq 1/4$. In this article we prove a generalization of this result for the joint $L^2$-eigenvalues of the algebra of commuting differential operators on Riemannian locally symmetric sp

Anqi Fu, Vicki T. Taasti, Masoud Zarepisheh

Purpose: The importance of robust proton treatment planning to mitigate the impact of uncertainty is well understood. However, its computational cost grows with the number of uncertainty scenarios, prolonging the treatment planning process. We developed a fast and scalable distributed optimization platform that parallelizes this computation over the scenario

Osamu Komori, Yusuke Saigusa, Shinto Eguchi

We discuss species distribution models (SDM) for biodiversity studies in ecology. SDM plays an important role to estimate abundance of a species based on environmental variables that are closely related with the habitat of the species. The resultant habitat map indicates areas where the species is likely to live, hence it is essential for conservation planni

Westley Brown, David R. Patton, Sara L. Ellison, Lawrence Faria

We study interacting galaxy pairs in the TNG100-1 and TNG300-1 cosmological simulations using previously generated closest companion samples. We study the specific star formation rates (sSFR) of massive ($10^{10} M_{\odot} < M_* < 10^{12} M_{\odot}$) galaxies at $z \leq 0.2$ as a function of separation from the closest companion galaxy. We split our sample b

Meenakshi S. Kagda, Bonita Lam, Casey Litton, Corinn Small

Spanning two decades, the Encyclopaedia of DNA Elements (ENCODE) is a collaborative research project that aims to identify all the functional elements in the human and mouse genomes. To best serve the scientific community, all data generated by the consortium is shared through a web-portal (https://www.encodeproject.org/) with no access restrictions. The fou

Sam Edwards, Mikolaj Fraczyk, Minju Lee, Hee Oh

Let $G$ be a higher rank simple real algebraic group, or more generally, any semisimple real algebraic group with no rank one factors and $X$ the associated Riemannian symmetric space. For any Zariski dense discrete subgroup $\Gamma<G$, we prove that $\operatorname{Vol}(\Gamma\backslash X)=\infty$ if and only if no positive Laplace eigenfunction belongs to $

Kenshiro Oguri

This paper proposes a new algorithm that solves non-convex optimal control problems with a theoretical guarantee for global convergence to a feasible local solution of the original problem. The proposed algorithm extends the recently proposed successive convexification (SCvx) algorithm by addressing one of its key limitations, that is, the converged solution

Fabio S. Bemfica, Mauricio Martinez, Masoud Shokri

We formulate the first-order dissipative anisotropic hydrodynamical theory for a relativistic conformal uncharged fluid, which generalizes the Bemfica-Disconzi-Noronha-Kovtun first-order viscous fluid framework. Our approach maintains causal behavior in the nonlinear regime with or without general relativity coupling, and we derive and analyze the constraint

Tianjun Han, Qiang Zhong, Amin Mivehchi, Daniel B. Quinn

A classic lift decomposition (von K\'arm\'an & Sears 1938) is conducted on potential flow simulations of a near-ground pitching hydrofoil. It is discovered that previously observed stable and unstable equilibrium altitudes are generated by a balance between positive wake-induced lift and negative quasi-steady lift while the added mass lift doesn't play a rol

Sebastián Tapia-García

We investigate dynamical properties of linear operators that are obtained as the linearization of Lipschitz self-maps defined on a pointed metric space. These operators are known as Lipschitz operators. More concretely, for a Lipschitz operator $\widehat{f}$, we study the set of recurrent vectors and the set of vectors $\mu$ such that the sequence $(\|\wideh

Jacob Thorstensen, Tyler Henderson, Justin Kavanagh

Animal models indicate that motor behaviour is shaped by monoamine neurotransmitters released diffusely throughout the brain and spinal cord. We present strong evidence that human motor pathways are equally affected by neuromodulation through noradrenergic and serotonergic projections arising from the brainstem. To do so, we have identified and collated huma

Yasaman Haghighi, Suryansh Kumar, Jean-Philippe Thiran, Luc Van Gool

Visual Simultaneous Localization and Mapping (vSLAM) is a widely used technique in robotics and computer vision that enables a robot to create a map of an unfamiliar environment using a camera sensor while simultaneously tracking its position over time. In this paper, we propose a novel RGBD vSLAM algorithm that can learn a memory-efficient, dense 3D geometr

Ahmet Kurt, Abdulhadi Sahin, Ricardo Harrilal-Parchment, Kemal Akkaya

Bitcoin is undoubtedly a great alternative to today's existing digital payment systems. Even though Bitcoin's scalability has been debated for a long time, we see that it is no longer a concern thanks to its layer-2 solution Lightning Network (LN). LN has been growing non-stop since its creation and enabled fast, cheap, anonymous, censorship-resistant Bitcoi

Sergey Bezuglyi, Palle E. T. Jorgensen

The purpose of this paper is to present new classes of function systems as part of multiresolution analyses. Our approach is representation theoretic, and it makes use of generalized multiresolution function systems (MRSs). It further entails new ideas from measurable endomorphisms-dynamics. Our results yield applications that are not amenable to more tradit

Austen Z. Fan, Paraschos Koutris, Hangdong Zhao

We study the fine-grained complexity of evaluating Boolean Conjunctive Queries and their generalization to sum-of-product problems over an arbitrary semiring. For these problems, we present a general semiring-oblivious reduction from the k-clique problem to any query structure (hypergraph). Our reduction uses the notion of embedding a graph to a hypergraph,

Anousheh Gholami, Nariman Torkzaban, John S. Baras

Network slicing enables the deployment of multiple dedicated virtual sub-networks, i.e. slices on a shared physical infrastructure. Unlike traditional one-size-fits-all resource provisioning schemes, each network slice (NS) in 5G is tailored to the specific service requirements of a group of customers. An end-to-end (E2E) mobile NS orchestration requires the

Debargha Banerjee, Tathagata Mandal, Sudipa Mondal

In this article, we study two important properties of ${\rm{sym}}^3$ transfers of the automorphic representation $\pi$ associated to a modular form. First we compute the conductor of ${\rm{sym}}^3(\pi)$. Then we detect the types of local automorphic representations at bad primes by the variation of the epsilon factors of symmetric cube transfer of the repres

Tingtao Zhou, Xuan Wan, Daniel Zhengyu Huang, Zongyi Li

Bacteria can swim upstream due to hydrodynamic interactions with the fluid flow in a narrow tube, and pose a clinical threat of urinary tract infection to patients implanted with catheters. Coatings and structured surfaces have been proposed as a way to suppress bacterial contamination in catheters. However, there is no surface structuring or coating approac

Hendrik Kempt, Alon Lavie, Saskia K. Nagel

The strive to make AI applications "safe" has led to the development of safety-measures as the main or even sole normative requirement of their permissible use. Similar can be attested to the latest version of chatbots, such as chatGPT. In this view, if they are "safe", they are supposed to be permissible to deploy. This approach, which we call "safety-norma

Sara Barrio, Laura Alba, Clara M. Prieto

X-ray radiography is an imaging technique widely used in the examination of works of art and heritage objects, however no references to its application to the study of daguerreotypes have been found. The results obtained in this study demonstrate for the first time the usefulness of X-ray radiography for locating, identifying, and characterizing the hallmark

Dmitriy N. Kim, Gerald A. Miller

An extension to our previous study on Nuclear Parton Distribution Functions (nPDFs) using Light-Front Holographic Quantum Chromodynamics (LFHQCD) is presented. We focus on applying the effects of nucleon motion inside the nucleus (Fermi motion/smearing) to deuterium, extending our nPDFs (and hence the DIS $F_2$ structure function for deuterium, $F_2^D$) to t

Timothée Bénard, Emmanuel Breuillard

We establish the (non-lattice) local limit theorem for products of i.i.d. random variables on an arbitrary simply connected nilpotent Lie group $G$, where the variables are allowed to be non-centered. Our result also improves on the known centered case by proving uniformity for two-sided moderate deviations and allowing measures with a moment of order $2(\di

Kenny Ballou, Elena Sherman

Verification techniques express program states as logical formulas over program variables. For example, symbolic execution and abstract interpretation encode program states as a set of integer inequalities. However, for real-world programs these formulas tend to become large, which affects scalability of analyses. To address this problem, researchers develop

Yang Xu, Loni Philip Tabb

Research on residential segregation has been active since the 1950s and originated in a desire to quantify the level of racial/ethnic segregation in the United States. The Index of Concentration at the Extremes (ICE), an operationalization of racialized economic segregation that simultaneously captures spatial, racial, and income polarization, has been a pop

Yi Zeng, Arian Jadbabaie, Ashay N. Patel, Phelan Yu

We have developed and demonstrated a scheme to achieve rotationally-closed photon cycling in polyatomic molecules with complex hyperfine structure and sensitivity to hadronic symmetry violation, specifically $^{171}$YbOH and $^{173}$YbOH. We calculate rotational branching ratios for spontaneous decay and identify repumping schemes which use electro-optical m

Seyedehdelaram Jahani, Katharina Boguslawski, Paweł Tecmer

We employ state-of-the-art quantum chemistry methods to study the structure-to-property relationship in polyanilines (PANIs) of different lengths and oxidation states. Specifically, we focus on leucoemeraldine, emeraldine, and pernigraniline in their tetramer and octamer forms. We scrutinize their structural properties, HOMO and LUMO energies, HOMO-LUMO gaps

Patrick Agostini, Zoran Utkovski, Slawomir Stanczak

This paper considers the massive MIMO unsourced random access problem in a quasi-static Rayleigh fading setting. The proposed coding scheme is based on a concatenation of a "conventional" channel code (such as, e.g., LDPC) serving as an outer code, and a sparse regression code (SPARC) serving as an inner code. The scheme combines channel estimation, single-u

David Bruns-Smith, Oliver Dukes, Avi Feller, Elizabeth L. Ogburn

We provide a novel characterization of augmented balancing weights, also known as automatic debiased machine learning (AutoDML). These popular doubly robust or de-biased machine learning estimators combine outcome modeling with balancing weights - weights that achieve covariate balance directly in lieu of estimating and inverting the propensity score. When t

Ali Lashgari

Forecasting financial time series (FTS) is an essential field in finance and economics that anticipates market movements in financial markets. This paper investigates the accuracy of text mining and technical analyses in forecasting financial time series. It focuses on the S&P500 stock market index during the pandemic, which tracks the performance of the lar

S. M. Seals, Valerie L. Shalin

The summarization of conversation, that is, discourse over discourse, elevates pragmatic considerations as a pervasive limitation of both summarization and other applications of contemporary conversational AI. Building on impressive progress in both semantics and syntax, pragmatics concerns meaning in the practical sense. In this paper, we discuss several ch

Bochuan Lyu, Illya V. Hicks, Joey Huchette

We study mixed-integer programming formulations for the piecewise linear lower and upper bounds (in other words, piecewise linear relaxations) of nonlinear functions that can be modeled by a new class of combinatorial disjunctive constraints (CDCs), generalized $n$D-ordered CDCs. We first introduce a general formulation technique to model piecewise linear lo

Omar Faruque, Francis Ndikum Nji, Mostafa Cham, Rohan Mandar Salvi

Clustering high-dimensional spatiotemporal data using an unsupervised approach is a challenging problem for many data-driven applications. Existing state-of-the-art methods for unsupervised clustering use different similarity and distance functions but focus on either spatial or temporal features of the data. Concentrating on joint deep representation learni

Jianhua Zhu, Ji Chen, Wei Wu

Recently topogical excitons have attracted much attention. However, studies on the topological properties of excitons in one dimension are still rare. Here we have computed the Zak phase for a generic one-dimensional dimerised excitonic model. Tuning relevant hopping parameters gives rise to a rich spectrum of physics, including non-trivial topological phase

Luís Cruz-Filipe, Eva Graversen, Fabrizio Montesi, Marco Peressotti

Choreographic programming is a paradigm where a concurrent or distributed system is developed in a top-down fashion. Programs, called choreographies, detail the desired interactions between processes, and can be compiled to distributed implementations based on message passing. Choreographic languages usually guarantee deadlock-freedom and provide an operatio

Neal Thomas

Stratification in both the design and analysis of randomized clinical trials is common. Despite features in automated randomization systems to re-confirm the stratifying variables, incorrect values of these variables may be entered. These errors are often detected during subsequent data collection and verification. Questions remain about whether to use the m

Sanjay Vishwakarma, Srinjoy Ganguly

The Naive Bayesian classifier is a popular classification method employing the Bayesian paradigm. The concept of having conditional dependence among input variables sounds good in theory but can lead to a majority vote style behaviour. Achieving conditional independence is often difficult, and they introduce decision biases in the estimates. In Naive Bayes,

Julio A. Mojica-Zárate, Daniel O-Campa, Erik Díaz-Bautista

In this article, we obtain the exact solutions for bound states of tilted anisotropic Dirac materials under the action of external electric and magnetic fields with translational symmetry. In order to solve the eigenvalue equation that arises from the effective Hamiltonian of these materials, we describe an algorithm that allow us to decouple the differentia