May 2023 arXiv papers — page 160

Gaurav Suri, Lily R. Slater, Ali Ziaee, Morgan Nguyen

A Large Language Model (LLM) is an artificial intelligence system that has been trained on vast amounts of natural language data, enabling it to generate human-like responses to written or spoken language input. GPT-3.5 is an example of an LLM that supports a conversational agent called ChatGPT. In this work, we used a series of novel prompts to determine wh

D. V. Khveshchenko

Generalized $1+0$-dimensional Liouvillean dynamics describing deformations of the Sachdev-Ye-Kitaev (SYK) model, as well as the various $1+1$-dimensional dilaton and Horava-Lifshitz gravity theories, can all be mapped onto single-particle quantum mechanics of a non-relativistic charge propagating in a (generally, curved) $2d$ space and subject to a (generall

Kei Funano

We obtain a new upper bound for Neumann eigenvalues of the Laplacian on a bounded convex domain in Euclidean space. As an application of the upper bound we derive universal inequalities for Neumann eigenvalues of the Laplacian.

Thomas Robinson, Guoxin Su

Allocation and planning with a collection of tasks and a group of agents is an important problem in multiagent systems. One commonly faced bottleneck is scalability, as in general the multiagent model increases exponentially in size with the number of agents. We consider the combination of random task assignment and multiagent planning under multiple-objecti

Yi Liu, Shoukun Xu, Dingwen Zhang, Jungong Han

Co-salient object detection targets at detecting co-existed salient objects among a group of images. Recently, a generalist model for segmenting everything in context, called SegGPT, is gaining public attention. In view of its breakthrough for segmentation, we can hardly wait to probe into its contribution to the task of co-salient object detection. In this

Runxin Zhang, Yulin Shao, Menghan Li, Lu Lu

This paper presents a new optical integrated sensing and communication (O-ISAC) framework tailored for cost-effective Light-Emitting Diode (LED) for enhanced Internet of Things (IoT) applications. Unlike prior research on ISAC, which predominantly focused on radio frequency (RF) band, O-ISAC capitalizes on the inherent advantages of the optical spectrum, inc

Srinivasa Rao. P

We present a thermodynamic analysis of a quantum engine that uses a single quantum particle as its working fluid, inspired by Szilard's classical single-particle engine. Our design is modeled after the classically-chaotic Szilard Map and involves a thermodynamic cycle of measurement, thermal-energy extraction, and memory reset. Our focus is on investigat

Woo Min Kim, Sutanoy Dasgupta, Anuj Srivastava

Estimating signals underlying noisy data is a significant problem in statistics and engineering. Numerous estimators are available in the literature, depending on the observation model and estimation criterion. This paper introduces a framework that estimates the shape of the unknown signal and the signal itself. The approach utilizes a peak-persistence diag

Sabiou Inoua

Contrary to conventional economic growth theory, which reduces a country's output to one aggregate variable (GDP), product diversity is central to economic development, as recent 'economic complexity' research suggests. A country's product diversity reflects its diversity of knowhow or 'capabilities'. Researchers proposed the Economic

Christopher Eling

We show that in two dimensions the incompressible Euler equations can be re-expressed in terms of an abelian gauge theory with a Chern-Simons term. The magnetic field corresponds to fluid vorticity and the electric field is the product of the vorticity and the gradient of the stream function. This picture can be extended to active scalar models, including th

Chang'an Yi, Haotian Chen, Yonghui Xu, Yifan Zhang

Federated domain adaptation (FDA) aims to collaboratively transfer knowledge from source clients (domains) to the related but different target client, without communicating the local data of any client. Moreover, the source clients have different data distributions, leading to extremely challenging in knowledge transfer. Despite the recent progress in FDA, w

Fazal-E-Asim, André L. F. de Almeida, Bruno Sokal, Behrooz Makki

This paper proposes a pilot decoupling-based two-dimensional channel parameter estimation method for intelligent reflecting surface (IRS)-assisted networks. We exploit the combined effect of Terahertz sparse propagation and the geometrical structure of arrays deployed at the base station, the IRS, and the user equipment to develop a low-complexity channel pa

Dongxia Wu, Ruijia Niu, Matteo Chinazzi, Yian Ma

To balance quality and cost, various domain areas of science and engineering run simulations at multiple levels of sophistication. Multi-fidelity active learning aims to learn a direct mapping from input parameters to simulation outputs at the highest fidelity by actively acquiring data from multiple fidelity levels. However, existing approaches based on Gau

Morteza Mardani, Jiaming Song, Jan Kautz, Arash Vahdat

Diffusion models have emerged as a key pillar of foundation models in visual domains. One of their critical applications is to universally solve different downstream inverse tasks via a single diffusion prior without re-training for each task. Most inverse tasks can be formulated as inferring a posterior distribution over data (e.g., a full image) given a me

Kerry M. Soileau

The Collatz conjecture implies that an iterated function sequence under a certain linear operator, beginning with a certain complex valued function, must converge to a certain complex function.

Steven W. Tarr, Joseph S. Brunner, Daniel Soto, Daniel I. Goldman

We study the dynamics of an oscillating, free-floating robot that generates radially expanding gravity capillary waves at a fluid surface. In open water, the device does not self-propel; near a rigid boundary, it can be attracted or repelled. Visualization of the wave field dynamics reveals that when near a boundary, a complex interference of generated and r

Mathias Braun, Shin-ichi Ohta

We prove that a Finsler spacetime endowed with a smooth reference measure whose induced weighted Ricci curvature $\smash{\mathrm{Ric}_N}$ is bounded from below by a real number $K$ in every timelike direction satisfies the timelike curvature-dimension condition $\smash{\mathrm{TCD}_q(K,N)}$ for all $q\in (0,1)$. A nonpositive-dimensional version ($N \le 0$)

Miles Turpin, Julian Michael, Ethan Perez, Samuel R. Bowman

Large Language Models (LLMs) can achieve strong performance on many tasks by producing step-by-step reasoning before giving a final output, often referred to as chain-of-thought reasoning (CoT). It is tempting to interpret these CoT explanations as the LLM's process for solving a task. This level of transparency into LLMs' predictions would yield significant

Eduardo Gonzalez, Cheuk Yu Mak, Daniel Pomerleano

Let $(\bar{M}, \omega)$ be a compact symplectic manifold with convex boundary and $c_1(T\bar{M})=0$. Suppose that $(\bar{M}, \omega)$ is equipped with a convex Hamiltonian $G$-action for some connected, compact Lie group $G$. We construct an action of the pure Coulomb branch of $G$ on the $G$-equivariant symplectic cohomology of $\bar{M}.$ Building on work o

Rohan Agarwal

There are many philosophies and theories on what creativity is and how it works, but one popular idea is that of variations on a theme and intersection of concepts. This literature review explores philosophical proposals of how creativity emerges from variations on a theme, and how formalizations of these proposals in human subject studies and computational

Mahajabin Rahman, Ilya Nemenman

To construct models of large, multivariate complex systems, such as those in biology, one needs to constrain which variables are allowed to interact. This can be viewed as detecting "local" structures among the variables. In the context of a simple toy model of 2D natural and synthetic images, we show that pairwise correlations between the variables -- even

M. Mazanov, V. A. Shklovskij

In this series of lectures, we discuss the basic theoretical concepts of magnonics and spintronics. We first briefly recall the relevant topics from quantum mechanics, electrodynamics of continuous media, and basic theory of magnetism. We then discuss the classical theory of magnetic dynamics: ferromagnetic and antiferromagnetic resonance, dynamic susceptibi

Johannes Michl, Giora Peniakov, Andreas Pfenning, Joonas Hilska

Creating single photons in the telecommunication wavelength range from semiconductor quantum dots (QDs) and interfacing them with spins of electrons or holes has been of high interest in recent years, with research mainly focusing on indium based QDs. However, there is not much data on the optical and spin properties of galliumantimonide (GaSb) QDs, despite

Hassiba Benseradj, Zohra Guessoum

In this paper, we establish weak consistency and asymptotic normality of an M-estimator of the regression function for left truncated and right censored (LTRC) model, where it is assumed that the observations form a stationary alpha-mixing sequence. The result holds with unbounded objective function, and are applied to derive weak consistency and asymptotic

Annagrazia Puglisi, Ugnė Dudzevičiūtė, Mark Swinbank, Steven Gillman

We present first results from the KMOS Ultra-deep Rotation Velocity Survey (KURVS), aimed at studying the outer rotation curves shape and dark matter content of 22 star-forming galaxies at $z\sim1.5$. These galaxies represent `typical' star-forming discs at $z \sim 1.5$, being located within the star-forming main sequence and stellar mass-size relation with

Dmitry I Mikhailov

As artificial intelligence and machine learning continue to advance, we must understand their strategic importance in national security. This paper focuses on unique AI applications in the military, emphasizes strategic imperatives for success, and aims to rekindle excitement about AI's role in national security. We will examine the United States progress in

Ian Laga, Jessica P. Kunke, Tyler H. McCormick, Xiaoyue Niu

The Network Scale-up Method (NSUM) uses social networks and answers to "How many X's do you know?" questions to estimate sizes of groups excluded by standard surveys. This paper addresses the bias caused by varying average social network sizes across populations, commonly referred to as the degree ratio bias. This bias is especially important for marginalize

Ezgi Ozyilkan, Johannes Ballé, Elza Erkip

We consider lossy compression of an information source when the decoder has lossless access to a correlated one. This setup, also known as the Wyner-Ziv problem, is a special case of distributed source coding. To this day, real-world applications of this problem have neither been fully developed nor heavily investigated. We propose a data-driven method based

Xin Shen, Praful Agrawal, Zhongwei Cheng

Multi-label classification models have a wide range of applications in E-commerce, including visual-based label predictions and language-based sentiment classifications. A major challenge in achieving satisfactory performance for these tasks in the real world is the notable imbalance in data distribution. For instance, in fashion attribute detection, there m

Daniel Blanquicett, Janko Gravner, David Sivakoff, Luke Wilson

We introduce a class of cellular automata growth models on the two-dimensional integer lattice with finite cross neighborhoods. These dynamics are determined by a Young diagram $\mathcal Z$ and the radius $\rho$ of the neighborhood, which we assume to be sufficiently large. A point becomes occupied if the pair of counts of currently occupied points on the ho

Ken Jen Lee, Adrian Davila, Hanlin Cheng, Joslin Goh

Much HCI research on prompting prosocial behaviors focuses on methods for increasing empathy. However, increased empathy may have unintended negative consequences. Our work offers an alternative solution that encourages critical reflection for nurturing compassion, which involves motivation and action to help others. In a between-subject experiment, particip

Meghal Gupta, Rachel Yun Zhang

In an interactive error-correcting code (iECC), Alice and Bob engage in an interactive protocol with the goal of Alice communicating a message $x \in \{ 0, 1 \}^k$ to Bob in such a way that even if some fraction of the total communicated bits are corrupted, Bob can still determine $x$. It was shown in works by Gupta, Kalai, and Zhang (STOC 2022) and by Efrem

Ken Jen Lee, Edith Law

Findings from existing research provide possible evidence that values are an important construct that should be part of the self-reflection process. However, many questions about value reflection remain unexplored. As such, this position paper aims to provide an overview of relevant research and frameworks, example studies on value reflection pursued by the

Zhengqin Li, Li Yu, Mikhail Okunev, Manmohan Chandraker

We propose a physically-motivated deep learning framework to solve a general version of the challenging indoor lighting estimation problem. Given a single LDR image with a depth map, our method predicts spatially consistent lighting at any given image position. Particularly, when the input is an LDR video sequence, our framework not only progressively refine

Daji Landis, Nikolaj I. Schwartzbach

We identify a subtle security issue that impacts mechanism design in scenarios in which agents can absolutely commit to strategies. Absolute commitments allow the strategy of an agent to depend on the commitments made by the other agents. This changes fundamental game-theoretic assumptions by inducing a meta-game in which agents choose which strategies they

Debjyoti Bardhan, Yevgeny Kats, Noam Wunch

If "dark quarks" from a confining hidden sector are produced at the LHC, they will shower and hadronize to dark sector hadrons, which may decay back to Standard Model particles within the detector, possibly resulting in a collimated spray of particles resembling a QCD jet. In this work we address scenarios in which dark hadrons decay with a measurable small

Evgenii Simakov, Sergey Galyamin, Andrey Tyukhtin

We investigate the diffraction of a slow symmetric TM mode by an open-ended corrugated cylindrical waveguide with a flange. This mode can be generated, in particular, by a charged particle bunch moving along the waveguide axis. We analyze the so-called ''longwave'' range when the wavelengths and the waveguide radius are much greater than the corrugation peri

M. Azeem, Q. Abbas, M. A. Abdelkareem, A. G. Olabi

Herein, we present a detailed study of the structural, optical, and electrochemical responses of Gd$_2$O$_3$ doped with nickel zinc ferrite nanoparticles. Doping of Ni$_{0.5}$Zn$_{0.5}$Fe$_2$O$_4$ nanoparticles to Gd$_2$O$_3$ powder was done through thermal decomposition at 1000 C. The average grain size of the mixture was determined to be approximately 95 n

Shizhuo Dylan Zhang, Talia Ringer, Emily First

Large language models have the potential to simplify formal theorem proving and make it more accessible. But how to get the most out of these models is still an open question. To answer this question, we take a step back and explore the failure cases of these models using common prompting-based techniques. Our talk will discuss these failure cases and what t

Ken Jen Lee, Edith Law

Research has found social VR to bring various benefits to users with and without disabilities. Given the success of social VR applications that support user-created worlds, it is important to consider how we can empower users in building inclusive virtual worlds by investigating how tools for world building can be built to better support collaborations betwe

Calin Iuliu Lazaroiu

I give a geometric construction of certain first order natural dynamical observables in multifield cosmological models with arbitrary target space topology and discuss a system of related dynamical approximations and regimes for such models.

Sukanya Ghosh, Soheil Ershadrad, Biplab Sanyal

Enhancement of Curie temperature ($T_\mathrm{C}$) of two-dimensional (2D) magnets is immensely desirable for room temperature spintronic applications. Fe$_{5}$GeTe$_{2}$ is an exceptional van der Waals metallic ferromagnet due to its tunable physical properties and relatively higher $T_\mathrm{C}$ than other 2D magnets. Using density functional theory combin

Patrick J. Burns

This paper introduces LatinCy, a set of trained general purpose Latin-language "core" pipelines for use with the spaCy natural language processing framework. The models are trained on a large amount of available Latin data, including all five of the Latin Universal Dependency treebanks, which have been preprocessed to be compatible with each other. The resul

Aravinth Chembu, Scott Sanner

Clustering is a powerful and extensively used data science tool. While clustering is generally thought of as an unsupervised learning technique, there are also supervised variations such as Spath's clusterwise regression that attempt to find clusters of data that yield low regression error on a supervised target. We believe that clusterwise regression is jus

Yuval Filmus, Itai Leigh, Artur Riazanov, Dmitry Sokolov

A circuit $\mathcal{C}$ samples a distribution $\mathbf{X}$ with an error $\epsilon$ if the statistical distance between the output of $\mathcal{C}$ on the uniform input and $\mathbf{X}$ is $\epsilon$. We study the hardness of sampling a uniform distribution over the set of $n$-bit strings of Hamming weight $k$ denoted by $\mathbf{U}^n_k$ for _decision fores

Gerson Mette, Kunie Ishioka, Steven Youngkin, Wolfgang Stolz

Ultrafast carrier and phonon dynamics at the buried heterointerface of GaP/Si(001) are investigated by means of two-color pump-probe reflectivity measurements. The carrier-induced reflectivity signal exhibits a resonant enhancement at pump-photon energies of 1.4 eV, which can be assigned to an optical transition between electronic interface states. The trans

Riccardo Poiani, Alberto Maria Metelli, Marcello Restelli

In Reinforcement Learning (RL), an agent acts in an unknown environment to maximize the expected cumulative discounted sum of an external reward signal, i.e., the expected return. In practice, in many tasks of interest, such as policy optimization, the agent usually spends its interaction budget by collecting episodes of fixed length within a simulator (i.e.

E. Ames, H. Andréasson, O. Rinne

We consider gravitational collapse for the axially symmetric Einstein-Vlasov system. We investigate the weak cosmic censorship conjecture in the case of highly prolate initial data and we investigate the ``only if" part of the Hoop conjecture. Shapiro and Teukolsky initiated a similar study in 1991 \cite{Shapiro1991} where they found support that the weak co

Magdalen Dobson Manohar, Zheqi Shen, Guy E. Blelloch, Laxman Dhulipala

Approximate nearest-neighbor search (ANNS) algorithms are a key part of the modern deep learning stack due to enabling efficient similarity search over high-dimensional vector space representations (i.e., embeddings) of data. Among various ANNS algorithms, graph-based algorithms are known to achieve the best throughput-recall tradeoffs. Despite the large sca

Leonid Barenboim, Uri Goldenberg

We obtain improved distributed algorithms in the CONGEST message-passing setting for problems on power graphs of an input graph $G$. This includes Coloring, Maximal Independent Set, and related problems. We develop a general deterministic technique that transforms R-round algorithms for $G$ with certain properties into $O(R \cdot \Delta^{k/2 - 1})$-round alg

Armin Abdehkakha, Craig Snoeyink

Single-Molecule Localization Microscopy (SMLM) has expanded our ability to visualize subcellular structures but is limited in its temporal resolution. Increasing emitter density will improve temporal resolution, but current analysis algorithms struggle as emitter images significantly overlap. Here we present a deep convolutional neural network called LUENN w

Fabio Massimo Zennaro, Paolo Turrini, Theodoros Damoulas

Structural causal models provide a formalism to express causal relations between variables of interest. Models and variables can represent a system at different levels of abstraction, whereby relations may be coarsened and refined according to the need of a modeller. However, switching between different levels of abstraction requires evaluating a trade-off b

Zanyar A. Ameen

Thangaraj and Balasubramanian introduced the so-called somewhat fuzzy semicontinuous and somewhat fuzzy semiopen functions. Two years later, the same authors defined two other types of functions called somewhat fuzzy continuous and somewhat fuzzy open without indicating connections between them. At first glance, we may easily conclude (from their definitions

Hee Jung Choi, Trevor Chow, Aaron Wan, Hong Meng Yam

In this paper, we discuss the methods we applied at SemEval-2023 Task 10: Towards the Explainable Detection of Online Sexism. Given an input text, we perform three classification tasks to predict whether the text is sexist and classify the sexist text into subcategories in order to provide an additional explanation as to why the text is sexist. We explored m

E. Rabinovici, A. Sánchez-Garrido, R. Shir, J. Sonner

There are various definitions of the concept of complexity in Quantum Field Theory as well as for finite quantum systems. For several of them there are conjectured holographic bulk duals. In this work we establish an entry in the AdS/CFT dictionary for one such class of complexity, namely Krylov or K-complexity. For this purpose we work in the double-scaled

James Taylor

A real number is a rule that, when provided with a rational interval, answers Yes or No depending on if the real number ought to be considered to be in the given interval. Since the goal is to define the real numbers, this can only motivate the definition of which rules should be considered a real number. The rule must satisfy five properties and any rule th

Zouhaïr Mouayn, Mohamed Mahboubi, Othmane El Moize

We discuss the mean and variance of the number \textquotedblleft point-particles\textquotedblright\ $\sharp _{D_{R}}$\ inside a disk $D_{R}$ centered at the origin of the complex plane $\mathbb{C}$ and of radius $R>0$ with respect to a Ginibre-type (polyanalytic) process of index $m\in \mathbb{Z}_{+}$ by quantizing the phase space $\mathbb{C} $ via a set of

Dan-Ştefan Marinescu, Constantin P. Niculescu

The present paper aims to survey known results and to point out the wealth of rather important open problems that are out there.

Nathaniel Moore Glaser, Zsolt Kira

In this work, we consider the task of collision-free trajectory planning for connected self-driving vehicles. We specifically consider communication-critical situations--situations where single-agent systems have blindspots that require multi-agent collaboration. To identify such situations, we propose a method which (1) simulates multi-agent perspectives fr

Grzegorz Rządkowski

In this paper we deal with the logistic wavelets introduced in \cite{RF}. We modify them by multiplying by appropriate coefficients so that their norm in the space $L^{2}(R)$ is equal to 1. We calculate the normalization coefficients using the Grosset-Veselov formula \cite{GV}, Eulerian numbers and Bernoulli numbers. Then we apply the logistic wavelets to mo

Y. Contoyiannis, P. Papadopoulos, L. Matiadou, S. G. Stavrinides

It is known that in thermal systems of finite size that are subject to second order phase transitions and until the spontaneous symmetry breaking is completed, the fluctuations of the order parameter obey to the dynamics of critical intermittency. Beyond the SSB, critical intermittency does not hold. Thus, it is not expected that the distribution of the wait

George Ionita, Frank Kutzschebauch

We prove that any null-homotopic special holomorphic vector bundle automorphisms of a rank 2 vector bundle E over a Stein space X can be written as a finite product of unipotent holomorphic vector bundle automorphism as well as a finite product of exponentials.

Vinicius Soares Silva Marques, Laurence Rodrigues do Amaral

Documentation is one of the most neglected activities in Software Engineering, although it is an important method of assuring quality and understanding. Bioinformatics software is generally written by researchers from fields other than Computer Science who usually do not provide documentation. Documenting bioinformatics software may ease its adoption in mult

Mohammed Mouhcine, Youness Kadil1, Imane Rahmoune, Houda Filali

Colorectal cancer is a public health problem.Approximately 30 to 50 \% of colorectal tumors are caused by mutations in the KRAS gene.These mutations induce uncontrolled proliferation.To date,There is no approved effective treatment for the mutated KRAS oncogene.Farnesyltransferase (FTI) inhibitors are considered a therapeutic target against the mutated KRAS

Jinkai Li

Children's well-being of immigrants is facing several challenges related to physical, mental, and educational risks, which may obstacle human capital accumulation and further development. In rural China, due to the restriction of Hukou registration system, nearly 9 million left-behind children (LBC) are in lack of parental care and supervision in 2020 when t

N. Devroye, A. Mulgund, R. Shekhar, Gy. Turán

As new deep-learned error-correcting codes continue to be introduced, it is important to develop tools to interpret the designed codes and understand the training process. Prior work focusing on the deep-learned TurboAE has both interpreted the learned encoders post-hoc by mapping these onto nearby ``interpretable'' encoders, and experimentally evaluated the

Maxwell Crouse, Pavan Kapanipathi, Subhajit Chaudhury, Tahira Naseem

Nearly all general-purpose neural semantic parsers generate logical forms in a strictly top-down autoregressive fashion. Though such systems have achieved impressive results across a variety of datasets and domains, recent works have called into question whether they are ultimately limited in their ability to compositionally generalize. In this work, we appr

Colin R. Twomey, David H. Brainard, Joshua B. Plotkin

Color naming in natural languages is not arbitrary: it reflects efficient partitions of perceptual color space modulated by the relative needs to communicate about different colors. These psychophysical and communicative constraints help explain why languages around the world have remarkably similar, but not identical, mappings of colors to color terms. Lang

Deepak Gupta, Dina Demner-Fushman

Pre-trained language models (PLMs) have proven to be effective for document re-ranking task. However, they lack the ability to fully interpret the semantics of biomedical and health-care queries and often rely on simplistic patterns for retrieving documents. To address this challenge, we propose an approach that integrates knowledge and the PLMs to guide the

Annabelle Oz, Abraham Nitzan, Oded Hod, Juan E. Peralta

A first-principles approach to describe electron dynamics in open quantum systems driven far from equilibrium via external time-dependent stimuli is introduced. Within this approach, the driven Liouville von Neumann methodology is used to impose open boundary conditions on finite model systems, whose dynamics is described using time-dependent density functio

Dun Mao, Yixiu Wang, Hwaseob Lee, Lorry Chang

Two-dimensional materials with unique physicochemical properties promote photocatalytic activities. As the 2D material composites research studies the statistical average of complex catalytic behaviors, an integrated photonic platform allows clean and single flake level photo-catalytic investigations with precisely quantified photocatalytic activities. In th

Sweta Rai, Alexis Hoffman, Soumendra Lahiri, Douglas W. Nychka

The heavy-tailed behavior of the generalized extreme-value distribution makes it a popular choice for modeling extreme events such as floods, droughts, heatwaves, wildfires, etc. However, estimating the distribution's parameters using conventional maximum likelihood methods can be computationally intensive, even for moderate-sized datasets. To overcome this

Dongming Huang, Songtao Tian, Qian Lin

In this work, we address the longstanding puzzle that Sliced Inverse Regression (SIR) often performs poorly for sufficient dimension reduction when the structural dimension $d$ (the dimension of the central space) exceeds 4. We first show that in the multiple index model $Y=f( \mathbf{P} \boldsymbol{X})+\epsilon$ where $\boldsymbol{X}$ is a $p$-standard norm

Hwaseob Lee, Ali Kecebas, Feifan Wang, Lorry Chang

Non-Hermitian systems with their spectral degeneracies known as exceptional points (EPs) have been explored for lasing, controlling light transport, and enhancing a sensor s response. A ring resonator can be brought to an EP by controlling the coupling between its frequency degenerate clockwise and counterclockwise traveling modes. This has been typically ac

Paweł Jędrejko, Jun-Ichi Yano, Marta Wacławczyk

An evolution of a spherical region, subjected to uniform buoyancy force, is investigated. Incompressibility and axial symmetry are assumed, together with a buoyancy discontinuity at the boundary. The boundary turns into a vortex sheet and the system evolves into a ring. Contrary to the case of mechanically generated rings, buoyancy-driven rings are unstable.

Yahui Xiao, Feifan Wang, Dun Mao, Thomas Kananen

Periodic or gradient subwavelength structures are basic configurations of photonic crystals and metamaterials. The measured linear losses of those nanophotonic devices are well-beyond theoretical predictions. Nanofabrication related geometric inhomogeneity is considered as the primary cause of the deleterious performance. The deep-UV photolithography in CMOS

Jitendra Kumar, Shafqat Ul Islam, Sushant G. Ghosh

We investigate gravitational lensing in the strong deflection regime by loop quantum gravity (LQG)-motivated rotating black hole (LMRBH) metrics with an additional parameter $l$ besides mass $M$ and rotation $a$. The LMRBH spacetimes are regular everywhere, asymptotically encompassing the Kerr black hole as a particular case and, depending on the parameters,

Nicholas Galbraith, Samory Kpotufe

We consider the problem of \emph{pruning} a classification tree, that is, selecting a suitable subtree that balances bias and variance, in common situations with inhomogeneous training data. Namely, assuming access to mostly data from a distribution $P_{X, Y}$, but little data from a desired distribution $Q_{X, Y}$ with different $X$-marginals, we present th

Andrej Janda, Pierre Merriaux, Pierre Olivier, Jonathan Kelly

Advances in lidar technology have made the collection of 3D point clouds fast and easy. While most lidar sensors return per-point intensity (or reflectance) values along with range measurements, flash lidar sensors are able to provide information about the shape of the return pulse. The shape of the return waveform is affected by many factors, including the

Giovanni Pelliccioli

Precise and accurate Standard Model predictions are needed for polarised weak bosons in LHC processes, in order to perform template fits of data and to enhance the sensitivity to possible new-physics effects. We have proposed a general strategy to compute polarised cross-sections including radiative QCD and electroweak corrections to the production and decay

Alexander J. Silenko

The relativistic Foldy-Wouthuysen transformation is used for an advanced description of planar graphene electrons in external fields and free (2+1)-space. It is shown that the initial Dirac equation should by based on the usual $(4\times4)$ Dirac matrices but not on the reduction of matrix dimensions and the use of $(2\times2)$ Pauli matrices. The latter app

Jakub Grzeszczyk, Michał Karwatowski, Daria Łukasik, Maciej Wielgosz

This paper shows the machine learning system which performs instance segmentation of cytological images in veterinary medicine. Eleven cell types were used directly and indirectly in the experiments, including damaged and unrecognized categories. The deep learning models employed in the system achieve a high score of average precision and recall metrics, i.e

Mickaël D. Chekroun, Honghu Liu, Kaushik Srinivasan, James C. McWilliams

Recent years have seen a surge in interest for leveraging neural networks to parameterize small-scale or fast processes in climate and turbulence models. In this short paper, we point out two fundamental issues in this endeavor. The first concerns the difficulties neural networks may experience in capturing rare events due to limitations in how data is sampl

Esa Ollila, Daniel P. Palomar, Frederic Pascal

We propose estimating the scale parameter (mean of the eigenvalues) of the scatter matrix of an unspecified elliptically symmetric distribution using weights obtained by solving Tyler's M-estimator of the scatter matrix. The proposed Tyler's weights-based estimate (TWE) of scale is then used to construct an affine equivariant Tyler's M-estimator as a weighte

Anku Rani, S. M Towhidul Islam Tonmoy, Dwip Dalal, Shreya Gautam

Automatic fact verification has received significant attention recently. Contemporary automatic fact-checking systems focus on estimating truthfulness using numerical scores which are not human-interpretable. A human fact-checker generally follows several logical steps to verify a verisimilitude claim and conclude whether its truthful or a mere masquerade. P

Lin Huang, Chung-Ching Lin, Kevin Lin, Lin Liang

We present a unified framework for camera-space 3D hand pose estimation from a single RGB image based on 3D implicit representation. As opposed to recent works, most of which first adopt holistic or pixel-level dense regression to obtain relative 3D hand pose and then follow with complex second-stage operations for 3D global root or scale recovery, we propos

Zahra Shomali

Recently, the two dimensional complex MA$_2$Z$_4$ structures have been suggested as suitable replacements for silicon channels in field-effect transistors (FETs). Specifically, two materials of MoSi$_2$N$_4$ and WSi$_2$N$_4$ due to their very desirable electrical and thermal properties are noticed. On the other hand, the reliability of transistors, which is

José D. Alvarado, Leonardo Gonçalves de Oliveira, Simon Griffiths

We consider the question of determining the probability of triangle count deviations in the Erd\H{o}s-R\'enyi random graphs $G(n,m)$ and $G(n,p)$ with densities larger than $n^{-1/2}(\log{n})^{1/2}$. In particular, we determine the log probability $\log\mathbb{P}(N_{\triangle}(G)\, >\, (1+\delta)p^3n^3)$ up to a constant factor across essentially the entire

Salim Rukhsar, Anil K. Tiwari

Background: Epilepsy is a neurological illness affecting the brain that makes people more likely to experience frequent, spontaneous seizures. There has to be an accurate automated method for measuring seizure frequency and severity in order to assess the efficacy of pharmacological therapy for epilepsy. The drug quantities are often derived from patient rep

Qi Liu, Joseph Y. J. Chow

A fast recovery from disruptions is of vital importance for the reliability of transit systems. This study presents a new attempt to tackle the transit disruption mitigation problem in a comprehensive and hierarchical way. A network level strategy selection optimization model is formulated as a joint routing and resource allocation (nJRRA) problem. By constr

Antonio Casares, Thomas Colcombet, Nathanaël Fijalkow, Karoliina Lehtinen

We study transformations of automata and games using Muller conditions into equivalent ones using parity or Rabin conditions. We present two transformations, one that turns a deterministic Muller automaton into an equivalent deterministic parity automaton, and another that provides an equivalent history-deterministic Rabin automaton. We show a strong optimal

Wei Lu, Hua Ma, Tien-Ping Tan

Emotion recognition using Electroencephalogram (EEG) signals has emerged as a significant research challenge in affective computing and intelligent interaction. However, effectively combining global and local features of EEG signals to improve performance in emotion recognition is still a difficult task. In this study, we propose a novel CNN Interactive Tran

Xinyu Du, Huanhuan Yuan, Pengpeng Zhao, Junhua Fang

Sequential recommendation (SR) aims to model user preferences by capturing behavior patterns from their item historical interaction data. Most existing methods model user preference in the time domain, omitting the fact that users' behaviors are also influenced by various frequency patterns that are difficult to separate in the entangled chronological items.

Hoang Ky Nguyen, Mustapha Azreg-Aïnou

The special Buchdahl-inspired metric obtained in a recent paper [Phys. Rev. D 107, 104008 (2023)] describes asymptotically flat spacetimes in pure $\mathcal{R}^{2}$ gravity. The metric depends on a new (Buchdahl) parameter $\tilde{k}$ of higher-derivative characteristic, and recovers the Schwarzschild metric when $\tilde{k}=0$. It is shown that the special B

Xiaonan Li, Kai Lv, Hang Yan, Tianyang Lin

In-context learning is a new learning paradigm where a language model conditions on a few input-output pairs (demonstrations) and a test input, and directly outputs the prediction. It has been shown highly dependent on the provided demonstrations and thus promotes the research of demonstration retrieval: given a test input, relevant examples are retrieved fr

Len Feremans, Boris Cule, Bart Goethals

Many organisations manage service quality and monitor a large set devices and servers where each entity is associated with telemetry or physical sensor data series. Recently, various methods have been proposed to detect behavioural anomalies, however existing approaches focus on multivariate time series and ignore communication between entities. Moreover, we

Yinong Wu, Dehui Wang

In the current study, a brand-new SINARS(1) model is proposed for stationary discrete time series defined on $\boldsymbol{Z}$, based on extended binomial distribution and the Pegram's operator. The model effectively characterizes the series of positive and negative integer values generated after differencing some non-stationary time series. The model's attri

Ruoyong Xu, Patrick Brown

Profile likelihoods are rarely used in geostatistical models due to the computational burden imposed by repeated decompositions of large variance matrices. Accounting for uncertainty in covariance parameters can be highly consequential in geostatistical models as some covariance parameters are poorly identified, the problem is severe enough that the differen

Nils Lukas, Florian Kerschbaum

Deep image classification models trained on vast amounts of web-scraped data are susceptible to data poisoning - a mechanism for backdooring models. A small number of poisoned samples seen during training can severely undermine a model's integrity during inference. Existing work considers an effective defense as one that either (i) restores a model's integri

Durga Prasad Challa, Divya Gangadaraiah, Mourad Sini

We propose a reconstruction method to extract the variable mass density from the elastic farfields, with a single incident direction, measured before and after injecting highly dense small scaled inclusions. We take as a model, the Lam\'e system where the mass density is the unknown in $\Omega$ and the Lam\'e parameters are known constants. The injected smal