May 2023 arXiv papers — page 84

Weizhi Nie, Chen Zhang, Dan song, Yunpeng Bai

The chest X-ray (CXR) is commonly employed to diagnose thoracic illnesses, but the challenge of achieving accurate automatic diagnosis through this method persists due to the complex relationship between pathology. In recent years, various deep learning-based approaches have been suggested to tackle this problem but confounding factors such as image resoluti

Ze-Hong Guo, Qizhong Zhu

Supersolid is an exotic state of matter characterized by both superfluid properties and periodic particle density modulation, due to spontaneous breaking of U(1) gauge symmetry and spatial translation symmetry, respectively. For conventional supersolids,continuous translation symmetry breaking is accompanied by one gapless Goldstone mode in the excitation sp

Hui Li, Carlos A. Pena Solorzano, Susan Wei, Davis J. McCarthy

The ADMANI datasets (annotated digital mammograms and associated non-image datasets) from the Transforming Breast Cancer Screening with AI programme (BRAIx) run by BreastScreen Victoria in Australia are multi-centre, large scale, clinically curated, real-world databases. The datasets are expected to aid in the development of clinically relevant Artificial In

Hiroyuki Kasahara, Paul Schrimpf, Michio Suzuki

This paper examines the nonparametric identifiability of production functions, considering firm heterogeneity beyond Hicks-neutral technology terms. We propose a finite mixture model to account for unobserved heterogeneity in production technology and productivity growth processes. Our analysis demonstrates that the production function for each latent type c

Lijun Zhang, Xiao Liu, Kaleel Mahmood, Caiwen Ding

Visual content understanding frequently relies on multi-task models to extract robust representations of a single visual input for multiple downstream tasks. However, in comparison to extensively studied single-task models, the adversarial robustness of multi-task models has received significantly less attention and many questions remain unclear: 1) How robu

Dale R. Harshman, Anthony T. Fiory

The Ginzburg-Landau-based upper critical magnetic field $H_{\textrm{C2}}$ (0) $\approx$ 88 T for N-doped lutetium hydride, reported in Dasenbrock-Gammon et al., Nature $\textbf{615}$, 244 (2023), is obtained therein by modeling resistance behavior, defining transitions widths, and applying magnetic fields $H$ = 1 T and 3 T. A method is presented herein for d

Hongliang Luo, Feifei Gao, Hai Lin, Shaodan Ma

Using communications signals for dynamic target sensing is an important component of integrated sensing and communications (ISAC). In this paper, we propose to utilize the beam squint effect to realize fast non-cooperative dynamic target sensing in massive multiple input and multiple output (MIMO) Terahertz band communications systems. Specifically, we const

Sai Srujana Buddi, Utkarsh Oggy Sarawgi, Tashweena Heeramun, Karan Sawnhey

The adoption of multimodal interactions by Voice Assistants (VAs) is growing rapidly to enhance human-computer interactions. Smartwatches have now incorporated trigger-less methods of invoking VAs, such as Raise To Speak (RTS), where the user raises their watch and speaks to VAs without an explicit trigger. Current state-of-the-art RTS systems rely on heuris

Junyu Tang, Ran Cheng

We resolve the debate over the existence and magnitude of cross-sublattice (CS) contributions to spin pumping and spin-transfer torques in a two-sublattice antiferromagnet connected to a non-magnetic metal. Guided by symmetry considerations, we first relate the controversial CS terms to specific components in the spin conductance matrix. Then we quantify the

Yan Wang, Dianpeng Wang, Xiaowei Yue

The two-layer computer simulators are commonly used to mimic multi-physics phenomena or systems. Usually, the outputs of the first-layer simulator (also called the inner simulator) are partial inputs of the second-layer simulator (also called the outer simulator). How to design experiments by considering the space-filling properties of inner and outer simula

Grier M. Jones, Run. R. Li, A. Eugene DePrince, Konstantinos D. Vogiatzis

The exponential computational cost of describing strongly correlated electrons can be mitigated by adopting a reduced density-matrix (RDM)-based description of the electronic structure. While variational two-electron RDM (v2RDM) methods can enable large-scale calculations on such systems, the quality of the solution is limited by the fact that only a subset

Parham Valipoorsalimi, Yuksel Asli Sari, Mihriban Pekguleryuz

Magnesium alloys are attractive options for temporary bio-implants because of their biocompatibility, controlled corrosion rate, and similarity to natural bone in terms of stiffness and density. Nevertheless, their low mechanical strength hinders their use as cardiovascular stents and bone substitutes. While it is possible to engineer alloys with the desired

Jodie A. Yuwono, Xinyu Li, Tyler D. Doležal, Adib J. Samin

Multi principal element alloys (MPEAs) comprise a unique class of metal alloys. MPEAs have been demonstrated to possess several exceptional properties, including, as most relevant to the present study, a high corrosion resistance. In the context of MPEA design, the vast number of potential alloying elements and the staggering number of elemental combinations

Menglin Kong, Muzhou Hou, Shaojie Zhao, Feng Liu

Click-Through Rate (CTR) prediction is one of the main tasks of the recommendation system, which is conducted by a user for different items to give the recommendation results. Cross-domain CTR prediction models have been proposed to overcome problems of data sparsity, long tail distribution of user-item interactions, and cold start of items or users. In orde

Hendra Setiawan

We propose utilizing n-best reranking to enhance Sequence-Level Knowledge Distillation (Kim and Rush, 2016) where we extract pseudo-labels for student model's training data from top n-best hypotheses and leverage a diverse set of models with different inductive biases, objective functions or architectures, including some publicly-available large language mod

Lingjiong Zhu, Mert Gurbuzbalaban, Anant Raj, Umut Simsekli

Algorithmic stability is an important notion that has proven powerful for deriving generalization bounds for practical algorithms. The last decade has witnessed an increasing number of stability bounds for different algorithms applied on different classes of loss functions. While these bounds have illuminated various properties of optimization algorithms, th

Vo Khuong Dien

This article discusses the pressure-induced structural phase transition and related phonon, electronic and optical properties of hexagonal {\epsilon}-GaSe using first-principles calculations. The study focuses on optimizing geometric and electronic band structures, analyzing the charge density distributions, atomic vibrations, and phonon spectra, and charact

Brian Barch, Namit Anand, Jeffrey Marshall, Eleanor Rieffel

The breakdown of Lieb-Robinson bounds in local, non-Hermitian quantum systems opens up the possibility for a rich landscape of quantum many-body phenomenology. We elucidate this by studying information scrambling and quantum chaos in non-Hermitian variants of paradigmatic local quantum spin-chain models. We utilize a mixture of exact diagonalization and tens

Yuichi Ueno

We construct and characterize quantum Garnier systems in two variables including degenerate cases by certain holomorphic properties under the quantum canonical transformations.

Zhineng Xie, Guowu Huang, Weihao Lin, Xin Jin

The vectorial evolution of polarized light interaction with a medium can reveal its microstructure and anisotropy beyond what can be obtained from scalar light interaction. Anisotropic properties (diattenuation, retardance, and depolarization) of a complex medium can be quantified by polarization imaging by measuring the Mueller matrix. However, polarization

Francisco Haces-Garcia, Natalya Maslennikova, Craig L Glennie, Hanadi S Rifai

Hydrodynamic flood modeling improves hydrologic and hydraulic prediction of storm events. However, the computationally intensive numerical solutions required for high-resolution hydrodynamics have historically prevented their implementation in near-real-time flood forecasting. This study examines whether several Deep Neural Network (DNN) architectures are su

Yusuke Nemoto

We construct some integral elements in the motivic cohomology of the Hesse cubic curves and express their regulators in terms of generalized hypergeometric functions and Kamp\'e de F\'eriet hypergeometric functions. By using these hypergeometric expressions, we obtain numerical examples of the Bloch-Beilinson conjecture on special values of $L$-functions.

Antonio Mauricio, Vladia Pinheiro, Vasco Furtado, João Araújo Monteiro Neto

A basic task for most Legal Artificial Intelligence (Legal AI) applications is Named Entity Recognition (NER). However, texts produced in the context of legal practice make references to entities that are not trivially recognized by the currently available NERs. There is a lack of categorization of legislation, jurisprudence, evidence, penalties, the roles o

Vijayaraghavan Murali, Chandra Maddila, Imad Ahmad, Michael Bolin

Generative LLMs have been shown to effectively power AI-based code authoring tools that can suggest entire statements or blocks of code during code authoring. In this paper we present CodeCompose, an AI-assisted code authoring tool developed and deployed at Meta internally. CodeCompose is based on the InCoder LLM that merges generative capabilities with bi-d

Juanjuan Ren, Sebastian Franke, Becca VanDrunen, Stephen Hughes

Recently the photonic golden rule, which predicts that the spontaneous emission rate of an atom depends on the projected local density of states (LDOS), was shown to fail in an optical medium with a linear gain amplifier. We present a classical light-matter theory to fix this widely used spontaneous emission rate, fully recovering the quantum mechanical rate

Jhonalbert Aponte, Álvaro Ruiz, Jacksson Sánchez, Miguel Martín-Landrove

Tumor vascularity detection and quantification are of high relevance in the assessment of cancer lesions not only for disease diagnostics but for therapy considerations and monitoring. The present work addressed the quantification of pharmacokinetic parameters derived from the two-compartment Brix model by analyzing and processing Dynamic Contrast-Enhanced M

Swetamber Das, Jason R. Green

We derive statistical-mechanical speed limits on dissipation from the classical, chaotic dynamics of many-particle systems. In one, the rate of irreversible entropy production in the environment is the maximum speed of a deterministic system out of equilibrium, $\bar S_e/k_B\geq 1/2\Delta t$, and its inverse is the minimum time to execute the process, $\Delt

Craig Gidney, Dave Bacon

We give the Bacon-Shor code a threshold purely by deleting gates from its circuit. Specifically: we use lattice surgery to concatenate the Bacon-Shor code with itself using local planar connectivity, and observe that the resulting circuit is a subset of the circuit that would be used by a larger Bacon-Shor code.

Christian Aganze, Sarah Pearson, Tjitske Starkenburg, Gabriella Contardo

Stellar streams form through the tidal disruption of satellite galaxies or globular clusters orbiting a host galaxy. Globular cluster streams are exciting since they are thin (dynamically cold) and, therefore sensitive to perturbations from low-mass subhalos. Since the subhalo mass function differs depending on the dark matter composition, these gaps can pro

Wenqi Cui, Guanya Shi, Yuanyuan Shi, Baosen Zhang

Ensuring the frequency stability of electric grids with increasing renewable resources is a key problem in power system operations. In recent years, a number of advanced controllers have been designed to optimize frequency control. These controllers, however, almost always assume that the net load in the system remains constant over a sufficiently long time.

Manisha Garg, Tyler Chang, Krishnan Raghavan

Due to the curse of dimensionality, it is often prohibitively expensive to generate deterministic space-filling designs. On the other hand, when using na{\"i}ve uniform random sampling to generate designs cheaply, design points tend to concentrate in a small region of the design space. Although, it is preferable in these cases to utilize quasi-random techniq

Tsukasa Isoshima, Masaki Ogawa

Gay and Meier asked whether or not a trisection diagram obtained by the Gluck twist on a spun or a twist spun 2-knot obtained from some method is standard. In this paper, we depict the trisection diagrams explicitly when the 2- knot is the spun $(2n + 1, -2)$-torus knot, where $n\geq1$, and show that the trisection diagram is standard when $n = 1$. Moreover,

Alexandr Kostochka, Duo Lin, Zimu Xiang

The Chen-Lih-Wu Conjecture states that each connected graph with maximum degree $\Delta\geq 3$ that is not the complete graph $K_{\Delta+1}$ or the complete bipartite graph $K_{\Delta,\Delta}$ admits an equitable coloring with $\Delta$ colors. For planar graphs, the conjecture has been confirmed for $\Delta\geq 13$ by Yap and Zhang and for $9\leq \Delta\leq

Kumar Shivam, Vishnu Paladugu, Yanhong A. Liu

Reliable distributed systems require replication and consensus among distributed processes to tolerate process and communication failures. Understanding and assuring the correctness of protocols for replication and consensus have been a significant challenge. This paper describes the precise specification and runtime checking of Derecho, a more recent, sophi

Qin Zhang, Dongsheng An, Tianjun Xiao, Tong He

In deep metric learning for visual recognition, the calibration of distance thresholds is crucial for achieving desired model performance in the true positive rates (TPR) or true negative rates (TNR). However, calibrating this threshold presents challenges in open-world scenarios, where the test classes can be entirely disjoint from those encountered during

Mahdi Esmaily, Dongjie Jia

Discretizing a solution in the Fourier domain rather than the time domain presents a significant advantage in solving transport problems that vary smoothly and periodically in time, such as cardiorespiratory flows. The finite element solution of the resulting time-spectral formulation is investigated here for the convection-diffusion equations. In addition t

Hoang Ky Nguyen

In Phys. Rev. D $\textbf{107}$, 104008 (2023) we reported a novel exact closed-form solution which describes asymptotically flat spacetimes in pure $R^2$ gravity. The solution is Ricci scalar flat, viz. $R\equiv0$ everywhere. Whereas any metric with a null Ricci scalar would $\textit{trivially}$ satisfy the $R^2$ vacuo field equation, $R\left(R_{\mu\nu}-\fra

Monika Kwiatkowski, Simon Matern, Olaf Hellwich

Image alignment and image restoration are classical computer vision tasks. However, there is still a lack of datasets that provide enough data to train and evaluate end-to-end deep learning models. Obtaining ground-truth data for image alignment requires sophisticated structure-from-motion methods or optical flow systems that often do not provide enough data

Thomas Oseroff, Zeming Sun, Matthias Liepe

Surface properties are critical to the capabilities of superconducting microwave devices. The native oxide of niobium-based devices is thought to consist of a thin normal conducting layer. To improve understanding on the importance of this layer, an attempt was made to replace it with a more easily controlled gold film. A niobium sample host microwave cavity

Fan Bu, Martijn J. Schuemie, Akihiko Nishimura, Louisa H. Smith

Post-market safety surveillance is an integral part of mass vaccination programs. Typically relying on sequential analysis of real-world health data as they accrue, safety surveillance is challenged by the difficulty of sequential multiple testing and by biases induced by residual confounding. The current standard approach based on the maximized sequential p

Krzysztof Piotrzkowski, Mariusz Przybycien

Bremsstrahlung spectra will be strongly distorted due to small lateral beam sizes at future colliders. That in turn will have large consequences for the electron and positron beam lifetimes as well as for the luminosity measurements in the case of electron-hadron colliders. We discuss in detail such consequences for the Future Circular Collider and Large Had

Nico Montali, John Lambert, Paul Mougin, Alex Kuefler

Simulation with realistic, interactive agents represents a key task for autonomous vehicle software development. In this work, we introduce the Waymo Open Sim Agents Challenge (WOSAC). WOSAC is the first public challenge to tackle this task and propose corresponding metrics. The goal of the challenge is to stimulate the design of realistic simulators that ca

Augustin Toma, Patrick R. Lawler, Jimmy Ba, Rahul G. Krishnan

We present Clinical Camel, an open large language model (LLM) explicitly tailored for clinical research. Fine-tuned from LLaMA-2 using QLoRA, Clinical Camel achieves state-of-the-art performance across medical benchmarks among openly available medical LLMs. Leveraging efficient single-GPU training, Clinical Camel surpasses GPT-3.5 in five-shot evaluations on

Krishnan Raghavan, Prasanna Balaprakash

Continual learning~(CL) is a field concerned with learning a series of inter-related task with the tasks typically defined in the sense of either regression or classification. In recent years, CL has been studied extensively when these tasks are defined using Euclidean data -- data, such as images, that can be described by a set of vectors in an n-dimensiona

Hua Shen, Vicky Zayats, Johann C. Rocholl, Daniel D. Walker

Current disfluency detection models focus on individual utterances each from a single speaker. However, numerous discontinuity phenomena in spoken conversational transcripts occur across multiple turns, hampering human readability and the performance of downstream NLP tasks. This study addresses these phenomena by proposing an innovative Multi-Turn Cleanup t

Arash Dehghan, Mucahit Cevik, Merve Bodur

Ride-pooling services have been growing in popularity, increasing the need for efficient and effective operations. The main goal of ride-pooling services is to maximize the number of passengers served while minimizing wait and delay times. However, factors such as the timing and volume of passenger requests, pick-up and drop-off locations, available vehicle

Mattia Atzeni, Mikhail Plekhanov, Frédéric A. Dreyer, Nora Kassner

Entity linking methods based on dense retrieval are an efficient and widely used solution in large-scale applications, but they fall short of the performance of generative models, as they are sensitive to the structure of the embedding space. In order to address this issue, this paper introduces DUCK, an approach to infusing structural information in the spa

Alexander Cerjan, Terry A. Loring

The Clifford spectrum is a form of joint spectrum for noncommuting matrices. This theory has been applied in photonics, condensed matter and string theory. In applications, the Clifford spectrum can be efficiently approximated using numerical methods, but this only is possible in low dimensional example. Here we examine the higher-dimensional spheres that ca

Md Razuan Hossain, Ahmed Salah Mohamed, Nicholas Xavier Armendarez, Joseph S. Najem

Reservoir computing is a highly efficient machine learning framework for processing temporal data by extracting features from the input signal and mapping them into higher dimensional spaces. Physical reservoir layers have been realized using spintronic oscillators, atomic switch networks, silicon photonic modules, ferroelectric transistors, and volatile mem

Cristiano S. Silva, Juliana F. R. Miranda, Marcio C. Araújo Filho

In this paper, we compute universal inequalities of eigenvalues of a large class of second-order elliptic differential operators in divergence form, that includes, e.g., the Laplace and Cheng-Yau operators, on a bounded domain in a complete Riemannian manifolds isometrically immersed in Euclidean space. A key step in order to obtain the sequence of our estim

Édouard Bonnet, Julien Duron

We introduce a new parameter, called stretch-width, that we show sits strictly between clique-width and twin-width. Unlike the reduced parameters [BKW '22], planar graphs and polynomial subdivisions do not have bounded stretch-width. This leaves open the possibility of efficient algorithms for a broad fragment of problems within Monadic Second-Order (MSO) lo

Yuri G. Zarhin

Let f(x) be a polynomial of degree at least 5 with complex coefficients and without repeated roots. Let p be an odd prime. Suppose that all the coefficients of f(x) lie in a subfield K such that: 1) K contains a primitive p-th root of unity; 2) f(x) is irreducible over K; 3) the Galois group \Gal(f) of f(x) acts doubly transitively on the set of roots of f(x

Bin Han, Hans D. Schotten

Unmanned aerial vehicle (UAV) is becoming increasingly important in modern civilian and military applications. However, its novel use cases is bottlenecked by conventional satellite and terrestrial localization technologies, and calling for complementary solutions. Multi-UAV mutual positioning can be a potential answer, but its accuracy and security are chal

Andrew T. Ton, Arthur K. MacKeith, Mark D. Shattuck, Corey S. O'Hern

During epithelial wound healing, cell morphology near the healed wound and the healing rate vary strongly among different developmental stages even for a single species like \textit{Drosophila}. We develop deformable particle (DP) model simulations to understand how variations in cell mechanics give rise to distinct wound closure phenotypes in the \textit{Dr

Srivathsan Amruth, Xin Yee Lam

In many modern data sets, High dimension low sample size (HDLSS) data is prevalent in many fields of studies. There has been an increased focus recently on using machine learning and statistical methods to mine valuable information out of these data sets. Thus, there has been an increased interest in efficient learning in high dimensions. Naturally, as the d

Xin Liu, Muhammad Khalifa, Lu Wang

Energy-based models (EBMs) have gained popularity for controlled text generation due to their high applicability to a wide range of constraints. However, sampling from EBMs is non-trivial, as it often requires a large number of iterations to converge to plausible text, which slows down the decoding process and makes it less practical for real-world applicati

Massimiliano Gubinelli, Martina Hofmanová, Nimit Rana

We present two approaches to establish the exponential decay of correlation functions of Euclidean quantum field theories (EQFTs) via stochastic quantization (SQ). In particular we consider the elliptic stochastic quantization of the H{\o}egh--Krohn (or $\exp (\alpha \phi)_2$) EQFT in two dimensions. The first method is based on a path-wise coupling argument

Said Zriaa, Mohammed Mouçouf

In this work, we define a more general family of polynomials in several variables satisfying a linear recurrence relation. Then we provide explicit formulas and determinantal expressions. Finally, we apply these results to recurrent polynomials of order $2$, we present several relations and interesting identities involving the Fibonacci polynomials of order

Nilin Abrahamsen, Jiahao Yao

We propose two procedures to create painting styles using models trained only on natural images, providing objective proof that the model is not plagiarizing human art styles. In the first procedure we use the inductive bias from the artistic medium to achieve creative expression. Abstraction is achieved by using a reconstruction loss. The second procedure u

Dustin Holley, Jovin D'sa, Hossein Nourkhiz Mahjoub, Gibran Ali

This paper discusses the limitations of existing microscopic traffic models in accounting for the potential impacts of on-ramp vehicles on the car-following behavior of main-lane vehicles on highways. We first surveyed U.S. on-ramps to choose a representative set of on-ramps and then collected real-world observational data from the merging vehicle's perspect

Safinah Ali, Daniella DiPaola, Randi Williams, Prerna Ravi

Generative AI tools introduce new and accessible forms of media creation for youth. They also raise ethical concerns about the generation of fake media, data protection, privacy and ownership of AI-generated art. Since generative AI is already being used in products used by youth, it is critical that they understand how these tools work and how they can be u

Gabe Murray, Jeff Field, Maxine Xiu, Yusef Farah

Second harmonic generation (SHG) microscopy is a valuable tool for optical microscopy. SHG microscopy is normally performed as a point scanning imaging method, which lacks phase information and is limited in spatial resolution by the spatial frequency support of the illumination optics. In addition, aberrations in the illumination are difficult to remove. We

Valentin Barriere, Martin Claverie, Maja Schneider, Guido Lemoine

Accurate in-season crop type classification is crucial for the crop production estimation and monitoring of agricultural parcels. However, the complexity of the plant growth patterns and their spatio-temporal variability present significant challenges. While current deep learning-based methods show promise in crop type classification from single- and multi-m

Anant Thazhemadam, Dhairya Gandhi, Venkatasubramanian Viswanathan, Rachel C. Kurchin

Chemellia is an open-source framework for atomistic machine learning in the Julia programming language. The framework takes advantage of Julia's high speed as well as the ability to share and reuse code and interfaces through the paradigm of multiple dispatch. Chemellia is designed to make use of existing interfaces and avoid ``reinventing the wheel'' wherev

Máximo Bustamante-Calabria, Susana Martín-Ruiz, Alejandro Sánchez de Miguel, J. L. Ortiz

Evidence of the negative impact of light pollution on ecosystems is increasing every year. Its monitoring and study requires the identification, characterisation and control of the emitting sources. This is the case of urban centres with outdoor lighting that spills light outside the place it is intended to illuminate. The quantity and nature of the pollutan

Mohira Rassel, Patrick Kilian, Vito Aberham, Felix Spanier

The electromagnetic emission from neutron star mergers is comprised of multiple components. Synchrotron emission from the disk-powered jet as well as thermal emission from the merger ejecta (powered by a variety of sources) are among the most studied sources. The low masses and high velocities of the merger ejecta quickly develop conditions where emission fr

John Shareshian, Sheila Sundaram

For a fixed nonnegative integer $u$ and positive integer $n$, we investigate the symmetric function \[\sum_{d|n} \left(c_d(\tfrac{n}{d})\right)^u p_d^{\tfrac{n}{d}},\] where $p_n$ denotes the $n$th power sum symmetric function, and $c_d(r)$ is a Ramanujan sum, equal to the sum of the $r$th powers of all the primitive $d$th roots of unity. We establish the Sc

Amy J. Pitts, Charlotte R. Fowler

Many software packages have been developed to assist researchers in drawing directed acyclic graphs (DAGs), each with unique functionality and usability. We examine five of the most common software to generate DAGs: TikZ, DAGitty, ggdag, dagR, and igraph. For each package, we provide a general description of the its background, analysis and visualization cap

Annastasia Haynie, Anthony L. Piro

Stripped-envelope supernovae (SESNe) are a subclass of core-collapse supernovae that are deficient in hydrogen (SN~IIb, SN~Ib) and possibly helium (SN~Ic) in their spectra. Their progenitors are likely stripped of this material through a combination of stellar winds and interactions with a close binary companion, but the exact ejecta mass ranges covered by e

K. A. Lopes Lima, L. A. Ribeiro Junior

Nanomaterial synthesis and characterization advancements have led to the discovery of new carbon allotropes, such as the biphenylene network (BPN). BPN consists of four-, six-, and eight-membered rings of sp2-hybridized carbon atoms. Here, we employ density functional theory (DFT) calculations to investigate the CO2 adsorption capabilities in pristine and va

Samir D. Mathur, Madhur Mehta

The thermodynamic properties of black holes -- temperature, entropy and radiation rates -- are usually associated with the presence of a horizon. We argue that any Extremely Compact Object (ECO) must have the {\it same} thermodynamic properties. Quantum fields just outside the surface of an ECO have a large negative Casimir energy similar to the Boulware vac

Xuanyu Zhang, Qing Yang, Dongliang Xu

In recent years, pre-trained language models have undergone rapid development with the emergence of large-scale models. However, there is a lack of open-sourced chat models specifically designed for the Chinese language, especially in the field of Chinese finance, at the scale of hundreds of billions. To address this gap, we introduce XuanYuan 2.0, the large

Badr AlKhamissi, Siddharth Verma, Ping Yu, Zhijing Jin

In this paper, we conduct a thorough investigation into the reasoning capabilities of Large Language Models (LLMs), focusing specifically on the Open Pretrained Transformers (OPT) models as a representative of such models. Our study entails finetuning three different sizes of OPT on a carefully curated reasoning corpus, resulting in two sets of finetuned mod

Kai North, Tharindu Ranasinghe, Matthew Shardlow, Marcos Zampieri

Lexical Simplification (LS) is the task of replacing complex for simpler words in a sentence whilst preserving the sentence's original meaning. LS is the lexical component of Text Simplification (TS) with the aim of making texts more accessible to various target populations. A past survey (Paetzold and Specia, 2017) has provided a detailed overview of LS. Si

Joseph M. Coale, Dmitriy Y. Anistratov

In this paper analysis is performed on a computational method for thermal radiative transfer (TRT) problems based on the multilevel quasidiffusion (variable Eddington factor) method with the method of long characteristics (ray tracing) for the Boltzmann transport equation (BTE). The method is formulated with a multilevel set of moment equations of the BTE wh

Faisal Hamman, Erfaun Noorani, Saumitra Mishra, Daniele Magazzeni

There is an emerging interest in generating robust counterfactual explanations that would remain valid if the model is updated or changed even slightly. Towards finding robust counterfactuals, existing literature often assumes that the original model $m$ and the new model $M$ are bounded in the parameter space, i.e., $\|\text{Params}(M){-}\text{Params}(m)\|{

Taher Anjary

The Floyd-Warshall(FW) algorithm, is an ancient but a largely important algorithm used to solve the all-pairs simple-paths(APSP) problem. While the algorithm is available for use in open-source graph optimization libraries such as NetworkX, they do not take advantage of modern parallel processing hardware such as Graphics Processing Units(GPUs), which would

Niklas Kueper, Kartik Chari, Judith Bütefür, Julia Habenicht

This paper presents a dataset containing recordings of the electroencephalogram (EEG) and the electromyogram (EMG) from eight subjects who were assisted in moving their right arm by an active orthosis device. The supported movements were elbow joint movements, i.e., flexion and extension of the right arm. While the orthosis was actively moving the subject's

D. Pitonyak, C. Cocuzza, A. Metz, A. Prokudin

We present a new quantum field-theoretic definition of fully unintegrated dihadron fragmentation functions (DiFFs) as well as a generalized version for $n$-hadron fragmentation functions. We demonstrate that this definition allows certain sum rules to be satisfied, making it consistent with a number density interpretation. Moreover, we show how our correspon

Matheus Henrique Marques da Silva, Jhessica Victoria Santos da Silva, Rodrigo Reis Arrais, Wladimir Barroso Guedes de Araújo Neto

The entire Image Signal Processor (ISP) of a camera relies on several processes to transform the data from the Color Filter Array (CFA) sensor, such as demosaicing, denoising, and enhancement. These processes can be executed either by some hardware or via software. In recent years, Deep Learning has emerged as one solution for some of them or even to replace

Mario Giulianelli, Iris Luden, Raquel Fernandez, Andrey Kutuzov

We propose using automatically generated natural language definitions of contextualised word usages as interpretable word and word sense representations. Given a collection of usage examples for a target word, and the corresponding data-driven usage clusters (i.e., word senses), a definition is generated for each usage with a specialised Flan-T5 language mod

Zhizhen Ma, Behrouz Movahhed Nouri, Mohammad Tahersima, Sikandar Khan

The ability to modulate light using 2-dimensional (2D) materials is fundamentally challenged by their small optical cross-section leading to miniscule modal confinements in diffraction-limited photonics despite intrinsically high electro-optic absorption modulation (EAM) potential given by their strong exciton binding energies. However the inherent polarizat

René Peinl, Johannes Wirth

Large language models (LLMs) have garnered significant attention, but the definition of "large" lacks clarity. This paper focuses on medium-sized language models (MLMs), defined as having at least six billion parameters but less than 100 billion. The study evaluates MLMs regarding zero-shot generative question answering, which requires models to provide elab

Eduardo Nascimento, John Just, Jurandy Almeida, Tiago Almeida

In precision agriculture, detecting productive crop fields is an essential practice that allows the farmer to evaluate operating performance separately and compare different seed varieties, pesticides, and fertilizers. However, manually identifying productive fields is often a time-consuming and error-prone task. Previous studies explore different methods to

Farhad Pourpanah, Chee Peng Lim, Ali Etemad, Q. M. Jonathan Wu

Most semi-supervised learning (SSL) models entail complex structures and iterative training processes as well as face difficulties in interpreting their predictions to users. To address these issues, this paper proposes a new interpretable SSL model using the supervised and unsupervised Adaptive Resonance Theory (ART) family of networks, which is denoted as

Z. Haba

We discuss path integrals for quantum mechanics with a potential which is a perturbation of the upside-down oscillator. We express the path integral (in the real time) by the Wiener measure. We obtain the Feynman integral for perturbations which are the Fourier-Laplace transforms of a complex measure and for polynomials of the fotm $x^{4n}$ and $x^{4n+2}$ (w

R. A. Street, X. Li, S. Khakpash, E. Bellm

Galactic science encompasses a wide range of subjects in the study of the Milky Way and Magellanic Clouds, from Young Stellar Objects to X-ray Binaries. Mapping these populations, and exploring transient phenomena within them, are among the primary science goals of the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST). While early versions o

Juexin Marfai, Ryan J. McGorty, Rae M. Robertson-Anderson

Polymer topology, which plays a principal role in the rheology of polymeric fluids, and non-equilibrium materials, which exhibit time-varying rheological properties, are topics of intense investigation. Here, we push composites of circular DNA and dextran out-of-equilibrium via enzymatic digestion of DNA rings to linear fragments. Our time-resolved rheology

Marian Kupczynski

In our contextual model, statistical independence is violated, thus it is not constrained by Bell Theorem. Individual outcomes are created locally in a deterministic way in a function of setting dependent variables describing measuring instruments and variables describing physical systems, at the moment of their interactions. These setting dependent variable

Gabriel Dias Carvalho, Luis Fernando dos Prazeres, Pedro Silva Correia, Thiago R de Oliveira

Many theoretical and experimental results show that even isolated quantum systems evolving unitarily may equilibrate, since the evolution of some observables may be around an equilibrium value with negligible fluctuations most of the time. There are rigorous theorems giving the conditions for such equilibration to happen. In particular, initial states prepar

Taigao Ma, Haozhu Wang, L. Jay Guo

Deep learning-based methods have recently been established as fast and accurate surrogate simulators for optical multilayer thin film structures. However, existing methods only work for limited types of structures with different material arrangements, preventing their applications towards diverse and universal structures. Here, we propose the Opto-Layer (OL)

Malek Abid, Christian Kharif

The stability of an exponential current in water to infinitesimal perturbations in the presence of gravity and capillarity is investigated. Some new results on the generation of gravity-capillary waves are presented which supplement the previous works of Morland, Saffman \& Yuen (1991) and Young \& Wolfe (2014), namely in finite depth. To consider perturbati

Mufeng Tang, Helen Barron, Rafal Bogacz

Forming accurate memory of sequential stimuli is a fundamental function of biological agents. However, the computational mechanism underlying sequential memory in the brain remains unclear. Inspired by neuroscience theories and recent successes in applying predictive coding (PC) to \emph{static} memory tasks, in this work we propose a novel PC-based model fo

Lukas Daniel Klausner, Maximilian Heimstädt, Leonhard Dobusch

The complexity and increasingly tight coupling of supply chains poses a major logistical challenge for leading companies. Another challenge is that leading companies -- under pressure from consumers, a critical public and legislative measures such as supply chain laws -- have to take more responsibility than before for their suppliers' labour standards. In t

Alaa Maalouf, Murad Tukan, Vladimir Braverman, Daniela Rus

A coreset is a tiny weighted subset of an input set, that closely resembles the loss function, with respect to a certain set of queries. Coresets became prevalent in machine learning as they have shown to be advantageous for many applications. While coreset research is an active research area, unfortunately, coresets are constructed in a problem-dependent ma

Robert Vacareanu, Siddharth Varia, Kishaloy Halder, Shuai Wang

We explore how weak supervision on abundant unlabeled data can be leveraged to improve few-shot performance in aspect-based sentiment analysis (ABSA) tasks. We propose a pipeline approach to construct a noisy ABSA dataset, and we use it to adapt a pre-trained sequence-to-sequence model to the ABSA tasks. We test the resulting model on three widely used ABSA

Sam Scheele, Pierce Howell, Harish Ravichandar

Effective close-proximity human-robot interaction (CP-HRI) requires robots to be able to both efficiently perform tasks as well as adapt to human behavior and preferences. However, this ability is mediated by many, sometimes competing, aspects of interaction. We propose a real-time motion-planning framework for robotic manipulators that can simultaneously op

W. David Wick

Einstein's 1905 analysis of the Brownian Motion of a pollen grain in a water droplet as due to statistical variations in the collisions of water molecules with the grain, followed up by Perrin's experiments, provided one of the most convincing demonstrations of the reality of atoms. But in 1926 Schroedinger replaced classical particles by wavefunctions, whic

Mario Krause

Change point detection is a crucial aspect of analyzing time series data, as the presence of a change point indicates an abrupt and significant change in the process generating the data. While many algorithms for the problem of change point detection have been developed over time, it can be challenging to select the appropriate algorithm for a specific probl

Alannah Neff, Andrew Keane, Henk A. Dijkstra, Bernd Krauskopf

The tipping of the Atlantic Meridional Overturning Circulation (AMOC) to a 'shutdown' state due to changes in the freshwater forcing of the ocean is of particular interest and concern due to its widespread ramifications, including a dramatic climatic shift for much of Europe. A clear understanding of how such a shutdown would unfold requires analyses of mode