October 2023 arXiv papers — page 31

Ranran Shen, Pan Peng

We address the problem of designing a sublinear-time spectral clustering oracle for graphs that exhibit strong clusterability. Such graphs contain $k$ latent clusters, each characterized by a large inner conductance (at least $\varphi$) and a small outer conductance (at most $\varepsilon$). Our aim is to preprocess the graph to enable clustering membership q

Martin Vejvar, Yasutaka Fujimoto

We present ASPIRO, an approach for structured data verbalisation into short template sentences in zero to few-shot settings. Unlike previous methods, our approach prompts large language models (LLMs) to directly produce entity-agnostic templates, rather than relying on LLMs to faithfully copy the given example entities, or validating/crafting the templates m

Himanshu Gupta, Kevin Scaria, Ujjwala Anantheswaran, Shreyas Verma

The rapid advancement of large language models (LLMs) has sparked interest in data synthesis techniques, aiming to generate diverse and high-quality synthetic datasets. However, these synthetic datasets often suffer from a lack of diversity and added noise. In this paper, we present TarGEN, a multi-step prompting strategy for generating high-quality syntheti

Qiankun Liu, Yichen Li, Yuqi Jiang, Ying Fu

The ability to detect and track the dynamic objects in different scenes is fundamental to real-world applications, e.g., autonomous driving and robot navigation. However, traditional Multi-Object Tracking (MOT) is limited to tracking objects belonging to the pre-defined closed-set categories. Recently, Open-Vocabulary MOT (OVMOT) and Generic MOT (GMOT) are p

Mengcheng Lan, Xinjiang Wang, Yiping Ke, Jiaxing Xu

Unsupervised semantic segmentation is a challenging task that segments images into semantic groups without manual annotation. Prior works have primarily focused on leveraging prior knowledge of semantic consistency or priori concepts from self-supervised learning methods, which often overlook the coherence property of image segments. In this paper, we demons

Liwei Duan

We investigate the dynamics of a particle in a binary lattice with staggered on-site energies. An additional static force is introduced which further adjusts the on-site energies. The binary lattice appears to be unrelated to the semiclassical Rabi model, which describes a periodically driven two-level system. However, in a certain parity subspace, the Floqu

Liangxin Qian, Jun Zhao

In the rapidly evolving landscape of large language models (LLMs) and mobile edge computing for 6G, the need for efficient service delivery to mobile users with constrained computational resources has become paramount. Addressing this, our paper delves into a collaborative framework for model training where user data and model adapters are shared with server

Yang Zhang, Ling-Fang Lin, Adriana Moreo, Thomas A. Maier

The recent discovery of superconductivity in bilayer La$_3$Ni$_2$O$_7$ (327-LNO) under pressure stimulated much interest in layered nickelates. However, superconductivity was not found in another bilayer nickelate system, La$_3$Ni$_2$O$_6$ (326-LNO), even under pressure. Using density functional theory and the random phase approximation (RPA), we systematica

Wentao Guo, Andrew Wang, Bradon Thymes, Thorsten Joachims

We introduce the problem of ranking with slot constraints, which can be used to model a wide range of application problems -- from college admission with limited slots for different majors, to composing a stratified cohort of eligible participants in a medical trial. We show that the conventional Probability Ranking Principle (PRP) can be highly sub-optimal

Zijie Song, Zhenzhen Hu, Richang Hong

Unsupervised representation learning for image clustering is essential in computer vision. Although the advancement of visual models has improved image clustering with efficient visual representations, challenges still remain. Firstly, existing features often lack the ability to represent the internal structure of images, hindering the accurate clustering of

Bokai Xu, Jiayi Zhang, Hongyang Du, Zhe Wang

Extremely large-scale multiple-input-multiple output (XL-MIMO) is a promising technology to achieve high spectral efficiency (SE) and energy efficiency (EE) in future wireless systems. The larger array aperture of XL-MIMO makes communication scenarios closer to the near-field region. Therefore, near-field resource allocation is essential in realizing the abo

Edward Raff, James Holt

Multiple Instance Learning (MIL) is a sub-domain of classification problems with positive and negative labels and a "bag" of inputs, where the label is positive if and only if a positive element is contained within the bag, and otherwise is negative. Training in this context requires associating the bag-wide label to instance-level information, and implicitl

Chichuan Jin, Elisabeta Lusso, Martin Ward, Chris Done

The inter-band correlations between optical/UV and X-ray luminosities of active galactic nuclei (AGN) are important for understanding the disc-coronal connection, as well as using AGN as standard candles for cosmology. It is conventional to measure the X-ray luminosity at rest frame 2 keV and compare to the UV luminosity at the rest-frame 2500 \AA, but the w

André L. G. Mandolesi

Metrics in Grassmannians, or distances between subspaces of same dimension, have many uses, and extending them to the Total Grassmannian of subspaces of different dimensions is an important problem, as usual extensions lack good properties or give little information. Dimensional asymmetries call for the use of asymmetric metrics, and we present a natural met

Jeff Hwang, Moto Hira, Caroline Chen, Xiaohui Zhang

TorchAudio is an open-source audio and speech processing library built for PyTorch. It aims to accelerate the research and development of audio and speech technologies by providing well-designed, easy-to-use, and performant PyTorch components. Its contributors routinely engage with users to understand their needs and fulfill them by developing impactful feat

Jinbo Wen, Jiangtian Nie, Jiawen Kang, Dusit Niyato

Generative Artificial Intelligence (GAI) possesses the capabilities of generating realistic data and facilitating advanced decision-making. By integrating GAI into modern Internet of Things (IoT), Generative Internet of Things (GIoT) is emerging and holds immense potential to revolutionize various aspects of society, enabling more efficient and intelligent I

Riki Shimizu, Hau-Tieng Wu

Objective: Sleep spindles contain crucial brain dynamics information. We introduce the novel non-linear time-frequency analysis tool 'Concentration of Frequency and Time' (ConceFT) to create an interpretable automated algorithm for sleep spindle annotation in EEG data and to measure spindle instantaneous frequencies (IFs). Methods: ConceFT effectively reduce

Hassen Nigatu, Doik Kim

This paper proposes a new methodology for deriving a point-based dimensionally homogeneous Jacobian, intended for performance evaluation and optimization of parallel manipulators with mixed degrees of freedom. Optimal manipulator often rely on performance indices obtained from the Jacobian matrix. However, when manipulators exhibit mixed translational and ro

Yayi Fu

Bakker, Brunebarbe, Tsimerman showed in \cite{bakker2022minimal} that the definable structure sheaf $\mathcal{O}_{\mathbb{C}^n}$ of $\mathbb{C}^n$ is a coherent $\mathcal{O}_{\mathbb{C}^n}$-module as a sheaf on the site $\underline{\mathbb{C}^n}$, where the coverings are finite coverings by definable open sets. In general, let $\mathcal{K}$ be an algebraical

Katalin Schäffer, Yasemin Ozkan-Aydin, Margaret M. Coad

Recently, soft actuator-based exosuits have gained interest, due to their high strength-to-weight ratio, inherent safety, and low cost. We present a novel wrist exosuit actuated by fabric pneumatic artificial muscles that has lightweight wearable components (160 g) and can move the wrist in flexion/extension and ulnar/radial deviation. We derive a model repr

Gunasekar Ramakrishnan, Shantanu Desai

We obtain the median, arithmetic mean, and the weighted mean-based central estimates for the distance to M87 using all the measurements collated in De Grijs et al (2020). We then reconstruct the error distribution for the residuals of the combined measurements and also splitting them based on the tracers used. We then checked for consistency with a Gaussian

Yang Huang, Yuejian Peng

An $(n, k_1, \dots, k_t)$-cross intersecting system is a set of non-empty pairwise cross-intersecting families $\mathcal{F}_1\subset{[n]\choose k_1}, \mathcal{F}_2\subset{[n]\choose k_2}, \dots, \mathcal{F}_t\subset{[n]\choose k_t}$ with $t\geq 2$ and $k_1\geq k_2\geq \cdots \geq k_t$. If an $(n, k_1, \dots, k_t)$-cross intersecting system contains at least

Mingyang Xu, Yujie Tan, Yurong Liang, Jiawen Zhi

In this work, we have built an experimental setup to simulate the clock noise transmission with two spacecrafts and two optical links, and further demonstrated the extraction of picometer level signal drowned by the large laser frequency noise and clock noise with the data post-processing method. Laser frequency noise is almost eliminated by using the idea o

S. J. Li, D. Zhao, S. Wang, S. T. Cui

Hund's metal is one kind of correlated metal, in which the electronic correlation is strongly influenced by the Hund's interaction. At high temperatures, while the charge and orbital degrees of freedom are quenched, the spin degrees of freedom can persist in terms of frozen moments. As temperature decreases, a coherent electronic state with characteristic or

Dongjun Kang, Joonsuk Park, Yohan Jo, JinYeong Bak

Being able to predict people's opinions on issues and behaviors in realistic scenarios can be helpful in various domains, such as politics and marketing. However, conducting large-scale surveys like the European Social Survey to solicit people's opinions on individual issues can incur prohibitive costs. Leveraging prior research showing influence of core hum

M. Akter, M. M. Rizvi, M. Forkan

This paper is concerned with determining the shortest path for a pursuer aiming to intercept a moving target travelling at a constant speed. To address this challenge, we introduce an efficient mathematical model outlined as an optimal control problem. The proposed model is based on Dubin's path, where we concatenate two possible paths: a left-circular curve

Pablo Shmerkin, Alexia Yavicoli

We prove that for $1\le k<d$, if $E$ is a Borel subset of $\mathbb{R}^d$ of Hausdorff dimension strictly larger than $k$, the set of $(k+1)$-volumes determined by $k+2$ points in $E$ has positive one-dimensional Lebesgue measure. In the case $k=d-1$, we obtain an essentially sharp lower bound on the dimension of the set of tuples in $E$ generating a given vo

Chenghao Xu

In this article, we investigate the incompressible 2D Euler equations on a rotating biaxial ellipsoid, which model the dynamics of the atmosphere of a Jovian planet. We study the non-zonal Rossby-Haurwitz solutions of the Euler equations on an ellipsoid, while previous works only considered the case of a sphere. Our main results include: the existence and un

Ryu Sasaki

Two very closely related Rahman polynomials are constructed explicitly as the left eigenvectors of certain multi-dimensional discrete time Markov chain operators $K_n^{(i)}({\boldsymbol x},{\boldsymbol y};N)$, $i=1,2$. They are convolutions of an $n+1$-nomial distribution $W_n({\boldsymbol x};N)$ and an $n$-tuple of binomial distributions $\prod_{i}W_1(x_i;N

Mingyang Xu, Yujie Tan, Hanzhong Wu, Panpan Wang

Clock noise is one of the dominant noises in the space-borne gravitational wave (GW) detection. To suppress this noise, the clock noise-calibrated time-delay-interferometry (TDI) technique is proposed. In this technique, an inter-spacecraft clock tone transfer chain is necessary to obtain the comparison information of the clock noises in two spacecraft, duri

Balhae Kim, Hyungi Lee, Juho Lee

A Bayesian pseudocoreset is a compact synthetic dataset summarizing essential information of a large-scale dataset and thus can be used as a proxy dataset for scalable Bayesian inference. Typically, a Bayesian pseudocoreset is constructed by minimizing a divergence measure between the posterior conditioning on the pseudocoreset and the posterior conditioning

Karthika Subramani, Roberto Perdisci, Pierros Skafidas

Domain fronting is a network communication technique that involves leveraging (or abusing) content delivery networks (CDNs) to disguise the final destination of network packets by presenting them as if they were intended for a different domain than their actual endpoint. This technique can be used for both benign and malicious purposes, such as circumventing

Milton Espinoza

Following a theorem of David R. Hayes, we give a geometric interpretation of the special value at $s=0$ of certain $1$-cocycle on $\mathrm{PGL}_2(\mathbb{Q})$ previously introduced by the author. This work yields three main results: an explicit formula for our cocycle at $s=0$, a generalization and a new proof of Hayes' theorem, and an elegant summation form

Rongrong Lin, Shimin Li, Yulan Liu

Computing the proximal operator of the sparsity-promoting piece-wise exponential (PiE) penalty $1-e^{-|x|/\sigma}$ with a given shape parameter $\sigma>0$, which is treated as a popular nonconvex surrogate of $\ell_0$-norm, is fundamental in feature selection via support vector machines, image reconstruction, zero-one programming problems, compressed sensing

Xiaotong Shen, Yifei Liu, Rex Shen

Synthetic data generation, a cornerstone of Generative Artificial Intelligence, promotes a paradigm shift in data science by addressing data scarcity and privacy while enabling unprecedented performance. As synthetic data becomes more prevalent, concerns emerge regarding the accuracy of statistical methods when applied to synthetic data in contrast to raw da

Saran Vijayan, Fei Zhou

In this article, we propose a practical way to realize topological surface Dirac fermions with tunable attractive interaction between them. The approach involves coating the surface of a topological insulator with a thin film metal and utilizing the strong-electron phonon coupling in the metal to induce interaction between the surface fermions. We found that

Yuwen Lu, Chao Zhang, Yuewen Yang, Yaxing Yao

The study of UX dark patterns, i.e., UI designs that seek to manipulate user behaviors, often for the benefit of online services, has drawn significant attention in the CHI and CSCW communities in recent years. To complement previous studies in addressing dark patterns from (1) the designer's perspective on education and advocacy for ethical designs; and (2)

Shenghao Qin, Bowen Gang, Yin Xia

Multiple testing is an important research area with widespread scientific applications, including in biology and neuroscience. Among popularly adopted multiple testing procedures, many are based on p-values or Local false discovery rate (Lfdr) statistics. However, p-values--often obtained via the probability integral transform of standard test statistics--ty

Zhiwei Gao, Liang Yan, Tao Zhou

The fundamental computational issues in Bayesian inverse problems (BIP) governed by partial differential equations (PDEs) stem from the requirement of repeated forward model evaluations. A popular strategy to reduce such costs is to replace expensive model simulations with computationally efficient approximations using operator learning, motivated by recent

Minbiao Han, Jonathan Light, Steven Xia, Sainyam Galhotra

Organizations often lack sufficient data to effectively train machine learning (ML) models, while others possess valuable data that remains underutilized. Data markets promise to unlock substantial value by matching data suppliers with demand from ML consumers. However, market design involves addressing intricate challenges, including data pricing, fairness,

Tianran Liu, Zeping Zhang, Morteza Mousa Pasandi, Robert Laganiere

Recent works have demonstrated the importance of object completion in 3D Perception from Lidar signal. Several methods have been proposed in which modules were used to densify the point clouds produced by laser scanners, leading to better recall and more accurate results. Pursuing in that direction, we present, in this work, a counter-intuitive perspective:

Yufei Li, Xihui Yang, Hua Bai, Mingzhi Wang

High-quality rare-earth iron garnet (ReIG) Tb$_3$Fe$_5$O$_{12}$ (TbIG) thin films are epitaxially grown on a series of (111)-oriented garnet substrates with various lattice constants. The coherent growth induces a substrate-dependent in-plane tensile or compressive strain in the TbIG film. Measurements of the anomalous Hall-like effect (AHLE) in TbIG/Pt hete

Guilong Li, Zibin Zhao, Xunda Jiang, Zhaopin Chen

We construct strongly anisotropic quantum droplets with embedded vorticity in the 3D space, with mutually perpendicular vortex axis and polarization of atomic magnetic moments. Stability of these anisotropic vortex quantum droplets (AVQDs) is verified by means of systematic simulations. Their stability area is identified in the parametric plane of the total

Rui Ma, Ke Hai Luo, Zhao Wang, Jing Song He

Characterizing the orbital angular momentum (OAM) of a vortex beam is critically important for OAM-encoded data transfer. However, in typical OAM-based applications where vortex beams transmit through diffusers, the accompanying scattering effect tends to be either deliberately prevented, or characterized and then modulated actively based on complex wavefron

Han Huang, Fernanda De La Torre, Cathy Mengying Fang, Andrzej Banburski-Fahey

We introduce a novel method for real-time animation control and generation on rigged models using natural language input. First, we embed a large language model (LLM) in Unity to output structured texts that can be parsed into diverse and realistic animations. Second, we illustrate LLM's potential to enable flexible state transition between existing animatio

Zhengyu Mao, Chen Wan, Lei Zhang

We propose two families of relative trace formula comparisons in the study of relative Langlands duality conjectured by Ben-Zvi--Sakellaridis--Venkatesh. This allows us to incorporate numerous relative trace formula comparisons studied during the last four decades under the BZSV duality framework. For the proposed relative trace formula comparisons associate

Zhiyi Song, Dipankar Chaki, Abdallah Lakhdari, Athman Bouguettaya

We propose a novel resident identification framework to identify residents in a multi-occupant smart environment. The proposed framework employs a feature extraction model based on the concepts of positional encoding. The feature extraction model considers the locations of homes as a graph. We design a novel algorithm to build such graphs from layout maps of

Sandeep Manandhar, Auguste Genovesio

In this paper, we propose a style-based conditional video generative model. We introduce a novel temporal generator based on a set of learned sinusoidal bases. Our method learns dynamic representations of various actions that are independent of image content and can be transferred between different actors. Beyond the significant enhancement of video quality

Sadi Ramirez, Miguel Icaza-Lizaola, Sebastien Fromenteau, Mariana Vargas-Magaña

Recently, a new wave of full modeling analyses have emerged within the Large-Scale Structure community, leading mostly to tighter constraints on the estimation of cosmological parameters, when compared with standard approaches used over the last decade by collaboration analyses of stage III experiments. However, the majority of these full-shape analyses have

Asish K. Kundu, Polina M. Sheverdyaeva, Paolo Moras, Krishnakumar S. R. Menon

The Zhang-Rice (ZR) state is a strongly hybridized bound state formed by the transition metal and oxygen atoms. The spin-fluctuations within the ZR state are known to play an important role in high-$T_\mathrm{c}$ superconductivity in cuprates. Here, we employ a combination of angle-resolved photoemission spectroscopy (ARPES), X-ray photoemission spectroscopy

Jake Buzhardt, Phanindra Tallapragada

The manipulation of a collection of fluid particles in a low Reynolds number environment has several important applications. As we demonstrate in this paper, this manipulation problem is related to the scientific question of how fluid flow structures direct Lagrangian transport. We investigate this problem of directing the transport by manipulating the flow,

Shubhrajit Bhattacharya, Andrew O'Desky

We compute the asymptotic number of monic trace-one integral polynomials with Galois group $C_3$ and bounded height. For such polynomials we compute a height function coming from toric geometry and introduce a parametrization using the quadratic cyclotomic field $\mathbb Q(\sqrt{-3})$. We also give a formula for the number of polynomials of the form $t^3 -t^

Dongwei Chen

This paper establishes Paley-Wiener perturbation theorems for probabilistic frames. The classical Paley-Wiener perturbation theorem shows that if a sequence is close to a basis in a Banach space, then this sequence is also a basis. Similar perturbation results have been established for frames in Hilbert spaces. In this work, we show that if a probability mea

Qiu Ji, Guilin Qi, Yuxin Ye, Jiaye Li

Ontology revision aims to seamlessly incorporate a new ontology into an existing ontology and plays a crucial role in tasks such as ontology evolution, ontology maintenance, and ontology alignment. Similar to repair single ontologies, resolving logical incoherence in the task of ontology revision is also important and meaningful, because incoherence is a mai

Ran Wang, Zhe Sage Chen

Recent advances in machine learning have made revolutionary breakthroughs in computer games, image and natural language understanding, and scientific discovery. Foundation models and large-scale language models (LLMs) have recently achieved human-like intelligence thanks to BigData. With the help of self-supervised learning (SSL) and transfer learning, these

Christopher Jellen, Charles Nelson, John Burkhardt, Cody Brownell

Accurate prediction of atmospheric optical turbulence in localized environments is essential for estimating the performance of free-space optical systems. Macro-meteorological models developed to predict turbulent effects in one environment may fail when applied in new environments. However, existing macro-meteorological models are expected to offer some pre

Patrick Bossert

We analyse a second-order SPDE model in multiple space dimensions and develop estimators for the parameters of this model based on discrete observations of a solution in time and space on a bounded domain. While parameter estimation for one and two spatial dimensions was established in recent literature, this is the first work which generalizes the theory to

Benjamin Lovitz, Nathaniel Johnston

We introduce a convergent hierarchy of lower bounds on the minimum value of a real form over the unit sphere. The main practical advantage of our hierarchy over the real sum-of-squares (RSOS) hierarchy is that the lower bound at each level of our hierarchy is obtained by a minimum eigenvalue computation, as opposed to the full semidefinite program (SDP) requ

Timothy J. Aveni, Armando Fox, Björn Hartmann

Predictive suggestion systems offer contextually-relevant text entry completions. Existing approaches, like autofill, often excel in narrowly-defined domains but fail to generalize to arbitrary workflows. We introduce a conceptual framework to analyze the compound demands of a particular suggestion context, yielding unique opportunities for large language mo

Adrián Bazaga, Pietro Liò, Gos Micklem

In recent years, the task of text-to-SQL translation, which converts natural language questions into executable SQL queries, has gained significant attention for its potential to democratize data access. Despite its promise, challenges such as adapting to unseen databases and aligning natural language with SQL syntax have hindered widespread adoption. To ove

T. Kurachi, T. Yamaguchi, E. Kobayashi, T. Soma

The complex dielectric functions of LiNbO$_2$ were determined using optical transmittance and reflectance spectroscopies at room temperature. The measured dielectric function spectra reveal distinct structures at several bandgap energies. The bandgaps (exciton resonances) in the spectrum were observed at ca. 2.3, 3.2, 3.9, and 5.1 eV, respectively. These exp

Sam Olin, Erekle Jmukhadze, Allan H. MacDonald, Wei-Cheng Lee

Moir\'e engineering in two-dimensional van der Waals bilayer crystals has emerged as a flexible platform for controlling strongly correlated electron systems. The competition between valleys for the band extremum energy position in the parent layers is crucial in deciding the qualitative nature of the moir\'e Hamiltonian since it controls the physics of the

Nikolaos D. Bagis

Using the elementary axioms of special relativity and quantum mechanics we construct a wave equation which generalizes the Schrodinger equation. We also solve the general second and some higher order differential equations.

P. G. Brown, P. J. A. McCausland, A. R Hildebrand, L. T. J. Hanton

The Golden (British Columbia, Canada) meteorite fall occurred on Oct 4, 2021 at 0534 UT with the first recovered fragment (1.3 kg) landing on an occupied bed. The meteorite is an unbrecciated, low-shock (S2) ordinary chondrite of intermediate composition, typed as an L/LL5. From noble gas measurements the cosmic ray exposure age is 25 Ma while gas retention

Ian Biringer, Cyril Lecuire

We show that except for one exceptional case, a lamination on the boundary of a 3-dimensional handlebody H is a Hausdorff limit of meridians if and only if it is commensurable to a lamination with a &#39;homoclinic leaf&#39;. This is a precise version of a philosophy called Casson&#39;s Criterion, which appeared in unpublished notes of A. Casson. Application

Kenneth Blakey, Soham Chanda, Yuhan Sun, Chris T. Woodward

This is the first in a sequence of papers where we show that Lagrangian fillings such as the Harvey-Lawson filling in any dimension define augmentations of Chekanov-Eliashberg differential graded algebras by counting configurations of holomorphic disks connected by gradient trajectories, as in Aganagic-Ekholm-Ng-Vafa; we also prove that for Legendrian lifts

Jiguang Li, Robert Gibbons, Veronika Rockova

Multivariate Item Response Theory (MIRT) is sought-after widely by applied researchers looking for interpretable (sparse) explanations underlying response patterns in questionnaire data. There is, however, an unmet demand for such sparsity discovery tools in practice. Our paper develops a Bayesian platform for binary and ordinal item MIRT which requires mini

Alon Eldan, Ofek Gilon, Asher Lagemi, Elai Fishman Furman

Protocols for processing of quantum information are the foundation of quantum technology, enabling to share secrets at a distance, teleport quantum states, and to implement quantum computation. While many protocols were realized, and even commercialized, the throughput and processing speed of current protocols is limited by the narrow electronic bandwidth of

R. Chesler, P. Bhattacharyya, A. Shukla, S. Mazumdar

We show from many-body quantum mechanical calculations that there occur structurally distinct triplet-pair eigenstates in the intramolecular singlet fission (iSF) compound pentacene-tetracene-pentacene. Triplet excitons occupy neigboring pentacene and tetracene monomers in the higher energy doubly degenerate triplet-triplet multiexcitons, and terminal pentac

Gui-Qiang G. Chen, Yun Pu, Yongqian Zhang

We are concerned with inverse problems for supersonic potential flows past infinite axisymmetric Lipschitz cones. The supersonic flows under consideration are governed by the steady isentropic Euler equations for axisymmetric potential flows, which involve a singular geometric source term. We first study the inverse problem for the stability of an oblique co

Luke S. Snyder, Jeffrey Heer

Dynamically Interactive Visualization (DIVI) is a novel approach for orchestrating interactions within and across static visualizations. DIVI deconstructs Scalable Vector Graphics charts at runtime to infer content and coordinate user input, decoupling interaction from specification logic. This decoupling allows interactions to extend and compose freely acro

Greg Yang, James B. Simon, Jeremy Bernstein

The push to train ever larger neural networks has motivated the study of initialization and training at large network width. A key challenge is to scale training so that a network's internal representations evolve nontrivially at all widths, a process known as feature learning. Here, we show that feature learning is achieved by scaling the spectral norm of w

Reuben R. W. Wang, John L. Bohn

We study anisotropic thermalization in dilute gases of microwave shielded polar molecular fermions. For collision energies above the threshold regime, we find that thermalization is suppressed due to a strong preference for forward scattering and a reduction in total cross section with energy, significantly reducing the efficiency of evaporative cooling. We

Benjamin Yan, Ruochen Liu, David E. Kuo, Subathra Adithan

Automatically generated reports from medical images promise to improve the workflow of radiologists. Existing methods consider an image-to-report modeling task by directly generating a fully-fledged report from an image. However, this conflates the content of the report (e.g., findings and their attributes) with its style (e.g., format and choice of words),

Grishma Adenkar, Viktor Lipovka, Nihar Prabhala, Srikar Vakkalagadda

The primary goal of this project was to use the given data of four emission lines from two ultra-luminous infrared galaxies to calculate the molecular gas mass and dynamical mass of each galaxy. These quantities can provide valuable information about a galaxy's age, star formation properties, and molecular make-up. Ultra-luminous infrared galaxies are formed

Chuyue Sun, Ying Sheng, Oded Padon, Clark Barrett

The use of large language models for code generation is a rapidly growing trend in software development. However, without effective methods for ensuring the correctness of generated code, this trend could lead to undesirable outcomes. In this paper, we introduce a new approach for addressing this challenge: the Clover paradigm, short for Closed-Loop Verifiab

Luigi Viola, Saeed Nordin, Daniel Dotta, Mohammad Reza Hesamzadeh

The expansion of variable generation has driven a transition toward a 100\% non-fossil power system. New system needs are challenging system stability and suggesting the need for a redesign of the ancillary service (AS) markets. This paper presents a comprehensive and broad review for industrial practitioners and academic researchers regarding the challenges

Audrey Renson, Ellicott C. Matthay, Kara E. Rudolph

Difference-in-differences (DID) is a popular approach to identify the causal effects of treatments and policies in the presence of unmeasured confounding. DID identifies the sample average treatment effect in the treated (SATT). However, a goal of such research is often to inform decision-making in target populations outside the treated sample. Transportabil

Ryan Sullivan, Akarsh Kumar, Shengyi Huang, John P. Dickerson

Most reinforcement learning methods rely heavily on dense, well-normalized environment rewards. DreamerV3 recently introduced a model-based method with a number of tricks that mitigate these limitations, achieving state-of-the-art on a wide range of benchmarks with a single set of hyperparameters. This result sparked discussion about the generality of the tr

Karthik Ganesan, Michal Fishkin, Ourong Lin, Natalie Enright Jerger

Neural networks are seeing increased use in diverse Internet of Things (IoT) applications such as healthcare, smart homes and industrial monitoring. Their widespread use makes neural networks a lucrative target for theft. An attacker can obtain a model without having access to the training data or incurring the cost of training. Also, networks trained using

Ehsan Nowroozi, Samaneh Ghelichkhani, Imran Haider, Ali Dehghantanha

Convolutional neural networks (CNNs) models play a vital role in achieving state-of-the-art performances in various technological fields. CNNs are not limited to Natural Language Processing (NLP) or Computer Vision (CV) but also have substantial applications in other technological domains, particularly in cybersecurity. The reliability of CNN's models can be

Victor Lovic, Davide G. Marangon, Peter. R. Smith, Robert I. Woodward

Quantum key distribution (QKD) enables private communications with information-theoretic security. To guarantee the practical security of QKD, it is essential that QKD systems are implemented in accordance to theoretical requirements and robust against side-channel attacks. Here we study a prominent attack on QKD transmitters known as the laser seeding attac

Sarah Alsayyahi, Riza Batista-Navarro

Temporal relation extraction models have thus far been hindered by a number of issues in existing temporal relation-annotated news datasets, including: (1) low inter-annotator agreement due to the lack of specificity of their annotation guidelines in terms of what counts as a temporal relation; (2) the exclusion of long-distance relations within a given docu

Jie Wei, Weicong Feng, Erik Blasch, Erika Ardiles-Cruz

It is important to quantify Damage Assessment (DA) for Human Assistance and Disaster Response (HADR) applications. In this paper, to achieve efficient and scalable DA in HADR, an image prior and posterior conditional probability (IP2CP) is developed as an effective computational imaging representation. Equipped with the IP2CP representation, the matching pre

Mai Zeng, Florence Regol, Mark Coates

Neural Temporal Point Processes (TPPs) have emerged as the primary framework for predicting sequences of events that occur at irregular time intervals, but their sequential nature can hamper performance for long-horizon forecasts. To address this, we introduce a novel approach that incorporates a diffusion generative model. The model facilitates sequence-to-

Saeed Nordin, Abolfazl Khodadadi, Priyanka Shinde, Evelin Blom

With the increasing integration of power plants into the frequency-regulation markets, the importance of optimal trading has grown substantially. This paper conducts an in-depth analysis of their optimal trading behavior in sequential day-ahead, intraday, and frequency-regulation markets. We introduce a probabilistic multi-product optimization model, derived

Xiaolei Chu, Ziqi Wang

Perturbed by natural hazards, community-level infrastructure networks operate like many-body systems, with behaviors emerging from coupling individual component dynamics with group correlations and interactions. It follows that we can borrow methods from statistical physics to study the response of infrastructure systems to natural disasters. This study aims

J. E. Smith

The bottom two layers of a neuromorphic architecture are designed and shown to be capable of online clustering and supervised classification. An active spiking dendrite model is used, and a single dendritic segment performs essentially the same function as a classic integrate-and-fire point neuron. A single dendrite is then composed of multiple segments and

Zhaoyang Liu, Zeqiang Lai, Zhangwei Gao, Erfei Cui

We present ControlLLM, a novel framework that enables large language models (LLMs) to utilize multi-modal tools for solving complex real-world tasks. Despite the remarkable performance of LLMs, they still struggle with tool invocation due to ambiguous user prompts, inaccurate tool selection and parameterization, and inefficient tool scheduling. To overcome t

Joshua Crouch, Chun-Hung Liu

Weak diameter coloring of graphs recently attracted attention partially due to its connection to asymptotic dimension of metric spaces. We consider weak diameter list-coloring of graphs in this paper. Dvo\v{r}\'{a}k and Norin proved that graphs with bounded Euler genus are 3-choosable with bounded weak diameter. In this paper, we extend their result by showi

Simone Marchesi, Elisa Palezzato, Michele Torielli

In this article, we study the weak and strong Lefschetz of higher dimensional quotients and dimension 1 almost complete intersections. We then apply the obtained results to the study of the Jacobian algebra of hyperplane arrangements.

Allyson Ettinger, Jena D. Hwang, Valentina Pyatkin, Chandra Bhagavatula

Large language models (LLMs) show amazing proficiency and fluency in the use of language. Does this mean that they have also acquired insightful linguistic knowledge about the language, to an extent that they can serve as an "expert linguistic annotator"? In this paper, we examine the successes and limitations of the GPT-3, ChatGPT, and GPT-4 models in analy

Eric J. Ching, Ryan F. Johnson, Andrew D. Kercher

A well-known issue associated with the use of fully conservative schemes in multicomponent-flow simulations is the generation of spurious pressure oscillations at contact interfaces. These oscillations can rapidly lead to solver divergence even in the presence of smooth interfaces that are not fully resolved. In this note, we compare various strategies for r

Shamiul Alam, Jack Hutchins, Nikhil Shukla, Kazi Asifuzzaman

Big data applications are on the rise, and so is the number of data centers. The ever-increasing massive data pool needs to be periodically backed up in a secure environment. Moreover, a massive amount of securely backed-up data is required for training binary convolutional neural networks for image classification. XOR and XNOR operations are essential for l

Marjan Zare, Ian Frigaard, Gregory Lawrence

The passage of single air bubbles through the horizontal interface between miscible viscoplastic and Newtonian fluids, considering various combinations of densities and viscosities for the fluid layers, is studied computationally. The primary focus is on the quantity of liquid transferred from the lower layer (Viscoplastic fluid) to the upper layer (Newtonia

Zeshun Zong, Xuan Li, Minchen Li, Maurizio M. Chiaramonte

We propose a hybrid neural network and physics framework for reduced-order modeling of elastoplasticity and fracture. State-of-the-art scientific computing models like the Material Point Method (MPM) faithfully simulate large-deformation elastoplasticity and fracture mechanics. However, their long runtime and large memory consumption render them unsuitable f

Mahsa Berahman, Seyed Sajad Tabasi, Javad T. Firouzjaee, S. Mobina Hosseini

All kinds of simulations of the intergalactic medium, such as hydrodynamic simulation, N-body simulation, numerical and semi-numerical simulation, etc., have been used to realize the history of this medium. In addition, emulators also have become widely used in recent years. Especially because they are very fast, they are used in many works instead of actual

Hao Xue, Flora D. Salim

Energy load forecasting plays a crucial role in optimizing resource allocation and managing energy consumption in buildings and cities. In this paper, we propose a novel approach that leverages language models for energy load forecasting. We employ prompting techniques to convert energy consumption data into descriptive sentences, enabling fine-tuning of lan

Aditya Joshi, Shruta Rawat, Alpana Dange

Large language models (LLMs) are typically evaluated on the basis of task-based benchmarks such as MMLU. Such benchmarks do not examine responsible behaviour of LLMs in specific contexts. This is particularly true in the LGBTI+ context where social stereotypes may result in variation in LGBTI+ terminology. Therefore, domain-specific lexicons or dictionaries

Nicholas E. Corrado, Josiah P. Hanna

Recently, data augmentation (DA) has emerged as a method for leveraging domain knowledge to inexpensively generate additional data in reinforcement learning (RL) tasks, often yielding substantial improvements in data efficiency. While prior work has demonstrated the utility of incorporating augmented data directly into model-free RL updates, it is not well-u