February 2024 arXiv papers — page 26

Zeyang Pan, Zhijie Qu, Joel N. Bregman, Jifeng Liu

The XMM-Newton Line Emission Analysis Program (X-LEAP) is designed to study diffuse X-ray emissions from the Milky Way (MW) hot gas, as well as emissions from the foreground solar wind charge exchange (SWCX). This paper reports an all-sky survey of spectral feature intensities corresponding to the O VII, O VIII, and iron L-shell (Fe-L) emissions. These inten

Jiajian Zheng, Duan Xin, Qishuo Cheng, Miao Tian

The stock market is a crucial component of the financial market, playing a vital role in wealth accumulation for investors, financing costs for listed companies, and the stable development of the national macroeconomy. Significant fluctuations in the stock market can damage the interests of stock investors and cause an imbalance in the industrial structure,

Biao Zhang, Zhongtao Liu, Colin Cherry, Orhan Firat

While large language models (LLMs) often adopt finetuning to unlock their capabilities for downstream applications, our understanding on the inductive biases (especially the scaling properties) of different finetuning methods is still limited. To fill this gap, we conduct systematic experiments studying whether and how different scaling factors, including LL

R. James Cotton

Recent developments have created differentiable physics simulators designed for machine learning pipelines that can be accelerated on a GPU. While these can simulate biomechanical models, these opportunities have not been exploited for biomechanics research or markerless motion capture. We show that these simulators can be used to fit inverse kinematics to m

Le Yang, Miao Tian, Duan Xin, Qishuo Cheng

The development of artificial intelligence has significantly transformed people's lives. However, it has also posed a significant threat to privacy and security, with numerous instances of personal information being exposed online and reports of criminal attacks and theft. Consequently, the need to achieve intelligent protection of personal information throu

K. S. Virbhadra

We recently hypothesized that a distortion parameter exists such that its signed sum for all images of singular gravitational lensing of a source vanishes identically [K. S. Virbhadra, Phys. Rev. D {\bf 106}, 064038 (2022)]. We found a distortion parameter (the ratio of the tangential to radial magnifications) that satisfied the hypothesis for the images of

Stanley Chinedu Okoro, Alexander Lopez, Austine Unuriode

Over the past few years, wildfires have become a worldwide environmental emergency, resulting in substantial harm to natural habitats and playing a part in the acceleration of climate change. Wildfire management methods involve prevention, response, and recovery efforts. Despite improvements in detection techniques, the rising occurrence of wildfires demands

Tzu-Ting Yang, Hsin-Wei Wang, Yi-Cheng Wang, Chi-Han Lin

With the massive developments of end-to-end (E2E) neural networks, recent years have witnessed unprecedented breakthroughs in automatic speech recognition (ASR). However, the codeswitching phenomenon remains a major obstacle that hinders ASR from perfection, as the lack of labeled data and the variations between languages often lead to degradation of ASR per

Wei Wei, Jiabin Tang, Yangqin Jiang, Lianghao Xia

Multimedia online platforms (e.g., Amazon, TikTok) have greatly benefited from the incorporation of multimedia (e.g., visual, textual, and acoustic) content into their personal recommender systems. These modalities provide intuitive semantics that facilitate modality-aware user preference modeling. However, two key challenges in multi-modal recommenders rema

Zhaoxin Guo, Zhipeng Wang, Ruiquan Ge, Jianxun Yu

The early detection of a pulmonary embolism (PE) is critical for enhancing patient survival rates. Both image-based and non-image-based features are of utmost importance in medical classification tasks. In a clinical setting, physicians tend to rely on the contextual information provided by Electronic Medical Records (EMR) to interpret medical imaging. Howev

Kristiāns Čerņevičs, Oleg V. Yazyev

Graphene nanoribbons (GNRs) produced by means of bottom-up chemical self-assembly are considered promising candidates for the next-generation nanoelectronic devices. We address the electronic transport properties of angled two-terminal GNR junctions, which are inevitable in the interconnects in graphene-based integrated circuits. We construct a library of ov

Ria Rashid, Komala Krishna, Clint Pazhayidam George, Nandakumar Nambath

We propose a machine learning-driven optimisation framework for analog circuit design in this paper. The primary objective is to determine the device sizes for the optimal performance of analog circuits for a given set of specifications. Our methodology entails employing machine learning models and spice simulations to direct the optimisation algorithm towar

Dule Shu, Wilson Zhen, Zijie Li, Amir Barati Farimani

Fluid data completion is a research problem with high potential benefit for both experimental and computational fluid dynamics. An effective fluid data completion method reduces the required number of sensors in a fluid dynamics experiment, and allows a coarser and more adaptive mesh for a Computational Fluid Dynamics (CFD) simulation. However, the ill-posed

Rohit Prabhavalkar, Zhong Meng, Weiran Wang, Adam Stooke

The accuracy of end-to-end (E2E) automatic speech recognition (ASR) models continues to improve as they are scaled to larger sizes, with some now reaching billions of parameters. Widespread deployment and adoption of these models, however, requires computationally efficient strategies for decoding. In the present work, we study one such strategy: applying mu

Shuaibing Wang, Shunli Wang, Dingkang Yang, Mingcheng Li

We propose a robust and accurate method for reconstructing 3D hand mesh from monocular images. This is a very challenging problem, as hands are often severely occluded by objects. Previous works often have disregarded 2D hand pose information, which contains hand prior knowledge that is strongly correlated with occluded regions. Thus, in this work, we propos

Taisuke Hosaka, Norio Konno, Etsuo Segawa

We consider the connection between this zeta function and quantum search via quantum walk. First, we give an explicit expression of the zeta function on the one-dimensional torus in the general case of the number and position of marked vertices. Moreover, we deal with the two special cases of the position of the marked vertices on the $d$-dimensional torus $

Miranda PG Zalusky, Danny E Miller

Long-read sequencing (LRS) is able to simultaneously capture information about both DNA sequence and modifications, such as CpG methylation in a single sequencing experiment. Here we present Methylation Operation Wizard (MeOW), a program to identify and prioritize differentially methylated regions (DMRs) genome-wide using LRS data. MeOW can be run using eith

Luca Candelori, Vladimir Y. Chernyak, John R. Klein

We introduce the notion of an X-state on $n$-qubits. After taking the Zariski closure of the set of X-states in the space of all mixed states, we obtain a complex algebraic variety $\scr X$ that is equipped with the action of the Lie group of local symmetries $G$. We show that the field of $G$-invariant rational functions on $\scr X$ is purely transcendental

Won-Kwang Park

Generally, to apply the MUltiple SIgnal Classification (MUSIC) algorithm for the rapid imaging of small inhomogeneities, the complete elements of the multi-static response (MSR) matrix must be collected. However, in real-world applications such as microwave imaging or bistatic measurement configuration, diagonal elements of the MSR matrix are unknown. Nevert

Alison Callahan, Duncan McElfresh, Juan M. Banda, Gabrielle Bunney

The impact of using artificial intelligence (AI) to guide patient care or operational processes is an interplay of the AI model's output, the decision-making protocol based on that output, and the capacity of the stakeholders involved to take the necessary subsequent action. Estimating the effects of this interplay before deployment, and studying it in real

Deqian Kong, Yuhao Huang, Jianwen Xie, Edouardo Honig

This work explores the challenging problem of molecule design by framing it as a conditional generative modeling task, where target biological properties or desired chemical constraints serve as conditioning variables. We propose the Latent Prompt Transformer (LPT), a novel generative model comprising three components: (1) a latent vector with a learnable pr

Yali Bian, Rebecca Faust, Chris North

An increasing number of studies have utilized interactive deep learning as the analytic model of visual analytics systems for complex sensemaking tasks. In these systems, traditional interactive dimensionality reduction (DR) models are commonly utilized to build a bi-directional bridge between high-dimensional deep learning representations and low-dimensiona

Yixin Liu, Kai Zhang, Yuan Li, Zhiling Yan

Sora is a text-to-video generative AI model, released by OpenAI in February 2024. The model is trained to generate videos of realistic or imaginative scenes from text instructions and show potential in simulating the physical world. Based on public technical reports and reverse engineering, this paper presents a comprehensive review of the model's background

Hongyu Shen, Yici Yan, Zhizhen Zhao

Model-X knockoff has garnered significant attention among various feature selection methods due to its guarantees for controlling the false discovery rate (FDR). Since its introduction in parametric design, knockoff techniques have evolved to handle arbitrary data distributions using deep learning-based generative models. However, we have observed limitation

Santiago Coelho, Ying Liao, Filip Szczepankiewicz, Jelle Veraart

Joint modeling of diffusion and relaxation has seen growing interest due to its potential to provide complementary information about tissue microstructure. For brain white matter, we designed an optimal diffusion-relaxometry MRI protocol that samples multiple b-values, B-tensor shapes, and echo times (TE). This variable-TE protocol (27 min) has as subsets a

Pavel Etingof

We prove a conjecture of D. Gaiotto on positivity of inner products arising in studying Landau-Ginzburg boundary conditions in the 1-dimensional case, and in special cases in higher dimensions, for 3d free hypermultiplets.

Jugal Garg, Aniket Murhekar, John Qin

We investigate the existence of fair and efficient allocations of indivisible chores to asymmetric agents who have unequal entitlements or weights. We consider the fairness notion of weighted envy-freeness up to one chore (wEF1) and the efficiency notion of Pareto-optimality (PO). The existence of EF1 and PO allocations of chores to symmetric agents is a maj

Kunyang Zhou

In this paper, we present a novel sequence generation-based framework for lane detection, called Lane2Seq. It unifies various lane detection formats by casting lane detection as a sequence generation task. This is different from previous lane detection methods, which depend on well-designed task-specific head networks and corresponding loss functions. Lane2S

Yiming Ren, Xiao Han, Chengfeng Zhao, Jingya Wang

For human-centric large-scale scenes, fine-grained modeling for 3D human global pose and shape is significant for scene understanding and can benefit many real-world applications. In this paper, we present LiveHPS, a novel single-LiDAR-based approach for scene-level human pose and shape estimation without any limitation of light conditions and wearable devic

Kazi Shahrukh Omar, Gustavo Moreira, Daniel Hodczak, Maryam Hosseini

Sunlight and shadow play critical roles in how urban spaces are utilized, thrive, and grow. While access to sunlight is essential to the success of urban environments, shadows can provide shaded places to stay during the hot seasons, mitigate heat island effect, and increase pedestrian comfort levels. Properly quantifying sunlight access and shadows in large

Yuge Zhang, Qiyang Jiang, Xingyu Han, Nan Chen

In the era of data-driven decision-making, the complexity of data analysis necessitates advanced expertise and tools of data science, presenting significant challenges even for specialists. Large Language Models (LLMs) have emerged as promising aids as data science agents, assisting humans in data analysis and processing. Yet their practical efficacy remains

Yonghan Li, Chenyu Wu, Taoran Wu, Shijie Wang

In this paper, we investigate the problem of verifying the finite-time safety of continuous-time perturbed deterministic systems represented by ordinary differential equations in the presence of measurable disturbances. Given a finite-time horizon, if the system is safe, it, starting from a compact initial set, will remain within an open and bounded safe reg

Huiyuan Zheng, Dawei Zhai, Cong Xiao, Wang Yao

We uncover a new origin of the planar Hall effect - as an intrinsic property of layer coherent electrons - that exists even in bilayer and trilayer atomically thin limit. It reforms the existing theories requiring three-dimensional orbital motion, or strong spin-orbit coupling of certain forms, which are absent in van der Waals thin films. We exemplify that

Ram J. Zaveri, Voke Brume, Gianfranco Doretto

Microscopy data collections are becoming larger and more frequent. Accurate and precise quantitative analysis tools like cell instance segmentation are necessary to benefit from them. This is challenging due to the variability in the data, which requires retraining the segmentation model to maintain high accuracy on new collections. This is needed especially

Hayden Brown

Consider a closed pooled annuity fund investing in n assets with discrete-time rebalancing. At time 0, each annuitant makes an initial contribution to the fund, committing to a predetermined schedule of withdrawals. Require annuitants to be homogeneous in the sense that their initial contributions and predetermined withdrawal schedules are identical, and the

Madeline A. Marshall, Laurie Amen, Tyrone E. Woods, Patrick J. Cote

The Cosmological Advanced Survey Telescope for Optical and UV Research (CASTOR) is a planned flagship space telescope, covering the blue-optical and UV part of the spectrum. Here we introduce the CASTOR image simulator, a Python GalSim package-based script capable of generating mock CASTOR images from an input catalogue. We generate example images from the C

Chentao Tan, Zhun Lu

We investigate the leading twist generalized transverse momentum dependent parton distributions (GTMDs) of the unpolarized and longitudinally polarized gluons in the nucleon. We adopt a light-front gluon-triquark model for the nucleon motivated by soft-wall AdS/QCD. The gluon GTMDs are defined through the off-forward gluon-gluon generalized correlator and ar

Zhilun Zhou, Yuming Lin, Depeng Jin, Yong Li

Participatory urban planning is the mainstream of modern urban planning that involves the active engagement of residents. However, the traditional participatory paradigm requires experienced planning experts and is often time-consuming and costly. Fortunately, the emerging Large Language Models (LLMs) have shown considerable ability to simulate human-like ag

Tomer Ezra, Michal Feldman, Zhihao Gavin Tang

In Bayesian online settings, every element has a value that is drawn from a known underlying distribution, which we refer to as the element's identity. The elements arrive sequentially. Upon the arrival of an element, its value is revealed, and the decision maker needs to decide, immediately and irrevocably, whether to accept it or not. While most previous w

Bingxi Liu, Yiqun Wang, Huaqi Tao, Tingjun Huang

Visual Place Recognition (VPR) is crucial in computer vision, aiming to retrieve database images similar to a query image from an extensive collection of known images. However, like many vision tasks, VPR always degrades at night due to the scarcity of nighttime images. Moreover, VPR needs to address the cross-domain problem of night-to-day rather than just

Michael Björklund, Alexander Fish

An extension of Szemer\'edi's Theorem is proved for sets of positive density in approximate lattices in general locally compact and second countable abelian groups. As a consequence, we establish a recent conjecture of Klick, Strungaru and Tcaciuc. Via a novel version of Furstenberg's Correspondence principle, which should be of independent interest, we show

Han Gao, Sebastian Kaltenbach, Petros Koumoutsakos

We introduce generative models for accelerating simulations of complex systems through learning and evolving their effective dynamics. In the proposed Generative Learning of Effective Dynamics (G-LED), instances of high dimensional data are down sampled to a lower dimensional manifold that is evolved through an auto-regressive attention mechanism. In turn, B

Lin Zongying, Li Hao, Lv Liuzhenghao, Lin Bin

Designing protein sequences with specific biological functions and structural stability is crucial in biology and chemistry. Generative models already demonstrated their capabilities for reliable protein design. However, previous models are limited to the unconditional generation of protein sequences and lack the controllable generation ability that is vital

Zi-Huai Zhang, Kadircan Godeneli, Justin He, Mutasem Odeh

The performance of superconducting quantum circuits is primarily limited by dielectric loss due to interactions with two-level systems (TLS). State-of-the-art circuits with engineered material interfaces are approaching a limit where dielectric loss from bulk substrates plays an important role. However, a microscopic understanding of dielectric loss in cryst

Sunhi Choi, Inwon C. Kim, Young-Heon Kim

We prove the global-time existence of weak solutions to the supercooled Stefan problem. Our result holds in general space dimensions and with a general class of initial data. In addition, our solution is maximal in the sense of a certain stochastic order, among all comparable weak solutions starting from the same initial data. Our approach is based on a free

Michael Celentano, William S. DeWitt, Sebastian Prillo, Yun S. Song

Many biological studies involve inferring the evolutionary history of a sample of individuals from a large population and interpreting the reconstructed tree. Such an ascertained tree typically represents only a small part of a comprehensive population tree and is distorted by survivorship and sampling biases. Inferring evolutionary parameters from ascertain

Jiaqi Zhai, Lucy Liao, Xing Liu, Yueming Wang

Large-scale recommendation systems are characterized by their reliance on high cardinality, heterogeneous features and the need to handle tens of billions of user actions on a daily basis. Despite being trained on huge volume of data with thousands of features, most Deep Learning Recommendation Models (DLRMs) in industry fail to scale with compute. Inspired

Zhengxiang Wang, Owen Rambow

We propose a novel clustering pipeline to detect and characterize influence campaigns from documents. This approach clusters parts of document, detects clusters that likely reflect an influence campaign, and then identifies documents linked to an influence campaign via their association with the high-influence clusters. Our approach outperforms both the dire

David Gao

In this paper, we shall prove that all actions of LERF groups on sets are sofic. As a corollary, we obtain that a large class of generalized wreath products are sofic.

Hao Jiang, Zhi-ming Lu, Bo-fu Wang, Xiao-hui Meng

This study investigates the spatial distribution of inertial particles in turbulent Taylor-Couette flow. Direct numerical simulations are performed using a one-way coupled Eulerian-Lagrangian approach, with a fixed inner wall Reynolds number of 2500 for the carrier flow, while the particle Stokes number varies from 0.034 to 1 for the dispersed phase. We firs

Nozomu Kobayashi, Yoshiyuki Suimon, Koichi Miyamoto

Finance, especially option pricing, is a promising industrial field that might benefit from quantum computing. While quantum algorithms for option pricing have been proposed, it is desired to devise more efficient implementations of costly operations in the algorithms, one of which is preparing a quantum state that encodes a probability distribution of the u

Mengran Zhu, Ye Zhang, Yulu Gong, Changxin Xu

Credit card fraud detection is a critical challenge in the financial sector, demanding sophisticated approaches to accurately identify fraudulent transactions. This research proposes an innovative methodology combining Neural Networks (NN) and Synthet ic Minority Over-sampling Technique (SMOTE) to enhance the detection performance. The study addresses the in

S. Moran, M. Arratia, J. Arrington, D. Gaskell

Analyzing global data on the EMC effect, which denotes differences in parton distribution functions in nuclei compared to unbound nucleons, reveals tensions. Precise measurements at Jefferson Lab, studying both x and A dependence, show systematic discrepancies among experiments, making the extraction of the A dependence of the EMC effect sensitive to the sel

Ruizhe Zhong, Junjie Ye, Zhentao Tang, Shixiong Kai

Pre-routing timing prediction has been recently studied for evaluating the quality of a candidate cell placement in chip design. It involves directly estimating the timing metrics for both pin-level (slack, slew) and edge-level (net delay, cell delay), without time-consuming routing. However, it often suffers from signal decay and error accumulation due to t

Xue Yang, Changchun Bao, Jing Zhou, Xianhong Chen

In target speaker extraction, many studies rely on the speaker embedding which is obtained from an enrollment of the target speaker and employed as the guidance. However, solely using speaker embedding may not fully utilize the contextual information contained in the enrollment. In this paper, we directly exploit this contextual information in the time-frequ

Jiawei He, Xiaogang Li

Recent classification of $\frac{3}{2}$-transitive permutation groups leaves us with six families of groups which are $2$-transitive, or Frobenius, or one-dimensional affine, or the affine solvable subgroups of $ \mathrm{AGL}(2, q)$, or special projective linear group $\mathrm{PSL}(2, q)$, or $\mathrm{P\Gamma L}(2, q)$, where $q=2^p $ with $p$ prime. Accordin

Yuankai Fan, Zhenying He, Tonghui Ren, Can Huang

The Natural Language Interface to Databases (NLIDB) empowers non-technical users with database access through intuitive natural language (NL) interactions. Advanced approaches, utilizing neural sequence-to-sequence models or large-scale language models, typically employ auto-regressive decoding to generate unique SQL queries sequentially. While these transla

Eric Balkanski, Noemie Perivier, Clifford Stein, Hao-Ting Wei

An important goal of modern scheduling systems is to efficiently manage power usage. In energy-efficient scheduling, the operating system controls the speed at which a machine is processing jobs with the dual objective of minimizing energy consumption and optimizing the quality of service cost of the resulting schedule. Since machine-learned predictions abou

Anton Kolotilin, Alexander Wolitzky

We offer a simple analysis of the problem of choosing a statistical experiment to optimize the induced distribution of posterior medians, or more generally $q$-quantiles for any $q \in (0,1)$. We show that all implementable distributions of the posterior $q$-quantile are implemented by a single experiment, the $q$-quantile matching experiment, which pools pa

Will Burstein

Let $\mathbb{F}_q$ denote the finite field of $q$ elements. For $E \subset \mathbb{F}_q^d$, denote the distance set $\Delta(E)= \{\|x-y\|^2:=(x_1-y_1)^2+ \cdots + (x_d-y_d)^2 : (x,y)\in E^2 \}$. The Erdos quotient set problem was introduced in \cite{Iosevich_2019} where it was shown that for even $d\geq2$ that if $|E| \subset \mathbb{F}_q^2$ such that $|E| >

Haipeng An, Shuailiang Ge, Jia Liu, Zhiyao Lu

In this study, we propose an investigation into dark photon dark matter (DPDM) within the infrared frequency band, utilizing highly sensitive infrared light detectors commonly integrated into space telescopes, such as the James Webb Space Telescope (JWST). The presence of DPDM induces electron oscillations in both the reflectors and the interior of the detec

Sherry Yang, Jacob Walker, Jack Parker-Holder, Yilun Du

Both text and video data are abundant on the internet and support large-scale self-supervised learning through next token or frame prediction. However, they have not been equally leveraged: language models have had significant real-world impact, whereas video generation has remained largely limited to media entertainment. Yet video data captures important in

Hao Sun, Junting Chen

This paper addresses the challenge of reconstructing a 3D power spectrum map from sparse, scattered, and incomplete spectrum measurements. It proposes an integrated approach combining interpolation and block-term tensor decomposition (BTD). This approach leverages an interpolation model with the BTD structure to exploit the spatial correlation of power spect

Vojtěch Rödl, Marcelo Sales

A set of points $S$ in Euclidean space $\mathbb{R}^d$ is called \textit{Ramsey} if any finite partition of $\mathbb{R}^{\infty}$ yields a monochromatic copy of $S$. While characterization of Ramsey set remains a major open problem in the area, a stronger ``density'' concept was considered in [J. Amer. Math. Soc. 3, 1--7, 1990]: If $S$ is a $d$-dimensional si

Tomáš Horych, Martin Wessel, Jan Philip Wahle, Terry Ruas

Media bias detection poses a complex, multifaceted problem traditionally tackled using single-task models and small in-domain datasets, consequently lacking generalizability. To address this, we introduce MAGPIE, the first large-scale multi-task pre-training approach explicitly tailored for media bias detection. To enable pre-training at scale, we present La

Hidetoshi Taya, Toru Nishimura, Akira Ohnishi

We estimate the spacetime profile of the electric field in head-on heavy-ion collisions at intermediate collision energies $\sqrt{s_{\rm NN}} = {\mathcal O}(3 \;-\; 10\;{\rm GeV})$. Using a hadronic cascade model (JAM; Jet AA Microscopic transport model), we numerically demonstrate that the produced field has strength $eE = {\mathcal O}((30 \;-\; 60\;{\rm Me

Kevin Frans, Seohong Park, Pieter Abbeel, Sergey Levine

Can we pre-train a generalist agent from a large amount of unlabeled offline trajectories such that it can be immediately adapted to any new downstream tasks in a zero-shot manner? In this work, we present a functional reward encoding (FRE) as a general, scalable solution to this zero-shot RL problem. Our main idea is to learn functional representations of a

Nguyen Nguyen, Yapeng Tian, Chenliang Xu

Incorporating linguistic knowledge can improve scene text recognition, but it is questionable whether the same holds for scene text spotting, which typically involves text detection and recognition. This paper proposes a method that leverages linguistic knowledge from a large text corpus to replace the traditional one-hot encoding used in auto-regressive sce

Chengcheng Wang, Zhiwei Hao, Yehui Tang, Jianyuan Guo

Diffusion-based super-resolution (SR) models have recently garnered significant attention due to their potent restoration capabilities. But conventional diffusion models perform noise sampling from a single distribution, constraining their ability to handle real-world scenes and complex textures across semantic regions. With the success of segment anything m

Hyun Gyu Park, Soohaeng Yoo Willow, D. ChangMo Yang, Chang Woo Myung

Large-scale computer simulations of chemical atoms are used in a wide range of applications, including batteries, drugs, and more. However, there is a problem with efficiency as it takes a long time due to the large amount of calculation. To solve these problems, machine learning interatomic potential (ML-IAP) technology is attracting attention as an alterna

Pedro Seber

O-GlcNAcylation, a subtype of glycosylation, has the potential to be an important target for therapeutics, but methods to reliably predict O-GlcNAcylation sites had not been available until 2023; a 2021 review correctly noted that published models were insufficient and failed to generalize. Moreover, many are no longer usable. In 2023, a considerably better

Eric Lepowsky, David Snyder, Alexander Glaser, Anirudha Majumdar

Performing an inspection task while maintaining the privacy of the inspected site is a challenging balancing act. In this work, we are motivated by the future of nuclear arms control verification, which requires both a high level of privacy and guaranteed correctness. For scenarios with limitations on sensors and stored information due to the potentially sec

Xiaokun Zhang, Bo Xu, Chenliang Li, Yao Zhou

The session-based recommendation (SBR) garners increasing attention due to its ability to predict anonymous user intents within limited interactions. Emerging efforts incorporate various kinds of side information into their methods for enhancing task performance. In this survey, we thoroughly review the side information-driven session-based recommendation fr

Nguyen Nguyen, Jing Bi, Ali Vosoughi, Yapeng Tian

The capability of intelligent models to extrapolate and comprehend changes in object states is a crucial yet demanding aspect of AI research, particularly through the lens of human interaction in real-world settings. This task involves describing complex visual environments, identifying active objects, and interpreting their changes as conveyed through langu

Taein Kang, Soyul Han, Sunmook Choi, Jaejin Seo

Conventional spoofing detection systems have heavily relied on the use of handcrafted features derived from speech data. However, a notable shift has recently emerged towards the direct utilization of raw speech waveforms, as demonstrated by methods like SincNet filters. This shift underscores the demand for more sophisticated audio sample features. Moreover

Juan Han, Yiwei Sun, Xiamin Huang, Wenjun Shuai

We have investigated both inter-band and intra-band second order nonlinear optical conductivity based on the velocity correlation formalism and the spectral expansion technique. We propose a scenario in which the second order intra-band process is nonzero while the inter-band process is zero. This occurs for a band structure with momentum asymmetry in the Br

J. J. Choi, C. Ha, E. J. Jeon, K. W. Kim

The lowering of the energy threshold in the NaI detector is crucial not only for comprehensive validation of DAMA/LIBRA but also for exploring new possibilities in the search for low-mass dark matter and observing coherent elastic scattering between neutrino and nucleus. Alongside hardware enhancements, extensive efforts have focused on refining event select

Xinran Zhao, Hongming Zhang, Xiaoman Pan, Wenlin Yao

For a LLM to be trustworthy, its confidence level should be well-calibrated with its actual performance. While it is now common sense that LLM performances are greatly impacted by prompts, the confidence calibration in prompting LLMs has yet to be thoroughly explored. In this paper, we explore how different prompting strategies influence LLM confidence calib

D. M. Fucci, R. M. Angelo

The distinctive features of quantum mechanics, which set it apart from other physical theories, challenge our notions of realism. Recovering realism from purely philosophical grounds, a quantitative and operational criterion was proposed in the past, but solely for the context of quantum mechanics. We use a framework of generalized probabilistic theories to

Tapas Tripura, Satyam Panda, Budhaditya Hazra, Souvik Chakraborty

Exploring the intersection of deterministic and stochastic dynamics, this paper delves into Lagrangian discovery for conservative and non-conservative systems under stochastic excitation. Traditional Lagrangian frameworks, adept at capturing deterministic behavior, are extended to incorporate stochastic excitation. The study critically evaluates recent compu

Ruitian Chen, Evelyn Li, Yu Zou

In materials science, a wide range of properties of materials are governed by various types of energies, including thermal, physicochemical, structural, and mechanical energies. In 2005, Dr. Frans Spaepen used crystalline face-centered-cubic (fcc) copper as an example to discuss a variety of phenomena that are associated with energies. Inspired by his pionee

Pedro Seber, Richard D. Braatz

Interpretable models can have advantages over black-box models, and interpretability is essential for the application of machine learning in critical settings, such as aviation or medicine. This article introduces the LASSO-Clip-EN (LCEN) algorithm for nonlinear, interpretable feature selection and machine learning modeling. In a wide variety of artificial a

Kaige Xie, Mark Riedl

Automated story generation has been one of the long-standing challenges in NLP. Among all dimensions of stories, suspense is very common in human-written stories but relatively under-explored in AI-generated stories. While recent advances in large language models (LLMs) have greatly promoted language generation in general, state-of-the-art LLMs are still unr

Hiroo Azuma, William J. Munro, Kae Nemoto

We propose a heralded single-photon source based on injecting a superposition of oppositely squeezed states onto a beam splitter. Our superposition of squeezed states is composed of only even photon number states (the number of photons is equal to $2,6,10,...$) meaning the probability for an emitted single photon given as a heralded single-photon event is hi

Maria R. Read, Chinmaya Dehury, Satish Narayana Srirama, Rajkumar Buyya

Streaming applications are becoming widespread across an extensive range of business domains as an increasing number of sources continuously produce data that need to be processed and analysed in real time. Modern businesses are aggressively using streaming data to generate valuable knowledge that can be used to automate processes, help decision-making, opti

Conor A. Nixon

In this article I summarize the current state of knowledge about the composition of Titan's atmosphere, and our current understanding of the suggested chemistry that leads to that observed composition. I begin with our present knowledge of the atmospheric composition, garnered from a variety of measurements including Cassini-Huygens, the Atacama Large Millim

Hussain Ahmad, Christoph Treude, Markus Wagner, Claudia Szabo

Microservice architectures have gained prominence in both academia and industry, offering enhanced agility, reusability, and scalability. To simplify scaling operations in microservice architectures, container orchestration platforms such as Kubernetes feature Horizontal Pod Auto-scalers (HPAs) designed to adjust the resources of microservices to accommodate

Eddy Chu, Yiyang Chen, Chedy Raissi, Anand Bhojan

3D modeling holds significant importance in the realms of AR/VR and gaming, allowing for both artistic creativity and practical applications. However, the process is often time-consuming and demands a high level of skill. In this paper, we present a novel approach to create volumetric representations of 3D characters from consistent turnaround concept art, w

Bo Yang, Hengwei Zhang, Jindong Wang, Yulong Yang

Transferable adversarial examples cause practical security risks since they can mislead a target model without knowing its internal knowledge. A conventional recipe for maximizing transferability is to keep only the optimal adversarial example from all those obtained in the optimization pipeline. In this paper, for the first time, we revisit this convention

Daniel Burgarth, Paolo Facchi, Hiromichi Nakazato, Saverio Pascazio

The product of two unitaries can normally be expressed as a single exponential through the famous Baker-Campbell-Hausdorff formula. We present here a counterexample in quantum optics, by showing that an expression in terms of a single exponential is possible only at the expense of the introduction of a new element (a central extension of the algebra), implyi

Lvmin Zhang, Maneesh Agrawala

We present LayerDiffuse, an approach enabling large-scale pretrained latent diffusion models to generate transparent images. The method allows generation of single transparent images or of multiple transparent layers. The method learns a "latent transparency" that encodes alpha channel transparency into the latent manifold of a pretrained latent diffusion mo

Philippe Gimenez, Hema Srinivasan

We show that any two homogeneous affine semigroups can be glued by embedding them suitably in a higher dimensional space. As a consequence, we show that the sum of their homogeneous toric ideals is again a homogeneous toric ideal, and that the minimal graded free resolution of the associated semigroup ring is the tensor product of the minimal resolutions of

Senjuti Dutta, Sherol Chen, Sunny Mak, Amnah Ahmad

Image generation models are poised to become ubiquitous in a range of applications. These models are often fine-tuned and evaluated using human quality judgments that assume a universal standard, failing to consider the subjectivity of such tasks. To investigate how to quantify subjectivity, and the scale of its impact, we measure how assessments differ amon

Bahman Abolhassani, Atilla Eryilmaz, Tom Hou

We introduce SwiftCache, a "fresh" learning-based caching framework designed for content distribution networks (CDNs) featuring distributed front-end local caches and a dynamic back-end database. Users prefer the most recent version of the dynamically updated content, while the local caches lack knowledge of item popularity and refresh rates. We first explor

Xiao Cui, Yulei Qin, Yuting Gao, Enwei Zhang

Knowledge distillation (KD) has been widely adopted to compress large language models (LLMs). Existing KD methods investigate various divergence measures including the Kullback-Leibler (KL), reverse Kullback-Leibler (RKL), and Jensen-Shannon (JS) divergences. However, due to limitations inherent in their assumptions and definitions, these measures fail to de

Kiran Tomlinson, Tanvi Namjoshi, Johan Ugander, Jon Kleinberg

A core tension in the study of plurality elections is the clash between the classic Hotelling-Downs model, which predicts that two office-seeking candidates should position themselves at the median voter's policy, and the empirical observation that real-world democracies often have two major parties with divergent policies. Motivated by this tension and draw

Natalie Collina, Varun Gupta, Aaron Roth

We study a repeated contracting setting in which a Principal adaptively chooses amongst $k$ Agents at each of $T$ rounds. The Agents are non-myopic, and so a mechanism for the Principal induces a $T$-round extensive form game amongst the Agents. We give several results aimed at understanding an under-explored aspect of contract theory -- the game induced whe

Guillaume Bal, Anjali Nair

Interference of randomly scattered classical waves naturally leads to familiar speckle patterns, where the wave intensity follows an exponential distribution while the wave field itself is described by a circularly symmetric complex normal distribution. In the It\^o-Schr\"odinger paraxial model of wave beam propagation, we demonstrate how a deterministic inc

Muhammad Faaiz Taufiq, Jean-Francois Ton, Yang Liu

In machine learning fairness, training models that minimize disparity across different sensitive groups often leads to diminished accuracy, a phenomenon known as the fairness-accuracy trade-off. The severity of this trade-off inherently depends on dataset characteristics such as dataset imbalances or biases and therefore, using a uniform fairness requirement

Eshwar Srinivasan, Ramesh Hariharasubramanian

A graph $G = (V, E)$ is said to be word-representable if a word $w$ can be formed using the letters of the alphabet $V$ such that for every pair of vertices $x$ and $y$, $xy \in E$ if and only if $x$ and $y$ alternate in $w$. Gaetz and Ji have recently introduced the notion of minimum length word-representants for word-representable graphs. They have also de