July 2023 arXiv papers — page 144

A. Halavanau, P. Piot, S. S. Baturin

Beam-driven wakefield accelerators are foreseen to enable compact accelerator-based light sources and play a critical role in future linear-collider concepts. This class of wakefield acceleration has been extensively studied over the last four decades with a focus on demonstrating its ability to support high-accelerating gradient and, most recently, enhanced

Xinlong Dong, Hrant Hakobyan

Thurston boundary of the universal Teichm\"uller space $T(\mathbb{D})$ is the space $PML_{bdd}(\mathbb{D})$ of projective bounded measured laminations of $\mathbb{D}$. A geodesic ray in $T(\mathbb{D})$ is of generalized Teichm\"uller type if it shrinks the vertical foliation of a holomorphic quadratic differential. We provide the first examples of generalize

Elena Grigorescu, Nithish Kumar, Young-San Lin

In the pairwise weighted spanner problem, the input consists of an $n$-vertex-directed graph, where each edge is assigned a cost and a length. Given $k$ vertex pairs and a distance constraint for each pair, the goal is to find a minimum-cost subgraph in which the distance constraints are satisfied. This formulation captures many well-studied connectivity pro

Hiroki Takeshita, Ashif Aminulloh Fathnan, Daisuke Nita, Shinya Sugiura

Wave phenomena can be artificially engineered by scattering from metasurfaces, which aids in the design of radio-frequency and optical devices for wireless communication, sensing, imaging, wireless power transfer and bio/medical applications. Scattering responses vary with changing frequency; conversely, they remain unchanged at a constant frequency, which h

Tuneer Khargonkar, Shwetank Choudhary, Sumit Kumar, Barath Raj KR

In this paper, we propose a sentiment-enriched lightweight network SeLiNet and an end-to-end on-device pipeline for contextual emotion recognition in images. SeLiNet model consists of body feature extractor, image aesthetics feature extractor, and learning-based fusion network which jointly estimates discrete emotion and human sentiments tasks. On the EMOTIC

Corwin Sinnamon, Robert E. Tarjan

Since the invention of the pairing heap by Fredman, Sedgewick, Sleator, and Tarjan, it has been an open question whether this or any other simple "self-adjusting" heap supports decrease-key operations in $O(\log\log n)$ time, where $n$ is the number of heap items. Using powerful new techniques, we answer this question in the affirmative. We prove that both s

Cesar Arias

We argue that the boundary of an asymptotically anti-de Sitter (AdS) space of dimension $d+1$, say $M^{d+1}$, can be locally reconstructed from a codimension-two defect located in the deep interior of a negatively curved Einstein manifold $X^{d+2}$ of one higher dimension. This means that there exist two different ways of thinking about the same $d$-submanif

TaeHo Yoon, Kibeom Myoung, Keon Lee, Jaewoong Cho

Diffusion models have recently shown remarkable success in high-quality image generation. Sometimes, however, a pre-trained diffusion model exhibits partial misalignment in the sense that the model can generate good images, but it sometimes outputs undesirable images. If so, we simply need to prevent the generation of the bad images, and we call this task ce

Sumanta Das, Siddhartha Gadgil, Ajay Kumar Nair

We show that a homotopy equivalence between two non-compact orientable surfaces is homotopic to a homeomorphism if and only if it preserves the Goldman bracket, provided our surfaces are neither the plane nor the punctured plane.

Mounica Maddela, Megan Ung, Jing Xu, Andrea Madotto

Many cognitive approaches to well-being, such as recognizing and reframing unhelpful thoughts, have received considerable empirical support over the past decades, yet still lack truly widespread adoption in self-help format. A barrier to that adoption is a lack of adequately specific and diverse dedicated practice material. This work examines whether current

Yu-Ji Shi, Zhi-Peng Xing

We study the heavy flavor conserved semi-leptonic decay $B_s\to B\ell\nu$ in the covariant light front approach. The covariant light front quark model is used to calculate the transition form factors of $B_s\to B^{(*)}$ as well as $D_s\to D$, which are consistent with the leading power predictions from the heavy quark symmetry. The angular distribution analy

Liwei Lu, Hailong Guo, Xu Yang, Yi Zhu

In this paper, we propose a deep learning framework for solving high-dimensional partial integro-differential equations (PIDEs) based on the temporal difference learning. We introduce a set of Levy processes and construct a corresponding reinforcement learning model. To simulate the entire process, we use deep neural networks to represent the solutions and n

Reed Essick

I investigate the sensitivity of gravitational-wave searches by analyzing the response of matched filters in stationary Gaussian noise. In particular, I focus on the ability to analytically model the distribution of observed filter responses maximized over coalescence phase and/or a template bank as well as the response of statistics defined for a network of

Wittawat Jitkrittum, Neha Gupta, Aditya Krishna Menon, Harikrishna Narasimhan

Cascades are a classical strategy to enable inference cost to vary adaptively across samples, wherein a sequence of classifiers are invoked in turn. A deferral rule determines whether to invoke the next classifier in the sequence, or to terminate prediction. One simple deferral rule employs the confidence of the current classifier, e.g., based on the maximum

Shreyanth S

By combining the undecimated wavelet transform within a Word Embedded Semantic Marginal Autoencoder (WESMA), this research study provides a novel strategy for improving security measures and denoising multiple languages. The incorporation of these strategies is intended to address the issues of robustness, privacy, and multilingualism in data processing appl

Hao Wang

In modern day industry, clustering algorithms are daily routines of algorithm engineers. Although clustering algorithms experienced rapid growth before 2010. Innovation related to the research topic has stagnated after deep learning became the de facto industrial standard for machine learning applications. In 2007, a density-based clustering algorithm named

David Jurgens, Agrima Seth, Jackson Sargent, Athena Aghighi

Understanding interpersonal communication requires, in part, understanding the social context and norms in which a message is said. However, current methods for identifying offensive content in such communication largely operate independent of context, with only a few approaches considering community norms or prior conversation as context. Here, we introduce

Ruosen Li, Teerth Patel, Xinya Du

Nowadays, the quality of responses generated by different modern large language models (LLMs) is hard to evaluate and compare automatically. Recent studies suggest and predominantly use LLMs for reference-free evaluation of open-ended question answering. More specifically, they use the recognized "strongest" LLM as the evaluator, which conducts pairwise comp

Haokai Ma, Zhuang Qi, Xinxin Dong, Xiangxian Li

Multimedia recommendation aims to fuse the multi-modal information of items for feature enrichment to improve the recommendation performance. However, existing methods typically introduce multi-modal information based on collaborative information to improve the overall recommendation precision, while failing to explore its cold-start recommendation performan

Wataru Urasaki, Yuki Wada, Tomoyuki Nakagawa, Kouji Tahata

In a two-way contingency table analysis with explanatory and response variables, the analyst is interested in the independence of the two variables. However, if the test of independence does not show independence or clearly shows a relationship, the analyst is interested in the degree of their association. Various measures have been proposed to calculate the

Abel Zandamela, Nicola Marchetti, Max J. Ammann, Adam Narbudowicz

This work proposes a small pattern and polarization diversity multi-sector annular antenna with electrical size and profile of ${ka=1.2}$ and ${0.018\lambda}$, respectively. The antenna is planar and comprises annular sectors that are fed using different ports to enable digital beamforming techniques, with efficiency and gain of up to 78% and 4.62 dBi, respe

Yuhao Yang, Chao Huang, Lianghao Xia, Chunzhen Huang

In this paper, we introduce a new self-supervised rationalization method, called KGRec, for knowledge-aware recommender systems. To effectively identify informative knowledge connections, we propose an attentive knowledge rationalization mechanism that generates rational scores for knowledge triplets. With these scores, KGRec integrates generative and contra

Aparna Ananthasubramaniam, Hong Chen, Jason Yan, Kenan Alkiek

Linguistic style matching (LSM) in conversations can be reflective of several aspects of social influence such as power or persuasion. However, how LSM relates to the outcomes of online communication on platforms such as Reddit is an unknown question. In this study, we analyze a large corpus of two-party conversation threads in Reddit where we identify all o

Hang Zou, Qiyang Zhao, Lina Bariah, Mehdi Bennis

The convergence of generative large language models (LLMs), edge networks, and multi-agent systems represents a groundbreaking synergy that holds immense promise for future wireless generations, harnessing the power of collective intelligence and paving the way for self-governed networks where intelligent decision-making happens right at the edge. This artic

Hao Ma, Youjun Lu, Xiao Guo, Siqi Zhang

Future ground-based gravitational wave (GW) detectors, i.e., Einstein telescope (ET) and Cosmic Explorer (CE), are expected to detect a significant number of lensed binary neutron star (BNS) mergers, which may provide a unique tool to probe cosmology. In this paper, we investigate the detectability of the optical/infrared electromagnetic (EM) counterparts (k

Katsuki Nihongi, Takanori Kida, Yasuo Narumi, Nobuyuki Kurita

We developed a metallic pressure cell made of nickel-chromium-aluminum (NiCrAl) for use with a non-destructive pulse magnet and a magnetic susceptibility measurement apparatus with a proximity detector oscillator (PDO) in pulsed magnetic fields of up to 51 T under pressures of up to 2.1 GPa. Both the sample and sensor coil of the PDO were placed in the cell

Md Arafat Habib, Hao Zhou, Pedro Enrique Iturria-Rivera, Medhat Elsayed

rApps and xApps need to be controlled and orchestrated well in the open radio access network (O-RAN) so that they can deliver a guaranteed network performance in a complex multi-vendor environment. This paper proposes a novel intent-driven intelligent control and orchestration scheme based on hierarchical reinforcement learning (HRL). The proposed scheme can

Jincheng Lu, Xipeng Yang, Jin Ye, Yifu Zhang

Multi-Camera Multi-Target Tracking (MCMT) is a computer vision technique that involves tracking multiple targets simultaneously across multiple cameras. MCMT in urban traffic visual analysis faces great challenges due to the complex and dynamic nature of urban traffic scenes, where multiple cameras with different views and perspectives are often used to cove

Li Jiang, Sijie Cheng, Jielin Qiu, Haoran Xu

The prevalent use of benchmarks in current offline reinforcement learning (RL) research has led to a neglect of the imbalance of real-world dataset distributions in the development of models. The real-world offline RL dataset is often imbalanced over the state space due to the challenge of exploration or safety considerations. In this paper, we specify prope

Zhifeng Wang, Chunyan Zeng, Surong Duan, Hongjie Ouyang

Speaker recognition is a biometric modality that utilizes the speaker's speech segments to recognize the identity, determining whether the test speaker belongs to one of the enrolled speakers. In order to improve the robustness of the i-vector framework on cross-channel conditions and explore the nova method for applying deep learning to speaker recognition,

Jun Hong, Xiaosheng Wu, Shixin Zhu

We consider the distribution of the binomial probability mass function (pmf) among arithmetic progressions and obtain an average-type theorem. As applications, we consider the possible visits to a kind of sieved sets of integers or lattice points, by an $\alpha$-random walker. We show that, almost surely, the asymptotic proportion of time the random walker i

Summer Haag, Clyde Kertzer, James Rickards, Katherine E. Stange

In a primitive integral Apollonian circle packing, the curvatures that appear must fall into one of six or eight residue classes modulo 24. The local-global conjecture states that every sufficiently large integer in one of these residue classes will appear as a curvature in the packing. We prove that this conjecture is false for many packings, by proving tha

Yuanpeng Zheng, Tiankui Zhang, Jonathan Loo, Yapeng Wang

Mobile edge computing (MEC) enables low-latency and high-bandwidth applications by bringing computation and data storage closer to end-users. Intelligent computing is an important application of MEC, where computing resources are used to solve intelligent task-related problems based on task requirements. However, efficiently offloading computing and allocati

Yuanpeng Zheng, Tiankui Zhang, Jonathan Loo

This paper investigates the semantic extraction task-oriented dynamic multi-time scale user admission and resourceallocation in mobile edge computing (MEC) systems. Amid prevalence artifi cial intelligence applications in various industries,the offloading of semantic extraction tasks which are mainlycomposed of convolutional neural networks of computer visio

Yuanpeng Zheng, Tiankui Zhang, Rong Huang, Yapeng Wang

This paper investigates the intelligent computing task-oriented computing offloading and semantic compression in mobile edge computing (MEC) systems. With the popularity of intelligent applications in various industries, terminals increasingly need to offload intelligent computing tasks with complex demands to MEC servers for computing, which is a great chal

Molla Basir Ahamed, Partha Pratim Roy

The sharp bound for the third Hankel determinant for the coefficients of the inverse function of starlike function of order $1/2$ is obtained. In light of this, we can deduce that the functionals $|H_3(1)(f)|$ and $|H_3(1)(f^{-1})|$ exhibit invariance on the class $\mathcal{S}^*(1/2)$.

Javier Salazar Cavazos, Jeffrey A. Fessler, Laura Balzano

Principal component analysis (PCA) is a key tool in the field of data dimensionality reduction that is useful for various data science problems. However, many applications involve heterogeneous data that varies in quality due to noise characteristics associated with different sources of the data. Methods that deal with this mixed dataset are known as heteros

Shuvendu Roy, Ali Etemad

Deep learning has played a significant role in the success of facial expression recognition (FER), thanks to large models and vast amounts of labelled data. However, obtaining labelled data requires a tremendous amount of human effort, time, and financial resources. Even though some prior works have focused on reducing the need for large amounts of labelled

Song He, Pujian Mao, Xin-Cheng Mao

Four-dimensional all-loop amplitudes in QED and gravity exhibit universal Infrared (IR) singularities with a factorization structure. This structure is governed by tree amplitudes and a universal IR-divergent factor representing the exchange of soft particles between external lines. This letter offers a precise dual interpretation of these universal IR-diver

Hao Ma, Youjun Lu, Zhiwei Chen, Yunfeng Chen

Pulsar timing array (PTA) experiments are expected to detect nano-Hertz gravitational waves (GWs) emitted from individual inspiralling supermassive binary black holes (SMBBHs). The GW signals from a small fraction of these SMBBHs may be diffractively lensed by intervening galaxies. In this paper, we investigate the diffractive lensing effects on the continuo

Sanju Mandal, Molla Basir Ahamed

The Hankel determinant $H_{2,1}(F_{f}/2)$ is defined as: \begin{align*} H_{2,1}(F_{f}/2):= \begin{vmatrix} \gamma_1 & \gamma_2 \gamma_2 & \gamma_3 \end{vmatrix}, \end{align*} where $\gamma_1, \gamma_2,$ and $\gamma_3$ are the first, second and third logarithmic coefficients of functions belonging to the class $\mathcal{S}$ of normalized univalent functions.

Hao Wang

Recommender system is adored in the internet industry as one of the most profitable technologies. Unlike other sectors such as fraud detection in the Fintech industry, recommender system is both deep and broad. In recent years, many researchers start to focus on the cold-start problem of recommender systems. In spite of the large volume of research literatur

Helia Hashemi, Yong Zhuang, Sachith Sri Ram Kothur, Srivas Prasad

In information retrieval (IR), domain adaptation is the process of adapting a retrieval model to a new domain whose data distribution is different from the source domain. Existing methods in this area focus on unsupervised domain adaptation where they have access to the target document collection or supervised (often few-shot) domain adaptation where they ad

Daniel J. Rhee, Ka Yee Yeung

This study aims to evaluate the accuracy of predicted eruption times of popular geysers in the Yellowstone National Park. The Yellowstone National Park was the first national park in the United States and is known for its geothermal features consisting of many highly popular geysers such as the Old Faithful. Geysers are fascinating to national park visitors

Matt DeVos, Kathryn Nurse

We give a compact variation of Seymour's proof that every $2$-edge-connected graph has a nowhere-zero $\mathbb{Z}_2 \times \mathbb{Z}_3$-flow.

Brandon Kynoch, Hugo Latapie, Dwane van der Sluis

Large Language Models (LLMs) have made extraordinary progress in the field of Artificial Intelligence and have demonstrated remarkable capabilities across a large variety of tasks and domains. However, as we venture closer to creating Artificial General Intelligence (AGI) systems, we recognize the need to supplement LLMs with long-term memory to overcome the

Haoran Xiong, Zicheng Ye, Huazi Zhang, Jun Wang

Elementary trapping sets (ETSs) are the main culprits for the performance of LDPC codes in the error floor region. Due to the large quantity, complex structures, and computational difficulties of ETSs, how to eliminate dominant ETSs in designing LDPC codes becomes a pivotal issue to improve the error floor behavior. In practice, researchers commonly address

Chaoxing Huang, Vincent Wai Sun Wong, Queenie Chan, Winnie Chiu Wing Chu

Objective: Quantitative $T_1\rho$ imaging has potential for assessment of biochemical alterations of liver pathologies. Deep learning methods have been employed to accelerate quantitative $T_1\rho$ imaging. To employ artificial intelligence-based quantitative imaging methods in complicated clinical environment, it is valuable to estimate the uncertainty of t

Anton Strezhnev

The triple-differences (TD) design is a popular identification strategy for causal effects in settings where researchers do not believe the parallel trends assumption of conventional difference-in-differences (DiD) is satisfied. TD designs augment the conventional 2x2 DiD with a "placebo" stratum -- observations that are nested in the same units and time per

Derek Reitz, Yaroslav Tserkovnyak

Schwinger boson mean-field theory (SBMFT) is a non-perturbative approach which treats ordered and disordered phases of magnetic systems on equal footing. We leverage its versatility to evaluate the spin correlators which determine thermally-induced spin transport (the spin Seebeck effect) in Heisenberg ferromagnets (FMs) and antiferromagnets (AFs), at arbitr

Ruiyang Xia, Decheng Liu, Jie Li, Lin Yuan

Advanced manipulation techniques have provided criminals with opportunities to make social panic or gain illicit profits through the generation of deceptive media, such as forged face images. In response, various deepfake detection methods have been proposed to assess image authenticity. Sequential deepfake detection, which is an extension of deepfake detect

Luísa Shimabucoro, Timothy Hospedales, Henry Gouk

Numerous benchmarks for Few-Shot Learning have been proposed in the last decade. However all of these benchmarks focus on performance averaged over many tasks, and the question of how to reliably evaluate and tune models trained for individual tasks in this regime has not been addressed. This paper presents the first investigation into task-level evaluation

Yuqing Fan, Shenghui Cheng

In today' s era of scientific and technological advancements, the importance of talent resources is increasingly highlighted. This article will attempt to summarize the academic trajectories and successes of numerous scientists from both past and present, aiming to reproduce the correlation between scientists' personal development and their academic output.

Sheng-Lan Liu, Yu-Ning Ding, Gang Yan, Si-Fan Zhang

The fine-grained action analysis of the existing action datasets is challenged by insufficient action categories, low fine granularities, limited modalities, and tasks. In this paper, we propose a Multi-modality and Multi-task dataset of Figure Skating (MMFS) which was collected from the World Figure Skating Championships. MMFS, which possesses action recogn

Yuheng Zha, Yichi Yang, Ruichen Li, Zhiting Hu

Large language models (LLMs), typically designed as a function of next-word prediction, have excelled across extensive NLP tasks. Despite the generality, next-word prediction is often not an efficient formulation for many of the tasks, demanding an extreme scale of model parameters (10s or 100s of billions) and sometimes yielding suboptimal performance. In p

Andrew Levy, Sreehari Rammohan, Alessandro Allievi, Scott Niekum

General purpose agents will require large repertoires of skills. Empowerment -- the maximum mutual information between skills and states -- provides a pathway for learning large collections of distinct skills, but mutual information is difficult to optimize. We introduce a new framework, Hierarchical Empowerment, that makes computing empowerment more tractab

Xiaoli Li, Ziyan Li, Hongxing Rui

In this paper, we construct an efficient linear and fully decoupled finite difference scheme for wormhole propagation with heat transmission process on staggered grids, which only requires solving a sequence of linear elliptic equations at each time step. We first derive the positivity preserving properties for the discrete porosity and its difference quotie

Nima Shahbazi, Nikola Danevski, Fatemeh Nargesian, Abolfazl Asudeh

Entity matching (EM) is a challenging problem studied by different communities for over half a century. Algorithmic fairness has also become a timely topic to address machine bias and its societal impacts. Despite extensive research on these two topics, little attention has been paid to the fairness of entity matching. Towards addressing this gap, we perform

Emily Williams, Gonzalo Arranz, Adrián Lozano-Durán

Wall-modeled large-eddy simulation (WMLES) is utilized to analyze the experimental aircraft X-59 Quiet SuperSonic Technology (QueSST) developed by Lockheed Martin at Skunk Works for NASA's Low-Boom Flight Demonstrator project. The simulations utilize the charLES solver and aim to assess the ability of WMLES to predict near-field noise levels under cruise con

Ziya Gulgun, Erik G. Larsson

We consider massive multiple-input multiple-output (MIMO) systems in the presence of Cauchy noise. First, we focus on the channel estimation problem. In the standard massive MIMO setup, the users transmit orthonormal pilots during the training phase and the received signal at the base station is projected onto each pilot. This processing is optimum when the

Guangyao Miao, Nuoyu Su, Ze Yu, Bo Li

Two-dimensional (2D) elemental semiconductors have great potential for device applications, but their performance is limited by the lack of efficient doping methods. Here, combining molecular beam epitaxy, scanning tunneling microscopy/spectroscopy, X-ray photoelectron spectroscopy, and density functional theory calculations, we investigate the evolution of

Jennifer A. Hardwick, Luca Cortese, Danail Obreschkow, Claudia Lagos

We use the EAGLE (Evolution and Assembly of GaLaxies and their Environments) and IllustrisTNG (The Next Generation) cosmological simulations to investigate the properties of the baryonic specific angular momentum (j), baryonic mass (M) and atomic gas fraction ($f_{\rm{atm}}$) plane for nearby galaxies. We find EAGLE and TNG to be in excellent agreement with

Hiroshi Tamura

Visual information is processed in hierarchically organized parallel pathways in the primate brain. In lower cortical areas, color information and shape information are processed in a parallel manner, while in higher cortical areas, various types of visual information, such as color, face, animate/inanimate, are processed in a parallel manner. In the present

Gene-Ping Yang, Yue Gu, Qingming Tang, Dongsu Du

Large self-supervised models are effective feature extractors, but their application is challenging under on-device budget constraints and biased dataset collection, especially in keyword spotting. To address this, we proposed a knowledge distillation-based self-supervised speech representation learning (S3RL) architecture for on-device keyword spotting. Our

Shang Liu, Xiaocheng Li

Uncertainty sampling is a classical active-learning strategy, yet the statistical objective induced by its query rule is often implicit. We introduce the equivalent loss, whose gradient is the original loss gradient multiplied by the query probability. This construction places probabilistic, margin-based, and threshold-based uncertainty rules within a common

Bradley W. Brock, Bruce W. Jordan, Lawren Smithline

We define an equivalence relation on periodic continued fractions with partial quotients in a ring $\mathcal{O} \subseteq \mathbf{C}$, a group law on these equivalence classes, and a map from these equivalence classes to matrices in $\mathrm{GL}_2(\mathcal{O})$ with determinant $\pm1$. We prove this group of equivalence classes is isomorphic to $\mathbf{Z}/2

Dengfeng Wang, Liukai Xu, Songyuan Liu, Zhi Li

Accommodating all the weights on-chip for large-scale NNs remains a great challenge for SRAM based computing-in-memory (SRAM-CIM) with limited on-chip capacity. Previous non-volatile SRAM-CIM (nvSRAM-CIM) addresses this issue by integrating high-density single-level ReRAMs on the top of high-efficiency SRAM-CIM for weight storage to eliminate the off-chip me

Min Xiao, Junnan Zhu, Haitao Lin, Yu Zhou

Multimodal summarization usually suffers from the problem that the contribution of the visual modality is unclear. Existing multimodal summarization approaches focus on designing the fusion methods of different modalities, while ignoring the adaptive conditions under which visual modalities are useful. Therefore, we propose a novel Coarse-to-Fine contributio

Kimberly J. Daas, Derk P. Kooi, Nina C. Peters, Eduardo Fabiano

Non-covalent interactions (NCIs) play a crucial role in biology, chemistry, material science, and everything in between. To improve pure quantum-chemical simulations of NCIs, we propose a methodology for constructing approximate correlation energies by combining an interpolation along the M{\o}ller adiabatic connection (MP AC) with a regularization and spin-

Koji Kaneko, Chihiro Tabata, Masato Hagihala, Hiroki Yamauchi

A magnetic order in orthorhombic GdCu$_2$ was investigated via a single crystal neutron diffraction with thermal neutron. Magnetic peaks were observed at incommensurate positions described by the ordering vector $q$=(${\delta}$,~1,~0) with ${\delta}$=0.678 at 4.6~K. This ordering vector is close to the commensurate one with $q_{\rm c}$=(2/3,~1,~0) reported e

Aziz Guergachi, Javid Hakim

This article has one single purpose: introduce a new and simple, yet highly insightful approach to capture, fully and quantitatively, the dynamics of the circular flow of income in economies. The proposed approach relies mostly on basic linear algebraic concepts and has deep implications for the disciplines of economics, physics and econophysics.

Emily Mu, John Guttag, Maggie Makar

Given a similarity metric, contrastive methods learn a representation in which examples that are similar are pushed together and examples that are dissimilar are pulled apart. Contrastive learning techniques have been utilized extensively to learn representations for tasks ranging from image classification to caption generation. However, existing contrastive

Nadia Lafrenière, Yan Zhuang

This paper studies the relationship between the modified Foata$\unicode{x2013}$Strehl action (a.k.a. valley-hopping)$\unicode{x2014}$a group action on permutations used to demonstrate the $\gamma$-positivity of the Eulerian polynomials$\unicode{x2014}$and the number of rixed points $\operatorname{rix}$$\unicode{x2014}$a recursively-defined permutation statis

Budhaditya Bhattacharjee, Jyotirmoy Mukherjee

We consider the HKLL bulk reconstruction procedure for $p-$form fields and graviton in empty AdS$_{d + 1}$. We derive spacelike bulk reconstruction kernels for the $p$-forms and the graviton in the Poinarc\'e patch of AdS$_{d+1}$. The kernels are first derived via a mode-sum approach in arbitrary even dimensions. The appropriate AdS-covariant fields are iden

Yongquan Yang, Hong Bu

The logical assessment formula (LAF) is a new theory proposed for evaluations with inaccurate ground-truth labels (IAGTLs) to assess the predictive models for artificial intelligence applications. However, the practicability of LAF for evaluations with IAGTLs has not yet been validated in real-world practice. In this paper, we applied LAF to two tasks of tum

Kepler Domurat-Sousa, Cameron Poe, Henry J. Frisch, Bernhard W. Adams

We present simulations of whole-body low-dose time-of-flight positron emission tomography (TOF-PET) based on the direct surface production [1] by 511 keV gamma rays of energetic electrons via the Photo-electric and Compton Effects, eliminating the scintillator and photodetector sub-systems in PET scanners. In Ref. [1] we described Microchannel Plates (MCP) c

Youzhi Luo, Chengkai Liu, Shuiwang Ji

We consider the problem of generating periodic materials with deep models. While symmetry-aware molecule generation has been studied extensively, periodic materials possess different symmetries, which have not been completely captured by existing methods. In this work, we propose SyMat, a novel material generation approach that can capture physical symmetrie

Albert S. Schwarz, Anton M. Zeitlin

Our goal is to describe superconformal structures on super Riemann surfaces (SRS), based on data assigned to a fatgraph. We start from the complex structures on punctured $(1|1)$-supermanifolds, characterizing the corresponding moduli and the deformations using Strebel differentials and certain \v{C}ech cocycles for a specific covering, which we reproduce fr

Zhiqiang Huang

The detailed fluctuation theorem implies symmetry in the generating function of entropy production probability. The integral fluctuation theorem directly follows from this symmetry and the normalization of the probability. In this paper, we rewrite the generating function by integrating measurements and evolution into a constructed mapping. This mapping is c

Ilaria Pascucci, Bennett N. Skinner, Dingshan Deng, Maxime Ruaud

We present an ACA search for [CI] emission at 492GHz toward large T Tauri disks (gas radii $\gtrsim 200$au) in the $\sim 1-3$Myr-old Lupus star-forming region. Combined with ALMA 12-m archival data for IM Lup, we report [CI] detections in 6 out of 10 sources, thus doubling the known detections toward T Tauri disks. We also identify four Keplerian double-peak

Glenn Bruns, Mauricio Cortes

Service providers commonly provide only a fixed catalog of services to their clients. Both clients and service providers can benefit from service negotiation, in which a client makes a query for a specific service, and the provider counters with an offer. The query could include parameters that control the performance, reliability, and function of the servic

Hann Woei Ho, Ye Zhou

This paper presents a novel approach for optical flow control of Micro Air Vehicles (MAVs). The task is challenging due to the nonlinearity of optical flow observables. Our proposed Incremental Nonlinear Dynamic Inversion (INDI) control scheme incorporates an efficient data-driven method to address the nonlinearity. It directly estimates the inverse of the t

Mirza S. Sarwar, Ryusuke Ishizaki, Kieran Morton, Claire Preston

Soft sensors that can discriminate shear and normal force could help provide machines the fine control desirable for safe and effective physical interactions with people. A capacitive sensor is made for this purpose, composed of patterned elastomer and containing both fixed and sliding pillars that allow the sensor to deform and buckle, much like skin itself

André C. A. Siqueira, Guillermo Palacios, Albert S. Reyna, Boris A. Malomed

We report results of systematic numerical analysis for multiple soliton generation by means of the recently reported multiple temporal compression (MTC) method, and compare its efficiency with conventional methods based on the use of photonic crystal fibers (PCFs) and fused silica waveguides (FSWs). The results show that the MTC method is more efficient to c

Sho Yokota, Taku Nonomura

The relationships between characteristic large-scale wake structures appearing behind a freestream-aligned circular cylinder are investigated and discussed from the velocity field obtained by wind tunnel tests. The tests were conducted under a supportless condition using a magnetic suspension and balance system and stereo PIV measurements at a Reynolds numbe

Vaibhav Vavilala, Faaris Shaik, David Forsyth

We demonstrate an image dequantizing diffusion model that enables novel edits on natural images. We propose operating on quantized images because they offer easy abstraction for patch-based edits and palette transfer. In particular, we show that color palettes can make the output of the diffusion model easier to control and interpret. We first establish that

Kumiko Tanaka-Ishii, Akira Tanaka

The Strahler number was originally proposed to characterize the complexity of river bifurcation and has found various applications. This article proposes computation of the Strahler number's upper and lower limits for natural language sentence tree structures. Through empirical measurements across grammatically annotated data, the Strahler number of natural

Guangyao Miao, Minghui Gu, Nuoyu Su, Weiliang Zhong

Two-dimensional (2D) magnetic transition metal compounds with atomic thickness exhibit intriguing physics in fundamental research and great potential for device applications. Understanding the correlations between their macrosopic magnetic properties and the dimensionality of microscopic magnetic exchange interactions are valuable for the designing and appli

Oscar Avalos-Ovando, Veronica A. Bahamondes Lorca, Lucas V. Besteiro, Artur Movsesyan

Chirality, either of light or matter, has proved to be very practical in biosensing and nanophotonics. However, the fundamental understanding of its temporal dynamics still needs to be discovered. A realistic setup for this are the so-called metastructures, since they are optically active and are built massively, hence rendering an immediate potential candid

Fabrizio Anella, Andreas Höring

K3 surfaces have been studied from many points of view, but the positivity of the cotangent bundle is not well understood. In this paper we explore the surprisingly rich geometry of the projectivised cotangent bundle of a very general polarised K3 surface $S$ of degree two. In particular, we describe the geometry of a surface $D_S \subset \mathbb{P}(Ω_S)$ th

Tatsuki Fushimi, Yusuke Koroyasu

Phased array transducers can shape acoustic fields for versatile manipulation; however, generating multiple focal points typically involves complex optimization. This study demonstrates that Dammann gratings - binary phase gratings originally used in optics to generate equal-intensity spot arrays - can be adapted for acoustics to create multiple equal-streng

Shushu Zhang, Xuming He, Kean Ming Tan, Wen-Xin Zhou

Expected shortfall is defined as the average over the tail below (or above) a certain quantile of a probability distribution. Expected shortfall regression provides powerful tools for learning the relationship between a response variable and a set of covariates while exploring the heterogeneous effects of the covariates. In the health disparity research, for

Juan Terven, Diana M. Cordova-Esparza, Alfonso Ramirez-Pedraza, Edgar A. Chavez-Urbiola

This paper presents a comprehensive review of loss functions and performance metrics in deep learning, highlighting key developments and practical insights across diverse application areas. We begin by outlining fundamental considerations in classic tasks such as regression and classification, then extend our analysis to specialized domains like computer vis

Francesco Cagnetta, Deborah Oliveira, Mahalakshmi Sabanayagam, Nikolaos Tsilivis

Lecture notes from the course given by Professor Julia Kempe at the summer school "Statistical physics of Machine Learning" in Les Houches. The notes discuss the so-called NTK approach to problems in machine learning, which consists of gaining an understanding of generally unsolvable problems by finding a tractable kernel formulation. The notes are mainly fo

Hideyuki Umeda, Chris Nagele

In this paper we revisit metal-enriched rotating pair instability supernovae (PISNe) models for metallicities consistent with the Small Magellanic Cloud (SMC), the Large Magellanic Cloud (LMC) and 0.1$Z_\odot$. By calculating multiple models, we intend to clarify mass ranges and the ejected $^{56}$Ni masses from the PISNe, and mass loss histories for progeni

Qiushi Lin, Hang Ma

Multi-Agent Path Finding (MAPF) is a crucial component for many large-scale robotic systems, where agents must plan their collision-free paths to their given goal positions. Recently, multi-agent reinforcement learning has been introduced to solve the partially observable variant of MAPF by learning a decentralized single-agent policy in a centralized fashio

Tianle Cai, Kaixuan Huang, Jason D. Lee, Mengdi Wang

The recent surge of large language models (LLMs) highlights their ability to perform in-context learning, i.e., "learning" to perform a task from a few demonstrations in the context without any parameter updates. However, their capabilities of in-context learning are limited by the model architecture: 1) the use of demonstrations is constrained by a maximum

Subhajit Chaudhury, Sarathkrishna Swaminathan, Daiki Kimura, Prithviraj Sen

Text-based reinforcement learning agents have predominantly been neural network-based models with embeddings-based representation, learning uninterpretable policies that often do not generalize well to unseen games. On the other hand, neuro-symbolic methods, specifically those that leverage an intermediate formal representation, are gaining significant atten

Yadu Kiran, Marc Riedel

Stochastic computing is a paradigm in which logical operations are performed on randomly generated bit streams. Complex arithmetic operations can be executed by simple logic circuits, resulting in a much smaller area footprint compared to conventional binary counterparts. However, the random or pseudorandom sources required for generating the bit streams are

Jessica Echterhoff, An Yan, Julian McAuley

It is time-consuming to find the best product among many similar alternatives. Comparative sentences can help to contrast one item from others in a way that highlights important features of an item that stand out. Given reviews of one or multiple items and relevant item features, we generate comparative review sentences to aid users to find the best fit. Spe