May 2023 arXiv papers — page 81

Tanmoy K. Paul, Atanu K. Saha, Sumeet K. Gupta

180{\deg} domains walls (DWs) of Head-to-Head/Tail-to-Tail (H-H/T-T) type in ferroelectric (FE) materials are of immense interest for a comprehensive understanding of the FE attributes as well as harnessing them for new applications. Our first principles calculation suggests that such DW formation in Hafnium Zirconium Oxide (HZO) based FEs depends on the uni

Eli Chien, Jiong Zhang, Cho-Jui Hsieh, Jyun-Yu Jiang

The eXtreme Multi-label Classification~(XMC) problem seeks to find relevant labels from an exceptionally large label space. Most of the existing XMC learners focus on the extraction of semantic features from input query text. However, conventional XMC studies usually neglect the side information of instances and labels, which can be of use in many real-world

Wenjun Peng, Weidong He, Derong Xu, Tong Xu

With the increasing demand for intelligent services of online video platforms, video character search task has attracted wide attention to support downstream applications like fine-grained retrieval and summarization. However, traditional solutions only focus on visual or coarse-grained social information and thus cannot perform well when facing complex scen

Han Huang, Leilei Sun, Bowen Du, Weifeng Lv

Designing new molecules is essential for drug discovery and material science. Recently, deep generative models that aim to model molecule distribution have made promising progress in narrowing down the chemical research space and generating high-fidelity molecules. However, current generative models only focus on modeling either 2D bonding graphs or 3D geome

A. M. Kamchatnov

We develop the theory of transformation of intensive initial nonlinear wave pulses to trains of solitons emerging at asymptotically large time of evolution. Our approach is based on the theory of dispersive shock waves in which the number of nonlinear oscillations in the shock becomes the number of solitons at the asymptotic state. We show that this number o

Jie Jiang, Ming Zhang

Recently, Fernandes discovered an analytic solution for rotating black holes in semiclassical gravity induced by the trace anomaly. These solutions exhibit some distinctive characteristics, including a non-spherically symmetric event horizon and violations of the Kerr bound. As a crucial assumption to uphold causality in spacetime, we investigate the validit

Wahyu Pebrianto, Panca Mudjirahardjo, Sholeh Hadi Pramono, Rahmadwati

Object detection with Unmanned Aerial Vehicles (UAVs) has attracted much attention in the research field of computer vision. However, not easy to accurately detect objects with data obtained from UAVs, which capture images from very high altitudes, making the image dominated by small object sizes, that difficult to detect. Motivated by that challenge, we aim

Kieran Ricardo, David Lee, Kenneth Duru

Atmospheric systems incorporating thermal dynamics must be stable with respect to both energy and entropy. While energy conservation can be enforced via the preservation of the skew-symmetric structure of the Hamiltonian form of the equations of motion, entropy conservation is typically derived as an additional invariant of the Hamiltonian system, and satisf

Seong-Gyu Yang, Hye Jin Park

Understanding the mechanisms that sustain high biodiversity remains a central challenge. MacArthur's classical consumer-resource model (MCRM) suggests that consumer diversity is limited by the number of available resources, yet empirical observations often exceed this bound. To address this, we extend the generalized consumer-resource model by incorporating

Sui Tang, Malik Tuerkoen, Hanming Zhou

In this paper, we tackle a critical issue in nonparametric inference for systems of interacting particles on Riemannian manifolds: the identifiability of the interaction functions. Specifically, we define the function spaces on which the interaction kernels can be identified given infinite i.i.d observational derivative data sampled from a distribution. Our

Shirong Chen, Yi C. Huang, Shaozhen Xu

We give a natural convexity proof of an elementary inequality used by Ozawa and Rogers in proving their bilinear estimate for the one-dimensional Klein-Gordon equation. This robust approach also enables us to derive the optimality of Ozawa-Rogers estimate and establish a new bilinear estimate. Our estimate is in sharp analogy with the bilinear estimate of Be

Jie Jiang, Jia Tan

We investigate whether the Strong Cosmic Censorship (SCC) Conjecture can be reinstated in Reissner-Nordstr\"om-de Sitter (RNdS) black holes by introducing non-minimal couplings between the electromagnetic and scalar fields in Einstein-Maxwell-scalar (EMS) theory. By conducting numerical calculations, we find that the SCC can be restored within a specific ran

Bingyi Chen, Jihao Liu, Lingyao Xie

We establish the Kodaira vanishing theorem and the Kawamata-Viehweg vanishing theorem for lc generalized pairs. As a consequence, we provide a new proof of the base-point-freeness theorem for lc generalized pairs. This new approach allows us to prove the contraction theorem for lc generalized pairs without using Koll\'ar's gluing theory.

Aditi Sen, Partha Lahiri

Empirical best prediction (EBP) is a well-known method for producing reliable proportion estimates when the primary data source provides only small or no sample from finite populations. There are potential challenges in implementing existing EBP methodology such as limited auxiliary variables in the frame (not adequate for building a reasonable working predi

Sinan Rasiya Koya, Tirthankar Roy

Over the past few decades, the hydrology community has witnessed notable advancements in streamflow prediction, particularly with the introduction of cutting-edge machine-learning algorithms. Recurrent neural networks, especially Long Short-Term Memory (LSTM) networks, have become popular due to their capacity to create precise forecasts and realistically mi

Guangsi Shi, Daokun Zhang, Ming Jin, Shirui Pan

The great learning ability of deep learning models facilitates us to comprehend the real physical world, making learning to simulate complicated particle systems a promising endeavour. However, the complex laws of the physical world pose significant challenges to the learning based simulations, such as the varying spatial dependencies between interacting par

Yihua Cheng, Ziyi Zhang, Hanchen Li, Anton Arapin

In real-time video communication, retransmitting lost packets over high-latency networks is not viable due to strict latency requirements. To counter packet losses without retransmission, two primary strategies are employed -- encoder-based forward error correction (FEC) and decoder-based error concealment. The former encodes data with redundancy before tran

Yixuan Huang, Jie Yang, Wankai Tang, Chao-Kai Wen

This study explores the use of non-line-of-sight (NLOS) components in millimeter-wave (mmWave) communication systems for joint localization and environment sensing. The radar cross section (RCS) of a reconfigurable intelligent surface (RIS) is calculated to develop a general path gain model for RISs and traditional scatterers. The results show that RISs have

Shubo Lv, Xiong Wang, Sining Sun, Long Ma

Real-world complex acoustic environments especially the ones with a low signal-to-noise ratio (SNR) will bring tremendous challenges to a keyword spotting (KWS) system. Inspired by the recent advances of neural speech enhancement and context bias in speech recognition, we propose a robust audio context bias based DCCRN-KWS model to address this challenge. We

Chen Zhang, Yang Yang, Jingang Wang, Dawei Song

Finetuning pretrained language models (LMs) have enabled appealing performance on a diverse array of tasks. The intriguing task-agnostic property has driven a shifted focus from task-specific to task-agnostic distillation of LMs. While task-agnostic, compute-efficient, performance-preserved LMs can be yielded by task-agnostic distillation, previous studies m

Tat-Bao-Thien Nguyen, Teh-Lu Liao, Tuan-Anh Vu

The article deals with anomaly detection of Juniper router logs. Abnormal Juniper router logs include logs that are usually different from the normal operation, and they often reflect the abnormal operation of router devices. To prevent router devices from being damaged and help administrator to grasp the situation of error quickly, detecting abnormal operat

Bosheng Qin, Juncheng Li, Siliang Tang, Tat-Seng Chua

We introduce InstructVid2Vid, an end-to-end diffusion-based methodology for video editing guided by human language instructions. Our approach empowers video manipulation guided by natural language directives, eliminating the need for per-example fine-tuning or inversion. The proposed InstructVid2Vid model modifies a pretrained image generation model, Stable

Chen Zhao, Zhihui Xu, Guang-Uei Hung, Weihua Zhou

Coronary artery disease (CAD) is one of the primary causes leading deaths worldwide. The presence of atherosclerotic lesions in coronary arteries is the underlying pathophysiological basis of CAD, and accurate extraction of individual arterial branches using invasive coronary angiography (ICA) is crucial for stenosis detection and CAD diagnosis. We propose a

Felipe Almeida-Fernandes, Vinicius Placco, Helio Rocha-Pinto, Marcelo Borges Fernandes

Metal-poor stars are key to our understanding of the early stages of chemical evolution in the Universe. New multi-filter surveys, such as the Southern Photometric Local Universe Survey (S-PLUS), are greatly advancing our ability to select low-metallicity stars. In this work, we analyse the chemodynamical properties and ages of 522 metal-poor candidates sele

Chao Chen, Anish Ghoshal, Zygmunt Lalak, Yudong Luo

We revisit the growth of curvature perturbations in non-minimal curvaton scenario with a non-trivial field metric $\lambda(\phi)$ where $\phi$ is an inflaton field, and incorporate the effect from the non-uniform onset of curvaton's oscillation in terms of an axion-like potential. The field metric $\lambda(\phi)$ plays a central role in the enhancement of cu

G. Sivanesan, C. Selvaraj

we obtain a necessary condition for the character degree graph with all of its vertices are odd degree of a finite solvable group G.

Cheng Liu, Haiguang Xu, Hoongwah Siew, Tao Zhu

An impressive feature of loop quantum gravity (LQG) is that it can elegantly resolve both the big bang and black hole singularities. By using the Newman-Janis algorithm, a regular and effective rotating self-dual black hole(SDBH) metric could be constructed, which alters the Kerr geometry with a polymeric function $P$ from the quantum effects of LQG geometry

Kaidi Cao, Phitchaya Mangpo Phothilimthana, Sami Abu-El-Haija, Dustin Zelle

Learning to predict properties of large graphs is challenging because each prediction requires the knowledge of an entire graph, while the amount of memory available during training is bounded. Here we propose Graph Segment Training (GST), a general framework that utilizes a divide-and-conquer approach to allow learning large graph property prediction with a

Shan-Zhong Li, Zhi Li

Inspired by the recently discovered phenomenon of re-entrant localization (REL) [Roy et al., PRL 126, 106803 (2021)], we propose a new approach to induce REL, i.e., to control the quasiperiodic potential's phase-shift between odd and even sites, as thus the system can be dubbed as a phase-shift AAH model. We then analyze the participation ratios and correspo

Junde Li, Swaroop Ghosh

Learning algorithms and data are the driving forces for machine learning to bring about tremendous transformation of industrial intelligence. However, individuals' right to retract their personal data and relevant data privacy regulations pose great challenges to machine learning: how to design an efficient mechanism to support certified data removals. Remov

Yue Xu, Qijie Shen, Jianwen Yin, Zengde Deng

Integrated recommendation, which aims at jointly recommending heterogeneous items from different channels in a main feed, has been widely applied to various online platforms. Though attractive, integrated recommendation requires the ranking methods to migrate from conventional user-item models to the new user-channel-item paradigm in order to better capture

Arnaud Cedric Kamkoum

This paper examines the monetary policies the Federal Reserve implemented in response to the Global Financial Crisis. More specifically, it analyzes the Federal Reserve's quantitative easing (QE) programs, liquidity facilities, and forward guidance operations conducted from 2007 to 2018. The essay's detailed examination of these policies culminates in an int

Marcin P. Joachimiak, J. Harry Caufield, Nomi L. Harris, Hyeongsik Kim

Molecular biologists frequently interpret gene lists derived from high-throughput experiments and computational analysis. This is typically done as a statistical enrichment analysis that measures the over- or under-representation of biological function terms associated with genes or their properties, based on curated assertions from a knowledge base (KB) suc

Wei Yu, Qing-Cui Bu, Zi-Xu Yang, He-Xin Liu

We present time-frequency analysis, based on the Hilbert-Huang transform (HHT), of the evolution on the low-frequency quasi-periodic oscillations (LFQPOs) observed in the black hole X-ray binary MAXI J1820+070. Through the empirical mode decomposition (EMD) method, we decompose the light curve of the QPO component and measure its intrinsic phase lag between

Mohamed Elmahallawy, Tie Luo

A Low Earth orbit (LEO) satellite constellation consists of a large number of small satellites traveling in space with high mobility and collecting vast amounts of mobility data such as cloud movement for weather forecast, large herds of animals migrating across geo-regions, spreading of forest fires, and aircraft tracking. Machine learning can be utilized t

Xin Zheng, Jonathan Dolde, Shimon Kolkowitz

The stability of an optical atomic clock is a critical figure of merit for almost all clock applications. To this end, much optical atomic clock research has focused on reducing clock instability by increasing the atom number, lengthening the coherent interrogation times, and introducing entanglement to push beyond the standard quantum limit. In this work, w

Kazuki Nakajima, Kazuyuki Shudo

Analysis of social networks with limited data access is challenging for third parties. To address this challenge, a number of studies have developed algorithms that estimate properties of social networks via a simple random walk. However, most existing algorithms do not assume private nodes that do not publish their neighbors' data when they are queried in e

Ryan Theisen, Hyunsuk Kim, Yaoqing Yang, Liam Hodgkinson

Ensembling has a long history in statistical data analysis, with many impactful applications. However, in many modern machine learning settings, the benefits of ensembling are less ubiquitous and less obvious. We study, both theoretically and empirically, the fundamental question of when ensembling yields significant performance improvements in classificatio

Bixiang Wang

This paper is concerned with the large deviation principle of the non-local fractional stochastic reaction-diffusion equation with a polynomial drift of arbitrary degree driven by multiplicative noise defined on unbounded domains. We first prove the strong convergence of the solutions of a control equation with respect to the weak topology of controls, and t

Ziyi Yang, Mahmoud Khademi, Yichong Xu, Reid Pryzant

The convergence of text, visual, and audio data is a key step towards human-like artificial intelligence, however the current Vision-Language-Speech landscape is dominated by encoder-only models which lack generative abilities. We propose closing this gap with i-Code V2, the first model capable of generating natural language from any combination of Vision, L

Amit Singer, Ruiyi Yang

In this paper we propose an algorithm for aligning three-dimensional objects when represented as density maps, motivated by applications in cryogenic electron microscopy. The algorithm is based on minimizing the 1-Wasserstein distance between the density maps after a rigid transformation. The induced loss function enjoys a more benign landscape than its Eucl

Dongwei Zhao, Audun Botterud, Marija Ilic

This paper evaluates market equilibrium under different pricing mechanisms in a two-settlement 100%-renewables electricity market. Given general probability distributions of renewable energy, we establish game-theoretical models to analyze equilibrium bidding strategies, market prices, and profits under uniform pricing (UP) and pay-as-bid pricing (PAB). We p

Lung-Sheng Tsai, Shang-Ling Shih, Pei-Kai Liao, Chao-Kai Wen

In 6G, the trend of transitioning from massive antenna elements to even more massive ones is continued. However, installing additional antennas in the limited space of user equipment (UE) is challenging, resulting in limited capacity scaling gain for end users, despite network side support for increasing numbers of antennas. To address this issue, we propose

Tanay Komarlu, Minhao Jiang, Xuan Wang, Jiawei Han

Fine-grained entity typing (FET), which assigns entities in text with context-sensitive, fine-grained semantic types, is a basic but important task for knowledge extraction from unstructured text. FET has been studied extensively in natural language processing and typically relies on human-annotated corpora for training, which is costly and difficult to scal

Mohammad Farajzadeh-Tehrani, Charles Frohman

This paper addresses some conjectures and questions regarding the absolute and relative compactifications of the $\SL(2,\C)$-character variety of an $n$-punctured Riemann surface without boundary. We study a class of projective compactifications determined by ideal triangulations of the surface and prove explicit results concerning the boundary divisors of t

Nikolaos Kidonakis, Alberto Tonero

We present theoretical calculations of total cross sections and top-quark transverse-momentum and rapidity distributions in the associated production of a top-antitop pair with a photon ($t{\bar t}\gamma$ production). We include complete QCD and electroweak corrections at NLO as well as soft-gluon corrections at approximate NNLO (aNNLO). The aNNLO correction

Alessio T. B. Celeste, Adriano M. Santos

This paper specifically focuses on the choice of a temporal background field over a spatial one in the context of Lorentz symmetry breaking. By examining the implications of this choice, we aim to understand the effects of Lorentz violation in different scenarios, with an emphasis on the consequences of adopting a purely temporal background field. In the ana

Shi Chen, Zhiyan Ding, Qin Li, Stephen J. Wright

PDE solutions are numerically represented by basis functions. Classical methods employ pre-defined bases that encode minimum desired PDE properties, which naturally cause redundant computations. What are the best bases to numerically represent PDE solutions? From the analytical perspective, the Kolmogorov $n$-width is a popular criterion for selecting repres

K. W. Ohm

We prove a restricted projection theorem for an n-2 dimensional family of projections from $\mathbb R^n$ to $\mathbb R$. The family we consider arises naturally in the context of the adjoint representation of the maximal unipotent subgroup of ${\rm SO}(n-1,1)$ on the Lie algebra of ${\rm SO}(n,1)$.

Yi Xuan Tan, Navonil Majumder, Soujanya Poria

The pre-trained speech encoder wav2vec 2.0 performs very well on various spoken language understanding (SLU) tasks. However, on many tasks, it trails behind text encoders with textual input. To improve the understanding capability of SLU encoders, various studies have used knowledge distillation to transfer knowledge from natural language understanding (NLU)

Jian Leng, Fan Yang, Xiang-Bin Wang

The original Grover's algorithm has a success probability to output a correct solution, while deterministic Grover's algorithms improve the success probability to 100%. However, the success probability of deterministic Grover's algorithm decreases in noisy environment. Here we improve the deterministic two-parameter (D2p) Grover's algorithm to reach the uppe

Vignon Oussa

The HRT (Heil-Ramanathan-Topiwala) posits the linear independence of any set of nonzero square-integrable vectors obtained from a single nonzero vector $f$ by applying a finite set of time-frequency shift operators. In this short note, we present findings centered on the zero set of the Zak transform of $f$, and a distinct arrangement involving a finite set

Saif E. Nouma, Attila A. Yavuz

Digital Twins (DT) virtually model cyber-physical objects using Internet of Things (IoT) components (e.g., sensors) to gather and process senstive information stored in the cloud. Trustworthiness of the streamed data is crucial which requires quantum safety and breach resiliency. Digital signatures are essential for scalable authentication and non-repudiatio

Ginestra Bianconi, Sergey N. Dorogovtsev

Hypergraphs capture the higher-order interactions in complex systems and always admit a factor graph representation, consisting of a bipartite network of nodes and hyperedges. As hypegraphs are ubiquitous, investigating hypergraph robustness is a problem of major research interest. In the literature the robustness of hypergraphs as been so far only treated a

Xilong Zhou, Miloš Hašan, Valentin Deschaintre, Paul Guerrero

Authoring high-quality digital materials is key to realism in 3D rendering. Previous generative models for materials have been trained exclusively on synthetic data; such data is limited in availability and has a visual gap to real materials. We circumvent this limitation by proposing PhotoMat: the first material generator trained exclusively on real photos

Kellie O'Neal-Ault

We provide a brief overview into recent tests of gravity, focusing on its foundational spacetime symmetries. In particular, we work with an agnostic, effective field-theory framework, named the Standard-Model Extension, that allows for analysis of tests of such symmetries. There have been a wide range of experiments and theory developments that have helped c

Andrei Kucharavy, Rachid Guerraoui, Ljiljana Dolamic

Whenever applicable, the Stochastic Gradient Descent (SGD) has shown itself to be unreasonably effective. Instead of underperforming and getting trapped in local minima due to the batch noise, SGD leverages it to learn to generalize better and find minima that are good enough for the entire dataset. This led to numerous theoretical and experimental investiga

Liangming Pan, Alon Albalak, Xinyi Wang, William Yang Wang

Large Language Models (LLMs) have shown human-like reasoning abilities but still struggle with complex logical problems. This paper introduces a novel framework, Logic-LM, which integrates LLMs with symbolic solvers to improve logical problem-solving. Our method first utilizes LLMs to translate a natural language problem into a symbolic formulation. Afterwar

Pablo Andújar Guerrero

We prove that any definable family of subsets of a definable infinite set $A$ in an o-minimal structure has cardinality at most $|A|$. We derive some consequences in terms of counting definable types and existence of definable topological spaces.

Jihun Lee, Ah-Hyoung Lee, Vincent Leung, Farah Laiwalla

We introduce a wireless RF network concept for capturing sparse event-driven data from large populations of spatially distributed autonomous microsensors, possibly numbered in the thousands. Each sensor is assumed to be a microchip capable of event detection in transforming time-varying inputs to spike trains. Inspired by brain information processing, we hav

Dimitris Bertsimas, Ryan Cory-Wright, Sean Lo, Jean Pauphilet

Low-rank matrix completion consists of computing a matrix of minimal complexity that recovers a given set of observations as accurately as possible. Unfortunately, existing methods for matrix completion are heuristics that, while highly scalable and often identifying high-quality solutions, do not provide an instance-wise certificate of optimality. We reexam

Holger Bech Nielsen, Colin D. Froggatt

We continue our work on a proposal for what dark matter could be, namely that the dark matter consists of essentially macroscopic objects built from ordinary matter. The only element of new physics is that there should exist several types or phases of vacuum. Then the dark matter particles are bubbles of a new type of vacuum filled with ordinary matter - say

Zachary Carlini, Yaolong Shen

Recently, Wang and the second author constructed a bar involution and canonical basis for a quasi-permutation module of the Hecke algebra associated to a type B Weyl group $W$, where the basis is parameterized by left cosets of a quasi-parabolic reflection subgroup in $W$. In this paper we provide an alternative approach to these constructions, and then gene

Haw-Shiuan Chang, Zonghai Yao, Alolika Gon, Hong Yu

Is the output softmax layer, which is adopted by most language models (LMs), always the best way to compute the next word probability? Given so many attention layers in a modern transformer-based LM, are the pointer networks redundant nowadays? In this study, we discover that the answers to both questions are no. This is because the softmax bottleneck someti

Jayashree Sengupta, Nirjhar Dhang, Arghya Deb

The present study explores a cost-effective method for using activated ground granulated blast furnace slag (GGBFS) and silica fume (SF) as cement substitutes. Instead of activating them with expensive alkali solutions, the present study employs industrial-grade powdered soda ash (SA) and hydrated lime (HL) as activators, reducing expenses by about 94.5% com

Sam Hawke, Hengrui Luo, Didong Li

Supervised dimension reduction (SDR) has been a topic of growing interest in data science, as it enables the reduction of high-dimensional covariates while preserving the functional relation with certain response variables of interest. However, existing SDR methods are not suitable for analyzing datasets collected from case-control studies. In this setting,

Rohit Khorana

Given the critical roles that satellites play in national defense, public safety, and worldwide communications, finding ways to determine satellite trajectories is a crucially important task for improved space situational awareness. However, it is increasingly common for satellites to lose connection to the ground stations with which they communicate due to

Nima Laal, William G Lamb, Joseph D. Romano, Xavier Siemens

We explore the use of Gibbs sampling in estimating the noise properties of individual pulsars and illustrate its effectiveness using the NANOGrav 11-year data set. We find that Gibbs sampling noise modeling (GM) is more efficient than the current standard Bayesian techniques (SM) for single pulsar analyses by yielding model parameter posteriors with average

Amir Ali Ahmadi, Abraar Chaudhry, Vikas Sindhwani, Stephen Tu

A fundamental challenge in learning an unknown dynamical system is to reduce model uncertainty by making measurements while maintaining safety. We formulate a mathematical definition of what it means to safely learn a dynamical system by sequentially deciding where to initialize trajectories. The state of the system must stay within a safety region for a hor

Shang Liu, Zhongze Cai, Xiaocheng Li

In this paper, we consider the uncertainty quantification problem for regression models. Specifically, we consider an individual calibration objective for characterizing the quantiles of the prediction model. While such an objective is well-motivated from downstream tasks such as newsvendor cost, the existing methods have been largely heuristic and lack of s

A. Morozov, N. Tselousov

We suggest an ansatz for representation of affine Yangian $Y(\hat{ \mathfrak{gl}}_1)$ by differential operators in the triangular set of time-variables ${\bf P}_{a,i}$ with $1\leqslant i\leqslant a$, which saturates the MacMahon formula for the number of $3d$ Young diagrams/plane partitions. In this approach the 3-Schur polynomials are defined as the common

Wuyang Chen, Yanqi Zhou, Nan Du, Yanping Huang

Pretraining on a large-scale corpus has become a standard method to build general language models (LMs). Adapting a model to new data distributions targeting different downstream tasks poses significant challenges. Naive fine-tuning may incur catastrophic forgetting when the over-parameterized LMs overfit the new data but fail to preserve the pretrained feat

Yi Yang, Hejie Cui, Carl Yang

The human brain is the central hub of the neurobiological system, controlling behavior and cognition in complex ways. Recent advances in neuroscience and neuroimaging analysis have shown a growing interest in the interactions between brain regions of interest (ROIs) and their impact on neural development and disorder diagnosis. As a powerful deep model for a

Darshan Deshpande, Zhivar Sourati, Filip Ilievski, Fred Morstatter

Automatic assessment of the quality of arguments has been recognized as a challenging task with significant implications for misinformation and targeted speech. While real-world arguments are tightly anchored in context, existing computational methods analyze their quality in isolation, which affects their accuracy and generalizability. We propose SPARK: a n

Peng Yang, Yuansong Zhao, Lei Nie, Jonathon Vallejo

Mixture priors provide an intuitive way to incorporate historical data while accounting for potential prior-data conflict by combining an informative prior with a non-informative prior. However, pre-specifying the mixing weight for each component remains a crucial challenge. Ideally, the mixing weight should reflect the degree of prior-data conflict, which i

Ali Raza Mirza, Adam Zaman Chaudhry

We demonstrate how using two qubits can drastically improve the estimation of environment parameters as compared to using only a single qubit. The two qubits are coupled to a common harmonic oscillatorenvironment, and the properties of the environment are imprinted upon the dynamics of the two qubits. The reduced density matrix of only one of these qubits co

Sarat Chandra Varanasi, Neeraj Mittal, Gopal Gupta

We present Locksynth, a tool that automatically derives synchronization needed for destructive updates to concurrent data structures that involve a constant number of shared heap memory write operations. Locksynth serves as the implementation of our prior work on deriving abstract synchronization code. Designing concurrent data structures involves inferring

Hiroki Sukeno, Tzu-Chieh Wei

Quantum simulation is one of the major applications of quantum devices. In the noisy intermediate-scale quantum era, however, the general quantum simulation is not yet feasible, such as that of lattice gauge theories, which is likely limited due to the violation of the Gauss law constraint and the complexity of the real-time dynamics, especially in the decon

Sara Court, Andrea D. Sims, Micha Elsner

Maltese is often described as having a hybrid morphological system resulting from extensive contact between Semitic and Romance language varieties. Such a designation reflects an etymological divide as much as it does a larger tradition in the literature to consider concatenative and non-concatenative morphological patterns as distinct in the language archit

Yuwen Chen, Paul Goulart

We present an efficient implementation of interior point methods for a family of nonsymmetric cones, including generalized power cones, power mean cones and relative entropy cones, by exploiting underlying low-rank and sparse properties of Hessians of homogeneous self-concordant barrier functions. We prove that the augmented linear system in our interior poi

Jay Bartroff

How do you bring your assets as close as possible to your target allocation by only investing a fixed amount of additional funds, and not selling any assets? We look at two versions of this problem which have simple, closed form solutions revealed by basic calculus and algebra.

Pinaki Patra

Parametric amplifiers are an integral part of measurements involving the conversion of propagating quantum information to mechanical motion. General time-dependent PT-symmetric parametric oscillators for unbroken parity and time reversal (PT) symmetry regimes are studied theoretically. By constructing an explicit metric operator, we have transformed the non-

Robert Pluta, Bernard Russo

We introduce a class of Banach algebras that we call anti-C*-algebras. We show that the normed standard embedding of a C*-ternary ring is the direct sum of a C*-algebra and an anti-C*-algebra. We prove that C*-ternary rings and anti-C*-algebras are semisimple. We give two new characterizations of C*-ternary rings which are isomorphic to a TRO (ternary ring o

Li Du, Hongyuan Mei, Jason Eisner

To predict the next token, autoregressive models ordinarily examine the past. Could they also benefit from also examining hypothetical futures? We consider a novel Transformer-based autoregressive architecture that estimates the next-token distribution by extrapolating multiple continuations of the past, according to some proposal distribution, and attending

Cristian Villavicencio

The quasistatic electric current density of fermions in the presence of an external electric field is determined through the utilization of a time-ordered Schwinger propagator. The study encompasses the necessary conditions for establishing a well-defined time-ordered propagator within the Schwinger formalism, specifically concentrating on constant and unifo

Yun Luo, Xiaotian Lin, Zhen Yang, Fandong Meng

Task-incremental continual learning refers to continually training a model in a sequence of tasks while overcoming the problem of catastrophic forgetting (CF). The issue arrives for the reason that the learned representations are forgotten for learning new tasks, and the decision boundary is destructed. Previous studies mostly consider how to recover the rep

Tainara Borges, Benjamin Foster

We prove $L^p\times L^q\rightarrow L^r$ bounds for certain lacunary bilinear maximal averaging operators with parameters satisfying the H\"older relation $1/p+1/q=1/r$. The boundedness region that we get contains at least the interior of the H\"older boundedness region of the associated single scale bilinear averaging operator. In the case of the lacunary bi

Bowen Jin, Wentao Zhang, Yu Zhang, Yu Meng

A real-world text corpus sometimes comprises not only text documents but also semantic links between them (e.g., academic papers in a bibliographic network are linked by citations and co-authorships). Text documents and semantic connections form a text-rich network, which empowers a wide range of downstream tasks such as classification and retrieval. However

Edward F. Redish

Even if students can make the blend, interpret physics correctly in mathematical symbology and graphs, they still need to be able to apply that knowledge in productive and coherent ways. As instructors, we can show our solutions to complex problems in class. We can give complex problems to students as homework. But our students are likely to still have troub

Ronit Das, Tie Luo

Anomaly detection is widely used in a broad range of domains from cybersecurity to manufacturing, finance, and so on. Deep learning based anomaly detection has recently drawn much attention because of its superior capability of recognizing complex data patterns and identifying outliers accurately. However, deep learning models are typically iteratively optim

Tao Long, Dorothy Zhang, Grace Li, Batool Taraif

Communicating science and technology is essential for the public to understand and engage in a rapidly changing world. Tweetorials are an emerging phenomenon where experts explain STEM topics on social media in creative and engaging ways. However, STEM experts struggle to write an engaging "hook" in the first tweet that captures the reader's attention. We pr

Masanori Hirano, Kentaro Imajo, Kentaro Minami, Takuya Shimada

Deep hedging is a framework for hedging derivatives in the presence of market frictions. In this study, we focus on the problem of hedging a given target option by using multiple options. To extend the deep hedging framework to this setting, the options used as hedging instruments also have to be priced during training. While one might use classical pricing

Wen Wu, Chao Zhang, Philip C. Woodland

This paper proposes handling training data sparsity in speech-based automatic depression detection (SDD) using foundation models pre-trained with self-supervised learning (SSL). An analysis of SSL representations derived from different layers of pre-trained foundation models is first presented for SDD, which provides insight to suitable indicator for depress

Julian Barbour

In my contribution to the collection at https://dd70th.weebly.com marking the 70th birthday of David Deutsch I suggest that hitherto unrecognised properties of the Newton gravitational potential made scale-invariant through multiplication by the N-body root-mean-square length hint at redundancy of quantum wave functions for the explanation of physical effect

P. Veres, P. N. Bhat, E. Burns, R. Hamburg

The recent discovery of a kilonova from the long duration gamma-ray burst, GRB 211211A, challenges classification schemes based on temporal information alone. Gamma-ray properties of GRB 211211A reveal an extreme event, which stands out among both short and long GRBs. We find very short variations (few ms) in the lightcurve of GRB 211211A and estimate ~1000

Zichao Zhang, Melda Yuksel, Halim Yanikomeroglu, Benjamin K. Ng

Faster-than-Nyquist (FTN) signaling is a nonorthogonal transmission technique, which brings in intentional inter-symbol interference. This way it can significantly enhance spectral efficiency for practical pulse shapes such as the root raised cosine pulses. This paper proposes an achievable rate region for the multiple antenna (MIMO) asynchronous multiple ac

Shengqiong Wu, Hao Fei, Wei Ji, Tat-Seng Chua

Unpaired cross-lingual image captioning has long suffered from irrelevancy and disfluency issues, due to the inconsistencies of the semantic scene and syntax attributes during transfer. In this work, we propose to address the above problems by incorporating the scene graph (SG) structures and the syntactic constituency (SC) trees. Our captioner contains the

Harish K. Dureppagari, Chiranjib Saha, Harpreet S. Dhillon, R. Michael Buehrer

Since the introduction of 5G Release 18, non-terrestrial networks (NTNs) based positioning has garnered significant interest due to its numerous applications, including emergency services, lawful intercept, and charging and tariff services. This release considers single low-earth-orbit (LEO) positioning explicitly for $\textit{location verification}$ purpose

Hao Fei, Meishan Zhang, Min Zhang, Tat-Seng Chua

Latest efforts on cross-lingual relation extraction (XRE) aggressively leverage the language-consistent structural features from the universal dependency (UD) resource, while they may largely suffer from biased transfer (e.g., either target-biased or source-biased) due to the inevitable linguistic disparity between languages. In this work, we investigate an

Ankur Sinha, Satishwar Kedas, Rishu Kumar, Pekka Malo

Fine-grained financial sentiment analysis on news headlines is a challenging task requiring human-annotated datasets to achieve high performance. Limited studies have tried to address the sentiment extraction task in a setting where multiple entities are present in a news headline. In an effort to further research in this area, we make publicly available SEn