May 2023 arXiv papers — page 69

Mollie E. Wood, Chase D. Latour, Lucia C. Petito

As a solution to methodologic challenges inherent to estimating causal effects of exposures in early pregnancy, we suggest emulating a target trial using a treatment decision design, wherein time zero is centered around clinical landmarks where treatment decisions may occur, such as the date of preconception counseling or prenatal care initiation. These idea

Ananth Hari, Uzi Vishkin

Horn-satisfiability or Horn-SAT is the problem of deciding whether a satisfying assignment exists for a Horn formula, a conjunction of clauses each with at most one positive literal (also known as Horn clauses). It is a well-known P-complete problem, which implies that unless P = NC, it is a hard problem to parallelize. In this paper, we empirically show tha

Linwei Sang, Yinliang Xu, Hongbin Sun

Decarbonizing the energy supply is essential and urgent to mitigate the increasingly visible climate change. Its basis is identifying emission responsibility during power allocation by the carbon emission flow (CEF) model. However, the main challenge of CEF application is the intractable nonlinear relationship between carbon emission and power allocation. So

Nelson Martins-Ferreira

Regardless of its environment, the category of internal groupoids is shown to be equivalent to the full subcategory of involutive-2-links that are unital and associative. The new notion of involutive-2-link originates from the study of triangulated surfaces and their application in additive manufacturing and 3d-printing. Thus, this result establishes a bridg

Dong Wang, Olga Saukh, Xiaoxi He, Lothar Thiele

While the deployment of deep learning models on edge devices is increasing, these models often lack robustness when faced with dynamic changes in sensed data. This can be attributed to sensor drift, or variations in the data compared to what was used during offline training due to factors such as specific sensor placement or naturally changing sensing condit

Ananth Balashankar, Xuezhi Wang, Yao Qin, Ben Packer

Counterfactual Data Augmentation (CDA) is a commonly used technique for improving robustness in natural language classifiers. However, one fundamental challenge is how to discover meaningful counterfactuals and efficiently label them, with minimal human labeling cost. Most existing methods either completely rely on human-annotated labels, an expensive proces

Muru Zhang, Ofir Press, William Merrill, Alisa Liu

A major risk of using language models in practical applications is their tendency to hallucinate incorrect statements. Hallucinations are often attributed to knowledge gaps in LMs, but we hypothesize that in some cases, when justifying previously generated hallucinations, LMs output false claims that they can separately recognize as incorrect. We construct t

William Hogan, Jiacheng Li, Jingbo Shang

Open-world Relation Extraction (OpenRE) has recently garnered significant attention. However, existing approaches tend to oversimplify the problem by assuming that all unlabeled texts belong to novel classes, thereby limiting the practicality of these methods. We argue that the OpenRE setting should be more aligned with the characteristics of real-world data

Simerjot Kaur, Andrea Stefanucci, Sameena Shah

Determining industry and product/service codes for a company is an important real-world task and is typically very expensive as it involves manual curation of data about the companies. Building an AI agent that can predict these codes automatically can significantly help reduce costs, and eliminate human biases and errors. However, unavailability of labeled

Amogh Joshi, Adarsh Kosta, Wachirawit Ponghiran, Manish Nagaraj

The ability of resource-constrained biological systems such as fruitflies to perform complex and high-speed maneuvers in cluttered environments has been one of the prime sources of inspiration for developing vision-based autonomous systems. To emulate this capability, the perception pipeline of such systems must integrate information cues from tasks includin

Alex H. Ardila, Jason Murphy, Jiqiang Zheng

We consider the nonlinear Schr\"odinger equation with focusing quintic and defocusing cubic nonlinearity in three space dimensions: \[ (i\partial_t+\Delta)u = |u|^2 u - |u|^4 u. \] In [18, 23], the authors classified the dynamics of solutions under the energy constraint $E(u)< E^c(W)$, where $W$ is the quintic NLS ground state and $E^c$ is the quintic NLS en

Daria Stetsenko, Inez Okulska

This paper provides an overview of a text mining tool the StyloMetrix developed initially for the Polish language and further extended for English and recently for Ukrainian. The StyloMetrix is built upon various metrics crafted manually by computational linguists and researchers from literary studies to analyze grammatical, stylistic, and syntactic patterns

Junho Peter Whang

We prove the existence of an effective universal upper bound for the order of any integral periodic orbit of any integral algebraic dynamical system in a fixed ambient space. Using this, we demonstrate the decidability of periodicity in arbitrary finitely generated algebraic dynamical systems over fields of characteristic zero.

Evgeniia Razumovskaia, Ivan Vulić, Anna Korhonen

Slot labeling (SL) is a core component of task-oriented dialogue (ToD) systems, where slots and corresponding values are usually language-, task- and domain-specific. Therefore, extending the system to any new language-domain-task configuration requires (re)running an expensive and resource-intensive data annotation process. To mitigate the inherent data sca

Tollef Emil Jørgensen, Andre Kåsen

This paper presents a merged collection of entity and coreference annotated data grounded in the Universal Dependencies (UD) treebanks for the two written forms of Norwegian: Bokm{\aa}l and Nynorsk. The aligned and converted corpora are the Norwegian Named Entities (NorNE) and Norwegian Anaphora Resolution Corpus (NARC). While NorNE is aligned with an older

Tian-Run Li, Yun-Ling Chen, Guang-Yuan Zhang

Ahlfors' theory of covering surfaces is one of the major mathematical achievement of last century. The most important part of his theory is the Second Fundamental Theorem (SFT). We are interested in the relation of errors of Ahlfors' SFT with the same boundary curve. In this paper we will prove a result which is used to establish the best bound of the consta

Siddharth Singh, Prajwal Singhania, Aditya K. Ranjan, Zack Sating

Heavy communication, in particular, collective operations, can become a critical performance bottleneck in scaling the training of billion-parameter neural networks to large-scale parallel systems. This paper introduces a four-dimensional (4D) approach to optimize communication in parallel training. This 4D approach is a hybrid of 3D tensor and data parallel

Khadija F. Zaidi, Michelle Harris-Love

Strokes are a leading cause of disability, with many experiencing difficulty in recovering arm movement, particularly hand function and grasping ability. There is currently no objective measure of movement quality, and without it, rehabilitative interventions remain at best estimations of the underlying neural structures response to produce movement. In this

Simerjot Kaur, Charese Smiley, Akshat Gupta, Joy Sain

A number of datasets for Relation Extraction (RE) have been created to aide downstream tasks such as information retrieval, semantic search, question answering and textual entailment. However, these datasets fail to capture financial-domain specific challenges since most of these datasets are compiled using general knowledge sources such as Wikipedia, web-ba

Xiuwen Zheng, Subhasis Dasgupta, Arun Kumar, Amarnath Gupta

Data science applications increasingly rely on heterogeneous data sources and analytics. This has led to growing interest in polystore systems, especially analytical polystores. In this work, we focus on a class of emerging multi-data model analytics workloads that fluidly straddle relational, graph, and text analytics. Instead of a generic polystore, we bui

Cheng Peng, Xi Yang, Aokun Chen, Kaleb E Smith

There is enormous enthusiasm and concerns in using large language models (LLMs) in healthcare, yet current assumptions are all based on general-purpose LLMs such as ChatGPT. This study develops a clinical generative LLM, GatorTronGPT, using 277 billion words of mixed clinical and English text with a GPT-3 architecture of 20 billion parameters. GatorTronGPT i

Danielle J. Harper, Yongjoo Kim, Alejandra Gómez-Ramírez, Benjamin J. Vakoc

We demonstrate that a simple, unscanned polarization-sensitive optical coherence tomography needle probe can be used to perform layer identification in biological tissues. Broadband light from a laser centered at 1310 nm was sent through a fiber that was embedded into a needle, and analysis of the polarization state of the returning light after interference

Nishchay Suri, Chong Wang, Benjamin M. Hunt, Di Xiao

We show that a superlattice potential can be employed to engineer topology in massive Dirac fermions in systems such as bilayer graphene, moir\'e graphene-boron nitride, and transition-metal dichalcogenide (TMD) monolayers and bilayers. We use symmetry analysis to analyze band inversions to determine the Chern number $\mathscr C$ for the valence band as a fu

Nan Xu, Hongming Zhang, Jianshu Chen

Existing event-centric NLP models often only apply to the pre-defined ontology, which significantly restricts their generalization capabilities. This paper presents CEO, a novel Corpus-based Event Ontology induction model to relax the restriction imposed by pre-defined event ontologies. Without direct supervision, CEO leverages distant supervision from avail

Emirhan Kurtulus, Zichao Li, Yann Dauphin, Ekin Dogus Cubuk

Data augmentation methods have played an important role in the recent advance of deep learning models, and have become an indispensable component of state-of-the-art models in semi-supervised, self-supervised, and supervised training for vision. Despite incurring no additional latency at test time, data augmentation often requires more epochs of training to

Patrick dos Anjos, Lucas A. Quaresma, Marcelo L. P. Machado

Electrical conductivity is of fundamental importance in electric arc furnaces (EAF) and the interaction of this phenomenon with the process slag results in energy losses and low optimization. As mathematical modeling helps in understanding the behavior of phenomena and it was used to predict the electrical conductivity of EAF slags through artificial neural

R. Oubagha, B. Hamil, B. C. Lütfüoğlu, M. Merad

In this manuscript, we investigate the extended uncertainty principle (EUP) effects on the Van der Waals (VdW) black holes whose thermal quantities mimic the VdW liquid. We find that the considered formalism imposes an upper bound on the event horizon radius. Thus, the mass, Hawking temperature, and heat capacity become physically meaningful within a certain

Ziyu Chen, Markos A. Katsoulakis, Luc Rey-Bellet, Wei Zhu

This work presents the first statistical performance guarantees for group-invariant generative models. Many real data, such as images and molecules, are invariant to certain group symmetries, which can be taken advantage of to learn more efficiently as we rigorously demonstrate in this work. Here we specifically study generative adversarial networks (GANs),

Vineel Pratap, Andros Tjandra, Bowen Shi, Paden Tomasello

Expanding the language coverage of speech technology has the potential to improve access to information for many more people. However, current speech technology is restricted to about one hundred languages which is a small fraction of the over 7,000 languages spoken around the world. The Massively Multilingual Speech (MMS) project increases the number of sup

George Theodorou, Bruno Barton-Singer, Stavros Komineas

Skyrmions in an antiferromagnet can travel as solitary waves in stark contrast to the situation in ferromagnets. Traveling skyrmion solutions have been found numerically in chiral antiferromagnets. We study head-on collision events between two skyrmions. We find that the result of the collision depends on the initial velocity of the skyrmions. For small velo

Giorgos Vernikos, Arthur Bražinskas, Jakub Adamek, Jonathan Mallinson

Despite the impressive performance of large language models (LLMs), they often lag behind specialized models in various tasks. LLMs only use a fraction of the existing training data for in-context learning, while task-specific models harness the full dataset for fine-tuning. In this work, we tackle the problem of leveraging training data to improve the perfo

Sergio Di Matteo

The division by N! in the expression of statistical entropy is usually justified to students by the statement that classical particles should be counted as indistinguishable. Sometimes, quantum indistinguishability is invoked to explain it. In this paper, we try to clarify the issue starting from Clausius thermodynamical entropy and deriving from it Boltzman

Mutian He, Philip N. Garner

Recently, large pretrained language models have demonstrated strong language understanding capabilities. This is particularly reflected in their zero-shot and in-context learning abilities on downstream tasks through prompting. To assess their impact on spoken language understanding (SLU), we evaluate several such models like ChatGPT and OPT of different siz

Sushanth Kini Manjeshwar, Anastasiia Ciers, Juliette Monsel, Hannes Pfeifer

Increasing the interaction between light and mechanical resonators is an ongoing endeavor in the field of cavity optomechanics. Optical microcavities allow for boosting the interaction strength through their strong spatial confinement of the optical field. In this work, we follow this approach by realizing a sub-wavelength-long, free-space optomechanical mic

Alexey A. Dmitriev, Vadim A. Porvatov, Alina D. Rozenblit, Mikhail K. Buzakov

Large assemblies of extremely simple robots capable only of basic motion activities (like propelling forward or self-rotating) are often applied to study swarming behavior or implement various phenomena characteristic of active matter composed of non-equilibrium particles that convert their energy to a directed motion. As a result, a great abundance of compa

Cuong Ly, Grayson Jorgenson, Dan Rosa de Jesus, Henry Kvinge

In the last decade, Convolutional Neural Network (CNN) and transformer based object detectors have achieved high performance on a large variety of datasets. Though the majority of detection literature has developed this capability on datasets such as MS COCO, these detectors have still proven effective for remote sensing applications. Challenges in this part

Haitham Khedr, Yasser Shoukry

Formal certification of Neural Networks (NNs) is crucial for ensuring their safety, fairness, and robustness. Unfortunately, on the one hand, sound and complete certification algorithms of ReLU-based NNs do not scale to large-scale NNs. On the other hand, incomplete certification algorithms are easier to compute, but they result in loose bounds that deterior

Mubashara Akhtar, Michael Schlichtkrull, Zhijiang Guo, Oana Cocarascu

Misinformation is often conveyed in multiple modalities, e.g. a miscaptioned image. Multimodal misinformation is perceived as more credible by humans, and spreads faster than its text-only counterparts. While an increasing body of research investigates automated fact-checking (AFC), previous surveys mostly focus on text. In this survey, we conceptualise a fr

Paul-Hermann Balduf

The amplitude of subdivergence-free logarithmically divergent Feynman graphs in $\phi^4$-theory in 4 spacetime dimensions is given by a single number, the Feynman period. We numerically compute the periods of 1.3 million completed graphs, this represents more than 33 million graphs contributing to the beta function. Our data set includes all primitive graphs

Florent P. Baudier, Christian Rosendal

We describe several ordinal indices that are capable of detecting, according to various metric notions of faithfulness, the embeddability between pairs of Polish spaces. These embeddability ranks are of theoretical interest but seem difficult to estimate in practice. Embeddability ranks, which are easier to estimate in practice, are embeddability ranks gener

Dharma KC, Clayton T. Morrison

Neural machine translation (NMT) methods developed for natural language processing have been shown to be highly successful in automating translation from one natural language to another. Recently, these NMT methods have been adapted to the generation of program code. In NMT for code generation, the task is to generate output source code that satisfies constr

Zhan-Lun Chang, Seyyedali Hosseinalipour, Mung Chiang, Christopher G. Brinton

Federated learning (FL) has emerged as a key technique for distributed machine learning (ML). Most literature on FL has focused on ML model training for (i) a single task/model, with (ii) a synchronous scheme for updating model parameters, and (iii) a static data distribution setting across devices, which is often not realistic in practical wireless environm

Mahdi Anbarloei

A multiplicative hyperring is a well-known type of algebraic hyperstructures which extend a ring to a structure in which the addition is an operation but multiplication is a hyperoperation. Let G be a commutative multiplicative hyperring and s,n \in Z^+. A proper hyperideal Q of G is called (weakly) (s,n)-closed if (0 \neq a^s \subseteq Q) s^s \subseteq Q fo

Davide Morelli, Alberto Baldrati, Giuseppe Cartella, Marcella Cornia

The rapidly evolving fields of e-commerce and metaverse continue to seek innovative approaches to enhance the consumer experience. At the same time, recent advancements in the development of diffusion models have enabled generative networks to create remarkably realistic images. In this context, image-based virtual try-on, which consists in generating a nove

Sitao Zhang, Yimu Pan, James Z. Wang

Emotion understanding is an essential but highly challenging component of artificial general intelligence. The absence of extensively annotated datasets has significantly impeded advancements in this field. We present EmotionCLIP, the first pre-training paradigm to extract visual emotion representations from verbal and nonverbal communication using only uncu

Kuan-Hao Huang, Liang Tan, Rui Hou, Sinong Wang

Many real-world applications require making multiple predictions from the same text. Fine-tuning a large pre-trained language model for each downstream task causes computational burdens in the inference time due to several times of forward passes. To amortize the computational cost, freezing the language model and building lightweight models for downstream t

Simon Carter, Lilianne Mujica-Parodi, Helmut H. Strey

This article aims to investigate the impact of noise on parameter fitting for an Ornstein-Uhlenbeck process, focusing on the effects of multiplicative and thermal noise on the accuracy of signal separation. To address these issues, we propose algorithms and methods that can effectively distinguish between thermal and multiplicative noise and improve the prec

Laura Di Gesu, Herman L. Marshall, Steven R. Ehlert, Dawoon E. Kim

The magnetic field conditions in astrophysical relativistic jets can be probed by multiwavelength polarimetry, which has been recently extended to X-rays. For example, one can track how the magnetic field changes in the flow of the radiating particles by observing rotations of the electric vector position angle $\Psi$. Here we report the discovery of a $\Psi

Bozidar Jovanovic

We present the basic concepts of space and time, the Galilean and pseudo-Euclidean geometry. We use an elementary geometric framework of affine spaces and groups of affine transformations to illustrate the natural relationship between classical mechanics and theory of relativity, which is quite often hidden, despite its fundamental importance. We have emphas

Pha Nguyen, Kha Gia Quach, Kris Kitani, Khoa Luu

One of the recent trends in vision problems is to use natural language captions to describe the objects of interest. This approach can overcome some limitations of traditional methods that rely on bounding boxes or category annotations. This paper introduces a novel paradigm for Multiple Object Tracking called Type-to-Track, which allows users to track objec

Hafiz Tayyab Rauf, Andre Freitas, Norman W. Paton

Deep Learning (DL) techniques now constitute the state-of-the-art for important problems in areas such as text and image processing, and there have been impactful results that deploy DL in several data management tasks. Deep Clustering (DC) has recently emerged as a sub-discipline of DL, in which data representations are learned in tandem with clustering, wi

Nunzio A. Letizia, Andrea M. Tonello, H. Vincent Poor

In this paper, the problem of determining the capacity of a communication channel is formulated as a cooperative game, between a generator and a discriminator, that is solved via deep learning techniques. The task of the generator is to produce channel input samples for which the discriminator ideally distinguishes conditional from unconditional channel outp

Veit Elser

We generate non-lattice packings of spheres in up to 22 dimensions using the geometrical constraint satisfaction algorithm RRR. Our aggregated data suggest that it is easy to double the density of Ball's lower bound, and more tentatively, that the exponential decay rate of the density can be improved relative to Minkowski's longstanding 1/2.

Andersen Chang, Lili Zheng, Gautam Dasarthy, Genevera I. Allen

Probabilistic graphical models have become an important unsupervised learning tool for detecting network structures for a variety of problems, including the estimation of functional neuronal connectivity from two-photon calcium imaging data. However, in the context of calcium imaging, technological limitations only allow for partially overlapping layers of n

B. G. Márkus, M. Gmitra, B. Dóra, G. Csősz

Graphite has been intensively studied, yet its electron spins dynamics remains an unresolved problem even 70 years after the first experiments. The central quantities, the longitudinal ($T_1$) and transverse ($T_2$) relaxation times were postulated to be equal, mirroring standard metals, but $T_1$ has never been measured for graphite. Here, based on a detail

Jai Vardhan, Kothapalli Sai Swetha

Monitoring plant health is crucial for maintaining agricultural productivity and food safety. Disruptions in the plant's normal state, caused by diseases, often interfere with essential plant activities, and timely detection of these diseases can significantly mitigate crop loss. In this study, we propose a deep learning-based approach for efficient detectio

H. Sharma, M. M. Hedman, S. Vahidinia

Enceladus' plume consists mainly of a mixture of water vapor and solid ice particles that may originate from a subsurface ocean. The physical processes underlying Enceladus' plume particle dynamics are still being debated, and quantifying the particles' size distribution and launch velocities can help constrain these processes. Cassini's Visual and Infrared

Charles F. Driscoll

A model of electric energization of the Solar Wind and Corona is developed, including electro-magnetic (EM) particle effects precluded by traditional magneto-hydro (MHD) assumptions. Using standard 1-D radial Solar models for particle density and temperature, the Core gravito-electric field is calculated; and the range of possible Photospheric photoelectric

Lianjun Li, Sai Sree Rayala, Jiarui Xu, Lizhong Zheng

In this paper we introduce StructNet-CE, a novel real-time online learning framework for MIMO-OFDM channel estimation, which only utilizes over-the-air (OTA) pilot symbols for online training and converges within one OFDM subframe. The design of StructNet-CE leverages the structure information in the MIMO-OFDM system, including the repetitive structure of mo

Yu Liu, Zachary Thurston, Alan Han, Pengyu Nie

We present pytest-inline, the first inline testing framework for Python. We recently proposed inline tests to make it easier to test individual program statements. But, there is no framework-level support for developers to write inline tests in Python. To fill this gap, we design and implement pytest-inline as a plugin for pytest, the most popular Python tes

Jill A. Kuhlberg, Irene Headen, Ellis A. Ballard, Donald Martin

Much attention and concern has been raised recently about bias and the use of machine learning algorithms in healthcare, especially as it relates to perpetuating racial discrimination and health disparities. Following an initial system dynamics workshop at the Data for Black Lives II conference hosted at MIT in January of 2019, a group of conference particip

Jinghan Yao, Nawras Alnaasan, Tian Chen, Aamir Shafi

Autoregressive models, despite their commendable performance in a myriad of generative tasks, face challenges stemming from their inherently sequential structure. Inference on these models, by design, harnesses a temporal dependency, where the current token's probability distribution is conditioned on preceding tokens. This inherent characteristic severely i

Qingkai Shi, Xiangzhe Xu, Xiangyu Zhang

Reverse engineering of protocol message formats is critical for many security applications. Mainstream techniques use dynamic analysis and inherit its low-coverage problem -- the inferred message formats only reflect the features of their inputs. To achieve high coverage, we choose to use static analysis to infer message formats from the implementation of pr

Jai Vardhan, Taraka Satya Krishna Teja Malisetti

Breast cancer (BC) remains a significant health threat, with no long-term cure currently available. Early detection is crucial, yet mammography interpretation is hindered by high false positives and negatives. With BC incidence projected to surpass lung cancer, improving early detection methods is vital. Thermography, using high-resolution infrared cameras,

Janusz Adamus

It is shown that every C-semianalytic arc-symmetric set can be realized as the zero locus of an arc-analytic function. As a consequence, a Nash globally subanalytic arc-symmetric set is the zero locus of a continuous globally-subanalytic function which is arc-analytic outside a simple normal crossings divisor.

Raúl Alvarez-Patiño

We use classical obstruction theory \`{a} la Eilenberg-Steenrod to obtain a homotopy classification of $\mathrm{Spin}(7)$-structures on compact $8$-manifolds with abelian fundamental group. As an application, we show that a compact, connected Riemannian $8$-manifold with holonomy contained inside the group $\mathrm{Spin}(7)$ has exactly two $\mathrm{Spin}(7)

Vivek Pandey, Sudhir K. Pandey

In this work, we report the existence of nodal-arc, which acts as the building block of all the nodal-rings in TaAs & TaP. This nodal-arc is found to be capable of generating all the nodal-rings in these materials upon the application of space-group symmetry operations including time-reversal symmetry. The arcs are obtained to be dispersive with the energy s

Mina Aganagic, Elise LePage, Miroslav Rapcak

There is a generalization of Heegaard-Floer theory from ${\mathfrak{gl}}_{1|1}$ to other Lie (super)algebras $^L{\mathfrak{g}}$. The corresponding category of A-branes is solvable explicitly and categorifies quantum $U_q(^L{\mathfrak{g}})$ link invariants. The theory was discovered in \cite{A1,A2}, using homological mirror symmetry. It has novel features, in

Behnaz Arzani, Siva Kesava Reddy Kakarla, Miguel Castro, Srikanth Kandula

We show communication schedulers' recent work proposed for ML collectives does not scale to the increasing problem sizes that arise from training larger models. These works also often produce suboptimal schedules. We make a connection with similar problems in traffic engineering and propose a new method, TECCL, that finds better quality schedules (e.g., fini

Joseph Marvin Imperial, Ekaterina Kochmar

In recent years, the main focus of research on automatic readability assessment (ARA) has shifted towards using expensive deep learning-based methods with the primary goal of increasing models' accuracy. This, however, is rarely applicable for low-resource languages where traditional handcrafted features are still widely used due to the lack of existing NLP

Nan Xu, Chunting Zhou, Asli Celikyilmaz, Xuezhe Ma

Given a prefix (context), open-ended generation aims to decode texts that are coherent, which do not abruptly drift from previous topics, and informative, which do not suffer from undesired repetitions. In this paper, we propose Look-back, an improved decoding algorithm that leverages the Kullback-Leibler divergence to track the distribution distance between

Sara Hosseinirad, Giulio Salizzoni, Alireza Alian Porzani, Maryam Kamgarpour

Our paper addresses characterizing conditions for a linear quadratic (LQ) game to be a potential game. The desired properties of potential games in finite action settings, such as convergence of learning dynamics to Nash equilibria, and the challenges of learning Nash equilibria in continuous state and action settings motivate us to characterize LQ potential

F. Lillo, G. Livieri, S. Marmi, A. Solomko

We investigate and prove the mathematical properties of a general class of one-dimensional unimodal smooth maps perturbed with a heteroscedastic noise. Specifically, we investigate the stability of the associated Markov chain, show the weak convergence of the unique stationary measure to the invariant measure of the map, and show that the average Lyapunov ex

Étienne Sandier, Peter Sternberg

We construct an entire solution $U:\mathbb{R}^2\to\mathbb{R}^2$ to the elliptic system \[ \Delta U=\nabla_uW(U), \] where $W:\mathbb{R}^2\to [0,\infty)$ is a `triple-well' potential. This solution is a local minimizer of the associated energy \[ \int \frac{1}{2}|\nabla U|^2+W(U)\,dx \] in the sense that $U$ minimizes the energy on any compact set among compe

Volodymyr Koverga, Selvaraj S. Chandrasekaran, Anh T. Ngo

Among the distinctive structural features of lithium ionic liquids (LILs), a novel class of single-component electrolytes, the variation of the electron-withdrawing group stands out as a key factor in determining their dynamics. To understand this phenomenon, we conducted molecular dynamics (MD) simulations for LILs based on hexafluoro-2-propanoxy (LIL2), he

Francesco Marchetti, Sabrina Guastavino, Cristina Campi, Federico Benvenuto

In many contexts, customized and weighted classification scores are designed in order to evaluate the goodness of the predictions carried out by neural networks. However, there exists a discrepancy between the maximization of such scores and the minimization of the loss function in the training phase. In this paper, we provide a complete theoretical setting

Hossein Taheri, Christos Thrampoulidis

Normalized gradient descent has shown substantial success in speeding up the convergence of exponentially-tailed loss functions (which includes exponential and logistic losses) on linear classifiers with separable data. In this paper, we go beyond linear models by studying normalized GD on two-layer neural nets. We prove for exponentially-tailed losses that

Jean-François Coeurjolly, Ismaïla Ba, Achmad Choiruddin

Point processes are stochastic models generating interacting points or events in time, space, etc. Among characteristics of these models, first-order intensity and conditional intensity functions are often considered. We focus on inhomogeneous parametric forms of these functions assumed to depend on a certain number of spatial covariates. When this number of

Saurabh Srivastava, Gaurav Singh, Shou Matsumoto, Ali Raz

In this work, we present the first dataset, MailEx, for performing event extraction from conversational email threads. To this end, we first proposed a new taxonomy covering 10 event types and 76 arguments in the email domain. Our final dataset includes 1.5K email threads and ~4K emails, which are annotated with totally ~8K event instances. To understand the

José D. V. Arbañil, Cesar V. Flores, César H. Lenzi, Juan M. Z. Pretel

The effects of the anisotropy on the fluid pulsation modes adopting the so-called Cowling approximation and tidal deformability of strange quark stars are investigated by using the numerical integration of the hydrostatic equilibrium, nonradial oscillations, and tidal deformability equations, being these equations modified from their standard form to include

Yiwei Lyu, Wenhao Luo, John M. Dolan

Decentralized control schemes are increasingly favored in various domains that involve multi-agent systems due to the need for computational efficiency as well as general applicability to large-scale systems. However, in the absence of an explicit global coordinator, it is hard for distributed agents to determine how to efficiently interact with others. In t

Agnieszka I. Pawłowska, Paweł Dąbczyński, Sebastian Lalik, Juan Pablo Carbajal

The memristive device is one of the basic elements of novel, brain-inspired, fast, and energy-efficient information processing systems in which there is no separation between memorization and information analysis functions. Since the first demonstration of the resistive switching effect, several types of memristive devices have been developed. In most of the

Hoda S. Hashemi, Shahed K. Mohammed, Qi Zeng, Reza Zahiri Azar

3D ultrasound imaging provides more spatial information compared to conventional 2D frames by considering the volumes of data. One of the main bottlenecks of 3D imaging is the long data acquisition time which reduces practicality and can introduce artifacts from unwanted patient or sonographer motion. This paper introduces the first shear wave absolute vibro

Bogdan Slavov, Kirill Polovnikov, Sergei Nechaev, Nikita Pospelov

We investigate the statistics of the largest eigenvalue, $\lambda_{\rm max}$, in an ensemble of $N\times N$ large ($N\gg 1$) sparse adjacency matrices, $A_N$. The most attention is paid to the distribution and typical fluctuations of $\lambda_{\rm max}$ in the vicinity of the percolation threshold, $p_c=\frac{1}{N}$. The overwhelming majority of subgraphs re

Cezary Adamczyk, Adrian Kliks

In Open Radio Access Networks, the Conflict Mitigation component, which is part of the Near-RT RIC, aims to detect and resolve any conflicts between xApp decisions. In this paper, we propose a universal method for detecting and resolving of indirect conflicts between xApps. Its efficiency is validated by extensive computer simulations. Our results demonstrat

Taeju Lee, Minkyu Je

This paper presents the trend of biopotential recording front-end channels developed from the 1970s to the 2020s while describing a basic background on the front-end channel design. Only the front-end channels that conduct electrical recording invasively and non-invasively are addressed. The front-end channels are investigated in terms of technology node, nu

Philippe Gagnon, Yuxi Wang

Generalized linear models (GLMs) form one of the most popular classes of models in statistics. The gamma variant is used, for instance, in actuarial science for the modelling of claim amounts in insurance. A flaw of GLMs is that they are not robust against outliers (i.e., against erroneous or extreme data points). A difference in trends in the bulk of the da

Jean Lelis, Diego Marques, Carlos Gustavo Moreira, Pavel Trojovský

In this note, the main focus is on a question about transcendental entire functions mapping $\mathbb{Q}$ into $\mathbb{Q}$ (which is related to a Mahler's problem). In particular, we prove that, for any $t>0$, there is no a transcendental entire function $f\in\mathbb{Q}[[z]]$ such that $f(\mathbb{Q})\subseteq\mathbb{Q}$ and whose denominator of $f(p/q)$ is $

Yiwen Huang, Zhiqiu Yu, Xinjie Yi, Yue Wang

3DMM conditioned face generation has gained traction due to its well-defined controllability; however, the trade-off is lower sample quality: Previous works such as DiscoFaceGAN and 3D-FM GAN show a significant FID gap compared to the unconditional StyleGAN, suggesting that there is a quality tax to pay for controllability. In this paper, we challenge the as

Sacha Cerf, Benjamin Doerr, Benjamin Hebras, Yakob Kahane

The Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is one of the most prominent algorithms to solve multi-objective optimization problems. Recently, the first mathematical runtime guarantees have been obtained for this algorithm, however only for synthetic benchmark problems. In this work, we give the first proven performance guarantees for a classic o

A. O. Nelson, L. Schmitz, C. Paz-Soldan, K. E. Thome

In a series of high performance diverted discharges on DIII-D, we demonstrate that strong negative triangularity (NT) shaping robustly suppresses all edge-localized mode (ELM) activity over a wide range of plasma conditions: $\langle n\rangle=0.1-1.5\times10^{20}$m$^{-3}$, $P_\mathrm{aux}=0-15$MW and $|B_\mathrm{t}|=1-2.2$T, corresponding to $P_\mathrm{loss}

Douglas D. Novaes, Luan V. M. F. Silva

Since Littlewood works in the 1960's, the boundedness of solutions of Duffing-type equations $\ddot{x}+g(x)=p(t)$ has been extensively investigated. More recently, some researches have focused on the family of non-smooth forced oscillators $ \ddot{x}+\text{sgn}(x)=p(t)$, mainly because it represents a simple limit scenario of Duffing-type equations for when

Isaac Corley, Caleb Robinson, Rahul Dodhia, Juan M. Lavista Ferres

Research in self-supervised learning (SSL) with natural images has progressed rapidly in recent years and is now increasingly being applied to and benchmarked with datasets containing remotely sensed imagery. A common benchmark case is to evaluate SSL pre-trained model embeddings on datasets of remotely sensed imagery with small patch sizes, e.g., 32x32 pixe

Kranti Chalamalasetti, Jana Götze, Sherzod Hakimov, Brielen Madureira

Recent work has proposed a methodology for the systematic evaluation of "Situated Language Understanding Agents"-agents that operate in rich linguistic and non-linguistic contexts-through testing them in carefully constructed interactive settings. Other recent work has argued that Large Language Models (LLMs), if suitably set up, can be understood as (simula

Patricio Orio, Pedro A. M. Mediano, Fernando E. Rosas

Recent research has provided a wealth of evidence highlighting the pivotal role of high-order interdependencies in supporting the information-processing capabilities of distributed complex systems. These findings may suggest that high-order interdependencies constitute a powerful resource that is, however, challenging to harness and can be readily disrupted.

Ali Owfi, ChunChih Lin, Linke Guo, Fatemeh Afghah

Indoor localization has gained significant attention in recent years due to its various applications in smart homes, industrial automation, and healthcare, especially since more people rely on their wireless devices for location-based services. Deep learning-based solutions have shown promising results in accurately estimating the position of wireless device

Julio Martinez, Felix Binder, Haoliang Wang, Nick Haber

Humans are interactive agents driven to seek out situations with interesting physical dynamics. Here we formalize the functional form of physical intrinsic motivation. We first collect ratings of how interesting humans find a variety of physics scenarios. We then model human interestingness responses by implementing various hypotheses of intrinsic motivation

Leonora Kaldaras, Carl Wieman

The ability to express scientific concepts in mathematical terms and integrate scientific and mathematical reasoning about a phenomenon is a foundational cognitive process involved in scientific thinking. This process called blended math-science sensemaking (blended MSS) is a desired skill for all STEM students, but few students are learning it, and there is

Abhinav Jangda, Saeed Maleki, Maryam Mehri Dehnavi, Madan Musuvathi

Machine Learning (ML) models execute several parallel computations including Generalized Matrix Multiplication, Convolution, Dropout, etc. These computations are commonly executed on Graphics Processing Units (GPUs), by dividing the computation into independent processing blocks, known as tiles. Since the number of tiles are usually higher than the execution

J. Michael Shull, S. R. Kulkarni

Bow-shocks are produced in the local interstellar medium by the passage of fast stars from the Galactic thin-disk and thick-disk populations with velocities $V_* = $ 40-80 km/s. Stellar transits of local H I clouds occur every 3500-7000 yr on average and last between $10^4$ and $10^5$ yr. There could be 10-20 active bow shocks around low-mass stars inside cl