March 2023 arXiv papers — page 39

O. Deniz Kose, Yanning Shen

Graphs can facilitate modeling various complex systems such as gene networks and power grids, as well as analyzing the underlying relations within them. Learning over graphs has recently attracted increasing attention, particularly graph neural network-based (GNN) solutions, among which graph attention networks (GATs) have become one of the most widely utili

Gustavo Caetano-Anollés

Biomolecular communication demands that interactions between parts of a molecular system act as scaffolds for message transmission. It also requires an evolving and organized system of signs - a communicative agency - for creating and transmitting meaning. Here I explore the need to dissect biomolecular communication with retrodiction approaches that make cl

Brad Windsor, Luke Martin, Anand Tyagi

Sentence simplification tends to focus on the generic simplification of sentences by making them more readable and easier to understand. This paper provides a dataset aimed at training models that perform subject aware sentence simplifications rather than simplifying sentences as a whole. We also test models on that dataset which are inspired by model archit

Tan-Ji Zhou, Yu-Han Ma, C. P. Sun

We propose a finite-time quantum Szilard engine (QSE) with a quantum particle with spin as the working substance (WS) to accelerate the operation of information engines. We introduce a Maxwell's demon (MD) to probe the spin state within a finite measurement time $t_{\rm M}$ to capture the which-way information of the particle, quantified by the mutual in

Brad Windsor, Kevin Choi

We present Thistle, a fully functional vector database. Thistle is an entry into the domain of latent knowledge use in answering search queries, an ongoing research topic at both start-ups and search engine companies. We implement Thistle with several well-known algorithms, and benchmark results on the MS MARCO dataset. Results help clarify the latent knowle

Eric Modesitt, Ruiqi Yang, Qi Liu

Classifying EEG data is integral to the performance of Brain Computer Interfaces (BCI) and their applications. However, external noise often obstructs EEG data due to its biological nature and complex data collection process. Especially when dealing with classification tasks, standard EEG preprocessing approaches extract relevant events and features from the

Bashar Al-Rfooh, Gheith Abandah, Rami Al-Rfou

Most of previous work on learning diacritization of the Arabic language relied on training models from scratch. In this paper, we investigate how to leverage pre-trained language models to learn diacritization. We finetune token-free pre-trained multilingual models (ByT5) to learn to predict and insert missing diacritics in Arabic text, a complex task that r

Shuhong Chen, Kevin Zhang, Yichun Shi, Heng Wang

We propose PAniC-3D, a system to reconstruct stylized 3D character heads directly from illustrated (p)ortraits of (ani)me (c)haracters. Our anime-style domain poses unique challenges to single-view reconstruction; compared to natural images of human heads, character portrait illustrations have hair and accessories with more complex and diverse geometry, and

Feng Long, Bin B. Ren, Nicole L. Wallack, Daniel Harsono

Planet formation imprints signatures on the physical structures of disks. In this paper, we present high-resolution ($\sim$50 mas, 8 au) Atacama Large Millimeter/submillimeter Array (ALMA) observations of 1.3 mm dust continuum and CO line emission toward the disk around the M3.5 star 2MASS J04124068+2438157. The dust disk consists only of two narrow rings at

Yuqing Wang, Yizhi Wang, Longhui Yu, Yuesheng Zhu

Vector font synthesis is a challenging and ongoing problem in the fields of Computer Vision and Computer Graphics. The recently-proposed DeepVecFont achieved state-of-the-art performance by exploiting information of both the image and sequence modalities of vector fonts. However, it has limited capability for handling long sequence data and heavily relies on

Phani Krishna Uppala, Abhishek Bamotra, Shriti Priya, Vaidehi Joshi

The recent success of the CLIP model has shown its potential to be applied to a wide range of vision and language tasks. However this only establishes embedding space relationship of language to images, not to the video domain. In this paper, we propose a novel approach to map video embedding space to natural langugage. We propose a two-stage approach that f

Lingyun Ding

In multispecies electrolyte solutions, even in the absence of an external electric field, differences in ion diffusivities induce an electric potential and generate additional fluxes for each species. This electro-diffusion process is well-described by the advection-Nernst-Planck equation. This study aims to analyze the long-time behavior of the governing eq

Dongyue Li, Huy L. Nguyen, Hongyang R. Zhang

Multitask learning is widely used in practice to train a low-resource target task by augmenting it with multiple related source tasks. Yet, naively combining all the source tasks with a target task does not always improve the prediction performance for the target task due to negative transfers. Thus, a critical problem in multitask learning is identifying su

Joseph Cohen, Xun Huan, Jun Ni

Data-driven artificial intelligence models require explainability in intelligent manufacturing to streamline adoption and trust in modern industry. However, recently developed explainable artificial intelligence (XAI) techniques that estimate feature contributions on a model-agnostic level such as SHapley Additive exPlanations (SHAP) have not yet been evalua

Yidong Chen, Marius Junge

We introduce a noncommutative Poisson random measure on a von Neumann algebra. This is a noncommutative generalization of the classical Poisson random measure. We call this construction Poissonization. Poissonization is a functor from the category of von Neumann algebras with normal semifinite faithful weights to the category of von Neumann algebras with nor

Thomas F. Lidbetter

Let $S$ be a finite subset of $\mathbb{Z}^n$. A vector sequence $(\mathbf{z}_i)$ is an $S$-walk if and only if $\mathbf{z}_{i+1} - \mathbf{z}_i$ is an element of $S$ for all $i$. Gerver and Ramsey showed in 1979 that for $S\subset \mathbb{Z}^3$ there exists an infinite $S$-walk in which no $5^{11} + 1=48{\small,}828{\small,}126$ points are collinear. Here, w

Pierluigi Contucci, Emanuele Mingione, Godwin Osabutey

We study a mean-field spin model with three- and two-body interactions. The equilibrium measure for large volumes is shown to have three pure states, the phases of the model. They include the two with opposite magnetization and an unpolarized one with zero magnetization, merging at the critical point. We prove that the central limit theorem holds for a suita

Tristan Bice, Noé de Rancourt, Jan Hubička, Matěj Konečný

Oscillation stability is an important concept in Banach space theory which happens to be closely connected to discrete Ramsey theory. For example, Gowers proved oscillation stability for the Banach space $c_0$ using his now famous Ramsey theorem for $\mathrm{FIN}_k$ as the key ingredient. We develop the theory behind this connection and introduce the notion

Cheng Zhang

Creating multiple-choice questions to assess reading comprehension of a given article involves generating question-answer pairs (QAPs) and adequate distractors. We present two methods to tackle the challenge of QAP generations: (1) A deep-learning-based end-to-end question generation system based on T5 Transformer with Preprocessing and Postprocessing Pipeli

Santosh V. Lohakare, Krishna Rathore, B. Mishra

In this paper, we have analyzed the geometrical and dynamical parameters of $\mathcal{F}(R, \mathcal{G})=\alpha R^2 \mathcal{G}^\beta$ cosmological model, ($R$, $\mathcal{G}$ being the Ricci scalar and Gauss-Bonnet invariant respectively), constraining the parameters through the cosmological data sets. It is exhibited that the model admits a viable radiation

Jack Borthwick, Eric Gourgoulhon, Jean-Philippe Nicolas

The starting point of this work was an intriguing similarity between the behaviour of fields near a degenerate horizon and near the infinity of an asymptotically flat spacetime, as revealed by the scattering theory for Dirac fields in the ``exterior'' region of the extreme Kerr - de Sitter black hole, developed by one of the authors (JB). However, in that si

Anees Peringal, Mohamad Chehadeh, Igor Boiko, Yahya Zweiri

In this paper, we present a real-time system identification method based on relay feedback testing with applications to multirotor unmanned aerial vehicles. The proposed identification method provides an alternative to the expensive lab testing of certain UAV dynamic parameters. Moreover, it has the advantage of identifying the parameters that get changed th

Timothy M. Chan, Virginia Vassilevska Williams, Yinzhan Xu

In this paper we carefully combine Fredman's trick [SICOMP'76] and Matou\v{s}ek's approach for dominance product [IPL'91] to obtain powerful results in fine-grained complexity: - Under the hypothesis that APSP for undirected graphs with edge weights in $\{1, 2, \ldots, n\}$ requires $n^{3-o(1)}$ time (when $\omega=2$), we show a variety of conditional lower

Arvind F. Gupta, Megan Bedell

With dedicated exoplanet surveys underway for multiple extreme precision radial velocity (EPRV) instruments, the near-future prospects of RV exoplanet science are promising. These surveys' generous time allocations are expected to facilitate the discovery of Earth analogs around bright, nearby Sun-like stars. But survey success will depend critically on the

Arvind F. Gupta, Jonathan M. Jackson, Guillaume Hebrard, Andrea S. Lin

We report the discovery of TOI-4127 b, a transiting, Jupiter-sized exoplanet on a long-period ($P = 56.39879^{+0.00010}_{-0.00010}$ d), high-eccentricity orbit around a late F-type dwarf star. This warm Jupiter was first detected and identified as a promising candidate from a search for single-transit signals in TESS Sector 20 data, and later characterized a

Adnan Qayyum, Muhammad Bilal, Muhammad Hadi, Paweł Capik

Recent advancements in technology, particularly in machine learning (ML), deep learning (DL), and the metaverse, offer great potential for revolutionizing surgical science. The combination of artificial intelligence and extended reality (AI-XR) technologies has the potential to create a surgical metaverse, a virtual environment where surgeries can be planned

Albert Pumarola, Artsiom Sanakoyeu, Lior Yariv, Ali Thabet

Surface reconstruction has been seeing a lot of progress lately by utilizing Implicit Neural Representations (INRs). Despite their success, INRs often introduce hard to control inductive bias (i.e., the solution surface can exhibit unexplainable behaviours), have costly inference, and are slow to train. The goal of this work is to show that replacing neural

Alexander M. Berenbeim, Iain J. Cruickshank, Susmit Jha, Robert H. Thomson

Quantitative characterizations and estimations of uncertainty are of fundamental importance in optimization and decision-making processes. Herein, we propose intuitive scores, which we call certainty and doubt, that can be used in both a Bayesian and frequentist framework to assess and compare the quality and uncertainty of predictions in (multi-)classificat

Benjamin Arroquia-Cuadros, Nestor Sanchez, Vicent Gomez, Pere Blay

Context: Given the current big data era in Astronomy, machine learning based methods have being applied over the last years to identify or classify objects like quasars, galaxies and stars from full sky photometric surveys. Aims: Here we systematically evaluate the performance of Random Forests (RF) in classifying quasars using either magnitudes or colours,

Hanlin Mo, Hongxiang Hao, Guoying Zhao

Rotational motion blur caused by the circular motion of the camera or/and object is common in life. Identifying objects from images affected by rotational motion blur is challenging because this image degradation severely impacts image quality. Therefore, it is meaningful to develop image invariant features under rotational motion blur and then use them in p

Chun-Tso Tsai, Seyed Mohammadhossein Tabatabaee, Stéphan Plassart, Jean-Yves Le Boudec

Time-sensitive networks, as in the context of IEEE-TSN and IETF-Detnet, require bounds on worst-case delays. Various network analysis tools compute such bounds; these tools are based on different methods and provide delay bounds that are all valid but may differ; furthermore, it is generally not known which tool will provide the best bound. To obtain the bes

Songyuan Zhang, Yumeng Xiu, Guannan Qu, Chuchu Fan

Developing stable controllers for large-scale networked dynamical systems is crucial but has long been challenging due to two key obstacles: certifiability and scalability. In this paper, we present a general framework to solve these challenges using compositional neural certificates based on ISS (Input-to-State Stability) Lyapunov functions. Specifically, w

Liangqi Yuan, Houlin Chen, Robert Ewing, Jia Li

Passive radio frequency (PRF)-based indoor positioning systems (IPS) have attracted researchers' attention due to their low price, easy and customizable configuration, and non-invasive design. This paper proposes a PRF-based three-dimensional (3D) indoor positioning system (PIPS), which is able to use signals of opportunity (SoOP) for positioning and also ca

Meiirkhan B. Borikhanov, Berikbol T. Torebek

The paper studies the large-time behavior of solutions to the Robin problem for PDEs with critical nonlinearities. For the considered problems, nonexistence results are obtained, which complements the interesting recent results by Ikeda et al. [J. Differential Equations, 269 (2020), no. 1, 563-594], where critical cases were left open. Moreover, our results

Daniel Nakhimovich, Yinglong Miao, Kostas E. Bekris

This work proposes a robot task planning framework for retrieving a target object in a confined workspace among multiple stacked objects that obstruct the target. The robot can use prehensile picking and in-workspace placing actions. The method assumes access to 3D models for the visible objects in the scene. The key contribution is in achieving desirable pr

Barnabás Szabó

Assuming the Generalised Riemann Hypothesis, we prove a sharp upper bound on moments of shifted Dirichlet $L$-functions. We use this to obtain conditional upper bounds on high moments of theta functions. Both of these results strengthen theorems of Munsch, who proved almost sharp upper bounds for these quantities. The main new ingredient of our proof comes f

Wenpin Tang, Yuming Paul Zhang

Motivated by numerical challenges in first-order mean field games (MFGs) and the weak noise theory for the Kardar-Parisi-Zhang equation, we consider the problem of vanishing viscosity approximations for MFGs. We provide the first results on the convergence rate to the vanishing viscosity limit in mean field games, with a focus on the dimension dependence of

Z. E. Musielak

A nonrelativistic quantum theory of dark matter particles in a spherical halo is developed by using a new asymmetric equation, which is complementary to the Schr\"odinger equation. The theory predicts that each dark matter halo has its core and envelope with very distinct physical properties. The core is free of any quantum structure and its dark matter part

Kaique Matias de Andrade Roberto, Hugo Rafael de Oliveira Ribeiro, Hugo Luiz Mariano, Kaique Ribeiro Prates Santos

Motivated by some recent developments in abstract theories of quadratic forms, we start to develop in this work an expansion of Linear Algebra to multivalued structures (a multialgebraic structure is essentially an algebraic structure but endowed with some multivalued operations). We introduce and study matrices and determinants over a commutative superrings

Sena A. Matsui, Tsutomu T. Takeuchi, Kai T. Kono, Suchetha Cooray

MASTER OT J004527.52+503213.8 (hereafter MASTER J004527) is a dwarf nova discovered by the MASTER project in 2013. At 18:20 UTC on 24 October 2020, brightening of this object was reported to vsnet-alert (24843 by Denisenko). This was the second report of a superoutburst after its discovery. Photometric observations were made using the 23.5-cm Schmidt-Cassegr

Zhuoyue Lyu

What if a clock could do more than tell time - what if it could look around? This project explores the conceptualization, design, and construction of a timepiece with visual perception capabilities, featuring three types of human-time interactions. Informal observations during a demonstration highlight its unique user experiences. https://www.zhuoyuelyu.com/

Debasish Jana, Sven Malama, Sriram Narasimhan, Ertugrul Taciroglu

Many networks, such as transportation, power, and water distribution, can be represented as graphs. Crucial challenge in graph representations is identifying the importance of graph edges and their influence on overall network efficiency and information flow performance. For example, important edges in a transportation network are those roads that, when affe

Daewon Chung, Weiyan Huang, Jean Carlo Moraes, María Cristina Pereyra

In this paper, we provide necessary and sufficient conditions on a triple of weights $(u,v,w)$ so that the $t$-Haar multipliers $T^t_{w,\sigma}$, $t\in \R$, %defined in \cite{P} when $\sigma=1$, are uniformly (on the choice of signs $\sigma$) bounded from $L^2(u)$ into $L^2(v)$. These dyadic operators have symbols $s(x,I)=\sigma_I\,(w(x)/\langle w\rangle_I)^

Albert Chern, Sadashige Ishida

We present a formula for the signed area of a spherical polygon via prequantization. In contrast to the traditional formula based on the Gauss-Bonnet theorem that requires measuring angles, the new formula mimics Green's theorem and is applicable to a wider range of degenerate spherical curves and polygons.

Mani Valleti, Rama K. Vasudevan, Maxim A. Ziatdinov, Sergei V. Kalinin

The ability of deep learning methods to perform classification and regression tasks relies heavily on their capacity to uncover manifolds in high-dimensional data spaces and project them into low-dimensional representation spaces. In this study, we investigate the structure and character of the manifolds generated by classical variational autoencoder (VAE) a

Sarah E. Marzen, Paul M. Riechers, James P. Crutchfield

Recurrent neural networks are used to forecast time series in finance, climate, language, and from many other domains. Reservoir computers are a particularly easily trainable form of recurrent neural network. Recently, a "next-generation" reservoir computer was introduced in which the memory trace involves only a finite number of previous symbols. We explore

Maciej Sypetkowski

In the majority of GAN architectures, the latent space is defined as a set of vectors of given dimensionality. Such representations are not easily interpretable and do not capture spatial information of image content directly. In this work, we define a family of spatial latent spaces for StyleGAN2, capable of capturing more details and representing images th

Zhongmin Zhang, Zhiyue Lu

At stationary environmental conditions, a catalyst's reaction rates may be restricted by thermodynamic laws, and certain performances can never be achieved (e.g., catalysts can not change the free energy difference between reactants and products). However, it has been reported that if environments change rapidly, catalysts can be driven away from stationary

Yufan Huang, C. Seshadhri, David F. Gleich

We study a new connection between a technical measure called $\mu$-conductance that arises in the study of Markov chains for sampling convex bodies and the network community profile that characterizes size-resolved properties of clusters and communities in social and information networks. The idea of $\mu$-conductance is similar to the traditional graph cond

Matheus S. M. de Sousa, Wei Chen

The opacity of graphene is known to be approximately given by the fine-structure constant $\alpha$ times $\pi$. We point out the fact that the opacity is roughly independent of the frequency and polarization of the light can be attributed to the topological charge of the Dirac points. As a result, one can literally see the topological charge by naked eyes fr

Dian Chen, Jie Li, Vitor Guizilini, Rares Ambrus

3D object detection from visual sensors is a cornerstone capability of robotic systems. State-of-the-art methods focus on reasoning and decoding object bounding boxes from multi-view camera input. In this work we gain intuition from the integral role of multi-view consistency in 3D scene understanding and geometric learning. To this end, we introduce VEDet,

G. Rüdiger, M. Schultz

The excitation conditions of the magnetorotational instability are studied for axially unbounded Taylor-Couette flows of various gap widths between the cylinders. The cylinders are considered as made from both perfect-conducting or insulating material and the conducting fluid with a finite but small magnetic Prandtl number rotates with a quasi-Keplerian velo

Cody J. Shakespeare, Jason H. Steffen

Tidally locked worlds provide a unique opportunity for constraining the probable climates of certain exoplanets. They are unique in that few exoplanet spin and obliquity states are known or will be determined in the near future: both of which are critical in modeling climate. A recent study shows the dynamical conditions present in the TRAPPIST-1 system make

Anirban Banerjee, Amitesh Sarkar

Here we study the spectral radii of some linear hypergraphs, that is, the maximum moduli of the eigenvalues of their corresponding adjacency matrices. We determine the hypertrees having the largest to seventh-largest spectral radii. The hypertrees with the largest and the second-largest spectral radii among all those with a given diameter are identified here

Simon Diemert, Jens H Weber

Large Language Models (LLMs), such as GPT-3, have demonstrated remarkable natural language processing and generation capabilities and have been applied to a variety tasks, such as source code generation. This paper explores the potential of integrating LLMs in the hazard analysis for safety-critical systems, a process which we refer to as co-hazard analysis

Fatemeh Mosaiyebzadeh, Seyedamin Pouriyeh, Reza M. Parizi, Quan Z. Sheng

Advancements in wearable medical devices in IoT technology are shaping the modern healthcare system. With the emergence of the Internet of Healthcare Things (IoHT), we are witnessing how efficient healthcare services are provided to patients and how healthcare professionals are effectively used AI-based models to analyze the data collected from IoHT devices

Yuzhou Chen, Yulia R. Gel

Graph neural networks (GNNs) have demonstrated a significant success in various graph learning tasks, from graph classification to anomaly detection. There recently has emerged a number of approaches adopting a graph pooling operation within GNNs, with a goal to preserve graph attributive and structural features during the graph representation learning. Howe

Junaed Younus Khan, Gias Uddin

Code example is a crucial part of good documentation. It helps the developers to understand the documentation easily and use the corresponding code unit (e.g., method) properly. However, many official documentation still lacks (good) code example and it is one of the common documentation issues as found by several studies. Hence in this paper, we consider au

David Rozenberszki, Or Litany, Angela Dai

3D instance segmentation is fundamental to geometric understanding of the world around us. Existing methods for instance segmentation of 3D scenes rely on supervision from expensive, manual 3D annotations. We propose UnScene3D, the first fully unsupervised 3D learning approach for class-agnostic 3D instance segmentation of indoor scans. UnScene3D first gener

Mehmet Mert Sahin, Onur Dizdar, Bruno Clerckx, Huseyin Arslan

Rate-splitting multiple access (RSMA) is a multiple access technique generalizing conventional techniques, such as, space-division multiple access (SDMA), non-orthogonal multiple access (NOMA), and physical layer multi-casting, which aims to address multi-user interference (MUI) in multiple-input multiple-output (MIMO) systems. In this study, we leverage the

James D. Currie

Good words are binary words avoiding factors 11 and 1001, and patterns 0000 and 00010100. We show that good words bear the same relationship to the period-doubling sequence that overlap-free words bear to the Thue-Morse sequence. We prove an analogue of Fife's Theorem for good words, exhibit the lexicographically least and greatest infinite good words, and d

Arzu Kurt

The non-Markovianity of open quantum system dynamics is often associated with the bidirectional interchange of information between the system and its environment, and it is thought to be a resource for various quantum information tasks. We have investigated the non-Markovianity of the dynamics of a two-state system driven by continuous time random walk-type

Rickard Brüel-Gabrielsson, Tongzhou Wang, Manel Baradad, Justin Solomon

Despite dropout's ubiquity in machine learning, its effectiveness as a form of data augmentation remains under-explored. We address two key questions: (i) When is dropout effective as an augmentation strategy? (ii) Is dropout uniquely effective under these conditions? To explore these questions, we propose Deep Augmentation, a network- and modality-agnostic

Haithem Turki, Jason Y. Zhang, Francesco Ferroni, Deva Ramanan

We extend neural radiance fields (NeRFs) to dynamic large-scale urban scenes. Prior work tends to reconstruct single video clips of short durations (up to 10 seconds). Two reasons are that such methods (a) tend to scale linearly with the number of moving objects and input videos because a separate model is built for each and (b) tend to require supervision v

Kilian Batzner, Lars Heckler, Rebecca König

Detecting anomalies in images is an important task, especially in real-time computer vision applications. In this work, we focus on computational efficiency and propose a lightweight feature extractor that processes an image in less than a millisecond on a modern GPU. We then use a student-teacher approach to detect anomalous features. We train a student net

E. Rongione, L. Baringthon, D. She, G. Patriarche

The helicity of 3D topological insulator surface states has drawn significant attention in spintronics owing to spin-momentum locking where the carriers' spin is oriented perpendicular to their momentum. This property can provide an efficient method to convert charge currents into spin currents, and vice-versa, through the Rashba-Edelstein effect. However, e

Carter Davis

What is the demand elasticity of statistical arbitrageurs that invest according to the advice of modern cross-sectional asset pricing models? Thirteen models from the literature exhibit strikingly inelastic demand, in contrast to classical models that rely on statistical arbitrageurs to create elastic market demand for assets. This inelasticity arises from t

Jingyang Zhang, Nathan Inkawhich, Randolph Linderman, Ryan Luley

Building up reliable Out-of-Distribution (OOD) detectors is challenging, often requiring the use of OOD data during training. In this work, we develop a data-driven approach which is distinct and complementary to existing works: Instead of using external OOD data, we fully exploit the internal in-distribution (ID) training set by utilizing generative models

Yuki Moritani, Akiko Kawachi, Atsuo T. Okazaki, Sho Chimasu

We have performed the near-infrared photometric monitoring observations of two TeV gamma-ray binaries with O-stars (LS 5039 and 1FGL J1018.6-5856), using IRSF/SIRIUS at SAAO, in order to study the stellar parameters and their perturbations caused by the binary interactions. The whole orbital phase was observed multiple times and no significant variabilities

Luiz Henrique Lopes dos Santos

Regarding the famous Sea Battle Argument, which Aristotle presents in De Interpretatione 9, there has never been a general agreement not only about its correctness but also, and mainly, about what the argument really is. According to the most natural reading of the chapter, the argument appeals to a temporal concept of truth and concludes that not every stat

Piotr Kubala, Michał Cieśla

Recent experimental discoveries of novel nematic types with polar order, including ferroelectric nematic and splay nematic have brought the resurgence of the interest in polar and modulated phases. One of the most important factors that is widely believed to be crucial for the formation of the new phases is the pear-like shape of the mesogenic molecules. Suc

Soumyadip Sarkar

Quantum generative models use the intrinsic probabilistic nature of quantum mechanics to learn and reproduce complex probability distributions. In this paper, we present an implementation of a 3-qubit quantum circuit Born machine trained to model a 3-bit Gaussian distribution using a Kullback-Leibler (KL) divergence loss and parameter-shift gradient optimiza

Stefan Stojku, Bojana Ilic, Igor Salom, Magdalena Djordjevic

We consider the problem of including a finite number of scattering centers in dynamical energy loss and classical DGLV formalism. Previously, either one or an infinite number of scattering centers were considered in energy loss calculations, while attempts to relax such approximations were largely inconclusive or incomplete. In reality, however, the number o

Jue Wang, Wentao Zhu, Pichao Wang, Xiang Yu

Effective modeling of complex spatiotemporal dependencies in long-form videos remains an open problem. The recently proposed Structured State-Space Sequence (S4) model with its linear complexity offers a promising direction in this space. However, we demonstrate that treating all image-tokens equally as done by S4 model can adversely affect its efficiency an

Ruoxi Chen, Haibo Jin, Jinyin Chen, Haibin Zheng

Deep neural networks (DNNs) are vulnerable to adversarial examples, which may lead to catastrophe in security-critical domains. Numerous detection methods are proposed to characterize the feature uniqueness of adversarial examples, or to distinguish DNN's behavior activated by the adversarial examples. Detections based on features cannot handle adversarial e

Sergio R. Fenley, Rafael Potrie

We show that if $\mathcal{F}_1$ and $\mathcal{F}_2$ are two transverse minimal foliations on $M = T^1S$ then either they intersect in an Anosov foliation or there exists a Reeb-surface in the intersection foliation. The existence of a Reeb surface is incompatible with partially hyperbolic foliations so we deduce from this that certain partially hyperbolic di

Yunfan Gao, Tao Sheng, Youlin Xiang, Yun Xiong

Large language models (LLMs) have demonstrated their significant potential to be applied for addressing various application tasks. However, traditional recommender systems continue to face great challenges such as poor interactivity and explainability, which actually also hinder their broad deployment in real-world systems. To address these limitations, this

Fernando Richter Vidal, Naghmeh Ivaki, Nuno Laranjeiro

Smart contracts are nowadays at the core of most blockchain systems, as they specify and allow an agreement between entities that wish to perform a transaction. As any computer program, smart contracts are subject to the presence of residual faults, including severe security vulnerabilities, which require that the vulnerable contract is terminated in the blo

Pedro Carvalho, Jessica Mégane, Nuno Lourenço, Penousal Machado

This work proposes Adaptive Facilitated Mutation, a self-adaptive mutation method for Structured Grammatical Evolution (SGE), biologically inspired by the theory of facilitated variation. In SGE, the genotype of individuals contains a list for each non-terminal of the grammar that defines the search space. In our proposed mutation, each individual contains a

Dávid Magyar, Máté Cserép, Zoltán Vincellér, Attila D. Molnár

One of the biggest environmental problems of our time is the increase in illegal landfills in forests, rivers, on river banks and other secluded places. In addition, waste in rivers causes damage not only locally, but also downstream, both in the water and washed ashore. Large islands of waste can also form at hydroelectric power stations and dams, and if th

Damião J. Araújo, Ginaldo S. Sá, José Miguel Urbano

This paper establishes sharp local regularity estimates for viscosity solutions of fully nonlinear parabolic free boundary problems with singular absorption terms. The main difficulties are due to the blow-up of the source along the free boundary and the lack of a variational structure. The proof combines the power of the Ishii-Lions method with intrinsicall

J. Pohlodek, H. Alsmeier, B. Morabito, C. Schlauch

Integrating measurements and historical data can enhance control systems through learning-based techniques, but ensuring performance and safety is challenging. Robust model predictive control strategies, like stochastic model predictive control, can address this by accounting for uncertainty. Gaussian processes are often used but have limitations with larger

Gabriele Giovannini, Yuzhu Cui, Kazuhiro Hada, Kunwoo Yi

We present here the East Asia to Italy Nearly Global VLBI (EATING VLBI) project. How this project started and the evolution of the international collaboration between Korean, Japanese, and Italian researchers to study compact sources with VLBI observations is reported. Problems related to the synchronization of the very different arrays and technical details

Made Raharja Surya Mahadi, Nugraha Priya Utama

Currently, text-to-image synthesis uses text encoder and image generator architecture. Research on this topic is challenging. This is because of the domain gap between natural language and vision. Nowadays, most research on this topic only focuses on producing a photo-realistic image, but the other domain, in this case, is the language, which is less concent

Stamatis Alexandropoulos, Christos Sakaridis, Petros Maragos

Semantic segmentation is a fundamental task in visual scene understanding. We focus on the supervised setting, where ground-truth semantic annotations are available. Based on knowledge about the high regularity of real-world scenes, we propose a method for improving class predictions by learning to selectively exploit information from neighboring pixels. In

Christian Mitsch

This paper focuses on a decentralized profit-center firm that uses negotiated transfer pricing as an instrument to coordinate the production process. Moreover, the firm's headquarters gives its divisions full authority over operating decisions and it is assumed that each division can additionally make an upfront investment decision that enhances the value of

Mensah Folly-Gbetoula

In this paper, we present formula solutions of a family of difference equations of higher order. We discuss the periodic nature of the solutions and we investigate the stability character of the equilibrium points. We utilize Lie symmetry analysis as part of our approach together with some number theoretic functions. Our findings generalize certain results i

Soumya Jana, Sayan Kar

Black holes with dyonic charges in Einstein-Maxwell-dilaton-axion supergravity theory are revisited in the context of black hole shadows. We consider static as well as rotating (namely the dyonic Kerr-Sen) black holes. The matter stress-energy tensor components, sourced by the Maxwell, axion and dilaton fields satisfy the standard energy conditions. The anal

Ayvaz I. Davletkhanov, Aram A. Mkrtchyan, Dmitry A. Chermoshentsev, Mikhail V. Shashkov

Optical waveguides, covered with thin films, which transmittance can be controlled by external action, are widely used in various applications from optical modulators to saturable absorbers. It is natural to suggest that the waveguide losses will be proportional to the covering material absorption. We demonstrate that under certain conditions this simple ass

Shan Huang, Wensheng Gan, Jinbao Miao, Xuming Han

Finding high-importance patterns in data is an emerging data mining task known as High-utility itemset mining (HUIM). Given a minimum utility threshold, a HUIM algorithm extracts all the high-utility itemsets (HUIs) whose utility values are not less than the threshold. This can reveal a wealth of useful information, but the precise needs of users are not wel

Christopher William Davis

In a groundbreaking work A. Levine proved the surprising result that there exist knots in homology spheres which are not smoothly concordant to any knot in $S^3$, even if one allows for concordances in homology cobordisms. Since then subsequent works due to Hom-Levine-Lidman and Zhou have strengthened this result showing that there are many knots in homology

Qianyong Wu, Jiang Hu

Community detection is a fundamental problem in complex network data analysis. Though many methods have been proposed, most existing methods require the number of communities to be the known parameter, which is not in practice. In this paper, we propose a novel goodness-of-fit test for the stochastic block model. The test statistic is based on the linear spe

Paulo D. Cordaro, Stefan Fürdös

In 1980 M{\'e}tivier characterized the analytic (and Gevrey) hypoellipticity of $L^2$-solvable partial linear differential operators by a-priori estimates. In this note we extend this characterization to ultradifferentiable hypoellipticity with respect to Denjoy-Carleman classes given by suitable weight sequences. We also discuss the case when the solutions

Jiacheng Li, Chang Chen, Zhen Cheng, Zhiwei Xiong

The widespread usage of high-definition screens on edge devices stimulates a strong demand for efficient image restoration algorithms. The way of caching deep learning models in a look-up table (LUT) is recently introduced to respond to this demand. However, the size of a single LUT grows exponentially with the increase of its indexing capacity, which restri

Chao Chen, Yu-Shen Liu, Zhizhong Han

It is vital to infer signed distance functions (SDFs) from 3D point clouds. The latest methods rely on generalizing the priors learned from large scale supervision. However, the learned priors do not generalize well to various geometric variations that are unseen during training, especially for extremely sparse point clouds. To resolve this issue, we present

Francois-Baptiste Cartiaux, Alain Ehrlacher, Frederic Legoll, Alex Libal

The standard stress-based approach to fatigue is based on the use of S-N curves. They are obtained by applying cyclic loading of constant amplitude $S$ to identical and standardised specimens until they fail. The S-N curves actually depend on a reference probability $p$: for a given cycle amplitude $S$, they provide the number of cycles at which a proportion

T. R. Garaev

Let $K$ be the graph on vertices $\{1, 2, 3, 4, 5\}$, and having all edges except $(4, 5)$. A continuous map $f:K\to \R^2$ is called an \emph{almost embedding} if $f$-images of non-adjacent edges are disjoint. Take the winding numbers of the $f$-image of the oriented cycle $(1, 2, 3)$ around $f(4)$ and around $f(5)$. We prove that the difference of these num

Adarsh Jagan Sathyamoorthy, Kasun Weerakoon, Tianrui Guan, Mason Russell

We propose a novel method for autonomous legged robot navigation in densely vegetated environments with a variety of pliable/traversable and non-pliable/untraversable vegetation. We present a novel few-shot learning classifier that can be trained on a few hundred RGB images to differentiate flora that can be navigated through, from the ones that must be circ

Bastian Wittmann, Johannes C. Paetzold, Chinmay Prabhakar, Daniel Rueckert

Link prediction algorithms aim to infer the existence of connections (or links) between nodes in network-structured data and are typically applied to refine the connectivity among nodes. In this work, we focus on link prediction for flow-driven spatial networks, which are embedded in a Euclidean space and relate to physical exchange and transportation proces

Junjie Luo, Jianjun Zhao

Quantum Intermediate Representation (QIR) is a Microsoft-developed, LLVM-based intermediate representation for quantum program compilers. QIR aims to provide a general solution for quantum program compilers independent of front-end languages and back-end hardware, thus avoiding duplicate development of intermediate representations and compilers. Since it is