May 2022 arXiv papers — page 158

Jiling Cao, Jeong-Hoon Kim, Xi Li, Wenjun Zhang

In this paper, we derive closed-form formulas of first-order approximation for down-and-out barrier and floating strike lookback put option prices under a stochastic volatility model, by using an asymptotic approach. To find the explicit closed-form formulas for the zero-order term and the first-order correction term, we use Mellin transform. We also conduct

Chenglong Lv, Ali Esamdin, Javier Pascual-Granado, Taozhi Yang

We propose that KIC 1573174 is a quadruple-mode $δ$ Scuti star with pulsation amplitudes between those of the HADS (high-amplitude Delta Scuti star) group and average low-amplitude pulsators. The radial modes detected in this star provide a unique opportunity to exploit asteroseismic techniques up to their limits. Detailed frequency analysis is given for the

David Wallace

I criticize the widely-defended view that the quantum measurement problem is an example of underdetermination of theory by evidence: more specifically, the view that the unmodified, unitary quantum formalism (interpreted following Everett) is empirically indistinguishable from Bohmian Mechanics and from dynamical-collapse theories like the GRW or CSL theorie

Valentin Vie, Ryan Sheatsley, Sophia Beyda, Sushrut Shringarputale

Planning algorithms are used in computational systems to direct autonomous behavior. In a canonical application, for example, planning for autonomous vehicles is used to automate the static or continuous planning towards performance, resource management, or functional goals (e.g., arriving at the destination, managing fuel fuel consumption). Existing plannin

Peter Lynch, Michael Mackey

We define an extension of parity from the integers to the rational numbers. Three parity classes are found -- even, odd and `none'. Using the 2-adic valuation, we partition the rationals into subgroups with a rich algebraic structure. The natural density provides a means of distinguishing the sizes of countably infinite sets. The Calkin-Wilf tree has a r

Pierfrancesco Guarino, Gabriel Ziegler

We study the behavioral implications of Rationality and Common Strong Belief in Rationality (RCSBR) with contextual assumptions allowing players to entertain misaligned beliefs, i.e., players can hold beliefs concerning their opponents' beliefs where there is no opponent holding those very beliefs. Taking the analysts' perspective, we distinguish the

Diane Donovan, Asha Rao, Elif Üsküplü, E. Ş. Yazıcı

We give a framework for generalizing LDPC code constructions that use Transversal Designs or related structures such as mutually orthogonal Latin squares. Our construction offers a broader range of code lengths and codes rates. Similar earlier constructions rely on the existence of finite fields of order a power of a prime. In contrast the LDPC codes constru

Rohan Chandra, Mingyu Wang, Mac Schwager, Dinesh Manocha

We present a novel approach for risk-aware planning with human agents in multi-agent traffic scenarios. Our approach takes into account the wide range of human driver behaviors on the road, from aggressive maneuvers like speeding and overtaking, to conservative traits like driving slowly and conforming to the right-most lane. In our approach, we learn a mapp

Sreetama Das, Jingfu Zhang, Stefano Martina, Dieter Suter

One of the most promising applications of quantum computing is the processing of graphical data like images. Here, we investigate the possibility of realizing a quantum pattern recognition protocol based on swap test, and use the IBMQ noisy intermediate-scale quantum (NISQ) devices to verify the idea. We find that with a two-qubit protocol, swap test can eff

Behrang Forghani, Keivan Mallahi-Karai

We define and study a new compactification, called the height compactification of the horospheric product of two infinite trees. We will provide a complete description of this compactification. In particular, we show that this compactification is isomorphic to the Busemann compactification when all the vertices of both trees have degrees of at least three, w

A. Clevinger, J. Corkish, K. Aryal, V. Dexheimer

In this contribution, we describe new chemically-equilibrated charge-neutral hybrid equations of state for neutron stars. They present a first-order phase transition to quark matter and differentiate by the particle population considered and how these particles interact. While some equations of state contain just nucleons and up, down-quarks, others also con

Eran Nevo, Simion Tarabykin

We prove that every triangulation of either of the torus, projective plane and Klein bottle, contains a vertex-spanning planar Laman graph as a subcomplex. Invoking a result of Kir{á}ly, we conclude that every $1$-skeleton of a triangulation of a surface of nonnegative Euler characteristic has a rigid realization in the plane using at most 26 locations for t

Guang-Yao Zhao, Jose L. Gomez, Antonio Fuentes, Thomas P. Krichbaum

We present the first very-long-baseline interferometric (VLBI) observations of the blazar OJ287 carried out jointly with the Global Millimeter VLBI Array (GMVA) and the phased Atacama Large Millimeter/submillimeter Array (ALMA) at 3.5 mm on April 2, 2017. Participation of phased-ALMA not only has improved the GMVA north-south resolution by a factor of ~3, bu

Saira Bano, Achilles Machumilane, Lorenzo Valerio, Pietro Cassarà

Automatic traffic classification is increasingly becoming important in traffic engineering, as the current trend of encrypting transport information (e.g., behind HTTP-encrypted tunnels) prevents intermediate nodes from accessing end-to-end packet headers. However, this information is crucial for traffic shaping, network slicing, and Quality of Service (QoS)

Ion Santra, Urna Basu, Sanjib Sabhapandit

We study the dynamics of a Brownian motion with a diffusion coefficient which evolves stochastically. We first study this process in arbitrary dimensions and find the scaling form and the corresponding scaling function of the position distribution. We find that the tails of the distribution have exponential tails with a ballistic scaling. We then introduce t

Ning Wang, Han Liu, Diego Klabjan

We develop an abstractive summarization framework independent of labeled data for multiple heterogeneous documents. Unlike existing multi-document summarization methods, our framework processes documents telling different stories instead of documents on the same topic. We also enhance an existing sentence fusion method with a uni-directional language model t

C. J. P. Flanagan, S. P. Ellingsen, A. A. Cole

We report the results of a sensitive search for water maser emission in the Local Group Galaxy NGC 6822 with the Karl G. Jansky Very Large Array. The observations provide tentative single-epoch detections of four candidates, associated with two infrared-bright star formation regions (Hubble I/III and Hubble IV). The candidate maser detections are all offset

Xiaohan Zhang, Bharat Lal Bhatnagar, Vladimir Guzov, Sebastian Starke

Humans interact with an object in many different ways by making contact at different locations, creating a highly complex motion space that can be difficult to learn, particularly when synthesizing such human interactions in a controllable manner. Existing works on synthesizing human scene interaction focus on the high-level control of action but do not cons

Nadia Creignou, Arnaud Durand, Heribert Vollmer

We refine the complexity landscape for enumeration problems by introducing very low classes defined by using Boolean circuits as enumerators. We locate well-known enumeration problems, e.g., from graph theory, Gray code enumeration, and propositional satisfiability in our classes. In this way we obtain a framework to distinguish between the complexity of dif

Michael Ridley

The objective of this paper is to explore the opportunities for human information behaviour research to inform and influence the field of machine learning and the resulting machine information behaviour. Using the development of foundation models in machine learning as an example, the paper illustrates how human information behaviour research can bring to ma

Mahmoud Raeisi, Mehdi Mahdavi, Ali Mohammad Doost Hosseini

This paper investigates a multi-hop cognitive radio network in terms of end-to-end bit delivery. The network exploits backscatter communication (BackCom) and harvest-then-transmit (HTT) mode in a hybrid manner. Such a network can be used in internet of things (IoT) applications in which IoT users coexist with a primary network (PN) and use the primary spectr

Pranay Bhardwaj, S. M. Zafaruddin

Recent research has focused on single antenna radio-frequency (RF) and terahertz (THz) wireless systems to mix the access link with the backhaul. In this paper, we evaluate the performance of a mixed RF-THz system employing multiple antenna-assisted access point (AP) for the RF link and single-antenna THz transmissions. We employ an equal gain combining (EGC

Jiahui Chen, Yuchi Qiu, Rui Wang, Guo-Wei Wei

Due to its high transmissibility, Omicron BA.1 ousted the Delta variant to become a dominating variant in late 2021 and was replaced by more transmissible Omicron BA.2 in March 2022. An important question is which new variants will dominate in the future. Topology-based deep learning models have had tremendous success in forecasting emerging variants in the

Xingbo Pu, Antonio Palermo, Alessandro Marzani

In this work, we investigate the dynamic behavior and the topological properties of quasiperiodic elastic metasurfaces, namely arrays of mechanical oscillators arranged over the free surface of an elastic half-space according to a quasiperiodic spatial distribution. An ad-hoc multiple scattering formulation is developed to describe the dynamic interaction be

P. Ciarletta, G. Pozzi, D. Riccobelli

Initially stressed plates are widely used in modern fabrication techniques, such as additive manufacturing and UV lithography, for their tunable morphology by application of external stimuli. In this work, we propose a formal asymptotic derivation of the Föppl-von Kármán equations for an elastic plate with initial stresses, using the constitutive theory of n

Manuel Enrique Cuesta, Rohit Chhiber, Sohom Roy, Joshua Goodwill

We examine the radial evolution of correlation lengths perpendicular (\(λ_C^{\perp}\)) and parallel (\(λ_C^{\parallel}\)) to the magnetic-field direction, computed from solar wind magnetic-field data measured by Parker Solar Probe (PSP) during its first eight orbits, Helios 1, Advanced Composition Explorer (ACE), WIND, and Voyager 1 spacecraft. Correlation l

Akshat Goel, Denise Gorse

While deep learning models have seen recent high uptake in the geosciences, and are appealing in their ability to learn from minimally processed input data, as black box models they do not provide an easy means to understand how a decision is reached, which in safety-critical tasks especially can be problematical. An alternative route is to use simpler, more

Eleonora Giunchiglia, Mihaela Catalina Stoian, Thomas Lukasiewicz

In recent years, there has been an increasing interest in exploiting logically specified background knowledge in order to obtain neural models (i) with a better performance, (ii) able to learn from less data, and/or (iii) guaranteed to be compliant with the background knowledge itself, e.g., for safety-critical applications. In this survey, we retrace such w

Ulf Peschel, Thomas Lettau, Stefanie Gräfe, Kurt Busch

We develop a quantum mechanical theory to describe the optical response of semiconductor nanostructures with a particular emphasis on higher-order harmonic Generation. Based on a tight-binding approach we take all two-particle correlations into account thus describing the creation, evolution and annihilation of electron and holes. In the limiting case of bul

Taishu Kayanoki, Yasushi Fukazawa

The {\it Fermi} satellite has detected $\sim$60 radio galaxies (RGs). In this study, we investigate the difference in the properties of X-ray spectra between GeV-loud RGs and GeV-quiet RGs. Our sample comprises 68 objects: 36 RGs detected with {\it Fermi} and 32 RGs not detected with gamma rays. We analyzed the X-ray spectra of these 68 objects using data fr

Arthur G. Rattew, Bálint Koczor

Quantum computers will be able solve important problems with significant polynomial and exponential speedups over their classical counterparts, for instance in option pricing in finance, and in real-space molecular chemistry simulations. However, key applications can only achieve their potential speedup if their inputs are prepared efficiently. We effectivel

Rahul Vaze

With multiple identical unit speed servers, the online problem of scheduling jobs that migrate between two phases, limitedly parallelizable or completely sequential, and choosing their respective speeds to minimize the total flow time is considered. In the limited parallelizable regime, allocating $k$ servers to a job, the speed extracted is $k^{1/α}, α>1$,

Wenzheng Zhao

Precise and timely traffic flow prediction plays a critical role in developing intelligent transportation systems and has attracted considerable attention in recent decades. Despite the significant progress in this area brought by deep learning, challenges remain. Traffic flows usually change dramatically in a short period, which prevents the current methods

Minglei Yang, Guannan Zhang, Diego Del-Castillo-Negrete, Yanzhao Cao

This work presents a probabilistic scheme for solving semilinear nonlocal diffusion equations with volume constraints and integrable kernels. The nonlocal model of interest is defined by a time-dependent semilinear partial integro-differential equation (PIDE), in which the integro-differential operator consists of both local convection-diffusion and nonlocal

Veronika S. Grach, Anton V. Artemyev, Andrei G. Demekhov, Xiao-Jia Zhang

Relativistic electron losses in Earth's radiation belts are usually attributed to electron resonant scattering by electromagnetic waves. One of the most important wave mode for such scattering is the electromagnetic ion cyclotron (EMIC) mode. Within the quasi-linear diffusion framework, the cyclotron resonance of relativistic electrons with EMIC waves re

Marek Kozoň, Ad Lagendijk, Matthias Schlottbom, Jaap J. W. van der Vegt

Functional defects in periodic media confine waves - acoustic, electromagnetic, electronic, spin, etc. - in various dimensions, depending on the structure of the defect. While defects are usually modelled by a superlattice with a typical band-structure representation of energy levels, determining the confinement associated with a given band is highly non-tri

Jussi Karlgren

Genres can be understood in many different ways. They are often perceived as a primarily sociological construction, or, alternatively, as a stylostatistically observable objective characteristic of texts. The latter view is more common in the research field of information and language technology. These two views can be quite compatible and can inform each ot

Jussi Karlgren

This chapter argues for more informed target metrics for the statistical processing of stylistic variation in text collections. Much as operationalised relevance proved a useful goal to strive for in information retrieval, research in textual stylistics, whether application oriented or philologically inclined, needs goals formulated in terms of pertinence, r

Viktor Petrov, Andrei Semenov

In this paper we generalize Atsuyama result on the geometry of symmetric spaces of type EIII to the case of arbitrary fields of characteristic not 2 or 3. As an application we prove a variant of "Chain lemma" for microweight tori in groups of type E6.

Zhe Wang, Jimei Yang, Charless Fowlkes

Nonparametric based methods have recently shown promising results in reconstructing human bodies from monocular images while model-based methods can help correct these estimates and improve prediction. However, estimating model parameters from global image features may lead to noticeable misalignment between the estimated meshes and image evidence. To addres

Yang Zhou, Zhanhao He, Keyu Lu, Guanhong Wang

Video-based action recognition is one of the most popular topics in computer vision. With recent advances of selfsupervised video representation learning approaches, action recognition usually follows a two-stage training framework, i.e., self-supervised pre-training on large-scale unlabeled sets and transfer learning on a downstream labeled set. However, ca

Nitesh Goyal, Ian Kivlichan, Rachel Rosen, Lucy Vasserman

Machine learning models are commonly used to detect toxicity in online conversations. These models are trained on datasets annotated by human raters. We explore how raters' self-described identities impact how they annotate toxicity in online comments. We first define the concept of specialized rater pools: rater pools formed based on raters' self-de

Simone Giombi, Elizabeth Helfenberger, Himanshu Khanchandani

We study operators with large charge $j$ in the $d$-dimensional $O(N)$ model with long range interactions that decrease with the distance as $1/r^{d+s}$, where $s$ is a continuous parameter. We consider the double scaling limit of large $N$, large $j$ with $j/N=\hat{j}$ fixed, and identify the semiclassical saddle point that captures the two-point function o

Yuanbo Hou, Bo Kang, Wout Van Hauwermeiren, Dick Botteldooren

In real life, acoustic scenes and audio events are naturally correlated. Humans instinctively rely on fine-grained audio events as well as the overall sound characteristics to distinguish diverse acoustic scenes. Yet, most previous approaches treat acoustic scene classification (ASC) and audio event classification (AEC) as two independent tasks. A few studie

Samuel Cole, Zachary Cowell, John M. Nunley, R. Alan Seals

We document the age-race-gender intersectionality in the distribution of occupational tasks in the United States. We also investigate how the task content of work changed from the early-2000s to the late-2010s for different age-race/ethnicity-gender groups. Using the Occupation Information Network (O*NET) and pooled cross-sectional data from the American Com

Adam W. Marcus

We use techniques from finite free probability to analyze matrix processes related to eigenvalues, singular values, and generalized singular values of random matrices. The models we use are quite basic and the analysis consists entirely of expected characteristic polynomials. A number of our results match known results in random matrix theory, however our ma

Markus Frembs, Andreas Döring

Gleason's theorem [A. Gleason, J. Math. Mech., \textbf{6}, 885 (1957)] is an important result in the foundations of quantum mechanics, where it justifies the Born rule as a mathematical consequence of the quantum formalism. Formally, it presents a key insight into the projective geometry of Hilbert spaces, showing that finitely additive measures on the p

Niclas Boehmer, Piotr Faliszewski, Rolf Niedermeier, Stanisław Szufa

Motivated by putting empirical work based on (synthetic) election data on a more solid mathematical basis, we analyze six distances among elections, including, e.g., the challenging-to-compute but very precise swap distance and the distance used to form the so-called map of elections. Among the six, the latter seems to strike the best balance between its com

Dominique Désérable

{\em Arrowhead} and {\em diamond} are two hierarchical Cayley graphs defined on the triangular grid. In their undirected version, they are isomorphic and merely define two distinct representations of the same graph. This paper gives the expression of their diameter, in the oriented and non--oriented case. It also displays the full distribution of antipodals.

A. A. Reshetnyak

The structure of a cubic Lagrangian vertex is clarified for irreducible fields of helicities $s_1, s_2, s_3$ in a $d$-dimensional Minkowski space. An explicit form of the operator $\mathcal{Z}_j$ entering the vertex in a non-multiplicative way (examined in arXiv:2105.12030[hep-th] for $j=1$) is obtained. The solution is found within the BRST approach with co

Nelson Martins-Ferreira

Semibiproducts of monoids are introduced here as a common generalization to biproducts (of abelian groups) and to semidirect products (of groups) for exploring a wide class of monoid extensions. More generally, abstract semibiproducts exist in any concrete category over sets in which map addition is meaningful thus reinterpreting Mac Lane's relative bipr

Liuhui Deng, Roger Hsiao, Arnab Ghoshal

In this paper, we investigate how the output representation of an end-to-end neural network affects multilingual automatic speech recognition (ASR). We study different representations including character-level, byte-level, byte pair encoding (BPE), and byte-level byte pair encoding (BBPE) representations, and analyze their strengths and weaknesses. We focus

Songlin Yang, Wei Liu, Kewei Tu

Hidden Markov Models (HMMs) and Probabilistic Context-Free Grammars (PCFGs) are widely used structured models, both of which can be represented as factor graph grammars (FGGs), a powerful formalism capable of describing a wide range of models. Recent research found it beneficial to use large state spaces for HMMs and PCFGs. However, inference with large stat

Horst Alzer, Omran Kouba

We give two new proofs of the Chaundy-Bullard formula $$ (1-x)^{n+1} \sum_{k=0}^m {n+k\choose k} x^k +x^{m+1}\sum_{k=0}^n {m+k\choose k} (1-x)^k=1 $$ and we prove the "twin formula" $$ \frac{ (1-x)^{(n+1)}}{(n+1)!} \sum_{k=0}^m \frac{n+1}{n+k+1} \frac{ x^{(k)}}{k!} + \frac{ x^{(m+1)}}{(m+1)!} \sum_{k=0}^n \frac{m+1}{m+k+1} \frac{ (1-x)^{(k)}}{k!}=1,

Zhixian Yang, Renliang Sun, Xiaojun Wan

k-nearest-neighbor machine translation (NN-MT), proposed by Khandelwal et al. (2021), has achieved many state-of-the-art results in machine translation tasks. Although effective, NN-MT requires conducting NN searches through the large datastore for each decoding step during inference, prohibitively increasing the decoding cost and thus leading to the difficu

M. Veselsky, V. Petousis, Ch. C. Moustakidis, G. A. Souliotis

The synthesis of hyper-heavy elements is investigated under conditions simulating neutron star environment. The Constrained Molecular Dynamics (CoMD) approach is used to simulate low energy collisions of extremely n-rich nuclei. A new type of the fusion barrier due to a "neutron wind" is observed when the effect of neutron star environment (screening

Jian Li, Yong Liu, Yingying Zhang

Recent theoretical studies illustrated that kernel ridgeless regression can guarantee good generalization ability without an explicit regularization. In this paper, we investigate the statistical properties of ridgeless regression with random features and stochastic gradient descent. We explore the effect of factors in the stochastic gradient and random feat

Joel D. Day, Vijay Ganesh, Nathan Grewal, Florin Manea

We investigate the properties of formal languages expressible in terms of formulas over quantifier-free theories of word equations, arithmetic over length constraints, and language membership predicates for the classes of regular, visibly pushdown, and deterministic context-free languages. In total, we consider 20 distinct theories and decidability questions

Alejandro Gangui

The Jesuit missions in South America were an important and unique advance in Christian evangelisation on the continent until the expulsion of the Order in 1767. Although the history and cultural aspects of these missions and their most iconic buildings have been extensively studied, the archaeoastronomy of the Guaraní peoples of the Province of Paraquaria (P

Wei-jie Fu

In this paper we present an overview on recent progress in studies of QCD at finite temperature and densities within the functional renormalization group (fRG) approach. The fRG is a nonperturbative continuum field approach, in which quantum, thermal and density fluctuations are integrated in successively with the evolution of the renormalization group (RG)

Federico Pigozzi

Biological agents possess bodies that are mostly of soft tissues. Researchers have resorted to soft bodies to investigate Artificial Life (ALife)-related questions; similarly, a new era of soft-bodied robots has just begun. Nevertheless, because of their infinite degrees of freedom, soft bodies pose unique challenges in terms of simulation, control, and opti

Razin A. Shaikh, Stefano Gogioso

We introduce a novel compositional description of Feynman diagrams, with well-defined categorical semantics as morphisms in a dagger-compact category. Our chosen setting is suitable for infinite-dimensional diagrammatic reasoning, generalising the ZX calculus and other algebraic gadgets familiar to the categorical quantum theory community. The Feynman diagra

Adel Daoud, Connor T. Jerzak, Richard Johansson

Causal inference methods that control for text-based confounders are becoming increasingly important in the social sciences and other disciplines where text is readily available. However, these methods rely on a critical assumption that there is no treatment leakage: that is, the text only contains information about the confounder and no information about tr

Abdelhamid Meziani

For a real analytic complex vector field $L$ in an open set of $\mathbb{R}^2$, with local first integrals that are open maps, we attach a number $μ\ge 1$ (obtained through Lojasiewicz inequalities) and show that the equation $Lu=f$ has bounded solutions when $f\in L^p$ with $p>1+μ$. We also establish a similarity principle between the bounded solutions of th

Calvin Flack, Emin Ucer, Charles Parker Smith, Mithat Kisacikoglu

Present grid infrastructure is unprepared for the wide-scale integration of electric vehicles (EVs). Real-time grid simulation and hardware testing are necessary for the fast and accurate development of EV charging control strategies that are applicable in the field to mitigate their adverse effects. To help facilitate this goal, in this study, we develop a

Md Rasel Hossen, Sonia Akter Ema, Krzysztof Bolejko, Geraint F. Lewis

An essential aspect of cosmic voids is that these underdense regions provide complementary information about the properties of our Universe. Unlike dense regions, voids are avoided by matter and are less contaminated by baryonic processes. The first step to understanding the properties of cosmic voids is to correctly infer their mass profiles. In the literat

Dinuka Piyadigama, Guhanathan Poravi

This research explores the methods that NFTs can be recommended to people who interact with NFT-marketplaces to explore NFTs of preference and similarity to what they have been searching for. While exploring past methods that can be adopted for recommendations, the use of NFT traits for recommendations has been explored. The outcome of the research highlight

Qian Zuo, Yimin Wei, Hua Xiang

Total least squares (TLS) methods have been widely used in data fitting. Compared with the least squares method, for TLS problem we takes into account not only the observation errors, but also the errors in the measurement matrix. This is more realistic in practical applications. For the large-scale discrete ill-posed problem $Ax \approx b$, we introduce the

Christiane Helling

This invited review for young researchers presents key ideas on cloud formation as key part for virtual laboratories for exoplanet atmospheres. The basic concepts are presented, followed by utilising a time-scale analysis to disentangle process hierarchies. The kinetic approach to cloud formation modelling is described in some detail to allow the discussion

Josua Unger

This thesis is devoted to the study of Lie bialgebra and Hopf algebra structures related to certain versions of non-commutative geometry constructed on infinite-dimensional Lie algebras that arise in the context of asymptotic symmetries of spacetime. We prove a number of theorems about cohomology groups that aid the classification of the Lie bialgebras and e

Wilton O. Júnior, Mauricio S. da Cruz, Andre Brasil Vieira Wyzykowski, Arnaldo Bispo de Jesus

This paper aims to present how the application of Natural Language Processing (NLP) and data augmentation techniques can improve the performance of a neural network for better detection of fake news in the Portuguese language. Fake news is one of the main controversies during the growth of the internet in the last decade. Verifying what is fact and what is f

Fatemeh Seifan, Lutz Schröder, Dirk Pattinson

A logic has uniform interpolation if its formulas can be projected down to given subsignatures, preserving all logical consequences that do not mention the removed symbols; the weaker property of (Craig) interpolation allows the projected formula -- the interpolant -- to be different for each logical consequence of the original formula. These properties are

Jaime Carracedo-Cosme, Rubén Pérez

Frequency modulation (FM) Atomic Force Microscopy (AFM) with metal tips functionalized with a CO molecule at the tip apex has provided access to the internal structure of molecules with totally unprecedented resolution. We propose a model to extract the chemical information from those AFM images in order to achieve a complete identification of the imaged mol

Ehud Karpas, Omri Abend, Yonatan Belinkov, Barak Lenz

Huge language models (LMs) have ushered in a new era for AI, serving as a gateway to natural-language-based knowledge tasks. Although an essential element of modern AI, LMs are also inherently limited in a number of ways. We discuss these limitations and how they can be avoided by adopting a systems approach. Conceptualizing the challenge as one that involve

María Alejandra Alvarez, Ivan Kaygorodov

This paper is devoted to the complete algebraic and geometric classification of complex $4$-dimensional nilpotent weakly associative, complex $4$-dimensional symmetric Leibniz algebras, and complex $5$-dimensional nilpotent symmetric Leibniz algebras. In particular, we proved that the variety of complex $4$-dimensional symmetric Leibniz algebras has no Vergn

Jędrzej Olkowski, Michał Pilipczuk, Mateusz Rychlicki, Karol Węgrzycki

We revisit classic string problems considered in the area of parameterized complexity, and study them through the lens of dynamic data structures. That is, instead of asking for a static algorithm that solves the given instance efficiently, our goal is to design a data structure that efficiently maintains a solution, or reports a lack thereof, upon updates i

Raghav R, Adarsh Vemali, Rajdeep Mukherjee

Structured Sentiment Analysis (SSA) deals with extracting opinion tuples in a text, where each tuple (h, e, t, p) consists of h, the holder, who expresses a sentiment polarity p towards a target t through a sentiment expression e. While prior works explore graph-based or sequence labeling-based approaches for the task, we in this paper present a novel unifie

Jeric Garrido, Jose Perico Esguerra

In this paper, a surface geometric constraint approach is used in designing the orbits of a solar sail. We solve the solar sail equation of motion by obtaining a generalized Laplace-Runge-Lenz (LRL) vector with the assumption that the cone angle is constant throughout the mission. A family of orbit equation solutions can then be specified by defining a const

Muhammad Mansur Zubairu, Abdullahi Umar, Muhammad Jada Aliyu

Let $[n]=\{1,2,\ldots,n\}$ be a finite chain and let $\mathcal{CT}_{n}$ be the semigroup of full contractions on $[n]$. Denote $\mathcal{ORCT}_{n}$ and $\mathcal{OCT}_{n}$ to be the subsemigroup of order preserving or reversing and the subsemigroup of order preserving full contractions, respectively. It was shown in [17] that the collection of all regular el

Joaquín Puig, Federico Elías, Jazmín Aragón Sanchez, Raúl Cortés Maldonado

Disordered hyperuniform materials with vanishing long-wavelength density fluctuations are attracting attention due to their unique physical properties. In these systems, the large-scale density fluctuations are strongly suppressed as in a perfect crystal, even though the system can be disordered like a liquid. Yet, hyperuniformity can be affected by the diff

Tingdi Ren, Haiyong Xu, Gangyi Jiang, Mei Yu

Underwater Image Enhancement (UIE) technology aims to tackle the challenge of restoring the degraded underwater images due to light absorption and scattering. To address problems, a novel U-Net based Reinforced Swin-Convs Transformer for the Underwater Image Enhancement method (URSCT-UIE) is proposed. Specifically, with the deficiency of U-Net based on pure

Jagdish Chand Bansal, Nikhil Sethi, Ogbonnaya Anicho, Atulya Nagar

Individual agents in natural systems like flocks of birds or schools of fish display a remarkable ability to coordinate and communicate in local groups and execute a variety of tasks efficiently. Emulating such natural systems into drone swarms to solve problems in defence, agriculture, industry automation and humanitarian relief is an emerging technology. H

Yuhang Li, Yi Luo, Bijie Bai, Aydogan Ozcan

Imaging through diffusive media is a challenging problem, where the existing solutions heavily rely on digital computers to reconstruct distorted images. We provide a detailed analysis of a computer-free, all-optical imaging method for seeing through random, unknown phase diffusers using diffractive neural networks, covering different deep learning-based tra

Dong Xing, Qian Zheng, Qianhui Liu, Gang Pan

Recent advances in deep reinforcement learning (DRL) have largely promoted the performance of adaptive traffic signal control (ATSC). Nevertheless, regarding the implementation, most works are cumbersome in terms of storage and computation. This hinders their deployment on scenarios where resources are limited. In this work, we propose TinyLight, the first D

Kesu Wang, Meng Yan, He Zhang, Haibo Hu

Program classification can be regarded as a high-level abstraction of code, laying a foundation for various tasks related to source code comprehension, and has a very wide range of applications in the field of software engineering, such as code clone detection, code smell classification, defects classification, etc. The cross-language program classification

Yuri Trakhinin, Tao Wang

We study the local well-posedness for an interface with surface tension that separates a perfectly conducting inviscid fluid from a vacuum. The fluid flow is governed by the equations of three-dimensional ideal compressible magnetohydrodynamics (MHD), while the vacuum magnetic and electric fields are supposed to satisfy the pre-Maxwell equations. The fluid a

Hideyuki Miyahara, Yiyou Chen, Vwani Roychowdhury, Louis-S. Bouchard

Quantum information stored in a qubit is rapidly lost to the environment. The realization of robust qubits is one of the most important challenges in quantum computing. Herein we propose to embed a logical qubit within the manifold of a qudit as a scheme to preserve quantum information over extended periods of time. Under identical conditions (e.g. decoheren

Kyu Hyun Kim, Daniel J. Caplan, Sangwook Kang

We consider a regression modeling of the quantiles of residual life, remaining lifetime at a specific time. We propose a smoothed induced version of the existing non-smooth estimating equations approaches for estimating regression parameters. The proposed estimating equations are smooth in regression parameters, so solutions can be readily obtained via stand

Yan Jiang, Wenqi Cui, Baosen Zhang, Jorge Cortés

Frequency control plays a pivotal role in reliable power system operations. It is conventionally performed in a hierarchical way that first rapidly stabilizes the frequency deviations and then slowly recovers the nominal frequency. However, as the generation mix shifts from synchronous generators to renewable resources, power systems experience larger and fa

Yuchan Yang, Wen-Biao Han, Qianyun Yun, Peng Xu

In this work, we discuss the improvement that the joint network of LISA and Taiji could provide on exploring two kinds of black hole formation mechanisms. For astrophysical origin, we consider light seed and heavy seed scenarios, and generate populations accordingly. We find that the joint network has the potential to observe growing light seeds in the range

Rhoan Lee, Soohyun Park, Soyi Jung, Joongheon Kim

Augmented reality (AR) is one of emerging applications in modern multimedia systems research. Due to intensive time-consuming computations for AR visualization in mobile devices, quality-aware real-time computing under delay constraints is essentially required. Inspired by Lyapunov optimization framework, this paper proposes a time-average quality maximizati

Onel L. A. López, Glauber Brante, Richard D. Souza, Markku Juntti

Grant-free protocols exploiting compressed sensing (CS) multi-user detection (MUD) are appealing for solving the random access problem in massive machine-type communications (mMTC) with sporadic device activity. Such protocols would greatly benefit from a prior deterministic knowledge of the sparsity level, i.e., instantaneous number of simultaneously active

Hanwen Kang, Xing Wang, Chao Chen

Accurate depth-sensing plays a crucial role in securing a high success rate of robotic harvesting in natural orchard environments. Solid-state LiDAR (SSL), a recently introduced LiDAR technique, can perceive high-resolution geometric information of the scenes, which can be potential utilised to receive accurate depth information. Meanwhile, the fusion of the

Nick Edelen, Luca Spolaor, Bozhidar Velichkov

We prove a strong maximum principle for minimizers of the one-phase Alt-Caffarelli functional. We use this to construct a Hardt-Simon-type foliation associated to any 1-homogenous global minimizer.

GyeongTaek Lee

The aim of reinforcement learning (RL) is to allow the agent to achieve the final goal. Most RL studies have focused on improving the efficiency of learning to achieve the final goal faster. However, the RL model is very difficult to modify an intermediate route in the process of reaching the final goal. That is, the agent cannot be under control to achieve

Hamid Hezari

We find new polynomial upper bounds for the size of nodal sets of eigenfunctions when the Riemannian manifold has a Gevrey or quasianalytic regularity.

Qing Yang, Yingzhi Tian

A conjecture of Luo, Tian and Wu (2022) says that for every positive integer $k$ and every finite tree $T$ with bipartition $X$ and $Y$ (denote $t = \max\{|X|,|Y |\})$, every $k$-connected bipartite graph $G$ with $δ(G) \geq k + t$ contains a subtree $T' \cong T$ such that $κ(G-V (T')) \geq k$. In this paper, we confirm this conjecture for caterpilla

Alexandru Kristály, Ildikó I. Mezei, Károly Szilák

We investigate a large class of elliptic differential inclusions on non-compact complete Riemannian manifolds which involves the Laplace-Beltrami operator and a Hardy-type singular term. Depending on the behavior of the nonlinear term and on the curvature of the Riemannian manifold, we guarantee non-existence and existence/multiplicity of solutions for the s

Vishal Venkatraman, Shantanu Pal, Zahra Jadidi, Alireza Jolfaei

Trust is a fundamental concept in large-scale distributed systems like the Internet of Things (IoT). Trust helps to resolve choices into a decision. However, the trust calculation depends on the amount of uncertainty present in data sources. Trust in an IoT network is proportional to the amount of uncertainty generated by such sources as hardware malfunction

Shutong Ni

There exists a phenomenon that subjectivity highly lies in the daily evaluation process. Our research primarily concentrates on a multi-person evaluation system with anomaly detection to minimize the possible inaccuracy that subjective assessment brings. We choose the two-stage screening method, which consists of rough screening and score-weighted Kendall-$τ