October 2022 arXiv papers — page 63

Xian Qian, Kai Hu, Jiaqiang Wang, Yifeng Liu

This report describes our VolcTrans system for the WMT22 shared task on large-scale multilingual machine translation. We participated in the unconstrained track which allows the use of external resources. Our system is a transformerbased multilingual model trained on data from multiple sources including the public training set from the data track, NLLB data

Maximilian Mozes, Bennett Kleinberg, Lewis D. Griffin

Adversarial examples in NLP are receiving increasing research attention. One line of investigation is the generation of word-level adversarial examples against fine-tuned Transformer models that preserve naturalness and grammaticality. Previous work found that human- and machine-generated adversarial examples are comparable in their naturalness and grammatic

Suleyman Gokhun Tanyer, Paul Dent, Murtaza Ali, Curtis Davis

The problem of multi-objective design of sparse MIMO arrays for better multitarget detection capabilities is considered. A novel approach for efficient utilization of the antenna design resources; namely, the number of available array elements, and array aperture are studied for the angular beamforming performance metrics such as, beam width, the peak to sid

Fabiano F. Santos, Oleksii Sokoliuk, Alexander Baransky

This work investigates the influence of a probe string on the complexity of braneworld according to the CA (Complexity equals action) conjecture within the Horndeski gravity. In the current study, it is considered that scalar fields that source Horndeski gravity has a spatial dependence. In addition, our system contains a particle moving on the boundary, whi

Daniel Puzzuoli, Sophia Fuhui Lin, Moein Malekakhlagh, Emily Pritchett

We develop general purpose algorithms for computing and utilizing both the Dyson series and Magnus expansion, with the goal of facilitating numerical perturbative studies of quantum dynamics. To enable broad applications to models with multiple parameters, we phrase our algorithms in terms of multivariable sensitivity analysis, for either the solution or the

Stanislaw Szymanowicz, Virginia Estellers, Tadas Baltrusaitis, Matthew Johnson

Delivering immersive, 3D experiences for human communication requires a method to obtain 360 degree photo-realistic avatars of humans. To make these experiences accessible to all, only commodity hardware, like mobile phone cameras, should be necessary to capture the data needed for avatar creation. For avatars to be rendered realistically from any viewpoint,

Brian Kwan, Lin Liu, David Strong, H. Irene Su

Identifying prognostic factors for disease progression is a cornerstone of medical research. Repeated assessments of a marker outcome are often used to evaluate disease progression, and the primary research question is to identify factors associated with the longitudinal trajectory of this marker. Our work is motivated by diabetic kidney disease (DKD), where

Miguel A. Ramirez, Sangyoung Yoon, Ernesto Damiani, Hussam Al Hamadi

Most recent studies have shown several vulnerabilities to attacks with the potential to jeopardize the integrity of the model, opening in a few recent years a new window of opportunity in terms of cyber-security. The main interest of this paper is directed towards data poisoning attacks involving label-flipping, this kind of attacks occur during the training

Erfan Hamdi, Rasool Dezhkam, Amir Shamloo, Ali Mashhadian

There have been multiple methods proposed to calculate lift coefficients in microfluidic channels. One of the most used methods is using Direct Numerical Simulation. DNS is a very accurate yet computationally expensive method. DNS computations comprise most of the time consumed on a microfluidic simulation done by commercial software. This paper proposes a u

Sunsheng Gu, Vahdat Abdelzad, Krzysztof Czarnecki

Explainable AI (XAI) methods are frequently applied to obtain qualitative insights about deep models' predictions. However, such insights need to be interpreted by a human observer to be useful. In this paper, we aim to use explanations directly to make decisions without human observers. We adopt two gradient-based explanation methods, Integrated Gradients (

Daniel LeJeune, Jiayu Liu, Reinhard Heckel

Machine learning systems are often applied to data that is drawn from a different distribution than the training distribution. Recent work has shown that for a variety of classification and signal reconstruction problems, the out-of-distribution performance is strongly linearly correlated with the in-distribution performance. If this relationship or more gen

Desh Raj, Junteng Jia, Jay Mahadeokar, Chunyang Wu

Neural transducers have achieved human level performance on standard speech recognition benchmarks. However, their performance significantly degrades in the presence of cross-talk, especially when the primary speaker has a low signal-to-noise ratio. Anchored speech recognition refers to a class of methods that use information from an anchor segment (e.g., wa

Daniel Crawford, Eric Mascot, Makoto Shimizu, Roland Wiesendanger

Magnet-superconductor hybrid (MSH) systems are a key platform for custom-designed topological superconductors. Ideally, the ends of a one-dimensional MSH structure will host Majorana zero-modes (MZMs), the fundamental unit of topological quantum computing. However, some of the experiments with ferromagnetic chains show a more complicated picture. Due to tiny

Vladimir Dragović, Borislav Gajić, Božidar Jovanović

We consider the nonholonomic systems of $n$ homogeneous balls $\mathbf B_1,\dots,\mathbf B_n$ with the same radius $r$ that are rolling without slipping about a fixed sphere $\mathbf S_0$ with center $O$ and radius $R$. In addition, it is assumed that a dynamically nonsymmetric sphere $\mathbf S$ with the center that coincides with the center $O$ of the fixe

Eanna E. Flanagan, Ibrahim Shehzad

Gauge and gravitational theories in asymptotically flat settings possess infinitely many conserved charges associated with large gauge transformations or diffeomorphisms that are nontrivial at infinity. To what extent do these charges constrain the scattering in these theories? It has been claimed in the literature that the constraints are trivial, due to a

Yao Rong, Tobias Leemann, Thai-trang Nguyen, Lisa Fiedler

Explainable AI (XAI) is widely viewed as a sine qua non for ever-expanding AI research. A better understanding of the needs of XAI users, as well as human-centered evaluations of explainable models are both a necessity and a challenge. In this paper, we explore how HCI and AI researchers conduct user studies in XAI applications based on a systematic literatu

Frances H. Cashman, Andrew J. Fox, Bart P. Wakker, Trisha Ashley

We characterize the chemical and physical conditions in an outflowing high-velocity cloud in the inner Galaxy. We report a super-solar metallicity of [O/H] = $+0.36\pm0.12$ for the high-velocity cloud at $v_\mathrm{LSR}$ = 125.6 km s$^{-1}$ toward the star HD 156359 ($l$ = 328.$^{\circ}$7, $b$ = $-$14.$^{\circ}$5, $d$ = 9 kpc, $z$ = $-$2.3 kpc). Using archiv

Sara L. Almonacid-Uribe, Oliverio J. Santana, Daniel Hernández-Sosa, David Freire-Obregón

An article published on Medical News Today in June 2022 presented a fundamental question in its title: Can an earlobe crease predict heart attacks? The author explained that end arteries supply the heart and ears. In other words, if they lose blood supply, no other arteries can take over, resulting in tissue damage. Consequently, some earlobes have a diagona

Jonathan A. Jackson, Nicolas Romeo, Alexander Mietke, Keaton J. Burns

The cell nucleus is enveloped by a complex membrane, whose wrinkling has been implicated in disease and cellular aging. The biophysical dynamics and spectral evolution of nuclear wrinkling during multicellular development remain poorly understood due to a lack of direct quantitative measurements. Here, we combine live-imaging experiments, theory, and simulat

F. Carnesecchi, G. Vignola, N. Agrawal, A. Alici

In this paper evidence that the increased response of SiPM sensors to the passage of charged particles is related mainly to Cherenkov light produced in the protection layer is reported. The response and timing properties of sensors with different protection layers have been studied.

Annette Möller, Ann Cathrice George, Jürgen Groß

Methods based on machine learning become increasingly popular in many areas as they allow models to be fitted in a highly-data driven fashion, and often show comparable or even increased performance in comparison to classical methods. However, in the area of educational sciences the application of machine learning is still quite uncommon. This work investiga

Haotian Fu, Shangqun Yu, Michael Littman, George Konidaris

We propose a model-based lifelong reinforcement-learning approach that estimates a hierarchical Bayesian posterior distilling the common structure shared across different tasks. The learned posterior combined with a sample-based Bayesian exploration procedure increases the sample efficiency of learning across a family of related tasks. We first derive an ana

Todd E. Humphreys, Peter A. Iannucci, Zacharias Komodromos, Andrew M. Graff

We develop a technique for blind signal identification of the Starlink downlink signal in the 10.7 to 12.7 GHz band and present a detailed picture of the signal's structure. Importantly, the signal characterization offered herein includes the exact values of synchronization sequences embedded in the signal that can be exploited to produce pseudorange measure

Xingang Guo, Bin Hu

Direct policy search has been widely applied in modern reinforcement learning and continuous control. However, the theoretical properties of direct policy search on nonsmooth robust control synthesis have not been fully understood. The optimal $\mathcal{H}_\infty$ control framework aims at designing a policy to minimize the closed-loop $\mathcal{H}_\infty$ n

Saber Jafarpour, Samuel Coogan

In this note, we study monotone dynamical systems with respect to polyhedral cones. Using the half-space representation and the vertex representation, we propose three equivalent conditions to certify monotonicity of a dynamical system with respect to a polyhedral cone. We then introduce the notion of gauge norm associated with a cone and provide closed-from

Ramesh Mainali, Jane R. Rigby, John Chisholm, Matthew Bayliss

We analyze spectra of a gravitationally lensed galaxy, known as the Sunburst Arc, that is leaking ionizing photons, also known as the Lyman continuum (LyC). Magnification from gravitational lensing permits the galaxy to be spatially resolved into one region that leaks ionizing photons, and several that do not. Rest-frame ultraviolet and optical spectra from

Reza Mohammadpour

In this paper, we study the size of the level sets of all Lyapunov exponents. For typical cocycles, we establish a variational relation between the topological entropy of the level sets of Lyapunov exponents and the topological pressure of the generalized singular value function.

Jiawei Zang, Jie Wang, Antoine Georges, Jennifer Cano

We construct a Wannier basis for twisted bilayer graphene that is projected only from the Bloch functions of the twisted bilayer flat bands. The $C_3$ and $C_{2} \mathcal{T}$ symmetries act locally on the Wannier functions while the Wannier function charge density is strongly peaked at the triangular sites and becomes fully sublattice-polarized in the chiral

Nadav Cohen, Itzik Klein

Autonomous underwater vehicles (AUVs) are employed for marine applications and can operate in deep underwater environments beyond human reach. A standard solution for the autonomous navigation problem can be obtained by fusing the inertial navigation system and the Doppler velocity log sensor (DVL). The latter measures four beam velocities to estimate the ve

Peiyao Sheng, Xuechao Wang, Sreeram Kannan, Kartik Nayak

Currently there exist many blockchains with weak trust guarantees, limiting applications and participation. Existing solutions to boost the trust using a stronger blockchain, e.g., via checkpointing, requires the weaker blockchain to give up sovereignty. In this paper, we propose a family of protocols in which multiple blockchains interact to create a combin

Farbod Ekbatani, Yiding Feng, Rad Niazadeh

We initiate the study of two-sided online resource allocation with costly cancellations. Our focus is on edge-weighted online bipartite matching (and several of its extensions), where nodes arrive online and request offline resources. In contrast to the classic literature, any fraction of an offline resource that was preallocated to an earlier online node ca

Leonid Frankfurt, Mark Strikman

In the current paper, we argue that the ground state of a hadron contains a significant perturbative quantum chromodynamics (pQCD) core as the result of color gauge invariance and of the values of chiral and gluon vacuum condensates. The evaluation within the method of dispersion sum rules (DSR) of the vacuum matrix elements of the correlator of local curren

Dmitri A. Ivanov

It is known that computing the permanent of the matrix $1+A$, where $A$ is a finite-rank matrix, requires a number of operations polynomial in the matrix size. Motivated by the boson-sampling proposal of restricted quantum computation, I extend this result to a generalization of the matrix permanent: an expectation value in a product of a large number of ide

Jinah Jeun, Gao Jun Wu, Sanjiva K. Lele

Twin-jet configuration allows two different scenarios to close the screech feedback. For each jet, there is one loop involving disturbances which originate in that jet and arrive at its own receptivity point in-phase (self-excitation). The other loop is associated with free-stream acoustic waves that radiate from the other jet, reinforcing the self-excited s

Megha Nawhal, Akash Abdu Jyothi, Greg Mori

Action anticipation involves predicting future actions having observed the initial portion of a video. Typically, the observed video is processed as a whole to obtain a video-level representation of the ongoing activity in the video, which is then used for future prediction. We introduce ANTICIPATR which performs long-term action anticipation leveraging segm

Nusrat Zahan, Shohanuzzaman Shohan, Dan Harris, Laurie Williams

Due to the ever-increasing security breaches, practitioners are motivated to produce more secure software. In the United States, the White House Office released a memorandum on Executive Order (EO) 14028 that mandates organizations provide self-attestation of the use of secure software development practices. The OpenSSF Scorecard project allows practitioners

Phil Saad, Douglas Stanford, Zhenbin Yang, Shunyu Yao

We conjecture a formula for the spectral form factor of a double-scaled matrix integral in the limit of large time, large density of states, and fixed temperature. The formula has a genus expansion with a nonzero radius of convergence. To understand the origin of this series, we compare to the semiclassical theory of "encounters" in periodic orbits. In Jacki

Hamid Mousavi, Mina Poozesh, Yousef Zamani

Let $G$ be a finite group, and let $x$ be an element of $G$. Denote by $\Sol_G(x)$ the set of all $y \in G$ such that the group generated by $x$ and $y$ is soluble. We investigate the influence of $\Sol_G(x)$ on the structure of $G$.

Jingxuan Tu, Kyeongmin Rim, Eben Holderness, James Pustejovsky

Understanding inferences and answering questions from text requires more than merely recovering surface arguments, adjuncts, or strings associated with the query terms. As humans, we interpret sentences as contextualized components of a narrative or discourse, by both filling in missing information, and reasoning about event consequences. In this paper, we d

Mike Nguyen, Charly Kirst, Nicole Mücke

We consider distributed learning using constant stepsize SGD (DSGD) over several devices, each sending a final model update to a central server. In a final step, the local estimates are aggregated. We prove in the setting of overparameterized linear regression general upper bounds with matching lower bounds and derive learning rates for specific data generat

Murat Çelik, Ali Baran Taşdemir, Lale Özkahya

Complex networks representing social interactions, brain activities, molecular structures have been studied widely to be able to understand and predict their characteristics as graphs. Models and algorithms for these networks are used in real-life applications, such as search engines, and recommender systems. In general, such networks are modelled by constru

Dan Friedman, Alexander Wettig, Danqi Chen

Many NLP datasets have been found to contain shortcuts: simple decision rules that achieve surprisingly high accuracy. However, it is difficult to discover shortcuts automatically. Prior work on automatic shortcut detection has focused on enumerating features like unigrams or bigrams, which can find only low-level shortcuts, or relied on post-hoc model inter

Rohaib Bhatti, Ali John Naqvi, Abdullah Tauqeer

Renewable sources of energy are the future due to the environmental problems caused by non-renewable sources to produce energy. The biggest issue with renewable energy sources is that the power produced by devices such as PV solar panels depend on many uncertain factors. These factors include Solar irradiation, wind speed, temperature, hours of sunlight per

Stephen Cantrell

We obtain orbital counting results for the class of strongly hyperbolic metrics on hyperbolic groups. To achieve this we combine ergodic theoretic techniques involving the Mineyev topological flow and symbolic dynamics. Our results apply to the Green metric associated to an admissible, finitely supported, symmetric random walk and to the Mineyev hat metric.

Sajjad Taravati, George V. Eleftheriades

Recently, nonreciprocal phase shifters have attracted a surge of interest thanks to the advent of nonreciprocal electromagnetic systems, such as nonreciprocal metasurfaces, nonreciprocal-beam antennas, and invisibility cloaks. To overcome the limitations associated with conventional technologies for realizing nonreciprocal phase shifters and gyrators, here w

Muhammad Gul Zain Ali Khan, Muhammad Ferjad Naeem, Luc Van Gool, Alain Pagani

Compositional zero-shot learning aims to recognize unseen compositions of seen visual primitives of object classes and their states. While all primitives (states and objects) are observable during training in some combination, their complex interaction makes this task especially hard. For example, wet changes the visual appearance of a dog very differently f

Emad Pirhadi, Juan M. Vanegas, Mithila Farin, Jeffrey W. Schertzer

Biological membranes are fundamental components of living organisms that play an undeniable role in their survival. Molecular dynamics (MD) serves as an essential computational tool for studying biomembranes on molecular and atomistic scales. The status quo of MD simulations of biomembranes studies a nanometer-sized membrane patch periodically extended under

Xin Yong, Ke Du

Cellular uptake of nanoplastics is instrumental in their environmental accumulation and transfer to humans through the food chain. Despite extensive studies using spherical plastic nanoparticles, the influence of the morphological characteristics of environmentally released nanoplastics is understudied. Using dissipative particle dynamics simulations, we mod

Jun Yang, Wenjie Xue, Sahar Ghavidel, Steven L. Waslander

6D pose estimation of textureless objects is a valuable but challenging task for many robotic applications. In this work, we propose a framework to address this challenge using only RGB images acquired from multiple viewpoints. The core idea of our approach is to decouple 6D pose estimation into a sequential two-step process, first estimating the 3D translat

S. Süleyman Kahraman, Kelvin Titimbo, Zhe He, Jung-Tsung Shen

The multi-stage Stern$\unicode{x2013}$Gerlach experiment conducted by Frisch and Segr\`e includes two cascaded quantum measurements with a nonadiabatic flipper in between. The Frisch and Segr\`e experiment has been modeled analytically by Majorana without the nuclear effect and subsequently revised by Rabi with the hyperfine interaction. However, the theoret

Sebastian Fajardo, Jetty Kleijn, Frank W. Takes, Geeske H. J. Langejans

Technologies and their production systems are used by archaeologists and anthropologists to study complexity of sociotechnical systems. However, there are several issues that hamper agreement about what constitutes complexity and how we can systematically compare the complexity of production systems. In this work, we propose a novel approach to assess the be

Lior Eldar

We consider a version of the nearest-codeword problem on finite fields $\mathbb{F}_q$ using the Manhattan distance, an analog of the Hamming metric for non-binary alphabets. Similarly to other lattice related problems, this problem is NP-hard even up to constant factor approximation. We show, however, that for $q = p^m$ where $p$ is small relative to the cod

Arghyadeep Basu, Nirupam Roy, Henrik Beuther, Jonas Syed

The neutral hydrogen 21 cm line is an excellent tracer of the atomic interstellar medium in the cold and the warm phases. Combined 21 cm emission and absorption observations are very useful to study the properties of the gas over a wide range of density and temperature. In this work, we have used 21 cm absorption spectra from recent interferometric surveys,

Stanisław M. Dubiel, Jan Żukrowski

The study was aimed at determination of the position of the Fe-rich border of the metastable miscibility gap (MMG) and of the solubility limit of Cr in iron at 858 K. Towards this end a Fe73.7Cr26.3 alloy was isothermally annealed at 858 K in vacuum up to 8144 hours and M\"ossbauer spectra were recorded at room temperature after every step of the annealing.

Ziyue Xiang, Paolo Bestagini, Stefano Tubaro, Edward J. Delp

Methods that can determine if two given video sequences are captured by the same device (e.g., mobile telephone or digital camera) can be used in many forensics tasks. In this paper we refer to this as "video device matching". In open-set video forensics scenarios it is easier to determine if two video sequences were captured with the same device than identi

E. G. C. Matias, K. S. Bezerra, A. H. Lima Costa, W. S. Clemente

The recent expansion of multidrug-resistant (MDR) pathogens poses significant challenges in treating healthcare-associated infections. Although antibacterial resistance occurs by numerous mechanisms, active efflux of the drugs is a critical concern. A single species of efflux pump can produce a simultaneous resistance to several drugs. One of the best-studie

Shane P. Kelly, Ulrich Poschinger, Ferdinand Schmidt-Kaler, Matthew P. A. Fisher

The coherent superposition of quantum states is an important resource for quantum information processing which distinguishes quantum dynamics and information from their classical counterparts. In this article we determine the coherence requirements to communicate quantum information in a broad setting encompassing monitored quantum dynamics and quantum error

Peiyao Sheng, Nikita Yadav, Vishal Sevani, Arun Babu

Recent years have seen the emergence of decentralized wireless networks consisting of nodes hosted by many individuals and small enterprises, reawakening the decades-old dream of open networking. These networks have been deployed in an organic, distributed manner and are driven by new economic models resting on tokenized incentives. A critical requirement fo

Junfeng Gao, Wenzhi Liao, David Nuyttens, Peter Lootens

Weed and crop segmentation is becoming an increasingly integral part of precision farming that leverages the current computer vision and deep learning technologies. Research has been extensively carried out based on images captured with a camera from various platforms. Unmanned aerial vehicles (UAVs) and ground-based vehicles including agricultural robots ar

Irina Zhuravleva, Mandy C. Chen, Eugene Churazov, Alexander A. Schekochihin

While awaiting direct velocity measurement of gas motions in the hot intracluster medium, we rely on indirect probes, including gas perturbations in galaxy clusters. Using a sample of $\sim 80$ clusters in different dynamic states from Omega500 cosmological simulations, we examine scaling relations between the fluctuation amplitudes of gas density, $\delta\r

Snehasis Banerjee, Brojeshwar Bhowmick, Ruddra Dev Roychoudhury

This paper presents an architecture and methodology to empower a service robot to navigate an indoor environment with semantic decision making, given RGB ego view. This method leverages the knowledge of robot's actuation capability and that of scenes, objects and their relations -- represented in a semantic form. The robot navigates based on GeoSem map - a r

Zhao Song, Xin Yang, Yuanyuan Yang, Lichen Zhang

Projection maintenance is one of the core data structure tasks. Efficient data structures for projection maintenance have led to recent breakthroughs in many convex programming algorithms. In this work, we further extend this framework to the Kronecker product structure. Given a constraint matrix ${\sf A}$ and a positive semi-definite matrix $W\in \mathbb{R}

Fabio Siringo, Giorgio Comitini

The analytic continuation of the gluon propagator is revised in the light of recent findings on the possible existence of complex conjugated poles. The contribution of the anomalous pole must be added when Wick rotating, leading to an effective Minkowskian propagator which is not given by the trivial analytic continuation of the Euclidean function. The effec

Brian Kwan, Wei Yang, Daniel Montemayor, Jing Zhang

Repeated longitudinal measurements are commonly used to model long-term disease progression, and timing and number of assessments per patient may vary, leading to irregularly spaced and sparse data. Longitudinal trajectories may exhibit curvilinear patterns, in which mixed linear regression methods may fail to capture true trends in the data. We applied func

Giulio Mattolin, Luca Zanella, Elisa Ricci, Yiming Wang

Unsupervised Domain Adaptation (UDA) for object detection aims to adapt a model trained on a source domain to detect instances from a new target domain for which annotations are not available. Different from traditional approaches, we propose ConfMix, the first method that introduces a sample mixing strategy based on region-level detection confidence for ada

Harshvardhan, Avishek Ghosh, Arya Mazumdar

In this paper, we address the dichotomy between heterogeneous models and simultaneous training in Federated Learning (FL) via a clustering framework. We define a new clustering model for FL based on the (optimal) local models of the users: two users belong to the same cluster if their local models are close; otherwise they belong to different clusters. A sta

Stefano Stramigioli

In this paper it is shown that the structure of the configuration space of any continua is what is called in differential geometry a {\it principle bundle} \cite{Frankel2011ThePhysics}. A principal bundle is a structure in which all points of the manifold (each configuration in this case) can be naturally projected to a manifold called the {\it base manifold

Tuhin Chakrabarty, Justin Lewis, Smaranda Muresan

Recent work on question generation has largely focused on factoid questions such as who, what, where, when about basic facts. Generating open-ended why, how, what, etc. questions that require long-form answers have proven more difficult. To facilitate the generation of open-ended questions, we propose CONSISTENT, a new end-to-end system for generating open-e

L. Fellay, C. Pezzotti, G. Buldgen, P. Eggenberger

Context. With the detection of thousands of exoplanets, characterising their dynamical evolution in detail represents a key step in the understanding of their formation. Studying the dissipation of tides occurring both in the host star and in the planets is of great relevance in order to investigate the distribution of the angular momentum occurring among th

Stephanie Newdick, Tony G. Chen, Benjamin Hockman, Edward Schmerling

In this paper we present a trade study-based method to optimize the architecture of ReachBot, a new robotic concept that uses deployable booms as prismatic joints for mobility in environments with adverse gravity conditions and challenging terrain. Specifically, we introduce a design process wherein we analyze the compatibility of ReachBot's design with its

Serhad Sarica, Ji Han, Jianxi Luo

Design representation is a common task in the design process to facilitate learning, analysis, redesign, communication, and other design activities. Traditional representation techniques rely on human expertise and manual construction and are difficult to repeat and scale. Here, we propose a methodology that utilizes a pre-trained large-scale cross-domain de

Ivan Letteri, Giuseppe Della Penna, Giovanni De Gasperis, Abeer Dyoub

In general, traders test their trading strategies by applying them on the historical market data (backtesting), and then apply to the future trades the strategy that achieved the maximum profit on such past data. In this paper, we propose a new trading strategy, called DNN-forwardtesting, that determines the strategy to apply by testing it on the possible fu

Alois Castellano, Francois Bottin, Johann Bouchet, Antoine Levitt

Finite temperature calculations, based on ab initio molecular dynamics (AIMD) simulations, are a powerful tool able to predict material properties that cannot be deduced from ground state calculations. However, the high computational cost of AIMD limits its applicability for large or complex systems. To circumvent this limitation we introduce a new method na

Mingliang Ma, Abolfazl Safikhani

Deep neural networks are powerful tools to model observations over time with non-linear patterns. Despite the widespread use of neural networks in such settings, most theoretical developments of deep neural networks are under the assumption of independent observations, and theoretical results for temporally dependent observations are scarce. To bridge this g

Gregor Ehrensperger, Thorsten Simon, Georg J. Mayr, Tobias Hell

Atmospheric environments favorable for lightning and convection are commonly represented by proxies or parameterizations based on expert knowledge such as CAPE, wind shears, charge separation, or combinations thereof. Recent developments in the field of machine learning, high resolution reanalyses, and accurate lightning observations open possibilities for i

John X. Morris, Justin T. Chiu, Ramin Zabih, Alexander M. Rush

Deidentification seeks to anonymize textual data prior to distribution. Automatic deidentification primarily uses supervised named entity recognition from human-labeled data points. We propose an unsupervised deidentification method that masks words that leak personally-identifying information. The approach utilizes a specially trained reidentification model

Jelena Đokić, Ksenija Doroslovački, Olga Bodroža-Pantić

We propose an algorithm for obtaining the common transfer digraph $ D^*_m$ for enumeration of 2-factors in graphs from the title all of which with $m n$ vertices ($m, n \in N, m >1 $). The numerical data gathered for $m <19$ reveal some matchings of the numbers of 2-factors for different types of torus or Klein bottle. In latter case we conjecture that these

Stefano Longhi

Nonlinear optical frequency conversion, observed more than half a century ago, is a corner stone in modern applications of nonlinear and quantum optics. It is well known that frequency conversion processes are constrained by conservation laws, such as momentum conservation that requires phase matching conditions for efficient conversion. However, conservatio

Francis X. Diebold

I offer reflections on adaptation to climate change, with emphasis on developing areas.

Carine P. Beatrici, Cassio A. Kirch, Silke Henkes, François Graner

Collectively coordinated cell migration plays a role in tissue embryogenesis, cancer, homeostasis and healing. To study these processes, different cell-based modelling approaches have been developed, ranging from lattice-based cellular automata to lattice-free models that treat cells as point-like particles or extended detailed cell shape contours. In the sp

Shuang Li, Yilun Du, Joshua B. Tenenbaum, Antonio Torralba

Large pre-trained models exhibit distinct and complementary capabilities dependent on the data they are trained on. Language models such as GPT-3 are capable of textual reasoning but cannot understand visual information, while vision models such as DALL-E can generate photorealistic photos but fail to understand complex language descriptions. In this work, w

Yulia Alexandr, Eliana Duarte, Julian Vill

We introduce a family of discrete context-specific models, which we call decomposable. We construct this family from the subclass of staged tree models known as CStree models. We give an algebraic and combinatorial characterization of all context-specific independence relations that hold in a decomposable context-specific model, which yields a Markov basis.

Huaiyu Hu, Ashis Gangopadhyay

One of the most important features of financial time series data is volatility. There are often structural changes in volatility over time, and an accurate estimation of the volatility of financial time series requires careful identification of change-points. A common approach to modeling the volatility of time series data is the well-known GARCH model. Alth

Donghyeon Kim, Kyungdeuk Ko, David K. Han, Hanseok Ko

Keyword Spotting (KWS) is an essential component in a smart device for alerting the system when a user prompts it with a command. As these devices are typically constrained by computational and energy resources, the KWS model should be designed with a small footprint. In our previous work, we developed lightweight dynamic filters which extract a robust featu

Jan Hohenheim, Mathias Fischler, Sara Zarubica, Jeremy Stucki

Current deep convolutional networks are fixed in their topology. We explore the possibilites of making the convolutional topology a parameter itself by combining NeuroEvolution of Augmenting Topologies (NEAT) with Convolutional Neural Networks (CNNs) and propose such a system using blocks of Residual Networks (ResNets). We then explain how our suggested syst

G. Younes, M. G. Baring, A. K. Harding, T. Enoto

Magnetars are a special subset of the isolated neutron star family, with X-ray and radio emission mainly powered by the decay of their immense magnetic fields. Many attributes of magnetars remain poorly understood: spin-down glitches or the sudden reductions in the star's angular momentum, radio bursts reminiscent of extra-galactic Fast Radio Bursts (FRBs),

José María Jorquera Valero, Manuel Gil Pérez, Gregorio Martínez Pérez

5G networks intend to cover user demands through multi-party collaborations in a secure and trustworthy manner. To this end, marketplaces play a pivotal role as enablers for network service consumers and infrastructure providers to offer, negotiate, and purchase 5G resources and services. Nevertheless, marketplaces often do not ensure trustworthy networking

Jane Allwright

Reaction-diffusion equations are studied on bounded, time-periodic domains with zero Dirichlet boundary conditions. The long-time behaviour is shown to depend on the principal periodic eigenvalue of a transformed periodic-parabolic problem. We prove upper and lower bounds on this eigenvalue under a range of different assumptions on the domain, and apply them

Mihir Kulkarni, Eli Visbal, Greg L. Bryan, Xinyu Li

Fuzzy dark matter (FDM) is a proposed modification for the standard cold dark matter (CDM) model motivated by small-scale discrepancies in low-mass galaxies. Composed of ultra-light (mass $\sim 10^{-22}$ eV) axions with kpc-scale de Broglie wavelengths, this is one of a class of candidates that predicts that the first collapsed objects form in relatively mas

Abhishek Anand, G J Sreejith

Real-space entanglement spectrum (RSES) of a quantum Hall (QH) wavefunction gives a natural route to infer the nature of its edge excitations. Computation of RSES becomes expensive with an increase in the number of particles and included Landau levels (LL). RSES can be efficiently computed using Monte Carlo (MC) methods for trial states that can be written a

Chester Holtz, Tsui-Wei Weng, Gal Mishne

There has been great interest in enhancing the robustness of neural network classifiers to defend against adversarial perturbations through adversarial training, while balancing the trade-off between robust accuracy and standard accuracy. We propose a novel adversarial training framework that learns to reweight the loss associated with individual training sa

Roberto Dessì, Eleonora Gualdoni, Francesca Franzon, Gemma Boleda

We compare the 0-shot performance of a neural caption-based image retriever when given as input either human-produced captions or captions generated by a neural captioner. We conduct this comparison on the recently introduced ImageCoDe data-set (Krojer et al., 2022) which contains hard distractors nearly identical to the images to be retrieved. We find that

Jinha Kim, Jun Jiang, Jinwei Gu

With the advent of deep learning methods replacing the ISP in transforming sensor RAW readings into RGB images, numerous methodologies solidified into real-life applications. Equally potent is the task of inverting this process which will have applications in enhancing computational photography tasks that are conducted in the RAW domain, addressing lack of a

Miaomiao Wang, Abdelhamid Tayebi

This paper considers the problem of nonlinear attitude estimation for a rigid body system using intermittent and multi-rate inertial vector measurements as well as continuous (high-rate) angular velocity measurements. Two types of hybrid attitude observers on Lie group $SO(3)$ are proposed. First, we propose a hybrid attitude observer where almost global asy

Sagar Ghorai, Daniel Hedlund, Martin Kapuscinski, Peter Svedlindh

In order to find a highly efficient, environment-friendly magnetic refrigerant, direct measurements of the adiabatic temperature change $\Delta T_{adb}$ is required. Here, in this work, a simple setup for the $\Delta T_{adb}$ measurement is presented. Using a permanent magnet Halbach array with a maximum magnetic field of $1.8$ T and a rate of magnetic field

Ilia Komissarov, Alberto Nicolis, John Staunton

At momenta much higher than the Hubble scale, the cosmological expansion can be thought of as a weak gravitational field. We consider QFT in a particularly convenient set of coordinates that makes this manifest, so that, for those high momenta, the effects of the cosmological expansion can be dealt with using the standard tools of perturbation theory in Mink

Yuncheng Mao, Daniele Guerci, Christophe Mora

Stacking three monolayers of graphene with a twist generally produces two moir\'e patterns. A moir\'e of moir\'e structure then emerges at larger distance where the three layers periodically realign. We devise here an effective low-energy theory to describe the spectrum at distances larger than the moir\'e lengthscale. In each valley of the underlying graphe

Ross Silver, Nuria Torres-Alba, Xiurui Zhao, Stefano Marchesi

We present the results of the broadband X-ray spectral analysis of simultaneous NuSTAR and XMM-Newton observations of four nearby Compton-thick active galactic nuclei (AGN) candidates selected from the Swift-Burst Alert Telescope (BAT) 150-month catalog. This work is part of a larger effort to identify and characterize all Compton-thick (NH >= 10^24 cm^-2) A

Yihui Quek, Daniel Stilck França, Sumeet Khatri, Johannes Jakob Meyer

Quantum error mitigation has been proposed as a means to combat unwanted and unavoidable errors in near-term quantum computing without the heavy resource overheads required by fault tolerant schemes. Recently, error mitigation has been successfully applied to reduce noise in near-term applications. In this work, however, we identify strong limitations to the

Josimar J. R. Aguirre, Victor G. L. Neumann

Let $\mathbb{F}_{q^n}$ be a finite field with $q^n$ elements and $r$ be a positive divisor of $q^n-1$. An element $\alpha \in \mathbb{F}_{q^n}^*$ is called $r$-primitive if its multiplicative order is $(q^n-1)/r$. Also, $\alpha \in \mathbb{F}_{q^n}$ is $k$-normal over $\mathbb{F}_q$ if the greatest common divisor of the polynomials $g_{\alpha}(x) = \alpha x^