May 2022 arXiv papers — page 64

G. S. Demyanov, P. R. Levashov

The density matrix for a system of particles interacting via the Coulomb potential is obtained in the high--temperature limit following almost entirely the original work by Kelbg. For this purpose the Bl\"och equation is solved in the first order of perturbation theory. We tried to explain all the transformations in the derivation in order to simplify the un

Jiuqi Elise Zhang, Di Wu, Benoit Boulet

Time series anomaly detection has been recognized as of critical importance for the reliable and efficient operation of real-world systems. Many anomaly detection methods have been developed based on various assumptions on anomaly characteristics. However, due to the complex nature of real-world data, different anomalies within a time series usually have div

Caleb John, Geoffrey G. Messier

This paper uses rule search techniques for the early identification of emergency homeless shelter clients who are at risk of becoming long term or chronic shelter users. Using a data set from a major North American shelter containing 12 years of service interactions with over 40,000 individuals, the optimized pruning for unordered search (OPUS) algorithm is

Patrick Gelß, Stefan Klus, Sebastian Knebel, Zarin Shakibaei

Quantum computing is arguably one of the most revolutionary and disruptive technologies of this century. Due to the ever-increasing number of potential applications as well as the continuing rise in complexity, the development, simulation, optimization, and physical realization of quantum circuits is of utmost importance for designing novel algorithms. We sh

Kamiokande Collaboration, L. N. Machado, K. Abe, Y. Hayato

In 2020, the Super-Kamiokande (SK) experiment moved to a new stage (SK-Gd) in which gadolinium (Gd) sulfate octahydrate was added to the water in the detector, enhancing the efficiency to detect thermal neutrons and consequently improving the sensitivity to low energy electron anti-neutrinos from inverse beta decay (IBD) interactions. SK-Gd has the potential

Shayan Fazeli, Alireza Samiei, Thomas D. Lee, Majid Sarrafzadeh

Analyzing and inspecting bone marrow cell cytomorphology is a critical but highly complex and time-consuming component of hematopathology diagnosis. Recent advancements in artificial intelligence have paved the way for the application of deep learning algorithms to complex medical tasks. Nevertheless, there are many challenges in applying effective learning

Li Xu, Yili Hong, Max D. Morris, Kirk W. Cameron

Although high-performance computing (HPC) systems have been scaled to meet the exponentially-growing demand for scientific computing, HPC performance variability remains a major challenge and has become a critical research topic in computer science. Statistically, performance variability can be characterized by a distribution. Predicting performance variabil

Mrinal Mathur, Archana Benkkallpalli Chandrashekhar, Venkata Krishna Chaithanya Nuthalapati

The National Football League and Amazon Web Services teamed up to develop the best sports injury surveillance and mitigation program via the Kaggle competition. Through which the NFL wants to assign specific players to each helmet, which would help accurately identify each player's "exposures" throughout a football play. We are trying to implement a computer

Agustín E. Martinez Suñé, Carlos G. Lopez Pombo

In software-as-a-service paradigms software systems are no longer monolithic pieces of code executing within the boundaries of an organisation, on the contrary, they are conceived as a dynamically changing collection of services, collectively executing, in pursuit of a common business goal. An essential aspect of service selection is determining whether the

Christopher G. Lester, Radha Mastandrea, Daniel Noel, Rupert Tombs

Non-Standard-Model parity violation may be occurring in LHC collisions. Any such violation would go unseen, however, as searches are for it are not currently performed. One barrier to searches for parity violation is the lack of model-independent methods sensitive to all of its forms. We remove this barrier by demonstrating an effective and model-independent

James Seale Smith, Zachary Seymour, Han-Pang Chiu

As progress is made on training machine learning models on incrementally expanding classification tasks (i.e., incremental learning), a next step is to translate this progress to industry expectations. One technique missing from incremental learning is automatic architecture design via Neural Architecture Search (NAS). In this paper, we show that leveraging

Bilal Saleem, Yang Weng

Distributed energy resources are better for the environment but may cause transformer overload in distribution grids, calling for recovering meter-transformer mapping to provide situational awareness, i.e., the transformer loading. The challenge lies in recovering meter-transformer (M.T.) mapping for two common scenarios, e.g., large distances between a mete

Fuheng Zhao, Dan Qiao, Rachel Redberg, Divyakant Agrawal

Linear sketches have been widely adopted to process fast data streams, and they can be used to accurately answer frequency estimation, approximate top K items, and summarize data distributions. When data are sensitive, it is desirable to provide privacy guarantees for linear sketches to preserve private information while delivering useful results with theore

David M. Chan, Shalini Ghosh

Deep neural networks have largely demonstrated their ability to perform automated speech recognition (ASR) by extracting meaningful features from input audio frames. Such features, however, may consist not only of information about the spoken language content, but also may contain information about unnecessary contexts such as background noise and sounds or

Vladimir Gasparian, Peng Guo, Esther Jódar

In this letter we discuss a phase transition-like anomalous behavior of Faraday rotation angles in a simple parity-time ($\mathcal{P}\mathcal{T}$) symmetric model with two complex $\delta$-potential placed at both boundaries of a regular dielectric slab. In anomalous phase, the value of one of Faraday rotation angles may turn negative, and both angles suffer

Claudia Rella, Babette Döbrich, Tien-Tien Yu

Proton beam-dump experiments are a high-intensity source of secondary muons and provide an opportunity to probe muon-specific dark sectors. We adopt a simplified-models framework for an exotic light scalar particle coupling predominantly or exclusively to muons. Equipped with state-of-the-art muon simulations, we compute the sensitivity reach in the paramete

Rui Liu, Barzan Mozafari

Transformers achieve state-of-the-art performance for natural language processing tasks by pre-training on large-scale text corpora. They are extremely compute-intensive and have very high sample complexity. Memory replay is a mechanism that remembers and reuses past examples by saving to and replaying from a memory buffer. It has been successfully used in r

Rui Chen, Dian Shi, Xiaoqi Qin, Dongjie Liu

Federated learning (FL) over mobile devices has fostered numerous intriguing applications/services, many of which are delay-sensitive. In this paper, we propose a service delay efficient FL (SDEFL) scheme over mobile devices. Unlike traditional communication efficient FL, which regards wireless communications as the bottleneck, we find that under many situat

Masahiro Kaneko, Danushka Bollegala, Naoaki Okazaki

Different methods have been proposed to develop meta-embeddings from a given set of source embeddings. However, the source embeddings can contain unfair gender-related biases, and how these influence the meta-embeddings has not been studied yet. We study the gender bias in meta-embeddings created under three different settings: (1) meta-embedding multiple so

A. A. Burkov

This chapter describes topological (Dirac and Weyl) semimetals from the viewpoint of their observable electromagnetic response. We argue that this response may be represented by topological terms with unquantized (non-integer) coefficients and make a connection with the Luttinger's theorem, which relates the size of the Fermi surface of an ordinary metal to

Marcel Koloschin, Thilo Krill, Max Pitz

We present a systematic investigation into how tree-decompositions of finite adhesion capture topological properties of the space formed by a graph together with its ends. As main results, we characterise when the ends of a graph can be distinguished, and characterise which subsets of ends can be displayed by a tree-decomposition of finite adhesion. In parti

Nigel Fernandez, Aritra Ghosh, Naiming Liu, Zichao Wang

Automated scoring of open-ended student responses has the potential to significantly reduce human grader effort. Recent advances in automated scoring often leverage textual representations based on pre-trained language models such as BERT and GPT as input to scoring models. Most existing approaches train a separate model for each item/question, which is suit

Emile Bouaziz

We study the space of loops into a hypersurface complement, and show that the corresponding topological algebra of Laurent series with coefficients in $\mathcal{O}(L\mathbf{A}^{d}_{f})$ is a topological localisation of $\mathcal{O}(L\mathbf{A}^{d})$. This requires introducing a small amount of non-Archimedean functional analysis. In particular we work with t

Chamalee Wickrama Arachchi, Nikolaj Tatti

A popular approach to model interactions is to represent them as a network with nodes being the agents and the interactions being the edges. Interactions are often timestamped, which leads to having timestamped edges. Many real-world temporal networks have a recurrent or possibly cyclic behaviour. For example, social network activity may be heightened during

Salome Wörner, Sebastian Becker, Stefan Küchemann, Katharina Scheiter

Optics is a core field in the curricula of secondary physics education. In this study, we present the development and validation of a test instrument in the field of optics, the Converging Lenses Concept Inventory (CLCI). It can be used as a formative or a summative assessment of middle school students' conceptual understanding of image formation by convergi

Jingwei Zhang, Xunpeng Huang, Jincheng Yu

We consider optimizing two-layer neural networks in the mean-field regime where the learning dynamics of network weights can be approximated by the evolution in the space of probability measures over the weight parameters associated with the neurons. The mean-field regime is a theoretically attractive alternative to the NTK (lazy training) regime which is on

Frédéric Genest, David Lafrenière, Anne Boucher, Antoine Darveau-Bernier

We present models designed to quantify the effects of stellar activity on exoplanet transit spectroscopy and atmospheric characterization at low (R = 100) and high (R = 100,000) spectral resolution. We study three model classes mirroring planetary system archetypes: a hot Jupiter around an early-K star (HD 189733 b); a mini-Neptune around an early-M dwarf (K

Matthew Conlen, Jeffrey Heer

Narrative visualization is a powerful communicative tool that can take on various formats such as interactive articles, slideshows, and data videos. These formats each have their strengths and weaknesses, but existing authoring tools only support one output target. We conducted a series of formative interviews with seven domain experts to understand needs an

V. E. Colussi, F. Caleffi, C. Menotti, A. Recati

We study the physics of a mobile impurity confined in a lattice, moving within a Bose-Hubbard bath at zero temperature. Within the Quantum Gutzwiller formalism, we develop a beyond-Fr\"ohlich model of the bath-impurity interaction. Results for the properties of the polaronic quasiparticle formed from the dressing of the impurity by quantum fluctuations of th

Glenn G. Chappell

For positive integers $a$ and $b$, a graph $G$ is $(a:b)$-choosable if, for each assignment of lists of $a$ colors to the vertices of $G,$ each vertex can be colored with a set of $b$ colors from its list so that adjacent vertices are colored with disjoint sets. We show that for positive integers $a$ and $b$, every bipartite planar graph is $(a:b)$-choosable

Thomas Kreuz, Federico Senocrate, Gloria Cecchini, Curzio Checcucci

Background: In neurophysiological data, latency refers to a global shift of spikes from one spike train to the next, either caused by response onset fluctuations or by finite propagation speed. Such systematic shifts in spike timing lead to a spurious decrease in synchrony which needs to be corrected. New Method: We propose a new algorithm of multivariate la

Naoki Koseki

In this article, we investigate semi-orthogonal decompositions of the symmetric products of dg-enhanced triangulated categories. Given a semi-orthogonal decomposition $\mathcal{D}=\langle \mathcal{A}, \mathcal{B} \rangle$, we construct semi-orthogonal decompositions of the symmetric products of $\mathcal{D}$ in terms of that of $\mathcal{A}$ and $\mathcal{B}

Vikram Voleti, Alexia Jolicoeur-Martineau, Christopher Pal

Video prediction is a challenging task. The quality of video frames from current state-of-the-art (SOTA) generative models tends to be poor and generalization beyond the training data is difficult. Furthermore, existing prediction frameworks are typically not capable of simultaneously handling other video-related tasks such as unconditional generation or int

Changchang Yin, Ruoqi Liu, Jeffrey Caterino, Ping Zhang

Despite intense efforts in basic and clinical research, an individualized ventilation strategy for critically ill patients remains a major challenge. Recently, dynamic treatment regime (DTR) with reinforcement learning (RL) on electronic health records (EHR) has attracted interest from both the healthcare industry and machine learning research community. How

Gennady Uraltsev, Michał Warchalski

We prove uniform uniform $L^{p}$ bounds for the family of bilinear Hilbert transforms $\mathrm{BHT}_{\beta} [f_1, f_2] (x) := \mathrm{p.v.} \int_{\mathbb{R}} f_1 (x - t) f_2 (x + \beta t) \frac{\mathrm{d} t}{t}$. We show that the operator $\mathrm{BHT}_{\beta}$ maps $L^{p_{1}}\times L^{p_{2}}$ into $L^{p}$ as long as $p_1 \in (1, \infty)$, $p_2 \in (1, \inft

I. Atas

Panoramic Dental Radiography (PDR) image processing is one of the most extensively used manual methods for gender determination in forensic medicine. With the assistance of the PDR images, a person's biological gender determination can be performed through analyzing skeletal structures expressing sexual dimorphism. Manual approaches require a wide range of m

Yingcong Li, Chandra Sekhar Mukherjee, Jiapeng Zhang

Unsupervised clustering algorithms for vectors has been widely used in the area of machine learning. Many applications, including the biological data we studied in this paper, contain some boundary datapoints which show combination properties of two underlying clusters and could lower the performance of the traditional clustering algorithms. We develop a con

Noura Djellali, Abdelbasset Hasni, Ahmed Mohammed Cherif, Mohamed Belkhelfa

In this paper, we give a classification of Codazzi hypersurfaces in a Lie group $(Nil^{4},\widetilde g)$. We also give a characterization of a class of minimal hypersurfaces in $(Nil^{4},\widetilde g)$ with an example of a minimal surface in this class.

Daniel Cummings, Anthony Sarah, Sharath Nittur Sridhar, Maciej Szankin

Recent advances in Neural Architecture Search (NAS) such as one-shot NAS offer the ability to extract specialized hardware-aware sub-network configurations from a task-specific super-network. While considerable effort has been employed towards improving the first stage, namely, the training of the super-network, the search for derivative high-performing sub-

Erico L. Rempel, Roman Chertovskih, Kamilla R. Davletshina, Suzana S. A. Silva

The analysis of the photospheric velocity field is essential for understanding plasma turbulence in the solar surface, which may be responsible for driving processes such as magnetic reconnection, flares, wave propagation, particle acceleration, and coronal heating. Currently, the only available methods to estimate velocities at the solar photosphere transve

Sumit Bam Shrestha, Longwei Zhu, Pengfei Sun

Spiking Neural Networks~(SNNs) are a promising research paradigm for low power edge-based computing. Recent works in SNN backpropagation has enabled training of SNNs for practical tasks. However, since spikes are binary events in time, standard loss formulations are not directly compatible with spike output. As a result, current works are limited to using me

Matt Wilson, Giulio Chiribella, Aleks Kissinger

We provide a new characterisation of quantum supermaps in terms of an axiom that refers only to sequential and parallel composition. Consequently, we generalize quantum supermaps to arbitrary monoidal categories and operational probabilistic theories. We do so by providing a simple definition of locally-applicable transformation on a monoidal category. The d

Zhiruo Wang, Zhengbao Jiang, Eric Nyberg, Graham Neubig

Tables are an important form of structured data for both human and machine readers alike, providing answers to questions that cannot, or cannot easily, be found in texts. Recent work has designed special models and training paradigms for table-related tasks such as table-based question answering and table retrieval. Though effective, they add complexity in b

Alexandra-Maria Tautan, Elias Casula, Ilaria Borghi, Michele Maiella

Transcranial magnetic stimulation co-registered with electroencephalographic (TMS-EEG) has previously proven a helpful tool in the study of Alzheimer's disease (AD). In this work, we investigate the use of TMS-evoked EEG responses to classify AD patients from healthy controls (HC). By using a dataset containing 17AD and 17HC, we extract various time domain f

Marija Habijan, Irena Galic

Deep learning has a great potential to alleviate diagnosis and prognosis for various clinical procedures. However, the lack of a sufficient number of medical images is the most common obstacle in conducting image-based analysis using deep learning. Due to the annotations scarcity, semi-supervised techniques in the automatic medical analysis are getting high

Syed Sameed Husain, Eng-Jon Ong, Dmitry Minskiy, Mikel Bober-Irizar

Unravelling protein distributions within individual cells is key to understanding their function and state and indispensable to developing new treatments. Here we present the Hybrid subCellular Protein Localiser (HCPL), which learns from weakly labelled data to robustly localise single-cell subcellular protein patterns. It comprises innovative DNN architectu

Workneh Yilma Ayele

The significance and abundance of data are increasing due to the growing digital data generated from social media, sensors, scholarly literature, patents, different forms of documents published online, databases, product manuals, etc. Various data sources can be used to generate ideas, yet, in addition to bias, the size of the available digital data is a maj

Alvaro J. Magdaleno, Riccardo Frisenda, Ferry Prins, Andres Castellanos-Gomez

Paper offers a low-cost and widely available substrate for electronics. It posses alternative characteristics to silicon, as it shows low density and high-flexibility, together with biodegradability. Solution processable materials, such as hybrid perovskites, also present light and flexible features, together with a huge tunability of the material compositio

Kinshuk Dua

Binary Neural Networks (BNNs), neural networks with weights and activations constrained to -1(0) and +1, are an alternative to deep neural networks which offer faster training, lower memory consumption and lightweight models, ideal for use in resource constrained devices while being able to utilize the architecture of their deep neural network counterpart. H

David Alvarez-Melis, Vikas Garg, Adam Tauman Kalai

This work offers a novel theoretical perspective on why, despite numerous attempts, adversarial approaches to generative modeling (e.g., GANs) have not been as popular for certain generation tasks, particularly sequential tasks such as Natural Language Generation, as they have in others, such as Computer Vision. In particular, on sequential data such as text

Keming Lu, I-Hung Hsu, Wenxuan Zhou, Mingyu Derek Ma

Relation extraction (RE) models have been challenged by their reliance on training data with expensive annotations. Considering that summarization tasks aim at acquiring concise expressions of synoptical information from the longer context, these tasks naturally align with the objective of RE, i.e., extracting a kind of synoptical information that describes

Luke Bhan, Marcos Quinones-Grueiro, Gautam Biswas

In this work, we address the problem of solving complex collaborative robotic tasks subject to multiple varying parameters. Our approach combines simultaneous policy blending with system identification to create generalized policies that are robust to changes in system parameters. We employ a blending network whose state space relies solely on parameter esti

Felipe Barra

A repeated interaction process assisted by auxiliary thermal systems charges a quantum battery. The charging energy is supplied by switching on and off the interaction between the battery and the thermal systems. The charged state is an equilibrium state for the repeated interaction process, and the ergotropy characterizes its charge. The working cycle consi

Fatih Cagatay Akyon, Yasar Kemal Alp, Gokhan Gok, Orhan Arikan

Detection and classification of radars based on pulses they transmit is an important application in electronic warfare systems. In this work, we propose a novel deep-learning based technique that automatically recognizes intra-pulse modulation types of radar signals. Re-assigned spectrogram of measured radar signal and detected outliers of its instantaneous

Ali Taghibakhshi, Nicolas Nytko, Tareq Zaman, Scott MacLachlan

Domain decomposition methods are widely used and effective in the approximation of solutions to partial differential equations. Yet the optimal construction of these methods requires tedious analysis and is often available only in simplified, structured-grid settings, limiting their use for more complex problems. In this work, we generalize optimized Schwarz

T. G. Akin, Bryan Hemingway, Steven Peil

We measure the spectrum of tellurium-130 in the vicinity of the 461~nm ${}^1\text{S}_0-{}^{1}\text{P}_1$ cycling transition in neutral strontium, a popular element for atomic clocks, quantum information, and quantum-degenerate gases. The lack of hyperfine structure in tellurium results in a spectral density of transitions nearly 50 times lower than that avai

Simon Hubmer, Ekaterina Sherina, Stefan Kindermann, Kemal Raik

The choice of a suitable regularization parameter is an important part of most regularization methods for inverse problems. In the absence of reliable estimates of the noise level, heuristic parameter choice rules can be used to accomplish this task. While they are already fairly well-understood and tested in the case of linear problems, not much is known ab

Samhita Honnavalli, Aesha Parekh, Lily Ou, Sophie Groenwold

Women are often perceived as junior to their male counterparts, even within the same job titles. While there has been significant progress in the evaluation of gender bias in natural language processing (NLP), existing studies seldom investigate how biases toward gender groups change when compounded with other societal biases. In this work, we investigate ho

Justin Jude, Matthew G. Perich, Lee E. Miller, Matthias H. Hennig

Decoding stimuli or behaviour from recorded neural activity is a common approach to interrogate brain function in research, and an essential part of brain-computer and brain-machine interfaces. Reliable decoding even from small neural populations is possible because high dimensional neural population activity typically occupies low dimensional manifolds that

Alexandru Paler, Austin G. Fowler

We describe a pipeline approach to decoding the surface code using minimum weight perfect matching, including taking into account correlations between detection events. An independent no-communication parallelizable processing stage reweights the graph according to likely correlations, followed by another no-communication parallelizable stage for high confid

K. D. Marquez, M. R. Pelicer, S. Ghosh, J. Peterson

Strong magnetic fields can modify the microscopic composition of matter with consequences on stellar macroscopic properties. Within this context, we study, for the first time, the possibility of the appearance of spin-3/2 $\Delta$ baryons in magnetars. We make use of two different relativistic models for the equation of state of dense matter under the influe

Vu H. N. Phan, Moshe Y. Vardi

In Bayesian inference, the maximum a posteriori (MAP) problem combines the most probable explanation (MPE) and marginalization (MAR) problems. The counterpart in propositional logic is the exist-random stochastic satisfiability (ER-SSAT) problem, which combines the satisfiability (SAT) and weighted model counting (WMC) problems. Both MAP and ER-SSAT have the

François-Pierre Paty, Philippe Choné, Francis Kramarz

The theory of weak optimal transport (WOT), introduced by [Gozlan et al., 2017], generalizes the classic Monge-Kantorovich framework by allowing the transport cost between one point and the points it is matched with to be nonlinear. In the so-called barycentric version of WOT, the cost for transporting a point $x$ only depends on $x$ and on the barycenter of

Benjamin Kompa, David R. Bellamy, Thomas Kolokotrones, James M. Robins

The No Unmeasured Confounding Assumption is widely used to identify causal effects in observational studies. Recent work on proximal inference has provided alternative identification results that succeed even in the presence of unobserved confounders, provided that one has measured a sufficiently rich set of proxy variables, satisfying specific structural co

Svenja M. Griesbach, Martin Hoefer, Max Klimm, Tim Koglin

We consider a largely untapped potential for the improvement of traffic networks that is rooted in the inherent uncertainty of travel times. Travel times are subject to stochastic uncertainty resulting from various parameters such as weather condition, occurrences of road works, or traffic accidents. Large mobility services have an informational advantage ov

Diogo Caetano, Charles M. Elliott, Maurizio Grasselli, Andrea Poiatti

We consider the Cahn-Hilliard equation with constant mobility and logarithmic potential on a two-dimensional evolving closed surface embedded in $\mathbb R^3$, as well as a related weighted model. The well-posedness of weak solutions for the corresponding initial value problems on a given time interval $[0,T]$ have already been established by the first two a

Dipan Mandal, Abhilash Jain

We propose DFPNet -- an unsupervised, joint learning system for monocular Depth, Optical Flow and egomotion (Camera Pose) estimation from monocular image sequences. Due to the nature of 3D scene geometry these three components are coupled. We leverage this fact to jointly train all the three components in an end-to-end manner. A single composite loss functio

Tianjiao Lei, Esther C. Hessong, Jungho Shin, Daniel S. Gianola

The mechanical behavior of two nanocrystalline Al alloys, Al-Mg-Y and Al-Fe-Y, is investigated with in-situ micropillar compression testing. Both alloys were strengthened by a hierarchical microstructure including grain boundary segregation, nanometer-thick amorphous complexions, carbide nanorod precipitates with sizes of a few nanometers, and submicron-scal

Vakhid A. Gani, Anastasia Gorina, Ilya Perapechka, Yakov Shnir

We study numerically the kink-fermion interactions in a 1+1 dimensional toy model, which describes sine-Gordon kinks coupled to the massless Dirac fermions with backreaction. We show that the spectrum of fermionic modes strongly depends on the choice of the coupling, in particular, there are no localized modes for a minimal Yukawa coupling. We analyze the sc

Roussey Sylvain

Let $\varphi$ and $\psi$ be quadratic forms over a field $K$ of characteristic different from 2. In this paper, we give a criterion for isotropy of $\varphi$ over the function field of $\psi$ in terms of representations and we apply it to stably birational equivalence of $\varphi$ and $\psi$. Then we use this criterion to investigate the case of stably birat

Navneet Agrawal, Yuqin Qiu, Matthias Frey, Igor Bjelakovic

Lagrange coded computation (LCC) is essential to solving problems about matrix polynomials in a coded distributed fashion; nevertheless, it can only solve the problems that are representable as matrix polynomials. In this paper, we propose AICC, an AI-aided learning approach that is inspired by LCC but also uses deep neural networks (DNNs). It is appropriate

Abhijit Suprem, Calton Pu

COVID-19 related misinformation and fake news, coined an 'infodemic', has dramatically increased over the past few years. This misinformation exhibits concept drift, where the distribution of fake news changes over time, reducing effectiveness of previously trained models for fake news detection. Given a set of fake news models trained on multiple domains, w

Pablo S Moya, Bea Zenteno-Quinteros, Iván Gallo-Méndez, Víctor A Pinto

Interactions between plasma particles and electromagnetic waves play a crucial role in the dynamics and regulation of the state of space environments. From plasma physics theory, the characteristics of the waves and their interactions with the plasma strongly depend on the composition of the plasma, among other factors. In the case of the Earth's magnetosphe

William Lefebvre, Grégoire Loeper, Huyên Pham

We propose machine learning methods for solving fully nonlinear partial differential equations (PDEs) with convex Hamiltonian. Our algorithms are conducted in two steps. First the PDE is rewritten in its dual stochastic control representation form, and the corresponding optimal feedback control is estimated using a neural network. Next, three different metho

Jing Fan Yang, Thomas A. Berrueta, Allan M. Brooks, Albert Tianxiang Liu

Spontaneous low-frequency oscillations on the order of several hertz are the drivers of many crucial processes in nature. From bacterial swimming to mammal gaits, the conversion of static energy inputs into slowly oscillating electrical and mechanical power is key to the autonomy of organisms across scales. However, the fabrication of slow artificial oscilla

Mohiuddeen Khan, Claus Aranha

Werewolf is a popular party game throughout the world, and research on its significance has progressed in recent years. The Werewolf game is based on conversation, and in order to win, participants must use all of their cognitive abilities. This communication game requires the playing agents to be very sophisticated to win. In this research, we generated a s

Erik Ekstedt, Gabriel Skantze

The modeling of turn-taking in dialog can be viewed as the modeling of the dynamics of voice activity of the interlocutors. We extend prior work and define the predictive task of Voice Activity Projection, a general, self-supervised objective, as a way to train turn-taking models without the need of labeled data. We highlight a theoretical weakness with prio

Jonathan H. Fetherolf, Petra Shih, Timothy C. Berkelbach

We study the impact of phonon anharmonicity on the electronic dynamics of soft materials using a nonperturbative quantum-classical approach. The method is applied to a one-dimensional model of doped organic semiconductors with low-frequency intermolecular lattice phonons. We find that anharmonicity that leads to phonon hardening increases the mobility and an

Mbaye Diouf, Joshua A. Burrow, Krishangi Krishna, Rachel Odessey

Surface plasmon polaritons (SPPs) are traditionally excited by plane waves within the Rayleigh range of a focused transverse magnetic (TM) Gaussian beam. Here, we investigate and confirm the coupling between SPPs and two-dimensional Gaussian and Bessel-Gauss wave packets, as well as one-dimensional light sheets and space-time wave packets. We encode the inco

Yewen Fan, Nian Si, Kun Zhang

Calibration is defined as the ratio of the average predicted click rate to the true click rate. The optimization of calibration is essential to many online advertising recommendation systems because it directly affects the downstream bids in ads auctions and the amount of money charged to advertisers. Despite its importance, calibration optimization often su

Thomas A. Schad, Sarah A. Jaeggli, Gabriel I Dima

Thomson scattered photospheric light is the dominant constituent of the lower solar corona's spectral continuum viewed off-limb at optical wavelengths. Known as the K-corona, it is also linearly polarized. We investigate the possibility of using the a priori polarized characteristics of the K-corona, together with polarized emission lines, to measure and cor

Hao Deng, Kazu Saitou

This paper presents a novel computational scheme for sensitivity analysis of the velocity field in the level set method using the discrete adjoint method. The velocity field is represented in B-spline space, and the adjoint equations are constructed based on the discretized governing equations. The key contribution of this work is the demonstration that the

Yuxin Lin, Silvia Spezzano, Olli Sipilä, Anton Vasyunin

Pre-stellar cores are the basic unit for the formation of stars and stellar systems. The anatomy of the physical and chemical structures of pre-stellar cores is critical for understanding the star formation process. L1544 is a prototypical pre-stellar core, which shows significant chemical differentiation surrounding the dust peak. We aim to constrain the ph

Urban Vernik, Alexey M. Lomonosov, Vladimir S. Vlasov, Leonid N. Kotov

We analyze resonant magneto-elastic interactions between standing perpendicular spin wave modes (exchange magnons) and longitudinal acoustic phonon modes in free-standing hybrid metal-ferromagnet bilayer and trilayer structures. Whereas the ferromagnetic layer acts as a magnetic cavity, all metal layers control the frequencies and eigenmodes of acoustic vibr

Maryam Aliakbarpour, Andrew McGregor, Jelani Nelson, Erik Waingarten

Recent work of Acharya et al. (NeurIPS 2019) showed how to estimate the entropy of a distribution $\mathcal D$ over an alphabet of size $k$ up to $\pm\epsilon$ additive error by streaming over $(k/\epsilon^3) \cdot \text{polylog}(1/\epsilon)$ i.i.d. samples and using only $O(1)$ words of memory. In this work, we give a new constant memory scheme that reduces

Michael Fromm, Max Berrendorf, Johanna Reiml, Isabelle Mayerhofer

Argumentation is one of society's foundational pillars, and, sparked by advances in NLP and the vast availability of text data, automated mining of arguments receives increasing attention. A decisive property of arguments is their strength or quality. While there are works on the automated estimation of argument strength, their scope is narrow: they focus on

Han Yue, Chunhui Zhang, Chuxu Zhang, Hongfu Liu

Recently, contrastiveness-based augmentation surges a new climax in the computer vision domain, where some operations, including rotation, crop, and flip, combined with dedicated algorithms, dramatically increase the model generalization and robustness. Following this trend, some pioneering attempts employ the similar idea to graph data. Nevertheless, unlike

Charilaos I. Kanatsoulis, Alejandro Ribeiro

Despite the remarkable success of Graph Neural Networks (GNNs), the common belief is that their representation power is limited and that they are at most as expressive as the Weisfeiler-Lehman (WL) algorithm. In this paper, we argue the opposite and show that standard GNNs, with anonymous inputs, produce more discriminative representations than the WL algori

David Kent, David Ruppert

This paper addresses the deconvolution problem of estimating a square-integrable probability density from observations contaminated with additive measurement errors having a known density. The estimator begins with a density estimate of the contaminated observations and minimizes a reconstruction error penalized by an integrated squared $m$-th derivative. Th

Marco Di Renzo, Abdelhamed Ahmed, Alessio Zappone, Vincenzo Galdi

Reconfigurable intelligent surface (RIS) is an emerging technology that is under investigation for different applications in wireless communications. RISs are often analyzed and optimized by considering simplified electromagnetic reradiation models. In this chapter, we aim to study the impact of realistic reradiation models for RISs as a function of the sub-

Pallavi Malavi, Prakash Kumar, Navita Jakhar, Surjeet Singh

The structural and magneto-transport properties of type-II Dirac semimetal candidate Ir2In8S have been investigated under high pressure. The ambient tetragonal structure (P4_2/mnm) is found to be stable up to 7 GPa, above which the system takes an orthorhombic Pnnm structure, possibly destroying the Dirac cones due to the loss of the four-fold screw symmetry

Ziniu Hu, Zhe Zhao, Xinyang Yi, Tiansheng Yao

Multi-Task Learning (MTL) is a powerful learning paradigm to improve generalization performance via knowledge sharing. However, existing studies find that MTL could sometimes hurt generalization, especially when two tasks are less correlated. One possible reason that hurts generalization is spurious correlation, i.e., some knowledge is spurious and not causa

Riccardo Ceccon, Giulia Livieri, Stefano Marmi

We study a random version of the population-market model proposed by Arlot, Marmi and Papini in Arlot et al. (2019). The latter model is based on the Yoccoz-Birkeland integral equation and describes a time evolution of livestock commodities prices which exhibits endogenous deterministic stochastic behaviour. We introduce a stochastic component inspired from

Ananth Jonnavittula, Shaunak A. Mehta, Dylan P. Losey

Assistive robot arms try to help their users perform everyday tasks. One way robots can provide this assistance is shared autonomy. Within shared autonomy, both the human and robot maintain control over the robot's motion: as the robot becomes confident it understands what the human wants, it intervenes to automate the task. But how does the robot know these

Ernest Ma

In the framework of seesaw neutrino masses from heavy fermion triplets $(\Sigma^+,\Sigma^0,\Sigma^-)$, the addition of a light fermion singlet $N$ and a heavy scalar triplet $(\rho^+,\rho^0,\rho^-)$ has some important consequences. The new particles are assumed to be odd under a new $Z_2$ symmetry which is only broken softly, both explicitly and spontaneousl

Tzvi Michelson, Hillel Aviezer, Shmuel Peleg

Despite much progress in the field of facial expression recognition, little attention has been paid to the recognition of peak emotion. Aviezer et al. [1] showed that humans have trouble discerning between positive and negative peak emotions. In this work we analyze how deep learning fares on this challenge. We find that (i) despite using very small datasets

Xinyao Zhang

The modularity of elliptic curves always intrigues number theorists. Recently, Thorne had proved a marvelous result that for a prime $ p $, every elliptic curve defined over a $ p $-cyclotomic extension of $ \mathbb{Q} $ is modular. The method is to use some automorphy lifting theorems and study non-cusp points on some specific elliptic curves by Iwasawa the

Lucas Kohn, Giuseppe E. Santoro, Michele Fabrizio, Erio Tosatti

The cyclic sudden switching of a magnetic impurity from Kondo to a non-Kondo state and back was recently shown to involve an important dissipation of the order of several $k_BT_K$ per cycle. The possibility to reveal this and other electronic processes through nanomechanical dissipation by e.g., ultrasensitive Atomic Force Microscope (AFM) tools currently re

John Buckleton, Jo-Anne Bright, Kevin Cheng, Duncan Taylor

We discuss a range of miscodes found in probabilistic genotyping (PG) software and from other industries that have been reported in the literature and have been used to inform PG admissibility hearings. Every instance of the discovery of a miscode in PG software with which we have been associated has occurred either because of testing, use, or repeat calcula

Fabrizio Russo, Francesca Toni

Neural networks have proven to be effective at solving machine learning tasks but it is unclear whether they learn any relevant causal relationships, while their black-box nature makes it difficult for modellers to understand and debug them. We propose a novel method overcoming these issues by allowing a two-way interaction whereby neural-network-empowered m