Miracle-dz/UniFAR-FTUs huggingface dataset:
Adding eccentricity to quasicircular binary-black-hole waveform models

The detection of gravitational-wave signals from coalescing eccentric binary black holes would yield unprecedented information about the formation and evolution of compact binaries in specific scenarios, such as dynamical formation in dense stellar clusters and three-body interactions. The gravitational-wave searches by the ground-based interferometers, LIGO and Virgo, rely on analytical waveform models for binaries on quasicircular orbits. Eccentric merger waveform models are less developed, and only few numerical simulations of eccentric mergers are publicly available, but several eccentric inspiral models have been developed from the post-Newtonian expansion. Here we present a novel method to convert the dominant quadrupolar mode of any circular analytical binary-black-hole model into an eccentric model. First, using numerical simulations, we examine the additional amplitude and frequency modulations of eccentric signals that are not present in their circular counterparts. Subsequently, we identify suitable analytical descriptions of those modulations and interpolate key parameters from twelve numerical simulations designated as our training dataset. This allows us to reconstruct the modulated amplitude and phase of any waveform up to mass ratio 3 and eccentricity 0.2. We find that the minimum overlap of the new model with numerical simulations is around 0.98 over all of our test dataset that are scaled to a 50M$_\odot$ black-hole binary starting at 35 Hz with aLIGO A+ design sensitivity. A Python package \pyrex easily carries out the computation of this method.
Adversarial Stylometry in the Wild: Transferable Lexical Substitution Attacks on Author Profiling

Written language contains stylistic cues that can be exploited to automatically infer a variety of potentially sensitive author information. Adversarial stylometry intends to attack such models by rewriting an author’s text. Our research proposes several components to facilitate deployment of these adversarial attacks in the wild, where neither data nor target models are accessible. We introduce a transformer-based extension of a lexical replacement attack, and show it achieves high transferability when trained on a weakly labeled corpus—decreasing target model performance below chance. While not completely inconspicuous, our more successful attacks also prove notably less detectable by humans. Our framework therefore provides a promising direction for future privacy-preserving adversarial attacks.
Motif-based tests for bipartite networks

Bipartite networks are a natural representation of the interactions between entities from two different types. The organization (or topology) of such networks gives insight to understand the systems they describe as a whole. Here, we rely on motifs which provide a meso-scale description of the topology. Moreover, we consider the bipartite expected degree distribution (B-EDD) model which accounts for both the density of the network and possible imbalances between the degrees of the nodes. Under the B-EDD model, we prove the asymptotic normality of the count of any given motif, considering sparsity conditions. We also provide close-form expressions for the mean and the variance of this count. This allows to avoid computationally prohibitive resampling procedures. Based on these results, we define a goodness-of-fit test for the B-EDD model and propose a family of tests for network comparisons. We assess the asymptotic normality of the test statistics and the power of the proposed tests on synthetic experiments and illustrate their use on ecological data sets.
“Can I Touch This?”: Survey of Virtual Reality Interactions via Haptic Solutions: Revue de Littérature des Interactions en Réalité Virtuelle par le biais de Solutions Haptiques

Haptic feedback has become crucial to enhance the user experiences in Virtual Reality (VR). This justifies the sudden burst of novel haptic solutions proposed these past years in the HCI community. This article is a survey of Virtual Reality interactions, relying on haptic devices. We propose two dimensions to describe and compare the current haptic solutions: their degree of physicality, as well as their degree of actuation. We depict a compromise between the user and the designer, highlighting how the range of required or proposed stimulation in VR is opposed to the haptic interfaces flexibility and their deployment in real-life use-cases. This paper (1) outlines the variety of haptic solutions and provides a novel perspective for analysing their associated interactions, (2) highlights the limits of the current evaluation criteria regarding these interactions, and finally (3) reflects the interaction, operation and conception potentials of ”encountered-type of haptic devices”.
Coverage Analysis of Broadcast Networks With Users Having Heterogeneous Content/Advertisement Preferences

This work is focused on the system-level performance of a broadcast network. Since all transmitters in a broadcast network transmit identical signals, received signals from multiple transmitters can be combined to improve system performance. We develop a stochastic geometry-based analytical framework to derive the coverage of a typical receiver. We show that there may exist an optimal connectivity radius that maximizes the rate coverage. Our analysis includes the fact that users may have their individual content/advertisement preferences. We assume that there are multiple classes of users with each user class preferring a particular type of content/advertisements and the users will pay the network only when they can see content aligned with their interest. The operator may choose to transmit multiple contents simultaneously to cater to more users’ interests to increase its revenue. We present revenue models to study the impact of the number of contents on the operator revenue. We consider two scenarios for users’ distribution- one where users’ interest depends on their geographical location and the other where it doesn’t. With the help of numerical results and analysis, we show the impact of various parameters including content granularity, connectivity radius, and rate threshold and present important design insights.
C-for-Metal: High Performance Simd Programming on Intel GPUs

The SIMT execution model is commonly used for general GPU development. CUDA and OpenCL developers write scalar code that is implicitly parallelized by compiler and hardware. On Intel GPUs, however, this abstraction has profound performance implications as the underlying ISA is SIMD and important hardware capabilities cannot be fully utilized. To close this performance gap we introduce C- For- Metal (CM), an explicit SIMD programming framework designed to deliver close-to-the-metal performance on Intel GPUs. The CM programming language and its vector/matrix types provide an intuitive interface to exploit the underlying hardware features, allowing fine-grained register management, SIMD size control and cross-lane data sharing. Experimental results show that CM applications from different domains outperform the best-known SIMT-based OpenCL implementations, achieving up to 2.7x speedup on the latest Intel GPU.
Multi-agent simulation of voter's behaviour

The goal of this paper is to simulate the voters’ behaviour given a voting method. Our approach uses a multi-agent simulation in order to model a voting process through many iterations, so that the voters can vote by taking into account the results of polls. Here we only tried basic rules and a single voting method, but further attempts could explore new features.
Sample Path Generation of the Stochastic Volatility CGMY Process and Its Application to Path-Dependent Option Pricing

This paper proposes the sample path generation method for the stochastic volatility version of the CGMY process. We present the Monte-Carlo method for European and American option pricing with the sample path generation and calibrate model parameters to the American style S&P 100 index options market, using the least square regression method. Moreover, we discuss path-dependent options, such as Asian and Barrier options.
Normalization for Cubical Type Theory

We prove normalization for (univalent, Cartesian) cubical type theory, closing the last major open problem in the syntactic metatheory of cubical type theory. Our normalization result is reduction-free, in the sense of yielding a bijection between equivalence classes of terms in context and a tractable language of β/η-normal forms. As corollaries we obtain both decidability of judgmental equality and the injectivity of type constructors.
Gaitgraph: Graph Convolutional Network for Skeleton-Based Gait Recognition

Gait recognition is a promising video-based biometric for identifying individual walking patterns from a long distance. At present, most gait recognition methods use silhouette images to represent a person in each frame. However, silhouette images can lose fine-grained spatial information, and most papers do not regard how to obtain these silhouettes in complex scenes. Furthermore, silhouette images contain not only gait features but also other visual clues that can be recognized. Hence these approaches can not be considered as strict gait recognition. We leverage recent advances in human pose estimation to estimate robust skeleton poses directly from RGB images to bring back model-based gait recognition with a cleaner representation of gait. Thus, we propose GaitGraph that combines skeleton poses with Graph Convolutional Network (GCN) to obtain a modern model-based approach for gait recognition. The main advantages are a cleaner, more elegant extraction of the gait features and the ability to incorporate powerful spatiotemporal modeling using GCN. Experiments on the popular CASIA-B gait dataset show that our method archives state-of-the-art performance in model-based gait recognition.The code and models are publicly available1
Powering COVID-19 Community Q&A with Curated Side Information

Community question answering and discussion platforms such as Reddit, Yahoo! answers or Quora provide users the flexibility of asking open ended questions to a large audience, and replies to such questions maybe useful both to the user and the community on certain topics such as health, sports or finance. Given the recent events around COVID-19, some of these platforms have attracted 2000+ questions from users about several aspects associated with the disease. Given the impact of this disease on general public, in this work we investigate ways to improve the ranking of user generated answers on COVID-19. We specifically explore the utility of external technical sources of side information (such as CDC guidelines or WHO FAQs) in improving answer ranking on such platforms. We found that ranking user answers based on question-answer similarity is not sufficient, and existing models cannot effectively exploit external (side) information. In this work, we demonstrate the effectiveness of different attention based neural models that can directly exploit side information available in technical documents or verified forums (e.g., research publications on COVID-19 or WHO website). Augmented with a temperature mechanism, the attention based neural models can selectively determine the relevance of side information for a given user question, while ranking answers.
Open-domain Topic Identification of Out-of-domain Utterances using Wikipedia

Users of spoken dialogue systems (SDS) expect high quality interactions across a wide range of diverse topics. However, the implementation of SDS capable of responding to every conceivable user utterance in an informative way is a challenging problem. Multi-domain SDS must necessarily identify and deal with out-of-domain (OOD) utterances to generate appropriate responses as users do not always know in advance what domains the SDS can handle. To address this problem, we extend the current state-of-the-art in multi-domain SDS by estimating the topic of OOD utterances using external knowledge representation from Wikipedia. Experimental results on real human-to-human dialogues showed that our approach does not degrade domain prediction performance when compared to the base model. But more significantly, our joint training achieves more accurate predictions of the nearest Wikipedia article by up to about 30% when compared to the benchmarks.
Scaling Regimes of Active Turbulence with External Dissipation

Active fluids exhibit complex turbulentlike flows at low Reynolds number. Recent predicted that 2D active nematic turbulence follows scaling laws with universal exponents. However, experimentally testing these predictions is conditioned by the coupling to the 3D environment. Here, we measure the spectrum of the kinetic energy E ð q Þ in an active nematic film in contact with a passive oil layer. At small and intermediate scales, we find the scaling regimes E ð q Þ ∼ q − 4 and E ð q Þ ∼ q − 1 , respectively, in agreement with the theoretical prediction for 2D active nematics. At large scales, however, we find a new scaling E ð q Þ ∼ q , which emerges when the dissipation is dominated by the 3D oil layer. In addition, we derive an explicit expression for the spectrum that spans all length scales, thus explaining and connecting the different scaling regimes. This allows us to fit the data and extract the length scale that controls the crossover to the new large-scale regime, which we tune by varying the oil viscosity. Overall, our work experimentally demonstrates the emergence of scaling laws with universal exponents in active turbulence, and it establishes how the spectrum is affected by external dissipation.
Language Modelling as a Multi-Task Problem

In this paper, we propose to study language modelling as a multi-task problem, bringing together three strands of research: multi-task learning, linguistics, and interpretability. Based on hypotheses derived from linguistic theory, we investigate whether language models adhere to learning principles of multi-task learning during training. To showcase the idea, we analyse the generalisation behaviour of language models as they learn the linguistic concept of Negative Polarity Items (NPIs). Our experiments demonstrate that a multi-task setting naturally emerges within the objective of the more general task of language modelling. We argue that this insight is valuable for multi-task learning, linguistics and interpretability research and can lead to exciting new findings in all three domains.
Purification and Characterization of Lipase from the Liver of Carp, Cyprinus carpio L. (1758), Living in Lake Tödürge (Sivas, TÜRKİYE)

In this study, a lipase from the liver of Cyprinus carpio Linnaeus (1758) living in Todurge Lake (Sivas) was purified with purification parameters of 90.38 μmol/dk.mg protein specific activity and 75.50 fold purity. The purification procedure consisted of preparation of homogenate, precipitation with polyethylene glycol-6000 (PEG-6000) and chromatographic techniques including in Q sepharose, sephacryl S 200 HR and phenyl sepharose CL-4B, respectively. The purified lipase showed a single band with approximately a molecular weight of 74 kDa by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE). pH and temperature optimums for the purified enzyme were determined as 8.0 and 37 °C using p-nitrophenyl butirate (p-NPB) as substrate. Km and Vmax values were found as 0.17 mM p-NPB and 2.6 μmol/mL.dk, respectively. It was observed that surface active agents, such as sodium dodecyl sulfate (SDS), Triton X-100 and Nataurocholate act as inhibitor on lipase activity.
TorchPRISM: Principal Image Sections Mapping, a novel method for Convolutional Neural Network features visualization

TorchPRISM: Principal Image Sections Mapping, a novel method for Convolutional Neural Network features visualization Authors / main developers Tomasz Szandała Abstract. In this paper we introduce a tool called Principal Image Sections Mapping PRISM, dedicated for PyTorch, but can be easily ported to other deep learning frameworks. Presented software relies on Principal Component Analysis to visualize the most significant features recognized by a given Convolutional Neural Network. Moreover, it allows to display comparative set features between images processed in the same batch, therefore PRISM can be a method well synerging with technique Explanation by Example.
Multiwavelength monitoring of NGC 1275 over a decade: evidence of a shift in synchrotron peak frequency and long-term multiband flux increase

We carried out a detailed study of the temporal and broadband spectral behaviour of one of the brightest misaligned active galaxies in gamma-rays, NGC 1275 utilising 11 years of Fermi, and available Swift and AstroSat observations. Based on the cumulative flux distribution of the gamma-ray lightcurve, we identified four distinct activity states and noticed an increase in the baseline flux during the first three states. Similar nature of the increase in the average flux was also noticed in X-ray and UV bands. A large flaring activity in gamma-rays was noticed in the fourth state. The source was observed twice by AstroSat for shorter intervals (~days) during the longer observing periods (~years) state 3 and 4. During AstroSat observing periods, the source gamma-ray flux was higher than the average flux observed during longer duration states. The increase in the average baseline flux from state 1 to state 3 can be explained considering a corresponding increase of jet particle normalisation. The inverse Comptonisation of synchrotron photons explained the average X-ray and gamma-ray emission by jet electrons during the first three longer duration states. However, during the shorter duration AstroSat observing periods, a shift of the synchrotron peak frequency was noticed, and the synchrotron emission of jet electrons well explained the observed X-ray flux.
Anomalies in the gravitational recoil of eccentric black-hole mergers with unequal mass ratios

The radiation of linear momentum imparts a recoil (or"kick") to the center of mass of a merging black hole binary system. Recent numerical relativity calculations have shown that eccentricity can lead to an approximate 25% increase in recoil velocities for equal-mass, spinning binaries with spins lying in the orbital plane ("superkick"configurations) [U Sperhake et al. Phys. Rev. D 101 (2020) 024044 (arXiv:1910.01598)]. Here we investigate the impact of nonzero eccentricity on the kick magnitude and gravitational-wave emission of nonspinning, unequal-mass black hole binaries. We confirm that nonzero eccentricities at merger can lead to kicks which are larger by up to ~25% relative to the quasicircular case. We also find that the kick velocity $v$ has an oscillatory dependence on eccentricity, that we interpret as a consequence of changes in the angle between the infall direction at merger and the apoapsis (or periapsis) direction.
Denoising Single Voxel Magnetic Resonance Spectroscopy with Deep Learning on Repeatedly Sampled In Vivo Data

— Objective: Magnetic Resonance Spectroscopy (MRS) is a noninvasive tool to reveal metabolic information. One challenge of MRS is the relatively low Signal-Noise Ratio (SNR) due to low concentrations of metabolites. To improve the SNR, the most common approach is to average signals that are acquired in multiple times. The data acquisition time, however, is increased by multiple times accordingly, resulting in the scanned objects uncomfortable or even unbearable. Methods: By exploring the multiple sampled data, a deep learning denoising approach is proposed to learn a mapping from the low SNR signal to the high SNR one. Results: Results on simulated and in vivo data show that the proposed method significantly reduces the data acquisition time with slightly compromised metabolic accuracy. Conclusion: A deep learning denoising method was proposed to significantly shorten the time of data acquisition, while maintaining signal accuracy and reliability. Significance: Provide a solution of the fundamental low SNR problem in MRS with artificial intelligence.
Enhanced solar photocurrent using a quantum dot molecule

We present a detailed study on the inﬂuence of coherent tunneling on the photovoltaic properties of a semiconductor molecule driven by solar radiation. The connection between the power delivered by the QDM and quantum coherence is not simply proportional but depends on an interplay between the interdot coherent tunneling, the interaction of the system with thermal phonon reservoirs, and the resonance between the QDM and conduction bands. We explored numerically various parameter regimes and found that the maximum power delivered by the molecule is up to 30% greater than the power delivered by a single quantum-dot device. The calculated photovoltaic conversion eﬃciency is presented in terms of accessible experimental parameters and, as expected, is constrained by the second law.
Using Finite-State Machines to Automatically Scan Classical Greek Hexameter

This paper presents a fully automatic approach to the scansion of Classical Greek hexameter verse. In particular, the paper describes an algorithm that uses deterministic finite-state automata and local linguistic rules to implement a targeted search for valid spondeus patterns and, in addition, a weighted finite-state transducer to correct and complete partial analyses and to reject invalid candidates. The paper also details the results of an empirical evaluation of the annotation quality resulting from this approach on hand-annotated data. It is shown that a finite-state approach provides quick and linguistically sound analyses of hexameter verses as well as an efficient formalisation of linguistic knowledge. The project code is available (see https://github.com/anetschka/greek_scansion).
Reinforcement Learning for Mean Field Games with Strategic Complementarities

Mean Field Games (MFG) are the class of games with a very large number of agents and the standard equilibrium concept is a Mean Field Equilibrium (MFE). Algorithms for learning MFE in dynamic MFGs are unknown in general. Our focus is on an important subclass that possess a monotonicity property called Strategic Complementar-ities (MFG-SC). We introduce a natural re-ﬁnement to the equilibrium concept that we call Trembling-Hand-Perfect MFE (T-MFE), which allows agents to employ a measure of randomization while accounting for the impact of such randomization on their payoﬀs. We propose a simple algorithm for computing T-MFE under a known model. We also introduce a model-free and a model-based approach to learning T-MFE and provide sample complexities of both algorithms. We also develop a fully online learning scheme that obviates the need for a simulator. Finally, we empirically evaluate the performance of the proposed algorithms via examples motivated by real-world applications.