nils-herrmann/clean_scirepeval huggingface dataset:
Simultaneous Segmentation and Recognition: Towards More Accurate Ego Gesture Recognition

Ego hand gestures can be used as an interface in AR and VR environments. While the context of an image is important for tasks like scene understanding, object recognition, image caption generation and activity recognition, it plays a minimal role in ego hand gesture recognition. An ego hand gesture used for AR and VR environments conveys the same information regardless of the background. With this idea in mind, we present our work on ego hand gesture recognition that produces embeddings from RBG images with ego hands, which are simultaneously used for ego hand segmentation and ego gesture recognition. To this extent, we achieved better recognition accuracy (96.9%) compared to the state of the art (92.2%) on the biggest ego hand gesture dataset available publicly. We present a gesture recognition deep neural network which recognises ego hand gestures from videos (videos containing a single gesture) by generating and recognising embeddings of ego hands from image sequences of varying length. We introduce the concept of simultaneous segmentation and recognition applied to ego hand gestures, present the network architecture, the training procedure and the results compared to the state of the art on the EgoGesture dataset
Class Feature Pyramids for Video Explanation

Deep convolutional networks are widely used in video action recognition. 3D convolutions are one prominent approach to deal with the additional time dimension. While 3D convolutions typically lead to higher accuracies, the inner workings of the trained models are more difficult to interpret. We focus on creating human-understandable visual explanations that represent the hierarchical parts of spatio-temporal networks. We introduce Class Feature Pyramids, a method that traverses the entire network structure and incrementally discovers kernels at different network depths that are informative for a specific class. Our method does not depend on the network's architecture or the type of 3D convolutions, supporting grouped and depth-wise convolutions, convolutions in fibers, and convolutions in branches. We demonstrate the method on six state-of-the-art 3D convolution neural networks (CNNs) on three action recognition (Kinetics-400, UCF-101, and HMDB-51) and two egocentric action recognition datasets (EPIC-Kitchens and EGTEA Gaze+).
Large e-retailer image dataset for visual search and product classification

Recent results of deep convolutional networks in visual recognition challenges open the path to a whole new set of disruptive user experiences such as visual search or recommendation. The list of companies offering this type of service is growing everyday but the adoption rate and the relevancy of results may vary a lot. We believe that the availability of large and diverse datasets is a necessary condition to improve the relevancy of such recommendation systems and facilitate their adoption. For that purpose, we wish to share with the community this dataset of more than 12M images of the 7M products of our online store classified into 5K categories. This original dataset is introduced in this article and several features are described. We also present some aspects of the winning solutions of our image classification challenge that was organized on the Kaggle platform around this set of images.
Horizon molecules in causal set theory

We propose a new definition of "horizon molecules" in Causal Set Theory following pioneering work by Dou and Sorkin. The new concept applies for any causal horizon and its intersection with any spacelike hypersurface. In the continuum limit, as the discreteness scale tends to zero, the leading behaviour of the expected number of horizon molecules is shown to be the area of the horizon in discreteness units, up to a dimension dependent factor of order one. We also determine the first order corrections to the continuum value, and show how such corrections can be exploited to obtain further geometrical information about the horizon and the spacelike hypersurface from the causal set.
ALMACAL – VI. Molecular gas mass density across cosmic time via a blind search for intervening molecular absorbers

We are just starting to understand the physical processes driving the dramatic change in cosmic star formation rate between z ∼ 2 and the present day. A quantity directly linked to star formation is the molecular gas density, which should be measured through independent methods to explore variations due to cosmic variance and systematic uncertainties. We use intervening CO absorption lines in the spectra of mm-bright background sources to provide a census of the molecular gas mass density of the Universe. The data used in this work are taken from ALMACAL, a wide and deep survey utilizing the ALMA calibrator archive. While we report multiple Galactic absorption lines and one intrinsic absorber, no extragalactic intervening molecular absorbers are detected. However, due to the large redshift path surveyed (Δz = 182), we provide constraints on the molecular column density distribution function beyond z ∼ 0. In addition, we probe column densities of N(H2) > 1016 atoms cm−2, 5 orders of magnitude lower than in previous studies. We use the cosmological hydrodynamical simulation IllustrisTNG to show that our upper limits of $\rho ({\rm H}_2)\lesssim 10^{8.3}\, \text{M}_{\odot }\, \text{Mpc}^{-3}$ at 0 < z ≤ 1.7 already provide new constraints on current theoretical predictions of the cold molecular phase of the gas. These results are in agreement with recent CO emission-line surveys and are complementary to those studies. The combined constraints indicate that the present decrease of the cosmic star formation rate history is consistent with an increasing depletion of molecular gas in galaxies compared to z ∼ 2.
The S-PLUS: a star/galaxy classification based on a Machine Learning approach

We present a star/galaxy classification for the Southern Photometric Local Universe Survey (S-PLUS), based on a Machine Learning approach: the Random Forest algorithm. We train the algorithm using the S-PLUS optical photometry up to $r$=21, matched to SDSS/DR13, and morphological parameters. The metric of importance is defined as the relative decrease of the initial accuracy when all correlations related to a certain feature is vanished. In general, the broad photometric bands presented higher importance when compared to narrow ones. The influence of the morphological parameters has been evaluated training the RF with and without the inclusion of morphological parameters, presenting accuracy values of 95.0\% and 88.1\%, respectively. Particularly, the morphological parameter {\rm FWHM/PSF} performed the highest importance over all features to distinguish between stars and galaxies, indicating that it is crucial to classify objects into stars and galaxies. We investigate the misclassification of stars and galaxies in the broad-band colour-colour diagram $(g-r)$ versus $(r-i)$. The morphology can notably improve the classification of objects at regions in the diagram where the misclassification was relatively high. Consequently, it provides cleaner samples for statistical studies. The expected contamination rate of red galaxies as a function of the redshift is estimated, providing corrections for red galaxy samples. The classification of QSOs as extragalactic objects is slightly better using photometric-only case. An extragalactic point-source catalogue is provided using the classification without any morphology feature (only the SED information) with additional constraints on photometric redshifts and {\rm FWHM/PSF} values.
Velocity debiasing for Hubble constant measurements from standard sirens

Gravitational wave (GW) sources are an excellent probe of the luminosity distance and offer a novel measure of the Hubble constant, $H_0$. This estimation of $H_0$ from standard sirens requires an accurate estimation of the cosmological redshift of the host galaxy of the GW source, after correcting for its peculiar velocity. Absence of an accurate peculiar velocity correction affects both the precision and accuracy of the measurement of $H_0$, particularly for nearby sources. We propose a framework to incorporate such a peculiar velocity correction for GW sources. The implementation of our method to the event GW170817 combined with the Very Large Baseline Interferometry (VLBI) observation leads to a revised value of $H_0= 69.3^{+ 4.5}_{-4.0}$ km s$^{-1}$Mpc$^{-1}$; more importantly, it demonstrates that our method will allow to achieve in practice unbiased, accurate measurements of $H_0$ at the precision promised by standard siren forecasts.
The MOSDEF Survey: The Metallicity Dependence of X-Ray Binary Populations at z ∼ 2

Population synthesis models predict that high-mass X-ray binary (HMXB) populations produced in low-metallicity environments should be more X-ray luminous, a trend supported by studies of nearby galaxies. This trend may be responsible for the observed increase of the X-ray luminosity (LX) per star formation rate (SFR) with redshift due to the decrease of metallicity (Z) at fixed stellar mass as a function of redshift. To test this hypothesis, we use a sample of 79 z ∼ 2 star-forming galaxies with oxygen abundance measurements from the MOSDEF survey, which obtained rest-frame optical spectra for ∼1500 galaxies in the CANDELS fields at 1.37 < z < 3.80. Using Chandra data from the Chandra AEGIS-X Deep, Chandra Deep Field North, and Chandra Deep Field South surveys, we stack the X-ray data at the galaxy locations in bins of redshift and Z because the galaxies are too faint to be individually detected. In agreement with previous studies, the average LX/SFR of our z ∼ 2 galaxy sample is enhanced by ≈0.4–0.8 dex relative to local HMXB LX–SFR scaling relations. Splitting our sample by Z, we find that LX/SFR and Z are anticorrelated with 97% confidence. This observed Z dependence for HMXB-dominated galaxies is consistent with both the local LX–SFR–Z relation and a subset of population synthesis models. Although the statistical significance of the observed trends is weak owing to the low X-ray statistics, these results constitute the first direct evidence connecting the redshift evolution of LX/SFR and the Z dependence of HMXBs.
The NANOGrav 11 yr Data Set: Evolution of Gravitational-wave Background Statistics

An ensemble of inspiraling supermassive black hole binaries should produce a stochastic background of very low frequency gravitational waves. This stochastic background is predicted to be a power law, with a gravitational-wave strain spectral index of −2/3, and it should be detectable by a network of precisely timed millisecond pulsars, widely distributed on the sky. This paper reports a new “time slicing” analysis of the 11 yr data release from the North American Nanohertz Observatory for Gravitational Waves (NANOGrav) using 34 millisecond pulsars. Methods to flag potential “false-positive” signatures are developed, including techniques to identify responsible pulsars. Mitigation strategies are then presented. We demonstrate how an incorrect noise model can lead to spurious signals, and we show how independently modeling noise across 30 Fourier components, spanning NANOGrav’s frequency range, effectively diagnoses and absorbs the excess power in gravitational-wave searches. This results in a nominal, and expected, progression of our gravitational-wave statistics. Additionally, we show that the first interstellar medium event in PSR J1713+0747 pollutes the common red-noise process with low spectral index noise, and we use a tailored noise model to remove these effects.
‘Observing’ unrelaxed clusters in dark matter simulations

We present a detailed study of relaxed and unrelaxed galaxy clusters in a large dark matter only simulation. Recent work has demonstrated clear differences between the galaxy populations in clusters that have Gaussian velocity distributions (relaxed) compared to those that do not (unrelaxed). To directly compare with observations, we identify unrelaxed clusters in the simulations using one-dimensional velocity distributions. We show that non-Gaussian clusters have had recent major mergers and enhanced rates of galaxy infall relative to systems with Gaussian velocity profiles. Furthermore, we find that the fraction of non-Gaussian clusters increases strongly with cluster mass and modestly with redshift. For comparison, we also make use of three-dimensional information available in the simulations to explore the impact of projection on observational measurements. Differences between Gaussian and non-Gaussian clusters are much stronger when three-dimensional information is considered, which demonstrates that the strength of oberserved trends with cluster dynamics are diluted because observed velocity information is limited to one line-of-sight.
New Policy Design for Food Accessibility to the People in Need

Food insecurity is a term used to measure hunger and food deprivation of a large population. As per the 2015 statistics provided by Feeding America - one of the largest domestic hunger-relief organizations in the United States, 42.2 million Americans live in food insecure households, including 29.1 million adults and 13.1 million children. This constitutes about 13.1% of households that are food insecure. Food Banks have been developed to improve food security for the needy. We have developed a novel food distribution policy using suitable welfare and poverty indices and functions. In this work, we propose an equitable and fair distribution of donated foods as per the demands and requirements of the people, thus ensuring minimum wastage of food (perishable and non-perishable) with focus towards nutrition. We present results and analysis based on the application of the proposed policy using the information of a local food bank as a case study. The results show that the new policy performs better than the current methods in terms of population being covered and reduction of food wastage obtaining suitable levels of nutrition.
Does the Leverage Effect Affect the Return Distribution?

The leverage effect refers to the generally negative correlation between the return of an asset and the changes in its volatility. There is broad agreement in the literature that the effect should be present for theoretical reasons, and it has been consistently found in empirical work. However, a few papers have pointed out a puzzle: the return distributions of many assets do not appear to be affected by the leverage effect. We analyze the determinants of the return distribution and find that the impact of the leverage effect comes primarily from an interaction between the leverage effect and the mean-reversion effect. When the leverage effect is large and the mean-reversion effect is small, then the interaction exerts a strong effect on the return distribution. However, if the mean-reversion effect is large, even a large leverage effect has little effect on the return distribution. To better understand the impact of the interaction effect, we propose an indirect method to measure it. We apply our methodology to empirical data and find that the S&P 500 data exhibits a weak interaction effect, and consequently its returns distribution is little impacted by the leverage effect. Furthermore, the interaction effect is closely related to the size factor: small firms tend to have a strong interaction effect and large firms tend to have a weak interaction effect.
Do We Need Neural Models to Explain Human Judgments of Acceptability?

Native speakers can judge whether a sentence is an acceptable instance of their language. Acceptability provides a means of evaluating whether computational language models are processing language in a human-like manner. We test the ability of language models, simple language features, and word embeddings to predict native speakers’ judgments of acceptability on English essays written by non-native speakers. We find that much sentence acceptability variance can be captured by a combination of misspellings, word order, and word similarity (r = 0.494). While predictive neural models fit acceptability judgments well (r = 0.527), we find that a 4-gram model is just as good (r = 0.528). Thanks to incorporating misspellings, our 4-gram model surpasses both the previous unsupervised state-of-the art (r = 0.472), and the average native speaker (r = 0.46), demonstrating that acceptability is well captured by n-gram statistics and simple language features.
Wasserstein Distance Based Domain Adaptation for Object Detection

In this paper, we present an adversarial unsupervised domain adaptation framework for object detection. Prior approaches utilize adversarial training based on cross entropy between the source and target domain distributions to learn a shared feature mapping that minimizes the domain gap. Here, we minimize the Wasserstein distance between the two distributions instead of cross entropy or Jensen-Shannon divergence to improve the stability of domain adaptation in high-dimensional feature spaces that are inherent to object detection task. Additionally, we remove the exact consistency constraint of the shared feature mapping between the source and target domains, so that the target feature mapping can be optimized independently, which is necessary in the case of significant domain gap. We empirically show that the proposed framework can mitigate domain shift in different scenarios, and provide improved target domain object detection performance.
Cross-Lingual Contextual Word Embeddings Mapping With Multi-Sense Words In Mind

Recent work in cross-lingual contextual word embedding learning cannot handle multi-sense words well. In this work, we explore the characteristics of contextual word embeddings and show the link between contextual word embeddings and word senses. We propose two improving solutions by considering contextual multi-sense word embeddings as noise (removal) and by generating cluster level average anchor embeddings for contextual multi-sense word embeddings (replacement). Experiments show that our solutions can improve the supervised contextual word embeddings alignment for multi-sense words in a microscopic perspective without hurting the macroscopic performance on the bilingual lexicon induction task. For unsupervised alignment, our methods significantly improve the performance on the bilingual lexicon induction task for more than 10 points.
LHC phenomenology with hidden sector dark matter: a long-lived stau and heavy Higgs in an observable range.

The presence of a hidden sector with very weak interactions with the standard model has significant implications on LHC signatures. In this work we discuss LHC phenomenology with the inclusion of a hidden sector by a $U(1)$ extension of MSSM/SUGRA. We consider both kinetic mixing and Stueckelberg mass mixing between the $U(1)$ gauge field of the hidden sector and $U(1)_Y$ of the visible sector. Such a model has an extended parameter space. We consider here two limited regions of this parameter space. In the first case we consider a $U(1)$ gauge field along with chiral fields needed for the Stueckelberg mechanism to operate and discuss the mixing between the hidden and the visible sectors. Here if the stau is the lightest sparticle in the MSSM sector and the neutralino of the hidden sector is the LSP of the full system and a dark matter candidate, the stau can be long-lived and decay inside an LHC detector tracker. In the second case we include extra vectorlike matter in the hidden sector which can give rise to a Dirac fermion in addition to the two neutralinos in the hidden sector. The neutralino sector now has six neutralinos and we assume that the lightest of these is the LSP and is higgsino-like. In this case the dark matter is constituted of a Majorana and a Dirac fermion, and a small $\mu$ leads to heavy Higgs boson masses which reside in the observable range of HL-LHC and HE-LHC.
Deep Latent Space Learning for Cross-Modal Mapping of Audio and Visual Signals

We propose a novel deep training algorithm for joint representation of audio and visual information which consists of a single stream network (SSNet) coupled with a novel loss function to learn a shared deep latent space representation of multimodal information. The proposed framework characterizes the shared latent space by leveraging the class centers which helps to eliminate the need of pairwise or triplet supervision. We quantitatively and qualitatively evaluate the proposed approach on VoxCeleb, a benchmarks audio-visual dataset on multitude of tasks including cross-modal verification, cross-modal matching and cross-modal retrieval. State-of-the-art performance is achieved on cross-modal verification and matching while comparable results are observed on the remaining applications. Our experiments demonstrate the effectiveness of the technique for cross-modal biometric applications.
Variable Population Memetic Search: A Case Study on the Critical Node Problem

Population-based memetic algorithms have been successfully applied to solve many difficult combinatorial problems. Often, a population of fixed size is used in such algorithms to record some best solutions sampled during the search. However, given the particular features of the problem instance under consideration, a population of variable size would be more suitable to ensure the best search performance possible. In this work, we propose a variable population memetic search (VPMS), where a strategic population sizing mechanism is used to dynamically adjust the population size during the search process. Our VPMS approach starts its search from a small population of only two solutions to focus on exploitation and then adapts the population size according to the search status to continuously influence the balancing between exploitation and exploration. We illustrate an application of the VPMS approach to solve the challenging critical node problem (CNP). We show that the VPMS algorithm integrating a variable population, an effective local optimization procedure, and a backbone-based crossover operator performs very well compared to state-of-the-art CNP algorithms. The algorithm is able to discover new upper bounds for 12 instances out of the 42 popular benchmark instances while matching 23 previous best-known upper bounds.
Redshift dependence of CMB temperature in BSBM α-varying theories

We have investigated some cosmological consequences of variation of the fine structure constant, α, in the context of the Bekenstein-Sandvik-Barrow-Magueijo (BSBM) model. In this context, such a variation is attributed to the electric charge by letting the latter take on the value of a real scalar field, ϕ, which varies generally in space and time. We consider the dynamics of ϕ in the case that it is allowed to have an exponential potential. We have found solutions for evolutions of ϕ and α in the radiation and the matter dominated eras. By employing observational bounds on the temporal variation of α, we constrain the model parameters. We then investigate the impact of α-variation on the redshift dependence of the CMB temperature. It is shown that there is a deviation from the standard evolution of the temperature which contains two different contributions, one is important at high-redshift eras and the other is mostly effective at low-redshift eras.
Introduction to orbifolds.

In this survey we introduce orbifolds from the classical point of view. We relate orbifolds with group actions, we see how elementary objects from Algebraic Topology generalize to orbifolds, such as the fundamental group and Euler characteristic, then we proceed to the generalizations of classical objects from Differential Geometry to orbifolds, studding orbibundles, differential forms, integration and (equivariant) De Rham cohomology. Finally, we endow orbifolds with Riemannian metrics and survey some generalizations of classical results from Riemannian Geometry to this setting.