nils-herrmann/clean_scirepeval huggingface dataset:
Computer-Aided Data Mining: Automating a Novel Knowledge Discovery and Data Mining Process Model for Metabolomics

This work presents MeKDDaM-SAGA, computer-aided automation software for implementing a novel knowledge discovery and data mining process model that was designed for performing justifiable, traceable and reproducible metabolomics data analysis. The process model focuses on achieving metabolomics analytical objectives and on considering the nature of its involved data. MeKDDaM-SAGA was successfully used for guiding the process model execution in a number of metabolomics applications. It satisfies the requirements of the proposed process model design and execution. The software realises the process model layout, structure and flow and it enables its execution externally using various data mining and machine learning tools or internally using a number of embedded facilities that were built for performing a number of automated activities such as data preprocessing, data exploration, data acclimatization, modelling, evaluation and visualization. MeKDDaM-SAGA was developed using object-oriented software engineering methodology and was constructed in Java. It consists of 241 design classes that were designed to implement 27 use-cases. The software uses an XML database to guarantee portability and uses a GUI interface to ensure its user-friendliness. It implements an internal embedded version control system that is used to realise and manage the process flow, feedback and iterations and to enable undoing and redoing the execution of the process phases, activities, and the internal tasks within its phases.
On Quantitative Comparison of Chemical Reaction Network Models

Chemical reaction networks (CRNs) provide a convenient language for modelling a broad variety of biological systems. These models are commonly studied with respect to the time series they generate in deterministic or stochastic simulations. Their dynamic behaviours are then analysed, often by using deterministic methods based on differential equations with a focus on the steady states. Here, we propose a method for comparing CRNs with respect to their behaviour in stochastic simulations. Our method is based on using the flux graphs that are delivered by stochastic simulations as abstract representations of their dynamic behaviour. This allows us to compare the behaviour of any two CRNs for any time interval, and define a notion of equivalence on them that overlaps with graph isomorphism at the lowest level of representation. The similarity between the compared CRNs can be quantified in terms of their distance. The results can then be used to refine the models or to replace a larger model with a smaller one that produces the same behaviour or vice versa.
Dark Matter Energy Deposition and Production from the Table-Top to the Cosmos

The discovery of nongravitational interactions between dark matter and the Standard Model would be an important step in unraveling the nature of dark matter. If such an interaction exists, it would have profound implications on how dark matter is produced in both the early universe and in collider experiments. In addition, it would also allow dark matter to deposit energy into Standard Model particles in unexpected ways. This thesis details some recent progress made in understanding these implications, including (i) a new freezeout mechanism for thermal dark matter dominated by a 3-to-2 process within a vector portal dark sector model; (ii) a study of how the existence of dark sector bound states can influence collider, direct and indirect searches for dark matter; (iii) a new axion dark matter interferometric search using a cavity that is sensitive to the axion-induced rotation of linearly polarized light; (iv) a definitive assessment of the potential contribution of dark matter annihilation and decay to cosmic reionization; (v) new constraints on dark matter annihilation rates and decay lifetimes from 21-cm cosmology, and (vi) a new numerical code, DarkHistory, which significantly improves the computation of the ionization and thermal histories of the universe in the presence of exotic sources of energy injection. These novel ideas span length scales ranging from table-top experiments to the entire cosmos, and represent just a few of the myriad of ways in which dark matter may yet surprise us.
Image based Eye Gaze Tracking and its Applications

Eye movements play a vital role in perceiving the world. Eye gaze can give a direct indication of the users point of attention, which can be useful in improving human-computer interaction. Gaze estimation in a non-intrusive manner can make human-computer interaction more natural. Eye tracking can be used for several applications such as fatigue detection, biometric authentication, disease diagnosis, activity recognition, alertness level estimation, gaze-contingent display, human-computer interaction, etc. Even though eye-tracking technology has been around for many decades, it has not found much use in consumer applications. The main reasons are the high cost of eye tracking hardware and lack of consumer level applications. In this work, we attempt to address these two issues. In the first part of this work, image-based algorithms are developed for gaze tracking which includes a new two-stage iris center localization algorithm. We have developed a new algorithm which works in challenging conditions such as motion blur, glint, and varying illumination levels. A person independent gaze direction classification framework using a convolutional neural network is also developed which eliminates the requirement of user-specific calibration. 
In the second part of this work, we have developed two applications which can benefit from eye tracking data. A new framework for biometric identification based on eye movement parameters is developed. A framework for activity recognition, using gaze data from a head-mounted eye tracker is also developed. The information from gaze data, ego-motion, and visual features are integrated to classify the activities.
Three-dimensional dust mapping of 12 supernovae remnants in the Galactic anticentre

We present three-dimensional (3D) dust mapping of 12 supernova remnants (SNRs) in the Galactic anticentre (Galactic longitude l between 150○ and 210○) based on a recent 3D interstellar extinction map. The dust distribution of the regions, which cover the full extents in the radio continuum for the individual SNRs, is discussed. Four SNRs show significant spatial coincidences between molecular clouds (MCs) revealed from the 3D extinction mapping and the corresponding radio features. The results confirm the interactions between these SNRs and their surrounding MCs. Based on these correlations, we provide new distance estimates of the four SNRs, G189.1+3.0 (IC443, $d=1729^{+116}_{-94} \rm \, pc$), G190.9–2.2 ($d=1036^{+17}_{-81} \rm \, pc$), G205.5+0.5 ($d=941^{+96}_{-94}$ or $1257^{+92}_{-101} \rm \, pc$), and G213.0–0.6 ($d=1146^{+79}_{-80} \rm \, pc$). In addition, we find indirect evidences of potential interactions between SNRs and MCs for three other SNRs. New distance constraints are also given for these three SNRs.
Universal interferometric signatures of a black hole’s photon ring

A black hole image contains a series of increasingly narrow rings measurable with progressively longer interferometers. The Event Horizon Telescope image of the supermassive black hole in the galaxy M87 is dominated by a bright, unresolved ring. General relativity predicts that embedded within this image lies a thin “photon ring,” which is composed of an infinite sequence of self-similar subrings that are indexed by the number of photon orbits around the black hole. The subrings approach the edge of the black hole “shadow,” becoming exponentially narrower but weaker with increasing orbit number, with seemingly negligible contributions from high-order subrings. Here, we show that these subrings produce strong and universal signatures on long interferometric baselines. These signatures offer the possibility of precise measurements of black hole mass and spin, as well as tests of general relativity, using only a sparse interferometric array.
The evolution of kicked stellar-mass black holes in star cluster environments - II. Rotating star clusters

In this paper, we continue our study on the evolution of black holes (BHs) that receive velocity kicks at the origin of their host star cluster potential. We now focus on BHs in rotating clusters that receive a range of kick velocities in different directions with respect to the rotation axis. We perform N-body simulations to calculate the trajectories of the kicked BHs and develop an analytic framework to study their motion as a function of the host cluster and the kick itself. Our simulations indicate that for a BH that is kicked outside of the cluster’s core, as its orbit decays in a rotating cluster the BH will quickly gain angular momentum as it interacts with stars with high rotational frequencies. Once the BH decays to the point where its orbital frequency equals that of local stars, its orbit will be circular and dynamical friction becomes ineffective since local stars will have low relative velocities. After circularization, the BH’s orbit decays on a longer time-scale than if the host cluster was not rotating. Hence BHs in rotating clusters will have longer orbital decay times. The time-scale for orbit circularization depends strongly on the cluster’s rotation rate and the initial kick velocity, with kicked BHs in slowly rotating clusters being able to decay into the core before circularization occurs. The implication of the circularization phase is that the probability of a BH undergoing a tidal capture event increases, possibly aiding in the formation of binaries and high-mass BHs.
Baryons in the Cosmic Web of IllustrisTNG – II. The connection among galaxies, haloes, their formation time, and their location in the Cosmic Web

The connections among galaxies, the dark matter haloes where they form and the properties of the large-scale Cosmic Web still need to be completely disentangled. We use the cosmological hydrodynamical simulation TNG100 of the IllustrisTNG suite to quantify the effects played by the large-scale density field and the Cosmic Web morphology on the relation between halo mass and galaxy stellar mass. We select objects with total dynamical mass in the range ${\ge}6.3\times 10^{10} \,h ^{-1}\, \mathrm{ M}_{\odot }$ up to a few $10^{14}\, h^{-1} \, \mathrm{ M}_{\odot }$ between redshift z = 4 and redshift z = 0. A Cosmic Web class (knot, filament, sheet, void) is assigned to each region of the volume using a density field deformation tensor-based method. We find that galaxy stellar mass strongly correlates with total dynamical mass and formation time, and more weakly with large-scale overdensity and Cosmic Web class. The latter two quantities correlate with each other, but are not entirely degenerate. Furthermore, we find that at fixed halo mass, galaxies with stellar mass lower than the median value are more likely to be found in voids and sheets, whereas galaxies with stellar mass higher than the median are more likely to be found in filaments and knots. Finally, we find that the dependence on environment is stronger for satellites than for centrals, and discuss the physical implications of these results.
The diversity of the circumgalactic medium around z = 0 Milky Way-mass galaxies from the Auriga simulations

Galaxies are surrounded by massive gas reservoirs (i.e. the circumgalactic medium; CGM) which play a key role in their evolution. The properties of the CGM, which are dependent on a variety of internal and environmental factors, are often inferred from absorption line surveys which rely on a limited number of single lines-of-sight. In this work we present an analysis of 28 galaxy haloes selected from the Auriga project, a cosmological magneto-hydrodynamical zoom-in simulation suite of isolated Milky Way-mass galaxies, to understand the impact of CGM diversity on observational studies. Although the Auriga haloes are selected to populate a narrow range in halo mass, our work demonstrates that the CGM of L⋆ galaxies is extremely diverse: column densities of commonly observed species span ∼3 − 4 dex and their covering fractions range from ${\sim } 5$ to $90{{\ \rm per\ cent}}$. Despite this diversity, we identify the following correlations: 1) the covering fractions (CF) of hydrogen and metals of the Auriga haloes positively correlate with stellar mass, 2) the CF of H i, C iv, and Si ii anticorrelate with active galactic nucleus luminosity due to ionization effects, and 3) the CF of H i, C iv, and Si ii positively correlate with galaxy disc fraction due to outflows populating the CGM with cool and dense gas. The Auriga sample demonstrates striking diversity within the CGM of L⋆ galaxies, which poses a challenge for observations reconstructing CGM characteristics from limited samples, and also indicates that long-term merger assembly history and recent star formation are not the dominant sculptors of the CGM.
Stellar Mass Growth of Brightest Cluster Galaxy Progenitors in COSMOS Since z ∼ 3

We examine the role of environment on the in situ star formation (SF) hosted by the progenitors of the most massive galaxies in the present-day universe, the brightest cluster galaxies (BCGs), from z ∼ 3 to present in the COSMOS field. Progenitors are selected from the COSMOS field using a stellar mass cut motivated by the evolving cumulative comoving number density of progenitors within the Illustris simulation, as well as the Millennium-II simulation and a constant comoving number density method for comparison. We characterize each progenitor using far-ultraviolet–far-infrared observations taken from the COSMOS field and fitting stellar, dust, and active galactic nucleus components to their spectral energy distributions. Additionally, we compare the SF rates of our progenitor sample to the local density maps of the COSMOS field to identify the effects of environment. We find that BCG progenitors evolve in three stages, starting with an in situ SF-dominated phase (z > 2.25). This is followed by a phase until z ∼ 1.25 where mass growth is driven by in situ SF and stellar mass deposited by mergers (both gas rich and poor) on the same order of magnitude independent of local environment. Finally, at low redshift dry mergers are the dominant stellar mass generation process. We also identify this final transition period as the time when progenitors quench, exhibiting quiescent NUVrJ colors.
Cross-Domain Generalization of Neural Constituency Parsers

Neural parsers obtain state-of-the-art results on benchmark treebanks for constituency parsing—but to what degree do they generalize to other domains? We present three results about the generalization of neural parsers in a zero-shot setting: training on trees from one corpus and evaluating on out-of-domain corpora. First, neural and non-neural parsers generalize comparably to new domains. Second, incorporating pre-trained encoder representations into neural parsers substantially improves their performance across all domains, but does not give a larger relative improvement for out-of-domain treebanks. Finally, despite the rich input representations they learn, neural parsers still benefit from structured output prediction of output trees, yielding higher exact match accuracy and stronger generalization both to larger text spans and to out-of-domain corpora. We analyze generalization on English and Chinese corpora, and in the process obtain state-of-the-art parsing results for the Brown, Genia, and English Web treebanks.
Transfer Learning from Audio-Visual Grounding to Speech Recognition

Transfer learning aims to reduce the amount of data required to excel at a new task by re-using the knowledge acquired from learning other related tasks. This paper proposes a novel transfer learning scenario, which distills robust phonetic features from grounding models that are trained to tell whether a pair of image and speech are semantically correlated, without using any textual transcripts. As semantics of speech are largely determined by its lexical content, grounding models learn to preserve phonetic information while disregarding uncorrelated factors, such as speaker and channel. To study the properties of features distilled from different layers, we use them as input separately to train multiple speech recognition models. Empirical results demonstrate that layers closer to input retain more phonetic information, while following layers exhibit greater invariance to domain shift. Moreover, while most previous studies include training data for speech recognition for feature extractor training, our grounding models are not trained on any of those data, indicating more universal applicability to new domains.
Hybrid system identification using switching density networks

Behaviour cloning is a commonly used strategy for imitation learning and can be extremely effective in constrained domains. However, in cases where the dynamics of an environment may be state dependent and varying, behaviour cloning places a burden on model capacity and the number of demonstrations required. This paper introduces switching density networks, which rely on a categorical reparametrisation for hybrid system identification. This results in a network comprising a classification layer that is followed by a regression layer. We use switching density networks to predict the parameters of hybrid control laws, which are toggled by a switching layer to produce different controller outputs, when conditioned on an input state. This work shows how switching density networks can be used for hybrid system identification in a variety of tasks, successfully identifying the key joint angle goals that make up manipulation tasks, while simultaneously learning image-based goal classifiers and regression networks that predict joint angles from images. We also show that they can cluster the phase space of an inverted pendulum, identifying the balance, spin and pump controllers required to solve this task. Switching density networks can be difficult to train, but we introduce a cross entropy regularisation loss that stabilises training.
Fast Estimating Pedestrian Moving State Based on Single 2D Body Pose by Shallow Neural Network

Crossing or Not-Crossing (C/NC) problem is important to autonomous vehicles (AVs) to safely interact with pedestrians. However, this problem setup ignores pedestrians walking along the direction of vehicles' movement (LONG). To enhance AVs' awareness of pedestrians behavior, we make the first step towards extending C/NC to C/NC/LONG problem and recognize them based on single body pose. In contrast, previous C/NC state classification work depend on multiple poses or contextual information. Our proposed shallow neural network classifier is able to recognize these three states within a very short time. We test our it on JAAD dataset and report average 81.23% accuracy. In order to further improve the classifier's performance, we introduce a computational-efficient method, action momentum optimizer (AMO), which correct prediction based on crossing behavior pattern. And our experiment shows that classifier perform at most 11.39% better on continuous pose test with the help of it. Furthermore, this model can cooperate with different sensors and algorithms that provide 2D pedestrian body pose so that it is able to work across multiple light and weather conditions. In addition, we have created extended annotations of pose for JAAD dataset, which will be publicly released soon
BASN - Learning Steganography with Binary Attention Mechanism

Secret information sharing through image carriers has aroused much research attention in recent years with images’ growing domination on the Internet and mobile applications. The technique of embedding secret information in images without being detected is called image steganography. With the booming trend of convolutional neural networks (CNN), neural-network-automated tasks have been embedded more deeply in our daily lives. However, a series of wrong labeling or bad captioning on the embedded images has left a trace of skepticism and finally leads to a self-confession like exposure. To improve the security of image steganography and minimize task result distortion, models must maintain the feature maps generated by task-specific networks being irrelative to any hidden information embedded in the carrier. This paper introduces a binary attention mechanism into image steganography to help alleviate the security issue, and, in the meantime, increase embedding payload capacity. The experimental results show that our method has the advantage of high payload capacity with little feature map distortion and still resist detection by state-of-the-art image steganalysis algorithms.
Capturing Financial markets to apply Deep Reinforcement Learning

In this paper we explore the usage of deep reinforcement learning algorithms to automatically generate consistently profitable, robust, uncorrelated trading signals in any general financial market. In order to do this, we present a novel Markov decision process (MDP) model to capture the financial trading markets. We review and propose various modifications to existing approaches and explore different techniques like the usage of technical indicators, to succinctly capture the market dynamics to model the markets. We then go on to use deep reinforcement learning to enable the agent (the algorithm) to learn how to take profitable trades in any market on its own, while suggesting various methodology changes and leveraging the unique representation of the FMDP (financial MDP) to tackle the primary challenges faced in similar works. Through our experimentation results, we go on to show that our model could be easily extended to two very different financial markets and generates a positively robust performance in all conducted experiments.
ALMA Spectral Imaging of Titan Contemporaneous with Cassini's Grand Finale

The Cassini mission performed 127 targeted flybys of Titan during its 13 yr mission to Saturn, culminating in the Grand Finale between 2017 April and September. Here we demonstrate the use of the Atacama Large Millimeter/submillimeter Array (ALMA) to continue Cassini’s legacy for chemical and climatological studies of Titan’s atmosphere. Whole-hemisphere, interferometric spectral maps of HCN, HNC, HC3N, CH3CN, C2H3CN, C2H5CN, and C3H8 were obtained using ALMA in 2017 May at moderate (≈0.″2, or ≈1300 km) spatial resolution, revealing the effects of seasonally variable chemistry and dynamics on the distribution of each species. The ALMA submillimeter observations of HCN and HC3N are consistent with Cassini infrared data on these species, obtained in the same month. Chemical/dynamical lifetimes of a few years are inferred for C2H3CN and C2H5CN, in reasonably close agreement with the latest chemical models incorporating the sticking of C2H5CN to stratospheric aerosol particles. ALMA radial limb flux profiles provide column density information as a function of altitude, revealing maximum abundances in the thermosphere (above 600 km) for HCN, HNC, HC3N, and C2H5CN. This constitutes the first detailed measurement of the spatial distribution of HNC, which is found to be confined predominantly to altitudes above 730 ± 60 km. The HNC emission map shows an east–west hemispheric asymmetry of 13% ± 3%. These results are consistent with very rapid production (and loss) of HNC in Titan’s uppermost atmosphere, making this molecule an effective probe of short-timescale (diurnal) ionospheric processes.
Convergence Rates for Gaussian Mixtures of Experts

We provide a theoretical treatment of over-specified Gaussian mixtures of experts with covariate-free gating networks. We establish the convergence rates of the maximum likelihood estimation (MLE) for these models. Our proof technique is based on a novel notion of \emph{algebraic independence} of the expert functions. Drawing on optimal transport theory, we establish a connection between the algebraic independence and a certain class of partial differential equations (PDEs). Exploiting this connection allows us to derive convergence rates and minimax lower bounds for parameter estimation.