nils-herrmann/clean_scirepeval huggingface dataset:
Rearrangement of two dimensional aggregates of droplets under compression: Signatures of the energy landscape from crystal to glass

We study signatures of the energy landscape's evolution through the crystal-to-glass transition by compressing 2D finite aggregates of oil droplets. Droplets of two distinct sizes are used to compose small aggregates in an aqueous environment. Aggregates range from perfectly ordered monodisperse single crystals to disordered bidisperse glasses. The aggregates are compressed between two parallel boundaries, with one acting as a force sensor. The compression force provides a signature of the aggregate composition and gives insight into the energy landscape. In particular, crystals dissipate all the stored energy through single catastrophic fracture events whereas the glassy aggregates break step-by-step. Remarkably, the yielding properties of the 2D aggregates are strongly impacted by even a small amount of disorder.
The glow of annihilating dark matter in Omega Centauri

Dark matter (DM) is the most abundant material in the Universe, but has so far been detected only via its gravitational effects. Several theories suggest that pairs of DM particles can annihilate into a flash of light at gamma-ray wavelengths. While gamma-ray emission has been observed from environments where DM is expected to accumulate, such as the centre of our Galaxy, other high energy sources can create a contaminating astrophysical gamma-ray background, thus making DM detection difficult. In principle, dwarf galaxies around the Milky Way are a better place to look -- they contain a greater fraction of DM with no astrophysical gamma-ray background -- but they are too distant for gamma-rays to have been seen. A range of observational evidence suggests that Omega Centauri (omega Cen or NGC 5139), usually classified as the Milky Way's largest globular cluster, is really the core of a captured and stripped dwarf galaxy. Importantly, Omega Cen is ten times closer to us than known dwarfs. Here we show that not only does Omega Cen contain DM with density as high as compact dwarf galaxies, but also that it emits gamma-rays with an energy spectrum matching that expected from the annihilation of DM particles with mass 31$\pm$4 GeV (68\% confidence limit). No astrophysical sources have been found that would otherwise explain Omega Cen's gamma-ray emission, despite deep multi-wavelength searches. We anticipate our results to be the starting point for even deeper radio observations of Omega Cen. If multi-wavelength searches continue to find no astrophysical explanations, this pristine, nearby clump of DM will become the best place to study DM interactions through forces other than gravity.
Integrality of matrices, finiteness of matrix semigroups, and dynamics of linear cellular automata

Let $\mathbb{K}$ be a finite commutative ring, and let $\mathbb{L}$ be a commutative $\mathbb{K}$-algebra. Let $A$ and $B$ be two $n \times n$-matrices over $\mathbb{L}$ that have the same characteristic polynomial. The main result of this paper states that the set $\left\{ A^0,A^1,A^2,\ldots\right\}$ is finite if and only if the set $\left\{ B^0,B^1,B^2,\ldots\right\}$ is finite. We apply this result to the theory of discrete time dynamical systems. Indeed, it gives a complete and easy-to-check characterization of sensitivity to initial conditions and equicontinuity for linear cellular automata over the alphabet $\mathbb{K}^n$ for $\mathbb{K} = \mathbb{Z}/m\mathbb{Z}$, i.e. cellular automata in which the local rule is defined by $n\times n$-matrices with elements in $\mathbb{Z}/m\mathbb{Z}$. To prove our main result, we derive an integrality criterion for matrices that is likely of independent interest. Namely, let $\mathbb{K}$ be any commutative ring (not necessarily finite), and let $\mathbb{L}$ be a commutative $\mathbb{K}$-algebra. Consider any $n \times n$-matrix $A$ over $\mathbb{L}$. Then, $A \in \mathbb{L}^{n \times n}$ is integral over $\mathbb{K}$ (that is, there exists a monic polynomial $f \in \mathbb{K}\left[t\right]$ satisfying $f\left(A\right) = 0$) if and only if all coefficients of the characteristic polynomial of $A$ are integral over $\mathbb{K}$. The proof of this fact relies on a strategic use of exterior powers (a trick pioneered by Gert Almkvist).
Spectral Analysis of Latent Representations

We propose a metric, Layer Saturation, defined as the proportion of the number of eigenvalues needed to explain 99% of the variance of the latent representations, for analyzing the learned representations of neural network layers. Saturation is based on spectral analysis and can be computed efficiently, making live analysis of the representations practical during training. We provide an outlook for future applications of this metric by outlining the behaviour of layer saturation in different neural architectures and problems. We further show that saturation is related to the generalization and predictive performance of neural networks.
Zermelo's problem: Optimal point-to-point navigation in 2D turbulent flows using Reinforcement Learning

To find the path that minimizes the time to navigate between two given points in a fluid flow is known as Zermelo's problem. Here, we investigate it by using a Reinforcement Learning (RL) approach for the case of a vessel that has a slip velocity with fixed intensity, Vs, but variable direction and navigating in a 2D turbulent sea. We show that an Actor-Critic RL algorithm is able to find quasioptimal solutions for both time-independent and chaotically evolving flow configurations. For the frozen case, we also compared the results with strategies obtained analytically from continuous Optimal Navigation (ON) protocols. We show that for our application, ON solutions are unstable for the typical duration of the navigation process and are, therefore, not useful in practice. On the other hand, RL solutions are much more robust with respect to small changes in the initial conditions and to external noise, even when Vs is much smaller than the maximum flow velocity. Furthermore, we show how the RL approach is able to take advantage of the flow properties in order to reach the target, especially when the steering speed is small.
Stimulated Emission Depletion Microscopy with Diamond Silicon Vacancy Centers

The spatial resolution and fluorescence signal amplitude in stimulated emission depletion (STED) microscopy is limited by the photostability of available fluorophores. Here, we show that negatively charged silicon vacancy (SiV) centers in diamond are promising fluorophores for STED microscopy, owing to their photostable, near-infrared emission and favorable photophysical properties. A home-built pulsed STED microscope was used to image shallow implanted SiV centers in bulk diamond at room temperature. The SiV stimulated emission cross section for 765–800 nm light is found to be (4.0 ± 0.3) × 10–17 cm2, which is approximately 2–4 times larger than that of the negatively charged diamond nitrogen vacancy center and approaches that of commonly used organic dye molecules. We performed STED microscopy on isolated SiV centers and observed a lateral full-width-at-half-maximum spot size of 89 ± 2 nm, limited by the low available STED laser pulse energy (0.4 nJ). For a pulse energy of 5 nJ, the resolution is expect...
Lookahead Optimizer: k steps forward, 1 step back

The vast majority of successful deep neural networks are trained using variants of stochastic gradient descent (SGD) algorithms. Recent attempts to improve SGD can be broadly categorized into two approaches: (1) adaptive learning rate schemes, such as AdaGrad and Adam, and (2) accelerated schemes, such as heavy-ball and Nesterov momentum. In this paper, we propose a new optimization algorithm, Lookahead, that is orthogonal to these previous approaches and iteratively updates two sets of weights. Intuitively, the algorithm chooses a search direction by looking ahead at the sequence of fast weights generated by another optimizer. We show that Lookahead improves the learning stability and lowers the variance of its inner optimizer with negligible computation and memory cost. We empirically demonstrate Lookahead can significantly improve the performance of SGD and Adam, even with their default hyperparameter settings on ImageNet, CIFAR-10/100, neural machine translation, and Penn Treebank.
Black hole–neutron star mergers from triples – II. The role of metallicity and spin–orbit misalignment

Observations of black hole–neutron star (BH–NS) mergers via gravitational waves (GWs) are of great interest for their electromagnetic counterparts, such as short gamma-ray bursts, and could provide crucial information on the nature of BHs and the NS crust and magnetosphere. While no event has been confirmed, a recent possible detection of a BH–NS merger event by the LIGO–Virgo collaboration has attracted a lot of attention to these sources. In this second paper of the series, we follow-up our study of the dynamical evolution of triples composed of an inner BH–NS binary. In particular, we examine how the progenitor metallicity affects the characteristics of the BH–NS mergers in triples. We determine the distributions of masses, orbital parameters, and merger times, as a function of the progenitor metallicity and initial triple orbital distributions, and show that the typical eccentricity in the LIGO band is ∼10−2–10−1. We derive a merger rate range of ΓBH–NS = 1.9 × 10−4–22 Gpc−3 yr−1, consistent the LIGO–Virgo upper limit. Finally, we study the expected spin–orbit misalignments of merging BH–NS binaries from this channel, and find that typically the effective spin distribution is peaked at χeff ∼ 0 with significant tails.
Detection of extensive cosmic ray air showers by measuring radio emission

So far, cosmic ray air showers have been detected using scintillation counter arrays on the ground widely. And also air Cherenkov detection method, which is limited its observation period in moonless nights, has been adopted. The detection method of radio emission from cosmic ray air showers is not new, but rather old method. Radio emission from cosmic ray air showers has not been detected with the method of self-trigger system. If the detection method of radio emission were available, there is no limit of the observation like Cherenkov counter. The developments of high radio frequency (RF) technology might make the detection of radio emission from extensive air showers possible. Antennas calibrated in laboratory are available. With those antennas, we can directly obtain the absolute intensity of radio frequency from cosmic ray air showers. Main issue to detect radio emission is that signal level is quite low. The thermal noise particularly causes background noise source. Those issues and detection method are discussed. We also describe an antenna detection method to generate self-trigger signal and a method to identify air shower events from natural and artificial noises.
Graph centrality is a question of scale

Classic measures of graph centrality capture distinct aspects of node importance, from the local (e.g., degree) to the global (e.g., closeness). Here we exploit the connection between diffusion and geometry to introduce a multiscale centrality measure. A node is defined to be central if it breaks the metricity of the diffusion as a consequence of the effective boundaries and inhomogeneities in the graph. Our measure is naturally multiscale, as it is computed relative to graph neighbourhoods within the varying time horizon of the diffusion. We find that the centrality of nodes can differ widely at different scales. In particular, our measure correlates with degree (i.e., hubs) at small scales and with closeness (i.e., bridges) at large scales, and also reveals the existence of multi-centric structures in complex networks. By examining centrality across scales, our measure thus provides an evaluation of node importance relative to local and global processes on the network.
Discovery and Spectroscopy of Dark Matter and Dark Sectors with Microscopic Black Holes at Next Generation Colliders.

If the length scale of possible extra dimensions is large enough, the effective Planck scale is lowered such that microscopic black holes could be produced in collisions of high-energy particles at colliders. These black holes evaporate through Hawking radiation of a handful of energetic particles drawn from the set of all kinematically and thermally allowed degrees of freedom, including dark matter. Here, we perform the first numerical black hole spectroscopic study of the dark sector. We find that if the next generation of colliders can produce microscopic black holes, then missing momentum signatures can reveal the existence of any new light (≲10  TeV) particle, regardless of the strength of its coupling to the standard model, even if there exists no such nongravitational coupling at all.
An Updated Small Magellanic Cloud and Magellanic Bridge Catalog of Star Clusters, Associations, and Related Objects

We present a catalog of star clusters, associations, and related extended objects in the Small Magellanic Cloud (SMC) and the Magellanic Bridge with 2741 entries, a factor 2 more than a previous version from a decade ago. Literature data up until 2018 December are included. The identification of star clusters was carried out with digital atlases in various bands currently available in the Digitized Sky Survey and the Machine Automatique à Mésurer pour l’Astronomie. imaging surveys. In particular, we cross-identified recent cluster samples from the Visible and Infrared Survey Telescope for Astronomy near-infrared YJKs survey of the Magellanic System, Optical Gravitational Lensing Experiment IV, and Survey of the MAgellanic Stellar History surveys, confirming new clusters and pointing out equivalencies. A major contribution of the present catalog consists of the accurate central positions for clusters and small associations, including a new sample of 45 clusters or candidates in the SMC and 19 in the Magellanic Bridge, as well as a compilation of the most reliable age and metallicity values from the literature. A general catalog must also deal with the recent discoveries of 27 faint and ultra-faint star clusters and galaxies projected on the far surroundings of the Clouds, most of them from the Dark Energy Survey. The information on these objects has been complemented with photometric, spectroscopic, and kinematical follow-up data from the literature. The underluminous galaxies around the Magellanic System, still very few as compared to the predictions from Λ Cold Dark Matter simulations, can bring constraints to galaxy formation and hierarchical evolution. Furthermore, we provide diagnostics, when possible, of the nature of the ultra-faint clusters, searching for borders of the Magellanic System extensions into the Milky Way gravitational potential.
Conditional Markov Chain Search for the Generalised Travelling Salesman Problem for Warehouse Order Picking

The Generalised Travelling Salesman Problem (GTSP) is a well-known problem that, among other applications, arises in warehouse order picking, where each stock is distributed between several locations – a typical approach in large modern warehouses. However, the instances commonly used in the literature have a completely different structure, and the methods are designed with those instances in mind. In this paper, we give a new pseudo-random instance generator that reflects the warehouse order picking and publish new benchmark testbeds. We also use the Conditional Markov Chain Search framework to automatically generate new GTSP metaheuristics trained specifically for warehouse order picking. Finally, we report the computational results of our metaheuristics to enable further competition between solvers.
Snomed2Vec: Random Walk and Poincaré Embeddings of a Clinical Knowledge Base for Healthcare Analytics

Representation learning methods that transform encoded data (e.g., diagnosis and drug codes) into continuous vector spaces (i.e., vector embeddings) are critical for the application of deep learning in healthcare. Initial work in this area explored the use of variants of the word2vec algorithm to learn embeddings for medical concepts from electronic health records or medical claims datasets. We propose learning embeddings for medical concepts by using graph-based representation learning methods on SNOMED-CT, a widely popular knowledge graph in the healthcare domain with numerous operational and research applications. Current work presents an empirical analysis of various embedding methods, including the evaluation of their performance on multiple tasks of biomedical relevance (node classification, link prediction, and patient state prediction). Our results show that concept embeddings derived from the SNOMED-CT knowledge graph significantly outperform state-of-the-art embeddings, showing 5-6x improvement in ``concept similarity" and 6-20\% improvement in patient diagnosis.
THE INTEGRAL HODGE CONJECTURE FOR 3-FOLDS OF KODAIRA DIMENSION ZERO

We prove the integral Hodge conjecture for all 3-folds $X$ of Kodaira dimension zero with $H^{0}(X,K_{X})$ not zero. This generalizes earlier results of Voisin and Grabowski. The assumption is sharp, in view of counterexamples by Benoist and Ottem. We also prove similar results on the integral Tate conjecture. For example, the integral Tate conjecture holds for abelian 3-folds in any characteristic.
Linked Crunchbase: A Linked Data API and RDF Data Set About Innovative Companies

Crunchbase is an online platform collecting information about startups and technology companies, including attributes and relations of companies, people, and investments. Data contained in Crunchbase is, to a large extent, not available elsewhere, making Crunchbase to a unique data source. In this paper, we present how to bring Crunchbase to the Web of Data so that its data can be used in the machine-readable RDF format by anyone on the Web. First, we give insights into how we developed and hosted a Linked Data API for Crunchbase and how sameAs links to other data sources are integrated. Then, we present our method for crawling RDF data based on this API to build a custom Crunchbase RDF knowledge graph. We created an RDF data set with over 347 million triples, including 781k people, 659k organizations, and 343k investments. Our Crunchbase Linked Data API is available online at http://linked-crunchbase.org.
Multi-modal Sentiment Analysis using Deep Canonical Correlation Analysis

This paper learns multi-modal embeddings from text, audio, and video views/modes of data in order to improve upon down-stream sentiment classification. The experimental framework also allows investigation of the relative contributions of the individual views in the final multi-modal embedding. Individual features derived from the three views are combined into a multi-modal embedding using Deep Canonical Correlation Analysis (DCCA) in two ways i) One-Step DCCA and ii) Two-Step DCCA. This paper learns text embeddings using BERT, the current state-of-the-art in text encoders. We posit that this highly optimized algorithm dominates over the contribution of other views, though each view does contribute to the final result. Classification tasks are carried out on two benchmark datasets and on a new Debate Emotion data set, and together these demonstrate that the one-Step DCCA outperforms the current state-of-the-art in learning multi-modal embeddings.
Calculated cross sections for low energy electron collision with OH

The hydroxyl radical, OH, is an important component of many natural and technological plasmas but there is little available information on processes involving its collisions with low-energy electrons. Low energy electron collisions with OH are studied in the framework of the R-matrix method. Potential energy curves of some of the low lying target states of doublet and quartet symmetry which go to the O(3P)+H(2S), O(1D)+H(2S) and O(1S)+H(2S) asymptotic limits are obtained for internuclear separations between 1 a n d 6 a 0 . Scattering calculations are performed at the OH equilibrium geometry R e = 1.8342 a 0 to yield cross sections for elastic scattering, electronic excitations from the X 2 Π ground state to the first three excited states of A 2 Σ + , a 4 Σ − , 1 2 Σ − symmetry and for electron impact dissociation of OH. The positions and widths for negative ion resonances in the e–OH system are used to estimate the cross section for dissociative electron attachment to OH which is found to be significant at electron energies about 1.5 eV.
Interpretable Modelling of Driving Behaviors in Interactive Driving Scenarios based on Cumulative Prospect Theory

Understanding human driving behavior is important for autonomous vehicles. In this paper, we propose an interpretable human behavior model in interactive driving scenarios based on the cumulative prospect theory (CPT). As a non-expected utility theory, CPT can well explain some systematically biased or "irrational" behavior/decisions of human that cannot be explained by the expected utility theory. Hence, the goal of this work is to formulate the human drivers’ behavior generation model with CPT so that some "irrational" behavior or decisions of human can be better captured and predicted. Towards such goal, we first develop a CPT-driven decision-making model focusing on driving scenarios with two interacting agents. A hierarchical learning algorithm is proposed afterwards to learn the utility function, the value function, and the decision weighting function in the CPT model. A case study for roundabout merging is also provided as verification. With real driving data, the prediction performances of three different models are compared: a predefined model based on time-to-collision (TTC), a learning-based model based on neural networks, and the proposed CPT-based model. The results show that the proposed model outperforms the TTC model and achieves similar performance as the learning-based model with much less training data and better interpretability.
Cross-Domain Car Detection Using Unsupervised Image-to-Image Translation: From Day to Night

Deep learning techniques have enabled the emergence of state-of-the-art models to address object detection tasks. However, these techniques are data-driven, delegating the accuracy to the training dataset which must resemble the images in the target task. The acquisition of a dataset involves annotating images, an arduous and expensive process, generally requiring time and manual effort. Thus, a challenging scenario arises when the target domain of application has no annotated dataset available, making tasks in such situation to lean on a training dataset of a different domain. Sharing this issue, object detection is a vital task for autonomous vehicles where the large amount of driving scenarios yields several domains of application requiring annotated data for the training process. In this work, a method for training a car detection system with annotated data from a source domain (day images) without requiring the image annotations of the target domain (night images) is presented. For that, a model based on Generative Adversarial Networks (GANs) is explored to enable the generation of an artificial dataset with its respective annotations. The artificial dataset (fake dataset) is created translating images from day-time domain to night-time domain. The fake dataset, which comprises annotated images of only the target domain (night images), is then used to train the car detector model. Experimental results showed that the proposed method achieved significant and consistent improvements, including the increasing by more than 10% of the detection performance when compared to the training with only the available annotated data (i.e., day images).