nils-herrmann/clean_scirepeval huggingface dataset:
SlimYOLOv3: Narrower, Faster and Better for Real-Time UAV Applications

Drones or general Unmanned Aerial Vehicles (UAVs), endowed with computer vision function by on-board cameras and embedded systems, have become popular in a wide range of applications. However, real-time scene parsing through object detection running on a UAV platform is very challenging, due to limited memory and computing power of embedded devices. To deal with these challenges, in this paper we propose to learn efficient deep object detectors through channel pruning of convolutional layers. To this end, we enforce channel-level sparsity of convolutional layers by imposing L1 regularization on channel scaling factors and prune less informative feature channels to obtain "slim" object detectors. Based on such approach, we present SlimYOLOv3 with fewer trainable parameters and floating point operations (FLOPs) in comparison of original YOLOv3 as a promising solution for real-time object detection on UAVs. We evaluate SlimYOLOv3 on VisDrone2018-Det benchmark dataset; compelling results are achieved by SlimYOLOv3 in comparison of unpruned counterpart, including ~90.8% decrease of FLOPs, ~92.0% decline of parameter size, running ~2 times faster and comparable detection accuracy as YOLOv3. Experimental results with different pruning ratios consistently verify that proposed SlimYOLOv3 with narrower structure are more efficient, faster and better than YOLOv3, and thus are more suitable for real-time object detection on UAVs. Our codes are made publicly available at https://github.com/PengyiZhang/SlimYOLOv3.
Using Answer Set Programming for Commonsense Reasoning in the Winograd Schema Challenge

The Winograd Schema Challenge (WSC) is a natural language understanding task proposed as an alternative to the Turing test in 2011. In this work we attempt to solve WSC problems by reasoning with additional knowledge. By using an approach built on top of graph-subgraph isomorphism encoded using Answer Set Programming (ASP) we were able to handle 240 out of 291 WSC problems. The ASP encoding allows us to add additional constraints in an elaboration tolerant manner. In the process we present a graph based representation of WSC problems as well as relevant commonsense knowledge.
Accurate and Robust Eye Contact Detection During Everyday Mobile Device Interactions

Quantification of human attention is key to several tasks in mobile human-computer interaction (HCI), such as predicting user interruptibility, estimating noticeability of user interface content, or measuring user engagement. Previous works to study mobile attentive behaviour required special-purpose eye tracking equipment or constrained users' mobility. We propose a novel method to sense and analyse visual attention on mobile devices during everyday interactions. We demonstrate the capabilities of our method on the sample task of eye contact detection that has recently attracted increasing research interest in mobile HCI. Our method builds on a state-of-the-art method for unsupervised eye contact detection and extends it to address challenges specific to mobile interactive scenarios. Through evaluation on two current datasets, we demonstrate significant performance improvements for eye contact detection across mobile devices, users, or environmental conditions. Moreover, we discuss how our method enables the calculation of additional attention metrics that, for the first time, enable researchers from different domains to study and quantify attention allocation during mobile interactions in the wild.
A numerical analysis focused comparison of several Finite Volume schemes for an Unipolar Degenerated Drift-Diffusion Model

In this paper, we consider an unipolar degenerated drift-diffusion system where the relation between the concentration of the charged species c and the chemical potential h is h(c) = log c 1−c. We design four different finite volume schemes based on four different formulations of the fluxes. We provide a stability analysis and existence results for the four schemes. The convergence proof with respect to the discretization parameters is established for two of them. Numerical experiments illustrate the behaviour of the different schemes.
Structure of percolating clusters in random clustered networks.

We examine the structure of the percolating cluster (PC) formed by site percolation on a random clustered network (RCN) model. Using the generating functions, we formulate the clustering coefficient and assortative coefficient of the PC. We analytically and numerically show that the PC in the highly clustered networks is clustered even at the percolation threshold. The assortativity of the PC depends on the details of the RCN. The PC at the percolation threshold is disassortative when the numbers of edges and triangles of each node are assigned by Poisson distributions, but assortative when each node in an RCN has the same small number of edges, most of which form triangles. This result seemingly contradicts the disassortativity of fractal networks, although the renormalization scheme unveils the disassortative nature of a fractal PC.
Lessons from Oz: design guidelines for automotive conversational user interfaces

This paper draws from literature and our experience of conducting Wizard-of-Oz (WoZ) studies using natural language, conversational user interfaces (CUIs) in the automotive domain. These studies have revealed positive effects of using in-vehicle CUIs on issues such as: cognitive demand/workload, passive task-related fatigue, trust, acceptance and environmental engagement. A nascent set of human-centred design guidelines that have emerged is presented. These are based on the analysis of users' behaviour and the positive benefits observed, and aim to make interactions with an in-vehicle agent interlocutor safe, effective, engaging and enjoyable, while conforming with users' expectations. The guidelines can be used to inform the design of future in-vehicle CUIs or applied experimentally using WoZ methodology, and will be evaluated and refined in ongoing work.
Searches for Connections Between Dark Matter and Neutrinos with the IceCube High-Energy Starting Event Sample

The nature of dark matter remains one of the most important open questions in physics. Although dark matter effects have only been observed gravitationally, the order-one ratio between conventional matter and dark matter hints to a non-gravitational link between them. In this contribution, we search for such a link using the IceCube high-energy starting event (HESE) sample, which contains some of the highest energy neutrinos ever observed. These are dominated by a yet unidentified high-energy diffuse astrophysical component. Using these events we look for evidence of an excess due to dark matter decay or annihilation, and also for a novel signature due to high-energy neutrinos scattering with dark matter. Finally, in this contribution, we will summarize and highlight the complementarity between the obtained limits for these two scenarios.
Hunting scalar leptoquarks with boosted tops in pair and single production channels

The LHC search strategies for leptoquarks that couple dominantly to the top quark are different than the ones that couple mostly to light quarks. We consider TeV-scale charge 1/3 ($\phi_1$) and 5/3 ($\phi_5$) scalar leptoquarks that can decay to a top quark and a charged lepton pair ($t\ell$) giving rise to a resonance system with a boosted top quark and a high-$p_{\rm T}$ lepton at the LHC. We implement a search strategy for these leptoquarks with a signal that is a combination of pair and single production channels by selecting events with at least one hadronically decaying boosted top and two high-$p_{\rm T}$ same flavour opposite sign leptons. While the pair production is best for discovery in the lower mass region, our combined signal can substantially improve the LHC discovery reach as single productions generally overcome pair production in the high mass region. Contrary to the general consensus, we find that the single production of top-philic $\phi = \{\phi_1,\phi_5\}$ in association with a lepton and jets could be significant for order one $\phi t\ell$ coupling if $\phi$ couples to a left-handed top quark. Our estimate shows that the leptoquark searches in the $t\ell\ell+X$ channel can discover a scalar leptoquark as heavy as $2.1$ TeV with $5\sigma$ significance for some coupling configurations at the $14$ TeV HL-LHC.
Multi-resolution Autoregressive Graph-to-Graph Translation for Molecules

The problem of accelerating drug discovery relies heavily on automatic tools to optimize precursor molecules to afford them with better biochemical properties. Our work in this paper substantially extends prior state-of-the-art on graph-to-graph translation methods for molecular optimization. In particular, we realize coherent multi-resolution representations by interweaving trees over substructures with the atom-level encoding of the original molecular graph. Moreover, our graph decoder is fully autoregressive, and interleaves each step of adding a new substructure with the process of resolving its connectivity to the emerging molecule. We evaluate our model on multiple molecular optimization tasks and show that our model outperforms previous state-of-the-art baselines by a large margin.
Boundedness in families with applications to arithmetic hyperbolicity

Motivated by conjectures of Demailly, Green-Griffiths, Lang, and Vojta, we show that several notions related to hyperbolicity behave similarly in families. We apply our results to show the persistence of arithmetic hyperbolicity along field extensions for projective surfaces with nonzero irregularity. These results rely on the mild boundedness of semi-abelian varieties. We also introduce and study the notion of pseudo-algebraic hyperbolicity which extends Demailly's notion of algebraic hyperbolicity for projective schemes.
Real-time Event Detection on Social Data Streams

Social networks are quickly becoming the primary medium for discussing what is happening around real-world events. The information that is generated on social platforms like Twitter can produce rich data streams for immediate insights into ongoing matters and the conversations around them. To tackle the problem of event detection, we model events as a list of clusters of trending entities over time. We describe a real-time system for discovering events that is modular in design and novel in scale and speed: it applies clustering on a large stream with millions of entities per minute and produces a dynamically updated set of events. In order to assess clustering methodologies, we build an evaluation dataset derived from a snapshot of the full Twitter Firehose and propose novel metrics for measuring clustering quality. Through experiments and system profiling, we highlight key results from the offline and online pipelines. Finally, we visualize a high profile event on Twitter to show the importance of modeling the evolution of events, especially those detected from social data streams.
STUDYING TRANSVERSE MOMENTUM DEPENDENT DISTRIBUTIONS IN POLARIZED PROTON COLLISIONS VIA AZIMUTHAL SINGLE SPIN ASYMMETRIES OF CHARGED PIONS IN JETS

A complete, fundamental understanding of the proton must include knowledge of the underlying spin structure. The transversity distribution, $h_1\left(x\right)$, which describes the transverse spin structure of quarks inside of a transversely polarized proton, is only accessible through channels that couple $h_1 \left(x\right)$ to another chiral odd distribution, such as the Collins fragmentation function ($\Delta^N D_{\pi/q^\uparrow}\left(z,j_T\right)$). Significant Collins asymmetries of charged pions have been observed in semi-inclusive deep inelastic scattering (SIDIS) data. These SIDIS asymmetries combined with $e^+e^-$ process asymmetries have allowed for the extraction of $h_1\left(x\right)$ and $\Delta^N D_{\pi/q^\uparrow}\left(z,j_T\right)$. However, the current uncertainties on $h_1\left(x\right)$ are large compared to the corresponding quark momentum and helicity distributions and reflect the limited statistics and kinematic reach of the available data. In transversely polarized hadronic collisions, Collins asymmetries may be isolated and extracted by measuring the spin dependent azimuthal distributions of charged pions in jets. This thesis will report on the first statistically significant Collins asymmetries extracted from $\sqrt{s}=200$ GeV hadronic collisions using $14$ pb$^{-1}$ of transversely polarized proton collisions at 57% average polarization.
A Frobenius norm regularization method for convolutional kernels to avoid unstable gradient problem

Convolutional neural network is a very important model of deep learning. It can help avoid the exploding/vanishing gradient problem and improve the generalizability of a neural network if the singular values of the Jacobian of a layer are bounded around $1$ in the training process. We propose a new penalty function for a convolutional kernel to let the singular values of the corresponding transformation matrix are bounded around $1$. We show how to carry out the gradient type methods. The penalty is about the structured transformation matrix corresponding to a convolutional kernel. This provides a new regularization method about the weights of convolutional layers.
Interactive Lungs Auscultation with Reinforcement Learning Agent

To perform a precise auscultation for the purposes of examination of respiratory system normally requires the presence of an experienced doctor. With most recent advances in machine learning and artificial intelligence, automatic detection of pathological breath phenomena in sounds recorded with stethoscope becomes a reality. But to perform a full auscultation in home environment by layman is another matter, especially if the patient is a child. In this paper we propose a unique application of Reinforcement Learning for training an agent that interactively guides the end user throughout the auscultation procedure. We show that \textit{intelligent} selection of auscultation points by the agent reduces time of the examination fourfold without significant decrease in diagnosis accuracy compared to exhaustive auscultation.
Force on a point charge source of the classical electromagnetic field

It is shown that a well-defined expression for the total electromagnetic force $f^{em}$ on a point charge source of the classical electromagnetic field can be extracted from the postulate of total momentum conservation whenever the classical electromagnetic field theory satisfies a handful of regularity conditions. Amongst these is the generic local integrability of the field momentum density over a neighborhood of the point charge. This disqualifies the textbook Maxwell-Lorentz field equations, while the Maxwell-Bopp-Lande-Thomas-Podolsky field equations qualify, and presumably so do the Maxwell-Born-Infeld field equations. Most importantly, when the usual relativistic relation between the velocity and the momentum of a point charge with bare rest mass $m_b \neq 0$ is postulated, Newton's law $\dot{p} = f$ with $f = f^{em}$ becomes an integral equation for the point particle's acceleration; the infamous third-order time derivative of the position which plagues the Abraham-Lorentz-Dirac equation of motion does not show up. No infinite bare mass renormalization is invoked, and no ad hoc averaging of fields over a neighborhood of the point charge. The approach lays the rigorous microscopic foundations of classical electrodynamics.
Metallicity of stars formed throughout the cosmic history based on the observational properties of star-forming galaxies

Metallicity is one of the crucial factors that determine stellar evolution. To characterize the properties of stellar populations one needs to know the fraction of stars forming at different metallicities. Knowing how this fraction evolves over time is necessary e.g. to estimate the rates of occurrence of any stellar evolution related phenomena (e.g. double compact object mergers, gamma-ray bursts). Such theoretical estimates can be confronted with observational limits to validate the assumptions about the evolution of the progenitor system leading to a certain transient. However, to perform the comparison correctly one needs to know the uncertainties related to the assumed star formation history and chemical evolution of the Universe. We combine the empirical scaling relations and other observational properties of the star-forming galaxies to construct the distribution of the cosmic star formation rate density at different metallicities and redshifts. We address the question of uncertainty of this distribution due to currently unresolved questions, such as the absolute metallicity scale, the flattening in the star formation−mass relation or the low-mass end of the galaxy mass function. We find that the fraction of stellar mass formed at metallicities <10 per cent solar (>solar) since z = 3 varies by ∼18 per cent (∼26 per cent) between the extreme cases considered in our study. This uncertainty stems primarily from the differences in the mass–metallicity relations obtained with different methods. We confront our results with the local core-collapse supernovae observations. Our model is publicly available.
J-PLUS: Impact of bars on quenching timescales in nearby green valley disc galaxies

Between the blue cloud and the red sequence peaks on the galaxy colour–magnitude diagram there is a region sparsely populated by galaxies called the green valley. In a framework where galaxies mostly migrate on the colour–magnitude diagram from star forming to quiescent, the green valley is considered a transitional galaxy stage. The details of the processes that drive galaxies from star-forming to passive systems still remain unknown.
Aims. We aim to measure the transitional timescales of nearby galaxies across the green valley, through the analysis of Galaxy Evolution Explorer and Javalambre Photometric of Local Universe Survey photometric data. Specifically, we seek to study the impact of bars on the quenching timescales.
Methods. We developed a method that estimates empirically the star formation quenching timescales of green valley galaxies, assuming an exponential decay model of the star formation histories and through a combination of narrow and broad bands from the Javalambre Photometric of Local Universe Survey and Galaxy Evolution Explorer. We correlated these quenching timescales with the presence of bars.
Results. We find that the Javalambre Photometric of Local Universe Survey colours F0395 −g and F0410 −g are sensitive to different star formation histories, showing, consequently, a clear correlation with the Dn(4000) and Hδ, A spectral indices. We measured quenching timescales based on these colours and we find that quenching timescales obtained with our new approach are in agreement with those determined using spectral indices. We also compared the quenching timescales of green valley disc galaxies as a function of the probability of hosting a bar. We find that galaxies with high bar probability tend to quench their star formation slowly.
 We conclude that: (1) Javalambre Photometric of Local Universe Survey filters can be used to measure quenching timescales in nearby green valley galaxies; and (2) the resulting star formation quenching timescales are longer for barred green valley galaxies. Considering that the presence of a bar indicates that more violent processes (e.g. major mergers) are absent in host galaxies, we conclude that the presence of a bar can be used as a morphological signature for slow star formation quenching.
Characterization of the Astrophysical Diffuse Neutrino Flux with IceCube High-Energy Starting Events

The IceCube neutrino observatory has established the existence of an astrophysical diffuse neutrino component above $\sim100$ TeV. This discovery was made using the high-energy starting event sample, which uses the outer layer of instrumented volume as a veto to significantly reduce atmospheric background. We present the latest astrophysical neutrino flux measurement using high-energy starting events. This latest iteration of the analysis extends the sample by $1.5$ years for a total of $7.5$, updates the event properties with newer models of light transport in the glacial ice, and has an improved systematic treatment. As part of this new analysis, we report on compatibility of our observations with detailed isotropic flux models proposed in the literature as well as the standard generic models such as single, double power-law scenarios. We find that none of the tested models are substantially preferred with respect to a single power law.
Towards meta-learning for multi-target regression problems

Several multi-target regression methods were devel-oped in the last years aiming at improving predictive performanceby exploring inter-target correlation within the problem. However, none of these methods outperforms the others for all problems. This motivates the development of automatic approachesto recommend the most suitable multi-target regression method. In this paper, we propose a meta-learning system to recommend the best predictive method for a given multi-target regression problem. We performed experiments with a meta-dataset generated by a total of 648 synthetic datasets. These datasets were created to explore distinct inter-targets characteristics toward recommending the most promising method. In experiments, we evaluated four different algorithms with different biases as meta-learners. Our meta-dataset is composed of 58 meta-features, based on: statistical information, correlation characteristics, linear landmarking, from the distribution and smoothness of the data, and has four different meta-labels. Results showed that induced meta-models were able to recommend the best methodfor different base level datasets with a balanced accuracy superior to 70% using a Random Forest meta-model, which statistically outperformed the meta-learning baselines.
Predominance of the weakest species in Lotka-Volterra and May-Leonard formulations of the rock-paper-scissors model.

We revisit the problem of the predominance of the "weakest" species in the context of Lotka-Volterra and May-Leonard formulations of a spatial stochastic rock-paper-scissors model in which one of the species has its predation probability reduced by 0<P_{w}<1. We show that, despite the different population dynamics and spatial patterns, these two formulations lead to qualitatively similar results for the late time values of the relative abundances of the three species (as a function of P_{w}), as long as the simulation lattices are sufficiently large for coexistence to prevail-the "weakest" species generally having an advantage over the others (specially over its predator). However, for smaller simulation lattices, we find that the relatively large oscillations at the initial stages of simulations with random initial conditions may result in a significant dependence of the probability of species survival on the lattice size.
Heat Transfer Prediction for Methane in Regenerative Cooling Channels with Neural Networks

Methane is considered being a good choice as a propellant for future reusable launch systems. However, the heat transfer prediction for supercritical methane flowing in cooling channels of a regeneratively cooled combustion chamber is challenging. Because accurate heat transfer predictions are essential to design reliable and efficient cooling systems, heat transfer modeling is a fundamental issue to address. Advanced computational fluid dynamics (CFD) calculations achieve sufficient accuracy, but the associated computational cost prevents an efficient integration in optimization loops. Surrogate models based on artificial neural networks (ANNs) offer a great speed advantage. It is shown that an ANN, trained on data extracted from samples of CFD simulations, is able to predict the maximum wall temperature along straight rocket engine cooling channels using methane with convincing precision. The combination of the ANN model with simple relations for pressure drop and enthalpy rise results in a complete reduced order model, which can be used for numerically efficient design space exploration and optimization.