nils-herrmann/clean_scirepeval huggingface dataset:
Ab Initio Study of Ground-state CS Photodissociation via Highly Excited Electronic States

Photodissociation by ultraviolet radiation is the key destruction pathway for CS in photon-dominated regions, such as diffuse clouds. However, the large uncertainties of photodissociation cross sections and rates of CS, resulting from a lack of both laboratory experiments and theoretical calculations, limit the accuracy of calculated abundances of S-bearing molecules by modern astrochemical models. Here we show a detailed ab initio study of CS photodissociation. Accurate potential energy curves of CS electronic states were obtained by choosing an active space CAS(8,10) in MRCI+Q/aug-cc-pV(5+d)Z calculation with additional diffuse functions, with a focus on the B and C 1Σ+ states. Cross sections for both direct photodissociation and predissociation from the vibronic ground state were calculated by applying the coupled-channel method. We found that the C − X (0 − 0) transition has extremely strong absorption due to a large transition dipole moment in the Franck–Condon region, and the upper state is resonant with several triplet states via spin–orbit couplings, resulting in predissociation to the main atomic products C (3P) and S (1D). Our new calculations show that the photodissociation rate under the standard interstellar radiation field is 2.9 × 10−9 s−1, with a 57% contribution from C − X (0 − 0) transition. This value is larger than that adopted by the Leiden photodissociation and photoionization database by a factor of 3.0. Our accurate ab initio calculations will allow more secure determination of S-bearing molecules in astrochemical models.
A CNN-based tool for automatic tongue contour tracking in ultrasound images

For speech research, ultrasound tongue imaging provides a non-invasive means for visualizing tongue position and movement during articulation. Extracting tongue contours from ultrasound images is a basic step in analyzing ultrasound data but this task often requires non-trivial manual annotation. This study presents an open source tool for fully automatic tracking of tongue contours in ultrasound frames using neural network based methods. We have implemented and systematically compared two convolutional neural networks, U-Net and DenseU-Net, under different conditions. Though both models can perform automatic contour tracking with comparable accuracy, Dense U-Net architecture seems more generalizable across test datasets while U-Net has faster extraction speed. Our comparison also shows that the choice of loss function and data augmentation have a greater effect on tracking performance in this task. This public available segmentation tool shows considerable promise for the automated tongue contour annotation of ultrasound images in speech research.
Motion-Aware Feature for Improved Video Anomaly Detection

Motivated by our observation that motion information is the key to good anomaly detection performance in video, we propose a temporal augmented network to learn a motion-aware feature. This feature alone can achieve competitive performance with previous state-of-the-art methods, and when combined with them, can achieve significant performance improvements. Furthermore, we incorporate temporal context into the Multiple Instance Learning (MIL) ranking model by using an attention block. The learned attention weights can help to differentiate between anomalous and normal video segments better. With the proposed motion-aware feature and the temporal MIL ranking model, we outperform previous approaches by a large margin on both anomaly detection and anomalous action recognition tasks in the UCF Crime dataset.
Vector self-interacting dark matter

We present a model of vector dark matter that interacts through a low-mass vector mediator based on the Higgsing of an SU(2) dark sector. The dark matter is charged under a U(1) gauge symmetry. Even though this symmetry is broken, the residual global symmetries of the theory prevent dark matter decay. We present the behavior of the model subject to the assumption that the dark matter abundance is due to thermal freeze out, including self-interaction targets for small scale structure anomalies and the possibility of interacting with the Standard Model through the vector mediator.
Efficient Circle-Based Camera Pose Tracking Free of PnP

Camera pose tracking attracts much interest both from academic and industrial communities, of which the methods based on planar markers are easy to be implemented. However, most of the existing methods need to identify multiple points in the marker images for matching to space points. Then, PnP and RANSAC are used to compute the camera pose. If cameras move fast or are far away from markers, matching is easy to generate errors and even RANSAC cannot remove incorrect matching. Then, the result by PnP cannot have good performance. To solve this problem, we design circular markers and represent 6D camera pose analytically and unifiedly as very concise forms from each of the marker by projective invariance. Afterwards, the pose is further optimized by a proposed nonlinear cost function based on a polar-n-direction geometric distance. The method is from imaged circle edges and without PnP/RANSAC, making pose tracking robust and accurate. Experimental results show that the proposed method outperforms the state of the arts in terms of noise, blur, and distance from camera to marker. Simultaneously, it can still run at about 100 FPS on a consumer computer with only CPU.
Movement science needs different pose tracking algorithms

Over the last decade, computer science has made progress towards extracting body pose from single camera photographs or videos. This promises to enable movement science to detect disease, quantify movement performance, and take the science out of the lab into the real world. However, current pose tracking algorithms fall short of the needs of movement science; the types of movement data that matter are poorly estimated. For instance, the metrics currently used for evaluating pose tracking algorithms use noisy hand-labeled ground truth data and do not prioritize precision of relevant variables like three-dimensional position, velocity, acceleration, and forces which are crucial for movement science. Here, we introduce the scientific disciplines that use movement data, the types of data they need, and discuss the changes needed to make pose tracking truly transformative for movement science.
Predicting Different Types of Conversions with Multi-Task Learning in Online Advertising

Conversion prediction plays an important role in online advertis- ing since Cost-Per-Action (CPA) has become one of the primary campaign performance objectives in the industry. Unlike click pre- diction, conversions have different types in nature, and each type may be associated with different decisive factors. In this paper, we formulate conversion prediction as a multi-task learning problem, so that the prediction models for different types of conversions can be learned together. These models share feature representa- tions, but have their specific parameters, providing the benefit of information-sharing across all tasks. We then propose Multi-Task Field-weighted Factorization Machine (MT-FwFM) to solve these tasks jointly. Our experiment results show that, compared with two state-of-the-art models, MT-FwFM improve the AUC by 0.74% and 0.84% on two types of conversions, and the weighted AUC across all conversion types is also improved by 0.50%.
Dark matter and H i in ultra-diffuse galaxy UGC 2162

Our GMRT (Giant Metrewave Radio Telescope) H i observations of the ultra-diffuse galaxy (UDG) UGC 2162, projected ∼ 300 kpc from the centre of the M 77 group, reveal it to a have an extended H i disc ($R_{\rm H\,{\small I}}/R_{25}$ ∼ 3.3) with a moderate rotational velocity (Vrot ∼ 31 km s−1). This Vrotis in line with that of dwarf galaxies with similar H i mass. We estimate an Mdyn of ∼1.14 × 109 M⊙ within the galaxy’s $R_ \rm {H\,{\small I}}$ ∼ 5.2 kpc. Additionally, our estimates of M200 for the galaxy from NFW models are in the range of 5.0–8.8 × 1010 M⊙. Comparing UGC 2162 to samples of UDGs with H i detections show it to have amongst the smallest Re with its MH i/M* being distinctly higher and g – icolour slightly bluer than typical values in those samples. We also compared H i and dark matter (DM) halo properties of UGC 2162 with dwarf galaxies in the LITTLE THINGS sample and find its DM halo mass and profile are within the range expected for a dwarf galaxy. While we were unable to determine the origin of the galaxy’s present-day optical form from our study, its normal H i rotation velocity in relation to its H i mass, H i morphology, environment, and dwarf mass DM halo ruled out some of the proposed ultra-diffuse galaxy formation scenarios for this galaxy.
Efficient Exploration with Self-Imitation Learning via Trajectory-Conditioned Policy

This paper proposes a method for learning a trajectory-conditioned policy to imitate diverse demonstrations from the agent’s own past experiences. We demonstrate that such self-imitation drives exploration in diverse directions and increases the chance of finding a globally optimal solution in reinforcement learning problems, especially when the reward is sparse and deceptive. Our method significantly outperforms existing self-imitation learning and count-based exploration methods on various sparse-reward reinforcement learning tasks with local optima. In particular, we report a state-of-the-art score of more than 25,000 points on Montezuma’s Revenge without using expert demonstrations or resetting to arbitrary states.
Learning Embedding of 3D models with Quadric Loss

Sharp features such as edges and corners play an important role in the perception of 3D models. In order to capture them better, we propose quadric loss, a point-surface loss function, which minimizes the quadric error between the reconstructed points and the input surface. Computation of Quadric loss is easy, efficient since the quadric matrices can be computed apriori, and is fully differentiable, making quadric loss suitable for training point and mesh based architectures. Through extensive experiments we show the merits and demerits of quadric loss. When combined with Chamfer loss, quadric loss achieves better reconstruction results as compared to any one of them or other point-surface loss functions.
Adaptive and Compressive Beamforming Using Deep Learning for Medical Ultrasound

In ultrasound (US) imaging, various types of adaptive beamforming techniques have been investigated to improve the resolution and the contrast-to-noise ratio of the delay and sum (DAS) beamformers. Unfortunately, the performance of these adaptive beamforming approaches degrades when the underlying model is not sufficiently accurate and the number of channels decreases. To address this problem, here, we propose a deep-learning-based beamformer to generate significantly improved images over widely varying measurement conditions and channel subsampling patterns. In particular, our deep neural network is designed to directly process full or subsampled radio frequency (RF) data acquired at various subsampling rates and detector configurations so that it can generate high-quality US images using a single beamformer. The origin of such input-dependent adaptivity is also theoretically analyzed. Experimental results using the B-mode focused US confirm the efficacy of the proposed methods.
Excitable solitons: Annihilation, crossover, and nucleation of pulses in mass-conserving activator-inhibitor media.

Excitable pulses are among the most widespread dynamical patterns that occur in many different systems, ranging from biological cells to chemical reactions and ecological populations. Traditionally, the mutual annihilation of two colliding pulses is regarded as their prototypical signature. Here we show that colliding excitable pulses may exhibit solitonlike crossover and pulse nucleation if the system obeys a mass conservation constraint. In contrast to previous observations in systems without mass conservation, these alternative collision scenarios are robustly observed over a wide range of parameters. We demonstrate our findings using a model of intracellular actin waves since, on time scales of wave propagations over the cell scale, cells obey conservation of actin monomers. The results provide a key concept to understand the ubiquitous occurrence of actin waves in cells, suggesting why they are so common, and why their dynamics is robust and long-lived.
Interpretable classification of time-series data using efficient enumerative techniques

Cyber-physical system applications such as autonomous vehicles, wearable devices, and avionic systems generate a large volume of time-series data. Designers often look for tools to help classify and categorize the data. Traditional machine learning techniques for time-series data offer several solutions to solve these problems; however, the artifacts trained by these algorithms often lack interpretability. On the other hand, temporal logic, such as Signal Temporal Logic (STL) have been successfully used in the formal methods community as specifications of time-series behaviors. In this work, we propose a new technique to automatically learn temporal logic formulas that are able to classify real-valued time-series data. Previous work on learning STL formulas from data either assumes a formula-template to be given by the user, or assumes some special fragment of STL that enables exploring the formula structure in a systematic fashion. In our technique, we relax these assumptions, and provide a way to systematically explore the space of all STL formulas. As the space of all STL formulas is very large, and contains many semantically equivalent formulas, we suggest a technique to heuristically prune the space of formulas considered. Finally, we illustrate our technique on various case studies from the automotive and transportation domains.
A Case for Stale Synchronous Distributed Model for Declarative Recursive Computation

A large class of traditional graph and data mining algorithms can be concisely expressed in Datalog, and other Logic-based languages, once aggregates are allowed in recursion. In fact, for most BigData algorithms, the difficult semantic issues raised by the use of non-monotonic aggregates in recursion are solved by Pre-Mappability ( 
${\cal P}$
 reM), a property that assures that for a program with aggregates in recursion there is an equivalent aggregate-stratified program. In this paper we show that, by bringing together the formal abstract semantics of stratified programs with the efficient operational one of unstratified programs, 
$\[{\cal P}\]$
 reM can also facilitate and improve their parallel execution. We prove that 
$\[{\cal P}\]$
 reM-optimized lock-free and decomposable parallel semi-naive evaluations produce the same results as the single executor programs. Therefore, 
$\[{\cal P}\]$
 reM can be assimilated into the data-parallel computation plans of different distributed systems, irrespective of whether these follow bulk synchronous parallel (BSP) or asynchronous computing models. In addition, we show that non-linear recursive queries can be evaluated using a hybrid stale synchronous parallel (SSP) model on distributed environments. After providing a formal correctness proof for the recursive query evaluation with 
$\[{\cal P}\]$
 reM under this relaxed synchronization model, we present experimental evidence of its benefits.
Backward-Forward Algorithm: An Improvement towards Extreme Learning Machine

The extreme learning machine needs a large number of hidden nodes to generalize a single hidden layer neural network for a given training data-set. The need for more number of hidden nodes suggests that the neural-network is memorizing rather than generalizing the model. Hence, a supervised learning method is described here that uses Moore-Penrose approximation to determine both input-weight and output-weight in two epochs, namely, backward-pass and forward-pass. The proposed technique has an advantage over the back-propagation method in terms of iterations required and is superior to the extreme learning machine in terms of the number of hidden units necessary for generalization.
Lectures on Differential Topology

This text arises from teaching advanced undergraduate courses in differential topology for the master curriculum in Mathematics at the University of Pisa. So it is mainly addressed to motivated and collaborative master undergraduate students, having nevertheless a limited mathematical background. Overall this text is a collection of themes, in some cases advanced and of historical importance, with the common feature that they can be treated with "bare hands". This means by just combining certain cut-and-paste procedures and applications of transversality, mainly through the multiplicative structure of the cobordism rings of every smooth compact manifold; topics developed from scratch along with the text.
Fine-Grained Sentence Functions for Short-Text Conversation

Sentence function is an important linguistic feature referring to a user’s purpose in uttering a specific sentence. The use of sentence function has shown promising results to improve the performance of conversation models. However, there is no large conversation dataset annotated with sentence functions. In this work, we collect a new Short-Text Conversation dataset with manually annotated SEntence FUNctions (STC-Sefun). Classification models are trained on this dataset to (i) recognize the sentence function of new data in a large corpus of short-text conversations; (ii) estimate a proper sentence function of the response given a test query. We later train conversation models conditioned on the sentence functions, including information retrieval-based and neural generative models. Experimental results demonstrate that the use of sentence functions can help improve the quality of the returned responses.
LOFAR Discovery of a Radio Halo in the High-redshift Galaxy Cluster PSZ2 G099.86+58.45

In this Letter, we report the discovery of a radio halo in the high-redshift galaxy cluster PSZ2 G099.86+58.45 (z = 0.616) with the LOw Frequency ARray (LOFAR) at 120–168 MHz. This is one of the most distant radio halos discovered so far. The diffuse emission extends over ∼1 Mpc and has a morphology similar to that of the X-ray emission as revealed by XMM-Newton data. The halo is very faint at higher frequencies and is barely detected by follow-up 1–2 GHz Karl G. Jansky Very Large Array observations, which enable us to constrain the radio spectral index to be α ≲ 1.5–1.6, i.e., with properties between canonical and ultra-steep spectrum radio halos. Radio halos are currently explained as synchrotron radiation from relativistic electrons that are re-accelerated in the intracluster medium by turbulence driven by energetic mergers. We show that in such a framework radio halos are expected to be relatively common at ∼150 MHz (∼30%–60%) in clusters with mass and redshift similar to PSZ2 G099.86+58.45; however, at least two-thirds of these radio halos should have a steep spectrum and thus be very faint above ∼1 GHz frequencies. Furthermore, because the luminosity of radio halos at high redshift depends strongly on the magnetic field strength in the hosting clusters, future LOFAR observations will also provide vital information on the origin and amplification of magnetic fields in galaxy clusters.
Testing new property of elliptical model for stock returns distribution

Wide class of elliptically contoured distributions is a popular model of stock returns distribution. However the important question of adequacy of the model is open. There are some results which reject and approve such model. Such results are obtained by testing some properties of elliptical model for each pair of stocks from some markets. New property of equality of $\tau$ Kendall correlation coefficient and probability of sign coincidence for any pair of random variables with elliptically contoured distribution is proved in the paper. Distribution free statistical tests for testing this property for any pair of stocks are constructed. Holm multiple hypotheses testing procedure based on the individual tests is constructed and applied for stock markets data for the concrete year. New procedure of testing the elliptical model for stock returns distribution for all years of observation for some period is proposed. The procedure is applied for the stock markets data of China, USA, Great Britain and Germany for the period from 2003 to 2014. It is shown that for USA, Great Britain and Germany stock markets the hypothesis of elliptical model of stock returns distribution could be accepted but for Chinese stock market is rejected for some cases.
Constraining warm dark matter power spectrum using the cross-correlation of HI 21 cm signal and the Lyman-α forest

We have considered the prospects for measuring the cross Warm Dark Matter (WDM) power spectrum of the redshifted HI 21-cm signal and the Lyman-α forest and thereby constraining WDM mass using observations with upcoming radio-interferometers—the Ooty Wide Field Array (OWFA) and SKA1-mid, and a spectroscopic survey of the quasars. We have considered a quasar survey with a mean observed quasar number density of n̄Q = 48 deg−2 over a collecting area of 14455 deg2, and a mean spectroscopic SNR = 5. Our analysis with OWFA shows that it is possible to measure the WDM power spectrum in several k-bins at k ⩽ 0.45 Mpc−1 with SNR > 5 using observations of 200 hours each in 100 different fields-of-view for mWDM = 0.25 keV . Considering the possibility of the joint measurement of the parameters, the warm dark matter density parameter ΩWDM, and the dark energy density parameter ΩΛ0, we find that the relative error on the 1−σ measurement of the parameter ΩWDM is ∼ 0.8 for a fiducial mWDM = 0.25 keV . However, we find that OWFA is not sensitive towards measuring the suppression of the WDM power spectrum, and therefore, cannot be used to constrain the WDM mass. Considering the analysis with SKA I mid, we find that for a fiducial mWDM= 0.25 keV, the suppression in the cross power spectrum can be measured at ∼ 10 − σ around k ∼ 0.2 Mpc−1 for a total observing time of 20000 hours distributed uniformly over 50 independent pointings where we have binned the available k-range as Δ k = k/5.
From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimation

Estimating accurate depth from a single image is challenging because it is an ill-posed problem as infinitely many 3D scenes can be projected to the same 2D scene. However, recent works based on deep convolutional neural networks show great progress with plausible results. The convolutional neural networks are generally composed of two parts: an encoder for dense feature extraction and a decoder for predicting the desired depth. In the encoder-decoder schemes, repeated strided convolution and spatial pooling layers lower the spatial resolution of transitional outputs, and several techniques such as skip connections or multi-layer deconvolutional networks are adopted to recover back to the original resolution for effective dense prediction. In this paper, for more effective guidance of densely encoded features to the desired depth prediction, we propose a network architecture that utilizes novel local planar guidance layers located at multiple stages in the decoding phase. We show that the proposed method outperforms the state-of-the-art works with significant margin evaluating on challenging benchmarks. We also provide results from an ablation study to validate the effectiveness of the proposed method.