Miracle-dz/UniFAR-FTUs huggingface dataset:
Questioning quark-gluon plasma formation in small collision systems.

A recent letter published in the journal Nature reports observation at the relativistic heavy ion collider (RHIC) of quark-gluon plasma (QGP) formation in small asymmetric collision systems denoted as $p$-Au, $d$-Au and $^3$He-Au. The claimed phenomenon, described as `short-lived QGP droplets,' is inferred from a combination of Glauber Monte Carlo simulations, measurement of two azimuth Fourier amplitudes $v_2$ and $v_3$ and hydro theory calculations. While that claim follows a trend in recent years to report `signals' conventionally attributed to QGP as appearing also in smaller collision systems, the new result remains surprising in the context of expectations before first RHIC operation that small systems, e.g. $d$-Au collisions, would provide control experiments in which a QGP was unlikely to appear. An alternative interpretation of the recent RHIC result is that small-system control experiments do convey an important message: The `signals' conventionally attributed to QGP formation in larger A-A collisions do not actually represent that phenomenon in any system. The present study reviews a broad array of experimental evidence for or against QGP formation in several collision systems. It examines in particular hydro theory descriptions of spectra and correlations usually interpreted to support QGP formation. Available evidence suggests that data features conventionally attributed to QGP formation represent either minimum-bias jet production or a nonjet azimuth quadrupole with properties inconsistent with a hydro hypothesis.
Connections Between Mirror Descent, Thompson Sampling and the Information Ratio

The information-theoretic analysis by Russo and Van Roy (2014) in combination with minimax duality has proved a powerful tool for the analysis of online learning algorithms in full and partial information settings. In most applications there is a tantalising similarity to the classical analysis based on mirror descent. We make a formal connection, showing that the information-theoretic bounds in most applications can be derived from existing techniques for online convex optimisation. Besides this, for $k$-armed adversarial bandits we provide an efficient algorithm with regret that matches the best information-theoretic upper bound and improve best known regret guarantees for online linear optimisation on $\ell_p$-balls and bandits with graph feedback.
ALLSAT compressed with wildcards: All, or all maximum independent sets.

An odd cycle cover is a vertex set whose removal makes a graph bipartite. We show that if a $k$-element odd cycle cover of a graph with w vertices is known then all $N$ maximum anticliques (= independent sets) can be generated in time $O(2^k w^3 + N w^2))$. Generating ${\it all}\ N'$ anticliques (maximum or not) is easier and works for arbitrary graphs in time $O(N'w^2)$. In fact the use of wildcards allows to compactly generate the anticliques in clusters.
Demonstration of PerformanceNet: A Convolutional Neural Network Model for Score-to-Audio Music Generation

We present in this paper PerformacnceNet, a neural network model we proposed recently to achieve score-to-audio music generation. The model learns to convert a music piece from the symbolic domain to the audio domain, assigning performance-level attributes such as changes in velocity automatically to the music and then synthesizing the audio. The model is therefore not just a neural audio synthesizer, but an AI performer that learns to interpret a musical score in its own way. The code and sample outputs of the model can be found online at https://github.com/bwang514/PerformanceNet.
An Analysis of Object Embeddings for Image Retrieval

We present an analysis of embeddings extracted from different pre-trained models for content-based image retrieval. Specifically, we study embeddings from image classification and object detection models. We discover that even with additional human annotations such as bounding boxes and segmentation masks, the discriminative power of the embeddings based on modern object detection models is significantly worse than their classification counterparts for the retrieval task. At the same time, our analysis also unearths that object detection model can help retrieval task by acting as a hard attention module for extracting object embeddings that focus on salient region from the convolutional feature map. In order to efficiently extract object embeddings, we introduce a simple guided student-teacher training paradigm for learning discriminative embeddings within the object detection framework. We support our findings with strong experimental results.
The Research on Safety Evaluation Method of ETC Lane Setting Based on traffic conflict

In order to improve the traffic capacity of the expressway, ETC toll lane is gradually introduced into the toll station system. However, new traffic questions are also caused by the differences in the speed, passing time and other characteristics of ETC vehicles and traditional vehicles. On the basis of the analysis of the development trend of Expressway and toll station and the factors influencing the traffic safety of toll station, this paper makes qualitative analysis on the factors that affect the traffic safety of toll stations, and the reference factor of traffic conflict is established by introducing the number of lane changing and the average change of vehicle speed. On the basis of fully obtaining the information of Nanjing toll station of Nanjing Hangzhou expressway, the simulation software VISSIM is used to establish the simulation model, and on the basis of verifying correctness of the model, orthogonal test is used to analyse the factors that impact traffic safety of toll station, such as the number of ETC lane, the location of ETC toll lane, the proportion of ETC vehicles, total number of vehicles and etc. The traffic safety situation of toll stations under different schemes is determined.
The Need for a Multimodal Means of Effective Digital Learning through Data Mining and Institutional Knowledge Repository: A Proposed System for Polytechnics in Northern Nigeria

Educational institutions, information and knowledge givers in Nigeria are faced with numerous challenges of carrying everyone on the same page through the lack of proper digital learning and knowledge preservation tools simply regarded as a digital library. In any learning institution, digital libraries and institutional repositories are the keys to a qualitative knowledge stockpiling and delivery. The main aim of the paper is to propose a viable means of effective digital learning for the Polytechnics in Northern Nigeria. This paper also attempted to highlight the major information system components needed to develop a digital learning that will fit the Institutions with an approach through Multimedia data mining and suggests an Institutional Knowledge repository for proper academic data storage such as journals, thesis, lecture notes, dissertations, undergraduate student projects etc. The proposed system is expected to serve as a step towards bridging the gap between the polytechnics learning system and the tertiary institutions in the developed nations.
Inhomogeneity in planar ventilation scintigraphy of emphysematous patients.

The aim of this study was to develop and evaluate a method for the quantification of inhomogeneity in ventilation scintigraphy. Ten healthy volunteers and 10 emphysematous patients were investigated. Anteroposterior (AP) and Posteroanterior (PA) images of planar ventilation scintigraphy were acquired. Lung regions of interest (ROI) were obtained by manual delineation of the lung contours and then divided into several 10-pixel-high horizontal-stripe regions. By allowing for the statistical noise of the pixel count rate, the biological coefficient of variation (CVB) of the pixel counts in each stripe region was calculated. The apex-to-base distribution of the CVB in the emphysematous lungs dispersed largely and with higher values than the corresponding distribution in the healthy lungs. The mean values of the CVB (MCVB), the ranges of the CVB (RCVB) and the maximum values of the CVB (MAXCVB) in the stripe regions in emphysematous lungs were significantly higher than the corresponding ones in the healthy lungs (all P < 0.001). The intraobserver variations of the MCVB, RCVB and MAXCVB (calculated using the standard deviations of the differences) were less than 2.3% units, 5.2% units and 3.9% units respectively. The corresponding values for interobserver variation were 5.7% units, 6.1% units and 6.4% units. A systematic decrease in lung ROI size, i.e. inclusion of successively less of the lung edge, resulted in a linear decrease of 1.7% units in the MCVB and MAXCVB of both emphysematous and normal lungs. In conclusion, the stripe-region method is a reliable tool for the quantification of inhomogeneity in the planar ventilation scintigraphy.
Study on the Stability of Platoon in High Speed under Cross Wind

It is considered that cross wind on highway has a significant influence on the vehicle platoon driving in high speed. Combined with the intelligent vehicle platoon driving in the future, this paper studies the control stability of cross wind on the high-speed of vehicle platoon. Based on the theory of vehicle system dynamics and using simulation software CarSim, the aerodynamic coefficients curve model and the characteristic parameters model are established. The process of crosswind effecting on the vehicle platoon with high speed is simulated. Taking driving situation of solo vehicle as the reference. Compared the general situation and the intelligent driving system failed, the deviation path of vehicle and the influence of steering wheel correction Angle on the control stability is obtained.
A Stochastic Decoupling Method for Minimizing the Sum of Smooth and Non-Smooth Functions

We consider the problem of minimizing the sum of three convex functions: i) a smooth function $f$ in the form of an expectation or a finite average, ii) a non-smooth function $g$ in the form of a finite average of proximable functions $g_j$, and iii) a proximable regularizer $R$. We design a variance reduced method which is able progressively learn the proximal operator of $g$ via the computation of the proximal operator of a single randomly selected function $g_j$ in each iteration only. Our method can provably and efficiently accommodate many strategies for the estimation of the gradient of $f$, including via standard and variance-reduced stochastic estimation, effectively decoupling the smooth part of the problem from the non-smooth part. We prove a number of iteration complexity results, including a general ${\cal O}(1/t)$ rate, ${\cal O}(1/t^2)$ rate in the case of strongly convex $f$, and several linear rates in special cases, including accelerated linear rate. For example, our method achieves a linear rate for the problem of minimizing a strongly convex function $f$ under linear constraints under no assumption on the constraints beyond consistency. When combined with SGD or SAGA estimators for the gradient of $f$, this leads to a very efficient method for empirical risk minimization with large linear constraints. Our method generalizes several existing algorithms, including forward-backward splitting, Douglas-Rachford splitting, proximal SGD, proximal SAGA, SDCA, randomized Kaczmarz and Point-SAGA. However, our method leads to many new specific methods in special cases; for instance, we obtain the first randomized variant of the Dykstra's method for projection onto the intersection of closed convex sets.
Combining the Hough Transform with MLP for Identifying Cashmere and Wool Fibers

The identification of cashmere and wool fibers has always been a challenge in the textile research field. At present, identification methods are mainly based on physical or biological properties such as DNA composition, which are time-consuming and costly to address. With the development of deep learning and computer vision, many fiber identification methods based on image analysis have emerged that effectively solve these problems, but there is much room for improvement regarding accuracy and time requirements. In this paper, a new identification algorithm is proposed that extracts the outline information of the fibers using the adaptive threshold method, performs the Hough transform on the binarized images, obtains the one-dimensional features from the Hough transform accumulator using a new feature descriptor called "theta_max", and lastly designs a Multi-Layer Perceptron (MLP) for classification. Experiments show that, the new algorithm can attain 96% accuracy in our datasets. The work significantly improves identification accuracy, reduces the complexity and time requirements of the classification models, and provides an effective method for the identification of cashmere and wool fibers.
Selection of sequence motifs and generative Hopfield-Potts models for protein families

Statistical models for families of evolutionary related proteins have recently gained interest: in particular pairwise Potts models, as those inferred by the Direct-Coupling Analysis, have been able to extract information about the three-dimensional structure of folded proteins, and about the effect of amino-acid substitutions in proteins. These models are typically requested to reproduce the one- and two-point statistics of the amino-acid usage in a protein family, i.e. to capture the so-called residue conservation and covariation statistics of proteins of common evolutionary origin. Pairwise Potts models are the maximum-entropy models achieving this. While being successful, these models depend on huge numbers of ad hoc introduced parameters, which have to be estimated from finite amount of data and whose biophysical interpretation remains unclear. Here we propose an approach to parameter reduction, which is based on selecting collective sequence motifs. It naturally leads to the formulation of statistical sequence models in terms of Hopfield-Potts models. These models can be accurately inferred using a mapping to restricted Boltzmann machines and persistent contrastive divergence. We show that, when applied to protein data, even 20-40 patterns are sufficient to obtain statistically close-to-generative models. The Hopfield patterns form interpretable sequence motifs and may be used to clusterize amino-acid sequences into functional sub-families. However, the distributed collective nature of these motifs intrinsically limits the ability of Hopfield-Potts models in predicting contact maps, showing the necessity of developing models going beyond the Hopfield-Potts models discussed here.
Dusty disk winds at the sublimation rim of the highly inclined, low mass young stellar object SU Aurigae

Context. T Tauri stars are low-mass young stars whose disks provide the setting for planet formation. Despite this, their structure is poorly understood. We present new infrared interferometric observations of the SU Aurigae circumstellar environment that offer resolution that is three times higher and a better baseline position angle coverage than previous observations. Aims. We aim to investigate the characteristics of the circumstellar material around SU Aur, constrain the disk geometry, composition and inner dust rim structure. Methods. The CHARA array offers unique opportunities for long baseline observations, with baselines up to 331 m. Using the CLIMB three-telescope combiner in the K-band allows us to measure visibilities as well as closure phase. We undertook image reconstruction for model-independent analysis, and fitted geometric models such as Gaussian and ring distributions. Additionally, the fitting of radiative transfer models constrain the physical parameters of the disk. For the first time, a dusty disk wind is introduced to the radiative transfer code TORUS to model protoplanetary disks. Our implementation is motivated by theoretical models of dusty disk winds, where magnetic field lines drive dust above the disk plane close to the sublimation zone. Results. Image reconstruction reveals an inclined disk with slight asymmetry along its minor-axis, likely due to inclination effects obscuring the inner disk rim through absorption of incident star light on the near-side and thermal re-emission and scattering of the far-side. Geometric modelling of a skewed ring finds the inner rim at 0.17 ± 0.02 au with an inclination of 50.9 ± 1.0° and minor axis position angle 60.8 ± 1.2°. Radiative transfer modelling shows a flared disk with an inner radius at 0.18 au which implies a grain size of 0.4 μm assuming astronomical silicates and a scale height of 15.0 at 100 au. Among the tested radiative transfer models, only the dusty disk wind successfully accounts for the K-band excess by introducing dust above the mid-plane.
Differentiable Sorting using Optimal Transport: The Sinkhorn CDF and Quantile Operator

Sorting an array is a fundamental routine in machine learning, one that is used to compute rank-based statistics, cumulative distribution functions (CDFs), quantiles, or to select closest neighbors and labels. The sorting function is however piece-wise constant (the sorting permutation of a vector does not change if the entries of that vector are infinitesimally perturbed) and therefore has no gradient information to back-propagate. We propose a framework to sort elements that is algorithmically differentiable. We leverage the fact that sorting can be seen as a particular instance of the optimal transport (OT) problem on $\mathbb{R}$, from input values to a predefined array of sorted values (e.g. $1,2,\dots,n$ if the input array has $n$ elements). Building upon this link , we propose generalized CDFs and quantile operators by varying the size and weights of the target presorted array. Because this amounts to using the so-called Kantorovich formulation of OT, we call these quantities K-sorts, K-CDFs and K-quantiles. We recover differentiable algorithms by adding to the OT problem an entropic regularization, and approximate it using a few Sinkhorn iterations. We call these operators S-sorts, S-CDFs and S-quantiles, and use them in various learning settings: we benchmark them against the recently proposed neuralsort [Grover et al. 2019], propose applications to quantile regression and introduce differentiable formulations of the top-k accuracy that deliver state-of-the art performance.
Integrated Neural Network and Machine Vision Approach For Leather Defect Classification

Leather is a type of natural, durable, flexible, soft, supple and pliable material with smooth texture. It is commonly used as a raw material to manufacture luxury consumer goods for high-end customers. To ensure good quality control on the leather products, one of the critical processes is the visual inspection step to spot the random defects on the leather surfaces and it is usually conducted by experienced experts. This paper presents an automatic mechanism to perform the leather defect classification. In particular, we focus on detecting tick-bite defects on a specific type of calf leather. Both the handcrafted feature extractors (i.e., edge detectors and statistical approach) and data-driven (i.e., artificial neural network) methods are utilized to represent the leather patches. Then, multiple classifiers (i.e., decision trees, Support Vector Machines, nearest neighbour and ensemble classifiers) are exploited to determine whether the test sample patches contain defective segments. Using the proposed method, we managed to get a classification accuracy rate of 84% from a sample of approximately 2500 pieces of 400 * 400 leather patches.
MolSSI and BioExcel Workflow Workshop 2018 Report

Workflows in biomolecular science are very important as they are intricately intertwined with the scientific outcomes, as well as algorithmic and methodological innovations. The use and effectiveness of workflow tools to meet the needs of the biomolecular science community is varied. MolSSI co-organized a biomolecular workflows workshop in December 2018 with the goal of identifying specific software gaps and opportunities for improved workflow practices. This report captures presentations and discussion from that workshop. The workshop participants were primary tools developers, along with "neutral observers" and some biomolecular domain scientists. After contextualizing and motivating the workshop, the report covers the existing roles and emerging trends in how workflow systems are utilized. A few recurring observations are presented as recommendations for improving the use and effectiveness of workflow tools. The tools presented are discussed in Appendix B.
Nonequilibrium Ionization Effects on Solar EUV and X-Ray Imaging Observations

During transient events such as major solar eruptions, the plasma can be far from the equilibrium ionization state because of rapid heating or cooling. Nonequilibrium ionization (NEI) is important in rapidly evolving systems where the thermodynamical timescale is shorter than the ionization or recombination timescales. We investigate the effects of NEI on EUV and X-ray observations by the Atmospheric Imaging Assembly (AIA) on board the Solar Dynamic Observatory and X-ray Telescope (XRT) on board Hinode. Our model assumes that the plasma is initially in ionization equilibrium at low temperature, and it is heated rapidly by a shock or magnetic reconnection. We tabulate the responses of the AIA and XRT passbands as functions of temperature and a characteristic timescale, net. We find that most of the ions reach equilibrium at net ≤ 1012 cm−3 s. Comparing ratios of the responses between different passbands allows us to determine whether a combination of plasmas at temperatures in ionization equilibrium can account for a given AIA and XRT observation. It also expresses how far the observed plasma is from equilibrium ionization. We apply the ratios to a supra-arcade plasma sheet on 2012 January 27. We find that the closer the plasma is to the arcade, the closer it is to a single-temperature plasma in ionization equilibrium. We also utilize the set of responses to estimate the temperature and density for shocked plasma associated with a coronal mass ejection on 2010 June 13. The temperature and density ranges we obtain are in reasonable agreement with previous works.