Miracle-dz/UniFAR-FTUs huggingface dataset:
OpenLORIS-Object: A Dataset and Benchmark towards Lifelong Object Recognition

—The recent breakthroughs in computer vision have beneﬁted from the availability of large representative datasets (e.g. ImageNet and COCO) for training. Yet, robotic vision poses unique challenges for applying visual algorithms developed from these standard computer vision datasets due to their implicit assumption over non-varying distributions for a ﬁxed set of tasks. Fully retraining models each time a new task becomes available is infeasible due to computational, storage and sometimes privacy issues, while na¨ıve incremental strategies have been shown to suffer from catastrophic forgetting. It is crucial for the robots to operate continuously under open-set and detrimental conditions with adaptive visual perceptual systems, where lifelong learning is a fundamental capability. However, very few datasets and benchmarks are available to evaluate and compare emerging techniques. To ﬁll this gap, we provide a new lifelong robotic vision dataset (“OpenLORIS-Object”) collected via RGB-D cameras mounted on mobile robots. The dataset embeds the challenges faced by a robot in the real-life application and provides new benchmarks for validating lifelong object recognition algorithms. More-over, we have provided a testbed of 9 state-of-the-art lifelong learning algorithms. Each of them involves 48 tasks with 4 evaluation metrics over the OpenLORIS-Object dataset. The results demonstrate that the object recognition task in the ever-changing difﬁculty environments is far from being solved and the bottlenecks are at the forward/backward transfer designs. Our dataset and benchmark are publicly available at this url.
Detecting regime transitions of the nocturnal and Polar boundary layer

Many natural systems undergo critical transitions, i.e. sudden shifts from one dynamical regime to another. In the climate system, the atmospheric boundary layer can experience sudden transitions between fully turbulent states and quiescent, quasi-laminar states. Such rapid transitions are observed in Polar regions or at night when the atmospheric boundary layer is stably stratified, and have important consequences in the strength of mixing with the higher levels of the atmosphere. To analyse the stable boundary layer, many approaches rely on the identification of regimes that are commonly denoted as weakly and very stable regimes. Detecting transitions between the regimes is crucial for modelling purposes. In this work a combination of methods from dynamical systems and statistical modelling is applied to study these regime transitions and to develop an early-warning signal. The analysis is based on an indicator for the dynamical stability (i.e. the resilience of the equilibrium flow to perturbations) and a conceptual model for regime transitions of near-surface temperature inversion at night as well as in Arctic conditions. A focus lies on transitions or bifurcation points in the dynamics, points in which the stability of the system changes drastically. The performance of the stability indicator and its reliability as an early-warning signal are assessed on the basis of simulated data and time series data from nocturnal and Polar meteorological measurements.
AGN feedback in a galaxy merger: multi-phase, galaxy-scale outflows with a fast molecular gas blob ∼6 kpc away from IRAS F08572+3915

To understand the role that active galactic nuclei (AGN) feedback plays in galaxy evolution, we need in-depth studies of the multi-phase structure and energetics of galaxy-wide outflows. In this work, we present new, deep (∼50 h) NOEMA CO(1-0) line observations of the molecular gas in the powerful outflow driven by the AGN in the ultra-luminous infrared galaxy IRAS F08572+3915. We spatially resolve the outflow, finding that its most likely configuration is a wide-angle bicone aligned with the kinematic major axis of the rotation disk. The molecular gas in the wind reaches velocities up to approximately ±1200 km s−1 and transports nearly 20% of the molecular gas mass in the system. We detect a second outflow component located ∼6 kpc northwest from the galaxy moving away at ∼900 km s−1, which could be the result of a previous episode of AGN activity. The total mass and energetics of the outflow, which includes contributions from the ionized, neutral, and warm and cold molecular gas phases, is strongly dominated by the cold molecular gas. In fact, the molecular mass outflow rate is higher than the star formation rate, even if we only consider the gas in the outflow that is fast enough to escape the galaxy, which accounts for ∼40% of the total mass of the outflow. This results in an outflow depletion time for the molecular gas in the central ∼1.5 kpc region of only ∼3 Myr, a factor of ∼2 shorter than the depletion time by star formation activity.
Pyrolysis Process as a Sustainable Management Option of Poultry Manure: Characterization of the Derived Biochars and Assessment of their Nutrient Release Capacities

Raw poultry manure (RPM) and its derived biochars at temperatures of 400 (B400) and 600 °C (B600) were physico-chemically characterized, and their ability to release nutrients was assessed under static conditions. The experimental results showed that RPM pyrolysis operation significantly affects its morphology, surface charges, and area, as well as its functional groups contents, which in turn influences its nutrient release ability. The batch experiments indicated that nutrient release from the RPM as well as biochars attains a pseudo-equilibrium state after a contact time of about 48 h. RPM pyrolysis increased phosphorus stability in residual biochars and, in contrast, transformed potassium to a more leachable form. For instance, at this contact time, P- and K-released amounts passed from 5.1 and 25.6 mg g−1 for RPM to only 3.8 and more than 43.3 mg g−1 for B400, respectively. On the other hand, six successive leaching batch experiments with a duration of 48 h each showed that P and K release from the produced biochars was a very slow process since negligible amounts continued to be released even after a total duration of 12 days. All these results suggest that RPM-derived biochars have specific physico-chemical characteristics allowing them to be used in agriculture as low-cost and slow-release fertilizers.
Tax Preference, R&D and Innovation Quality: An Empirical Study Based on the Mediating Effect Model

This paper studies the relationship among tax preferences, R&D and innovation quality by taking the listed enterprises in Shanghai and Shenzhen from 2008 to 2017 as samples. The study finds that (1) tax preferences can affect innovation quality by R&D, there is a “partial intermediary effect” among tax preferences, R&D and innovation quality. Based on the further test of the heterogeneity of property rights, it finds that the relations among the three show obvious differences in property rights. In non-state-owned enterprises, “partial intermediary effect” is established; in state-owned enterprises, “partial intermediary effect” is not established. Further study on the impact of R&D and innovation quality on enterprise performance shows that both R&D and innovation quality can significantly promote the improvement of enterprise performance. Innovation quality plays a “partial mediating effect” in the relationship between R&D and enterprise performance, and this mediating effect is only significant in non-state-owned enterprises.
Do Citizens Vote for Parties or Policies? Evidence from Israel

This paper measures the relative importance of party platforms versus expected policy outcomes in the voting decisions of individuals in a proportional representation (PR) system. It uses survey pre-electoral data from the 2006 Israeli elections. This survey is critical as it provides a set of questions on perceived likelihood and support for different potential coalitions that is unique in its richness and scope I find that the proportion of policy voters is slightly below 10\%. This is smaller than the proportion found in previous studies. The key to explaining this difference is the fact that this paper uses individual subjective perceptions about party platforms and likelihood of the different coalitions. Critically, it is shown that the inclusion of such subjective perceptions in the model improves the fit. Finally, there seems to be no correlation between policy voting and sociodemographics.
UCSY-SC1: A Myanmar speech corpus for automatic speech recognition

This paper introduces a speech corpus which is developed for Myanmar Automatic Speech Recognition (ASR) research. Automatic Speech Recognition (ASR) research has been conducted by the researchers around the world to improve their language technologies. Speech corpora are important in developing the ASR and the creation of the corpora is necessary especially for low-resourced languages. Myanmar language can be regarded as a low-resourced language because of lack of pre-created resources for speech processing research. In this work, a speech corpus named UCSY-SC1 (University of Computer Studies Yangon - Speech Corpus1) is created for Myanmar ASR research. The corpus consists of two types of domain: news and daily conversations. The total size of the speech corpus is over 42 hrs. There are 25 hrs of web news and 17 hrs of conversational recorded data.The corpus was collected from 177 females and 84 males for the news data and 42 females and 4 males for conversational domain. This corpus was used as training data for developing Myanmar ASR. Three different types of acoustic models such as Gaussian Mixture Model (GMM) - Hidden Markov Model (HMM), Deep Neural Network (DNN), and Convolutional Neural Network (CNN) models were built and compared their results. Experiments were conducted on different data sizes and evaluation is done by two test sets: TestSet1, web news and TestSet2, recorded conversational data. It showed that the performance of Myanmar ASRs using this corpus gave satisfiable results on both test sets. The Myanmar ASR using this corpus leading to word error rates of 15.61% on TestSet1 and 24.43% on TestSet2.
New Hard-TeV Extreme Blazars Detected with the MAGIC Telescopes

Extreme high-frequency-peaked BL Lac objects (EHBLs) are blazars that exhibit extremely energetic synchrotron emission. They also feature nonthermal gamma-ray emission whose peak lies in the very high-energy (VHE, E > 100 GeV) range, and in some sources exceeds 1 TeV: this is the case for hard-TeV EHBLs such as 1ES 0229+200. With the aim of increasing the EHBL population, 10 targets were observed with the MAGIC telescopes from 2010 to 2017, for a total of 265 hr of good-quality data. The data were complemented by coordinated Swift observations. The X-ray data analysis confirms that all but two sources are EHBLs. The sources show only a modest variability and a harder-when-brighter behavior, typical for this class of objects. At VHE gamma-rays, three new sources were detected and a hint of a signal was found for another new source. In each case, the intrinsic spectrum is compatible with the hypothesis of a hard-TeV nature of these EHBLs. The broadband spectral energy distributions (SEDs) of all sources are built and modeled in the framework of a single-zone, purely leptonic model. The VHE gamma-ray-detected sources were also interpreted with a spine–layer model and a proton synchrotron model. The three models provide a good description of the SEDs. However, the resulting parameters differ substantially in the three scenarios, in particular the magnetization parameter. This work presents the first mini catalog of VHE gamma-ray and multiwavelength observations of EHBLs.
An algorithm for detection of tuberculosis bacilli in Ziehl-Neelsen sputum smear images

This work proposes an algorithm oriented to the detection of tuberculosis bacilli in digital images of sputum samples, inked with the Ziehl Neelsen method and prepared with the direct, pellet and diluted pellet methods. The algorithm aims at automating the optical analysis of bacilli count and the calculation of the concentration level. Several algorithms have been proposed in the literature with the same objective, however, in no case is the performance in sensitivity and specificity evaluated for the 3 preparation methods. The proposed algorithm improves the contrast of the colors of interest, then thresholds the image and segments by labeling the objects of interest (bacilli). Each object then has its geometrical descriptors and photometric descriptors. With all this, a characteristic vector is formed, which are used in the training and classification process of an SVM. For the training 225 images obtained by the 3 preparation methods were used. The proposed algorithm reached, for the direct method, a sensitivity level of 93.67% and a specificity level of 89.23%. In the case of the Pellet method, a sensitivity of 92.13% and a specificity of 82.58% was obtained, while for diluted Pellet the sensitivity was 92.81% and the specificity 83.61%.
Accuracy and speci city trade-o in k-nearest neighbors classi cation

The k-NN rule is a simple, exible and widely used nonparametric decision method, also connected to many problems in image classi cation and retrieval such as annotation and content-based search. As the number of classes increases and ner classi cation is considered (e.g. speci c dog breed), high accuracy is often not possible in such challenging conditions, resulting in a system that will often suggest a wrong label. However, predicting a broader concept (e.g. dog) is much more reliable, and still useful in practice. Thus, sacri cing certain speci city for a more secure prediction is often desirable. This problem has been recently posed in terms of accuracy-speci city trade-o . In this paper we study the accuracy-speci city trade-o in k-NN classi cation, evaluating the impact of related techniques (posterior probability estimation and metric learning). Experimental results show that a proper combination of k-NN and metric learning can be very e ective and obtain good performance.
WHAT IMPACT DOES EDUCATIONAL SERVICE ATTENDANCE

from the Montréal Survey on the Preschool Experiences of Children in Kindergarten (MSPECK). They build on the portrait presented in issue number 1 on the use of different types of childcare and educational services by Montréal families. By retracing the preschool experiences of children from birth onwards, the portrait highlights the wide range of preschool educational trajectories in Québec. In this number, we strive to answer the question ‘What impact does educational service attendance have on child development in kindergarten? ’ while taking family socioeconomic status into account.
Optional versus Standard Features

Competing brands differ in the extent to which they offer a feature as standard or optional in their product line. In this paper, we study the competitive basis for this difference in brands’ product line strategies. Specifically, we analyze the relationship between a brand’s quality image and its propensity to offer a more differentiated, wider product line, starting from a relatively stripped-down base model to a more feature-rich model. We consider a duopoly model with two vertically differentiated firms, and show that a low-quality firm would offer the feature as optional, i.e., offer a feature-added product as well as a stripped-down base product, if it chooses to add the feature to its product. On the other hand, a high-quality firm would offer the feature as a standard component unless the cost of the feature is high. We test this asymmetry in the propensity of highand low-quality firms to offer stripped-down versions of the product with data from the US passenger car market and find empirical support for our model.
The Role of Autophagy in Myocardial Ischemia/Reperfusion Injury in Isolated Rat Hearts

Autophagy is a housekeeping process used to remove damaged cytoplasmic constituents and protein aggregates. However, a debate persists on whether autophagy is beneficial or detrimental when an ischemic/reperfusion (I/R) insult occurs in the heart. This study tested the effects of autophagy enhancers (rapamycin and trehalose) and an autophagy inhibitor (3-methyladenine) on cardiac function and infarct size after global ischemia (30 minutes) and reperfusion (45 minutes) when given prior to ischemia (pre-treatment) or at the beginning of reperfusion (post-treatment). Rapamycin (25nM) pre-treatment and post-treatment significantly restored final left ventricular developed pressure (LVDP) to 75.4±9.1% and 60±5% of initial baseline respectively (both n=5, p<0.05), compared to control I/R group (n=9) that recovered to 35±5.5% of initial baseline. Likewise, trehalose (5mM) pre-treatment and post-treatment also significantly restored final LVDP to 61.4±3.7% (n=6) and 69.1±2.7% (n=5) of their initial baseline respectively, compared to control I/R group (p<0.05). However, 3-Methyladenine (1mM) pre-treatment (n=6) and posttreatment (n=5) showed similar reduction in final LVDP to 24.7±9.1% and 33.4±12.8 % of their initial baseline respectively, as the control I/R group. Moreover, infarction percentage was significantly reduced by rapamycin pre-treatment and post-treatment (14 ± 2.8% and 21.4 ± 5.3%, respectively; both p<0.05); and trehalose pre-treatment and post-treatment (19.2 ± 3% and 15.2% ± 3, respectively; both p<0.05), but not by 3-Methyladenine pre-treatment or posttreatment (26±2% and 28±4.1%, respectively) when compared to control I/R group (38.6±4.3%). The data suggests that autophagy enhancement before ischemia or at reperfusion reduces I/R injury.
Acute stroke monitoring . European standards

__________________________________________________________________________ Acute stroke is one of the leading causes of morbidity and mortality worldwide. At the same time stroke has increasingly been recognised as a medical emergency like myocardial infarction or severe trauma. Acute care of stroke patients in specialised stroke units has been proven to be effective in acute ischaemic stroke. Accordingly, several neurological societies have established guidelines for the management of acute stroke. A comprehensive set of recommendations, together with an overview of established and widely used therapeutic strategies, has been published by the European Stroke Initiative (EUSI). While revascularisation therapies such as IV and IA thrombolysis emerged as the only specific treatments in acute stroke to have been demonstrated as being effective in multicentre randomised controlled trials, the general supportive care and treatment of acute complications are of equal importance, even if these procedures have not been investigated in randomised controlled trials. The present article aims to review the state-of-the-art general supportive care which is usually provided on an acute care stroke unit.
GET: Global envelopes in R

This work describes the R package GET that implements global envelopes for a general set of $d$-dimensional vectors $T$ in various applications. A $100(1-\alpha)$% global envelope is a band bounded by two vectors such that the probability that $T$ falls outside this envelope in any of the $d$ points is equal to $\alpha$. Global means that the probability is controlled simultaneously for all the $d$ elements of the vectors. The global envelopes can be employed for central regions of functional or multivariate data, for graphical Monte Carlo and permutation tests where the test statistic is multivariate or functional, and for global confidence and prediction bands. Intrinsic graphical interpretation property is introduced for global envelopes, and the global envelopes included in the GET package that have the property are described. Examples of different uses of global envelopes and their implementation in the GET package are presented, including global envelopes for single and several one- or two-dimensional functions, Monte Carlo goodness-of-fit tests for simple and composite hypotheses, comparison of distributions, graphical functional analysis of variance (ANOVA), and general linear model (GLM), and confidence bands in polynomial regression.
Tracking the circulation routes of fresh coins in Bitcoin: A way of identifying coin miners with transaction network structural properties

Bitcoin draws the highest degree of attention among cryptocurrencies, while coin mining is one of the most important fashion of profiting in the Bitcoin ecosystem. This paper constructs fresh coin circulation networks by tracking the fresh coin transfer routes with transaction referencing in Bitcoin blockchain. This paper proposes a heuristic algorithm to identifying coin miners by comparing coin circulation networks from different mining pools and thereby inferring the common profit distribution schemes of Bitcoin mining pools. Furthermore, this paper characterizes the increasing trend of Bitcoin miner numbers during recent years.
Asymptotically Exact Variational Bayes for High-Dimensional Binary Regression Models

State-of-the-art methods for Bayesian inference on regression models with binary responses are either computationally impractical or inaccurate in high dimensions. To cover this gap we propose a novel variational approximation for the posterior distribution of the coeﬃcients in high-dimensional probit regression. Our method leverages a representation with global and local variables but, unlike for classical mean-ﬁeld assumptions, it avoids a fully factorized approximation, and instead assumes a factorization only for the local variables. We prove that the resulting variational approximation belongs to a tractable class of uniﬁed skew-normal distributions that preserves the skewness of the actual posterior and, unlike for state-of-the-art variational Bayes solutions, converges to the exact posterior as the number of predictors p increases. A scalable coordinate ascent variational algorithm is proposed to obtain the optimal parameters of the approximating densities. As we show with both theoretical results and an application to Alzheimer’s data, such a routine requires a number of iterations converging to 1 as p → ∞ , and can easily scale to large p settings where expectation-propagation and state-of-the-art Markov chain Monte Carlo algorithms are computationally impractical.
A Gridless Compressive Sensing Based Channel Estimation for Millimeter Wave MIMO OFDM Systems with One-Bit Quantization

This paper considers the problem of estimating the sparse mmWave massive multiple input - multiple output (MIMO) OFDM channel from one-bit quantized measurements. Unlike previous grid-based one-bit compressive sensing approaches, we present a gridless convex method to recover sparse channel form one-bit measurements via Binary Atomic Norm Minimization (BiANM) and Reweighted Binary Atomic Norm Minimization (ReBiANM). Simulation results verify the accuracy of the binary and reweighted binary atomic norm minimization techniques.
Resist and Transfer Free Patterned CVD Graphene Growth on ALD Molybdenum Carbide Nano Layers

Multilayer graphene (MLG) films were grown by chemical vapour deposition (CVD) on molybdenum carbide ($MoC_{x}$) substrates. We fabricated the catalytic $MoC_{x}$ films by plasma enhanced atomic layer deposition (PEALD). The mechanism of graphene growth is studied and analysed for amorphous and crystalline $MoC_{x}$ films. In addition, the unique advantages of catalytic substrate PEALD are demonstrated in two approaches to graphene device fabrication. First, we present a complete bottom up, resist-free patterned graphene growth (GG) on pre-patterned $MoC_{x}$ PEALD performed at 50$^{\circ}C$. Selective CVD GG eliminates the need to pattern or transfer the graphene film to retain its pristine, as grown, qualities. Furthermore, we fabricated MLG directly on 100 nm suspended SiN membrane. We characterise the MLG qualities using Raman spectroscopy, and analyse the samples by optical microscopy, scanning electron microscopy and X-ray diffraction measurements. The techniques of graphene device manufacturing demonstrated here pave the path for large scale production of graphene applications.