Miracle-dz/UniFAR-FTUs huggingface dataset:
A Large-scale Analysis of App Inventor Projects

App Inventor is a popular block-based programming environment used by a wide audience, from K-12 to higher education, including end-users to create mobile applications to support their primary job or hobbies. Although learning computational thinking with App Inventor has been investigated, a question that remains is which programming concepts are typically used and how this compares to other block-based programming environments. Therefore, we explore the characteristics of App Inventor projects through a large-scale analysis of 88,606 apps from the App Inventor Gallery. We discovered that the size of App Inventor projects varies from projects with very few blocks to some surprisingly large projects with more than 60,000 blocks. In general, much fewer design components are used than programming blocks, as typically, to work properly, several programming blocks are necessary for each design component in an App Inventor project. In addition, we also compare our results with the analysis of 233,491 Scratch projects reported by Aivaloglou and Hermans [4]. Several differences can be observed, as in App Inventor projects events are more predominant, with lesser use of conditionals and loops. These findings may guide the decision on the adoption of App Inventor for teaching computing depending on the specific learning objectives or indicate the need for tailoring the curricula.
Functional and evolutionary analysis of two CBF genes in Prunus mume

Zhang, J., Yang, W. R. , Cheng, T. R. , Pan, H. T. and Zhang, Q. X. 2013. Functional and evolutionary analysis of two CBF genes inPrunus mume. Can. J. Plant Sci. 93: 455-464. Primers based on the C-repeat (CRT)/dehydration responsive element (DRE) binding factor of peach (Prunus persica), sweet cherry (Prunus avim) and other related family member sequences found in GenBank were designed. Fragments of C-repeat binding factor (CBF) genes were isolated from Prunus mume by PCR and RT-PCR. The two CBF genes, designated PmCBFa and PmCBFb, were 821 bp and 741 bp long, encoding putative proteins of 238 and 225 amino acids, respectively, which contain all the conserved CBF protein domains. Similar to other CBF homologs, PmCBFa and PmCBFb may be constitutive and can be induced at a low temperature. Phylogenetic analysis using known CBF homologs indicated that all monocot CBF genes belong to the same group, separated from the eudicot CBF genes. The PmCBF genes are the homologs of the sweet cherry PaDREB gene. Sequencing of 16 cultivars and a wild species, ‘Zang’ Mei, characterized the intraspecific molecular evolution of the Prunus mume CBF genes, and the preliminary analysis indicates that the nucleotide diversity is low in coding area of PmCBFa.
Hippo: Taming Hyper-parameter Optimization of Deep Learning with Stage Trees

Hyper-parameter optimization is crucial for pushing the accuracy of a deep learning model to its limits. A hyper-parameter optimization job, referred to as a study, involves numerous trials of training a model using different training knobs, and therefore is very computation-heavy, typically taking hours and days to finish. We observe that trials issued from hyper-parameter optimization algorithms often share common hyper-parameter sequence prefixes. Based on this observation, we propose Hippo, a hyper-parameter optimization system that removes redundancy in the training process to reduce the overall amount of computation significantly. Instead of executing each trial independently as in existing hyper-parameter optimization systems, Hippo breaks down the hyper-parameter sequences into stages and merges common stages to form a tree of stages (called a stage-tree), then executes a stage once per tree on a distributed GPU server environment. Hippo is applicable to not only single studies, but multi-study scenarios as well, where multiple studies of the same model and search space can be formulated as trees of stages. Evaluations show that Hippo's stage-based execution strategy outperforms trial-based methods such as Ray Tune for several models and hyper-parameter optimization algorithms, reducing GPU-hours and end-to-end training time significantly.
Study of an Air Curtain in the Context of Individual Protection from Exposure to Coronavirus (SARS-CoV-2) Contained in Cough-Generated Fluid Particles

The ongoing respiratory COVID-19 pandemic has heavily impacted the social and private lives of the majority of the global population. This infection is primarily transmitted via virus-laden fluid particles (i.e., droplets and aerosols) that are formed in the respiratory tract of infected individuals and expelled from the mouth in the course of breathing, talking, coughing, and sneezing. To mitigate the risk of virus transmission, in many places of the world, the public has been asked or even obliged to use face covers. It is plausible that in the years ahead we will see the use of face masks, face shields and respirators become a normal practice in our life. However, wearing face covers is uncomfortable in some situations, like, for example, in summer heat, while staying on beaches or at hotel swimming pools, doing exercises in gyms, etc. Also, most types of face cover become contaminated with time and need to be periodically replaced or disinfected. These nuisances are caused by the fact that face covers are based on material barriers, which prevent inward and outward propagation of aerosol and droplets containing the pathogen. Here, we study a non-material based protection barrier created by a flow of well directed down stream of air across the front of the open face. The protection is driven by dragging virus-laden particles inside the width of the air flow and hence, as a consequence, displacing them away from their primary trajectories. Applying well established gas-particle flow formalism, we analyzed the dynamics of aerosols and droplets at different regimes of the flow laying over the bodies of the fluid particles. The analysis allowed us to establish the rates of velocity gain of the fluid particles of dimensions relevant for the pathogen transmissions, while they are crossing the width of the air barrier. On the basis of this analysis, we provide a comprehensive study of the protection effectiveness of the air barrier for a susceptible individual located indoor, in an infected environment. Our study shows that such, potentially portable, air curtains can effectively provide both inward and outward protection and serve as an effective personal protective equipment (PPE) mitigating human to human transmission of virus infection like COVID-19.
Overlook: Differentially Private Exploratory Visualization for Big Data

Data exploration systems that provide differential privacy must manage a privacy budget that measures the amount of privacy lost across multiple queries. One effective strategy to manage the privacy budget is to compute a one-time private synopsis of the data, to which users can make an unlimited number of queries. However, existing systems using synopses are built for offline use cases, where a set of queries is known ahead of time and the system carefully optimizes a synopsis for it. The synopses that these systems build are costly to compute and may also be costly to store. We introduce Overlook, a system that enables private data exploration at interactive latencies for both data analysts and data curators. The key idea in Overlook is virtual synopsis that can be evaluated \emph{incrementally}, without extra space storage or expensive precomputation. Overlook simply executes queries using an existing engine, such as a SQL DBMS, and adds noise to their results. Because Overlook's synopses do not require costly precomputation or storage, data curators can also use Overlook to explore the impact of privacy parameters interactively. Overlook offers a rich visual query interface based on the open source Hillview system. Overlook achieves accuracy comparable to existing synopsis-based systems, while offering better performance and removing the need for extra storage.
Deep Low-rank Prior in Dynamic MR Imaging

The deep learning methods have achieved attractive performance in dynamic MR cine imaging. However, all of these methods are only driven by the sparse prior of MR images, while the important low-rank (LR) prior of dynamic MR cine images is not explored, which limits the further improvements on dynamic MR reconstruction. In this paper, a learned singular value thresholding (Learned-SVT) operation is proposed to explore deep low-rank prior in dynamic MR imaging for obtaining improved reconstruction results. In particular, we come up with two novel and distinct schemes to introduce the learnable low-rank prior into deep network architectures in an unrolling manner and a plug-and-play manner respectively. In the unrolling manner, we put forward a model-based unrolling sparse and low-rank network for dynamic MR imaging, dubbed SLR-Net. The SLR-Net is defined over a deep network flow graph, which is unrolled from the iterative procedures in the Iterative Shrinkage-Thresholding Algorithm (ISTA) for optimizing a sparse and low-rank based dynamic MRI model. In the plug-and-play manner, we present a plug-and-play LR network module that can be easily embedded into any other dynamic MR neural networks without changing the network paradigm. Experimental results show that both schemes can further improve the state-of-the-art CS methods, such as k-t SLR, and sparsity-driven deep learning-based methods, such as DC-CNN and CRNN, both qualitatively and quantitatively.
Sparsifying and Down-scaling Networks to Increase Robustness to Distortions

It has been shown that perfectly trained networks exhibit drastic reduction in performance when presented with distorted images. Streaming Network (STNet) is a novel architecture capable of robust classification of the distorted images while been trained on undistorted images. The distortion robustness is enabled by means of sparse input and isolated parallel streams with decoupled weights. Recent results prove STNet is robust to 20 types of noise and distortions. STNet exhibits state-of-the-art performance for classification of low light images, while being of much smaller size when other networks. In this paper, we construct STNets by using scaled versions (number of filters in each layer is reduced by factor of n) of popular networks like VGG16, ResNet50 and MobileNetV2 as parallel streams. These new STNets are tested on several datasets. Our results indicate that more efficient (less FLOPS), new STNets exhibit higher or equal accuracy in comparison with original networks. Considering a diversity of datasets and networks used for tests, we conclude that a new type of STNets is an efficient tool for robust classification of distorted images.
Representation via Representations: Domain Generalization via Adversarially Learned Invariant Representations

We investigate the power of censoring techniques, first developed for learning {\em fair representations}, to address domain generalization. We examine {\em adversarial} censoring techniques for learning invariant representations from multiple "studies" (or domains), where each study is drawn according to a distribution on domains. The mapping is used at test time to classify instances from a new domain. In many contexts, such as medical forecasting, domain generalization from studies in populous areas (where data are plentiful), to geographically remote populations (for which no training data exist) provides fairness of a different flavor, not anticipated in previous work on algorithmic fairness. We study an adversarial loss function for $k$ domains and precisely characterize its limiting behavior as $k$ grows, formalizing and proving the intuition, backed by experiments, that observing data from a larger number of domains helps. The limiting results are accompanied by non-asymptotic learning-theoretic bounds. Furthermore, we obtain sufficient conditions for good worst-case prediction performance of our algorithm on previously unseen domains. Finally, we decompose our mappings into two components and provide a complete characterization of invariance in terms of this decomposition. To our knowledge, our results provide the first formal guarantees of these kinds for adversarial invariant domain generalization.
Dependence of the Fundamental Plane of Early-type Galaxies on Age and Internal Structure

We investigate the scatter in the fundamental plane (FP) of early-type galaxies (ETGs) and its dependence on age and internal structure of ETGs, using 16,283 ETGs with Mr ≤ −19.5 and 0.025 ≤ z < 0.055 in Sloan Digital Sky Survey data. We use the relation between the age of ETGs and photometric parameters such as color, absolute magnitude, and central velocity dispersion of ETGs and find that the scatter in the FP depends on age. The FP of old ETGs with age ≳9 Gyr has a smaller scatter of ∼0.06 dex (∼14%), while that of young ETGs with age ≲6 Gyr has a larger scatter of ∼0.075 dex (∼17%). In the case of young ETGs, less compact ETGs have a smaller scatter in the FP (∼0.065 dex; ∼15%) than more compact ones (∼0.10 dex; ∼23%). On the other hand, the scatter in the FP of old ETGs does not depend on the compactness of galaxy structure. Thus, among the subpopulations of ETGs, compact young ETGs have the largest scatter in the FP. This large scatter in compact young ETGs is caused by ETGs that have low dynamical mass-to-light ratio (Mdyn/L) and blue color in the central regions. By comparing with a simple model of the galaxy that has experienced a gas-rich major merger, we find that the scenario of recent gas-rich major merger can reasonably explain the properties of the compact young ETGs with excessive light for a given mass (low Mdyn/L) and blue central color.
ÉTUDE DE LA DIGESTION DES FOURRAGES DANS LE RUMEN PAR LA MÉTHODE DES SACHETS DE NYLON ; LIAISONS AVEC LA VALEUR ALIMENTAIRE

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. ÉTUDE DE LA DIGESTION DES FOURRAGES DANS LE RUMEN PAR LA MÉTHODE DES SACHETS DE NYLON ; LIAISONS AVEC LA VALEUR ALIMENTAIRE C. Demarquilly, M. Chenost, Jacqueline Aubry, Danièle Chevalier, Marie Jailler
An Evaluation of the Interaction of Lysine and Methionine in Diets for Growing Broilers 1

Levels of lysine (Lys) and methionine (Met) in excess of NRC recommendations may result in enhanced performance, especially in regard to breast meat yield (BMY). Some people have interpreted the "ideal Protein" concept to suggest that amino acids such as Met should be kept in an "Ideal" ratio with Lys even though Lys may be in excess of actual needs. The objectives of this study were twofold: 1) to evaluate the effects of levels of Lys and Met in excess of NRC recommendations in diets of male broilers grown for further processing; 2) to determine if any interaction exists between levels of Lys and Met when minimum levels are exceeded. Three studies of identical design were conducted. Corn, soybean meal and corn gluten meal of known composition were used to formulate basal diets for 0 to 3, 3 to 6, and 6 to 9 wk which provided a minimum of 110% of NRC (1994) amino acid recommendations other than Lys, Met and TSAA which were at 100% of recommended levels. From the base diets, experimental diets were derived by additions of lysine HCl and DL methionine to provide a factorial arrangement with three levels of Lys (NRC, NRC + 0.15%, NRC + 0.3%) and four levels of Met and TSAA (NRC, NRC + 0.05%, NRC + 0.1%, NRC + 0.15%). Each of the 12 diets was fed to two (trial 1) or four (trial 2 and 3) replicate pens of 50 male broilers (Cobb 500). Birds were weighed and feed consumption determined at 3, 6, 7, 8, and 9 week. Samples of birds were processed at 7, 8, 9 wk for parts yield. Although significant differences in performance were noted among trials due to environmental temperature there was no trial x treatment interaction so data were combined. There were no significant interactions between Lys and Met for any parameter when both were fed equal to or in excess of NRC recommendations. Increasing Lys above NRC recommendations significantly improved BW and FCR at 21 d but not at later ages; BMY was improved by increasing Lys only at 63 d. Increasing Met above NRC significantly improved FCR at 42 and 56d; there was no significant effect of Met levels on BMY at any age. Results of this study suggest that people formulating diet on "Ideal Protein" basis should not elevate the level of Met if lysine is in excess of its minimum needs.
Quantile-Quantile Embedding for Distribution Transformation, Manifold Embedding, and Image Embedding with Choice of Embedding Distribution

We propose a new embedding method, named Quantile-Quantile Embedding (QQE), for distribution transformation, manifold embedding, and image embedding with the ability to choose the embedding distribution. QQE, which uses the concept of quantile-quantile plot from visual statistical tests, can transform the distribution of data to any theoretical desired distribution or empirical reference sample. Moreover, QQE gives the user a choice of embedding distribution in embedding manifold of data into the low dimensional embedding space. It can also be used for modifying the embedding distribution of different dimensionality reduction methods, either basic or deep ones, for better representation or visualization of data. We propose QQE in both unsupervised and supervised manners. QQE can also transform the distribution to either the exact reference distribution or shape of the reference distribution; and one of its many applications is better discrimination of classes. Our experiments on different synthetic and image datasets show the effectiveness of the proposed embedding method.
Remarks on the Genus Necroscia (Phasmatodea, Diapheromeridae, Necrosciinae) from China, with Two New Records, One New Synonym, and One New Combination and Key to the Species

Abstract There are nine Necroscia species recognized in China, among which N. inflata (Redtenbacher) and N. marginata (Gray) are reported for the first time from China. One new synonym is discovered: N. maculata (Chen & He) is regarded as a junior synonym of N. multicolor (Redtenbacher) n. syn. First description is given for the female and egg of N. hainanensis (Chen & He) n. comb. , which is transferred from Sosibia Stål. A list and key to all Chinese species are given.
Operational performance analysis of trans mebidang bus (case study: Binjai terminal - Medan)

The purpose of this study is to analyze the performance of parameters and the satisfaction of passengers of Trans Mebidang bus which serves from the Binjai terminal to Medan City Market Center. Based on SK.687/AJ.206/DRJD/2002, the performance of parameters is comprised of: the travel time, the headway, the circulation time, the load factor, and the comfort of the passenger area on the bus. The method that is used to measure the satisfaction of the passengers is Importance Performance Analysis. Results of the analysis found that the parameters of headway, circulation time, and load factor should be corrected because do not meet the standard, whereas the results of the analysis of the satisfaction of passengers indicated that the parameters that need to get improvement are the physical condition of the bus stop also the comfort and the safety of the bus stop.
Creative Unemployment

We examine the impact of job loss on entrepreneurship behavior in Norway. Our identification strategy relies on the use of mass layoffs caused by bankruptcies as indicators of exogenous displacement. We find that working in a company which is going to close down due to bankruptcy during the next four years raises the subsequent entrepreneur rate by 3.7 percentage points (155%) for men and 1.8 percentage points (180%) for women, compared to working in a stable firm. These estimates are much larger than what has previously been reported in the literature. Taking into account that many workers lose their jobs in the comparison group of stable firms also, we reckon that the full effects of displacement are even larger.
Growth, Upgrading and Limited Catch-up in China’s Semiconductor Industry

Semiconductors represent a major building block of high-tech industry. This chapter analyzes the trajectory of China’s rapidly growing semiconductor sector, focusing on the interplay among global technology developments, Chinese government policy, and three groups of firms: purely domestic manufacturers, Chinese operations of multi-national corporations, and hybrid firms based in China but with offshore ownership and financing. <br><br>China has pursued policies aiming to build a globally competitive domestic industry. Policy instruments include outlays on technical education, and more focused initiatives that channel investment funds, imported technology and product demand toward domestic firms, mostly state-owned ones. Despite strong government backing, domestic state-owned firms have generally failed to generate technological dynamism. Only the hybrids have delivered substantial technological advance. <br><br>Recent policy initiatives continue to lavish resources on state-owned firms while limiting the capacity of potentially more innovative hybrid and domestic private operators to expand and upgrade. As a result, China’s semiconductor industry appears locked into a path that combines quantitative expansion with low financial returns and limited qualitative improvement. This trajectory offers little prospect for attaining China’s long-term objective of propelling domestic semiconductor manufacture toward the global frontier of technology and innovation.