Miracle-dz/UniFAR-FTUs huggingface dataset:
Coindexed null elements for a Japanese parsed corpus

To capture language phenomena such as prodrop, wh-movement, control, and discontinuous structures, special tokens, called null elements, are used in Penn Treebanks (Marcus et al 1994, Xue and Xia 2000, Bies and Maamouri 2003). Concerned with resource creation for Japanese, this paper describes supplementing hand annotation of a parsed corpus with the generation of formal semantic representations which are used to obtain coindexing information to invest back into the hand annotation. This yields annotation with the full range of coindexed null elements available in Penn Treebanks.
A New Putative Chitinase from Reticulitermes speratus KMT001

1) Termites are pests that cause serious economic and cultural damage by digesting wood cellulose. Termites are arthropods and have an epidermis surrounded by a chitin layer. To maintain a healthy epidermis, termites have chitinase (β -1,4-poly-N-acetyl glucosamidinase, EC 3.2.1.14), an enzyme that hydrolyzes the β-1,4 bond of chitin. In this study, the amino acid sequence of the gene, which is presumed to be termite chitinolytic enzyme (NCBI accession no. KC477099), was obtained from a transcriptomic analysis of Reticulitermes speratus KMT001 in Bukhan Mountain, Korea. An NCBI protein BLAST search confirmed that the protein is a glycoside hydrolase family 18 (GH18). The highest homology value found was 47%, with a chitinase from Araneus ventricosus. Phylogenetic analysis indicated that the KC477099 protein has the same origins as those of arthropods but has a very low similarity with other arthropod chitinases, resulting in separation at an early stage of evolution. The KC477099 protein contains two conserved motifs, which encode the general enzymatic characteristics of the GH18 group. The amino acid sequences Asp-Trp-Glu, which play an important role in the enzymatic activity of the GH18 group, were also present. This study suggests that the termite KC477099 protein is a new type of chitinase, which is evolutionarily distant from other insect chitinases.
Biological, Pathological, Molecular Diagnosis of Microbes and their Role in Pharmaceutical Field as Biological Target

Different infections in blood leads to infectious diseases that are caused by viruses, bacteria and parasites. Microorganisms are used in the production of antibiotics, vaccines, steroids, etc. The advantages and limitation of molecular techniques including real-time polymerase chain reaction, genome sequencing, molecular typing, microarrays, PCR and multiplexing required advancements at molecular level. Nucleic acid-based tests are used in diagnosing infectious diseases for isolating nucleic acids from through gel electrophoresis, and nucleic acid hybridizationsn techniques to analyze DNA or RNA. Polymerase chain reaction (PCR) is an in situ DNA replication process that allows for the exponential amplification of target DNA in the presence of synthetic oligonucleotide primers. The advances in chemistry that made real-time PCR possible were also significant, and modifications of these chemical reactions continue today. In situ hybridization has been introduced into the clinical microbiology laboratory and should prove to be a useful technology for the rapid characterization of bacteria and fungi in positive blood culture samples. Multilocus sequence typing (MLST) is an unambiguous, portable and nucleotide-based technique for typing bacteria using the sequences of internal fragments of (usually) seven house-keeping genes. Microorganisms are used in the production of antibiotics, vaccines, steroids.
Research on Financial Distress Early Warning of Listed Companies Based on GBDT Model and SHAP

: Early warning of financial distress is of great significance to the healthy development of enterprises and stakeholders’ decision-making. Nowadays, the data-driven machine learning method has replaced the statistical methods to become the mainstream method of financial early warning. However, the complex model brings higher prediction accuracy, and brings the model unexplained embarrassment as well. In this paper, the ensemble learning algorithm GBDT(Gradient Boosting Decision Tree) is introduced to establish a financial distress early warning model, and the interpretability of the model is studied based on the SHAP(SHAPley Additive exPlanations) framework. Then the empirical study on more than 2000 listed manufacturing companies in China shows that the GBDT model has better generalization ability than the model based on Logistic regression, and SHAP gives a clear explanation about how financial distress contributed by feature variables. This study is valuable not only in theory but also in application for financial distress risk early warning.
A Bayesian hierarchical mixture model with continuous-time Markov chains to capture bumblebee foraging behavior

Some say that unto bees a share is given of divine intelligence. –Virgil, Georgics IV: 221–222 The standard statistical methodology for analyzing complex case-control studies in ethology is often limited by approaches that force researchers to model distinct aspects of biological processes in a piecemeal, disjointed fashion. By developing a hierarchical Bayesian model, this work demonstrates that statistical inference in this context can be done using a single coherent framework. To do this, we construct a continuous-time Markov chain (CTMC) to model bumblebee foraging behavior. To connect the experimental design with the CTMC, we employ a mixture model controlled by a logistic regression on the two-factor design matrix. We then show how to infer these model parameters from experimental data using Markov chain Monte Carlo and interpret the results from a motivating experiment.
MORPHOLOGICAL AND CHEMICAL CHARACTERISTICS OF DIFFERENT CASSAVA VARIETIES IN RELATION TO THE QUALITY ATTRIBUTES OF THEIR GARI (ROASTED FERMENTED CASSAVA GRITS)

Gari is the most popular cassava product in West Africa. Therefore, the suitability of any cassava root introduced into this region for gari production is important. This study determined some selected morphological and chemical characteristics of six cassava varieties (TMS 30001, TMS 30572, TME 419, TMS 98/0505, TMS 98/0581 and Biofortified) being promoted for widespread cultivation in Kwara State, Nigeria. The yield, physical, chemical and sensory attributes of gari made from these roots were also investigated. Principal Component Analysis (PCA) was conducted to identify parameters contributing to major sensory variations in the gari samples while correlation analysis was adopted to determine relationships between cassava and gari properties. Biofortified cassava had the highest root size attributes. Moisture, starch and cyanide contents of the cassava roots were 64.01-77.38%, 31.47-61.94% and 40.52-58.86 mg/kg, respectively. TMS 30001 had highest pulp-to-peel ratio (3.96) and gari yield (20.11%). The particle size distributions, angles of repose, pH, and total titratable acidity of the gari samples differed significantly (p≤0.05). Cyanide content of gari from TMS 98/0581 was above WHO recommended safe limit of 10 mg/kg while gari from TMS 30001 had highest sesnory ratings, except in taste. PCA revealed overall acceptability, taste and colour as the top three sensory components contributing to variation in the gari samples. Root size negatively correlated with dry matter, starch content and gari yield. It was concluded that the various cassava varieties exhibited some distinct morpholgical and chemical characteristics which influenced their potentials for gari production.
FlexPushdownDB: Hybrid Pushdown and Caching in a Cloud DBMS

Modern cloud databases adopt a storage-disaggregation architecture that separates the management of computation and storage. A major bottleneck in such an architecture is the network connecting the computation and storage layers. Two solutions have been explored to mitigate the bottleneck: caching and computation pushdown. While both techniques can significantly reduce network traffic, existing DBMSs consider them as orthogonal techniques and support only one or the other, leaving potential performance benefits unexploited. In this paper we present FlexPushdownDB (FPDB) , an OLAP cloud DBMS prototype that supports fine-grained hybrid query execution to combine the benefits of caching and computation pushdown in a storage-disaggregation architecture. We build a hybrid query executor based on a new concept called separable operators to combine the data from the cache and results from the pushdown processing. We also propose a novel Weighted-LFU cache replacement policy that takes into account the cost of pushdown computation. Our experimental evaluation on the Star Schema Benchmark shows that the hybrid execution outperforms both the conventional caching-only architecture and pushdown-only architecture by 2.2X. In the hybrid architecture, our experiments show that Weighted-LFU can outperform the baseline LFU by 37%.
Family Planning Utilization among PLWHIV Attending TASO Masaka: Evidence from a Cross-Sectional Study

Family Planning Utilization among PLWHIV Attending TASO Masaka: Evidence from a Cross-Sectional Study Kizito Benjamin Birungi, Ayebare Angella Arigye, Namubiru Sophia, Nabukeera Swalha Kabogo, Hussein Mukasa Kafeero and Fred Kironde 1 Islamic University in Uganda, Faculty of Health Sciences, Habib Medical School, Kampala Campus P.O Box, 7689, KampalaUganda 2 Islamic University in Uganda, Faculty of Health Sciences, Habib Medical School, Department of Medical Microbiology, Kampala Campus P.O Box, 7689, KampalaUganda
A new wear facet terminology for mammalian dentitions

Teeth of vertebrates are under constant use and therefore show wear-induced traces on the surface. According to Williams (2005), the mechanism by which wear occurs is a mechanical and/or chemical process resulting in material loss. Wear facets, the smooth and polished contact areas between opposing teeth, form during force-fit chewing activity between antagonists due to attritional (tooth-tooth) and abrasional (tooth-substrate-tooth) contacts involving either some kind of resistant alimentary bolus or some other dental behavior with an empty mouth cavity (Maier & Schneck 1981). In mammals, the occlusion of dental surfaces produces characteristic facet patterns on the crown’s surface. Orientation and inclination of a facet are determined by tooth morphology, while the texture of the surface (e. g., complexity and roughness) is the result of the food ingested (e. g., Schulz et al. 2010, Winkler et al. 2019, Schulz-Kornas et al. 2020, this volume). Complementary patterns of wear facets on the occlusal surface of opposing teeth change in shape and size during ontogeny and therefore can be used for age determination (e. g., Baumann 1949, Grau et al. 1970, Habermehl 1985, Anders et al. 2011a,b, Ruf et al. 2020, this volume). Wear facet patterns begin to form as soon as the tooth crown erupts and antagonists occlude. Fortelius (1985) differentiated between primary and secondary occlusal surfaces. Facets on primary surfaces develop gradually on the enamel cover (i. e., enamel facets) and only later expose the dentin with increasing wear due to age. In those cases, the exposed dentin has no explicit function. Facets on a secondary surface are composed of enamel and dentin. Dentin exposure happens relatively fast and it forms depressions in close proximity to protruding enamel, and the combination of both has a specific function. Those facets remain constant for a relatively long period of time in the life of an animal (i. e., dentin facets interrupted and/ or surrounded by enamel bands). Facets on secondary surfaces are specialized occlusal surfaces that are mostly developed in herbivorous taxa with a horizontal power stroke movement, regardless of the direction (Koenigswald 2020, this volume). In these specialized cases enamel and exposed dentin function as a unit, and neighboring facets merge and may cover large areas composed of both tissues (e. g., occlusal surface of elephant molars). In correspondence, some mammal groups developed specialized dentitions (e. g. , hypsodonty, euhypsodonty, A new wear facet terminology for mammalian dentitions
One Buffer Manager to Rule Them All: Using Distributed Memory with Cache Coherence over RDMA

Remote direct memory access (RDMA) allows query processing on distributed systems when data exceeds the size of local memory on a single machine. Cache coherence protocols generalize distributed memory as they abstract remote data access and cache data locally. Unfortunately, existing protocols are limited to main memory only. In this work, we extend a recently presented cache-co-herence protocol to deal with background storage as well. With its memory input/output primitives, this protocol ser-ves as basis for a distributed buﬀer manager that is capable of pinning and evicting pages to frames of remote nodes. Whereas locking is part of the cache coherence protocol, the buﬀer manager is responsible for a distributed buﬀer pool. This results in two-stage caching with almost no overhead, due to the quickly converging performance characteristics of SSDs and RDMA capable networks like InﬁniBand to not waste compute performance. Benchmarking the input/output primitives results in a eviction strategy of fully utilizing the distributed buﬀer pool before swapping out pages.
Relevance Constrained Re-ranking in Sponsored Listing Recommendations

Advertising (Ad) revenue is a major revenue source for many technology and e-commerce companies; most of the revenue optimization research has been around third party display ads or Cost-Per-Click based first party ads. This paper discusses the Cost-Per-Action ad product at eBay; and the challenge of balancing ad revenue and relevance. We proposed a new measurement that uses Kullback–Leibler (KL) divergence to both optimize ad revenue and improve buyer experience in item recommendations. KL divergence is adopted in the re-ranking algorithm as a constraint for revenue optimization and it is solved by a greedy grid search algorithm. In addition, we are able to approximate KL divergence with inventory based features, and that simplified a full greedy search operation to a regression. Overall, we designed and A/B tested three different approaches, all of them showed significant improvement over the baseline. Through effective re-ranking, we showed that we can achieve significant revenue gain in a sponsored listing recommendation system, even without making any improvement on conversion estimation. We launched one of the implementations to production that yielded more than 12% revenue lift with minimum impact on user experience. CCS CONCEPTS  Information systems → Sponsored search advertising ; Content match advertising.
Optimal Minimax Variable Selection for Large-Scale Matrix Linear Regression Model

Large-scale matrix linear regression models with high-dimensional responses and highdimensional variables have been widely employed in various large-scale biomedical studies. In this article, we propose an optimal minimax variable selection approach for the matrix linear regression model when the dimensions of both the response matrix and predictors diverge at the exponential rate of the sample size. We develop an iterative hard-thresholding algorithm for fast computation and establish an optimal minimax theory for the parameter estimates. The finite sample performance of the method is examined via extensive simulation studies and a real data application from the Alzheimer’s Disease Neuroimaging Initiative study is provided.
Extreme-Scale Interactive Cross-Platform Streaming Analytics - The INFORE Approach

Nowadays, analytics are at the core of many emerging applications and can greatly benefit from the abundance of data and the progress in the processing capabilities of modern hardware. Still, new challenges arise with the extreme complexity of designing applications to exploit in full the potential of such offerings. In this work, we describe the main challenges in delivering advanced, cross-platform, streaming analytics at scale with the primary goal of interactivity. We present the architecture of a prototype system designed from scratch to tackle such challenges and we describe the internals of its components. Finally, we discuss key added-value synergies upon serving target analytics workflows in real-world applications and present a preliminary performance evaluation.
Research on Model Compression Based on Convolutional Neural Network

: Deep learning methods have achieved remarkable success in the variety of applications with various variants. The most popular instance is perhaps Convolutional neural networks(CNN) consisting numerous of numbers of convolutional layers to proceed image based input to yield desired output. Typically, CNNs contains enormous number of parameters and requires huge number of float operations for inference. Hence how to filter out redundant parameters become more and more necessary. In this paper, we study how to compress CNN architectures based on sparsity-inducing regularization optimization. We validate the method on one benchmark architecture VGG16 and dataset MNIST.
The Art of Balance: A RateupDB Experience of Building a CPU/GPU Hybrid Database Product

GPU-accelerated database systems have been studied for more than 10 years, ranging from prototyping development to industry products serving in multiple domains of data applications. Existing GPU database research solutions are often focused on specific aspects in parallel algorithms and system implementations for specific features, while industry product development generally concentrates on delivering a whole system by considering its holistic performance and cost. Aiming to fill this gap between academic research and industry development, we present a comprehensive industry product study on a complete CPU/GPU HTAP system, called Ra-teupDB. We firmly believe “the art of balance" addresses major issues in the development of RateupDB. Specifically, we consider balancing multiple factors in the software development cycle, such as the trade-off between OLAP and OLTP, the trade-off between system performance and development productivity, and balanced choices of algorithms in the product. We also present RateupDB’s complete TPC-H test performance to demonstrate its significant advantages over other existing GPU DBMS products.
Effectiveness of Disinfectants on Environmental Multidrug Resistance Contaminants Causing Skin Abscess in Farm Animals

Corresponding Author: Sultan Farag Nagati Department of Bacteriology, Animal Health Research Institute (AHRI), Agriculture Research Center (ARC), Egypt Email: sultan_farag99@yahoo.com Abstract: Resistance to antimicrobial agents is recognized as a growing problem for different farms and infection of farm animals with abscesses is detrimental to livestock due to the enormous economic losses. This study aimed to find out the bacterial infection of some cases in farm animals suffering from wounds and abscesses in Fayoum Governorate, Egypt and Related Antimicrobial Resistance (AMR) procedures and technical investigations focused on disinfectant effects or the effects of AMR on bacterial persistence. The samples were collected from 130 different animals (24 buffaloes, 6 cows, 35 sheep, 40 goats, 10 camels and 15 donkeys). By bacteriology examination, eight isolates (33.3%) of Corynebacterium pseudotuberculosis biovarequi (C. equi) was isolated only from buffalo with edematous skin lesions (OSD), while Corynebacterium pseudotuberculosis biovar ovis (C. ovis) were isolated from sheep and goats with caseous lymphadenitis lesions in a rate 28.6% and 27.5% respectively. On susceptibility test against antimicrobial agents. Pipracillin-tazablam was the best of choice for Pseudomonas aeruginosa and C. equi (20%). The result showed the bacterial growth at OD595 after treatment with Povidone-iodine (PI) 10% at different time intervals, where all types of isolates showed the lowest growth after 24 h, C. ovis showed the lowest bacterial growth, while the C. equi showed the lowest bacterial growth with H2O21%. Conclusion: Caseous lymphadenitis remains an important challenge for the sheep and goat industries. C. equi also plays a role in buffalo raring and caused edematous skin disease which affects milk and meat production. The high resistance of both C. ovis and C. equi to antibiotics and disinfectants reflected the necessity to adopt control measures. MRSA, as well as PDR Pseudomonas aeruginosa, were observed animal wounds which prove the bad hygiene. Increasing contact time is more effective against microbes.
A Novel Convolutional Neural Network for COVID-19 detection and classification using Chest X-Ray images

The early and rapid diagnosis of severe acute respiratory syndrome coronavirus 2 (SARS CoV-2), the main cause of fatal pandemic coronavirus disease 2019 (COVID-19), with the analysis of patients chest X-ray (CXR) images has lifesaving importance for both patients and medical professionals. In this research a very simple novel and robust deep-learning convolutional neural network (CNN) model with less number of trainable-parameters is proposed to assist the radiologists and physicians in the early detection of COVID-19 patients. It also helps to classify patients into COVID-19, pneumonia and normal on the bases of analysis of augmented X-ray images. This augmented dataset contains 4803 COVID-19 from 686 publicly available chest X-ray images along with 5000 normal and 5000 pneumonia samples. These images are divided into 80% training and 20 % validation. The proposed CNN model is trained on training dataset and then tested on validation dataset. This model has a promising performance with a mean accuracy of 92.29%, precision of 99.96%, Specificity of 99.85% along with Sensitivity value of 85.92%. The result can further be improved if more data of expert radiologist is publically available.
Review on Thin Layer Chromatography

Thin-layer chromatography (TLC) is a technique which is used to distinct non-volatile mixtures. This Thin-layer chromatography is can be executed on a sheet of glass, plastic, or aluminium foil, which is covered with a thin layer of adsorbent material, usually silica gel, cellulose or aluminium oxide (alumina). In this, the layer of adsorbent is called as the stationary phase. Chromatography is a division procedure that each natural scientist and organic chemist knows about.
Welfare-based Fairness through Optimization

We propose optimization as a general paradigm for formalizing welfare-based fairness in AI systems. We argue that optimization models allow formulation of a wide range of fairness criteria as social welfare functions, while enabling AI to take advantage of highly advanced solution technology. In particular, we highlight that social welfare optimization supports a broad perspective on fairness motivated by general distributive justice considerations. We illustrate this advantage by reviewing a collection of social welfare functions that capture various concepts of equity. Most of these functions have tractable optimization formulations that can be eﬃciently solved by state-of-the-art methods. To further demonstrate the potentials of social welfare optimization in AI, we show how to integrate optimization with rule-based AI and machine learning, and outline research directions to explore for practical implementation of integrated methods.