Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling sequences with quantum states: predicting generalization quality of generative models

Classical probability distributions on sets of sequences can be modeled using a mathematical formalism identical to that used for quantum states. Here, we do so with a quantum state that is pure and entangled. One advantage of this approach is that the reduced densities describing subsystems also carry information about the complementary subsystem. This is in contrast to the classical marginal distributions on a subsystem for which information about the complementary system is lost. A training algorithm based on the density matrix renormalization group (DMRG) procedure uses the extra information contained in the reduced densities and organizes it into a tensor network model. An understanding of the extra information contained in the reduced densities allow us to examine the mechanics and results of this DMRG algorithm and to study the generalization error of the resulting model. As an illustration, we work with the even-parity dataset and produce an estimate for the generalization error as a function of the fraction of the dataset used in training.
Bacterial identification, somatic cell count, antimicrobial profile and toxigenic Staphylococcus strains search from mastitic cow milk samples on small farms properties

ABSTRACT: Bovine mastitis has a negative impact on milk production and can pose risks to public health. The present study aimed to evaluate the quality of bovine milk from small farms in the Botucatu/SP region. Somatic cell counts (SCC), identification of pathogens involved in mastitis, and sensitivity antimicrobial profile of staphylococci isolated were performed. The presence of enterotoxin encoding genes in isolates of staphylococci obtained from milk was investigated. Milk samples from individual mammary quarters of cows were submitted to the California mastitis test (CMT) and SCC. Of the 239 dairy cows from 21 dairy herds evaluated (mean = 11.4 animals/property), two cows (0.8%) presented clinical mastitis and 86 (35.9%) subclinical mastitis. Bacterial culture was performed in 177 quarter milk samples. Staphylococci were identified in 55 (31.1%), corynebacteria in 45 (25.4%), streptococci in 25 (14.1%) and coliforms in four (2.3%) milk samples. Average SCC from culture-positive samples was 1598x103 cells/mL, in case of staphylococci was 1362x103 cells/ml, streptococci was 2857x103 cells/mL, corynebacteria was 976x103 cells/mL and in the cases of coliforms 1161x103 cells/mL were obtained. Staphylococci showed a high sensitivity (>95%) to cephalothin, cotrimoxazole, enrofloxacin, and gentamicin, with a 41.2% resistance to penicillin and 11.8% to oxacillin. Both coagulase positive (CPS) and negative staphylococci (CNS) carried genes encoding enterotoxins in 21.6% of the first group and 41.9% in the second. The sea gene was the most detected 45.8% (n=24) between them, followed by seb with 29.2% and sec with 25.0%. The sed gene was not identified. We highlight the potential risk to public health in the possibility of strains of Staphylococcus spp. enterotoxin-producing genes that can cause staphylococcal food poisoning.
Calculation of the barometric coefficients for the particle detectors belonging to the world-wide networks at the start of the 24 th solar activity cycle

After major modernization of the data acquisition electronics of the Aragats Space Environmental Center (ASEC) particle detectors the calculations of the barometric coefficients of all ASEC particle monitors were performed in the beginning of 24 solar activity cycle. The time periods of minimal disturbances of the interplanetary magnetic field (IMF) were selected to avoid biases due to transient solar events. The barometric coefficients of different particle detectors measuring various secondary cosmic ray fluxes located at altitudes of 1000 m, 2000 and 3200 m. a.s. l. are calculated and compared. The barometric coefficients for the several Neutron Monitors of recently established Eurasian data base (NMDB) and SEVAN particle detector networks also were calculated. The latitude and altitude dependencies of the barometric coefficients were investigated, as well as the dependence of coefficients on the most probable energy of the primary protons generated definite species of the secondary fluxes.
Development of High-Pressure Multigrain X-Ray Diffraction for Exploring the Earthâ€TMs Interior

The lower mantle makes up more than a half of our planet’s volume. Mineralogical and petrological experiments on realistic bulk compositions under high pressure–temperature (P–T) conditions are essential for understanding deep mantle processes. Such high P–T experiments are commonly conducted in a laser-heated diamond anvil cell, producing a multiphase assemblage consisting of 100 nm to submicron crystallite grains. The structures of these lower mantle phases often cannot be preserved upon pressure quenching; thus, in situ characterization is needed. The X-ray diffraction (XRD) pattern of such a multiphase assemblage usually displays a mixture of diffraction spots and rings as a result of the coarse grain size relative to the small X-ray beam size (3–5 lm) available at the synchrotron facilities. Severe peak overlapping from multiple phases renders the powder XRD method inadequate for indexing new phases and minor phases. Consequently, structure determination of new phases in a high P–T multiphase assemblage has been extremely difficult using conventional XRD techniques. Our recent development of multigrain XRD in high-pressure research has enabled the indexation of hundreds of individual crystallite grains simultaneously through the determination of crystallographic orientations for these individual grains. Once indexation is achieved, each grain can be treated as a single crystal. The combined crystallographic information from individual grains can be used to determine the crystal structures of new phases and minor phases simultaneously in a multiphase system. With this new development, we have opened up a new area of crystallography under the high P–T conditions of the deep lower mantle. This paper explains key challenges in studying multiphase systems and demonstrates the unique capabilities of high-pressure multigrain XRD through successful examples of its applications. 2019 THE AUTHORS. Published by Elsevier LTD on behalf of Chinese Academy of Engineering and Higher Education Press Limited Company. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Efficient Parameter Sampling for Neural Network Construction

The customizable nature of deep learning models have allowed them to be successful predictors in various disciplines. These models are often trained with respect to thousands or millions of instances for complicated problems, but the gathering of such an immense collection may be infeasible and expensive. However, what often occurs is the pollution of redundant information from these instances to the deep learning models. This paper outlines an algorithm that dynamically selects and appends instances to a training dataset from uncertain regions of the parameter space based on differences in predictions from multiple convolutional neural networks (CNNs). These CNNs are also simultaneously trained on this growing dataset to construct more accurate and knowledgable models. The methodology presented has reduced training dataset sizes by almost 90% and maintained predictive power in two diagnostics of high energy density physics.
Enhancing Fluorescence Light Microscopy Image Sequences for the Purpose of Vesicle Tracking

Tracking software is increasingly being used to extract useful information from fluorescence light microscopy image sequences, which can be difficult due to the high level of noise, low contrast and convolution inherent in the imaging process. This project aimed to improve tracking performance through the use of image enhancement techniques. A broad selection of image enhancement algorithms were implemented and the impact on tracking performance assessed. From these assessments a combined workflow has been proposed which ensures all candidate vesicles are detected, without large amounts of false detections that occur without pre-processing. The proposed workflow includes a novel pre-processing technique to remove noise, contrast enhancement and deconvolution in the wavelet domain using Stein’s Unbiased Risk Estimate (SURE) to optimise the process. It is shown that this process greatly improves the tracking performance.
The genomic survey of Tc1-like elements in the silkworm microsporidia Nosema bombycis

Microsporidia Nosema bombycis is the destructive pathogen in the production of sericulture. The Tc1/mariner elements belong to important component of DNA transposon. The genomic data of N. bombycis and related Nosema species were screened to identify the Tc1-like elements and analyzed the phylogenetic relationship, based on bioinformational analysis. High-throughput data of transcriptomes and small RNAs were used to evaluate the expressed level and potential rasiRNAs for the Tc1-like elements of N. bombycis. Twelve complete Tc1-like elements belonging to DD34,E clade is confirmed in the whole genome of N. bombycis, and divided into two branches. Six of them are sole in N. bombycis and thereby would be the molecular marker to differentiate this species from others Nosema spp. Most of the elements have the transcriptional active and are the source of sRNAs. Abundant Tc1-like elements in N. bombycis reflect the expansion of transposons for this genomic characters, comparing with others Nosema spp. The finding of distribution, phylogeny and potential functional activity for Tc1Nbs in N. bombycis will help understanding the role of the DNA transposon in genomic evolution of microsporidia.
Good Corporate Governance and Bondholder-Shareholder Conflict: Case of Implementation Accounting Conservatism for Mining Company

The application of Accounting Conservatism Principle can lead to different kind of assessment in various companies. Due to the application of this principle, financial statements are no longer considered as obligatory tools to evaluate company's risk. On the contrary, other parties state that the application of the Accounting Conservatism Principle can generate reasonable financial reports and provide relevance in Financial Information. The objective of this study is to analyze the effect of good corporate governance and bondholder-shareholder conflict against company accounting conservatism. The analysis of accounting conservatism is done using quantitative methods. In addition, data testing is done using classical assumption and hypothesis testing is conducted using multiple linear regression models. The level of accounting conservatism is measured using the value of accruals and the market value. The sample used is a mining company listed on the Indonesia Stock Exchange in 2014-2018. The results of the research show that each company implements accounting conservatism on a different level. In managerial ownership of accrual accounting, the effects of conservatism can be seen in several aspects; it is different from the approach in which the market value of managerial ownership did not affect the significance of accounting conservatism. Institutional ownership does not affect accounting conservatism when measured using accrual approach, but it has the effect on market value when measured. The presence of independent directors and market value when measuring accrual are able to influence the level of accounting conservatism.
Deep seq2seq architecture for DNA sequence decoding from noisy data

We investigate the use of a deep seq2seq architecture to solve a complex inverse problem in biophysics, that is to accurately decode DNA strands from noisy experimental data. The data corresponds to the thermal unbinding time of collections of oligonucleotides on unzipped DNA together with approximate hybridization location along the strand. Without noise, this data for all 64 possible triucleotides build from the four bases allows for DNA deciphering. However, experimental noise severely impairs this reconstruction. By modeling the various sources of noise, we train a deep neural network on synthetic data to solve this inverse problem. Various benchmarks against noise intensity demonstrate the relevance of this approach to real world DNA decoding.
TIME AND INTENSITY WEIGHTED INDICES OF FLUVIAL PROCESSES: A CASE STUDY FROM THE KOOTENAI RIVER, USA

Within riparian landscapes, river flows and stages determine habitat gradients from less to more dynamic, and these support different plant species and their life history stages that are adapted to specific positions along these gradients. The gradients are characterized by physical processes that vary in magnitude and duration, and these shape the riparian vegetation communities. Consequently, natural riparian ecosystems are very dynamic, and the river disturbance regime is essential for sustaining ecosystem health. However, although the importance of disturbance is well accepted, disturbance regimes are poorly understood. This study was undertaken to develop indices capable of characterizing riparian habitats by considering flood magnitude and the elapsed time after flood disturbance, that is, the history that influenced the present vegetation composition. The indices were tested along two reaches of the Kootenai River in Idaho, USA, with braided versus meandering channel forms. The case study spanned a 31-year period and emphasized two major disturbance components, the morphodynamic influence of velocity and shear stress and the flood or inundation duration. Computed indices were tested for consistency and then used to characterize different riparian vegetation development and succession phases. The statistical analysis revealed high correspondence among the calculated indices and differences across the different successional stages and between the two reaches. This demonstrated the utility of the time and intensity weighted indices to analyse the fluvial patterns that support different riparian vegetation communities, and this could be applicable for riparian management, mitigation, conservation and restoration. Copyright © 2016 John Wiley & Sons, Ltd. key words: disturbance regime; flood history; riparian vegetation; Kootenai River; plant succession Received 30 March 2015; Revised 21 November 2015; Accepted 03 December 2015
The Impact of Violent Crime on Obesity

Dwelling in a violence-plagued neighborhood may amplify obesity by engendering psychological distress or by cultivating a sedentary, homebound lifestyle. This relationship is speculated to be especially relevant for black and Hispanic citizens because they are much more likely than whites to live in violence-beleaguered neighborhoods. Results from two multilevel analyses of 12,645 residents living in 34 New York City neighborhoods show that, while the violent crime rate does not have a direct effect on obesity, it does condition the relationships between race, ethnicity, and obesity. As the violent crime rate rises in a neighborhood, the probability of both a black and Hispanic resident being obese increases, controlling for both individual and neighborhood factors. The BMI of black and Hispanic residents is also higher in neighborhoods beset by violence. These findings suggest that violent crime may be a salient but unappreciated factor in explaining both racial and ethnic differences in obesity.
Performance Analysis of Implementation of Construction Projects with Customer Satisfaction Index (CSI) in Building Projects

: Construction projects with different backgrounds and interests determine the overall performance of the project. Due to the different interests of contractors and service users can cause problems during construction, one of the problems caused by service users such as poor communication, late payment and final design changes. This study aims to determine the variables that affect the level of service user performance and satisfaction level perceived contractor to service users in a multi-storey building project. The study consisted of 6 variables on project performance, finance, decision making, management capability, contractor support and attitude developed into 37 questions on 30 (thirty) respondents. The method used is Customer Satisfaction Index (CSI). Based on the results of data processing using Customer Satisfaction Index (CSI) obtained by the contractor's satisfaction level of 73.40%, it is categorized that the contractor is satisfied with the service user's performance in the multi-storey building project in Palu City. In this case the satisfied category is included in the value of the satisfaction index. Contractor's satisfaction ranges from 66% - 80.90%. Thus, CSI is used to calculate all contractor satisfaction to service users in general by looking at two sides: the level of importance and the level of satisfaction
Observational and Modelling Study of a Major Downburst Event in Liguria: The 14 October 2016 Case

Downbursts are very disruptive weather events that can produce large amounts of damage. The most studied downbursts are those occurring in the United States and continental Europe, but they can happen globally. This work is an observational and modelling analysis of a major downburst event that occurred on 14 October 2016 over eastern Liguria (Italy). This downburst affected an area 30 km long and 10 km wide, producing observed wind gusts of 40 m/s with major impacts to railways, trees, and houses, with more than 2.5 million euros of damage. First, the general environment influencing this downburst is identified and analyzed, then the event is reproduced with a small multi-physics high-resolution ensemble using the Weather Research and Forecasting (WRF)–advanced research WRF (ARW) model, with 1 km horizontal grid spacing. The event was poorly predicted beforehand, and the difficulty in forecasting this event is confirmed by the fact that so few ensemble members suggested the occurrence of damaging winds over eastern Liguria. However, one of the eight members performed well and its output helped to reveal the primary mechanisms for the downburst, suggesting that high-resolution ensembles using mixed physics may be a useful tool for improving the prediction of similar extreme events in the Mediterranean region in the future.
Joint Image Reconstruction and Motion Estimation for Spatiotemporal Imaging

We propose a variational model for joint image reconstruction and motion estimation applicable to 4 spatiotemporal imaging. This model consists of two parts, one that conducts image reconstruction in 5 a static setting and another that estimates the motion by solving a sequence of coupled indirect image 6 registration problems, each formulated within the large deformation diffeomorphic metric mapping 7 framework. The proposed model is compared against alternative approaches (optical flow based 8 model and diffeomorphic motion models). Next, we derive efficient algorithms for a time-discretized 9 setting and show that the optimal solution of the time-discretized formulation is consistent with that 10 of the time-continuous one. The complexity of the algorithm is characterized and we conclude by 11 giving some numerical examples in 2D space + time tomography with very sparse and/or highly 12 noisy data. 13
PRANDTL BOUNDARY LAYER FLOW OF A CASSON NANOFLUID PAST A PERMEABLE VERTICAL PLATE

This paper demonstrates the non-Newtonian flow behavior of a nanofluid under the permeability of a porous medium. In particular, this investigation focused on the flow properties of Silica (SiO2) nanofluid subject to the buoyancy driven Casson fluid flow. The volume fraction of nanoparticles influences the velocity and temperature field. The higher percentage of volume fraction thickens the thermal boundary layer which in turn reduces the growth of velocity boundary layer. The effects of rheological parameters such as Prandtl number, thermal Grashof number, permeability, Casson parameter, Nusselt number and skin friction coefficient has been explored abundantly. The quantitative comparison of this study with the already existing results produces an excellent correlation.
Effect of stocking density on survival , growth and production of mud eel , Monoterus cuchia ( Hamilton ) under semi-intensive pond aquaculture

Effect of stocking density on the growth, survival and production of mud eel Monopterus cuchia was tested in three Fish Seed Multiplication Farm of Bangladesh. The experiment was conducted for a rearing period of 240 days in six earthen ponds having an area of 0.14 ha each. The initial length and weight of fingerlings 29.20±1.80 cm and 99.90 g was stocked at 7410 fingerlings ha-1 was designated as treatment T1-, 9880 fingerlings ha -1 as treatment T2 and 12350 fingerlings ha -1 as treatment T3. Physico-chemical parameters were at the optimum level for culture period. Highest weight gain was observed in treatment T1 and lowest in treatment T3. Final length, weight and survival also followed the same trends as weight gain. In treatment T1 M. cuchia was produced significantly higher specific growth rate than treatment T2 and T3. Feed conversion ratio was significantly lower in treatment T1 followed by treatment T2 and T3 in that order. Significantly highest production was produced in treatment T1 than in treatment T2 and T3, respectively. In despite of this, consistently higher net benefits were also found from treatment T1 than from treatment T2 and T3. Overall, highest growth, survival and net benefits of were recorded in treatment T1 at a density of 7410 fingerlings ha-1. Therefore, of the three stocking densities, 7410 fingerlings ha-1 appears to be most suitable stocking density for rearing of M. cuchia in semi intensive mud eel culture system.
SmartAgent — Creating Reinforcement Learning Tetris AI

For an NP-complete problem with a large amount of possible states, such as playing the popular videogame of Tetris, learning an effective artificial intelligence (AI) strategy can be hard using standard machine learning techniques because of the large number of examples required. Reinforcement learning, which learns by interacting with the environment through actions and receiving reward based on those actions, is well suited to the task. By learning which actions receive the highest rewards, the AI agent becomes a formidable player. This project discusses the application of reinforcement learning to a Tetris playing AI agent which was entered into the Reinforcement Learning Competition 2008 and presents the results and conclusions formed from its development and performance.
Trigger Rate Anomaly Detection with Conditional Variational Autoencoders at the CMS Experiment

Exploiting the rapid advances in probabilistic inference, in particular variational autoencoders (VAEs) for machine learning (ML) anomaly detection (AD) tasks, remains an open research question. In this work, we use the deep conditional varia-tional autoencoders (CVAE), and we define an original loss function together with a metric that targets AD for hierarchically structured data. Our target application is a real world problem: monitoring the trigger system which is a component of many particle physics experiments at the CERN Large Hadron Collider (LHC). Experiments show the superior performance of this method over vanilla VAEs.