Miracle-dz/UniFAR-FTUs huggingface dataset:
BIOACTIVITY OF PENICILLIUM SPORES SUSPENSION AND PLANT EXTRACTS AGAINST Liriomyza sativae (BLACHARD) AND THEIR ASSOCIATED PARASITOIDS

The toxicity of two plant extracts Allium sativum L. and Euphorbia helioscopia L. in addition to virulence of Pencillium funiculosum and their latent effect were investigated on the tomato leaf miner, Liriomyza sativae and two of associated parasitoids, ectoparasitoid, Neochrysocharis formosa (Westood) and endoparasitoid, Neochrysocharis punctiventris (Crawford). Regarding toxic effect, A. sativum extract was the most toxic at LC50 values against L. sativae while penicillium spores suspension was the least active and E. helioscopia extract lies in between. Also, the adult stage was more sensitive for all used plant materials than pupae followed by larvae. Concerning latent effect, E. helioscopia extract was more effective on the different stages of L. sativae whereas P. funiculosum was more effective on the ecto and endo parasitoids.
EFFECT OF USING ORGANIC ACIDS AND ENZYMES ON PERFORMANCE OF JAPANESE QUAIL FED OPTIMAL AND SUB- OPTIMAL ENERGY AND PROTEIN LEVELS 1. MALIC ACID

An experiment was conducted to study the effectiveness of dietary malic acid supplementation as a growth promoter on Japanese quail performance, carcass characteristics, intestinal villi and pH, bacteria enumeration, blood parameters, digestibility coefficients and economical efficiency. A total number of 360 unsexed day-old Japanese quail chicks were equally divided into 4 groups of 6 replicates each. Two starter-grower corn-soybean meal (C-SBM) basal diets were formulated to contain 24 % CP & 2900 kcal ME /kg diet and 22 % CP & 2750 kcal ME /kg diet, respectively. Also, two layer C-SBM basal diets were formulated to contain 20 % CP & 2900 kcal ME /kg diet and 18 % CP & 2750 kcal ME /kg diet, respectively. Each of the 4 basal diets was either unsupplemented or supplemented with 0.05 % malic acid. Therefore, 4 experimental treatments were used in both startinggrowing and laying periods. Each chick group fed one of the 4 experimental diets. At 35 days of age, a slaughter test was performed to determine carcass traits, edible giblets, lymphoid organs and intestinal villi, microflora count and pH. Blood samples were taken and assayed to determine some serum blood parameters. Digestibility trials were conducted to determine nutrients digestibility for starter-grower experimental diets. At laying period, egg number, weight, mass and production as well as feed intake and conversion were recorded. At the end of the 90-day period, egg samples were taken and broken out to determine internal egg quality and analysis. From nutritional and economical point of view, it was observed that using malic acid at a level of 0.05 % in Japanese quail diets containing sub-optimal energy and protein levels helped in reducing microflara count, particularly pathogens and in turn, improving quail performance and immunity. This can alleviate the financial pressure on the farmer.
Influence of practitioners' characteristics on risk assessment in Green Building projects in emerging economies: a case of Vietnam

PurposeRisks are considered a significant obstacle to Green Building (GB) development and have recently received significant attention from both construction practitioners and academics. This study aimed to identify critical GB risks and explore the relationship between participants' characteristics and risk assessment.Design/methodology/approachFirstly, a list of GB risks was developed based on a comprehensive literature review and interviewing GB experts. After that, a survey of 207 construction professionals was then conducted to validate these GB risk factors. Finally, this research adopted the ANOVA test and hierarchical regression analysis to examine the relationship between participants' characteristics and risk assessment.FindingsThe results provided a list of GB risks classified and evaluated according to the GB project life cycle and, thus, may serve as a helpful reference for GB practitioners. Notably, the ANOVA analysis revealed that risk assessment negatively correlates with participants' GB experience, while their industry experience does not affect risk assessment. Furthermore, the hierarchical regression analysis proved that participant roles do not moderate the association between risk assessment and GB experience.Originality/valueThis study contributed to GB literature by implementing empirical research on GB risks in a developing country. The results implied the essential role of professionals with rich GB experience in risk management in GB projects. Furthermore, this research could help construction practitioners understand GB risks adequately and thereby have better risk-management strategies for future GB projects.
ReviewR: a light-weight and extensible tool for manual review of clinical records

OBJECTIVES: Manual record review is a crucial step for electronic health record (EHR)-based research, but it has poor workflows and is error prone. We sought to build a tool that provides a unified environment for data review and chart abstraction data entry. MATERIALS AND METHODS: ReviewR is an open-source R Shiny application that can be deployed on a single machine or made available to multiple users. It supports multiple data models and database systems, and integrates with the REDCap API for storing abstraction results. RESULTS: We describe two real-world uses and extensions of ReviewR. Since its release in April 2021 as a package on CRAN it has been downloaded 2,204 times. DISCUSSION AND CONCLUSION: ReviewR provides an easily accessible review interface for clinical data warehouses. Its modular, extensible, and open source nature afford future expansion by other researchers.
Adaptive Single-Channel EEG Artifact Removal With Applications to Clinical Monitoring

Electroencephalography (EEG) has become very common in clinical practice due to its relatively low cost, ease of installation, non-invasiveness, and good temporal resolution. Portable EEG devices are increasingly popular in clinical monitoring applications such as sleep scoring or anesthesia monitoring. In these situations, for reasons of speed and simplicity only few electrodes are used and contamination of the EEG signal by artifacts is inevitable. Visual inspection and manual removal of artifacts is often not possible, especially in real-time applications. Our goal is to develop a flexible technique to remove EEG artifacts in these contexts with minimal supervision. We propose here a new wavelet-based method which allows to remove artifacts from single-channel EEGs. The method is based on a data-driven renormalization of the wavelet components and is capable of adaptively attenuate artifacts of different nature. We benchmark our method against alternative artifact removal techniques. We assessed the performance of the proposed method on publicly available datasets comprising ocular, muscular, and movement artifacts. The proposed method shows superior performances on different kinds of artifacts and signal-to-noise levels. Finally, we present an application of our method to the monitoring of general anesthesia. We show that our method can successfully attenuate various types of artifacts in single-channel EEG. Thanks to its data-driven approach and low computational cost, the proposed method provides a valuable tool to remove artifacts in real-time EEG applications with few electrodes, such as monitoring in special care units.
Evaluating Visual Photoplethysmography Method

Regular monitoring of common physiological signs, including heart rate, blood pressure, and oxygen saturation, can be an effective way to either prevent or detect many kinds of chronic conditions. In particular, cardiovascular diseases (CVDs) are a worldwide concern. According to the World Health Organization, 32% of all deaths worldwide are from CVDs. In addition, stress-related issues cost $190 billion in healthcare costs per year. Currently, contact devices are required to extract most of an individuals physiological information, which can be uncomfortable for users and can cause discomfort. However, in recent years, remote photoplethysmography (rPPG) technology is gaining growing interest, which enables contactless monitoring of the blood volume pulse signal using a regular camera, and ultimately can provide the same physiological information as a contact device. In this paper, we propose a benchmark comparison using a new multimodal database consisting of 56 subjects where each subject was submitted to three different tasks. Each subject wore a wearable device capable of extracting photoplethysmography signals and was filmed to allow simultaneous rPPG signal extraction. Several experiments were conducted, including a comparison between information from contact and remote signals and stress state recognition. Results have shown that in this dataset, rPPG signals were capable of dealing with motion artifacts better than contact PPG sensors and overall had better quality if compared to the signals from the contact sensor. Moreover, the statistical analysis of the variance method had shown that at least two HRV features, NNi 20 and SAMPEN were capable of differentiating between Stress and Non-Stress states. In addition, three features, IBI, NNi 20, and SAMPEN were capable of differentiating between tasks relating to different levels of difficulty. Furthermore, using machine learning to classify a stressed or unstressed state, the models were able to achieve an accuracy score of 83.11%.
Response to the coronavirus disease 2019 (COVID-19) pandemic at private retail pharmacies in Kenya: a mixed methods study

Background: Private retail pharmacies in developing countries present a unique channel for COVID-19 prevention. We assessed the response to the COVID-19 pandemic by pharmacies in Kenya, aiming to identify strategies for maximising their contribution to the national response. Methods: We conducted a prospective mixed-methods study, consisting of a questionnaire survey (n=195), a simulated client survey (n=103), and in-depth interviews (n=18). Data collection started approximately seven months after the pandemic reached Kenya. Quantitative data were summarized using measures of central tendency and multivariable modelling done using logistic regression. Qualitative analysis followed a thematic approach. Results: The initial weeks of the pandemic were characterized by fear and panic among service providers and a surge in client flow. Subsequently, 61% of pharmacies experienced a dip in demand to below pre-pandemic levels and 31% reported challenges with unavailability, high price, and poor-quality of products. Almost all pharmacies were actively providing preventive materials and therapies; educating clients on prevention measures; counselling anxious clients; and handling and referring suspect cases. Fifty-nine pharmacies (55% [95% CI 45-65%]) reported ever receiving a client asking for COVID-19 testing and a similar proportion supported pharmacy-based testing. For treatment, most pharmacies (71%) recommended alternative therapies and nutritional supplements such as vitamin C; only 27% recommended conventional therapies such as antibiotics. While 48% had at least one staff member trained on COVID-19, a general feeling of disconnection from the national program prevailed. Conclusions: Private pharmacies in Kenya were actively contributing to the COVID-19 response, but more deliberate engagement, support and linkages are required. Notably, there is an urgent need to develop guidelines for pharmacy-based COVID-19 testing, a service that is clearly needed and which could greatly increase test coverage. Roll-out of this and other pharmacy-based COVID-19 programs should be accompanied with implementation research in order to inform current and future pandemic responses.
Capital Allocation and Risk Sharing ∗ March 29 , 2021

We develop an optimal contracting model in which limited enforcement of financial contracts generates dispersion in marginal products of capital across firms. We show that the optimal contract can be implemented using state-contingent transfers and a simple collateral constraint that limits the capital input of firms by a fraction of the financial wealth of the firm owner. Compared to models with exogenous collateral constraint and incomplete markets (for example Moll (2014)), we find that the degree of measured misallocation is increasing in the persistence of the idiosyncratic productivity shocks. Under the optimal contract, the possibility to transfer wealth from high productivity states to low productivity states allows firm owners to trade off efficient allocation of consumption against the efficient allocation of capital. We show that for reasonable values of risk aversion, insurance needs more than offset production efficiency concerns. ∗University of Minnesota. We thank seminar participants at the Finance Theory Group Meetings 2019, Society for Economic Dynamics 2019, Einaudi Institute for Economics and Finance, University of Toronto for helpful comments. Electronic copy available at: https://ssrn.com/abstract=3949045
Learning to Pre-process Laser Induced Breakdown Spectroscopy Signals Without Clean Data

This work tests whether deep neural networks can clean laser induced breakdown spectroscopy (LIBS) signals by using only uncleaned raw measurements. Our view of this problem considers a disentanglement of the effects of the target of interest from those of the nuisance factors (with non-zero mean) by leveraging the vast amounts of redundancies in LIBS data and our proposed learning formulation. This later aims at promoting consistency between repeated measurement views of a target while simultaneously removing consistencies with all other LIBS measurements taken throughout the history of the instrument. Evaluations on real data from the ChemCam instrument onboard the Martian Curiosity rover show a superior performance in cleaning LIBS signals compared to the standard approaches being used by the ChemCam team.
Inference in Regression Discontinuity Designs with High-Dimensional Covariates

We study regression discontinuity designs in which many predetermined covariates, possibly much more than the number of observations, can be used to increase the precision of treatment effect estimates. We consider a two-step estimator which first selects a small number of “important” covariates through a localized Lasso-type procedure, and then, in a second step, estimates the treatment effect by including the selected covariates linearly into the usual local linear estimator. We provide an in-depth analysis of the algorithm’s theoretical properties, showing that, under an approximate sparsity condition, the resulting estimator is asymptotically normal, with asymptotic bias and variance that are conceptually similar to those obtained in low-dimensional settings. Bandwidth selection and inference can be carried out using standard methods. We also provide simulations and an empirical application.
EnTRPO: Trust Region Policy Optimization Method with Entropy Regularization

Trust Region Policy Optimization (TRPO) is a popular and empirically successful policy search algorithm in reinforcement learning (RL). It iteratively solved the surrogate problem which restricts consecutive policies to be close to each other. TRPO is an on-policy algorithm. On-policy methods bring many benefits, like the ability to gauge each resulting policy. However, they typically discard all the knowledge about the policies which existed before. In this work, we use a replay buffer to borrow from the off-policy learning setting to TRPO. Entropy regularization is usually used to improve policy optimization in reinforcement learning. It is thought to aid exploration and generalization by encouraging more random policy choices. We add an Entropy regularization term to advantage over {\pi}, accumulated over time steps, in TRPO. We call this update EnTRPO. Our experiments demonstrate EnTRPO achieves better performance for controlling a Cart-Pole system compared with the original TRPO
Market Design for Drone Traffic Management

The rapid development of drone technology is leading to more and more use cases being proposed. In response, regulators are drawing up drone traffic management frameworks. However, to design solutions that are efficient, fair, simple, non-manipulable, and scalable, we need market design and AI expertise. To this end, we introduce the drone traffic management problem as a new research challenge to the market design and AI communities. We present five design desiderata that we have derived from our interviews with stakeholders from the regulatory side as well as from public and private enterprises. Finally, we provide an overview of the solution space to point out possible directions for future research.
PARIS: Personalized Activity Recommendation for Improving Sleep Quality

The quality of sleep has a deep impact on people's physical and mental health. People with insufficient sleep are more likely to report physical and mental distress, activity limitation, anxiety, and pain. Moreover, in the past few years, there has been an explosion of applications and devices for activity monitoring and health tracking. Signals collected from these wearable devices can be used to study and improve sleep quality. In this paper, we utilize the relationship between physical activity and sleep quality to find ways of assisting people improve their sleep using machine learning techniques. People usually have several behavior modes that their bio-functions can be divided into. Performing time series clustering on activity data, we find cluster centers that would correlate to the most evident behavior modes for a specific subject. Activity recipes are then generated for good sleep quality for each behavior mode within each cluster. These activity recipes are supplied to an activity recommendation engine for suggesting a mix of relaxed to intense activities to subjects during their daily routines. The recommendations are further personalized based on the subjects' lifestyle constraints, i.e. their age, gender, body mass index (BMI), resting heart rate, etc, with the objective of the recommendation being the improvement of that night's quality of sleep. This would in turn serve a longer-term health objective, like lowering heart rate, improving the overall quality of sleep, etc.
Deep learning exotic hadrons

We perform the first amplitude analysis of experimental data using deep neural networks to determine the nature of an exotic hadron. Specifically, we study the line shape of the P c ð 4312 Þ signal reported by the LHCb collaboration, and we find that its most likely interpretation is that of a virtual state. This method can be applied to other near-threshold resonance candidates. DOI: 10.1103/PhysRevD.105. L091501
Gauge-invariant perturbation theory on the Schwarzschild background spacetime Part I : -- Formulation and odd-mode perturbations

This is the Part I paper of our series of full papers on a gauge-invariant {\it linear} perturbation theory on the Schwarzschild background spacetime which was briefly reported in our short papers [K. ~Nakamura, Class. Quantum Grav. {\bf 38} (2021), 145010; K. ~Nakamura, Letters in High Energy Physics {\bf 2021} (2021), 215. ]. We first review our general framework of the gauge-invariant perturbation theory, which can be easily extended to the {\it higher-order} perturbation theory. When we apply this general framework to perturbations on the Schwarzschild background spacetime, a gauge-invariant treatments of $l=0,1$ mode perturbations are required. On the other hand, in the current consensus on the perturbations of the Schwarzschild spacetime, gauge-invariant treatments for $l=0,1$ modes are difficult if we keep the reconstruction of the original metric perturbations in our mind. Based on this situation, we propose a strategy of a gauge-invariant treatments of $l=0,1$ mode perturbations through the decomposition of the metric perturbations by singular harmonic functions at once and the regularization of this singularity through the imposition of the boundary conditions to the Einstein equations. Following this proposal, we derive the linearized Einstein equations for any modes of $l\geq 0$ in a gauge-invariant manner. We discuss the solutions to the odd-mode perturbation equations in the linearized Einstein equations and show that these perturbations include the Kerr parameter perturbation in these odd-mode perturbation, which is physically reasonable.
High-Throughput and Energy-Efficient VLSI Architecture for Ordered Reliability Bits GRAND

Ultrareliable low-latency communication (URLLC), a major 5G new-radio (NR) use case, is the key enabler for applications with strict reliability and latency requirements. These applications necessitate the use of short-length and high-rate channel codes. Guessing random additive noise decoding (GRAND) is a recently proposed maximum likelihood (ML) decoding technique for these short-length and high-rate codes. Rather than decoding the received vector, GRAND tries to infer the noise that corrupted the transmitted codeword during transmission through the communication channel. As a result, GRAND can decode any code, structured or unstructured. GRAND has hard-input as well as soft-input variants. Among these variants, ordered reliability bits GRAND (ORBGRAND) is a soft-input variant that outperforms hard-input GRAND and is suitable for parallel hardware implementation. This work reports the first hardware architecture for ORBGRAND, which achieves an average throughput of up to 42.5 Gb/s for a code length of 128 at a target frame error rate (FER) of 10<sup>−7</sup>. Furthermore, the proposed hardware can be used to decode any code as long as the length and rate constraints are met. In comparison to the GRAND with ABandonment (GRANDAB), a hard-input variant of GRAND, the proposed architecture enhances decoding performance by at least 2 dB. When compared to the state-of-the-art fast dynamic successive cancellation flip decoder (Fast-DSCF) using a 5G polar code (PC) (128, 105), the proposed ORBGRAND VLSI implementation has <inline-formula> <tex-math notation="LaTeX">$49\times $ </tex-math></inline-formula> higher average throughput, <inline-formula> <tex-math notation="LaTeX">$32\times $ </tex-math></inline-formula> times more energy efficiency, and <inline-formula> <tex-math notation="LaTeX">$5\times $ </tex-math></inline-formula> more area efficiency while maintaining similar decoding performance.
Generating GitHub Repository Descriptions: A Comparison of Manual and Automated Approaches

Given the vast number of repositories hosted on GitHub, project discovery and retrieval have become increasingly important for GitHub users. Repository descriptions serve as one of the first points of contact for users who are accessing a repository. However, repository owners often fail to provide a high-quality description; instead, they use vague terms, the purpose of the repository is poorly explained, or the description is omitted entirely. In this work, we examine the current practice of writing GitHub repository descriptions. Our investigation leads to the proposal of the LSP (Language, Software technology, and Purpose) template to formulate good descriptions for GitHub repositories that are clear, concise, and informative. To understand the extent to which current automated techniques can support generating repository descriptions, we compare the performance of state-of-the-art text summarization methods on this task. Finally, our user study with GitHub users reveals that automated summarization can adequately be used for default description generation for GitHub repositories, while the descriptions which follow the LSP template offer the most effective instrument for communicating with GitHub users.