Miracle-dz/UniFAR-FTUs huggingface dataset:
Small Area Estimation of Food Insecurity in the Eastern Indo-Gangetic Plain

Achieving food security for all citizens is an important policy issue in India. While the existing data based on socio-economic surveys provide accurate estimates of food insecurity indicators at state and national level, due to small sample sizes, the surveys cannot be used directly to produce reliable estimates at the district or lower administrative levels. The availability of reliable and representative disaggregated measures of food insecurity is necessary for effective policy planning and monitoring, as food insecurity is often distributed unevenly within relatively small areas. This article explores a small area estimation (SAE) approach to derive reliable and representative estimates of food insecurity prevalence (FIP), gap (FIG), and severity (FIS) among people in different districts of the rural areas of the Eastern Indo-Gangetic Plain (EIGP) region by linking the latest round of available data from the Household Consumer Expenditure Survey collected by the National Sample Survey Office of India as well as the latest available Indian Population Census data. District-specific food insecurity indicators such as FIP, FIG, and FIS were estimated based on a recommended threshold of per capita caloric intake of 2400 kilocalories per day, as defined by the Ministry of Health and Family Welfare, Government of India. Spatial maps showing district-level inequality in the distribution of the indicators of food insecurity among the population in the EIGP region are also produced. Our disaggregated estimates can provide district-specific focused insights into food insecurity to policy analysts and decision-makers, and could thereby prove to be useful and relevant to the U.N. Sustainable Development Goal Indicator 2.1. 2.
Differential gene expression and co-regulated expression of genes in leukemia: an in-silico approach to identify potent biomarker

A biomarker can be measured, used to diagnose or classify disease, and measure progress as well as the therapeutic response of the disease. Early diagnosis and selection of appropriate treatment can be critical for the successful treatment of diseases. Identification and characterization of potent diagnostic biomarkers, and therapeutic targets rely heavily on traditional in vitro screens which require extensive resources and time. Integration of in silico screens prior to experimental validation can improve the efficiency and potency of biomarkers as well as reduce the cost and time of biomarker discovery. Considering the need, present work was undertaken to identify biomarkers for different classes of leukemia. Differential Gene Expression (DGE) analysis and co-regulated expression analysis were used for in silico identification and characterise a potent biomarker for leukemia. On the basis of in silico screening, the present study proposed seven protein-coding (CD38, TSC22D3, TNFRSF25, AGL, LARGE1, ARHGAP32, and PARM1) genes for the diagnosis of leukemia. The study also proposed a novel three-step lineage-specific model for the diagnosis of leukemia. In the three-step diagnosis model, the first group of biomarkers with an association of clinical and hematological parameters diagnose leukemia. The second group of biomarkers diagnoses acute and chronic form of leukemia. The third group of biomarkers identifies whether it belongs to myeloid lineage or lymphoid lineage.
A Lens Model Based Judge-Participant Benchmark Repository and Its Application to Select Ranking Methods

In a judging system where multiple participants are evaluated by multiple judges, selecting a suitable ranking method to rank participants is a challenge for the organizer(s). More so in cases where the participant pool is large, the number of judges evaluating them is limited and the outcome is expected to be fair. Adding to the already existing complexities, each judging system has its own unique features and discrepancies that are required to be considered. This work introduces a multi-label classification approach that considers several features inherent to the score matrix in the judging system to recommend suitable ranking methods. The performance of existing ranking methods has been compared using a true rank condition as benchmark using a simulated bed. The simulation bed, called the judge-participant benchmark repository, has been constructed using the Lens Model framework to mimic real world score matrices and is further used to train the proposed method recommender. The repository of score matrices has been made available for researchers to be used as a benchmark for newly devised ranking methods in decision making problems.
Association of Serum Uric Acid with Biventricular Myocardial Dysfunction in Patients with type 2 Diabetes Mellitus: A Cross-sectional Study

BackgroundIncreased serum uric acid (SUA) is common in patients with type 2 diabetes mellitus (T2DM) and is associated with left ventricular (LV) myocardial dysfunction. Nonetheless the association of SUA with right ventricular (RV) function in patients with T2DM has not been studied. This study aimed to investigate the association of SUA with biventricular myocardial function in patients with T2DM.MethodsA total of 560 patients with T2DM were enrolled and divided into four groups according to quartile of SUA. Transthoracic echocardiography was performed and two-dimensional speckle tracking used to measure biventricular myocardial strain, including LV global longitudinal strain (GLS), circumferential strain (CS), radial strain (RS), and RV free wall longitudinal stra in (RV-FWLS). Results The absolute value of all biventricular strain parameters showed a stepwise decrease across SUA quartiles (all P<0.01). In particular, LV assessment by GLS, CS and RS demonstrated that those in the 4th quartile were impaired compared with the other quartiles (all P<0.05). Similarly, RV-FWLS of the 4th quartile was significantly impaired compared with the 1st and 2nd quartiles (all P<0.05). The same reduction in biventricular strain across SUA quartiles was observed in patients with estimated glomerular filtration rate＜or ≥60 ml/min/1.73 m2, and glycated hemoglobin < or ≥7.0% (all P<0.05). Multivariable linear regression analysis demonstrated that higher quartile of SUA was independently associated with impaired biventricular myocardial strain (all P<0.05). ConclusionsSUA was independently associated with biventricular myocardial dysfunction in asymptomatic T2DM patients, regardless of renal function or diabetic control.
Toward an Ecology of Gaming

1954. West Germany gains an unexpected 3-2 victory over Hungary in the World Cup, known from then on as The Miracle of Bern. Officials announce that an American hydrogen bomb test had been conducted on Bikini Atoll in the Pacific Ocean. Marilyn Monroe weds Joe DiMaggio. The Geneva Conference partitions Vietnam into North Vietnam and South Vietnam. Mathematician Alan Turing commits suicide. “Gaming as a Technique of Analysis” is released, praising games as designed models with which to think. When viewed from this perspective, 1954 looks a lot like 2007: a year of instability and transformation on the world stage, a year shadowed by the promise and threat of competing ideologies, a year colored by fear, hope, and the advent of new technology. 1954 was also a year, like this year, when games entered the popular lexicon and man the player was seized upon as a harbinger of change. By 1954, Piaget had mapped the moral judgment of a child through a study of his or her coming to know the rules of a game;2 Turing had contributed
Characterization of Jatropha hybrid clones grown under subtropical conditions of South India

Jatropha curcas L. is a tree borne oilseed crop and growing as a shrub as a living fence in a farmers’ field. Characterization is an initial step for the development of any plant database. The characterization of plant characters (plant height and growth habit), stem characters (stem colour and young shoot colour) and leaf characters viz., juvenile leaf colour, leaf shape, leaf texture, leaf length, leaf width, petiole length and petiole colour was recorded in 56 Jatropha hybrids. There was no difference in stem characters and leaf shape in all the hybrids. The other traits viz., plant height, growth habit, juvenile leaf colour, leaf texture, leaf length, leaf width, petiole length and petiole colour showed variation and categorized qualitatively as per the PPV&FRA descriptors. The plant height varies from 65.6 cm (CJH 1 and CJH 22) to 162.8 cm (CBM 1) and mature leaf length ranged between 4.4 cm (CBM 5) and 17.7 cm (TNMC 7). The results obtained in the present study could be useful for making the plant database for the Jatropha hybrids.
Building a QA System using R-net

Question-answering (QA) task is an important problem for research in natural language processing, for which many deep learning models have been designed. In this report we implement R-Net and evaluate its performance on SQUAD 2.0. While the performance of R-Net itself is worse than BiDAF, it showed a strong capability of its attention mechanism. We have also experimented with an ensemble model using BiDAF and R-Net that achieved better performance than the baseline BiDAF. Our study suggests that a promising future direction is to combine BiDAF and R-Net for building better models.
Mixture of Experts and Back-Translation to improve QA robustness

This work improves the generalization of a DistiIBERT-based Question Answering (QA) model with the addition of a Mixture of Experts (MoE) layer as well as through data augmentation via back-translation. QA models generally struggle to perform in contexts that differ from those present in the model’s training data. As a step towards addressing this limitation, our MoE implementation effectively learns domain-invariant features without explicitly training each expert on individual subdomains. We also apply top-k sampling back-translation and introduce a new technique to more effectively retrieve the answer span from the back-translated context. We find that the addition of the MoE layer yields an improvement of 3.19 in F1 score on an out-of-domain validation set, with back-translation granting a further 1.75 in Fl score. This represents a net improvement of 10.1% over the DistilBERT baseline.
Question Answering System Implementation Using QANet Architecture

QANet [10] is one of the most important papers in the domain of machine reading comprehension and automated question answering (QA). The main goal of the QANet architecture is efficiency and speed which results in significant training and inference performance gains compared to RNN based architectures. In this project, my goal is to 1) implement, from scratch, the baseline QANet model, as described in the QANet original paper, using Pytorch, 2) implement all the required dataset pre-processing, and 3) evaluate my model on the SQuAD 1.1 data set [6]
Controlled, High Rate Composting of Mixtures of Food Residuals, Yard Trimmings and Chicken Manure

Three mixtures of ground yard trimmings (50 percent by volume in each mix), chicken manure (50, 27 and 40 percent) and potato processing, gelatin and bakery wastes were composted in four pilot-scale, 208 liter, insulated vessels (the first mixture was replicated). The mixes were obtained from a commercial composter and were studied both to obtain kinetic parameters for materials of this type and to help solve a problem of inconsistent maturity of product at the commercial operation. A 65°C set point controlled a high/low fan aeration system for each vessel. Temperature (at several points), air flow, oxygen consumption, carbon dioxide evolution and ammonia production were monitored. The vessels were weighed and materials were remixed twice each week during a four week composting period. At each remix, water was added to maintain a 50 percent by weight moisture content, and samples were taken for moisture content, volatile solids, chemical composition (including C:N ratio), pH and compost stability determin.
Impact of Lockdown Due to COVID-19 Outbreak on O3 and its Precursor Gases, PM and BC Over Northeast India

Copernicus Atmosphere Monitoring Service (CAMS) data are used to evaluate the impact of the lockdown (24 March-3 May 2020) on the concentrations of surface O-3, NOx, CO, SO2, PM and BC compared to those measured during the same period in 2015-2019 over northeast India and adjoining areas Measurements made at Dibrugarh complements the CAMS observations The NOx, NO2, CO, SO2, BC and PM concentrations dipped appreciably over northeast India and nearby countries Similar decrement is observed in Dibrugarh in 2020 over their reference levels Reduction of precursor gases triggered an increase in O-3 concentration across northeast India and adjoining South Asia and at Dibrugarh The air quality over the region improved from moderate to satisfactory levels due to the lockdown
Opti mizing Settings for Pedestrian-Actuated S ignal Control Systems

A pedestrian-actuated traffic signal control system has been implemented at intersections where most traffic conflicts are between vehicle flows and pedestrians. When properly implemented, such a system can process traffic flows more efficiently than a pretimed control system. lt is not yet clear, however, how a pedestrian-actuated system can be best utilized, and misuse is not uncommon. This study provides a basis for proper use of a pedestr¡an-actuated system by characterizing the performance of the system at ¡solated ¡ntersect¡ons in terms of traffic delay. Three optimization problems of signal sett¡ng-the minimization of (a) average vehicle delay, (b) total delay of pedestrians and vehicle riders, and (c) the d¡fference between average pedestrian delay and average vehicle delay-are dis-
Exploring the Limits of the Wake-Sleep Algorithm on a Low-Resource Language

Machine Translation (MT) remains an open sub-problem in Natural Language Processing (NLP). Recently developed systems have achieved impressive, near-human performances on certain languages, but these systems are heavily dependent on large parallel corpora in the source and target languages. Thus, this approach is impractical for languages with sparse bodies of text; the real challenge lies in creating machine translation systems that are able to provide high quality, syntactically-correct translations with small amounts of parallel corpora. This work aims to utilize the wake-sleep back-translation algorithm introduced by Cotterell and Kreutzer [1] in order to generate synthetic training data that will then be used on OpenNMT’s pre-built models in order to achieve high-quality Yoruba-English translations. We also explore the effectiveness of the wake-sleep algorithm when used in-conjunction with in-domain data (in domain meaning data of a similar—or very closely related—subject as the original training data) versus out-of-domain-data (data not necessarily related in topic to the training data). We examine the performance differences between a smaller, in-domain dataset and a larger, less topical out-of-domain dataset and compare those results to our baseline. We find that performing the wake sleep algorithm on a small, in-domain dataset leads to a decrease in BLEU score of about 5 points when compared to the baseline model (14.47). Training on an out-of-domain dataset leads to a 6 point decrease in the BLEU when compared to the baseline.
Full Paper Identifi cation and characterization of the dominant lactic acid bacteria from kurut: The naturally fermented yak milk in Qinghai, China

The Tibetan tribe living in Qinghai of Northwest China, which lies in the Qing-Tibetan highlands at an average elevation of 3,000 m, has a conserved rich traditional heritage that revolves around yak. Yak thrive in treeless uplands, including plains, hills, and mountains, from as low as 1,600 m up to the limit of vegetation at about 5,400 m (Cai, 1992). Kurut, the traditional naturally-fermented yak milk, is one of the staple foods of the Tibetan nomadic people. Consumption of kurut is a part of Tibetan tribe’s culture. It is a common custom for the Tibetan people to prepare their own fermented milks using traditional methods in Qinghai. Kurut is produced using raw yak milk from the plateau breed of yaks. To produce acidity, alcohol and fl avor to the desired level, kurut is made by fermentation in a special big tung-made jar for at least 7‒8 days at ambient temperature around 10‒25°C. A special characteristic of kurut is the presence of alcohol and lactic acid, just like kefi r and koumiss. Kurut was popular among most regions of Qinghai, and was considered as sacred food by Tibetan people. Moreover, kurut was the most important indigenous fermented milk product, and of considerable economic and dietary importance to the people of Qinghai as reported by Cai (1985) and Cao et al. (2004). Given the growing public awareness on the importance of food nutrition and quality, knowledge of the microbiological distribution of kurut was of great signifi cance for further development of its processing into high quality consumer products. Some of the major fermentation processes are based on the use of J. Gen. Appl. Microbiol. , 56, 1‒10 (2010)
Comparison of The Two Lipoprotein Apheresis Methods in The Treatment of Familial Hypercholesterolemia

Amaç: Ailesel hiperkolesterolemi düşük dansiteli lipoprotein kolesterol düzeyinin önemli derecede yükselmesine ve erken yaşta koroner arter hastalığı ile kalp nedenli ölüme yol açan otozomal dominant kalıtımlı bir genetik hastalıktır. Bu hastalığın heterozigot ve homozigot formları vardır ve insidansları sırasıyla 1:500 ve 1:1 000 000 olarak rapor edilmiştir. Bu hastalık sıklıkla LDL reseptörü (en sık), apolipoprotein B (Apo B), proprotein konvertaz subtilisin/cexin 9 (PCSK9) ve LDL reseptör adaptör proteini (LDLRAP) gen mutasyonları nedeniyle ortaya çıkmaktadır. Ailesel hiperkolesterolemili hastalar statin gibi lipid-düşürücü tedavilere iyi yanıt vermez ve bu nedenle lipoprotein aferezi seçkin tedavi yöntemidir. Hastalar ve Yöntem: Homozigot ailesel hiperkolesterolemi tanısı alan 20 yaşında kadın hastada, çift filtrasyon plazmaferez (DFPP) ve dekstran sülfat kolonu (DSC) olmak üzere farklı iki lipoprotein aferez yönteminin sonuçlarını karşılaştırdık. 20 seans çift filtrasyon plazmaferez (DFPP) ve 20 seans dekstran sülfat kolonu (DSC), toplam 40 aferez seansı değerlendirildi. Bulgular: İki yöntemin karşılaştırılmasında, yüksek dansiteli lipoprotein (YDL), lökosit, platelet, potasyum, kalsiyum, protrombin zamanı ve aktive parsiyel tromboplastin zamanı değerleri değişiklikleri istatistiksel olarak anlamlı bulundu. Sonuç: Sonuç olarak, çalışmamız iki farklı yöntemin serum elektrolit değerleri, hemostaz ölçütleri ve lökosit, trombosit sayılarına farklı etkileri olduğunu gösterdiği için, aferez yöntemi seçiminin hastanın klinik ve laboratuvar bulgularına göre yapılmasının daha uygun olacağını düşündürmektedir.
THE PERFORMANCE OF THE P/B, P/E AND RIVM VALUATION MODELS FOR U.S. RATE-REGULATED COMPANIES

This paper studies the performance of the P/B, P/E and RIVM valuation models in finding out the intrinsic value of equity of U.S. utilities companies. Utilities are businesses whose revenues are set by regulators such as to allow only a “normal” rate of return and align market and book values. I expect that, if regulation is efficient in accomplishing this goal, the application of the sophisticated RIVM is not advantageous vis-à-vis the application of less complex multiples-based approaches because figures drawn from financial statements are very close to market values. U.S. manufacturing companies are used as a control group in order to maximize the comparability of the performance metrics. The performance of the valuation models is evaluated in terms of accuracy (how close to zero the valuation errors are) and bias (whether the model under or over estimates observed share price). The results show that for rate-regulated companies the RIVM generates value estimates that are neither more accurate (biggest absolute valuation errors) nor less biased (greatly over estimates the observed share price) than the ones obtained by the simple application of multiples-based approaches. This result suggests that residual income is quite low for rate-regulated companies in the United States. This can also indicate the regulation practiced in that country is relatively efficient and that the accruals measures recorded according to Regulatory Accounting Principles (RAP) perform well in terms of capturing the intrinsic value of utilities companies' shares.
Managing Tacit and Explicit Knowledge

Organizational learning forms an essential aspect and it is this ability that helps build the required knowledge resource to sustain growth and long term survival in the competitive environment. Most of the experts working in the area of Knowledge Management (KM) believes that the extent to which knowledge is accessible is the major distinguishing factor among organizations. The strategic orientation of different firms is significantly related to the type of knowledge base they exploit and share. Particularly we argue that knowledge (K) is predominantly explicit in defenders or cost leaders (Miles and Snow, 1978; Porter, 1980) and it is implicit in differentiators (Porter, 1980) and innovators (Miller and Roth, 1994). We also suggest that culture is important in KM. We give interesting propositions regarding KM such as use of Information Technology, collectivism and formalizing key aspects of K.
QUALITATIVE RESEARCH IN PHARMACY EDUCATION Demystifying Content Analysis

Objective. In the course of daily teaching responsibilities, pharmacy educators collect rich data that can provide valuable insight into student learning. This article describes the qualitative data analysis method of content analysis, which can be useful to pharmacy educators because of its application in the investigation of a wide variety of data sources, including textual, visual, and audio ﬁles. Findings. Both manifest and latent content analysis approaches are described, with several examples used to illustrate the processes. This article also offers insights into the variety of relevant terms and visualizations found in the content analysis literature. Finally, common threats to the reliability and validity of content analysis are discussed, along with suitable strategies to mitigate these risks during analysis. Summary. This review of content analysis as a qualitative data analysis method will provide clarity and actionable instruction for both novice and experienced pharmacy education researchers.
Importance Weighting for Robust QA

Machine Reading Comprehension (MRC) Questions Answering (QA) systems are commonly used within conversational agents and search engines to support users information needs while saving users the effort of navigation in documents, when the information need is a question for which the user seeks an answer. While state of the art approaches have shown to be successful for QA on a general domain, enterprise retrieval problems where the information need for QA exists in domains that are specialized and have limited or none annotated data remain open. In this work we address adaptation to new specialized domains with very little training data for MRC-QA , focusing on importance weighting. We propose two features for importance weighting that are applicable for an unsupervised setting, and present preliminary results comparing importance weighting with transfer learning