Miracle-dz/UniFAR-FTUs huggingface dataset:
Threshold models for the determination of genetic variability among reproductive traits in Nellore cattle

The objective of this work was to evaluate the potential of different threshold models to determine the genetic variability in Nellore cattle, with basis on the heritability estimates for the traits stayability (STA) and first calving probability at 36 months of age (CP36). Data came from the Nellore herds participating in the animal breeding program of the Embrapa-Geneplus partnership. Binomial and multi-threshold models were defined for the STA and CP36 traits. Heritability estimates were obtained following Bayesian procedures in the Multiple-trait Gibbs Sampler for Animal Models (MTGSAM) software, using a sire-maternal grandsire model. The heritability estimates, provided by the binary and alternative models, were, respectively, 0.08 and 0.12 for STA and 0.17 and 0.12 for CP36. The multi-threshold model can efficiently detect the genetic variability for stayability, but not for probability of calving for 36-month-old cows.
Foraging by Hippodamia Convergens1 for the Aphid Sitobion Avenae2 on Wheat Plants Growing in Greenhouse Plots

Abstract. We investigated predation by adult convergent lady beetle, Hippodamia convergens Guerin-Meneville, on English grain aphid, Sitobion avenae L. , on wheat, Triticum aestivum L. , growing in 1.8 × 1.8 m plantings in a greenhouse with a soil floor. The wheat was planted to simulate a typical production field, with one seed per 2.5 cm in each row, and rows planted 15.2 cm apart. The plot was artificially infested with aphids. Beetles in one group (designated “light-fed”) were provided with 20 English grain aphids of mixed instars per day, whereas beetles in a second group (designated “heavy-fed”) were provided with 80 English grain aphids per day for 4–6 days before observation. Beetles were observed foraging in the plot for a period of 12 minutes. For each visit to plants, and for movements of beetles on the soil floor of the plot, the amount of time spent searching and eating were recorded, as were incidents of beetle flight from one location to another in the plot or out of the plot. Air temperature, intensity of solar radiation, height of the wheat plants, and density of the aphids were measured concomitant with foraging observations. The number of aphids eaten in the wheat plots averaged 0.16 per minute. The number of aphids eaten per minute increased as the number of aphids per tiller increased (t = 3.80; df = 1, 46; P = 0.0004). Analysis of covariance applied to predation data from the aphid-infested plots indicated that light-fed beetles ate a significantly greater number of aphids per minute than did heavy-fed beetles (F = 5.35; df = 1, 212; P = 0.02). However, the number of aphids per tiller, the covariable in the analysis, was not significant (F = 1.64; df = 1, 211; P = 0.20), indicating that predation rate was not related to aphid density. This observation suggested that the physiological state of the beetles (poorly fed or well fed) was dominant over aphid density in influencing predation rate.
<inline-formula><tex-math notation="LaTeX">$\mathsf{NCF}$</tex-math><alternatives><mml:math><mml:mi mathvariant="sans-serif">NCF</mml:mi></mml:math><inline-graphic xlink:href="hu-ieq1-3003542.gif"/></alternatives></inline-formula>: A Neural Context Fusion Approach to Raw Mobility Annotation

Understanding human mobility patterns at the point-of-interest (POI) scale plays an important role in enhancing business intelligence in mobile environments. While large efforts have been made in this direction, most studies simply utilize POI check-ins to mine the concerned mobility patterns, the effectiveness of which is usually hindered due to data sparsity. To obtain better POI-based human mobility for mining, in this paper, we strive to directly annotate the POIs associated with raw user-generated mobility records. We propose a neural context fusion approach which integrates various context factors in people's POI-visiting behaviors. Our approach evaluates the preference and transition factors via representation learning. Notably, we incorporate an attention mechanism to deal with the randomized transitions in raw mobility. The domain knowledge factors, i.e., distance, time and popularity, remain effective and our approach further includes them from a data-driven perspective. Factors are automatically fused with a feed-forward neural network. Furthermore, we exploit a multi-head architecture to enhance the model expressiveness. Using two real-life data sets, we conduct our experimental study and find that our approach consistently outperforms the state-of-the-art baselines by at least 32 percent in accuracy. Besides, we demonstrate the utility of the obtained POI-based human mobility with a POI recommendation example.
Efficient Active Learning for New Domains

The promise of active learning is to reduce the number of labeled examples required by supervised machine learning algorithms. The largest potential benefits lie in entirely new domains, for which no labeled examples yet exist. Yet to date, most active learning studies are retroactive and demonstrate the benefits that could have been gained if active learning had been used. What are the barriers to true adoption and utilization of active learning? We focus on two: (1) the cold start or class discovery problem, in which active learning methods may struggle to make progress with zero labeled examples, and (2) the cost of having the classifier in the loop to select the next example to be labeled. We assess different active learning approaches in the context of these two barriers and conclude with recommendations for how to employ active learning in new domains. As an example, we report on the use of active learning on a large, novel data set of Mars surface images.
Study on the Effect of Irradiation on Storage Quality of Preserved Tomato Crush

Tomatoes were processed into tomato crush using standard methods and they were packed in 150 guage polythene bags. Each product was subjected to following treatments T1in which chemical preservatives were added (0.4g of potassium metabisulphite+0.2g of sodium benzoate per kg, glacial acetic acid 5ml/kg) which was taken as control and T2, T3, T4 were prepared without added preservatives and are subjected to irradiation at 0.50kGy, 1.00kGy and 2.00kGy. The storage studies were carried out up to 60 days at ambient temperature. Samples were analyzed for physico – chemical and microbial parameters on the day of preparation (0 day), 30th day and 60th day. No change was observed in TSS content and pH of crush samples during storage period. Moisture content decreased on storage at 5% significant level in the products. 2.00kGy irradiation treatment showed highest retention of moisture. There was drastic decrease seen in vitamin C content and lycopene content during entire storage period. 1.00kGy irradiation treatment showed highest retention of vitamin C content and lycopene content. TBARS (%), MDA and reducing sugars increased with the increase in doses of irradiation. As the irradiation dose and storage period increased antioxidant activity decreased. Total bacterial count increased on storage but, as the irradiation dosage increased there was decrease in bacterial count. No mould growth was noticed in crush samples throughout the period of storage.
STUDY OF PROBABILISTIC TOPIC REPRESENTATIONS FOR THE CLASSIFICATION OF GENOMIC

In this study, we utilize probabilistic topic modeling and machine learning methods to develop an efficient pipeline for the representation and classification of DNA sequences ab-initio. By treating genomic sequences as natural language text, we infer their underlying topics which are formed by patterns in their sub-sequences. Subsequently, the genomic sequences are projected into a lower-dimensional space according to their topic compositions. A preliminary investigation was conducted via a wide experimental evaluation on a diverse dataset containing different types of genomic sequences originating from key-organisms. The results suggest that topic representations can prove beneficial to the classification process as they outperformed preceding methods and have been neglected in the literature.
SE(n)++: An Efficient Solution to Multiple Pose Estimation Problems

In robotic applications, many pose problems involve solving the homogeneous transformation based on the special Euclidean group <inline-formula> <tex-math notation="LaTeX">${\mathrm{ SE}}(n)$ </tex-math></inline-formula>. However, due to the nonconvexity of <inline-formula> <tex-math notation="LaTeX">${\mathrm{ SE}}(n)$ </tex-math></inline-formula>, many of these solvers treat rotation and translation separately, and the computational efficiency is still unsatisfactory. A new technique called the <inline-formula> <tex-math notation="LaTeX">${\mathrm{ SE}}(n)++$ </tex-math></inline-formula> is proposed in this article that exploits a novel mapping from <inline-formula> <tex-math notation="LaTeX">${\mathrm{ SE}}(n)$ </tex-math></inline-formula> to <inline-formula> <tex-math notation="LaTeX">${\mathrm{ SO}}(n + 1)$ </tex-math></inline-formula>. The mapping transforms the coupling between rotation and translation into a unified formulation on the Lie group and gives better analytical results and computational performances. Specifically, three major pose problems are considered in this article, that is, the point-cloud registration, the hand–eye calibration, and the <inline-formula> <tex-math notation="LaTeX">${\mathrm{ SE}}(n)$ </tex-math></inline-formula> synchronization. Experimental validations have confirmed the effectiveness of the proposed <inline-formula> <tex-math notation="LaTeX">${\mathrm{ SE}}(n)++$ </tex-math></inline-formula> method in open datasets.
Documentation of Callable and Putable bonds

• For putable bonds, there are two cases: I. at issuance date, the issuer may set a fixed increasing amount δ of coupon rate after option time, if the bond is not redeemed. That is the coupon rate between issuance date and option date Tm is C, and is C + δ from option date Tm to maturity Tn. II. Right before the date Tm, the issuer could decide whether to increase the coupon in the future coupon dates Tm+1 < Tm+2 < . < Tn by certain amount δ ∈ [δmin, δmax] or not, and announce the decision to the holder. • For callable bonds, there is just one case: on issuance date, the issuer may set a fixed amount δ of coupon rate after option time Tm, if the bond is not redeemed by the issuer.
Development of mucinous adenocarcinoma in chronic fistula-in-ano: A case study

Chronic perianal fistulas are a common clinical condition. However, their evolution to carcinoma is rare. We report a case of recurrent chronic perineal fistula which turned into malignant tumor. The diagnosis was established by clinical examination, repeated biopsy, and colonoscopy. Clinical examination features suggested malignancy, but initial biopsy report showed cellular atypia and colonoscopy followed with biopsy revealed as large ulcerated rectal growth suggesting Carcinoma (CA) rectum and histopathological report showed infiltrating mucinous adenocarcinoma. It is suggested that this rare complication of chronic fistula-in-ano may be prevented by prompt expert management of complex primary fistula.
Optimal Batch Variance with Second-Order Marginals

Obtaining unbiased, low-variance estimates of the mean of a ground set of points by sampling a small subset of points is a crucial machine learning task, arising in stochastic learning procedures, experimental design, and active learning. In the purely stochastic case, it is well-known that importance sampling achieves unbiased estimates of minimal variance; but finding optimal distributions over batches of size greater than 1 has proven difficult. For batches of arbitrary size, we show a) that importance sampling achieves the lowest variance when sampling with replacement, and b) that parametric distributions over batches can be optimized via a quadratic form in the distribution’s first and second order marginals when sampling without replacement. We verify that such learned distributions outperform other batch selection methods, and achieve faster SGD convergence in downstream experiments. As a side-effect of our analysis, we show that distributions over fixed-sized subsets cannot be characterized by their firstand secondorder marginals in polynomial time.
Technical and Economic Models for Distributed Energy Resources Integration

Technical and Economic Models for Distributed Energy Resources Integration Jesus E. Contreras Ocaña Co-Chairs of the Supervisory Committee: Assistant Professor Baosen Zhang Electrical Engineering Affiliate Assistant Professor Miguel A. Ortega-Vazquez Electrical Engineering The increase in distributed energy resources (DERs) in power systems presents society with both opportunities and challenges. On the one hand, DERs tend to reduce carbon emissions, introduce competition, and increase the flexibility to power systems. On the other hand, DERs fundamentally disrupt the technical and economic nature of power systems. As a result, they raise challenges such as less predictable load, more complex control of resources, disruption of power flow patterns, and obsolescence of market designs. This dissertation addresses five of those challenges: i) the need for models of building load flexibility and ii) models of interaction between DERs and their aggregator, iii) the problem of coordination among DERs, iv) the risk of market power abuse, and v) the long-term DERs planning problem. Regarding i), we present a data-driven and statistically robust model that describes the flexibility of thermostatically controlled loads in buildings. The model’s simplicity makes it suitable for a wide-range of power system applications such as economic dispatch, optimal power flow, and reserve allocation problems. Regarding ii), we present an interaction model for DERs and their aggregator that predicts their long-term equilibrium. Predicting the eventual equilibrium is valuable because it predicts the aggregator’s behavior in the electricity market and profit allocation among players. Regarding iii), we present a mixed-integer linear program adaptation of the DatzingWolfe decomposition algorithm for decentralized coordination of a building and a fleet of electric vehicles (EVs). This algorithm is suitable for buildings and EVs whose operation is coupled (e.g., by common infrastructure) and cannot formulate a joint problem due to data privacy concerns or software impediments. Regarding iv), we present a pricing mechanism that mitigates the market power of a generic firm (e.g., a generator, a demand-side bidder, or an aggregator of DERs). The pricing mechanism is attractive because it incentivizes socially optimal bids by the firm, requires no private information to be formulated, and provides an instrument for the regulation of the firm’s profit. Finally, regarding v), we present a DER planning problem for deferral of capacity expansion (i.e., a non-wire alternatives planning problem) and an algorithm to solve it. Our contributions in this domain are twofold. We explicitly model deferral of capacity expansion an additional value stream of DERs and provide a scalable and tractable algorithm to solve this non-convex, large-scale problem.
Data-Driven Identification of Dialogue Acts in Chat Messages

We present an approach to classify chat messages into dialogue acts, focusing on questions and directives (“to-dos”). Our multi-lingual system uses word lexica, a specialized tokenizer and rule-based shallow syntactic analysis to compute relevant features, and then trains statistical models (support vector machines, random forests, etc.) for dialogue act prediction. The classi-ﬁcation scores we achieve are very satisfactory on question detection and promising on to-do detection, on English and German data collections.
Learning Graph-Based Priors for Generalized Zero-Shot Learning

The task of zero-shot learning (ZSL) requires correctly predicting the label of samples from classes which were unseen at training time. This is achieved by leveraging side information about class labels, such as label attributes or word embeddings. Recently, attention has shifted to the more realistic task of generalized ZSL (GZSL) where test sets consist of seen and unseen samples. Recent approaches to GZSL have shown the value of generative models, which are used to generate samples from unseen classes. In this work, we incorporate an additional source of side information in the form of a relation graph over labels. We leverage this graph in order to learn a set of prior distributions, which encourage an aligned variational autoencoder (VAE) model to learn embeddings which respect the graph structure. Using this approach we are able to achieve improved performance on the CUB and SUN benchmarks over a strong baseline.
Microsponge: An Aeon in Therapeutics

The drug delivery technology has become vastly competitive and rapidly evolving. More and more developments in delivery systems are being assimilated to elevate the efficacy and cost-effectiveness of the therapy. To govern the delivery rate of active pharmaceutical agents to a predetermined site inside the body has been one of the biggest challenges faced by the drug industry. Microsponge releases its active pharmaceutical ingredient in a time mode and also in response to other stimuli (rubbing, temperature, pH, etc.). Microsponge drug delivery technology offers entrapment of active pharmaceutical ingredients and is believed to contribute towards reduced side effects, improved stability, increased elegance, and enhanced formulation flexibility. In addition, number of studies have confirmed that microsponges systems are non-irritating, non-mutagenic, non-allergenic, and non-toxic. Microsponge technology is being used currently in a wide range of formulations.
IRIT-UPS DCASE 2020 AUDIO CAPTIONING SYSTEM Technical Report

This technical report is a short description of the sequence-to-sequence model used in the DCASE 2020 task 6 dedicated to audio captioning. Four submissions were made: i) a baseline one using greedy search, ii) beam search, iii) beam search integrating a 2g language model, iv) with a model trained with a vocabulary limited to the most frequent word types (1k words instead of about 5k words).
An Enhancement Of Cluster Wrapper In Grid Computing Environment To Support Internet Of Things (Iot)

: The term Internet of Things (IoT) includes everything associated with the internet; however, it is progressively being utilized to characterize objects that discuss one another. It is possible to accumulate data, evaluate it to build an achievement to support a precise task. To gain process, IoT allows devices on private internetto communicate with each other. By joining these integrated devices with computerized frameworks, IoT brings those networks together. An argument has arisen because something that can be associated with the internet doesn't mean it should be. However, every device gathers information for a particular reason that might be helpful to a client and affect the more extensive economy. An Operating System (OS) intended for IoTs devices based on their structural design, scheduling techniques, networking methods, programming methods, control and memory control process together with different functions which essential for real-time IoTs.
Antioxidant Activities in Sweet Potatoes Leaves Steamed with Spices

Sweet potato leaves contain phenolic and flavonoids compounds which give them a potential antioxidant capacity. However, these antioxidant activities can be lost during cooking due to high temperature. In the aim to enhance their antioxidant activities, sweet potato leaves were steamed with antioxidant spices. The spices were used alone or mixed: nutmeg, Guinea pepper, cloves, nutmeg-Guinea pepper, nutmeg-cloves, Guinea pepper-cloves and nutmeg-Guinea pepper-cloves. The phytochemical analysis was carried-out on total phenolic compounds and flavonoids, while the antioxidant activities were determined via free radical-scavenging and inhibition power of lipid peroxidation. The results showed that cloves total phenolic content (513.33 mg Gallic Acid Equivalent (GAE)/g dry matter (DM)) increased to 1786.67 mg GAE/g DM when mixed with Guinea pepper. Flavonoids content was high in Guinea pepper-cloves (57.17 mg Quercetin Equivalent (QE)/g DM). In sweet potato steamed leaves, total phenolic compounds content is improved by incorporating cloves (625.83 mg GAE/g DM), while flavonoids content is slightly improved by Guinea pepper (125.00 mg QE/g DM). Among spices, cloves, Guinea pepper-cloves and nutmeg IC50 values (0.45, 0.83 and 1.50 μg/ml respectively) did not differ significantly to that of vitamin C (1.67 μg/ml), indicating that they had a good antiradical activity. The capacity of spices and steamed sweet potato leaves with and without spices to inhibit lipid peroxidation was higher than that of Gallic acid which is the standard reference. Spices, particularly cloves and Guinea pepper can improve antioxidant activities during sweet potato leaves steaming.
IoMT Based Smart Health Care Monitoring System

The present health care system is, for the most part, in-hospital based and incorporates occasional visits that has turned as a monotonous activity for the patients. In this paper, a complete and integrated healthcare model is described enabling Remote Health Monitoring (RPM) patients to daily collect vital signs at home and sending them to caretakers using the Internet of Medical Things (IoMT). This enables doctors to screen patients at a distance and take occasional activities if there should be an occurrence of need. A set of health parameters has been identified i.e. Electrocardiogram (ECG), Pulse rate, Temperature, and Blood Pressure by using wearable sensors. These sensors are connected to an Intel Edison Board. Once the Intel Edison Board is connected to the internet, it collects data from sensors and sends to the server. The vital parameters can be visualized and monitored on any remote smart device including laptops or smartphones which are connected under the same network. The proposed demonstrate empowers users to enhance monitoring of health-related dangers and lessen hospital costs by gathering, recording, breaking down and sharing extensive health information continuously and productively. The possibility of this task came so to decrease the cerebral pain of patient to visit a specialist each time he has to check his pulse, heartbeat rate, temperature and so forth. With the assistance of this proposal, the time of both patients and doctors are saved and doctors can also help in an emergency scenario as much as possible.
Genetic Variation at the IL 10 Gene Locus Is Associated with Severity of Respiratory Syncytial Virus Bronchiolitis

The intense airway inflammatory response associated with respiratory syncytial virus (RSV) infection may be an important determinant in the severity of the disease. Interleukin (IL)–10 is a key regulatory cytokine known to be secreted during this infection. We investigated the role that IL-10 plays in RSV disease by studying the effects that variation in the IL10 gene has on the outcome of the disease. Eight single nucleotide polymorphisms (SNPs) spanning the IL10 gene were selected, and haplotypes were constructed. SNPs that efficiently tagged these haplotypes were then typed in 580 infants with severe RSV bronchiolitis and in 580 control subjects. None of the SNPs or haplotypes was associated with RSV bronchiolitis. In a subgroup analysis, 2 SNPs (IL1