Miracle-dz/UniFAR-FTUs huggingface dataset:
Prevalence and antibiotic sensitivity pattern of Salmonella isolated from eggs sold in commercial markets in Thanjavur, Tamil Nadu

Salmonellosis is a major public health problem globally. Poultry species acts as reservoir of this bacterium and most of the infections in humans results from the ingestion of contaminated eggs and poor hygienic practices followed by food handlers. Hence, the study was carried out with the objective to evaluate the prevalence of Salmonella in eggs from poultry and Japanese quail sold in local markets in Thanjavur district, Tamil Nadu. A total of 125 eggs were collected randomly for isolation of Salmonella in and around Thanjavur district. Briefly, the collected eggs were pre-enriched, enriched and streaked onto Xylose-Lysine Deoxycholate agar. The characteristic Salmonella colonies were subjected to biochemical tests, PCR and antibiotic susceptibility testing (ABST). A 20% prevalence of Salmonella was observed from poultry eggs, however, no Salmonella was recovered from eggs of Japanese Quails. The ABST results suggested that, all the isolates were sensitive to erythromycin, streptomycin, nalidixc acid, gentamicin, doxycycline, tetracycline and amikacin and resistant to methicillin and vancomycin. Results of the study suggest that Salmonella was detected in poultry eggs whereas; it was not detected in Japanese quail eggs in Thanjavur region of Tamil Nadu. Hence, effective salmonellosis control strategies should be followed to control the egg borne transmission. In addition, proper hygienic food safety practices and strict bio-safety measures while handling eggs and avoiding cross contamination needs to be employed for controlling salmonellosis.
A Compact Eight-port CPW-fed UWB MIMO Antenna with Band-notched Characteristic

A compact eight-port coplanar waveguide (CPW)-fed ultra-wideband (UWB) multiple-input-multiple-output (MIMO) antenna with band-notched characteristics in a small size of 54×54×0.8 mm3 is proposed in this paper. The eight-port MIMO antenna consists of four two-port MIMO antennas. For each two-port MIMO antenna, two monopole antenna elements are printed on the FR4 substrate and placed perpendicularly to each other. To increase impedance bandwidth and improve the isolation, a stub is positioned in the middle of two radiating elements. The band-notched characteristic are achieved by etching two L-shaped resonator slots on each radiating elements, respectively. The S11 reflection coefficients, coupling isolation, radiation patterns, peak gain and radiation efficiencies of the MIMO antenna are measured. The MIMO performance of the proposed antenna is analyzed and evaluated by the envelope correlation coefficient (ECC) and total active reflection coefficient (TARC).
Ontology Learning for Facilitating Ontology Matching in Automotive

All manufacturing companies need to monitor a large number of devices and from which critical data must be captured and analyzed. The increasing complexity of these ecosystems emphasizes the requirement for a flexible and versatile data model architecture. Ontologies may facilitate a proper understanding of the problem domain as well as the interoperability with surrounding systems using ontology matching approach. However, data models of surrounding systems are not always ontologies. Thus, concepts and relations among them have to be extracted from the models to enable their integration with the ontology. The definition of concepts, their hierarchy, relations between concepts, and properties from a general architecture is a complex task and has to be tailored to an application’s needs. In this paper, we propose an involvement of the ontology learning approach to the process of ontology matching in the automotive.
Auto-Detection of Click-Frauds using Machine Learning

: In the current web advertising activities, the fraud increases the number of risks for online marketing, advertising industry and e-business. The click-fraud is considered one of the most critical issues in online advertising. On-line advertisement has become one of the most important funding models to support Internet sites. Given that large sums of money are involved in on-line advertisement; malicious parties are unfortunately attempting to gain an unfair advantage. Even if the online advertisers make permanent efforts to improve the traffic filtering techniques, they are still looking for the best protection methods to detect click-frauds. Click-Fraud occurs by intentional clicking of online advertisements with no actual interest in the advertised product or service. Click Fraud is an important threat to advertisement world that affects the revenue and trust of the advertisers also. Click-fraud attacks are one instance of such malicious behavior, where software imitates a human clicking on an advertisement link. Hence, an effective fraud detection algorithm is essential for online advertising businesses. The purpose of our paper is to identify the precision of one of the modern machine learning algorithms in order to detect the click fraud in online environment. In this paper, we have studied click patterns over a dataset that handles millions of clicks over few days. The main goal was to assess the journey of a user ’ s click across their portfolio and flag IP addresses who produce lots of clicks, but never end up in installing apps. We have focused on the issue while using various single and ensemble-typed classification algorithms for the fraud detection task. As our single classifiers, we employed the Support Vector Machine, kNN algorithms. We have also employed decision tree-based ensemble classifiers, which have been used in data mining. These algorithms are Random Forest and Gradient Tree Boosting.
Genetic Algorithm with Comprehensive Sequential Constructive Crossover for the Travelling Salesman Problem

The travelling salesman problem (TSP) is a very famous NP-hard problem in operations research as well as in computer science. To solve the problem several genetic algorithms (GAs) are developed which depend primarily on crossover operator. The crossover operators are classified as distance-based crossover operators and blind crossover operators. The distance-based crossover operators use distances between nodes to generate the offspring(s), whereas blind crossover operators are independent of any kind of information of the problem, except follow the problem’s constraints. Selecting better crossover operator can lead to successful GA. Several crossover operators are available in the literature for the TSP, but most of them are not leading good GA. In this study, we propose reverse greedy sequential constructive crossover (RGSCX) and then comprehensive sequential constructive crossover (CSCX) for developing better GAs for solving the TSP. The usefulness of our proposed crossover operators is shown by comparing with some distance-based crossover operators on some TSPLIB instances. It can be concluded from the comparative study that our proposed operator CSCX is the best crossover in this study for the TSP.
A Modified Adaptive Integral Method for Analysis of Large-scale Finite Periodic Array

─ A fast algorithm based on AIM is proposed to analyze the scattering problem of the large-scale finite array. In this method, by filling zeros into the local transformation matrix, the near and far fields are isolated thoroughly to eliminate the near correction process. In the far part, a 5-level block-toeplitz matrix is employed to avoid saving the idle grids without adding artificial interfaces. In the near part, only one local cube is required to compute the local translation matrix and near impedance matrix, which can be shared by all elements. Furthermore, the block Jacobi preconditioning technique is applied to improve the convergence, and the principle of pattern multiplication is used to accelerate the calculation of the scattering pattern. Numerical results show that the proposed method can reduce not only the CPU time in filling and solving matrix but also the whole memory requirement dramatically for the large-scale finite array with large spacings. Index Terms ─ Adaptive integral method, diagonal block preconditioning, large-scale finite periodic array, multilevel block-toeplitz, scattering problem.
Context-aware Memory Enhanced Transformer for End-to-end Task-Oriented Dialogue Systems

Recent studies try to build task-oriented dialogue systems in an end-to-end manner and the existing works make great progress on this task. However, there is still an issue need to be further considered, i.e., how to effectively represent the knowledge bases and incorporate that into dialogue systems. To solve this issue, we design a novel Contextaware Memory Generation module to model the knowledge bases, which can generate context-aware entity representations with perceiving relevant entities. Furthermore, we incorporate this module into Transformer and propose Context-aware Memory Enhanced Transformer (CMET), which can aggregate information from the dialogue history and knowledge bases to generate better responses. Through extensive experiments, our method can achieve superior performance over the state-of-the-art methods.
Effects of Gel,Polysaccharide and Acemannan from Aloe vera on Broiler Gut Flora,Microvillidensity,Immune Function and Growth Performance

The effects of gel,polysaccharide and acemannan from Aloe vera on broiler gut flora,microvillidensity,immune function and growth performance were studied.600 broilers of Guangdong Qingyuan-huang are randomly assigned to 5 groups,each group consisted of six replications,half male and half female.Five groups were fed with diets as follows respectively:①basic diet+0.1% acemannan;②basic diet+0.05% acemannan;③basic diet+0.1% polysaccharide;④basic diet+0.1% aloe gel;⑤basic diet as control.The testing period was 56 days.The results indicated that dietary gel,polysaccharide,acemannan from Aloe vera significantly decreased the number of E.coli and increased the number of bifdobacteria and lactobacill,the density of microvilli.The relative weights of thymus and bursa Fabricii increased significantly(P0.05). The serum antibody titres to the Newcastle disease virus increased(P0.05). Compared to the control group,the other groups has no significant difference on the weight/day increased and the feed/gain decreased.During the early and midlle growth stages of broiler,gel,polysaccharide,acemannan from Aloe vera can inhibit the harmful gut flora and improve the multiplication of beneficial bacteria colony.Aloe gel,polysaccharide,acemannan also improve growth of microvilli,and increase the immune function and performance.
Investigating Cross-Linguistic Adjective Ordering Tendencies with a Latent-Variable Model

Across languages, multiple consecutive adjectives modifying a noun (e.g. "the big red dog") follow certain unmarked ordering rules. While explanatory accounts have been put forward, much of the work done in this area has relied primarily on the intuitive judgment of native speakers, rather than on corpus data. We present the first purely corpus-driven model of multi-lingual adjective ordering in the form of a latent-variable model that can accurately order adjectives across 24 different languages, even when the training and testing languages are different. We utilize this novel statistical model to provide strong converging evidence for the existence of universal, cross-linguistic, hierarchical adjective ordering tendencies.
Software Sustainability & High Energy Physics

New facilities of the 2020s, such as the High Luminosity Large Hadron Collider (HL-LHC), will be relevant through at least the 2030s. This means that their software efforts and those that are used to analyze their data need to consider sustainability to enable their adaptability to new challenges, longevity, and efficiency, over at least this period. This will help ensure that this software will be easier to develop and maintain, that it remains available in the future on new platforms, that it meets new needs, and that it is as reusable as possible. This report discusses a virtual half-day workshop on "Software Sustainability and High Energy Physics" that aimed 1) to bring together experts from HEP as well as those from outside to share their experiences and practices, and 2) to articulate a vision that helps the Institute for Research and Innovation in Software for High Energy Physics (IRIS-HEP) to create a work plan to implement elements of software sustainability. Software sustainability practices could lead to new collaborations, including elements of HEP software being directly used outside the field, and, as has happened more frequently in recent years, to HEP developers contributing to software developed outside the field rather than reinventing it. A focus on and skills related to sustainable software will give HEP software developers an important skill that is essential to careers in the realm of software, inside or outside HEP. The report closes with recommendations to improve software sustainability in HEP, aimed at the HEP community via IRIS-HEP and the HEP Software Foundation (HSF).
Open-sourced Dataset Protection via Backdoor Watermarking

The rapid development of deep learning has benefited from the release of some high-quality open-sourced datasets ($e.g.$, ImageNet), which allows researchers to easily verify the effectiveness of their algorithms. Almost all existing open-sourced datasets require that they can only be adopted for academic or educational purposes rather than commercial purposes, whereas there is still no good way to protect them. In this paper, we propose a \emph{backdoor embedding based dataset watermarking} method to protect an open-sourced image-classification dataset by verifying whether it is used for training a third-party model. Specifically, the proposed method contains two main processes, including \emph{dataset watermarking} and \emph{dataset verification}. We adopt classical poisoning-based backdoor attacks ($e.g.$, BadNets) for dataset watermarking, ie, generating some poisoned samples by adding a certain trigger ($e.g.$, a local patch) onto some benign samples, labeled with a pre-defined target class. Based on the proposed backdoor-based watermarking, we use a hypothesis test guided method for dataset verification based on the posterior probability generated by the suspicious third-party model of the benign samples and their correspondingly watermarked samples ($i.e.$, images with trigger) on the target class. Experiments on some benchmark datasets are conducted, which verify the effectiveness of the proposed method.
MS-Ranker: Accumulating Evidence from Potentially Correct Candidates for Answer Selection

As conventional answer selection (AS) methods generally match the question with each candidate answer independently, they suffer from the lack of matching information between the question and the candidate. To address this problem, we propose a novel reinforcement learning (RL) based multi-step ranking model, named MS-Ranker, which accumulates information from potentially correct candidate answers as extra evidence for matching the question with a candidate. In specific, we explicitly consider the potential correctness of candidates and update the evidence with a gating mechanism. Moreover, as we use a listwise ranking reward, our model learns to pay more attention to the overall performance. Experiments on two benchmarks, namely WikiQA and SemEval-2016 CQA, show that our model significantly outperforms existing methods that do not rely on external resources.
An Empirical Investigation of Beam-Aware Training in Supertagging

Structured prediction is often approached by training a locally normalized model with maximum likelihood and decoding approximately with beam search. This approach leads to mismatches as, during training, the model is not exposed to its mistakes and does not use beam search. Beam-aware training aims to address these problems, but unfortunately, it is not yet widely used due to a lack of understanding about how it impacts performance, when it is most useful, and whether it is stable. Recently, Negrinho et al. (2018) proposed a meta-algorithm that captures beam-aware training algorithms and suggests new ones, but unfortunately did not provide empirical results. In this paper, we begin an empirical investigation: we train the supertagging model of Vaswani et al. (2018) and a simpler model with instantiations of the meta-algorithm. We explore the influence of various design choices and make recommendations for choosing them. We observe that beam-aware training improves performance for both models, with large improvements for the simpler model which must effectively manage uncertainty during decoding. Our results suggest that a model must be learned with search to maximize its effectiveness.
PHICON: Improving Generalization of Clinical Text De-identification Models via Data Augmentation

De-identification is the task of identifying protected health information (PHI) in the clinical text. Existing neural de-identification models often fail to generalize to a new dataset. We propose a simple yet effective data augmentation method PHICON to alleviate the generalization issue. PHICON consists of PHI augmentation and Context augmentation, which creates augmented training corpora by replacing PHI entities with named-entities sampled from external sources, and by changing background context with synonym replacement or random word insertion, respectively. Experimental results on the i2b2 2006 and 2014 de-identification challenge datasets show that PHICON can help three selected de-identification models boost F1-score (by at most 8.6%) on cross-dataset test setting. We also discuss how much augmentation to use and how each augmentation method influences the performance.
Two- and three-point functions at criticality: Monte Carlo simulations of the three-dimensional 
(q+1)
-state clock model

We simulate the improved $(q+1)$-state clock model on the simple cubic lattice at the critical point on lattices of a linear size up to $L=960$. We compute operator product expansion (OPE) coefficients for the three-dimensional XY universality class. These are compared with highly accurate estimates obtained by using the conformal bootstrap method. We find that the results are consistent.
End-to-End Deep Learning for Reliable Cardiac Activity Monitoring using Seismocardiograms

Continuous monitoring of cardiac activity is paramount to understanding the functioning of the heart in addition to identifying precursors to conditions such as Atrial Fibrillation. Through continuous cardiac monitoring, early indications of any potential disorder can be detected before the actual event, allowing timely preventive measures to be taken. Electrocardiography (ECG) is an established standard for monitoring the function of the heart for clinical and non-clinical applications, but its electrode-based implementation makes it cumbersome, especially for uninterrupted monitoring. Hence we propose SeismoNet, a Deep Convolutional Neural Network which aims to provide an end-to-end solution to robustly observe heart activity from Seismocardiogram (SCG) signals. These SCG signals are motion-based and can be acquired in an easy, user-friendly fashion. Furthermore, the use of deep learning enables the detection of R-peaks directly from SCG signals in spite of their noise-ridden morphology and obviates the need for extracting hand-crafted features. SeismoNet was modelled on the publicly available CEBS dataset and achieved a high overall Sensitivity and Positive Predictive Value of 0.98 and 0.98 respectively.
Robust Fairness under Covariate Shift

Making predictions that are fair with regard to protected attributes (race, gender, age, etc.) has become an important requirement for classification algorithms. Existing techniques derive a fair model from sampled labeled data relying on the assumption that training and testing data are identically and independently drawn (iid) from the same distribution. In practice, distribution shift can and does occur between training and testing datasets as the characteristics of individuals interacting with the machine learning system change. We investigate fairness under covariate shift, a relaxation of the iid assumption in which the inputs or covariates change while the conditional label distribution remains the same. We seek fair decisions under these assumptions on target data with unknown labels. We propose an approach that obtains the predictor that is robust to the worst-case testing performance while satisfying target fairness requirements and matching statistical properties of the source data. We demonstrate the benefits of our approach on benchmark prediction tasks.
The Number Densities and Stellar Populations of Massive Galaxies at 3 < z < 6: A Diverse, Rapidly Forming Population in the Early Universe

We present the census of massive (log(M */M ⊙) > 11) galaxies at 3 < z < 6 identified over the COSMOS/UltraVISTA Ultra-Deep field stripes: consisting of ≈100 and ≈20 high-confidence candidates at 3 < z < 4 and 4 < z < 6, respectively. The 3 < z < 4 population is comprised of post-starburst, UV-star-forming, and dusty star-forming galaxies in roughly equal fractions, while UV-star-forming galaxies dominate at 4 < z < 6 . We account for various sources of biases in the spectral energy distribution (SED) modeling, finding that the treatment of emission line contamination is essential for understanding the number densities and mass growth histories of massive galaxies at z > 3. The significant increase in observed number densities at z ∼ 4 (> × 5 in ≲600 Myr) implies that this is the epoch at which log(M */M ⊙) > 11 galaxies emerge in significant numbers, with stellar ages (≈500–900 Myr) indicating rapid formation epochs as early as z ∼ 7. Leveraging ancillary multiwavelength data sets, we perform panchromatic SED modeling to constrain the total star formation activity of the sample. The star formation activity of the sample is generally consistent with being on the star formation main sequence at the considered redshifts, with ≈15%–25% of the population showing evidence of suppressed star formation rates, indicating that quenching mechanisms are already at play by z ∼ 4. We stack the available Hubble Space Telescope imaging, confirming their compact nature (r e ≲ 2.2 kpc), consistent with expected sizes of high-z star-forming galaxies. Finally, we discuss how our results are in-line with the early formation epochs and short formation timescales inferred from the fossil records of the most massive galaxies in the universe.
Using Information to Amplify Competition

I characterize the consumer-optimal market segmentation in competitive markets where multiple firms selling differentiated products to consumers with unit demand. This segmentation is public---in that each firm observes the same market segments---and takes a simple form: in each market segment, there is a dominant firm favored by all consumers in that segment. By segmenting the market, all but the dominant firm maximally compete to poach the consumer's business, setting price to equal marginal cost. Information, thus, is being used to amplify competition. This segmentation simultaneously generates an efficient allocation and delivers to each firm its minimax profit.
Radiation reaction: charge distributions or point charges?.

A point charge is frequently approximated by charge distributions deriving the Lorentz-Abraham-Dirac (LAD) equation. Here it is shown that the field of a uniformly charged rigid spherical shell cannot be substituted by the field of a central effective point charge. The related calculations are short and transparent due to a reference frame free representation of spacetime and the mathematical theory of generalised functions.