Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimation of genetic parameters for reproductive traits between first and later parities in pig

The objective of this study was to estimate genetic parameters between first and later parities as different traits in reproductive traits of pigs using multiple trait animal model procedures. Data related to reproductive traits from a total of 2,371 individuals maintained at a farm were taken from the pedigree file. Sires and dams were consisted of Duroc, Landrace, and Yorkshire breeds, respectively. The first and later parity records were considered as different traits. Traits included in analyses were total pigs born (TB1), number of pigs born alive (NBA1), number of pigs weaned (NW1), and litter weaning weight (LWT1) in the first parity, and total pigs born (TB2), number of pigs born alive (NBA2), number of pigs weaned (NW2), litter weaning weight (LWT2) and interval between farrowing events (FTF) in later parities. Heritability estimates of TB1, NBA1, NW1 and LWT1 in the first parity were 0.27, 0.25, 0.16 and 0.20, respectively. For TB2, NBA2, NW2, LWT2 and FTF in later parities, heritabilities were estimated as 0.15, 0.15, 0.08, 0.11 and 0.07, respectively. Genetic correlations between sow reproductive traits in the first parity and in the second and later parity were estimated to be 0.89, 0.77, 0.58 and 0.66, respectively, between TB1 and TB2, NBA1 and NBA2, NW1 and NW2, and LWT1 and LWT2. While phenotypic correlations between TB1 and TB2, NBA1 and NBA2, NW1 and NW2, and LWT1 and LWT2 were estimated as 0.18, 0.15, 0.06 and 0.10, respectively. Genetic correlations between reproductive traits of first and later parities were not high indicating that reproductive traits for sows should be analyzed while considering the parities as different traits. (Asian-Aust. J. Anim. Sci. 2006. Vol 19, No. 1 : 7-12)
Modeling sums of exchangeable binary variables

Abstract We introduce a new model for sums of exchangeable binary random variables. The proposed distribution is an approximation to the exact distributional form, and relies on the theory of completely monotone functions and the Laplace transform of a gamma distribution function. Using Monte Carlo methods, we show that this new model compares favorably to the beta-binomial model with respect to estimating the success probability of the Bernoulli trials and the correlation between any two variables in the exchangeable set. We apply the new methodology to two classic data sets and the results are summarized.
The Elusive Distance Gradient in the Ultrafaint Dwarf Galaxy Hercules: A Combined Hubble Space Telescope and Gaia View

The ultrafaint dwarf galaxy Hercules has an extremely elongated morphology with both photometric overdensities and kinematic members at large radii, suggesting that it may be tidally disrupting due to a previous close encounter with the Milky Way. To explain its observational peculiarities, we present a deep Hubble Space Telescope (HST) imaging study of Hercules and its surrounding regions and investigate its tidal history through a careful search for a distance gradient along its stretched body. Our off-center HST data clearly resolve a main sequence, showing that the stellar extension seen along the major axis of Hercules is genuine, not a clump of background galaxies. Utilizing Gaia DR2 data, we clean the region around the Hercules of field contamination, and find four new plausible member stars, all of which are located on the outskirts of the dwarf galaxy. We update the distance to Hercules, and find 130.6 ± 6.1 kpc ( ) for the main body, which is consistent with earlier estimates in the literature. While we find no conclusive evidence for a distance gradient, our work demonstrates that constraining a distance gradient in such a faint system is not trivial, and the possible thickness of the dwarf along the line of sight and field contamination make it harder to make decisive conclusions even with these high-precision data. Future studies coupled with tailored theoretical models are needed to understand the true nature of Hercules and of tidal distortion observables in ultrafaint galaxies in general.
Complex scaling: Physics of unbound light nuclei and perspective

The complex scaling method (CSM) is one of the most powerful methods of describing the resonances with complex energy eigenstates based on non-Hermitian quantum mechanics. We present the basic application of CSM to the properties of the unbound phenomena of light nuclei. In particular, we focus on many-body resonant and non-resonant continuum states observed in unstable nuclei. We also investigate the continuum level density (CLD) in the scattering problem in terms of the Green’s function with CSM. We discuss the explicit effects of resonant and non-resonant contributions in CLD and transition strength functions.
Venting and Reef Fish Survival: Perceptions and Participation Rates among Recreational Anglers in the Northern Gulf of Mexico

Abstract Fisheries scientists, managers, and industry have developed innovative tools and techniques to improve the survival of fishes captured and released in deepwater settings. Venting involves the insertion of a hollow needle to deflate a fish's swim bladder and is among the most widely promoted barotrauma mitigation techniques. However, its efficacy has been the subject of intense debate. In the northern Gulf of Mexico, venting tools are mandatory tackle for offshore reef anglers, but current mandates on usage are being reconsidered. We surveyed recreational and tournament anglers to understand the popularity and perceived effectiveness of venting and identify factors that affect these measures. Our survey results indicate that approximately two-thirds of anglers vent the fish they release offshore and most perceive it to be effective for improving survival rates. Among recreational anglers, we found that primary fishing locale (inshore, offshore) and experience were powerful predictors of perception.
On a Bernoulli Autoregression Framework for Link Discovery and Prediction

We present a dynamic prediction framework for binary sequences that is based on a Bernoulli generalization of the auto-regressive process. Our approach lends itself easily to variants of the standard link prediction problem for a sequence of time dependent networks. Focusing on this dynamic network link prediction/recommendation task, we propose a novel problem that exploits additional information via a much larger sequence of auxiliary networks and has important real-world relevance. To allow discovery of links that do not exist in the available data, our model estimation framework introduces a regularization term that presents a trade-off between the conventional link prediction and this discovery task. In contrast to existing work our stochastic gradient based estimation approach is highly efficient and can scale to networks with millions of nodes. We show extensive empirical results on both actual product-usage based time dependent networks and also present results on a Reddit based data set of time dependent sentiment sequences.
HCMS at SemEval-2020 Task 9: A Neural Approach to Sentiment Analysis for Code-Mixed Texts

Problems involving code-mixed language are often plagued by a lack of resources and an absence of materials to perform sophisticated transfer learning with. In this paper we describe our submission to the Sentimix Hindi-English task involving sentiment classification of code-mixed texts, and with an F1 score of 67.1%, we demonstrate that simple convolution and attention may well produce reasonable results.
Detecting the Insider Threat with Long Short Term Memory (LSTM) Neural Networks

Information systems enable many organizational processes in every industry. The efficiencies and effectiveness in the use of information technologies create an unintended byproduct: misuse by existing users or somebody impersonating them - an insider threat. Detecting the insider threat may be possible if thorough analysis of electronic logs, capturing user behaviors, takes place. However, logs are usually very large and unstructured, posing significant challenges for organizations. In this study, we use deep learning, and most specifically Long Short Term Memory (LSTM) recurrent networks for enabling the detection. We demonstrate through a very large, anonymized dataset how LSTM uses the sequenced nature of the data for reducing the search space and making the work of a security analyst more effective.
Relative wealth concerns with partial information and heterogeneous priors

We establish a Nash equilibrium in a market with $ N $ agents with the performance criteria of relative wealth level when the market return is unobservable. Each investor has a random prior belief on the return rate of the risky asset. The investors can be heterogeneous in both the mean and variance of the prior. By a separation result and a martingale argument, we show that the optimal investment strategy under a stochastic return rate model can be characterized by a fully-coupled linear FBSDE. Two sets of deep neural networks are used for the numerical computation to first find each investor's estimate of the mean return rate and then solve the FBSDEs. We establish the existence and uniqueness result for the class of FBSDEs with stochastic coefficients and solve the utility game under partial information using deep neural network function approximators. We demonstrate the efficiency and accuracy by a base-case comparison with the solution from the finite difference scheme in the linear case and apply the algorithm to the general case of nonlinear hidden variable process. Simulations of investment strategies show a herd effect that investors trade more aggressively under relativeness concerns. Statistical properties of the investment strategies and the portfolio performance, including the Sharpe ratios and the Variance Risk ratios (VRRs) are examed. We observe that the agent with the most accurate prior estimate is likely to lead the herd, and the effect of competition on heterogeneous agents varies more with market characteristics compared to the homogeneous case.
The phylogenetic position of an unusual Protoperidinium species, P. bipes (Peridiniales, Dinophyceae), based on small and large subunit ribosomal RNA gene sequences

A. Yamaguchi, H. Kawamura and T. Horiguchi. 2007. The phylogenetic position of an unusual Protoperidinium species, P. bipes (Peridiniales, Dinophyceae), based on small and large subunit ribosomal RNA gene sequences. Phycologia 46: 270–276. DOI: 10.2216/06-82.1 Protoperidinium bipes is a minute heterotrophic dinoflagellate with a very distinctive shape and a tabulation unique for the genus, resulting in its placement into the subgenus Minusculum. The small subunit rDNA and the large subunit rDNA sequences of this species were determined using the single-cell polymerase chain reaction technique and analyzed to estimate its phylogenetic position. The results of the phylogenetic analyses revealed that P. bipes positioned in the clade corresponding to the section Protoperidinium of the subgenus Protoperidinium. Although the species occasionally has been regarded to belong to a different genus, our molecular analysis clearly confirmed that it is a true member of the genus Protoperidinium. It was inferred from our analysis that the reduction of the number of precingular plates took place relatively recently during the course of evolution of Protoperidinium.
Augmentation adversarial training for unsupervised speaker recognition

The goal of this work is to train robust speaker recognition models without speaker labels. Recent works on unsupervised speaker representations are based on contrastive learning in which they encourage within-utterance embeddings to be similar and across-utterance embeddings to be dissimilar. However, since the within-utterance segments share the same acoustic characteristics, it is difficult to separate the speaker information from the channel information. To this end, we propose augmentation adversarial training strategy that trains the network to be discriminative for the speaker information, while invariant to the augmentation applied. Since the augmentation simulates the acoustic characteristics, training the network to be invariant to augmentation also encourages the network to be invariant to the channel information in general. Extensive experiments on the VoxCeleb and VOiCES datasets show significant improvements over previous works using self-supervision, and the performance of our self-supervised models far exceed that of humans.
Planetary systems in a star cluster II: intermediate-mass black holes and planetary systems

Most stars form in dense stellar environments. It is speculated that some dense star clusters may host intermediate-mass black holes (IMBHs), which may have formed from runaway collisions between high-mass stars, or from the mergers of less massive black holes. Here, we numerically explore the evolution of populations of planets in star clusters with an IMBH. We study the dynamical evolution of single-planet systems and free-floating planets, over a period of 100~Myr, in star clusters without an IMBH, and in clusters with a central IMBH of mass $100~M_\odot$ or $200~M_\odot$. In the central region ($r\lesssim 0.2$~pc), the IMBH's tidal influence on planetary systems is typically 10~times stronger than the average neighbour star. For a star cluster with a $200M_\odot$ IMBH, the region in which the IMBH's influence is stronger within the virial radius ($\sim 1$~pc). The IMBH quenches mass segregation, and the stars in the core tend to move towards intermediate regions. The ejection rate of both stars and planets is higher when an IMBH is present. The rate at which planets are expelled from their host star rate is higher for clusters with higher IMBH masses, for $t<0.5 t_{rh}$, while remains mostly constant while the star cluster fills its Roche lobe, similar to a star cluster without an IMBH. The disruption rate of planetary systems is higher in initially denser clusters, and for wider planetary orbits, but this rate is substantially enhanced by the presence of a central IMBH.
Diagnostic Questions: The NeurIPS 2020 Education Challenge

Digital technologies are becoming increasingly prevalent in education, enabling personalized, high quality education resources to be accessible by students across the world. Importantly, among these resources are diagnostic questions: the answers that the students give to these questions reveal key information about the specific nature of misconceptions that the students may hold. Analyzing the massive quantities of data stemming from students' interactions with these diagnostic questions can help us more accurately understand the students' learning status and thus allow us to automate learning curriculum recommendations. In this competition, participants will focus on the students' answer records to these multiple-choice diagnostic questions, with the aim of 1) accurately predicting which answers the students provide; 2) accurately predicting which questions have high quality; and 3) determining a personalized sequence of questions for each student that best predicts the student's answers. These tasks closely mimic the goals of a real-world educational platform and are highly representative of the educational challenges faced today. We provide over 20 million examples of students' answers to mathematics questions from Eedi, a leading educational platform which thousands of students interact with daily around the globe. Participants to this competition have a chance to make a lasting, real-world impact on the quality of personalized education for millions of students across the world.
Early detection of eating disorders in reddit

This thesis proposes an approach for the early detection of Anorexia Nervosa (AN) on social media. Our method is based on machine learning techniques using the processed texts written by social media users. This method relies on a set of features based on domain-speciﬁc vocabulary, topic modelling, psychological processes and linguistic information extracted from the users’ writings. Moreover, other features are studied in order to exploit all the dataset resources. Additionally, we have compared some of the most known learning algorithms and we have introduced a minimum amount of information threshold to avoid some false positive predictions. This approach penalises the delay in the detection of positive cases in order to classify the users at risk as early as possible. By the early identiﬁcation of anorexia, along with an appropriate treatment, the speed of recovery and the likelihood of staying free of the illness improves. The results of this thesis showed that our proposal is suitable for the early detection of AN symptoms in social media. Further research in this topic is needed to solve the problems stated in this project.
Building High Rate Capability and Ultrastable Dendrite‐Free Organic Anode for Rechargeable Aqueous Zinc Batteries

Aqueous zinc‐ion batteries (ZIBs) are an alternative energy storage system for large‐scale grid applications compared with lithium‐ion batteries, when the low cost, safety, and durability are taken into consideration. However, the reliability of the battery systems always suffers from the serious challenge of the large Zn dendrite formation and “dead Zn,” thus bringing out the inferior cycling stability, and even cell shorting. Herein, a dendrite‐free organic anode, perylene‐3,4,9,10‐tetracarboxylic diimide (PTCDI) polymerized on the surface of reduced graphene oxide (PTCDI/rGO) utilized in ZIBs is reported. Moreover, the theoretical calculations prove the reason for the low redox potential. Due to the protons and zinc ions coparticipant phase transfer mechanism and the high charge transfer capability, the PTCDI/rGO electrode provides superior rate capability (121 mA h g−1 at 5000 mA g−1, retaining the 95% capacity of that compared with 50 mA g−1) and a long cycling life span (96% capacity retention after 1500 cycles at 3000 mA g−1). In addition, the proton coparticipation energy storage mechanism of active materials is elucidated by various ex‐situ methods.
Characterization and Identification of Cloudified Mobile Network Performance Bottlenecks

This study is a first attempt to experimentally explore the range of performance bottlenecks that 5G mobile networks can experience. To this end, we leverage a wide range of measurements obtained with a prototype testbed that captures the key aspects of a cloudified mobile network. We investigate the relevance of the metrics and a number of approaches to accurately and efficiently identify bottlenecks across the different locations of the network and layers of the system architecture. Our findings validate the complexity of this task in the multi-layered architecture and highlight the need for novel monitoring approaches that intelligently fuse metrics across network layers and functions. In particular, we find that distributed analytics performs reasonably well both in terms of bottleneck identification accuracy and incurred computational and communication overhead.
Climate change induced vulnerability and adaption for dengue incidence in Colombo and Kandy Districts: The first detailed investigation in Sri Lanka

Background: Assessing the vulnerability of an infectious disease such as dengue among endemic population is an important requirement to design proactive programmes in order to improve resilience capacity of vulnerable communities. Despite the significance in decision making process especially for rational resource allocation, the recognition of actual burden of dengue and the vulnerability towards dengue, has been limitedly addressed in many countries including Sri Lanka, due to deficiencies in a holistic methodology. Methods: A total of 42 variables (entomological, epidemiological, meteorological parameters, land-use practices and socio-demographic data) of all the 38 Medical Officer of Health (MOH) areas in the districts of Colombo and Kandy were considered as candidate variables for a composite index based vulnerability assessment. The Principal Component Analysis (PCA) was used in selecting and setting the weight for each indicator. Exposure, Sensitivity, Adaptive Capacity and Vulnerability of all the MOH areas for dengue incidence were calculated. Results: Out of 42 candidate variables, only 23 parameters (Exposure Index: 6 variables; Sensitivity Index: 11 variables; Adaptive Capacity Index: 6 variables) were selected as indicators to assess climate change vulnerability to dengue. Colombo Municipal Council (CMC) MOH area denoted the highest values for exposure (0.89: exceptionally high exposure), sensitivity (0.86: exceptionally high sensitivity) in the district of Colombo, while Kandy Municipal Council (KMC) area reported the highest exposure (0.79: high exposure) and sensitivity (0.77: high sensitivity) in Kandy. Piliyandala MOH area denoted the highest level of adaptive capacity (0.66) in the district of Colombo followed by Menikhinna (0.68) in Kandy. The highest vulnerability (0.45: moderate vulnerability) to dengue was indicated from CMC and the lowest indicated from Galaha MOH (0.15; very low vulnerability) in Kandy. Interestingly the Kandy Municipal Council MOH area had a notable vulnerability of 0.41 (moderate vulnerability), which was the highest within Kandy. Conclusions: In general, vulnerability for dengue was relatively higher within the MOH areas of Colombo, than in Kandy, suggesting a higher degree of potential susceptibility to dengue within and among local communities of Colombo. The Vector Controlling Entities (VCE) are recommended to consider the spatial variations in vulnerability of local communities to dengue for decision making, especially in allocation of limited financial, human and mechanical resources for dengue epidemic management
Tail inference for high-dimensional data

Identifying directions where severe events occur is a major challenge in multivariate Extreme Value Analysis. The support of the spectral measure of regularly varying vectors brings out which coordinates contribute to the extremes. This measure is defined via weak convergence which fails at providing an estimator of its support, especially in high dimension. The estimation of the support is all the more challenging since it relies on a threshold above which the data are considered to be extreme and the choice of such a threshold is still an open problem. In this paper we extend the framework of sparse regular variation introduced by Meyer and Wintenberger (2020) to infer tail dependence. This approach relies on the Euclidean projection onto the simplex which exhibits sparsity and reduces the dimension of the extremes' analysis. We provide an algorithmic approach based on model selection to tackle both the choice of an optimal threshold and the learning of relevant directions on which extreme events appear. We apply our method on numerical experiments to highlight the relevance of our findings. Finally we illustrate our approach with financial return data.
HeRTA: Heaviside Real-Time Analysis

We investigate the mathematical properties of event bound functions as they are used in the worst-case response time analysis and utilization tests. We figure out the differences and similarities between the two approaches. Based on this analysis, we derive a more general form do describe events and event bounds. This new unified approach gives clear new insights in the investigation of real-time systems, simplifies the models and will support algebraic proofs in future work. In the end, we present a unified analysis which allows the algebraic definition of any scheduler. Introducing such functions to the real-time scheduling theory will lead two a more systematic way to integrate new concepts and applications to the theory. Last but not least, we show how the response time analysis in dynamic scheduling can be improved.