Miracle-dz/UniFAR-FTUs huggingface dataset:
Time-series Imputation and Prediction with Bi-Directional Generative Adversarial Networks

Multivariate time-series data are used in many classification and regression predictive tasks, and recurrent models have been widely used for such tasks. Most common recurrent models assume that time-series data elements are of equal length and the ordered observations are recorded at regular intervals. However, real-world time-series data have neither a similar length nor a same number of observations. They also have missing entries, which hinders the performance of predictive tasks. In this paper, we approach these issues by presenting a model for the combined task of imputing and predicting values for the irregularly observed and varying length time-series data with missing entries. Our proposed model (Bi-GAN) uses a bidirectional recurrent network in a generative adversarial setting. The generator is a bidirectional recurrent network that receives actual incomplete data and imputes the missing values. The discriminator attempts to discriminate between the actual and the imputed values in the output of the generator. Our model learns how to impute missing elements in-between (imputation) or outside of the input time steps (prediction), hence working as an effective any-time prediction tool for time-series data. Our method has three advantages to the state-of-the-art methods in the field: (a) single model can be used for both imputation and prediction tasks; (b) it can perform prediction task for time-series of varying length with missing data; (c) it does not require to know the observation and prediction time window during training which provides a flexible length of prediction window for both long-term and short-term predictions. We evaluate our model on two public datasets and on another large real-world electronic health records dataset to impute and predict body mass index (BMI) values in children and show its superior performance in both settings.
Deviation from the First Law of Thermodynamics for Particle-like Quantum Kerr-de Sitter Black Holes

The goal of this paper is to extend the particle-like quantization scheme presented in Pacheco and Silk (2020), to extremal Kerr-de Sitter black holes in four spacetime dimensions, thereby obtaining various quantized parameters, like the black hole mass and angular momentum, consistent with existing results, in the proper limits. Moreover, we show numerically, that for such extremal quantum black holes, there is a root mean square deviation from the First Law of black hole thermodynamics, of the order $\mathcal{O}(\Lambda^{0.45 \pm 0.01})$, where $\Lambda$ denotes the Cosmological Constant.
Automatic differentiation to simultaneously identify nonlinear dynamics and extract noise probability distributions from data

The sparse identification of nonlinear dynamics (SINDy) is a regression framework for the discovery of parsimonious dynamic models and governing equations from time-series data. As with all system identification methods, noisy measurements compromise the accuracy and robustness of the model discovery procedure. In this work we develop a variant of the SINDy algorithm that integrates automatic differentiation and recent time-stepping constrained motivated by Rudy et al (2019 J. Computat. Phys. 396 483–506) for simultaneously (1) denoising the data, (2) learning and parametrizing the noise probability distribution, and (3) identifying the underlying parsimonious dynamical system responsible for generating the time-series data. Thus within an integrated optimization framework, noise can be separated from signal, resulting in an architecture that is approximately twice as robust to noise as state-of-the-art methods, handling as much as 40% noise on a given time-series signal and explicitly parametrizing the noise probability distribution. We demonstrate this approach on several numerical examples, from Lotka-Volterra models to the spatio-temporal Lorenz 96 model. Further, we show the method can learn a diversity of probability distributions for the measurement noise, including Gaussian, uniform, Gamma, and Rayleigh distributions.
Kohn-Sham equations as regularizer: building prior knowledge into machine-learned physics

Including prior knowledge is important for effective machine learning models in physics and is usually achieved by explicitly adding loss terms or constraints on model architectures. Prior knowledge embedded in the physics computation itself rarely draws attention. We show that solving the Kohn-Sham equations when training neural networks for the exchange-correlation functional provides an implicit regularization that greatly improves generalization. Two separations suffice for learning the entire one-dimensional H_{2} dissociation curve within chemical accuracy, including the strongly correlated region. Our models also generalize to unseen types of molecules and overcome self-interaction error.
Sequential changepoint detection for label shift in classification

Classifier predictions often rely on the assumption that new observations come from the same distribution as training data. When the underlying distribution changes, so does the optimal classifier rule, and predictions may no longer be valid. We consider the problem of detecting a change to the overall fraction of positive cases, known as label shift, in sequentially-observed binary classification data. We reduce this problem to the problem of detecting a change in the one-dimensional classifier scores, which allows us to develop simple nonparametric sequential changepoint detection procedures. Our procedures leverage classifier training data to estimate the detection statistic, and converge to their parametric counterparts in the size of the training data. In simulations, we show that our method compares favorably to other detection procedures in the label shift setting.
Factors affecting Callus Induction in Mothbean [Vigna aconitifolia (Jacq.)

Mothbean [Vigna aconitifolia (Jacq.) Marechal] belongs to fabaceae family is an important food grain legume crop that is cultivated over the entire world. Five varieties of mothbean viz., GMO 1, GMO 2, CZM 2, Jwala and RMO 40 were investigated for callus induction. Cent per cent callus induction was obtained in both the explants of five varieties on different medium combinations tested. Significant differences were observed among the varieties, explants, medium combinations and their two as well as three way interactions for both the days taken for callus initiation and callus fresh weight except for variety × explant × medium interaction in case of days taken for callus initiation. Among the different medium combinations tried, MS + 2.0 mg l−1 2, 4-D + 1.0 mg l−1 BAP recorded significantly minimum number of days (6.356) for callus initiation and produced the highest callus fresh weight (2.457 g). Of the different varieties tested, Jwala took minimum number of days to callus initiation (6.944) and produced the highest callus fresh weight (2.056 g). Among the explant types, the hypocotyl explants recorded the least number of days to callus initiation (7.660) and leaf explants produced the highest callus fresh weight (1.932 g). Considering the interaction effects, MS + 2.0 mg l−1 2, 4-D + 1.0 mg l−1 BAP for days to callus initiation and callus fresh weight were identified as the most suitable media for callus induction. MS medium supplemented with 1.0 mg l−1 2, 4-D promoted the growth of friable and organized callus in all the varieties and hence, this medium was identified as the most suitable medium for callus maintenance.
Deep Learning for 3D Point Cloud Understanding: A Survey

The development of practical applications, such as autonomous driving and robotics, has brought increasing attention to 3D point cloud understanding. While deep learning has achieved remarkable success on image-based tasks, there are many unique challenges faced by deep neural networks in processing massive, unstructured and noisy 3D points. To demonstrate the latest progress of deep learning for 3D point cloud understanding, this paper summarizes recent remarkable research contributions in this area from several different directions (classification, segmentation, detection, tracking, flow estimation, registration, augmentation and completion), together with commonly used datasets, metrics and state-of-the-art performances. More information regarding this survey can be found at: this https URL.
Automated Stroke Rehabilitation Assessment using Wearable Accelerometers in Free-Living Environments

Stroke is known as a major global health problem, and for stroke survivors it is key to monitor the recovery levels. However, traditional stroke rehabilitation assessment methods (such as the popular clinical assessment) can be subjective and expensive, and it is also less convenient for patients to visit clinics in a high frequency. To address this issue, in this work based on wearable sensing and machine learning techniques, we developed an automated system that can predict the assessment score in an objective and continues manner. With wrist-worn sensors, accelerometer data was collected from 59 stroke survivors in free-living environments for a duration of 8 weeks, and we aim to map the week-wise accelerometer data (3 days per week) to the assessment score by developing signal processing and predictive model pipeline. To achieve this, we proposed two new features, which can encode the rehabilitation information from both paralysed/non-paralysed sides while suppressing the high-level noises such as irrelevant daily activities. We further developed the longitudinal mixed-effects model with Gaussian process prior (LMGP), which can model the random effects caused by different subjects and time slots (during the 8 weeks). Comprehensive experiments were conducted to evaluate our system on both acute and chronic patients, and the results suggested its effectiveness.
MUTANT: A Training Paradigm for Out-of-Distribution Generalization in Visual Question Answering

While progress has been made on the visual question answering leaderboards, models often utilize spurious correlations and priors in datasets under the i.i. d. setting. As such, evaluation on out-of-distribution (OOD) test samples has emerged as a proxy for generalization. In this paper, we present MUTANT, a training paradigm that exposes the model to perceptually similar, yet semantically distinct mutations of the input, to improve OOD generalization, such as the VQA-CP challenge. Under this paradigm, models utilize a consistency-constrained training objective to understand the effect of semantic changes in input (question-image pair) on the output (answer). Unlike existing methods on VQA-CP, MUTANT does not rely on the knowledge about the nature of train and test answer distributions. MUTANT establishes a new state-of-the-art accuracy on VQA-CP with a $10.57\%$ improvement. Our work opens up avenues for the use of semantic input mutations for OOD generalization in question answering.
Some Hookworms of the Genus Ancylostoma from Colombia and Panama1

The following species of Ancylostoma are reported for the Colombia-Panama area: A. duo- denale (Dubini, 1843); A. caninum (Ercolani, 1859); A. tubaeforme (Zeder, 1800); A. pluridentaturn (Alessandrini, 1905); and A. buckleyi Le Roux and Biocca, 1957. The latter two species are reported from Colombia for the first time, and A. pluridentaturn is reported from Panama for the first time. Addi- tionally, this paper constitutes the first report of A. buckleyi since the original description. This species is also recorded from a new host (Atelocynus microtis Schlater). Measurements of A. pluridentaturn and A. buckleyi are presented. Methods of distinguishing the four species of animal ancylostomes are dis- cussed and illustrated. Hookworms of the genus Ancylostoma are widespread and important pathogens of man and his domestic animals. Other species of as yet unassessed infectivity for man occur in wild carnivorous mammals. Although it had been thought that these nematodes were highly host specific (Scott, 1930), in recent years it has become apparent that some of the animal ancylostomes pose a threat to man. Ancylos- toma ceylanicum, for example, has been found to mature in the intestine of man. Other species, such as A. caninum and A. braziliense, seem not to be able to mature in man, but they may produce larval invasions of the skin or internal organs. The series of papers by Biocca (eg. 1954, 1961) and by Biocca and Le Roux (1957) have helped to clarify the formerly confused systematics of the genus. The subgenera pro- posed in the latter paper are herein accepted and followed.
Genetic diversity analysis in Indian mustard (Brassica juncea)

Genetic divergence determine in thirty six genotypes of Indian mustard [Brassica juncea (L.) Czern & Coss. ] by applying D2 statistics for ten characters facilitate grouping of all the genotypes into eleven clusters. Days to 50% flowering, plant height, days to maturity, siliqua on main shoot, total no of siliqua per plant, no of seed per siliqua, seed yield, biological yield per plant, test weight, harvest index were the main contributors for genetic diversity among the genotypes. Out of 11 clusters, cluster I is used to be the most important comprising 11 genotypes pursuing by cluster IV consisting of seven genotypes, cluster II and V have 5 genotypes, cluster VIII has 2 genotype whereas, rest have single genotypes each. The cluster V presented maximum intra-cluster distance (8.20), while maximum inter-cluster distance was detected between cluster XI and VI (41.82).
The implication of school admission rules for segregation and educational inequality: Research report

In this research project we investigate how the design of school assignment rules affects social segregation and its implications for social inequality. The context of our study is the Hungarian school system, which is characterized by early ability tracking combined with a centralized assignment mechanism based on the student-proposing deferred-acceptance algorithm. The project is split into three interrelated parts. First, we investigate the different factors that affect social segregation through the school assignment process. Second, we analyze how segregation resulting through school assignments affects inequality in student achievement. In a final step, we plan to combine insights from the first two parts in a counterfactual analysis to evaluate different assignment rules.
Heterosis studies on thermotolerant hybrids of bivoltine silkworm (Bombyx mori L.)

The investigation was carried out to evaluate the twenty-eight silkworm bivoltine hybrids raised by involving half (8 × 8) diallel set of bivoltine breeds in order to identify the thermotolerant, adaptable, high yielding hybrids suited for sub-tropical climate. The evaluation of hybrids was done on heterosis worked at mid parent value. Maximum heterosis was depicted by non-breakable filament length (37.75) followed by filament length (35.62), cocoon yield (by weight) (35.22), larval weight (28.21), single shell weight (24.46) and cocoon weight (17.10). On the basis of significant heterosis displayed for thirteen important commercial parameters, four hybrid combinations ND5 × PO1 (Cumulative heterosis 362.83), ND5 × PO3 (Cumulative heterosis 323.17), PO1 × Udhey-6 (Cumulative heterosis 291.31) and PO1 × Udhey-3 (Cumulative heterosis 250.42) were identified to be heterotic. The superiority of these hybrids was further confirmed by Evaluation Index (E.I.) method. Hybrid ND5 × PO3 (E.I. 55.61) ranked 1 followed by ND5 × PO1 (E.I. 54.40) and PO1 × Udhey-6 (E.I. 51.97). The control hybrid (SH6 × NB4D2) scored the cumulative heterosis value of 155.36 and the average E.I. value of 39.17 only.
Cross sections for νμ and ν̄μ induced pion production on hydrocarbon in the few-GeV region using MINERvA

C.L. McGivern, ∗ T. Le, 3 B. Eberly, † L. Aliaga, 5 O. Altinok, L. Bellantoni, A. Bercellie, M. Betancourt, A. Bodek, A. Bravar, H. Budd, T. Cai, M.F. Carneiro, M.E. Christy, H. da Motta, S.A. Dytman, G.A. Dı́az, 5 E. Endress, J. Felix, L. Fields, 12 R. Fine, R.Galindo, H. Gallagher, T. Golan, 6 R. Gran, D.A. Harris, A. Higuera, 11, ‡ K. Hurtado, 15 M. Kiveni, J. Kleykamp, M. Kordosky, E. Maher, S. Manly, W.A. Mann, C.M. Marshall, D.A. Martinez Caicedo, § K.S. McFarland, 6 A.M. McGowan, B. Messerly, J. Miller, A. Mislivec, J.G. Morf́ın, J. Mousseau, ¶ D. Naples, J.K. Nelson, A. Norrick, Nuruzzaman, 13 V. Paolone, J. Park, C.E. Patrick, G.N. Perdue, 7 L. Rakotondravohitra, ∗∗ M.A. Ramirez, R.D. Ransome, H. Ray, L. Ren, D. Rimal, P.A. Rodrigues, D. Ruterbories, H. Schellman, 12 D.W. Schmitz, 6 C. Simon, C.J. Solano Salinas, S. Sánchez Falero, B.G. Tice, E. Valencia, T. Walton, †† J. Wolcott, ‡‡ M.Wospakrik, and D. Zhang Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, USA Physics Department, Tufts University, Medford, Massachusetts 02155, USA Rutgers, The State University of New Jersey, Piscataway, New Jersey 08854, USA Department of Physics, College of William & Mary, Williamsburg, Virginia 23187, USA Sección F́ısica, Departamento de Ciencias, Pontificia Universidad Católica del Perú, Apartado 1761, Lima, Perú Fermi National Accelerator Laboratory, Batavia, Illinois 60510, USA University of Rochester, Rochester, New York 14627 USA University of Geneva, 1211 Geneva 4, Switzerland Centro Brasileiro de Pesquisas F́ısicas, Rua Dr. Xavier Sigaud 150, Urca, Rio de Janeiro, Rio de Janeiro, 22290-180, Brazil Hampton University, Dept. of Physics, Hampton, VA 23668, USA Campus León y Campus Guanajuato, Universidad de Guanajuato, Lascurain
Distribution of different forms of soil potassium in surface and sub-surface layers of paddy growing areas of Nagpur District

The potassium dynamics in black soils of paddy growing areas of Nagpur district, Maharashtra was studied during 2018-19. Potassium exists in soil in different forms such as water soluble, exchangeable, non-exchangeable, lattice K. The available potassium on an average obtained highest in both the depths (190.75 mg kg-1 and 173.43 mg kg-1 in surface and sub-surface depths, respectively). The surface samples recorded high available potassium compared to sub-surface layer. The water-soluble K of black soils ranged from 5.10 to 8.10 mg kg-1 with a mean of 6.75 mg kg-1 in surface depth. The water-soluble potassium in sub-surface layer varied from 3.90 to 7.70 mg kg-1 with a mean of 6.21 mg kg-1. In the black soils, exchangeable potassium varied from 139.76 to 217.25 mg kg-1 in surface layer and in sub-surface layer it varied from 120.84 to 207.29 mg kg-1. The trend of decrease in water soluble and exchangeable potassium was noticed in these soils from surface to sub-surface layers. The non-exchangeable potassium of black soils in surface samples varied from 673.13 mg kg-1 to 789.50 mg kg-1. The non-exchangeable potassium varied from 628.12 mg kg-1 to 778.00 mg kg-1 in sub-surface. The lattice potassium ranged from 5316.40 mg kg-1 to 7324.64 mg kg-1 in surface layer and 6013.73 mg kg-1 to 7241.60 mg kg-1 was observed in sub-surface depth. The black soils total potassium ranged from 6250 to 8322 mg kg-1 in surface and 6900 to 8140 mg kg-1 in sub-surface layer.
Assortment for host resistance and eco-friendly management of mulberry powdery mildew caused by Phyllactinia corylea (Pers.) Karst

Powdery mildew caused by Phyllactinia corylea (Pers.) Karst, is an obligate parasitic disease of mulberry inflicting considerable qualitative as well as quantitative losses of leaf yield. Screening of 16 mulberry varieties against powdery mildew disease under field conditions yielded two resistant varieties with the lower powdery mildew disease severity of 2.5 and 5.42 per cent in a variety S-13 and in variety S-34 had 3 and 5.37 per cent after 55 and 70 days after planting respectively. The three varieties viz., Kanva-2 / M-5, Mysore local and S-1635 were moderately resistant with the less DS values of 11.43, 11.47 and 11.48 per cent respectively. In management studies, the plant extracts at all concentrations tested were effective in suppressing the disease and at higher concentration of 15%, Zinger officinale reduced the disease to 60.31 per cent and Lantana camara to 59.04 per cent disease over control and were considered as best for managing powdery mildew disease in mulberry. Among the culture filtrate of different bioagents tested, the Trichoderma harzianum reduced the disease to 51.56 per cent and Pseudomonas fluorescens to 51.09 per cent over control at 15% concentration and were shown to be potent in fading away the disease.
EARNINGS QUALITY AND CORPORATE GOVERNANCE

We develop and test the proposition that earnings quality reflects both the scope for moral hazard (when associated mainly with volatile business fundamentals) and the outcome of moral hazard (when associated with management’s discretionary reporting choices). As a consequence, earnings quality can exhibit opposite sign associations with corporate governance structures depending on its source: business fundamentals (innate quality) and managerial incentives (discretionary quality). We provide consistent evidence in broad samples, using a methodology that splits earnings quality into an innate and discretionary component, and in subsamples likely to be dominated by either source of earnings quality. We further document interaction effects, i.e., corporate governance structures being increasingly effective on discretionary earnings quality as innate quality worsens. The study highlights the importance of specifying the source of earnings quality effects when researching associations with corporate governance, as a theoretical matter as well as in the empirical design.
DISTINCTION BETWEEN CONVENTIONAL BANKS AND ISLAMIC BANKS IN CREDIT RISK MANAGEMENT PROCESSES: A UNI-VARIATE AND MULTIVARIATE NON-PARAMETRIC ANALYSIS

The paper analyses the perceptions of the 96 credit officers from 17 conventional and 5 Islamic banks of Bangladesh in order to identify the distinction in credit risk management processes. The investigation process uses descriptive statistics and both Mann Whitney U test as univariate approach and Binary Logistic Regression as multivariate approach to identify the differences in credit risk management processes between conventional and Islamic banks in Bangladesh. The findings reveal that there is a significant perceived difference between conventional banks and Islamic banks in credit risk identification, assessment, understanding the risk and risk management practices. The study also finds that respondents of conventional banks are more sensitive in overall risk management control system where as Islamic banks are more careful in credit risk identification, monitoring and controlling processes.