Miracle-dz/UniFAR-FTUs huggingface dataset:
The Dynamics of Gradient Descent for Overparametrized Neural Networks

We consider the dynamics of gradient descent (GD) in overparameterized single hidden layer neural networks with a squared loss function. Recently, it has been shown that, under some conditions, the parameter values obtained using GD achieve zero training error and generalize well if the initial conditions are chosen appropriately. Here, through a Lyapunov analysis, we show that the dynamics of neural network weights under GD converge to a point which is close to the minimum norm solution subject to the condition that there is no training error when using the linear approximation to the neural network. To illustrate the application of this result, we show that the GD converges to a prediction function that generalizes well, thereby providing an alternative proof of the generalization results in Arora et al. (2019).
Dynamic Portfolio Allocation in High Dimensions using Sparse Risk Factors

We propose a fast and flexible method to scale multivariate return volatility predictions up to high-dimensions using a dynamic risk factor model. Our approach increases parsimony via time-varying sparsity on factor loadings and is able to sequentially learn the use of constant or time-varying parameters and volatilities. We show in a dynamic portfolio allocation problem with 452 stocks from the S&P 500 index that our dynamic risk factor model is able to produce more stable and sparse predictions, achieving not just considerable portfolio performance improvements but also higher utility gains for the mean-variance investor compared to the traditional Wishart benchmark and the passive investment on the market index.
Sketch2Model: View-Aware 3D Modeling from Single Free-Hand Sketches

We investigate the problem of generating 3D meshes from single free-hand sketches, aiming at fast 3D modeling for novice users. It can be regarded as a single-view reconstruction problem, but with unique challenges, brought by the variation and conciseness of sketches. Ambiguities in poorly-drawn sketches could make it hard to determine how the sketched object is posed. In this paper, we address the importance of viewpoint specification for overcoming such ambiguities, and propose a novel view-aware generation approach. By explicitly conditioning the generation process on a given viewpoint, our method can generate plausible shapes automatically with predicted viewpoints, or with specified viewpoints to help users better express their intentions. Extensive evaluations on various datasets demonstrate the effectiveness of our view-aware design in solving sketch ambiguities and improving reconstruction quality.
Sanity Simulations for Saliency Methods

Saliency methods are a popular class of feature attribution explanation methods that aim to capture a model's predictive reasoning by identifying"important"pixels in an input image. However, the development and adoption of these methods are hindered by the lack of access to ground-truth model reasoning, which prevents accurate evaluation. In this work, we design a synthetic benchmarking framework, SMERF, that allows us to perform ground-truth-based evaluation while controlling the complexity of the model's reasoning. Experimentally, SMERF reveals significant limitations in existing saliency methods and, as a result, represents a useful tool for the development of new saliency methods.
PEMBELAJARAN KONTEKSTUAL MELALUI PERMAINAN KELERENG PADA SISWA KELAS III SD UNTUK MENINGKATKAN PEMAHAMAN KONSEP PERKALIAN

The purpose of this study was to describe an increase in the ability to understand mathematical concepts, teaching skills of teachers in managing mathematics learning and student learning activities using contextual learning models through marble games. This type of research is Classroom Action Research (PTK). The subjects of the research were 15-grade students of SDI Tetandara. The data collection techniques used were observation, interview, test, and documentation. The instrument in this study used test questions, observation sheets, and interview sheets. Data analysis in this research is quantitative and qualitative data analysis. The results of this study indicate that (1) the increase in teacher activity from pre-cycle to cycle I am 15.75%, cycle I to cycle II is 17.45%. (2) Student activity has increased from the pre-cycle to cycle I by 27%, and cycle I to cycle II by 26%. (3) Increased understanding of the multiplication concept of students in the pre-cycle to cycle I amounted to 26.66% for indicator 1, 20% for indicator 2, 6.67% for indicator 3, and 33.34% for indicator 4. As for the first cycle to cycle II has increased by 26.67% for indicator 1, 26.66% for indicator 2, 33.33% for indicator 3, and 26.66% for indicator 4.
Meta Auxiliary Learning for Facial Action Unit Detection

Despite the success of deep neural networks on facial action unit (AU) detection, better performance depends on a large number of training images with accurate AU annotations. However, labeling AU is time-consuming, expensive, and error-prone. Considering AU detection and facial expression recognition (FER) are two highly correlated tasks, and facial expression (FE) is relatively easy to annotate, we consider learning AU detection and FER in a multi-task manner. However, the performance of the AU detection task cannot be always enhanced due to the negative transfer in the multi-task scenario. To alleviate this issue, we propose a Meta Auxiliary Learning method (MAL) that automatically selects highly related FE samples by learning adaptative weights for the training FE samples in a meta learning manner. The learned sample weights alleviate the negative transfer from two aspects: 1) balance the loss of each task automatically, and 2) suppress the weights of FE samples that have large uncertainties. Experimental results on several popular AU datasets demonstrate MAL consistently improves the AU detection performance compared with the state-of-the-art multi-task and auxiliary learning methods. MAL automatically estimates adaptive weights for the auxiliary FE samples according to their semantic relevance with the primary AU detection task.
Efficient Spiking Neural Networks With Radix Encoding

Spiking neural networks (SNNs) have advantages in latency and energy efficiency over traditional artificial neural networks (ANNs) due to their event-driven computation mechanism and the replacement of energy-consuming weight multiplication with addition. However, to achieve high accuracy, it usually requires long spike trains to ensure accuracy, usually more than 1000 time steps. This offsets the computation efficiency brought by SNNs because a longer spike train means a larger number of operations and larger latency. In this article, we propose a radix-encoded SNN, which has ultrashort spike trains. Specifically, it is able to use less than six time steps to achieve even higher accuracy than its traditional counterpart. We also develop a method to fit our radix encoding technique into the ANN-to-SNN conversion approach so that we can train radix-encoded SNNs more efficiently on mature platforms and hardware. Experiments show that our radix encoding can achieve $25\times $ improvement in latency and 1.7% improvement in accuracy compared to the state-of-the-art method using the VGG-16 network on the CIFAR-10 dataset.
Vibrational Modes of Hydrogen Hydrates: A First-Principles Molecular Dynamics and Raman Spectra Study

We have employed classically propagated molecular dynamics (MD), within the framework of density functional theory (DFT), to calculate vibrational spectral band of molecular hydrogen trapped in clathrate hydrate, with large-cage occupancy from 1 to 4, at ∼260 K and ∼2 kbar. The predicted vibrations, obtained by applying a state-of-the-art generalized gradient approximation (GGA) functional with nonlocal correlation (VdW-DF), reproduce satisfactorily our own accurate Raman spectra (at the same temperature and pressure conditions). We decomposed the MD-sampled vibrational band to individual peaks and assigned them to the vibration of H2 molecules enclosed in small and large cages of SII hydrate. By summing the resulting spectral bands, we have demonstrated that the measured spectral response is a complex composition of signals originating from H2 molecules experiencing different local, intracage environments.
Determinants of Treatment Adherence among Patients with Severe Mental Disorders at the Kedungmundu Primary Health Center in Semarang City

Background: Treatment adherence among patients with severe mental disorders is needed to avoid drop-outs, relapses, and shackling. Kedungmundu Primary Health Center in Semarang City is known to have a low treatment adherence to patients with severe mental disorders. This study aimed to determine the factors associated with treatment adherence among patients with severe mental disorders.Methods: A quantitative study with a cross-sectional approach. The samples were 73 patients with severe mental disorders at The Kedungmundu Primary Health Center. Data were collected using questionnaires and analyzed using frequency distribution analysis and Spearman Rank Correlation Test.Results: The results indicated that knowledge (p = 0,000; r = 0,715), attitudes (p = 0,000; r = 0,839), family support (p = 0,000; r = 0,427), assessment of health workers (p = 0,000; r = 0,456), and the need for health services (p = 0,000; r = 0,716) have a positive correlation with treatment adherence among severe mental disorders patients.Conclusion: Kedungmundu Primary Health Center is suggested to provide psychoeducation to patients and families, improve the implementation of mental health programs, and empower mental health cadres.
Neutral CGM as damped Ly α absorbers at high redshift

Recent searches for the hosts of z ∼ 4 damped Ly α absorbers (DLAs) have detected bright galaxies at distances of tens of kpc from the DLA. Using the FIRE-2 cosmological zoom simulations, we argue that these relatively large distances are due to a predominantly cool and neutral inner circumgalactic medium (CGM) surrounding high-redshift galaxies. The inner CGM is cool because of the short cooling time of hot gas in ${\lesssim}10^{12}\, {\rm M_{\odot }}$ haloes, which implies that accretion and feedback energy are radiated quickly, while it is neutral due to high volume densities and column densities at high redshift that shield cool gas from photoionization. Our analysis predicts large DLA covering factors (${\gtrsim}50{{\ \rm per\ cent}}$) out to impact parameters ∼0.3[(1 + z)/5]3/2Rvir from the central galaxies at z ≳ 1, equivalent to a proper distance of ${\sim}21\, M_{12}^{1/3} \left(\left(1+z\right)/5\right)^{1/2}\, {\rm kpc}$ (Rvir and M12 are the halo virial radius and mass in units of $10^{12}\, {\rm M_{\odot }}$, respectively). This implies that DLA covering factors at z ∼ 4 may be comparable to unity out to a distance ∼10 times larger than stellar half-mass radii. A predominantly neutral inner CGM in the early universe suggests that its mass and metallicity can be directly constrained by absorption surveys, without resorting to the large ionization corrections as required for ionized CGM.
Benchmarking VQE with the LMG model

The Variational Quantum Eigensolver (VQE) is a promising algorithm for Noisy Intermediate Scale Quantum (NISQ) computation. Veriﬁcation and validation of NISQ algorithms is an important task. We consider the exactly-diagonalizable Lipkin-Meshkov-Glick (LMG) model as a candidate for benchmarking. We use the Bethe ansatz to construct eigenstates of the trigonometric LMG model using quantum circuits inspired by the LMG’s underlying algebraic structure. We construct circuits with depth O ( N ) and O (log 2 N ) that can prepare any trigonometric LMG eigenstate of N particles. The number of gates required for both circuits is O ( N ) .
Sociodemographic Correlates of Perceived Psychosocial Health in Times of the COVID-19 Pandemic: A Community-Based Online Study in China

The coronavirus (COVID-19) pandemic and the ensuing sociopolitical measures to control and curb its spread have been affecting people's psychosocial health and well-being through various complex pathways and in unprecedented ways. The present study aims to assess the sociodemographic correlates of psychosocial health situation of Chinese community-dwelling residents. Methods: This was a cross-sectional survey that was carried out online and using a structured questionnaire during April 2020. In total, 4788 men and women with the age range of 11-98 years were included in the analysis. Outcome variables were the change in the experience of hopelessness, loneliness and depression before and during the pandemic, and the explanatory variables included demographic and social capital related variables.Results: Respectively 34.80%, 32.50% and 44.84% of the participants expressed feeling more hopeless, lonely, and depressed during the pandemic. The percentage of all three indicators was comparatively higher among women than among men: hopelessness (50.67% vs 49.33%), loneliness (52.44% vs 47.56%), and depression (56.22% vs 43.78%). Being married was associated lower odds of loneliness among men [Odds ratio= 0.63, 95% CI=0.45,0.90]. Loneliness was negatively associated with smoking [Odds ratio= 0.67, 95% CI=0.45,0.99] and positively with drinking [Odds ratio= 1.45, 95% CI=1.04,2.02]. Compared with those in the lowest income bracket (<10K), men [Odds ratio= 0.34, 95% CI=0.21,0.55] and women [Odds ratio= 0.36, 95% CI=0.23,0.56] in the highest (>40K) had the lowest odds of reporting perceived hopelessness [Odds ratio= 0.35, 95% CI=0.25,0.48]. Smoking also showed negative association with depression only among men [Odds ratio=0.63, 95% CI=0.43,0.91]. Conclusion: More about one-third of the participants reported worsening in the experience of hopelessness and loneliness, with more than two-fifth of worsening depression during the pandemic compared with the time before. Several socioeconomic and lifestyle factors were found to be associated with the outcome variables, most notably participants marital status, household income, smoking, alcohol drinking, existing chronic conditions, and urbanicity.
Antiferromagnetic character of the quantum phase transition in the Hubbard model on the honeycomb lattice

Johann Ostmeyer,1 Evan Berkowitz,2, 3 Stefan Krieg,3, 4, 1 Timo A. Lähde,3, 5 Thomas Luu,3, 5, 1 and Carsten Urbach1 Helmholtz-Institut für Strahlenund Kernphysik, Rheinische Friedrich-Wilhelms-Universität Bonn, 53115 Bonn, Germany Maryland Center for Fundamental Physics, University of Maryland, College Park 20742, USA Institute for Advanced Simulation, Forschungszentrum Jülich, 54245 Jülich, Germany JARA & Jülich Supercomputing Center, Forschungszentrum Jülich, 54245 Jülich, Germany Institut für Kernphysik, Forschungszentrum Jülich, 54245 Jülich, Germany (Dated: 17th May 2021)
Safety-Constrained Learning and Control using Scarce Data and Reciprocal Barriers

We develop a control algorithm that ensures the safety, in terms of confinement in a set, of a system with unknown, 2nd-order nonlinear dynamics. The algorithm establishes novel connections between data-driven and robust, nonlinear control. It is based on data obtained online from the current trajectory and the concept of reciprocal barriers. More specifically, it first uses the obtained data to calculate set-valued functions that over-approximate the unknown dynamic terms. For the second step of the algorithm, we design a robust control scheme that uses these functions as well as reciprocal barriers to render the system forward invariant with respect to the safe set. In addition, we provide an extension of the algorithm that tackles issues of controllability loss incurred by the nullspace of the control-direction matrix. The algorithm removes a series of standard, limiting assumptions considered in the related literature since it does not require global boundedness, growth conditions, or a priori approximations of the unknown dynamics' terms.
Facial Age Estimation using Convolutional Neural Networks

This paper is a part of a student project in Machine Learning at the Norwegian University of Science and Technology. In this paper, a deep convolutional neural network with five convolutional layers and three fully-connected layers is presented to estimate the ages of individuals based on images. The model is in its entirety trained from scratch, where a combination of three different datasets is used as training data. These datasets are the APPA dataset, UTK dataset, and the IMDB dataset. The images were preprocessed using a proprietary face-recognition software. Our model is evaluated on both a held-out test set, and on the Adience benchmark. On the test set, our model achieves a categorical accuracy of 52%. On the Adience benchmark, our model proves inferior compared with other leading models, with an exact accuray of 30%, and an one-off accuracy of 46%. Furthermore, a script was created, allowing users to estimate their age directly using their web camera. The script, alongside all other code, is located in our GitHub repository: AgeNet.
TriPose: A Weakly-Supervised 3D Human Pose Estimation via Triangulation from Video

Estimating 3D human poses from video is a challenging problem. The lack of 3D human pose annotations is a major obstacle for supervised training and for generalization to unseen datasets. In this work, we address this problem by proposing a weakly-supervised training scheme that does not require 3D annotations or calibrated cameras. The proposed method relies on temporal information and triangulation. Using 2D poses from multiple views as the input, we first estimate the relative camera orientations and then generate 3D poses via triangulation. The triangulation is only applied to the views with high 2D human joint confidence. The generated 3D poses are then used to train a recurrent lifting network (RLN) that estimates 3D poses from 2D poses. We further apply a multi-view re-projection loss to the estimated 3D poses and enforce the 3D poses estimated from multi-views to be consistent. Therefore, our method relaxes the constraints in practice, only multi-view videos are required for training, and is thus convenient for in-the-wild settings. At inference, RLN merely requires single-view videos. The proposed method outperforms previous works on two challenging datasets, Human3.6M and MPI-INF-3DHP. Codes and pretrained models will be publicly available.
EASE: Extractive-Abstractive Summarization with Explanations

Current abstractive summarization systems outperform their extractive counterparts, but their widespread adoption is inhibited by the inherent lack of interpretability. To achieve the best of both worlds, we propose EASE, an extractive-abstractive framework for evidence-based text generation and apply it to document summarization. We present an explainable summarization system based on the Information Bottleneck principle that is jointly trained for extraction and abstraction in an end-to-end fashion. Inspired by previous research that humans use a two-stage framework to summarize long documents (Jing and McKeown, 2000), our framework first extracts a pre-defined amount of evidence spans as explanations and then generates a summary using only the evidence. Using automatic and human evaluations, we show that explanations from our framework are more relevant than simple baselines, without substantially sacrificing the quality of the generated summary.
SMEs Management Success Determinants and its Implications on Their Performance in South West, Nigeria

Purpose: In developing nations, like Nigeria, Small and Medium scale Enterprise (SME) is supposed to be a catalyst for socio-economic development and employment generation. Eighty-five percent of the SMEs failed before their fifth anniversary. The study undertook to identify influential management success determinants and the concomitant effects on SMEs’ performance in Nigerian unstable business environment. Approach/Methodology/Design: The study used a cross sectional survey research design and probability sampling methods of stratified, random and proportional samplingto select 1102 CEOs. There were 988 completed questionnairesreceived back from respondents, giving and excellent response rate of 89.66%. Findings: The result of the study revealed significant difference in the evaluations of management success determinants to performance and management skills of the SMEs operators, organizational structure and innovation as being the most critical success factors for the SMEs in the Nigerian business environment. The most significant individual effects on SMEs performance are traceable to organizational structure, management skills and environmental scanning. There is a combined strong effect of the management success determinants on SMEs performance. Practical Implications: Apart from contributing to the existing literature on SMEs performance, the study also noted that new entrants into Nigerian market environment will succeed if the right organizational structure is put in place and innovation as well as management skills are taken seriously. Originality/Value: This study contributes by discovering that management skills of the SMEs operators, organizational structure and innovation are the most critical success factors for the SMEs in the Nigerian business environment.
Organic Manure Seed Pelleting for Enhancing Soil Properties, Nutrient Uptake and Yield of Rice

Background: Seed pelleting involves the sticking of target materials on to the surface of seeds. Pelleted seeds make planting of small seeds much easier by altering their shape, size and density. The call out on organic farming explains the requisite of user friendly and ecofriendly technique for seed management practices. The current study aimed to assess the effect of organic manure seed pelleting for enhancing soil properties, nutrient uptake and yield of rice.Methods: A pot culture experiment was carried out at College of Agriculture, Vellayani during the year 2018-19 to prepare organic manure seed pellets for rice and to evaluate its influence on yield, nutrient uptake and soil nutrient status. Pelleting materials were prepared using various treatment combinations namely, FYM + Azospirillum + Phosphobacteria (T2), vermicompost + Azospirillum + Phosphobacteria (T3), bioslurry flakes + Azospirillum + Phosphobacteria (T4), charcoal powder + Azospirillum + Phosphobacteria (T5), fly ash + Azospirillum + Phosphobacteria (T6), pongamia leaf powder + Azospirillum + Phosphobacteria (T7) and bioslurry + plant extracts + Azospirillum + Pseudomonas (T8: Farmer practice). Result: The results revealed that the highest grain yield (54.84 g pot-1) and straw yield (66.10 g pot-1) were recorded by T3. With regard to the uptake of nutrients, T3 registered the highest uptake of N, K, Ca, S, Zn, Cu and Si in grain and shoot. P uptake in grain was highest in the treatment T4 while T3 had the maximum P uptake in shoot. Nutrient status of the post-harvest soil was significantly influenced by the treatments.