Miracle-dz/UniFAR-FTUs huggingface dataset:
School start times and academic achievement - a systematic review on grades and test scores

School start times have been at the centre of many scientific and political debates given the accumulating evidence that bell times are generally too early, and thus lead to an epidemic of sleep restriction in the student population. Recent media attention has conveyed the message that later school starts not only improve sleep but also result in better academic achievement. Several studies have been recently published on this topic requiring a comprehensive review of the results to clarify the relationship between later school start times and academic achievement to inform the general public and policy makers. To this end, we conducted a systematic review of the current literature on school starting times and academic achievement in middle and high school students, considering grades and standardised test scores as achievement measures. We followed the PRISMA guidelines for searching, including, and reporting relevant literature and identified 21 studies for detailed analysis. Evidence quality of included studies was assessed with a pre-defined risk of bias assessment using modified items from the GRADE scheme and ROBINS-I tool. About half of the reviewed studies reported no (positive or negative) effect of delaying school times on grades and test scores, while the other half reported either mixed or positive results. Given the strong heterogeneity of included studies, we grouped them according to various characteristics, such as academic outcomes, dose of delay, evidence quality, or study design to identify potential hidden effects. Despite this, we could not identify any generalisable effect beyond single studies as to whether delaying school times has clear beneficial effects on academic performance. Given that grades and scores determine future career trajectories and predict future success, the question whether school start times contribute to academic achievement is of great interest for the general public and needs to be further clarified. Mechanistically, it is very likely that improved sleep leads to or mediates improved cognitive performance and learning, but definitive conclusions on whether this also translates into better grades and scores across all students requires better evidence at this stage. Importantly, this does not preclude other positive outcomes of later start times such as improved sleep (quality), motivation or learning but draws attention on current gaps and shortcomings. To this end, we also highlight critical methodological aspects and provide suggestions to increase the evidence-level and to guide the direction of research in future studies.
The Adoption of Image-Driven Machine Learning for Microstructure Characterization and Materials Design: A Perspective

The recent surge in the adoption of machine learning techniques for materials design, discovery, and characterization has resulted in an increased interest and application of Image Driven Machine Learning (IDML) approaches. In this work, we review the application of IDML to the field of materials characterization. A hierarchy of six action steps is defined which compartmentalizes a problem statement into well-defined modules. The studies reviewed in this work are analyzed through the decisions adopted by them at each of these steps. Such a review permits a granular assessment of the field, for example the impact of IDML on materials characterization at the nanoscale, the number of images in a typical dataset required to train a semantic segmentation model on electron microscopy images, the prevalence of transfer learning in the domain, etc. Finally, we discuss the importance of interpretability and explainability, and provide an overview of two emerging techniques in the field: semantic segmentation and generative adversarial networks.
ThundeRiNG: generating multiple independent random number sequences on FPGAs

In this paper, we propose ThundeRiNG, a resource-efficient and high-throughput system for generating multiple independent sequences of random numbers (MISRN) on FPGAs. Generating MISRN can be a time-consuming step in many applications such as numeric computation and approximate computing. Despite that decades of studies on generating a single sequence of random numbers on FPGAs have achieved very high throughput and high quality of randomness, existing MISRN approaches either suffer from heavy resource consumption or fail to achieve statistical independence among sequences. In contrast, ThundeRiNG resolves the dependence by using a resource-efficient decorrelator among multiple sequences, guaranteeing a high statistical quality of randomness. Moreover, ThundeRiNG develops a novel state sharing among a massive number of pseudo-random number generator instances on FPGAs. The experimental results show that ThundeRiNG successfully passes the widely used statistical test, TestU01, only consumes a constant number of DSPs (less than 1% of the FPGA resource capacity) for generating any number of sequences, and achieves a throughput of 655 billion random numbers per second. Compared to the state-of-the-art GPU library, ThundeRiNG demonstrates a 10.62x speedup on MISRN and delivers up to 9.15x performance and 26.63x power efficiency improvement on two applications (pi estimation and Monte Carlo option pricing). This work is open-sourced on Github at https://github.com/Xtra-Computing/ThundeRiNG.
Biologically Inspired Semantic Lateral Connectivity for Convolutional Neural Networks

Lateral connections play an important role for sensory processing in visual cortex by supporting discriminable neuronal responses even to highly similar features. In the present work, we show that establishing a biologically inspired Mexican hat lateral connectivity profile along the filter domain can significantly improve the classification accuracy of a variety of lightweight convolutional neural networks without the addition of trainable network parameters. Moreover, we demonstrate that it is possible to analytically determine the stationary distribution of modulated filter activations and thereby avoid using recurrence for modeling temporal dynamics. We furthermore reveal that the Mexican hat connectivity profile has the effect of ordering filters in a sequence resembling the topographic organization of feature selectivity in early visual cortex. In an ordered filter sequence, this profile then sharpens the filters' tuning curves.
Supplementary material for DEHB: Evolutionary Hyperband for Scalable, Robust and Efﬁcient Hyperparameter Optimization

Differential Evolution (DE) is a simple, well-performing evolutionary algorithm to solve a variety of optimization problems [K. Price and Lampinen, 2006] [Das et al., 2016]. This algorithm was originally introduced in 1995 by Storn and Price [Storn and Price, 1997], and later attracted the attention of many researchers to propose new improved state-of-the-art algorithms [Chakraborty, 2008]. DE is based on four steps: initialization, mutation, crossover and selection. Algorithm 1 presents the DE pseudo-code. Initialization. DE is a population-based meta-heuristic algorithm which consists of a population of N individuals. Each individual is considered a solution and expressed as a vector of D-dimensional decision variables as follows:
DeepDarts: Modeling Keypoints as Objects for Automatic Scorekeeping in Darts using a Single Camera

Existing multi-camera solutions for automatic score-keeping in steel-tip darts are very expensive and thus inaccessible to most players. Motivated to develop a more accessible low-cost solution, we present a new approach to keypoint detection and apply it to predict dart scores from a single image taken from any camera angle. This problem involves detecting multiple keypoints that may be of the same class and positioned in close proximity to one another. The widely adopted framework for regressing keypoints using heatmaps is not well-suited for this task. To address this issue, we instead propose to model keypoints as objects. We develop a deep convolutional neural network around this idea and use it to predict dart locations and dartboard calibration points within an overall pipeline for automatic dart scoring, which we call DeepDarts. Additionally, we propose several task-specific data augmentation strategies to improve the generalization of our method. As a proof of concept, two datasets comprising 16k images originating from two different dartboard setups were manually collected and annotated to evaluate the system. In the primary dataset containing 15k images captured from a face-on view of the dartboard using a smartphone, DeepDarts predicted the total score correctly in 94.7% of the test images. In a second more challenging dataset containing limited training data (830 images) and various camera angles, we utilize transfer learning and extensive data augmentation to achieve a test accuracy of 84.0%. Because DeepDarts relies only on single images, it has the potential to be deployed on edge devices, giving anyone with a smartphone access to an automatic dart scoring system for steel-tip darts. The code and datasets are available1.
Tuza's Conjecture for Threshold Graphs

Tuza famously conjectured in 1981 that in a graph without k+1 edge-disjoint triangles, it suffices to delete at most 2k edges to obtain a triangle-free graph. The conjecture holds for graphs with small treewidth or small maximum average degree, including planar graphs. However, for dense graphs that are neither cliques nor 4-colorable, only asymptotic results are known. Here, we confirm the conjecture for threshold graphs, i.e. graphs that are both split graphs and cographs, and for co-chain graphs with both sides of the same size divisible by 4.
Graphene–Silicon Device for Visible and Infrared Photodetection

The fabrication of a graphene–silicon (Gr-Si) junction involves the formation of a parallel metal–insulator–semiconductor (MIS) structure, which is often disregarded but plays an important role in the optoelectronic properties of the device. In this work, the transfer of graphene onto a patterned n-type Si substrate, covered by Si3N4, produces a Gr-Si device, in which the parallel MIS consists of a Gr-Si3N4-Si structure surrounding the Gr-Si junction. The Gr-Si device exhibits rectifying behavior with a rectification ratio up to 104. The investigation of its temperature behavior is necessary to accurately estimate the Schottky barrier height (SBH) at zero bias, φb0 = 0.24 eV, the effective Richardson’s constant, A* = 7 × 10–10 AK–2 cm–2, and the diode ideality factor n = 2.66 of the Gr-Si junction. The device is operated as a photodetector in both photocurrent and photovoltage mode in the visible and infrared (IR) spectral regions. A responsivity of up to 350 mA/W and an external quantum efficiency (EQE) of up to 75% are achieved in the 500–1200 nm wavelength range. Decreases in responsivity to 0.4 mA/W and EQE to 0.03% are observed above 1200 nm, which is in the IR region beyond the silicon optical band gap, in which photoexcitation is driven by graphene. Finally, a model based on two parallel and opposite diodes, one for the Gr-Si junction and the other for the Gr-Si3N4-Si MIS structure, is proposed to explain the electrical behavior of the Gr-Si device.
Development and Evaluation of Polymer-bound Glibenclamide Oral Thin Film

Glibenclamide (GLB) is a second-generation sulfonylurea oral hypoglycemic agent used in the treatment of non-insulin dependent diabetes mellitus. The use of this drug is limited due to its poor dissolution and pharmacokinetic profile. In order to nullify these drawbacks, GLB oral thin films were prepared and evaluated to study the influence of preparatory conditions on the physicochemical properties by using Scanning Electron Microscopy (SEM), Fourier Transform Infra-red Spectroscopy (FTIR) and Differential Scanning Calorimetric (DSC). The surface pH of the GLB thin film was found to be in the range of 6.50 ± 0.10 and it is non-irritant to mucosal lining of the oral cavity. The results of the SEM analysis showed uniform surface morphology with homogenous distribution of GLB pure drug in PVP matrix. The in vitro drug release profile at pH 5.0 and 7.5 revealed sustained release patterns for a period of 8 h. The GLB oral thin film showed an enhanced in vivo insulin release profiles as compared to pure GLB drug. Thus, the results of the present study revealed that, the prepared GLB oral thin film can be used as alternative strategy to deliver glibenclamide for diabetes mellitus as it showed a significant role in increasing in vitro dissolution and in vivo insulin release profiles.
Monte Carlo Filtering Objectives: A New Family of Variational Objectives to Learn Generative Model and Neural Adaptive Proposal for Time Series

Learning generative models and inferring latent trajectories have shown to be challenging for time series due to the intractable marginal likelihoods of flexible generative models. It can be addressed by surrogate objectives for optimization. We propose Monte Carlo filtering objectives (MCFOs), a family of variational objectives for jointly learning parametric generative models and amortized adaptive importance proposals of time series. MCFOs extend the choices of likelihood estimators beyond Sequential Monte Carlo in state-of-the-art objectives, possess important properties revealing the factors for the tightness of objectives, and allow for less biased and variant gradient estimates. We demonstrate that the proposed MCFOs and gradient estimations lead to efficient and stable model learning, and learned generative models well explain data and importance proposals are more sample efficient on various kinds of time series data.
Characterizing Abhorrent, Misinformative, and Mistargeted Content on YouTube

YouTube has revolutionized the way people discover and consume video. Although YouTube facilitates easy access to hundreds of well-produced and trustworthy videos, abhorrent, misinformative, and mistargeted content is also common. The platform is plagued by various types of problematic content: 1) disturbing videos targeting young children;2) hateful and misogynistic content;and 3) pseudoscientific misinformation. While YouTube's recommendation algorithm plays a vital role in increasing user engagement and YouTube's monetization, its role in unwittingly promoting problematic content is not entirely understood. In this thesis, we shed some light on the degree of problematic content on YouTube and the role of the recommendation algorithm in the dissemination of such content. Following a data-driven quantitative approach, we analyze thousands of videos on YouTube, to shed light on: 1) the risks of YouTube media consumption by young children;2) the role of the recommendation algorithm in the dissemination of misogynistic content, by focusing on the Involuntary Celibates (Incels) community;and 3) user exposure to pseudoscientific content on various parts of the platform and how this exposure changes based on the user's watch history. Our analysis reveals that young children are likely to encounter disturbing content when they randomly browse the platform. By analyzing the Incel community on YouTube, we find that Incel activity is increasing over time and that platforms may play an active role in steering users towards extreme content. Finally, when studying pseudoscientific misinformation, we find that YouTube suggests more pseudoscientific content regarding traditional pseudoscientific topics (e.g., flat earth) than for emerging ones (like COVID-19) and that these recommendations are more common on the search results page than on a user's homepage or the video recommendations section.
Congestion-Aware Routing in Dynamic IoT Networks: A Reinforcement Learning Approach

The innovative services empowered by the Internet of Things (IoT) require a seamless and reliable wireless infras-tructure that enables communications within heterogeneous and dynamic low-power and lossy networks (LLNs). The Routing Protocol for LLNs (RPL) was designed to meet the communication requirements of a wide range of IoT application domains. How-ever, a load balancing problem exists in RPL under heavy traffic-load scenarios, degrading the network performance in terms of delay and packet delivery. In this paper, we tackle the problem of load-balancing in RPL networks using a reinforcement-learning framework. The proposed method adopts Q-learning at each node to learn an optimal parent selection policy based on the dynamic network conditions. Each node maintains the routing information of its neighbours as Q-values that represent a composite routing cost as a function of the congestion level, the link-quality and the hop-distance. The Q-values are updated continuously exploiting the existing RPL signalling mechanism. The performance of the proposed approach is evaluated through extensive simulations and compared with the existing work to demonstrate its effectiveness. The results show that the proposed method substantially improves network performance in terms of packet delivery and average delay with a marginal increase in the signalling frequency.
FreshDiskANN: A Fast and Accurate Graph-Based ANN Index for Streaming Similarity Search

Approximate nearest neighbor search (ANNS) is a fundamental building block in information retrieval with graph-based indices being the current state-of-the-art and widely used in the industry. Recent advances in graph-based indices have made it possible to index and search billion-point datasets with high recall and millisecond-level latency on a single commodity machine with an SSD. However, existing graph algorithms for ANNS support only static indices that cannot reflect real-time changes to the corpus required by many key real-world scenarios (e.g. index of sentences in documents, email, or a news index). To overcome this drawback, the current industry practice for manifesting updates into such indices is to periodically re-build these indices, which can be prohibitively expensive. In this paper, we present the first graph-based ANNS index that reflects corpus updates into the index in real-time without compromising on search performance. Using update rules for this index, we design FreshDiskANN, a system that can index over a billion points on a workstation with an SSD and limited memory, and support thousands of concurrent real-time inserts, deletes and searches per second each, while retaining $>95\%$ 5-recall@5. This represents a 5-10x reduction in the cost of maintaining freshness in indices when compared to existing methods.
Towards a Sample Efficient Reinforcement Learning Pipeline for Vision Based Robotics

Deep Reinforcement learning holds the guarantee of empowering self-ruling robots to master enormous collections of conduct abilities with negligible human mediation. The improvements brought by this technique enables robots to perform difficult tasks such as grabbing or reaching targets. Nevertheless, the training process is still time consuming and tedious especially when learning policies only with RGB camera information. This way of learning is capital to transfer the task from simulation to the real world since the only external source of information for the robot in real life is video. In this paper, we study how to limit the time taken for training a robotic arm with 6 Degrees Of Freedom (DOF) to reach a ball from scratch by assembling a pipeline as efficient as possible. The pipeline is divided into two parts: the first one is to capture the relevant information from the RGB video with a Computer Vision algorithm. The second one studies how to train faster a Deep Reinforcement Learning algorithm in order to make the robotic arm reach the target in front of him. Follow this link to find videos and plots in higher resolution: \url{https://drive.google. com/drive/folders/1_lRlDSoPzd_GTcVrxNip10o_lm-_DPdn? usp=sharing}
Semi-supervised, Topology-Aware Segmentation of Tubular Structures from Live Imaging 3D Microscopy

Motivated by a challenging tubular network segmentation task, this paper tackles two commonly encountered problems in biomedical imaging: Topological consistency of the segmentation, and limited annotations. We propose a topological score which measures both topological and geometric consistency between the predicted and ground truth seg-mentations, applied for model selection and validation. We apply our topological score in three scenarios: i. a U-net ii. a U-net pretrained on an autoencoder, and iii. a semisu-pervised U-net architecture, which oﬀers a straightforward approach to jointly training the network both as an autoencoder and a segmentation algorithm. This allows us to utilize un-annotated data for training a representation that generalizes across test data variability, in spite of our annotated training data having very limited variation. Our contributions are validated on a challenging segmentation task, locating tubular structures in the fetal pancreas from noisy live imaging confocal microscopy.
LAST at SemEval-2021 Task 1: Improving Multi-Word Complexity Prediction Using Bigram Association Measures

This paper describes the system developed by the Laboratoire d’analyse statistique des textes (LAST) for the Lexical Complexity Prediction shared task at SemEval-2021. The proposed system is made up of a LightGBM model fed with features obtained from many word frequency lists, published lexical norms and psychometric data. For tackling the specificity of the multi-word task, it uses bigram association measures. Despite that the only contextual feature used was sentence length, the system achieved an honorable performance in the multi-word task, but poorer in the single word task. The bigram association measures were found useful, but to a limited extent.
A Biased Deep Tensor Factorization Network For Tensor Completion

Tensor decomposition is a popular technique for tensor completion, However most of the existing methods are based on linear or shallow model, when the data tensor becomes large and the observation data is very small, it is prone to over fitting and the performance decreases significantly. To address this problem, the completion method for a tensor based on a Biased Deep Tensor Factorization Network (BDTFN) is proposed. This method can not only overcome the shortcomings of traditional tensor factorization, but also deal with complex non-linear data. Firstly, the horizontal and lateral tensors corresponding to the observed values of the input tensors are used as inputs and projected to obtain their horizontal (lateral) potential feature tensors. Secondly, the horizontal (lateral) potential feature tensors are respectively constructed into a multilayer perceptron network. Finally, the horizontal and lateral output tensors are fused by constructing a bilinear pooling layer. Tensor forward-propagation is composed of those three step, and its parameters are updated by tensor back-propagation using the multivariable chain rule. In this paper, we consider the large-scale 5-minute traffic speed data set and use it to address the missing data imputation problem for large-scale spatiotemporal traffic data. In addition, we compare the numerical performance of the proposed algorithm with those for state-of-the-art approaches on video recovery and color image recovery. Numerical experimental results illustrate that our approach is not only much more accurate than those state-of-the-art methods, but it also has high speed.