Miracle-dz/UniFAR-FTUs huggingface dataset:
Do Volunteer Community-Based Preceptors Value Students’ Feedback?

Purpose. A key component of educational practice is to provide feedback and evaluation to teachers and learners to improve the teaching and learning process. The purpose of this study was to determine whether volunteer community preceptors value evaluation and feedback by students as much as they value other resources or rewards. Method. In Fall 1999, a questionnaire concerning the resources and rewards of preceptorship was mailed to 236 community preceptors affiliated with the Mercer University School of Medicine, Macon, Georgia. Preceptors were asked to rate 20 factors on a five-point Likert scale (5 = very important to 1 = not very important). The mean values were compared using t-tests. Results. One hundred sixty-eight preceptors (71%) completed questionnaires. Preceptors rated evaluation and feedback from students significantly higher (p < .001) than all other factors (mean = 4.02, standard deviation [SD] = .87). Continuing medical education for teaching was the next most highly valued factor (mean = 3.67, SD = 1.14). Preceptors rated financial compensation the lowest (mean = 2.01, SD = 1.19) of all factors. The high rank of feedback and evaluation from students persisted across gender, specialty, length of time as a preceptor, practice location, and years practicing medicine. Conclusion. This study demonstrates that feedback and evaluation from students is highly valued. The knowledge that community-based preceptors highly value feedback and evaluation from students should stimulate medical school programs to provide feedback and evaluation to preceptors that will enhance the educational outcomes for both faculty and learners.
Comparison between acarbose, metformin, and insulin treatment in type 2 diabetic patients with secondary failure to sulfonylurea treatment.

The purpose of this study was to determine the most suitable treatment for Type 2 (non-insulin-dependent) diabetic patients with secondary failure to sulfonylureas (SFS). In a four-month comparative study, 36 Type 2 diabetic patients given SFS were allocated to three treatment groups: A (n = 12, M/F 6/6, HbAlc 9.1 +/- 1.6%) received 0.3 IU/Kg body weight (BW) of insulin-Zn between 10 and 11 p.m. ; B (n = 12, M/F 6/6, HbAlc 9.2 +/- 1.6%) SFS plus 850 mg/day of metformin; and C (n = 12, M/F 6/6, HbAlc 9.5 +/- 2.4%) SFS plus acarbose 3 x 100 mg daily. Modifications in HbAlc, BW, blood pressure (BP), lipoprotein profile and insulin sensitivity were evaluated. HbAlc decreased in the three groups (A: 17.9 +/- 13.5%; B: 18.2 +/- 4.5%; C: 7.6 +/- 16.8%; all p < 0.05; A and B vs C = p < 0.05). BW increased in group A and decreased in the other groups. BP decreased statistically in group B. HDL-cholesterol increased (1.26 +/- 0.46 vs 1.49 +/- 0.36 mmol/L; p < 0.05) and triglyceride levels decreased (1.68 +/- 0.85 vs 1.16 +/- 0.43 mmol/L; p < 0.05) in group A. There were no significant changes in the other studied parameters. We conclude that, for Type 2 diabetic patients given SFS, both insulin and metformin plus SFS provided better glycaemic control than acarbose plus SFS. Metformin combined with SFS offered further advantages for the control of BW and BP.
Communication Complexity in Locally Private Distribution Estimation and Heavy Hitters

We consider the problems of distribution estimation and heavy hitter (frequency) estimation under privacy and communication constraints. While these constraints have been studied separately, optimal schemes for one are sub-optimal for the other. We propose a sample-optimal $\varepsilon$-locally differentially private (LDP) scheme for distribution estimation, where each user communicates only one bit, and requires no public randomness. We show that Hadamard Response, a recently proposed scheme for $\varepsilon$-LDP distribution estimation is also utility-optimal for heavy hitter estimation. Finally, we show that unlike distribution estimation, without public randomness where only one bit suffices, any heavy hitter estimation algorithm that communicates $o(\min \{\log n, \log k\})$ bits from each user cannot be optimal.
Modeling socially responsible consumption and the need for uniqueness: a PLS-SEM approach

Purpose The purpose of the present study is intended to fill a research gap, by advancing a conceptual model which brings novel insights on the relationships between socially responsible consumption and consumer’s need for uniqueness. Design/methodology/approach Relying on a questionnaire-based survey for data collection, the proposed model was tested using the partial least squares (PLS) algorithm for structural equation modeling (SEM), which allows the assessment of the models containing both formative and reflective constructs. Findings The research found that 73.7 per cent of the variance in the consumer’s belief in the importance of personal power to make a difference through socially responsible choices is explained by the proposed model. Moreover, four of the five factors of socially responsible consumption fully mediate the positive effect of the consumer’s need for uniqueness on the importance attached to personal socially responsible choices in driving positive change. Research limitations/implications The study avails a phenomenological perspective by offering novel insights from a Romanian sample. The limitation associated with a country-centric vision is compensated through the contextual analysis and integration of a new point of reference in the overall framework of socially responsible consumption. Practical implications The evidence is indicative of new consumption insights and should be closely considered by companies. Originality/value The study draws upon a conceptual model integrating the relationships between socially responsible consumption and a psychological characteristic – the consumer’s need for uniqueness – which has been never tested as such before.
OICSR: Out-In-Channel Sparsity Regularization for Compact Deep Neural Networks

Channel pruning can significantly accelerate and compress deep neural networks. Many channel pruning works utilize structured sparsity regularization to zero out all the weights in some channels and automatically obtain structure-sparse network in training stage. However, these methods apply structured sparsity regularization on each layer separately where the correlations between consecutive layers are omitted. In this paper, we first combine one out-channel in current layer and the corresponding in-channel in next layer as a regularization group, namely out-in-channel. Our proposed Out-In-Channel Sparsity Regularization (OICSR) considers correlations between successive layers to further retain predictive power of the compact network. Training with OICSR thoroughly transfers discriminative features into a fraction of out-in-channels. Correspondingly, OICSR measures channel importance based on statistics computed from two consecutive layers, not individual layer. Finally, a global greedy pruning algorithm is designed to remove redundant out-in-channels in an iterative way. Our method is comprehensively evaluated with various CNN architectures including CifarNet, AlexNet, ResNet, DenseNet and PreActSeNet on CIFAR-10, CIFAR-100 and ImageNet-1K datasets. Notably, on ImageNet-1K, we reduce 37.2% FLOPs on ResNet-50 while outperforming the original model by 0.22% top-1 accuracy.
Dirac geometry and integration of Poisson homogeneous spaces.

Using tools from Dirac geometry, we show through an explicit construction that any Poisson homogeneous space of any Poisson Lie group admits an integration to a symplectic groupoid. Our theorem follows from a more general result which relates, for a principal bundle $M\to B$, integrations of a Poisson structure on $B$ to integrations of its pullback Dirac structure on $M$ by pre-symplectic groupoids. Our construction gives a distinguished class of explicit real or holomorphic pre-symplectic and symplectic groupoids over semi-simple Lie groups and some of their homogeneous spaces, including their symmetric spaces, conjugacy classes, and flag varieties.
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

Convolutional Neural Networks (ConvNets) are commonly developed at a fixed resource budget, and then scaled up for better accuracy if more resources are available. In this paper, we systematically study model scaling and identify that carefully balancing network depth, width, and resolution can lead to better performance. Based on this observation, we propose a new scaling method that uniformly scales all dimensions of depth/width/resolution using a simple yet highly effective compound coefficient. We demonstrate the effectiveness of this method on scaling up MobileNets and ResNet. To go even further, we use neural architecture search to design a new baseline network and scale it up to obtain a family of models, called EfficientNets, which achieve much better accuracy and efficiency than previous ConvNets. In particular, our EfficientNet-B7 achieves state-of-the-art 84.4% top-1 / 97.1% top-5 accuracy on ImageNet, while being 8.4x smaller and 6.1x faster on inference than the best existing ConvNet. Our EfficientNets also transfer well and achieve state-of-the-art accuracy on CIFAR-100 (91.7%), Flowers (98.8%), and 3 other transfer learning datasets, with an order of magnitude fewer parameters. Source code is at this https URL.
Empirical review of Java program repair tools: a large-scale experiment on 2,141 bugs and 23,551 repair attempts

In the past decade, research on test-suite-based automatic program repair has grown significantly. Each year, new approaches and implementations are featured in major software engineering venues. However, most of those approaches are evaluated on a single benchmark of bugs, which are also rarely reproduced by other researchers. In this paper, we present a large-scale experiment using 11 Java test-suite-based repair tools and 2,141 bugs from 5 benchmarks. Our goal is to have a better understanding of the current state of automatic program repair tools on a large diversity of benchmarks. Our investigation is guided by the hypothesis that the repairability of repair tools might not be generalized across different benchmarks. We found that the 11 tools 1) are able to generate patches for 21% of the bugs from the 5 benchmarks, and 2) have better performance on Defects4J compared to other benchmarks, by generating patches for 47% of the bugs from Defects4J compared to 10-30% of bugs from the other benchmarks. Our experiment comprises 23,551 repair attempts, which we used to find causes of non-patch generation. These causes are reported in this paper, which can help repair tool designers to improve their approaches and tools.
A Gram-Gauss-Newton Method Learning Overparameterized Deep Neural Networks for Regression Problems

First-order methods such as stochastic gradient descent (SGD) are currently the standard algorithm for training deep neural networks. Second-order methods, despite their better convergence rate, are rarely used in practice due to the prohibitive computational cost in calculating the second-order information. In this paper, we propose a novel Gram-Gauss-Newton (GGN) algorithm to train deep neural networks for regression problems with square loss. Our method draws inspiration from the connection between neural network optimization and kernel regression of neural tangent kernel (NTK). Different from typical second-order methods that have heavy computational cost in each iteration, GGN only has minor overhead compared to first-order methods such as SGD. We also give theoretical results to show that for sufficiently wide neural networks, the convergence rate of GGN is \emph{quadratic}. Furthermore, we provide convergence guarantee for mini-batch GGN algorithm, which is, to our knowledge, the first convergence result for the mini-batch version of a second-order method on overparameterized neural networks. Preliminary experiments on regression tasks demonstrate that for training standard networks, our GGN algorithm converges much faster and achieves better performance than SGD.
Between Habit and Addiction: An Overview of Preliminary Finding on Social Networking Sites Usage among Teenagers

Social networking sites (SNS) usage have impacted our lives in many ways. Researches on SNS usage mostly were focus on adults compared to teenagers. Nowadays, teenagers are so mesmerize with the SNS applications offered by the provider until they get addicted to it. Yet, SNS addiction is not an isolation process. It started off with high engagement and originated from habit behaviour. This study focuses on exploring and analyzing the usage of SNS among the teenagers in Malaysia specifically in urban area. It also examined the SNS habit and addiction behaviors of the teenagers and emphasized the differences of the behaviors between genders. An online survey form was distributed among the teenagers in urban area. The data were analyzed using SPSS and cross tabulations were constructed to understand the correlation between gender and SNS behaviors. From the analysis, comparisons between genders were done based on the frequency of the SNS habit and addiction behaviors. Based on the preliminary findings, it shows that there are slightly differences between the genders across the behaviors that may have shed some light for other researchers in the niche area.
Discrete value-distribution of Artin $L$-functions associated with cubic fields

Arising from the factorizations of Dedekind zeta-functions of non-Galois cubic fields, we obtain Artin $L$-functions of two-dimensional irreducible representations. In this paper, we study the distribution of values of such Artin $L$-functions as the cubic fields are varying. We prove that various mean values of the Artin $L$-functions are represented by integrals involving a density function which can be explicitly constructed. The result is applied to the study on the distribution of class numbers of cubic fields.
Validation of Approximate Likelihood and Emulator Models for Computationally Intensive Simulations

Complex phenomena in engineering and the sciences are often modeled with computationally intensive feed-forward simulations for which a tractable analytic likelihood does not exist. In these cases, it is sometimes necessary to estimate an approximate likelihood or fit a fast emulator model for efficient statistical inference; such surrogate models include Gaussian synthetic likelihoods and more recently neural density estimators such as autoregressive models and normalizing flows. To date, however, there is no consistent way of quantifying the quality of such a fit. Here we propose a statistical framework that can distinguish any arbitrary misspecified model from the target likelihood, and that in addition can identify with statistical confidence the regions of parameter as well as feature space where the fit is inadequate. Our validation method applies to settings where simulations are extremely costly and generated in batches or "ensembles" at fixed locations in parameter space. At the heart of our approach is a two-sample test that quantifies the quality of the fit at fixed parameter values, and a global test that assesses goodness-of-fit across simulation parameters. While our general framework can incorporate any test statistic or distance metric, we specifically argue for a new two-sample test that can leverage any regression method to attain high power and provide diagnostics in complex data settings.
A new algorithm for decremental single-source shortest paths with applications to vertex-capacitated flow and cut problems

We study the vertex-decremental Single-Source Shortest Paths (SSSP) problem: given an undirected graph G=(V,E) with lengths ℓ(e)≥ 1 on its edges that undergoes vertex deletions, and a source vertex s, we need to support (approximate) shortest-path queries in G: given a vertex v, return a path connecting s to v, whose length is at most (1+є) times the length of the shortest such path, where є is a given accuracy parameter. The problem has many applications, for example to flow and cut problems in vertex-capacitated graphs. Decremental SSSP is a fundamental problem in dynamic algorithms that has been studied extensively, especially in the more standard edge-decremental setting, where the input graph G undergoes edge deletions. The classical algorithm of Even and Shiloach supports exact shortest-path queries in O(mn) total update time. A series of recent results have improved this bound to O(m1+o(1)logL), where L is the largest length of any edge. However, these improved results are randomized algorithms that assume an oblivious adversary. To go beyond the oblivious adversary restriction, recently, Bernstein, and Bernstein and Chechik designed deterministic algorithms for the problem, with total update time Õ(n2logL), that by definition work against an adaptive adversary. Unfortunately, their algorithms introduce a new limitation, namely, they can only return the approximate length of a shortest path, and not the path itself. Many applications of the decremental SSSP problem, including the ones considered in this paper, crucially require both that the algorithm returns the approximate shortest paths themselves and not just their lengths, and that it works against an adaptive adversary. Our main result is a randomized algorithm for vertex-decremental SSSP with total expected update time O(n2+o(1)logL), that responds to each shortest-path query in Õ(nlogL) time in expectation, returning a (1+є)-approximate shortest path. The algorithm works against an adaptive adversary. The main technical ingredient of our algorithm is an Õ(|E(G)|+ n1+o(1))-time algorithm to compute a core decomposition of a given dense graph G, which allows us to compute short paths between pairs of query vertices in G efficiently. We use our result for vertex-decremental SSSP to obtain (1+є)-approximation algorithms for maximum s-t flow and minimum s-t cut in vertex-capacitated graphs, in expected time n2+o(1), and an O(log4n)-approximation algorithm for the vertex version of the sparsest cut problem with expected running time n2+o(1). These results improve upon the previous best known algorithms for these problems in the regime where m= ω(n1.5 + o(1)).
Revisiting Low-Resource Neural Machine Translation: A Case Study

It has been shown that the performance of neural machine translation (NMT) drops starkly in low-resource conditions, underperforming phrase-based statistical machine translation (PBSMT) and requiring large amounts of auxiliary data to achieve competitive results. In this paper, we re-assess the validity of these results, arguing that they are the result of lack of system adaptation to low-resource settings. We discuss some pitfalls to be aware of when training low-resource NMT systems, and recent techniques that have shown to be especially helpful in low-resource settings, resulting in a set of best practices for low-resource NMT. In our experiments on German–English with different amounts of IWSLT14 training data, we show that, without the use of any auxiliary monolingual or multilingual data, an optimized NMT system can outperform PBSMT with far less data than previously claimed. We also apply these techniques to a low-resource Korean–English dataset, surpassing previously reported results by 4 BLEU.
Adversarial Attacks on Remote User Authentication Using Behavioural Mouse Dynamics

Mouse dynamics is a potential means of authenticating users. Typically, the authentication process is based on classical machine learning techniques, but recently, deep learning techniques have been introduced for this purpose. Although prior research has demonstrated how machine learning and deep learning algorithms can be bypassed by carefully crafted adversarial samples, there has been very little research performed on the topic of behavioural biometrics in the adversarial domain. In an attempt to address this gap, we built a set of attacks, which are applications of several generative approaches, to construct adversarial mouse trajectories that bypass authentication models. These generated mouse sequences will serve as the adversarial samples in the context of our experiments. We also present an analysis of the attack approaches we explored, explaining their limitations. In contrast to previous work, we consider the attacks in a more realistic and challenging setting in which an attacker has access to recorded user data but does not have access to the authentication model or its outputs. We explore three different attack strategies: 1) statistics-based, 2) imitation-based, and 3) surrogate-based; we show that they are able to evade the functionality of the authentication models, thereby impacting their robustness adversely. We show that imitation-based attacks often perform better than surrogate-based attacks, unless, however, the attacker can guess the architecture of the authentication model. In such cases, we propose a potential detection mechanism against surrogate-based attacks.
Self-supervised audio representation learning for mobile devices

We explore self-supervised models that can be potentially deployed on mobile devices to learn general purpose audio representations. Specifically, we propose methods that exploit the temporal context in the spectrogram domain. One method estimates the temporal gap between two short audio segments extracted at random from the same audio clip. The other methods are inspired by Word2Vec, a popular technique used to learn word embeddings, and aim at reconstructing a temporal spectrogram slice from past and future slices or, alternatively, at reconstructing the context of surrounding slices from the current slice. We focus our evaluation on small encoder architectures, which can be potentially run on mobile devices during both inference (re-using a common learned representation across multiple downstream tasks) and training (capturing the true data distribution without compromising users' privacy when combined with federated learning). We evaluate the quality of the embeddings produced by the self-supervised learning models, and show that they can be re-used for a variety of downstream tasks, and for some tasks even approach the performance of fully supervised models of similar size.
Bregman forward-backward splitting for nonconvex composite optimization: superlinear convergence to nonisolated critical points

We introduce Bella, a locally superlinearly convergent Bregman forward-backward splitting method for minimizing the sum of two nonconvex functions, one of which satisfying a relative smoothness condition and the other one possibly nonsmooth. A key tool of our methodology is the Bregman forward-backward envelope (BFBE), an exact and continuous penalty function with favorable first- and second-order properties, and enjoying a nonlinear error bound when the objective function satisfies a Lojasiewicz-type property. The proposed algorithm is of linesearch type over the BFBE along candidate update directions, and converges subsequentially to stationary points, globally under a KL condition, and owing to the given nonlinear error bound can attain superlinear convergence rates even when the limit point is a nonisolated minimum, provided the directions are suitably selected.