Miracle-dz/UniFAR-FTUs huggingface dataset:
Non-Local Video Denoising by CNN

Non-local patch based methods were until recently state-of-the-art for image denoising but are now outperformed by CNNs. Yet they are still the state-of-the-art for video denoising, as video redundancy is a key factor to attain high denoising performance. The problem is that CNN architectures are hardly compatible with the search for self-similarities. In this work we propose a new and efficient way to feed video self-similarities to a CNN. The non-locality is incorporated into the network via a first non-trainable layer which finds for each patch in the input image its most similar patches in a search region. The central values of these patches are then gathered in a feature vector which is assigned to each image pixel. This information is presented to a CNN which is trained to predict the clean image. We apply the proposed architecture to image and video denoising. For the latter patches are searched for in a 3D spatio-temporal volume. The proposed architecture achieves state-of-the-art results. To the best of our knowledge, this is the first successful application of a CNN to video denoising.
Customer Lifetime Value in Video Games Using Deep Learning and Parametric Models

Nowadays, video game developers record every virtual action performed by their players. As each player can remain in the game for years, this results in an exceptionally rich dataset that can be used to understand and predict player behavior. In particular, this information may serve to identify the most valuable players and foresee the amount of money they will spend in in-app purchases during their lifetime. This is crucial in free-to-play games, where up to 50% of the revenue is generated by just around 2% of the players, the so-called whales.To address this challenge, we explore how deep neural networks can be used to predict customer lifetime value in video games, and compare their performance to parametric models such as Pareto/NBD. Our results suggest that convolutional neural network structures are the most efficient in predicting the economic value of individual players. They not only perform better in terms of accuracy, but also scale to big data and significantly reduce computational time, as they can work directly with raw sequential data and thus do not require any feature engineering process. This becomes important when datasets are very large, as is often the case with video game logs.Moreover, convolutional neural networks are particularly well suited to identify potential whales. Such an early identification is of paramount importance for business purposes, as it would allow developers to implement in-game actions aimed at retaining big spenders and maximizing their lifetime, which would ultimately translate into increased revenue.
Measure, Manifold, Learning, and Optimization: A Theory Of Neural Networks

We present a formal measure-theoretical theory of neural networks (NN) built on probability coupling theory. Our main contributions are summarized as follows. * Built on the formalism of probability coupling theory, we derive an algorithm framework, named Hierarchical Measure Group and Approximate System (HMGAS), nicknamed S-System, that is designed to learn the complex hierarchical, statistical dependency in the physical world. * We show that NNs are special cases of S-System when the probability kernels assume certain exponential family distributions. Activation Functions are derived formally. We further endow geometry on NNs through information geometry, show that intermediate feature spaces of NNs are stochastic manifolds, and prove that "distance" between samples is contracted as layers stack up. * S-System shows NNs are inherently stochastic, and under a set of realistic boundedness and diversity conditions, it enables us to prove that for large size nonlinear deep NNs with a class of losses, including the hinge loss, all local minima are global minima with zero loss errors, and regions around the minima are flat basins where all eigenvalues of Hessians are concentrated around zero, using tools and ideas from mean field theory, random matrix theory, and nonlinear operator equations. * S-System, the information-geometry structure and the optimization behaviors combined completes the analog between Renormalization Group (RG) and NNs. It shows that a NN is a complex adaptive system that estimates the statistic dependency of microscopic object, e.g., pixels, in multiple scales. Unlike clear-cut physical quantity produced by RG in physics, e.g., temperature, NNs renormalize/recompose manifolds emerging through learning/optimization that divide the sample space into highly semantically meaningful groups that are dictated by supervised labels (in supervised NNs).
A Framework for Studying the Heterogeneity of Risk Factors in Math Anxiety

Math anxiety is a prevalent disorder which affects many people worldwide. Here, we draw together ample evidence to suggest a dynamic developmental bio-psycho-social model. The model highlights the complex pathways towards the development of math anxiety, with a focus on dynamism. That is, math anxiety is viewed here as a dynamic interplay between environmental (parenting style, as well as social style including teachers’ attitude, instruction strategies and wider social effects) and intrinsic factors (i.e., neuro-cognitive and genetic predispositions, including brain malfunctions, heritability, predisposition towards general anxiety) and basic numerical cognition and affective factors. The model predicts that the dynamic interplay between these factors can either prevent or promote math anxiety’s effects on the development of heterogeneous symptoms. Considering the universal nature of math anxiety, a systematic description of the vulnerability factors that contribute to the development of math anxiety is vital. Such information may be of particular value in informing the design of preventive interventions as well as of specific intervention tools.
Factors Influence EFL Teacher’s Adoption of BYOD

The role of BYOD (Bring Your Own Device) has significantly increased and been emphasized in English education. However, research investigating EFL teachers’ attitudes and behaviors related to BYOD has been limited in descriptive aspects of the technology. Therefore, this study proposed a research model that empirically examines behaviors of EFL teachers’ BYOD acceptance by using Fred Davis’s Technology Acceptance Model (TAM) as the research framework. As external variables, this research model includes instant connectivity, compatibility, interaction, content enrichment, influencing the perceived usefulness of TAM. Structural Equation Modeling (SEM) with the data of 17 EFL teachers was used to analyze causal relationships between external variables and TAM variables. The results provide evidence that supports the tested hypotheses. The implications of the findings suggest a new direction for future studies on BYOD.
The Cholinergic Pathways in Inflammation: A Potential Pharmacotherapeutic Target for COPD

In COPD, the activity of the cholinergic system is increased, which is one of the reasons for the airflow limitation caused by the contraction of airway smooth muscles. Therefore, blocking the contractive actions with anticholinergics is a useful therapeutic intervention to reduce the airflow limitation. In addition to the effects of bronchoconstriction and mucus secretion, accumulating evidence from animal models of COPD suggest acetylcholine has a role in inflammation. Experiments using muscarinic M3-receptor deficient mice or M3 selective antagonists revealed that M3-receptors on parenchymal cells, but not on hematopoietic cells, are involved in the pro-inflammatory effect of acetylcholine. Recently, combinations of long-acting β2 adrenergic agonists (LABAs) and long-acting muscarinic antagonists (LAMAs) have become available for COPD treatment. These dual long-acting bronchodilators may have synergistic anti-inflammatory effects because stimulation of β2 adrenergic receptors induces inhibitory effects in inflammatory cells via a different signaling pathway from that by antagonizing M3-receptor, though these anti-inflammatory effects have not been clearly demonstrated in COPD patients. In contrast to the pro-inflammatory effects by ACh via muscarinic receptors, it has been demonstrated that the cholinergic anti-inflammatory pathway, which involves the parasympathetic nervous systems, regulates excessive inflammatory responses to protect organs during tissue injury and infection. Stimulation of acetylcholine via the α7 nicotinic acetylcholine receptor (α7nAChR) exerts inhibitory effects on leukocytes including macrophages and type 2 innate lymphoid cells. Although it remains unclear whether the inhibitory effects of acetylcholine via α7nAChR in inflammatory cells can regulate inflammation in COPD, neuroimmune interactions including the cholinergic anti-inflammatory pathway might serve as potential therapeutic targets.
On the Computational Inefficiency of Large Batch Sizes for Stochastic Gradient Descent

Increasing the mini-batch size for stochastic gradient descent offers significant opportunities to reduce wall-clock training time, but there are a variety of theoretical and systems challenges that impede the widespread success of this technique. We investigate these issues, with an emphasis on time to convergence and total computational cost, through an extensive empirical analysis of network training across several architectures and problem domains, including image classification, image segmentation, and language modeling. Although it is common practice to increase the batch size in order to fully exploit available computational resources, we find a substantially more nuanced picture. Our main finding is that across a wide range of network architectures and problem domains, increasing the batch size beyond a certain point yields no decrease in wall-clock time to convergence for \emph{either} train or test loss. This batch size is usually substantially below the capacity of current systems. We show that popular training strategies for large batch size optimization begin to fail before we can populate all available compute resources, and we show that the point at which these methods break down depends more on attributes like model architecture and data complexity than it does directly on the size of the dataset.
On the Existential Road From Regret to Heroism: Searching for Meaning in Life

We investigated whether regret predicted the motivation to act heroically. In a series of studies, we examined the relationship between regret, search for meaning in life, and heroism motivation. First, Study 1 (a and b) investigated the association between regret and search for meaning in life, considering regret as a whole, action regret, and inaction regret. As expected, regret correlated positively with search for meaning in life. In two additional studies (Study 2 and 3), we examined whether regret predicted heroism motivation and whether this effect was mediated through search for meaning in life. Study 2 confirmed this hypothesis for individual differences in regret, using a correlational design. Study 3 confirmed the hypothesis for temporary experiences of regret, using an experimental design. In addition, in Study 3 we found that heroism motivation was stronger for people with high self-enhancement needs than for those with lower self-enhancement needs. We discuss the relationship between regret and heroism in light of these results and explore their implications.
Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity

Sensitive statistics are often collected across sets of users, with repeated collection of reports done over time. For example, trends in users' private preferences or software usage may be monitored via such reports. We study the collection of such statistics in the local differential privacy (LDP) model, and describe an algorithm whose privacy cost is polylogarithmic in the number of changes to a user's value. More fundamentally---by building on anonymity of the users' reports---we also demonstrate how the privacy cost of our LDP algorithm can actually be much lower when viewed in the central model of differential privacy. We show, via a new and general privacy amplification technique, that any permutation-invariant algorithm satisfying e-local differential privacy will satisfy [MATH HERE]-central differential privacy. By this, we explain how the high noise and [MATH HERE] overhead of LDP protocols is a consequence of them being significantly more private in the central model. As a practical corollary, our results imply that several LDP-based industrial deployments may have much lower privacy cost than their advertised e would indicate---at least if reports are anonymized.
Hybrid Distortion Aggregated Visual Comfort Assessment for Stereoscopic Image Retargeting

Visual comfort is a quite important factor in 3D media service. Few research efforts have been carried out in this area especially in case of 3D content retargeting which may introduce more complicated visual distortions. In this paper, we propose a Hybrid Distortion Aggregated Visual Comfort Assessment (HDA-VCA) scheme for stereoscopic retargeted images (SRI), considering aggregation of hybrid distortions including structure distortion, information loss, binocular incongruity and semantic distortion. Specifically, a Local-SSIM feature is proposed to reflect the local structural distortion of SRI, and information loss is represented by Dual Natural Scene Statistics (D-NSS) feature extracted from the binocular summation and difference channels. Regarding binocular incongruity, visual comfort zone, window violation, binocular rivalry, and accommodation-vergence conflict of human visual system (HVS) are evaluated. Finally, the semantic distortion is represented by the correlation distance of paired feature maps extracted from original stereoscopic image and its retargeted image by using trained deep neural network. We validate the effectiveness of HDA-VCA on published Stereoscopic Image Retargeting Database (SIRD) and two stereoscopic image databases IEEE-SA and NBU 3D-VCA. The results demonstrate HDA-VCA's superior performance in handling hybrid distortions compared to state-of-the-art VCA schemes.
Rethinking clinical prediction: Why machine learning must consider year of care and feature aggregation

Machine learning for healthcare often trains models on de-identified datasets with randomly-shifted calendar dates, ignoring the fact that data were generated under hospital operation practices that change over time. These changing practices induce definitive changes in observed data which confound evaluations which do not account for dates and limit the generalisability of date-agnostic models. In this work, we establish the magnitude of this problem on MIMIC, a public hospital dataset, and showcase a simple solution. We augment MIMIC with the year in which care was provided and show that a model trained using standard feature representations will significantly degrade in quality over time. We find a deterioration of 0.3 AUC when evaluating mortality prediction on data from 10 years later. We find a similar deterioration of 0.15 AUC for length-of-stay. In contrast, we demonstrate that clinically-oriented aggregates of raw features significantly mitigate future deterioration. Our suggested aggregated representations, when retrained yearly, have prediction quality comparable to year-agnostic models.
Pancreatic Grafts from Pediatric Donors Do Not Appear to Grow After Transplantation into Adults

Unlike pediatric kidney donors, there is no literature regarding the growth of pediatric donor pancreatic transplant grafts. Our center prospectively followed three pediatric donor grafts after transplant by measuring two dimensions of the graft at postoperative day one and then at one, two, and three months post-transplant surgery with the hypothesis that the grafted pancreas would not grow like pediatric kidney donors given the fundamental physiologic differences between these two organs. Two grafts were stable to minimally larger in size, the third case decreased in size. Interestingly, all patients had an excellent clinical response with normalization of HbA1c. Further study will be required to understand the natural history of pancreatic transplants from a pediatric donor. Volumetric assessment with magnetic resonance imaging (MRI) is proposed as the next step for better evaluation of graft size.
Automated Bug Reporting System in Web Applications

Bug reporting system is the approach for tracking and reporting bugs of a web application. It is the system which filters the duplicate bugs and generates the bug report. Bug reporting system permits individual or group of developers to observe and scan the bugs in their product adequately. The bug reporting system can greatly raise the work rate and responsibilities of particular working stiff by accommodating a recorded progress and optimistic response for pleasing performance. Manually generated the bug report is a time-consuming process and it is very expensive to maintain. In this paper, we designed a framework that automatically generates the bug report. It provides the facility of report logging, reusable method and multiple browser support which helps to reduce the human effort and time requires to performing regression testing.
Local Economic Conditions and Worker Participation in the Online Gig Economy

Online gig economy platforms maintain low barriers to entry, and they enable flexible working arrangements, allowing individuals to work in an ad-hoc fashion, wherever and whenever they desire. Therefore, online labor platforms could absorb negative employment shocks in the traditional offline local labor market. However, as the bulk of workers in the gig economy treat it as a temporary source of employment, these individuals may scale back their participation as they focus on identifying a new position. Leveraging data from a leading online labor platform, and statistics on unemployment rates and mass layoff events from the Bureau of Labor Statistics, we use a difference-indifferences framework to estimate the effect of longitudinal state-level variation in unemployment, attributable to the 2008 financial crisis, on the supply of online labor at Freelancer. Our estimates show that a 1% change in unemployment around the financial crisis leads to an approximate 6.4% increase in the volume of new workers registering at Freelancer, an 8% increase in the total number of active workers, and a 12.1% increase in the number of submitted project bids. We then consider an alternative identification, examining the impact of mass layoff events, by industry and location, on the supply of online labor residing in the same location. We demonstrate a positive effect from mass layoff events in IT-related industries, yet no effect from mass layoffs in non-IT industries, primarily because most online labor platforms cater to IT-related projects that are easily outsourced and delivered via the Internet.
General Support-Effective Decomposition for Multi-Directional 3-D Printing

We present a method for fabricating general models with multi-directional 3-D printing systems by printing different model regions along with different directions. The core of our method is a support-effective volume decomposition algorithm that minimizes the area of the regions with large overhangs. A beam-guided searching algorithm with manufacturing constraints determines the optimal volume decomposition, which is represented by a sequence of clipping planes. While current approaches require manually assembling separate components into a final model, our algorithm allows for directly printing the final model in a single pass. It can also be applied to models with loops and handles. A supplementary algorithm generates special supporting structures for models where supporting structures for large overhangs cannot be eliminated. We verify the effectiveness of our method using two hardware systems: a Cartesian-motion-based system and an angular-motion-based system. A variety of 3-D models have been successfully fabricated on these systems. Note to Practitioners—In conventional planar-layer-based 3-D printing systems, supporting structures need to be added at the bottom of large overhanging regions to prevent material collapse. Supporting structures used in single-material 3-D printing technologies have three major problems: being difficult to remove, introducing surface damage, and wasting material. This article introduces a method to improve 3-D printing by adding rotation during the manufacturing process. To keep the hardware system relatively inexpensive, the hardware, called a multi-directional 3-D printing system, only needs to provide unsynchronized rotations. In this system, models are subdivided into different regions, and then, the regions are printed in different directions. We develop a general volume decomposition algorithm for effectively reducing the area that needs supporting structures. When supporting structures cannot be eliminated, we provide a supplementary algorithm for generating supports compatible with multi-directional 3-D printing. Our method can speed up the process of 3-D printing by saving time in producing and removing supports.
Fast and Accurate Reconstruction of Pan-Tilt RGB-D Scans via Axis Bound Registration

A fast and accurate algorithm is presented for registering scans from an RGB-D camera on a pan-tilt platform. The pan-tilt RGB-D camera rotates and scans the entire scene in an automated fashion. The proposed algorithm exploits the movement of the camera that is bound by the two rotation axes of the servo motors so as to realize fast and accurate registration of acquired point clouds. The rotation parameters, including the rotation axes, pan-tilt transformations and the servo control mechanism, are calibrated beforehand. Subsequently, fast global registration can be performed during online operation with transformation matrices formed by the calibrated rotation axes and angles. In local registration, features are extracted and matched between two scenes. False-positive correspondences, whose distances to the rotation trajectories exceed a threshold, are rejected. Then, a more accurate registration can be achieved by minimizing the residual distances between corresponding points, while transformations are bound to the rotation axes. Finally, the preliminary alignment result is input to the iterative closed point algorithm to compute the final transformation. Results of comparative experiments validate that the proposed method outperforms state-of-the-art algorithms of various approaches based on camera calibration, global registration, and simultaneous-localization-and-mapping in terms of root-mean-square error and computation time.
Automatic Facial Attractiveness Prediction by Deep Multi-Task Learning

Facial Attractiveness Prediction (FAP) is a useful yet challenging problem in the domain of computer vision. In this paper, we propose a deep learning based approach. Different from the existing deep methods, the proposed one models both the texture and shape clues within a multi-task learning framework consisting of attractiveness score prediction and fiducial landmark localization, thus highlighting both of their roles in assessing attractiveness of faces. Considering that the training data are not extensive, a lightweight CNN is designed to jointly learn the facial representation, landmark location, and facial attractiveness score. The proposed method is evaluated on the SCUT-FBP database, and a prediction correlation 0.92, is delivered, which shows the effectiveness of our method. Furthermore, two additional experiments in terms of comparison between facial images before and after make-up or beautification are conducted. The results also prove the advantage of the proposed method.