Miracle-dz/UniFAR-FTUs huggingface dataset:
NTIRE 2020 Challenge on Image Demoireing: Methods and Results

This paper reviews the Challenge on Image Demoireing that was part of the New Trends in Image Restoration and Enhancement (NTIRE) workshop, held in conjunction with CVPR 2020. Demoireing is a difficult task of removing moire patterns from an image to reveal an underlying clean image. The challenge was divided into two tracks. Track 1 targeted the single image demoireing problem, which seeks to remove moire patterns from a single image. Track 2 focused on the burst demoireing problem, where a set of degraded moire images of the same scene were provided as input, with the goal of producing a single demoired image as output. The methods were ranked in terms of their fidelity, measured using the peak signal-to-noise ratio (PSNR) between the ground truth clean images and the restored images produced by the participants’ methods. The tracks had 142 and 99 registered participants, respectively, with a total of 14 and 6 submissions in the final testing stage. The entries span the current state-of-the-art in image and burst image demoireing problems.
Collective Loss Function for Positive and Unlabeled Learning

People learn to discriminate between classes without explicit exposure to negative examples. On the contrary, traditional machine learning algorithms often rely on negative examples, otherwise the model would be prone to collapse and always-true predictions. Therefore, it is crucial to design the learning objective which leads the model to converge and to perform predictions unbiasedly without explicit negative signals. In this paper, we propose a Collectively loss function to learn from only Positive and Unlabeled data (cPU). We theoretically elicit the loss function from the setting of PU learning. We perform intensive experiments on the benchmark and real-world datasets. The results show that cPU consistently outperforms the current state-of-the-art PU learning methods.
Promotion of flowering in potted Piceaengelmannii (Perry) grafts: effects of heat, drought, gibberellin A4/7, and their timing

Young, potted grafts ofPiceaengelmannii were moved into a 30:20 °C (day:night) heated polyethylene house at different stages of lateral shoot elongation; and there they were subjected to low, moderate, or severe drought stress with and without branch applications of gibberellin A4/7 (GA4/7). The critical time for promoting flowering by high temperature was the late stage of slow shoot elongation, whereas for drought it was during early and rapid shoot growth. Each treatment inhibited flowering at the time the other was maximally effective and the effective treatment period for GA4/7 appeared to include that both for high temperature and drought. In contrast to drought, optimally timed heat treatment did not retard shoot elongation, nor did it result in a decreased needle water potential relative to well-watered grafts outdoors. It appears that heat and drought promote flowering through different mechanisms, albeit mechanisms which may be mediated, at least in part, through their influence on gibberellin m.
Deep Learning based Person Re-identification

Automated person re-identification in a multi-camera surveillance setup is very important for effective tracking and monitoring crowd movement. In the recent years, few deep learning based re-identification approaches have been developed which are quite accurate but time-intensive, and hence not very suitable for practical purposes. In this paper, we propose an efficient hierarchical re-identification approach in which color histogram based comparison is first employed to find the closest matches in the gallery set, and next deep feature based comparison is carried out using Siamese network. Reduction in search space after the first level of matching helps in achieving a fast response time as well as improving the accuracy of prediction by the Siamese network by eliminating vastly dissimilar elements. A silhouette part-based feature extraction scheme is adopted in each level of hierarchy to preserve the relative locations of the different body structures and make the appearance descriptors more discriminating in nature. The proposed approach has been evaluated on five public data sets and also a new data set captured by our team in our laboratory. Results reveal that it outperforms most state-of-the-art approaches in terms of overall accuracy.
Constraining Alfvénic turbulence with helicity invariants

In this paper, we study the constraints imposed by the invariants (generalized helicities and energy) of extended magnetohydrodynamics on some global characteristics of turbulence. We show that the global turbulent kinetic and magnetic energies will approach equipartition only under certain circumstances that depend on the ratio of the generalized helicities. In systems with minimal thermal energy, we demonstrate that the three invariants collectively determine the characteristic length scale associated with Alfvenic turbulence.
Recognizing Exercises and Counting Repetitions in Real Time

Artificial intelligence technology has made its way absolutely necessary in a variety of industries including the fitness industry. Human pose estimation is one of the important researches in the field of Computer Vision for the last few years. In this project, pose estimation and deep machine learning techniques are combined to analyze the performance and report feedback on the repetitions of performed exercises in real-time. Involving machine learning technology in the fitness industry could help the judges to count repetitions of any exercise during Weightlifting or CrossFit competitions.
Overview of Surgical Simulation

Motivated by the current demand of clinical governance, surgical simulation is now a well-established modality for basic skills training and assessment. The practical deployment of the technique is a multi-disciplinary venture encompassing areas in engineering, medicine and psychology. This paper provides an overview of the key topics involved in surgical simulation and associated technical challenges. The paper discusses the clinical motivation for surgical simulation, the use of virtual environments for surgical training, model acquisition and simplification, deformable models, collision detection, tissue property measurement, haptic rendering and image synthesis. Additional topics include surgical skill training and assessment metrics as well as challenges facing the incorporation of surgical simulation into medical education curricula.
Mice with Deficiency of G Protein γ3 Are Lean and Have Seizures

ABSTRACT Emerging evidence suggests that the γ subunit composition of an individual G protein contributes to the specificity of the hundreds of known receptor signaling pathways. Among the twelve γ subtypes, γ3 is abundantly and widely expressed in the brain. To identify specific functions and associations for γ3, a gene-targeting approach was used to produce mice lacking the Gng3 gene (Gng3−/−). Confirming the efficacy and specificity of gene targeting, Gng3 −/− mice show no detectable expression of the Gng3 gene, but expression of the divergently transcribed Bscl2 gene is not affected. Suggesting unique roles for γ3 in the brain, Gng3 −/− mice display increased susceptibility to seizures, reduced body weights, and decreased adiposity compared to their wild-type littermates. Predicting possible associations for γ3, these phenotypic changes are associated with significant reductions in β2 and αi3 subunit levels in certain regions of the brain. The finding that the Gng3 −/− mice and the previously reported Gng7 −/− mice display distinct phenotypes and different αβγ subunit associations supports the notion that even closely related γ subtypes, such as γ3 and γ7, perform unique functions in the context of the organism.
Testing Scenario Library Generation for Connected and Automated Vehicles: An Adaptive Framework

How to generate testing scenario libraries for connected and automated vehicles (CAVs) is a major challenge faced by the industry. In previous studies, to evaluate maneuver challenge of a scenario, surrogate models (SMs) are often used without explicit knowledge of the CAV under test. However, performance dissimilarities between the SM and the CAV under test usually exist, and it can lead to the generation of suboptimal scenario libraries. In this article, an adaptive testing scenario library generation (ATSLG) method is proposed to solve this problem. A customized testing scenario library for a specific CAV model is generated through an adaptive process. To compensate for the performance dissimilarities and leverage each test of the CAV, Bayesian optimization techniques are applied with classification-based Gaussian Process Regression and a newly designed acquisition function. Comparing with a pre-determined library, a CAV can be tested and evaluated in a more efficient manner with the customized library. To validate the proposed method, a cut-in case study is investigated and the results demonstrate that the proposed method can further accelerate the evaluation process by a few orders of magnitude.
Entropy, irreversibility and cascades in the inertial range of isotropic turbulence

Abstract This paper analyses the turbulent energy cascade from the perspective of statistical mechanics, and relates interscale energy fluxes to statistical irreversibility and information entropy production. The microscopical reversibility of the energy cascade is tested by constructing a reversible three-dimensional turbulent system using a dynamic model for the sub-grid stresses. This system, when reversed in time, develops a sustained inverse cascade towards the large scales, evidencing that the characterisation of the inertial energy cascade must consider the possibility of an inverse regime. This experiment is used to study the origin of statistical irreversibility and the prevalence of direct over inverse energy cascades in isotropic turbulence. Statistical irreversibility, a property of statistical ensembles in phase space related to entropy production, is connected to the dynamics of the energy cascade in physical space by considering the space locality of the energy fluxes and their relation to the local structure of the flow. A mechanism to explain the probabilistic prevalence of direct energy transfer is proposed based on the dynamics of the rate-of-strain tensor, which is identified as the most important source of statistical irreversibility in the energy cascade.
Fact-based Dialogue Generation with Convergent and Divergent Decoding

Fact-based dialogue generation is a task of generating a human-like response based on both dialogue context and factual texts. Various methods were proposed to focus on generating informative words that contain facts effectively. However, previous works implicitly assume a topic to be kept on a dialogue and usually converse passively, therefore the systems have a difficulty to generate diverse responses that provide meaningful information proactively. This paper proposes an end-to-end fact-based dialogue system augmented with the ability of convergent and divergent thinking over both context and facts, which can converse about the current topic or introduce a new topic. Specifically, our model incorporates a novel convergent and divergent decoding that can generate informative and diverse responses considering not only given inputs (context and facts) but also inputs-related topics. Both automatic and human evaluation results on DSTC7 dataset show that our model significantly outperforms state-of-the-art baselines, indicating that our model can generate more appropriate, informative, and diverse responses.
Contribution of entrainment and vertical plumes to the winter cascading of cold shelf waters in a deep lake

Observations made from vertical moorings of thermistors and current meters are used to analyze the relative contribution of the entrainment of overlying water and the cold plumes convecting from the surface to the cascade of dense shelf waters down the sides of Lake Geneva. Assuming a steady state and ignoring the effects of the Earth’s rotation, a simple model was introduced and 25 individual slugs of gravity currents were analyzed. The average drag coefficient, which includes the effects of friction on the slope and the interface between the gravity current and the ambient water, is found to be equal to CD = (4.6 ± 1.5) × 10−3. The volume flux in the gravity current increases with downslope distance—a consequence of both the entrainment of water into the gravity current and the supply of cold water from the plumes. Entrainment was found to be, on average, 1.9 ± 0.9 times the contribution of vertical plumes, suggesting that the latter may be important in the dynamics of the cascade.
Novel solutions toward high accuracy automatic brain tissue classification in young children

Accurate automatic classification of major tissue classes and the cerebrospinal fluid in pediatric MR scans of early childhood brains remains a challenge. A poor and highly variable grey matter and white matter contrast on T1-weighted MR scans of developing brains complicates the automatic categorization of voxels into major tissue classes using state-of-the-art classification methods (Partial Volume Estimation). Varying intensities across brain tissues and possible tissue artifacts further contribute to misclassification. In order to improve the accuracy of automatic detection of major tissue types and the cerebrospinal fluid in infant's brains within the age range from 10 days to 4.5 years, we propose a new classification method based on Kernel Fisher Discriminant Analysis (KFDA) for pattern recognition, combined with an objective structural similarity index (SSIM) for perceptual image quality assessment. The proposed method performs an optimal partitioning of the image domain into subdomains having different average intensity values and relatively homogeneous tissue intensity. In the KFDA-based framework, a complex non-linear structure of grey matter, white matter and cerebrospinal fluid intensity clusters in a 3D (T1W, T2W, PDW)-space is exploited to find an accurate classification. Based on Computer Vision hypothesis that the Human Visual System is an optimal structural information extractor, the SSIM finds a new role in the evaluation of the quality of classification. A comparison with the state-of-the-art Partial Volume Estimation method using the SSIM index demonstrates superior performance of the local KFDA-based algorithm in low contrast subdomains and more accurate detection of grey matter, white matter, and cerebrospinal fluid patterns in the brain volume.
History, Use, and Future of Microbial Insecticides

ALTHOUGH MICROBIAL INSECTICIDES account for <1% of the total insecticide market, microbial control of insect pests is gaining in importance. This is because of the improved performance and cost competitiveness of microbials in addition to increasing arthropod resistance to chemical insecticides, environmental concerns, and lack of development of new insecticidal chemistries. The use of microbial insecticides is growing at a rapid pace of 10–25% per year compared with chemical insecticides at 1–2% per year. Bacillus thuringiensis Berliner is primarily responsible for this growth. Microbial pest control agents including plants genetically engineered to produce pesticidal chemicals represent the largest number of recent registrations obtained by industry. Currently there are 20 microbial pesticides registered with the Environmental Protection Agency (EPA) (Table 1). Since 1984, EPA has reviewed 44 applications for small-scale field testing of genetically modified microbial pesticides. Since 1986, EPA has reviewed 40 applications for small-scale field testing of transgenic pesticidal plants.
The formation of trapped surfaces in the gravitational collapse of spherically symmetric scalar fields with a positive cosmological constant

Given spherically symmetric characteristic initial data for the Einstein-scalar field system with a positive cosmological constant, we provide a criterion, in terms of the dimensionless size and dimensionless renormalized mass content of an annular region of the data, for the formation of a future trapped surface. This corresponds to an extension of Christodoulou’s classical criterion by the inclusion of the cosmological term.
Percolation on the gene regulatory network

We consider a simplified model for gene regulation, where gene expression is regulated by transcription factors (TFs), which are single proteins or protein complexes. Proteins are in turn synthesised from expressed genes, creating a feedback loop of regulation. This leads to a directed bipartite network in which a link from a gene to a TF exists if the gene codes for a protein contributing to the TF, and a link from a TF to a gene exists if the TF regulates the expression of the gene. Both genes and TFs are modelled as binary variables, which indicate, respectively, whether a gene is expressed or not, and a TF is synthesised or not. We consider the scenario where for a TF to be synthesised, all of its contributing genes must be expressed. This results in an ‘AND’ gate logic for the dynamics of TFs. By adapting percolation theory to directed bipartite graphs, evolving according to the AND logic dynamics, we are able to determine the necessary conditions, in the network parameter space, under which bipartite networks can support a multiplicity of stable gene expression patterns, under noisy conditions, as required in stable cell types. In particular, the analysis reveals the possibility of a bi-stability region, where the extensive percolating cluster is or is not resilient to perturbations. This is remarkably different from the transition observed in standard percolation theory. Finally, we consider perturbations involving single node removal that mimic gene knockout experiments. Results reveal the strong dependence of the gene knockout cascade on the logic implemented in the underlying network dynamics, highlighting in particular that avalanche sizes cannot be easily related to gene-gene interaction networks.
coSense

We present coSense—the collaborative, fault-tolerant, and adaptive sensing middleware for the Internet-of-Things (IoT). By actively harnessing the greatest asset of the IoT, the sheer number of devices, coSense is able to provide easy data acquisition with quality-of-service-based data cleaning by combining unsupervised learning and information fusion. It can also greatly improve sensor accuracy and fault tolerance to produce measurements specifically tailored for modern data-driven IoT empowered applications. In this article, we focus on the general concepts behind coSense and evaluate the accuracy gain based on a real-world dataset.
Personality Sensing

Personality detection is an important task in psychology, as different personality traits are linked to different behaviours and real-life outcomes. Traditionally it involves filling out lengthy questionnaires, which is time-consuming, and may also be unreliable if respondents do not fully understand the questions or are not willing to honestly answer them. In this article, we propose a framework for objective personality detection that leverages humans’ physiological responses to external stimuli. We exemplify and evaluate the framework in a case study, where we expose subjects to affective image and video stimuli, and capture their physiological responses using non-invasive commercial-grade eye-tracking and skin conductivity sensors. These responses are then processed and used to build a machine learning classifier capable of accurately predicting a wide range of personality traits. We investigate and discuss the performance of various machine learning methods, the most and least accurately predicted traits, and also assess the importance of the different stimuli, features, and physiological signals. Our work demonstrates that personality traits can be accurately detected, suggesting the applicability of the proposed framework for robust personality detection and use by psychology practitioners and researchers, as well as designers of personalised interactive systems.
A User-centric Approach toward Resilient Frequency-regulating Wind Generators

Smart microgrids are rapidly being developed and deployed, even as concerns over their cyber-physical security are increasing. The high penetration of these power electronic-interfaced energy resources has resulted in weaker power grids and an increase in cyberattack surface. The implementation of frequency regulation in these new resources—particularly in wind generators—is on the rise. This article investigates how malicious controllable loads can threaten the integrity of frequency-regulating wind generators. Adopting a user-centric approach and benefiting from small-signal analyses, the article shows for the first time how these wind generators can be the target of attackers. Effective methods to enhance system resilience are sought by mitigating the attack risk in the extended end-users, wind generators. The article models and explores how proper tuning and design of the physical system can improve cyber-physical security. The work also extends the user-centric method to the physical layer of smart grids. Detailed time-domain simulations verify the results of the analyses.
PolicyFlow

Stability in social, technical, and financial systems, as well as the capacity of organizations to work across borders, requires consistency in public policy across jurisdictions. The diffusion of laws and regulations across political boundaries can reduce the tension that arises between innovation and consistency. Policy diffusion has been a topic of focus across the social sciences for several decades, but due to limitations of data and computational capacity, researchers have not taken a comprehensive and data-intensive look at the aggregate, cross-policy patterns of diffusion. This work combines visual analytics and text and network analyses to help understand how policies, as represented in digitized text, spread across states. As a result, our approach can quickly guide analysts to progressively gain insights into policy adoption data. We evaluate the effectiveness of our system via case studies with a real-world policy dataset and qualitative interviews with domain experts.
Progressive Disclosure

It is essential that users understand how algorithmic decisions are made, as we increasingly delegate important decisions to intelligent systems. Prior work has often taken a techno-centric approach, focusing on new computational techniques to support transparency. In contrast, this article employs empirical methods to better understand user reactions to transparent systems to motivate user-centric designs for transparent systems. We assess user reactions to transparency feedback in four studies of an emotional analytics system. In Study 1, users anticipated that a transparent system would perform better but unexpectedly retracted this evaluation after experience with the system. Study 2 offers an explanation for this paradox by showing that the benefits of transparency are context dependent. On the one hand, transparency can help users form a model of the underlying algorithm's operation. On the other hand, positive accuracy perceptions may be undermined when transparency reveals algorithmic errors. Study 3 explored real-time reactions to transparency. Results confirmed Study 2, in showing that users are both more likely to consult transparency information and to experience greater system insights when formulating a model of system operation. Study 4 used qualitative methods to explore real-time user reactions to motivate transparency design principles. Results again suggest that users may benefit from initially simplified feedback that hides potential system errors and assists users in building working heuristics about system operation. We use these findings to motivate new progressive disclosure principles for transparency in intelligent systems and discuss theoretical implications.