Miracle-dz/UniFAR-FTUs huggingface dataset:
Combining Evolutionary and Adaptive Control Strategies for Quadruped Robotic Locomotion

In traditional robotics, model-based controllers are usually needed in order to bring a robotic plant to the next desired state, but they present critical issues when the dimensionality of the control problem increases and disturbances from the external environment affect the system behavior, in particular during locomotion tasks. It is generally accepted that the motion control of quadruped animals is performed by neural circuits located in the spinal cord that act as a Central Pattern Generator and can generate appropriate locomotion patterns. This is thought to be the result of evolutionary processes that have optimized this network. On top of this, fine motor control is learned during the lifetime of the animal thanks to the plastic connections of the cerebellum that provide descending corrective inputs. This research aims at understanding and identifying the possible advantages of using learning during an evolution-inspired optimization for finding the best locomotion patterns in a robotic locomotion task. Accordingly, we propose a comparative study between two bio-inspired control architectures for quadruped legged robots where learning takes place either during the evolutionary search or only after that. The evolutionary process is carried out in a simulated environment, on a quadruped legged robot. To verify the possibility of overcoming the reality gap, the performance of both systems has been analyzed by changing the robot dynamics and its interaction with the external environment. Results show better performance metrics for the robotic agent whose locomotion method has been discovered by applying the adaptive module during the evolutionary exploration for the locomotion trajectories. Even when the motion dynamics and the interaction with the environment is altered, the locomotion patterns found on the learning robotic system are more stable, both in the joint and in the task space.
The adhesion-GPCR BAI1 shapes dendritic arbors via Bcr-mediated RhoA activation causing late growth arrest

Dendritic arbor architecture profoundly impacts neuronal connectivity and function, and aberrant dendritic morphology characterizes neuropsychiatric disorders. Here, we identify the adhesion-GPCR BAI1 as an important regulator of dendritic arborization. BAI1 loss from mouse or rat hippocampal neurons causes dendritic hypertrophy, whereas BAI1 overexpression precipitates dendrite retraction. These defects specifically manifest as dendrites transition from growth to stability. BAI1-mediated growth arrest is independent of its Rac1-dependent synaptogenic function. Instead, BAI1 couples to the small GTPase RhoA, driving late RhoA activation in dendrites coincident with growth arrest. BAI1 loss lowers RhoA activation and uncouples it from dendrite dynamics, causing overgrowth. None of BAI1’s known downstream effectors mediates BAI1-dependent growth arrest. Rather, BAI1 associates with the Rho-GTPase regulatory protein Bcr late in development and stimulates its cryptic RhoA-GEF activity, which functions together with its Rac1-GAP activity to terminate arborization. Our results reveal a late-acting signaling pathway mediating a key transition in dendrite development.
On the Theory of a Non-Periodic Quasilattice Associated with the Icosahedral Group

A quasilattice in the euclidean space E3 is generated by dualization of a hexagrid. The construction is based on a general theory of periodic and non-periodic space filling by projection from hypergrids. Global and local properties of the quasilattice are discussed, including the structure factor, the form and packing of the cells, and the point symmetry. The quasilattice is expected to give a description of quasicrystals without periodic order which have recently been found in experiments.
Visual Discrimination, Serial Reversal, and Extinction Learning in the mdx Mouse

Duchenne muscular dystrophy (DMD) is the most common form of muscular dystrophy and the most common neuromuscular disorder. In addition to neuromuscular consequences, some individuals with DMD experience global intellectual dysfunction and executive dysfunction of unknown mechanistic origin. The cognitive profile of the mdx mouse, the most commonly used mouse model of DMD, has been incompletely characterized and has never been assessed using the touchscreen operant conditioning paradigm. The touchscreen paradigm allows the use of protocols that are virtually identical to those used in human cognitive testing and may, therefore, provide the most translational paradigm for quantifying mouse cognitive function. In the present study, we used the touchscreen paradigm to assess the effects of the mdx mutation on visual discrimination learning, serial reversal learning, and extinction learning. To enable measuring task-dependent learning and memory processes while holding demands on sensory-driven information processing constant, we developed equally salient visual stimuli and used them on all experimental stages. Acquisition of the initial pairwise visual discrimination was facilitated in mdx mice relative to wildtype littermates; this effect was not explained by genotypic differences in impulsivity, motivation, or motor deficits. The mdx mutation had no effect on serial reversal or extinction learning. Together, findings from this study and previous studies suggest that mdx effects on cognitive function are task-specific and may be influenced by discrimination type (spatial, visual), reward type (food, escape from a non-preferred environment), sex, and genetic background.
Identification of Important Features in Mobile Health Applications for Surgical Site Infection Surveillance.

Background: A landscape analysis of mobile health (mHealth) applications and published literature related to their use in surgical site infection (SSI) detection and surveillance was conducted by the Assessing Surgical Site Infection Surveillance Technologies (ASSIST) investigators. Methods: The literature review focused on post-discharge SSI detection or tracking by caregivers or patients using mHealth technology. This report is unique in its review across both commercial and research-based mHealth apps. Apps designed for long-term wound tracking and those focused on care coordination and scheduling were excluded. A structured evaluation framework was used to assess the operational, technical, and policy features of the apps. Results: Of the 10 apps evaluated, only two were in full clinical use. A variety of data were captured by the apps including wound photographs (eight apps), wound measurements (three apps), dressing assessments (two apps), physical activity metrics (three apps), medication adherence (three apps) as well as structured surveys, signs, and symptoms. Free-text responses were permitted by at least two apps. The extent of integration with the native electronic health record system was variable. Conclusion: The examination of rapidly evolving technologies is challenged by lack of standard evaluative methods, such as those more commonly used in clinical research. This review is unique in its application of a structured evaluation framework across both commercial and research-based mHealth apps.
Miniaturized Near-Infrared (MicroNIR) Spectrometer in Plastic Waste Sorting

Valorisation of the urban plastic waste in high-quality recyclates is an imperative challenge in the new paradigm of the circular economy. In this scenario, a key role in the improvement of the recycling process is exerted by the optimization of waste sorting. In spite of the enormous developments achieved in the field of automated sorting systems, the quest for the reduction of cross-contamination of incompatible polymers as well as a rapid and punctual sorting of the unmatched polymers has not been sufficiently developed. In this paper, we demonstrate that a miniaturized handheld near-infrared (NIR) spectrometer can be used to successfully fingerprint and classify different plastic polymers. The investigated urban plastic waste comprised polyethylene (PE), polypropylene (PP), poly(vinyl chloride) (PVC), poly(ethylene terephthalate) (PET), and poly(styrene) (PS), collected directly in a recycling plastic waste plant, without any kind of sample washing or treatment. The application of unsupervised and supervised chemometric tools such as principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) on the NIR dataset resulted in a complete classification of the polymer classes. In addition, several kinds of PET (clear, blue, coloured, opaque, and boxes) were correctly classified as PET class, and PE samples with different branching degrees were properly separated.
LANTERN: learn analysis transform network for dynamic magnetic resonance imaging with small dataset

This paper proposes to learn analysis transform network for dynamic magnetic resonance imaging (LANTERN) with small dataset. Integrating the strength of CS-MRI and deep learning, the proposed framework is highlighted in three components: (i) The spatial and temporal domains are sparsely constrained by using adaptively trained CNN. (ii) We introduce an end-to-end framework to learn the parameters in LANTERN to solve the difficulty of parameter selection in traditional methods. (iii) Compared to existing deep learning reconstruction methods, our reconstruction accuracy is better when the amount of data is limited. Our model is able to fully exploit the redundancy in spatial and temporal of dynamic MR images. We performed quantitative and qualitative analysis of cardiac datasets at different acceleration factors (2x-11x) and different undersampling modes. In comparison with state-of-the-art methods, extensive experiments show that our method achieves consistent better reconstruction performance on the MRI reconstruction in terms of three quantitative metrics (PSNR, SSIM and HFEN) under different undersamling patterns and acceleration factors.
A Fast Method to Calculate Hitting Time Distribution for a Random Walk on Connected Undirected Graph

With the advent of increasingly large graphs, we need to find a quick and reliable method to measure the distance and similarity between any pair of nodes on a large graph. One way to measure the distance is by performing random walks on a graph, and people already designed plenty of algorithms to accomplish this goal. However, most of the implementations of random walk algorithms are computationally expensive due to the use of Monte Carlo simulations which could be pretty time-consuming. Here, we propose an alternative measure of the distance between any pair of nodes on a connected and undirected graph using the notion of hitting time for a random walk. We also give an analytical solution to the hitting time distribution of a random walk on a graph. This analytical method, which can be conveniently implemented using SciPy linear algebra packages, is more time-saving to run and yields more accurate results than that obtained via Monte Carlo simulations. It is further noted that the hitting times also provide a glimpse of the community structure of a graph. This algorithm for measuring the distance between any pair of nodes is specifically devised to measure the influence of a fraudulent user upon all the other users that co-occur in the same social network. We employ our algorithm to weed out potential fraudsters from among tens of thousands of online retail users.
A Flexible Neural Renderer for Material Visualization

Photo realism in computer generated imagery is crucially dependent on how well an artist is able to recreate real-world materials in the scene. The workflow for material modeling and editing typically involves manual tweaking of material parameters and uses a standard path tracing engine for visual feedback. A lot of time may be spent in iterative selection and rendering of materials at an appropriate quality. In this work, we propose a convolutional neural network that quickly generates high-quality ray traced material visualizations on a shaderball. Our novel architecture allows for control over environment lighting which assists in material selection and also provides the ability to render spatially-varying materials. Comparison with state-of-the-art denoising and neural rendering techniques suggests that our neural renderer performs faster and better. We provide an interactive visualization tool and an extensive dataset to foster further research in this area.
Priority Setting for the Management of Chemicals Using the Globally Harmonized System and Multivariate Analysis: Use of the Mahalanobis-Taguchi System

This study aims to provide a new methodology using the Globally Harmonized System (GHS) and the Mahalanobis–Taguchi System (MTS) that can be used to assess the overall hazard of a chemical using GHS information. Previously, hazardous chemicals were designated and managed by the Chemical Management Act, but many more chemicals are now in use. Damage prediction modeling programs predict the extent of damage and proactively manage high-risk chemicals, but the lack of physical and chemical characterization information relating to chemicals has limitations that cannot be modeled. To overcome such limitations, a new method of chemical management prioritization was developed using the GHS and Mahalanobis–Taguchi System (MTS). For effective management, the risk of a chemical can be ranked according to a comprehensive risk assessment and calculated through multivariate analysis using the GHS. Relative hazards are then identified using MTS multivariate analysis with GHS information, even when there is insufficient information about the chemical’s characteristics, and the method can be applied to a large number of different chemicals.
Detecting Toxicity in News Articles: Application to Bulgarian

Online media aim for reaching ever bigger audience and for attracting ever longer attention span. This competition creates an environment that rewards sensational, fake, and toxic news. To help limit their spread and impact, we propose and develop a news toxicity detector that can recognize various types of toxic content. While previous research primarily focused on English, here we target Bulgarian. We created a new dataset by crawling a website that for five years has been collecting Bulgarian news articles that were manually categorized into eight toxicity groups. Then we trained a multi-class classifier with nine categories: eight toxic and one non-toxic. We experimented with different representations based on ElMo, BERT, and XLM, as well as with a variety of domain-specific features. Due to the small size of our dataset, we created a separate model for each feature type, and we ultimately combined these models into a meta-classifier. The evaluation results show an accuracy of 59.0% and a macro-F1 score of 39.7%, which represent sizable improvements over the majority-class baseline (Acc=30.3%, macro-F1=5.2%).
A Comparison of CNN and Classic Features for Image Retrieval

Feature detectors and descriptors have been successfully used for various computer vision tasks, such as video object tracking and content-based image retrieval. Many methods use image gradients in different stages of the detection-description pipeline to describe local image structures. Recently, some, or all, of these stages have been replaced by convolutional neural networks (CNNs), in order to increase their performance. A detector is defined as a selection problem, which makes it more challenging to implement as a CNN. They are therefore generally defined as regressors, converting input images to score maps and keypoints can be selected with non-maximum suppression. This paper discusses and compares several recent methods that use CNNs for keypoint detection. Experiments are performed both on the CNN based approaches, as well as a selection of conventional methods. In addition to qualitative measures defined on keypoints and descriptors, the bag-of-words (BoW) model is used to implement an image retrieval application, in order to determine how the methods perform in practice. The results show that each type of features are best in different contexts.
Evaluating and documenting affect in the art museum

The importance of affect in free-choice learning settings such as museums, and the ability of artworks to elicit affective responses, is widely recognized yet remains absent from museum documentation. In order to be integrated into museum information systems, the affective experiences that visitors have in museums must first be researched and documented, and as such, methods must be developed that seek to accurately and holistically capture the affective elements of these experiences while maintaining the agency of the object, context, and viewer. This paper explores the methods used as part of a larger study on affective metadata for art object experiences [1], and presents a set of mixed-method procedural tools for researching affective experiences in the art museum setting. This discussion focuses on the current state of methods for researching felt experiences with art objects, as well as the potential and limitations of the approach taken in this study.
Computing ground states of Bose-Einstein Condensates with higher order interaction via a regularized density function formulation

We propose and analyze a new numerical method for computing the ground state of the modified Gross-Pitaevskii equation for modeling the Bose-Einstein condensate with a higher order interaction by adapting the density function formulation and the accelerated projected gradient method. By reformulating the energy functional $E(\phi)$ with $\phi$, the wave function, in terms of the density $\rho=|\phi|^2$, the original non-convex minimization problem for defining the ground state is then reformulated to a convex minimization problem. In order to overcome the semi-smoothness of the function $\sqrt{\rho}$ in the kinetic energy part, a regularization is introduced with a small parameter $0<\varepsilon\ll1$. Convergence of the regularization is established when $\varepsilon\to0$. The regularized convex optimization problem is discretized by the second order finite difference method. The convergence rates in terms of the density and energy of the discretization are established. The accelerated projected gradient method is adapted for solving the discretized optimization problem. Numerical results are reported to demonstrate the efficiency and accuracy of the proposed numerical method. Our results show that the proposed method is much more efficient than the existing methods in the literature, especially in the strong interaction regime.
Safety and antioxidant potential of traditional thai poly-herbal tea “phy-blica-d” used as a rejuvenation formula

Background: The rising popularity of phytonutrient consumption may be due to a vast number of scientific studies that have revealed their health benefits; however, concerns regarding the medical safety of herbal-based products are increasing. Phy-Blica-O is Phyllanthus emblica- based herbal tea used in Thai traditional medicine as a rejuvenating remedy. However, its consumption has been limited due to its strong bitter taste with unpleasant odor. Objective: The objective of this study is to evaluate the safety and antioxidant potential of Phy-Blica-D, the modified formula of Phy-Blica-O which gave high sensory acceptability scores. Materials and Methods: Subacute toxicity studies of Phy-Blica-D infusion was conducted by repeated oral administration of the extract at doses of 5, 50, and 300 mg/kg/day in Sprague-Dawley rats. Results: The formula exhibited antioxidant activity with an IC50of 0.243 ± 0.006, 0.486 ± 0.002 and 0.108 ± 0.004 mg/mL using 1,1-diphenyl-2-picrylhydrazyl, 2,20-azino-bis-3-ethylbenzthiazoline-6-sulfonic acid and metal chelating assays, respectively. There was no significant treatment-related toxicity as indicated by clinical signs, body weight, food consumption, serum biochemical and hematological parameters, organ weight, and histopathological examination of the animals treated with Phy-Blica-D infusion. These results suggest that the oral no-observed-adverse-effect-level of Phy-Blica-D extract is >300 mg/kg body weight/day, or ~3.7 L/kg body weight/day for both sexes. The calculated human equivalent dose value is 48.39 mg/kg/day, or ~600 mL/kg body weight/day. There were no target organs affected. Conclusion: This study demonstrates that Phy-Blica-D infusion can be regarded as safe and could potentially be used as a functional ingredient to reduce oxidative stress in non-communicable diseases.
Improvability Through Semi-Supervised Learning: A Survey of Theoretical Results

Semi-supervised learning is a setting in which one has labeled and unlabeled data available. In this survey we explore different types of theoretical results when one uses unlabeled data in classification and regression tasks. Most methods that use unlabeled data rely on certain assumptions about the data distribution. When those assumptions are not met in reality, including unlabeled data may actually decrease performance. Studying such methods, it therefore is particularly important to have an understanding of the underlying theory. In this review we gather results about the possible gains one can achieve when using semi-supervised learning as well as results about the limits of such methods. More precisely, this review collects the answers to the following questions: What are, in terms of improving supervised methods, the limits of semi-supervised learning? What are the assumptions of different methods? What can we achieve if the assumptions are true? Finally, we also discuss the biggest bottleneck of semi-supervised learning, namely the assumptions they make.
Towards Practical Driver Cognitive Load Detection Based on Visual Attention Information

Towards Practical Driver Cognitive Load Detection Based on Visual Attention Information Cheng Chen Liu Master of Applied Science Graduate Department of Electrical and Computer Engineering University of Toronto 2017 With growing popularities of intelligent in-vehicle technologies, monitoring of driver cognitive load is becoming more important for both safety and comfort concerns. This task is recognized to be challenging due to limited prior knowledge. This thesis explores the feasibility of classifying driver cognitive load levels based on visual attention features. First, we contribute a dataset collected from 37 experienced drivers. The collection process focuses on eliciting three levels of cognitive load effectively. The resulting dataset consists a total of eight measurements, gathered from visual, vehicular, physiological sensors and subjective-questionnaires. Next, we focus on the eye-tracking modality and propose meta-features for capturing variations in visual attention intensity and direction. Then, five machine learning algorithms are applied for subject-independent classification. Issues arose from the machine learning workflow, such as evaluation bias, are examined. The most promising algorithm (Random Forest) achieves 70.3% accuracy on classifying between high and low cognitive