Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Reinforcement Learning and the Deadly Triad

We know from reinforcement learning theory that temporal difference learning can fail in certain cases. Sutton and Barto (2018) identify a deadly triad of function approximation, bootstrapping, and off-policy learning. When these three properties are combined, learning can diverge with the value estimates becoming unbounded. However, several algorithms successfully combine these three properties, which indicates that there is at least a partial gap in our understanding. In this work, we investigate the impact of the deadly triad in practice, in the context of a family of popular deep reinforcement learning models - deep Q-networks trained with experience replay - analysing how the components of this system play a role in the emergence of the deadly triad, and in the agent's performance
Photo Wake-Up: 3D Character Animation From a Single Photo

We present a method and application for animating a human subject from a single photo. E.g., the character can walk out, run, sit, or jump in 3D. The key contributions of this paper are: 1) an application of viewing and animating humans in single photos in 3D, 2) a novel 2D warping method to deform a posable template body model to fit the person's complex silhouette to create an animatable mesh, and 3) a method for handling partial self occlusions. We compare to state-of-the-art related methods and evaluate results with human studies. Further, we present an interactive interface that allows re-posing the person in 3D, and an augmented reality setup where the animated 3D person can emerge from the photo into the real world. We demonstrate the method on photos, posters, and art. The project page is at https://grail.cs. washington.edu/projects/wakeup/.
Lead and Cadmium Levels in Residential Soils of Lagos and Ibadan, Nigeria

Background. Lead and cadmium are components of paints sold in Nigeria. These heavy metals are associated with adverse neurological, cardiovascular and other human health effects. Objectives. This study assesses the levels of lead and cadmium in topsoil of residential areas in Lagos and Ibadan potentially resulting from painting of buildings. Methods. Samples were pre-treated prior to metal determination using atomic absorption spectrophotometry. Metal speciation was determined using modified Tessier sequential extraction. Soil characteristics were determined by standard methods. Soil contamination was assessed using contamination factor, geo-accumulation and pollution load indices. The United States Environmental Protection Agency integrated exposure uptake biokinetic (IEUBK) model was used to estimate and predict children's blood lead levels (BLL). Results. Lead and cadmium concentrations in residential areas in Lagos and Ibadan ranged from 1.56–419 mg/kg and not detected–2.8 mg/kg, respectively. Metal contamination factor and pollution load index were highest at the Lagos low income settlement. Results of IEUBK modelling showed that the Lagos low income settlement had the highest probability density for children between 1–7 years of age with an estimated BLL of >10 μg/dL. This population made up less than 0.01% of those within this age range. Conclusions. Lead and cadmium concentrations in soil around the residential buildings were higher than those in the control sample. Contamination factor and pollution load index showed significant contamination in average and low income settlements. These results suggested that there was accumulation of the metals in the soil, which can persist in the environment. This may pose serious health risks, especially to vulnerable groups such as children.
Effect of auditory-motor training on auditory processing of school children

ABSTRACT Objective To compare performance in Avaliação Simplificada do Processamento Auditivo Central and Scale of Auditory Behaviors scores before and after auditory and motor training. Methods Sample comprising 162 children aged 9 to 11 years and attending public schools in the city of São Paulo (SP), Brazil; 122 out of 162 children were allocated to one of three experimental groups: Multisensory; Auditory/Motor and Motor/Auditory. Experimental groups were submitted to 8 hours of auditory, visuospatial and motor stimulation over the course of 8 weeks. The remaining 40 children formed the Control Group and received no stimulation. Results Relation between child behavior as perceived by school teachers and auditory test responses revealed that the better the performance in auditory processing assessment, the higher the Scale of Auditory Behaviors scores. Conclusion Auditory and motor training led to improvements in auditory processing skills as rated by Avaliação Simplificada do Processamento Auditivo Central and Scale of Auditory Behaviors; this intervention model proved to be a good tool for use in school settings.
Effect of Temporomandibular Disorders on Quality of Life for Elderly Individuals

1. Dept. of Basic Health, City Hall of Porto Alegre, Porto Alegre, Rio Grande do Sul, Brazil 2. Dept. of Epidemiology, Federal University of Pelotas, Pelotas, Rio Grande do Sul, Brazil 3. Dept. Odontology, Luteran University of Brazil, Canoas, Rio Grande do Sul, Brazil 4. Federal University of Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil 5. The Graduate Program in Dentistry, Luteran University of Brasil, Canoas, Rio Grande do Sul, Brazil
Segmentation-Driven 6D Object Pose Estimation

The most recent trend in estimating the 6D pose of rigid objects has been to train deep networks to either directly regress the pose from the image or to predict the 2D locations of 3D keypoints, from which the pose can be obtained using a PnP algorithm. In both cases, the object is treated as a global entity, and a single pose estimate is computed. As a consequence, the resulting techniques can be vulnerable to large occlusions. In this paper, we introduce a segmentation-driven 6D pose estimation framework where each visible part of the objects contributes a local pose prediction in the form of 2D keypoint locations. We then use a predicted measure of confidence to combine these pose candidates into a robust set of 3D-to-2D correspondences, from which a reliable pose estimate can be obtained. We outperform the state-of-the-art on the challenging Occluded-LINEMOD and YCB-Video datasets, which is evidence that our approach deals well with multiple poorly-textured objects occluding each other. Furthermore, it relies on a simple enough architecture to achieve real-time performance.
Cost effectiveness of the cancer prevention program for carriers of the BRCA1/2 mutation

ABSTRACT OBJECTIVE To analyze the cost effectiveness of the diagnostic program for the germline mutation in BRCA1/2 genes and of preventative strategies for the relatives of patients diagnosed with ovarian cancer associated with this mutation. METHODS The study analyzed the cost effectiveness by developing an analysis of the Markov decision process from the perspective of the National Health System. The strategies compared reflect upon the adoption of genetic testing and preventative strategies for relatives or the usual care currently proposed. The incremental cost-effectiveness ratio was expressed in terms of cost per case avoided. The sensitivity analysis was performed in a univariate and deterministic manner. RESULTS The study showed increments for effectiveness and for costs when performing genetic testing and adopting prophylactic measures for family members. The incremental cost-effectiveness ratio was estimated at R$908.58 per case of cancer avoided, a figure considered lower than the study's cost-effectiveness threshold (R$7,543.50). CONCLUSIONS The program analyzed should be considered a cost-effective strategy for the national situation. Studies in various other countries have reached similar conclusions. One possible ramification of this research might the need to perform a budgetary-impact analysis of making the program one of the country's health policies.
Distinguishing extensive and intensive properties for meaningful geocomputation and mapping

ABSTRACT A most fundamental and far-reaching trait of geographic information is the distinction between extensive and intensive properties. In common understanding, originating in Physics and Chemistry, extensive properties increase with the size of their supporting objects, while intensive properties are independent of this size. It has long been recognized that the decision whether analytical and cartographic measures can be meaningfully applied depends on whether an attribute is considered intensive or extensive. For example, the choice of a map type as well as the application of basic geocomputational operations, such as spatial intersections, aggregations or algebraic operations such as sums and weighted averages, strongly depend on this semantic distinction. So far, however, the distinction can only be drawn in the head of an analyst. We still lack practical ways of automation for composing GIS workflows and to scale up mapping and geocomputation over many data sources, e.g. in statistical portals. In this article, we test a machine-learning model that is capable of labeling extensive/intensive region attributes with high accuracy based on simple characteristics extractable from geodata files. Furthermore, we propose an ontology pattern that captures central applicability constraints for automating data conversion and mapping using Semantic Web technology.
Satellite cell function, intramuscular inflammation and exercise in chronic kidney disease

ABSTRACT Skeletal muscle wasting is a common feature of chronic kidney disease (CKD) and is clinically relevant due to associations with quality of life, physical functioning, mortality and a number of comorbidities. Satellite cells (SCs) are a population of skeletal muscle progenitor cells responsible for accrual and maintenance of muscle mass by providing new nuclei to myofibres. Recent evidence from animal models and human studies indicates CKD may negatively affect SC abundance and function in response to stimuli such as exercise and damage. The aim of this review is to collate recent literature on the effect of CKD on SCs, with a particular focus on the myogenic response to exercise in this population. Exercise is widely recognized as important for the maintenance of healthy skeletal muscle mass and is increasingly advocated in the care of a number of chronic conditions. Therefore a greater understanding of the impact of uraemia upon SCs and the possible altered myogenic response in CKD is required to inform strategies to prevent uraemic cachexia.
OMNIA Faster R-CNN: Detection in the wild through dataset merging and soft distillation

Object detectors tend to perform poorly in new or open domains, and require exhaustive yet costly annotations from fully labeled datasets. We aim at benefiting from several datasets with different categories but without additional labelling, not only to increase the number of categories detected, but also to take advantage from transfer learning and to enhance domain independence. Our dataset merging procedure starts with training several initial Faster R-CNN on the different datasets while considering the complementary datasets' images for domain adaptation. Similarly to self-training methods, the predictions of these initial detectors mitigate the missing annotations on the complementary datasets. The final OMNIA Faster R-CNN is trained with all categories on the union of the datasets enriched by predictions. The joint training handles unsafe targets with a new classification loss called SoftSig in a softly supervised way. Experimental results show that in the case of fashion detection for images in the wild, merging Modanet with COCO increases the final performance from 45.5% to 57.4%. Applying our soft distillation to the task of detection with domain shift on Cityscapes enables to beat the state-of-the-art by 5.3 points. We hope that our methodology could unlock object detection for real-world applications without immense datasets.
MMA Training: Direct Input Space Margin Maximization through Adversarial Training

We study adversarial robustness of neural networks from a margin maximization perspective, where margins are defined as the distances from inputs to a classifier's decision boundary. Our study shows that maximizing margins can be achieved by minimizing the adversarial loss on the decision boundary at the ``shortest successful perturbation'', demonstrating a close connection between adversarial losses and the margins. We propose Max-Margin Adversarial (MMA) training to directly maximize the margins to achieve adversarial robustness. Instead of adversarial training with a fixed $\eps$, MMA offers an improvement by enabling adaptive selection of the ``correct'' $\eps$ as the margin individually for each datapoint. In addition, we rigorously analyze adversarial training with the perspective of margin maximization, and provide an alternative interpretation for adversarial training, maximizing either a lower or an upper bound of the margins. Our experiments empirically confirm our theory and demonstrate MMA training's efficacy on the MNIST and CIFAR10 datasets w.r. t. $\ell_\infty$ and $\ell_2$ robustness.
Dynamic Spatio-temporal Graph-based CNNs for Traffic Prediction

Forecasting future traffic flows from previous ones is a challenging problem because of their complex and dynamic nature of spatio-temporal structures. Most existing graph-based CNNs attempt to capture the static relations while largely neglecting the dynamics underlying sequential data. In this paper, we present dynamic spatio-temporal graph-based CNNs (DST-GCNNs) by learning expressive features to represent spatio-temporal structures and predict future traffic flows from surveillance video data. In particular, DST-GCNN is a two stream network. In the flow prediction stream, we present a novel graph-based spatio-temporal convolutional layer to extract features from a graph representation of traffic flows. Then several such layers are stacked together to predict future flows over time. Meanwhile, the relations between traffic flows in the graph are often time variant as the traffic condition changes over time. To capture the graph dynamics, we use the graph prediction stream to predict the dynamic graph structures, and the predicted structures are fed into the flow prediction stream. Experiments on real datasets demonstrate that the proposed model achieves competitive performances compared with the other state-of-the-art methods.
Clinical and Biochemical Features in a Patient With Mitochondrial Fission Factor Gene Alteration

Mitochondrial Fission Factor (MFF) is part of a protein complex that promotes mitochondria and peroxisome fission. Hitherto, only 5 patients have been reported harboring mutations in MFF, all of them with the clinical features of a very early onset Leigh-like encephalopathy. We report on an 11-year-old boy with epileptic encephalopathy. He presented with neurological regression, epileptic myoclonic seizures, severe intellectual disability, microcephaly, tetraparesis, optic atrophy, and ophthalmoplegia. Brain MRI pattern was compatible with Leigh syndrome. NGS-based analysis of a gene panel for mitochondrial disorders revealed a homozygous c.892C>T (p. Arg298*) in the MFF gene. Fluorescence staining detected abnormal morphology of mitochondria and peroxisomes in fibroblasts from the patient; a strong reduction in MFF protein levels and the presence of truncated forms were observed. No biochemical alterations denoting peroxisomal disorders were found. As reported in other disorders affecting the dynamics of intracellular organelles, our patient showed clinical features suggesting both mitochondrial and peroxisomal impairment. High levels of lactate in our case suggested an involvement of the energetic metabolism but without clear respiratory chain deficiency, while biomarkers of peroxisomal dysfunction were normal. We confirm that MFF mutations are associated with epileptic encephalopathy with Leigh-like MRI pattern.
Evaluating cytotoxic effects of recombinant fragaceatoxin C pore forming toxin against AML cell lines

Objective(s): Current therapeutic strategies for cancer are associated with side effects and lack of specificity in treatments. Biological therapies including monoclonal antibodies and immune effectors have been the subject of multiple research projects. Pore-forming proteins may become the other biological strategy to overcome the problems associated with current treatments. But detailed mechanisms of their action on target membranes remained to be elucidated. We aimed to study the cytotoxic effects of recombinant form of fragaceatoxin C on AML cell lines HL-60 and KG-1. Materials and Methods: We cloned the FraC gene in pET-28a (+) bacterial expression vector and the expressed recombinant FraC protein was purified by affinity chromatography. Then, cytotoxic effects of the recombinant protein were examined on two AML cell lines, HL-60 and KG-1. Effects of serum and calcium ion were explored by hemolysis assay in more details. Results: Our results showed that the recombinant C-terminal polyhistidine-tagged FraC protein has potent cytotoxic effects on both AML cell lines, with IC50=5.6, and 4.6 µg. ml-1 for HL-60 and KG-1 cells, respectively. Serum showed dose-dependent and also time-dependent inhibitory effects on the hemolytic and cytotoxic activities of the FraC protein. Pre-incubation of the toxin with different concentrations of calcium ion also inhibited hemolytic activity of FraC toxin. Conclusion: Results of the present study showed that FraC has potential anti-tumor effects. By detailed investigation of the inhibition mechanism of serum and calcium effects in the future, it can be possible to design target sites for clinical applications of the toxin.
Cluster Editing in Multi-Layer and Temporal Graphs

Motivated by the recent rapid growth of research for algorithms to cluster multi-layer and temporal graphs, we study extensions of the classical Cluster Editing problem. In Multi-Layer Cluster Editing we receive a set of graphs on the same vertex set, called layers and aim to transform all layers into cluster graphs (disjoint unions of cliques) that differ only slightly. More specifically, we want to mark at most d vertices and to transform each layer into a cluster graph using at most k edge additions or deletions per layer so that, if we remove the marked vertices, we obtain the same cluster graph in all layers. In Temporal Cluster Editing we receive a sequence of layers and we want to transform each layer into a cluster graph so that consecutive layers differ only slightly. That is, we want to transform each layer into a cluster graph with at most k edge additions or deletions and to mark a distinct set of d vertices in each layer so that each two consecutive layers are the same after removing the vertices marked in the first of the two layers. We study the combinatorial structure of the two problems via their parameterized complexity with respect to the parameters d and k, among others. Despite the similar definition, the two problems behave quite differently: In particular, Multi-Layer Cluster Editing is fixed-parameter tractable with running time k^{O(k + d)} s^{O(1)} for inputs of size s, whereas Temporal Cluster Editing is W[1]-hard with respect to k even if d = 3.
GSM-RF Channel Characterization Using a Wideband Subspace Sensing Mechanism for Cognitive Radio Networks

In this paper, we examine a spectrum sharing opportunities over the existing Global System of Mobile Communication (GSM) networks, by identifying the unused channels at a specific time and location. For this purpose, we propose a wideband spectrum sensing mechanism to analyze the status of 51 channels at once, belonging to the 10 MHz bandwidth centered at the frequency 945 MHz, in four different areas. We propose a subspace based spectral estimation mechanism, adapted to deal with real measurements. The process begins with data collection using Secondary User (SU) device enabled with Software Defined Radio (SDR) technology, configured to operate in the GSM band. Obtained samples are used then to feed the sensing mechanism. Spectral analysis is delivered to estimate power density peaks and corresponding frequencies. Decision making phase brings together power thresholding technique and GSM control channel decoding to identify idle and busy channels. Experiments are evaluated using detection and false alarm probabilities emulated via Receiver Operating Characteristic (ROC) curves. Obtained performances show better detection accuracy and robustness against variant noise/fading effects, when using our mechanism compared to Energy Detection (ED) based ones as Welch method, and Beamforming based ones as Minimum Variance Distortionless Response (MVDR) method. Occupancy results exhibit considerable potential of secondary use in GSM based primary network.
Melanopsin Retinal Ganglion Cells and Pupil: Clinical Implications for Neuro-Ophthalmology

Melanopsin retinal ganglion cells (mRGCs) are intrinsically photosensitive RGCs that mediate many relevant non-image forming functions of the eye, including the pupillary light reflex, through the projections to the olivary pretectal nucleus. In particular, the post-illumination pupil response (PIPR), as evaluated by chromatic pupillometry, can be used as a reliable marker of mRGC function in vivo. In the last years, pupillometry has become a promising tool to assess mRGC dysfunction in various neurological and neuro-ophthalmological conditions. In this review we will present the most relevant findings of pupillometric studies in glaucoma, hereditary optic neuropathies, ischemic optic neuropathies, idiopathic intracranial hypertension, multiple sclerosis, Parkinson's disease, and mood disorders. The use of PIPR as a marker for mRGC function is also proposed for other neurodegenerative disorders in which circadian dysfunction is documented.
Efficient and Robust Machine Learning for Real-World Systems

While machine learning is traditionally a resource intensive task, embedded systems, autonomous navigation and the vision of the Internet-of-Things fuel the interest in resource efficient approaches. These approaches require a carefully chosen trade-off between performance and resource consumption in terms of computation and energy. On top of this, it is crucial to treat uncertainty in a consistent manner in all but the simplest applications of machine learning systems. In particular, a desideratum for any real-world system is to be robust in the presence of outliers and corrupted data, as well as being `aware' of its limits, i.e.\ the system should maintain and provide an uncertainty estimate over its own predictions. These complex demands are among the major challenges in current machine learning research and key to ensure a smooth transition of machine learning technology into every day's applications. In this article, we provide an overview of the current state of the art of machine learning techniques facilitating these real-world requirements. First we provide a comprehensive review of resource-efficiency in deep neural networks with focus on techniques for model size reduction, compression and reduced precision. These techniques can be applied during training or as post-processing and are widely used to reduce both computational complexity and memory footprint. As most (practical) neural networks are limited in their ways to treat uncertainty, we contrast them with probabilistic graphical models, which readily serve these desiderata by means of probabilistic inference. In that way, we provide an extensive overview of the current state-of-the-art of robust and efficient machine learning for real-world systems.