Miracle-dz/UniFAR-FTUs huggingface dataset:
Precise Extraction of Anatomical Segment Orientations from the Kinect One Sensor

— The recent advances for the Kinect One, a low-cost marker-less motion capture sensor, and its human motion tracking algorithm have led to the development of applications on the most diversified fields such as clinical rehabilitation, biomechanical analysis and entertainment. The tracking algorithm estimates efficiently the three-dimensional position of twenty-five anatomical joints which all together constitute a human skeleton. However, for the simulation of a human biomechanical model, the orientations it provides to guide each anatomical segment are poorly implemented and the documentation available is inconsiderable. For the purpose of obtaining a better estimation for the anatomical segments’ orientations, six techniques based on vector orthogonalization were implemented. To validate the orientations estimated by each method, twenty-eight subjects performed a set of ten movements that were recorded by the Kinect One sensor and by a marker-based motion capture system that is considered the reference. Discrepancies between orientations estimated by the marker-based system and by each technique used on the marker-less system were graphically compared and were evaluated using the Pearson’s correlation coefficient. The results obtained show that the six techniques implemented estimated with good to very good correlation the orientation around one axis of rotation while for the orientations around the other axes of rotation, the performance was different between the different techniques and movements. These findings encourage the development of a parametrized model to predict the optimal orientation of each anatomical segment taking into account the position of the human joints estimated by the Kinect One sensor.
Traffic simulation for interactive applications

— The road traffic simulation is a crucial component for interactive applications, entertainment and educational purposes, such as the driving simulators. The objective is to create realistic and plausible situations, where the human player can interact with other agents while driving his virtual vehicle. The key to a realistic motorized vehicle simulation is the sub-microscopic model, where the decision process is based on the physical, mechanical characteristics of the vehicle and the human driver mental processes. This model also allows the interaction with physics simulation engines to improve the realism of the simulation. The actions of the vehicles and pedestrians are based on steering behaviors specified by Craig W. Reynolds and adapted for the dynamics and physics properties of the agents.
Impact of 24 Systemic Cryotherapy Treatments on the Rheological and Morphological Properties of Blood in Healthy Men

Cryotherapy and cryostimulation have recently become popular in the prevention and treatment of musculoskeletal system overloads in sports disciplines and sports medicine, as well as in biological regeneration of healthy persons and in increasing the immunity (Szygula et al 2014). The aim of this paper was to assess the impact of a series of whole body cryotherapy (WBC) treatments on the rheological and morphological properties of blood in healthy men.
Graph-based Point Cloud Compression

Recently, 3D visual representation models such as light fields and point clouds are becoming popular due to their capability to represent the real world in a more complete and immersive way, paving the road for new and more advanced visual experiences. The point cloud representation model is able to efficiently represent the surface of objects/scenes by means of a set of 3D points and associated attributes and is increasingly being used from autonomous cars to augmented reality. Emerging advanced imaging sensors have made easier to perform richer and denser point cloud acquisitions, notably with millions of points, making impossible to store and transmit these very high amounts of data without appropriate coding. This bottleneck has raised the need for efficient point cloud coding solutions in order more immersive visual experiences and better quality of experience (QoE) may be offered to the users. In this context, this paper proposes an efficient lossy coding solution for the geometry of static point clouds. The proposed coding solution uses an octreebased approach for a base layer and a graph-based transform approach for an enhancement layer where an Inter-layer residual is coded. The performance assessment shows very significant compression gains regarding the state-of-the-art, especially for the most relevant lower and medium rates.
A Deep Learning based Fast Signed Distance Map Generation

Signed distance map (SDM) is a common representation of surfaces in medical image analysis and machine learning. The computational complexity of SDM for 3D parametric shapes is often a bottleneck in many applications, thus limiting their interest. In this paper, we propose a learning based SDM generation neural network which is demonstrated on a tridimensional cochlea shape model parameterized by 4 shape parameters. The proposed SDM Neural Network generates a cochlea signed distance map depending on four input parameters and we show that the deep learning approach leads to a 60 fold improvement in the time of computation compared to more classical SDM generation methods. Therefore, the proposed approach achieves a good trade-off between accuracy and efficiency.
Can RoBERTa Reason? A Systematic Approach to Probe Logical Reasoning in Language Models

Humans can map natural language into a logical representation that is robust to linguistic variations and useful for reasoning. While pre-trained language models (LM) have dramatically improved performance on commonsense reasoning benchmarks, it remains unclear whether they share this ability to reason consistently amid linguistic variations. Prior studies of LMs have found speciﬁc deﬁcits but failed to provide a comprehensive or systematic means of understanding whether LMs deﬁcits are due to commonsense reasoning or linguistic variation. In this work, we address this gap and explore the LM’s ability to reason by developing a general procedure that allows the systematic creation of logically-equivalent, but syntactically-diﬀerent statements. To demonstrate the power of our approach, we construct a large corpus of 14,400 predictive tasks that evaluate both the abstract reasoning abilities and robustness of LMs. We ﬁnd that despite the current success of large LMs on commonsense benchmarks, their performance on these tasks is no better than random guessing, heavily dependent on biases, and fails even after ﬁne-tuning. We have released our code 1 and data will be released upon acceptance.
A RETROSPECTIVE ANALYSIS OF DENTAL PROCEDURES PERFORMED UNDER GENERAL ANESTHESIA

Objective: This study is an evaluation of the records of the patients who underwent dental treatment under sedation/general anesthesia. Method: The data of 549 patients who underwent dental treatment under sedation/general anesthesia in Samsun Oral and Dental Health Hospital between 1st January 2013 and 31st December 2013 were evaluated retrospectively. Demographic data of the patients, indications for the dental treatment under anesthesia, type of treatment, anesthetic method, duration of surgery, duration of anesthesia, intubation method, duration of recovery and complications caused by anesthesia were recorded. Results: Dental treatment under sedation/general anesthesia was mostly performed on pediatric patients (49.4%) and on patients with mental illnesses (26.9%) due to inadequate cooperation. In 76% of the patients, dental treatment was performed under general anesthesia, in 17.1%, under deep sedation and in 6.9%, under conscious sedation. The most common dental treatments performed under anesthesia were tooth extraction (62.3%), filling therapy (37.2%) and compacted tooth extraction (20.9%). It was observed that the recovery duration from anesthesia in mentally-disabled patients was longer than mentally normal patients, and it was statistically significant. Conclusion: Due to the lack of cooperation, dental treatment under general anesthesia is mostly preferred in pediatric or in mentally-disabled patients. Against all negative aspects, dental treatment under general anesthesia is inevitable for many patients, and its outcomes are satisfactory.
Subspace Segmentation Problem

Segmenting a mixture of linear subspaces in the presence of noise and outliers is a challenging problem which finds direct applications in computer vision, e.g., motion segmentation, structure-from-motion and object recognition. We present two optimization formulations for the subspace segmentation problem together with the associated numerical algorithms to solve them. In the first formulation, the segmentation problem is cast as the maximization of a convex function over a compact convex set. We propose a monotone optimization algorithm which loops between a Minimization-Maximization (MM) routine and a Escape from only-Local Maximizers (ELM) routine. The MM routine locates a local maximizer and ELM tries to escape from it towards the global one, reinitializing the MM routine when successful. For the MM routine, convergence is proved to be achieved in a finite number of steps (for a certain cost function). Our technique shows a competitive performance with both synthetic and real data image sequences, which includes the hard articulated scenario. The main drawback is that the number of subspaces to be segmented and their dimensions are required beforehand. In the second formulation, the segmentation problem is approached through successive minimization of a l1-norm based convex function over the unit-norm sphere. The goal is to construct, recursively, the orthogonal complements of the subspaces. Prior knowledge of the number of subspaces or their dimensions is not required. To handle the optimization problems over the unit-sphere, we present a locallyconvergent optimization algorithm with quadratic rate of convergence (Newton method on a Riemannian manifold). The performance of our approach is tested through computer simulations. Finally, we introduce a novel theoretical framework to analyse the subspace segmentation problem. This is based on optimality criteria for convex optimization and subdifferential calculus. This framework shows that, in the absence of outliers, the subspace segmentation problem can be solved by a certain random linear program, with probability one. Incorporation of outliers lowers the probability of success, but our framework enables its theoretical computation and offers an intuitive geometrical interpretation.
Holistic Environmental Approaches and Aichi Biodiversity Targets: accomplishments and perspectives for marine ecosystems

In order to help safeguard biodiversity from global changes, the Conference of the Parties developed a Strategic Plan for Biodiversity for the period 2011–2020 that included a list of twenty specific objectives known as the Aichi Biodiversity Targets. With the end of that timeframe in sight, and despite major advancements in biodiversity conservation, evidence suggests that the majority of the Targets are unlikely to be met. This article is part of a series of perspective pieces from the 4th World Conference on Marine Biodiversity (May 2018, Montréal, Canada) to identify next steps towards successful biodiversity conservation in marine environments. We specifically reviewed holistic environmental assessment studies (HEA) and their contribution to reaching the Targets. Our analysis was based on multiple environmental approaches which can be considered as holistic, and we discuss how HEA can contribute to the Aichi Biodiversity Targets in the near future. We found that only a few HEA articles considered a specific Biodiversity Target in their research, and that Target 11, which focuses on marine protected areas, was the most commonly cited. We propose five research priorities to enhance HEA for marine biodiversity conservation beyond 2020: (i) expand the use of holistic approaches in environmental assessments, (ii) standardize HEA vocabulary, (iii) enhance data collection, sharing and management, (iv) consider ecosystem spatio-temporal variability and (v) integrate ecosystem services in HEA. The consideration of these priorities will promote the value of HEA and will benefit the Strategic Plan for Biodiversity.
Lung Nodule Classification Using Convolutional Autoencoder and Clustering Augmented Learning Method(CALM)

Early detection of lung cancer can help in a sharp decrease in the lung cancer mortality rate, which accounts for more than 17% percent of total cancer-related deaths. A large number of cases are encountered by radiologists daily for initial diagnosis. Computer-Aided Diagnosis(CAD) systems can assist radiologists by offering a second opinion and making the whole process faster. However, one drawback of CAD systems is a large amount of data needed to train them, which can be expensive in the medical field. In this paper, we propose using a generative adversarial network(GAN) as a potential data augmentation strategy to generate more training data to improve CAD systems. We also propose a convolutional au-toencoder deep learning framework to support unsupervised image features learning for lung nodule through unlabeled data. The paper also introduces Clustering Augmented Learning Method (CALM) classifier which is based on the concept of simultaneous heterogeneous clustering and classification to learn deep feature representations of the features obtained from Convolutional autoencoder. The classification model within CALM consists of a Feedforward Neural Net (FNN) architecture. To improve the accuracy of the classification model, CALM iterates between clustering and learning to form robust clusters, thereby leveraging the learning process of the FNN. Computational experiments using the National Cancer Institute (NCI) Lung Image Database Consortium (LIDC) dataset resulted in an overall accuracy of 95.3% with a precision of 94.9%.
Indoors Localization of a Robot Using RFID Tags

Radio Frequency Identiﬁcation - RFID - has become widely popular in recent years. In robotics it has been implemented mainly for localization and object detection. This dissertation proposes two methods using RFID technology for both people and robot localization in social robotics scenarios, in a pediatric inﬁrmary of an oncological hospital. A novel approach for people localization with passive RFID tags is proposed, involving the estimation in real time of a measure of the probability of tags carried by people being detected. The method estimates the location of the tag relative to the RFID reader with an accuracy suitable for a wide range of human-robot interactions. The method for robot localization is implemented using Support Vector Machines, with tags sparsely placed on walls and doors. Through learning classiﬁcation, the robot is able to estimate its position, which can be used as a complement for other localization systems. Experimental results show that both methods provide useful information for purposes of human-robot interaction and for robots to be accepted in human social scenarios.
Clustering Single-cell RNA-sequencing Data based on Matching Clusters Structures

Single-cell sequencing technology can generate RNA-sequencing data at the single cell level, and one important single-cell RNA-sequencing data analysis method is to identify their cell types without supervised information. Clustering is an unsupervised approach that can help find new insights into biology especially for exploring the biological functions of specific cell type. However, it is challenging for traditional clustering methods to obtain high-quality cell type recognition results. In this research, we propose a novel Clustering method based on Matching Clusters Structures (MCSC) for identifying cell types among single-cell RNA-sequencing data. Firstly, MCSC obtains two different groups of clustering results from the same K-means algorithm because its initial centroids are randomly selected. Then, for one group, MCSC uses shared nearest neighbour information to calculate a label transition matrix, which denotes label transition probability between any two initial clusters. Each initial cluster may be reassigned if merging results after label transition satisfy a consensus function that maximizes structural matching degree of two different groups of clustering results. In essence, the MCSC may be interpreted as a label training process. We evaluate the proposed MCSC with five commonly used datasets and compare MCSC with several classical and state-of-the-art algorithms. The experimental results show that MCSC outperform other algorithms.
Disentangling multiple factors shaping the activity of mice prefrontal cortical neurons during Working Memory

There is a wide sphere of situations in any animal’s life where a decision has to be made by using information that will no longer be present in an immediate future. The framework of the brain that manages this type of information is called working memory (WM). It calls for complex mechanisms and beyond the mnemonic representations of events, animals often recur to behavioural strategies to achieve the goals, creating a bridge between relevant events and goal achievement, bypassing working memory mechanisms. The Medial Prefrontal Cortex (mPFC) has traditionally been implicated in combining sensory and contextual information, in order to develop information representations that guide behaviour. This work focuses on analyzing data from a head-fixed spatial memoryguided behavioural task, in an attempt to understand the effect of behavioural cues and mnemonic representation in mPFC cells. The task consists on a head-fixed mouse running on a treadmill, which has to make a binary decision (stop/not stop) upon reaching a decision area (DA) where the stimulus (5 or 12 kHz sound) that identifies the decision is no longer present. Through analysis of the mice’s speed trajectories and cell spiking activity it was possible to observe that the decision was made mostly on inner representations coded into persistent activity, and that behavioural cues show a significant contribution to the outcome. The results point out relevant contributions from behavioural cues, as well as the existence of an active control mechanism determined varied classes of cells that are context selective and sensitive to stimulus, showing characteristic spiking profiles across trials.
Comparing the effectiveness of six missing value imputation methods for longitudinal classiﬁcation datasets

Missing values are common in longitudinal datasets because often data is not collected at all waves (time points), and choosing the best method to estimate these values is a challenging task. This work compares the effectiveness of six imputation methods in a classiﬁcation dataset created from the English Longitudinal Study of Ageing (ELSA), where the classes are age-related diseases and the predictive features are mainly biomedical data. Three of the imputation methods are based on different approaches for computing the mean or mode of a feature (for numeric or nominal features, respectively), two methods use longitudinal (temporal) information, and one method uses the 7-NN (7-Nearest Neighbours) machine learning algo-rithm. The methods’ effectiveness were measured using two criteria: (a) how often each imputation method can be applied (the two longitudinal meth-ods often cannot be applied to some features); (b) how accurately they estimate the missing values. Overall, 7-NN was the most accurate method, but the two longitudinal methods also performed well when they could be applied.
The Potential Use Of Mri Guidance For Computerized Surgical Procedures

We report recent work in the development of computerized reconstruction and image processing techniques which allow planning and rehearsal of surgical procedures, and which will also be used for real-time control during therapeutical interventions. Sample applications are described, including neurosurgical and radiation therapy planning for astrocytomas; laser energy deposition via fiberoptic waveguide transmission deep within tissue for ablative surgery in the brain and other organs, hyperthermia of tumors, or laser angioplasty; and computer-guided stereotactic techniques for image-guided robotic arms or other intraoperative mechanical navigation systems. Technical requirements for acquisition and processing of 3D rendered data applicable for surgical planning are delineated, and avenues for further development suggested.
Applying Support Vector Machines and Direct Reinforcement Learning to Financial Forecasting

—Many ﬁnance companies offer services like making recommendations for investments or sharing targeted trading strategies with clients. Financial forecasting tools play an essential role in all of these, offering insight regarding the identiﬁcation of good entry and exit points for the transaction of ﬁnancial instruments. This paper presents an approach combining Support Vector Machines (SVMs), Direct Reinforcement Learning (DRL) and Genetic Algorithms (GAs), capable of generating forecasts relative to which buy and sell points should be set in order to obtain high returns with low associated risk. The system in question uses a single-objective GA to select a set of technical indicators (derived from raw ﬁnancial data) that, when used as inputs in a SVM, produce the best model for predicting the rise or fall of a securitys price short term Simple Moving Average (SMA). The DRL algorithm searches for patterns and repetitive mistakes made by the previously established model, constructing the ﬁnal trading signal. The developed system’s learning capabilities are studied, being demonstrated its ability to identify important pieces of information within data and appropriate behaviour when dealing with high transaction costs. Additionally, its performance is evaluated with real daily data from nine ﬁnancial instruments with very different characteristics. The obtained results demonstrated that the system possesses a high degree of robustness, achieving positive total proﬁt in eight out of the nine tested assets. Furthermore, its concluded that the GA and SVM combination adds signiﬁcant value to the proposed approach.
On the Relevance of Query Expansion Using Parallel Corpora and Word Embeddings to Boost Text Document Retrieval Precision

In this paper we implement a document retrieval system using the Lucene tool and we conduct some experiments in order to compare the efficiency of two different weighting schema: the well-known TF-IDF and the BM25. Then, we expand queries using a comparable corpus (wikipedia) and word embeddings. Obtained results show that the latter method (word embeddings) is a good way to achieve higher precision rates and retrieve more accurate documents.