Zero-Shot Object Recognition Based on Haptic Attributes

International audienceRobots operating in household environments need to recognize a variety of objects. Several touch-based object recognition systems have been proposed in the last few years [2]– [5]. They map haptic data to object classes using machine learning techniques, and then use the learned mapping to recognize one of the previously encountered objects. The accuracy of these proposed methods depends on the mass of the the training samples available for each object class. On the other hand, haptic data collection is often system (robot) specific and labour intensive. One way to cope with this problem is to use a knowledge transfer based system, that can exploit object relationships to share learned models between objects. However, while knowledge-based systems, such as zero shot learning [6], have been regularly proposed for visual object recognition, a similar system is not available for haptic recognition. Here we developed [1] the first haptic zero-shot learning system that enables a robot to recognize, using haptic exploration alone, objects that it encounters for the first time. Our system first uses the so called Direct Attributes Prediction (DAP) model [7] to train on the semantic representation of objects based on a list of haptic attributes, rather than the object itself. The attributes (including physical properties such as shape, texture, material) constitute an intermediate layer relating objects, and is used for knowledge transfer. Using this layering, our system can predict the attribute-based representation of a new (previously non-trained) object and use it to infer its identity. A. System Overview An overview of our system is given in Fig. 1. Given distinct training and test data-sets Y and Z, that are described by an attribute basis a, we first associate a binary label a o m to each object o with o ∈ Y ∪ Z and m = 1. .. M. This results in a binary object-attribute matrix K. For a given attributes list during training, haptic data collected from Y are used to train a binary classifier for each attribute a m. Finally, to classify a test sample x as one of Z objects, x is introduced to each one of the learned attribute classifiers and the output attributes posteriors p(a m | x) are used to predict the corresponding object, provided that the ground truth is available in K. This extended abstract is a summary of submission [1] B. Experimental Setup To collect haptic data, we use the Shadow anthropo-morphic robotic hand equipped with a BioTac multimodal tactile sensor on each fingertip. We developed a force-based grasp controller that enables the hand to enclose an object. The joint encoder readings provides us with information on object shape, while the BioTac sensors provides us with information about objects material, texture and compliance at each fingertip 1. In order to find the appropriate list of attributes describing our object set (illustrated in Fig. 2), we used online dictionaries to collect one or multiple textual definitions of each object. From this data, we extracted 11 haptic adjectives, or descriptions that could be " felt " using our robot hand. These adjectives served as our attributes: made of porcelain, made of plastic, made of glass, made of cardboard, made of stainless steel, cylindrical, round, rectangular, concave, has a handle, has a narrow part. We grouped the attributes into material attributes, and shape attributes. During the training phase, we use the Shadow hand joint readings x sh to train an SVM classifier for each shape, and BioTacs readings x b to train an SVM classifier for each material attribute. SVM training returns a distance s m (x) measure for each sample x that gives how far x lies from the discriminant hyper-plane. We transform this score to an attribute posterior p(a m | x) using a sigmoid function

Performance of a wastewater treatment plant in south-eastern Algeria

The experimental study was carried out on an urban wastewater purification station located in the region of El Oued -Kounine- in south-eastern Algeria. During 6 months, samples were taken every month to study the Physico-chemical parameters of this station. Monthly monitoring of SS, COD, BOD5 was made from September 2017 to February 2018 and the results obtained show that the average elimination rates were 77.76, 74.10 and 80% respectively for BOD5, DCO and SS. The average of the ratio COD/BOD5 during the 6 months of follow-up is equal to 2.9

Reconnaissance visio-haptique des objets de la vie quotidienne : à partir de peu de données d'entraînement

Recognizing surrounding objects is an important skill for the autonomy of robots performing in daily-life. Nowadays robots are equipped with sophisticated sensors imitating the human sense of touch. This allows the recognition of an object based on information ensuing from robot-object physical interaction. Such information can include the object texture, compliance and material. In this thesis, we exploit haptic data to perform haptic recognition of daily life objects using machine learning techniques. The main challenge faced in our work is the difficulty of collecting a fair amount of haptic training data for all daily-life objects. This is due to the continuously growing number of objects and to the effort and time needed by the robot to physically interact with each object for data collection. We solve this problem by developing a haptic recognition framework capable of performing Zero-shot, One-shot and Multi-shot Learning. We also extend our framework by integrating vision to enhance the robot’s recognition performance, whenever such sense is available.Il est important pour les robots de pouvoir reconnaître les objets rencontrés dans la vie quotidienne afin d’assurer leur autonomie. De nos jours, les robots sont équipés de capteurs sophistiqués permettant d’imiter le sens humain du toucher. C’est ce qui permet aux robots interagissant avec les objets de percevoir les propriétés (telles la texture, la rigidité et la matière) nécessaires pour leur reconnaissance. Dans cette thèse, notre but est d’exploiter les données haptiques issues de l’interaction robot-objet afin de reconnaître les objets de la vie quotidienne, et cela en utilisant les algorithmes d’apprentissage automatique. Le problème qui se pose est la difficulté de collecter suffisamment de données haptiques afin d’entraîner les algorithmes d’apprentissage supervisé sur tous les objets que le robot doit reconnaître. En effet, les objets de la vie quotidienne sont nombreux et l’interaction physique entre le robot et chaque objet pour la collection des données prend beaucoup de temps et d’efforts. Pour traiter ce problème, nous développons un système de reconnaissance haptique permettant de reconnaître des objets à partir d'aucune, de une seule, ou de plusieurs données d’entraînement. Enfin, nous intégrons la vision afin d’améliorer la reconnaissance d'objets lorsque le robot est équipé de caméras

New hybrid algorithm based on nonmonotone spectral gradient and simultaneous perturbation

International audienceIn this paper, we introduce a new hybrid method called nonmonotone spectral gradient and simultaneous perturbation (NSGSP). It combines the advantages of nonmonotone spectral gradient (NSG), and simultaneous perturbation (SP) methods. The main idea of our approach is to use the simultaneous perturbation (SP) method in order to get a non expensive estimate of the gradient, and exploit the good properties of the nonmonotone spectral gradient (NSG) method in order to compute an efficient line search. Several numerical experiments are provided. The results indicate that the new method is effective and outperforms most of other popular methods

Visuo-Tactile Recognition of Daily-Life Objects Never Seen or Touched Before

International audienceThis study proposes a visuo-tactile Zero-Shot object recognition framework. The proposed framework recognizes a set of novel objects for which no tactile or visual training data are available. It uses visuo-tactile training data collected from known objects to recognize the novel ones, given their attributes. This framework extends the haptic Zero-Shot Learning framework that we proposed in [1] with vision, which enables a multimodal recognition system. In our test with the PHAC-2 dataset, the system was able to get a recognition accuracy of 72% among 6 objects that were never touched or seen during the training phase

BRCA1 Promoter Hypermethylation in Malignant Breast Tumors and in the Histologically Normal Adjacent Tissues to the Tumors: Exploring Its Potential as a Biomarker and Its Clinical Significance in a Translational Approach

The hypermethylation status of the promoter region of the breast cancer 1 (BRCA1), a well-known tumor suppressor gene, has been extensively investigated in the last two decades as a potential biomarker for breast cancer. In this retrospective study, we investigated the prevalence of BRCA1 promoter methylation in 84 human breast tissues, and we correlated this epigenetic silencing with the clinical and histopathological parameters of breast cancer. We used methylation-specific PCR (MSP) to analyze BRCA1 promoter hypermethylation in 48 malignant breast tumors (MBTs), 15 normal adjacent tissues (NATs), and 21 benign breast lesions (BBLs). The results showed that BRCA1 promoter hypermethylation was higher in MBTs (20/48; 41.67%) and NATs (7/15; 46.67%) compared to BBLs (4/21; 19.05%). The high percentage of BRCA1 hypermethylation in the histologically normal adjacent tissues to the tumors (NATs) suggests the involvement of this epigenetic silencing as a potential biomarker of the early genomic instability in NATs surrounding the tumors. The detection of BRCA1 promoter hypermethylation in BBLs reinforces this suggestion, knowing that a non-negligible rate of benign breast lesions was reported to evolve into cancer. Moreover, our results indicated that the BRCA1 promoter hypermethylated group of MBTs exhibited higher rates of aggressive features, as indicated by the SBR III grade (14/19; 73.68%), elevated Ki67 levels (13/16; 81.25%), and Her2 receptor overexpression (5/20; 25%). Finally, we observed a concordance (60%) in BRCA1 promoter hypermethylation status between malignant breast tumors and their paired histologically normal adjacent tissues. This study highlights the role of BRCA1 promoter hypermethylation as a potential useful biomarker of aggressiveness in MBTs and as an early marker of genomic instability in both histological NATs and BBLs

Cross-sectional study of recurrent disc herniation risk factors and predictors of outcomes after primary lumbar discectomy: A STROBE compliance

Objectives: The purpose of this study is to determine if lifting weight, smoking status, occupational work, and diabetes were predictors for recurrent lumbar disk herniation (rLDH) leading to reoperation and if the outcome can be influenced by the reoperated level and side. Methods: From June 2010 to July 2019, the 2196 consecutive patients who underwent first-time single-level lumbar discectomy at our institution were revised. Data on first lumbar spine surgery, reoperation as well as preoperative data were brought into the analysis. Multivariate Logistic Regression Analysis was performed to determine whether lifting weight, smoking status, occupational work, and diabetes were predictors for recurrent lumbar disk herniation. The outcome level was assessed by the Multivariable Cox-regression Kaplan–Meier analysis for repeated lumbar disc herniation excision at the L4L5 and L5S1 levels independently. Results: From the 101(4.59%) patients that presented with recurrent lumbar disc herniation (rLDH), 75 cases (3.41%) met the inclusion criteria. There were 54 cases of ipsilateral recurrent herniation and 21 contralateral with a male predominance of 64% (n = 48). The average age at the time of recurrence was 48 ± 9.34 years (age range 29–67 years). The group of diabetes patients who smoke is at high risk (Odds 2.77) of rapid recurrence for lumbar disc prolapse; about 3 months after the first surgery followed by the group of diabetes who lift weight (Odds 0.83), about 4 months after the first surgery. At the L4L5 level, only the group of patients operated for opposite side recurrence (Odd ratio 1.01) did well and were pain-free immediately after surgery compared to the group of patients operated for recurrence on the same side (Odd ratio 6.73). Conclusion: Coexisting diabetes and smoking status in the same patient increase the risk of rLDH and the patient’s outcome is favorable with pain-free after reoperation without the need for physiotherapy when the recurrence is on the same level and opposite side

Abstracts of 1st International Conference on Computational & Applied Physics

This book contains the abstracts of the papers presented at the International Conference on Computational & Applied Physics (ICCAP’2021) Organized by the Surfaces, Interfaces and Thin Films Laboratory (LASICOM), Department of Physics, Faculty of Science, University Saad Dahleb Blida 1, Algeria, held on 26–28 September 2021. The Conference had a variety of Plenary Lectures, Oral sessions, and E-Poster Presentations. Conference Title: 1st International Conference on Computational & Applied PhysicsConference Acronym: ICCAP’2021Conference Date: 26–28 September 2021Conference Location: Online (Virtual Conference)Conference Organizer: Surfaces, Interfaces, and Thin Films Laboratory (LASICOM), Department of Physics, Faculty of Science, University Saad Dahleb Blida 1, Algeria