Shaping in Practice: Training Wheels to Learn Fast Hopping Directly in Hardware

Learning instead of designing robot controllers can greatly reduce engineering effort required, while also emphasizing robustness. Despite considerable progress in simulation, applying learning directly in hardware is still challenging, in part due to the necessity to explore potentially unstable parameters. We explore the concept of shaping the reward landscape with training wheels: temporary modifications of the physical hardware that facilitate learning. We demonstrate the concept with a robot leg mounted on a boom learning to hop fast. This proof of concept embodies typical challenges such as instability and contact, while being simple enough to empirically map out and visualize the reward landscape. Based on our results we propose three criteria for designing effective training wheels for learning in robotics. A video synopsis can be found at https://youtu.be/6iH5E3LrYh8.Comment: Accepted to the IEEE International Conference on Robotics and Automation (ICRA) 2018, 6 pages, 6 figure

FootTile: a Rugged Foot Sensor for Force and Center of Pressure Sensing in Soft Terrain

In this paper we present FootTile, a foot sensor for reaction force and center of pressure sensing in challenging terrain. We compare our sensor design to standard biomechanical devices, force plates and pressure plates. We show that FootTile can accurately estimate force and pressure distribution during legged locomotion. FootTile weighs 0.9g, has a sampling rate of 330Hz, a footprint of 10 by 10mm and can easily be adapted in sensor range to the required load case. In three experiments we validate: first the performance of the individual sensor, second an array of FootTiles for center of pressure sensing and third the ground reaction force estimation during locomotion in granular substrate. We then go on to show the accurate sensing capabilities of the waterproof sensor in liquid mud, as a showcase for real world rough terrain use

Exploring Supervised Techniques for Automated Recognition of Intention Classes from Portuguese Free Texts on Agriculture

Technical and scientific knowledge is vast and complex, particularly in interdisciplinary fields such as sustainable agriculture, which is available in several interrelated, geographically dispersed and interdisciplinary online textual information sources. In this context, it is essential to support people with computational mechanisms that allow them to retrieve and interpret information in an appropriate way, as communication in these software systems is typically asynchronous and textual. User’s intention recognition and analysis in textual documents results in benefits for better information retrieval. However, intentions are expressed implicitly in texts in natural language and the specificities of the domain and cultural aspects of language make it difficult to process and analyze the text by computer systems. This requires the study of methods for the automatic recognition of intention classes in text. In this article, we conduct extensive experimental analyses on techniques based on language models and machine learning to detect instances of intention classes in texts about sustainable agriculture written in Portuguese. In our methodology, we perform a morphological analysis of the sentences and evaluate four Word Embeddings techniques (Word2Vec, Wang2Vec, FastText and Glove) combined with four machine learning techniques (Support Vector Machine, Artificial Neural Network, Random Forest and Transfer Learning). The results obtained by applying the techniques proposed in a database with textual information on sustainable agriculture indicate promising possibilities in the recognition of intentions in free texts  in  Portuguese language on sustainable agriculture

Методы и механизмы геттерирования кремниевых структур в производстве интегральных микросхем

Увеличение степени интеграции элементной базы предъявляет все более жесткие требования к уменьшению концентрации загрязняющих примесей и окислительных дефектов упаковки в исходных кремниевых пластинах с ее сохранением в технологическом цикле изготовления ИМС. Это обуславливает высокую актуальность применения геттерирования в современной технологии микроэлектроники. В статье рассмотрены существующие методы геттерирования кремниевых пластин и механизмы их протекания.Збільшення ступеня інтеграції елементної бази пред'являє все більш жорсткі вимоги до зменшення концентрації забруднюючих домішок та окислювальних дефектів упаковки у вихідних кремнієвих пластинах за її збереження у технологічному циклі виготовлення ІМС. Це обумовлює високу актуальність застосування гетерування в сучасній технології мікроелектроніки. Розглянуто існуючі методи гетерування кремнієвих пластин та розглянуто механізми їх перебігу.Increasing the degree of integration of hardware components imposes more stringent requirements for the reduction of the concentration of contaminants and oxidation stacking faults in the original silicon wafers with its preservation in the IC manufacturing process cycle. This causes high relevance of the application of gettering in modern microelectronic technology. The existing methods of silicon wafers gettering and the mechanisms of their occurrence are considered

Epigenomic and transcriptomic approaches in the post-genomic era: path to novel targets for diagnosis and therapy of the ischemic heart?

Despite advances in myocardial reperfusion therapies, acute myocardial ischemia/reperfusion injury and consequent ischemic heart failure represent the number one cause of morbidity and mortality in industrialized societies. Although different therapeutic interventions have been shown beneficial in preclinical settings, an effective cardioprotective or regenerative therapy has yet to be successfully introduced in the clinical arena. Given the complex pathophysiology of the ischemic heart, large scale, unbiased, global approaches capable of identifying multiple branches of the signaling networks activated in the ischemic/reperfused heart might be more successful in the search for novel diagnostic or therapeutic targets. High-throughput techniques allow high-resolution, genome-wide investigation of genetic variants, epigenetic modifications and associated gene expression profiles. Platforms such as proteomics and metabolomics (not described here in detail) also offer simultaneous readouts of hundreds of proteins and metabolites. Isolated omics analyses usually provide Big Data requiring large data storage, advanced computational resources and complex bioinformatics tools. The possibility of integrating different omics approaches gives new hope to better understand the molecular circuitry activated by myocardial ischemia, putting it in the context of the human "diseasome".Since modifications of cardiac gene expression have been consistently linked to pathophysiology of the ischemic heart, the integration of epigenomic and transcriptomic data seems a promising approach to identify crucial disease networks. Thus, the scope of this Position Paper will be to highlight potentials and limitations of these approaches, and to provide recommendations to optimize the search for novel diagnostic or therapeutic targets for acute ischemia/reperfusion injury and ischemic heart failure in the post-genomic era

Genome-wide association analysis of genetic generalized epilepsies implicates susceptibility loci at 1q43, 2p16.1, 2q22.3 and 17q21.32

Genetic generalized epilepsies (GGEs) have a lifetime prevalence of 0.3% and account for 20-30% of all epilepsies. Despite their high heritability of 80%, the genetic factors predisposing to GGEs remain elusive. To identify susceptibility variants shared across common GGE syndromes, we carried out a two-stage genome-wide association study (GWAS) including 3020 patients with GGEs and 3954 controls of European ancestry. To dissect out syndrome-related variants, we also explored two distinct GGE subgroups comprising 1434 patients with genetic absence epilepsies (GAEs) and 1134 patients with juvenile myoclonic epilepsy (JME). Joint Stage-1 and 2 analyses revealed genome-wide significant associations for GGEs at 2p16.1 (rs13026414, Pmeta = 2.5 × 10−9, OR[T] = 0.81) and 17q21.32 (rs72823592, Pmeta = 9.3 × 10−9, OR[A] = 0.77). The search for syndrome-related susceptibility alleles identified significant associations for GAEs at 2q22.3 (rs10496964, Pmeta = 9.1 × 10−9, OR[T] = 0.68) and at 1q43 for JME (rs12059546, Pmeta = 4.1 × 10−8, OR[G] = 1.42). Suggestive evidence for an association with GGEs was found in the region 2q24.3 (rs11890028, Pmeta = 4.0 × 10−6) nearby the SCN1A gene, which is currently the gene with the largest number of known epilepsy-related mutations. The associated regions harbor high-ranking candidate genes: CHRM3 at 1q43, VRK2 at 2p16.1, ZEB2 at 2q22.3, SCN1A at 2q24.3 and PNPO at 17q21.32. Further replication efforts are necessary to elucidate whether these positional candidate genes contribute to the heritability of the common GGE syndrome