Imitation and Mirror Systems in Robots through Deep Modality Blending Networks

Learning to interact with the environment not only empowers the agent with manipulation capability but also generates information to facilitate building of action understanding and imitation capabilities. This seems to be a strategy adopted by biological systems, in particular primates, as evidenced by the existence of mirror neurons that seem to be involved in multi-modal action understanding. How to benefit from the interaction experience of the robots to enable understanding actions and goals of other agents is still a challenging question. In this study, we propose a novel method, deep modality blending networks (DMBN), that creates a common latent space from multi-modal experience of a robot by blending multi-modal signals with a stochastic weighting mechanism. We show for the first time that deep learning, when combined with a novel modality blending scheme, can facilitate action recognition and produce structures to sustain anatomical and effect-based imitation capabilities. Our proposed system, can be conditioned on any desired sensory/motor value at any time-step, and can generate a complete multi-modal trajectory consistent with the desired conditioning in parallel avoiding accumulation of prediction errors. We further showed that given desired images from different perspectives, i.e. images generated by the observation of other robots placed on different sides of the table, our system could generate image and joint angle sequences that correspond to either anatomical or effect based imitation behavior. Overall, the proposed DMBN architecture not only serves as a computational model for sustaining mirror neuron-like capabilities, but also stands as a powerful machine learning architecture for high-dimensional multi-modal temporal data with robust retrieval capabilities operating with partial information in one or multiple modalities

DeepSym: Deep Symbol Generation and Rule Learning from Unsupervised Continuous Robot Interaction for Planning

Autonomous discovery of discrete symbols and rules from continuous interaction experience is a crucial building block of robot AI, but remains a challenging problem. Solving it will overcome the limitations in scalability, flexibility, and robustness of manually-designed symbols and rules, and will constitute a substantial advance towards autonomous robots that can learn and reason at abstract levels in open-ended environments. Towards this goal, we propose a novel and general method that finds action-grounded, discrete object and effect categories and builds probabilistic rules over them that can be used in complex action planning. Our robot interacts with single and multiple objects using a given action repertoire and observes the effects created in the environment. In order to form action-grounded object, effect, and relational categories, we employ a binarized bottleneck layer of a predictive, deep encoder-decoder network that takes as input the image of the scene and the action applied, and generates the resulting object displacements in the scene (action effects) in pixel coordinates. The binary latent vector represents a learned, action-driven categorization of objects. To distill the knowledge represented by the neural network into rules useful for symbolic reasoning, we train a decision tree to reproduce its decoder function. From its branches we extract probabilistic rules and represent them in PPDDL, allowing off-the-shelf planners to operate on the robot's sensorimotor experience. Our system is verified in a physics-based 3d simulation environment where a robot arm-hand system learned symbols that can be interpreted as 'rollable', 'insertable', 'larger-than' from its push and stack actions; and generated effective plans to achieve goals such as building towers from given cubes, balls, and cups using off-the-shelf probabilistic planners

Minimizing the Limitations in Improving Historical Aerial Photographs with Super-Resolution Technique

Compared to natural images in artificial datasets, it is more challenging to improve the spatial resolution of remote sensing optical image data using super-resolution techniques. Historical aerial images are primarily grayscale due to single-band acquisition, which further limits their recoverability. To avoid data limitations, it is advised to employ a data collection consisting of images with homogeneously distributed intensity values of land use/cover objects at various resolution values. Thus, two different datasets were created. In line with the proposed approach, images of bare land, farmland, residential areas, and forested regions were extracted from orthophotos of different years with different spatial resolutions. In addition, images with intensity values in a more limited range for the same categories were obtained from a single year’s orthophoto to highlight the contribution of the suggested approach. Training of two different datasets was performed independently using a deep learning-based super-resolution model, and the same test images were enhanced individually with the weights of both models. The results were assessed using a variety of quality metrics in addition to visual interpretation. The findings indicate that the suggested dataset structure and content can enable the recovery of more details and effectively remove the smoothing effect. In addition, the trend of the metric values matches the visual perception results

Hepatotoxic effects of melamine exposure from the weaning period in rats: a flow cytometric, electron microscopic, and histopathologic study

This study aims to investigate the effects of melamine exposure from the weaning period (21st postnatal days in rats) on liver tissue. Female Wistar albino rats (n = 18) were divided into three groups. About 0.1-ml saline was applied to the control group by gavage for 21 days from the postnatal 21st day. The second group was taken 50-mg/kg melamine (in 0.1-ml saline) and the third group was taken 75-mg/kg melamine (in 0.1-ml saline) p.o. On the postnatal 45th day, all rats were sacrificed under anesthesia. Then, liver tissues were cut into three parts and two of them placed in neutral formalin for histopathological and flow cytometric analysis, and one of them placed in 2.5% glutaraldehyde. Histopathological analysis was performed with hematoxylin & eosin, Masson trichrome, periodic acid Schiff stained sections, and also with transmission electron microscopy. Apoptosis (Annexin V positivity) was analyzed by flow cytometry. According to histopathological analysis, hepatocyte damage, sinusoidal dilatation, and inflammatory cell infiltration significantly increased in both melamine groups compared with the control group. Apoptosis significantly increased in the 50 and 75-mg melamine groups compared with the control group. In the results of transmission electron microscopy analysis, there was abnormal chromatin distribution in the hepatocyte nuclei, loss in the cristae of the mitochondria, and organelle loss in large areas in the cytoplasm in both melamine exposure groups. As result, melamine exposure from the weaning period causes liver damage with increasing doses

Ensemble Classification over Stock Market Time Series and Economy News

Aim of this study is applying the ensemble classification methods over the stock market closing values, which can be assumed as time series and finding out the relation between the economy news. In order to keep the study back ground clear, the majority voting method has been applied over the three classification algorithms, which are the k-nearest neighborhood, support vector machine and the C4.5 tree. The results gathered from two different feature extraction methods are correlated with majority voting meta classifier (ensemble method) which is running over three classifiers. The results show the success rates are increased after the ensemble at least 2 to 3 percent success rate

TIME SERIES ANALYSIS ON STOCK MARKET FOR TEXT MINING CORRELATION OF ECONOMY NEWS

This paper proposes an information retrieval methodfor the economy news. Theeffect of economy news, are researched in the wordlevel and stock market valuesare considered as the ground proof.The correlation between stock market prices and economy news is an already ad-dressed problem for most of the countries. The mostwell-known approach is ap-plying the text mining approaches to the news and some time series analysis tech-niques over stock market closing values in order toapply classification or cluster-ing algorithms over the features extracted. This study goes further and tries to askthe question what are the available time series analysis techniques for the stockmarket closing values and which one is the most suitable? In this study, the newsand their dates are collected into a database and text mining is applied over thenews, the text mining part has been kept simple with only term frequency – in-verse document frequency method. For the time series analysis part, we havestudied 10 different methods such as random walk, moving average, acceleration,Bollinger band, price rate of change, periodic average, difference, momentum orrelative strength index and their variation. In this study we have also explainedthese techniques in a comparative way and we have applied the methods overTurkish Stock Market closing values for more than a2 year period. On the otherhand, we have applied the term frequency – inversedocument frequency methodon the economy news of one of the high-circulatingnewspapers in Turkey

Mesangioproliferative Glomerulonephritis Due to Hepatic Hydatid Disease: A Case Report and Literature Review

WOS: 000374928100013Hydatid cyst (CH), which is quite common in the world, mostly transmitted by dog faeces, is a parasitic disease caused by Echinococcus granulosus. CH often infects the liver and lungs. During the clinical course, renal involvement is rarely seen. In this article; due to liver hydatid disease, mezengioproliferatif glomerulonephritis case is presented

COMPARISON OF NEUROPROTECTIVE EFFECTS ON ALPHA LIPOIC ACID TO METHYPREDNIZONOL AT EXPERIMENTAL SPINAL CORD TRAUMA. A STUDY HISTOPATHOLOGICAL AND ULTRASTRUCTURAL

Background: Traumatic and ischemic injuries of spinal cord are active at malfunctioning of damaged tissue at primary and secondary mechanisms. Monoammins free radicales, neuropeptits,arachidonic acids metobolites,and extracellular Ca variations are important at development of early ischemic and main causes of the secondary damage at progrediens tissue ischemic

Targeting soluble guanylate cyclase with Riociguat has potency to alleviate testicular ischaemia reperfusion injury via regulating various cellular pathways

Testicular ischaemia reperfusion (I/R) injury results with serious dysfunctions in testis. This study aims to explore effects of soluble guanylate cyclase (sGC) stimulator Riociguat on experimental testicular I/R injury in rats. Twenty-one male rats were divided into three groups (Control, IR and IRR). The control group was not exposed to any application. Bilateral testis from IR and IRR animals were rotated 720 degrees in opposite directions for 3 h to induce experimental testicular ischaemia. Animals in IR and IRR groups were subjected to 3 h of reperfusion. Isotonic and Riociguat were administered to the animals 30 min prior reperfusion by oral gavage. At the end of experiment, animals were sacrificed and tissue samples were used for analyses. Riociguat treatment significantly decreased tissue malondialdehyde and Luminol levels compared to the IR group (p < 0.05). The pathological changes, pro-apoptotic proteins (Bax, Caspase 3, and Caspase 9) and apoptotic index in the IR group were down regulated in Riociguat treated animals (p < 0.05). Riociguat treatment was also significantly increased anti-apoptotic Bcl-2 expression, but alleviated tissue injury via modulating pro-inflammatory cytokine IL-1 beta levels and significantly (p < 0.05) down-regulating NF-kappa B activity. Moreover, mTOR and ERK phosphorylation increased in IR group (p < 0.05), but Riociguat treatment reduced protein phosphorylation. Our experiment indicated that targeting sGC might support surgical interventions in testicular I/R injury by modulating oxidative stress, inflammation, and apoptotic protein expression levels, but more detailed studies are required to explore the protective activity of Riociguat and underlying mechanisms in testicular I/R injury