Motion Switching with Sensory and Instruction Signals by designing Dynamical Systems using Deep Neural Network

To ensure that a robot is able to accomplish an extensive range of tasks, it is necessary to achieve a flexible combination of multiple behaviors. This is because the design of task motions suited to each situation would become increasingly difficult as the number of situations and the types of tasks performed by them increase. To handle the switching and combination of multiple behaviors, we propose a method to design dynamical systems based on point attractors that accept (i) "instruction signals" for instruction-driven switching. We incorporate the (ii) "instruction phase" to form a point attractor and divide the target task into multiple subtasks. By forming an instruction phase that consists of point attractors, the model embeds a subtask in the form of trajectory dynamics that can be manipulated using sensory and instruction signals. Our model comprises two deep neural networks: a convolutional autoencoder and a multiple time-scale recurrent neural network. In this study, we apply the proposed method to manipulate soft materials. To evaluate our model, we design a cloth-folding task that consists of four subtasks and three patterns of instruction signals, which indicate the direction of motion. The results depict that the robot can perform the required task by combining subtasks based on sensory and instruction signals. And, our model determined the relations among these signals using its internal dynamics.Comment: 8 pages, 6 figures, accepted for publication in RA-L. An accompanied video is available at this https://youtu.be/a73KFtOOB5

Tumor cell invasion from the marginal sinus into extranodal veins during early-stage lymph node metastasis can be a starting point for hematogenous metastasis

Aim: To investigate whether tumor cells in a lymph node (LN) can invade from the marginal sinus into extranodal veins via vessel branches that communicate with intranodal veins and whether this can be a starting point for hematogenous metastasis at the early stage of LN metastasis.Methods: Vascular and lymphatic networks of LNs in MXH10/Mo-lpr/lpr mice were investigated using three-dimensional micro-computed tomography and histological methods. Flow in the blood vessel networks of LNs was investigated by fluorescence microscopy. Tumor cells were injected into the subiliac LNs of MXH10/Mo-lpr/lpr mice to induce metastasis to the proper axillary LNs. Tumor development in the proper axillary LN was detected using an in vivo bioluminescence imaging system. A two-dimensional image of the proper axillary LN microvasculature was reconstructed using a contrast-enhanced high-frequency ultrasound system.Results: Extranodal veins communicated with intranodal veins via branches that penetrated the capsule, and blood flowed from intranodal veins to extranodal veins. Tumor cells that had metastasized to the marginal sinus invaded these communicating veins to develop hematogenous metastases.Conclusion: Metastatic LNs that would be considered by clinical imaging to be stage N0 can be a starting point for hematogenous metastasis. The study findings highlight the need for the development of novel techniques for the diagnosis and treatment of early-stage LN metastasis, i.e., when standard diagnostic imaging might incorrectly classify the LN as stage N0

Compensation for undefined behaviors during robot task execution by switching controllers depending on embedded dynamics in RNN

Robotic applications require both correct task performance and compensation for undefined behaviors. Although deep learning is a promising approach to perform complex tasks, the response to undefined behaviors that are not reflected in the training dataset remains challenging. In a human-robot collaborative task, the robot may adopt an unexpected posture due to collisions and other unexpected events. Therefore, robots should be able to recover from disturbances for completing the execution of the intended task. We propose a compensation method for undefined behaviors by switching between two controllers. Specifically, the proposed method switches between learning-based and model-based controllers depending on the internal representation of a recurrent neural network that learns task dynamics. We applied the proposed method to a pick-and-place task and evaluated the compensation for undefined behaviors. Experimental results from simulations and on a real robot demonstrate the effectiveness and high performance of the proposed method.Comment: To appear in IEEE Robotics and Automation Letters (RA-L) and IEEE International Conference on Robotics and Automation (ICRA 2021

Atiyah-Patodi-Singer index on a lattice

We propose a non-perturbative formulation of the Atiyah-Patodi-Singer(APS) index in lattice gauge theory, in which the index is given by the $\eta$ invariant of the domain-wall Dirac operator. Our definition of the index is always an integer with a finite lattice spacing. To verify this proposal, using the eigenmode set of the free domain-wall fermion, we perturbatively show in the continuum limit that the curvature term in the APS theorem appears as the contribution from the massive bulk extended modes, while the boundary $\eta$ invariant comes entirely from the massless edge-localized modes.Comment: 14 pages, appendices added, details of key equations added, typos corrected, to appear in PTE

Tool-Use Model to Reproduce the Goal Situations Considering Relationship Among Tools, Objects, Actions and Effects Using Multimodal Deep Neural Networks

We propose a tool-use model that enables a robot to act toward a provided goal. It is important to consider features of the four factors; tools, objects actions, and effects at the same time because they are related to each other and one factor can influence the others. The tool-use model is constructed with deep neural networks (DNNs) using multimodal sensorimotor data; image, force, and joint angle information. To allow the robot to learn tool-use, we collect training data by controlling the robot to perform various object operations using several tools with multiple actions that leads different effects. Then the tool-use model is thereby trained and learns sensorimotor coordination and acquires relationships among tools, objects, actions and effects in its latent space. We can give the robot a task goal by providing an image showing the target placement and orientation of the object. Using the goal image with the tool-use model, the robot detects the features of tools and objects, and determines how to act to reproduce the target effects automatically. Then the robot generates actions adjusting to the real time situations even though the tools and objects are unknown and more complicated than trained ones

Usefulness of NIRS for medication adherence

The symptoms of attention deficit hyperactivity disorder (ADHD) are inattention, hyperactivity, and impulsiveness. Physicians often prescribe methylphenidate (MPH) for children with ADHD for long periods of time. The purpose of the present study was to investigate the usefulness of near-infrared spectroscopy (NIRS) for evaluating drug effects and improvements in medication adherence in children with ADHD. Subjects were 10 male children diagnosed with ADHD : average age, 9.3 years, and 10 boys with typical development : average age 9.5 years. Children with intellectual disability, autism, and obvious depressive symptoms were excluded. The present study revealed that in the ADHD group, oxy-Hb concentrations in the left and right lateral prefrontal cortex significantly increased during the execution of the Stroop color-word test in both channels when taking MPH. This method was considered to be useful for assessing drug effects on ADHD because NIRS is an objective indicator for evaluating ADHD executive dysfunction and visualizes the activation of frontal lobe function by MPH. A pediatric neurologist explained the results of NIRS while presenting images to the ADHD group, and medication adherence and the drug-taking ratio both markedly improved. Therefore, this therapeutic explanation is an effective strategy for improving medication compliance and adherence among patients

Tapping but Not Massage Enhances Vasodilation and Improves Venous Palpation of Cutaneous Veins

This paper investigated whether tapping on the median cubital vein or massaging the forearm was more effective in obtaining better venous palpation for venipuncture. Forty healthy volunteers in their twenties were subjected to tapping (10 times in 5 sec) or massage (10 strokes in 20 sec from the wrist to the cubital fossa) under tourniquet inflation on the upper arm. Venous palpation was assessed using the venous palpation score (0-6, with 0 being impalpable). Three venous factors―venous depth, cross-sectional area, and elevation―were also measured using ultrasonography. The venous palpation score increased significantly by tapping but not by massage. Moreover, all 3 venous measurements changed significantly by tapping, while only the depth decreased significantly by massage. The three venous measurements correlated significantly with the venous palpation score, indicating that they are useful objective indicators for evaluating vasodilation. We suggest that tapping is an effective vasodilation technique

Regulation of Oxidative Stress and Cardioprotection in Diabetes Mellitus

Analysis of the Framingham data has shown that the risk of heart failure is increased substantially among diabetic patients, while persons with the metabolic syndrome have an increased risk of both atherosclerosis and diabetes mellitus. Sleep apnea may be related to the metabolic syndrome and systemic inflammation through hypoxia, which might also cause the cardiac remodeling by increased oxidative stress. On the other hand, the renin-angiotensin system is activated in diabetes, and local angiotensin II production may lead to oxidative damage via the angiotensin II type 1 receptor. Basic and clinical data indicate that angiotensin II receptor blockers have the potential to preserve left ventricular function and prevent cardiac remodeling that is exaggerated by oxidative stress in patients with diabetes. Thus, alleviation of oxidative stress might be one possible strategy in the treatment of diabetic patients associated with sleep apnea