Causal Patterns: Extraction of multiple causal relationships by Mixture of Probabilistic Partial Canonical Correlation Analysis

In this paper, we propose a mixture of probabilistic partial canonical correlation analysis (MPPCCA) that extracts the Causal Patterns from two multivariate time series. Causal patterns refer to the signal patterns within interactions of two elements having multiple types of mutually causal relationships, rather than a mixture of simultaneous correlations or the absence of presence of a causal relationship between the elements. In multivariate statistics, partial canonical correlation analysis (PCCA) evaluates the correlation between two multivariates after subtracting the effect of the third multivariate. PCCA can calculate the Granger Causal- ity Index (which tests whether a time-series can be predicted from an- other time-series), but is not applicable to data containing multiple partial canonical correlations. After introducing the MPPCCA, we propose an expectation-maxmization (EM) algorithm that estimates the parameters and latent variables of the MPPCCA. The MPPCCA is expected to ex- tract multiple partial canonical correlations from data series without any supervised signals to split the data as clusters. The method was then eval- uated in synthetic data experiments. In the synthetic dataset, our method estimated the multiple partial canonical correlations more accurately than the existing method. To determine the types of patterns detectable by the method, experiments were also conducted on real datasets. The method estimated the communication patterns In motion-capture data. The MP- PCCA is applicable to various type of signals such as brain signals, human communication and nonlinear complex multibody systems.Comment: DSAA2017 - The 4th IEEE International Conference on Data Science and Advanced Analytic

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

A generative framework for conversational laughter: Its 'language model' and laughter sound synthesis

As the phonetic and acoustic manifestations of laughter in conversation are highly diverse, laughter synthesis should be capable of accommodating such diversity while maintaining high controllability. This paper proposes a generative model of laughter in conversation that can produce a wide variety of laughter by utilizing the emotion dimension as a conversational context. The model comprises two parts: the laughter "phones generator," which generates various, but realistic, combinations of laughter components for a given speaker ID and emotional state, and the laughter "sound synthesizer," which receives the laughter phone sequence and produces acoustic features that reflect the speaker's individuality and emotional state. The results of a listening experiment indicated that conditioning both the phones generator and the sound synthesizer on emotion dimensions resulted in the most effective control of the perceived emotion in synthesized laughter.Comment: Submitted to INTERSPEEC

Thylakoid ΔpH-dependent precursor proteins bind to a cpTatC–Hcf106 complex before Tha4-dependent transport

The thylakoid ΔpH-dependent pathway transports folded proteins with twin arginine–containing signal peptides. Identified components of the machinery include cpTatC, Hcf106, and Tha4. The reaction occurs in two steps: precursor binding to the machinery, and transport across the membrane. Here, we show that a cpTatC–Hcf106 complex serves as receptor for specific binding of twin arginine–containing precursors. Antibodies to either Hcf106 or cpTatC, but not Tha4, inhibited precursor binding. Blue native gel electrophoresis and coimmunoprecipitation of digitonin-solubilized thylakoids showed that Hcf106 and cpTatC are members of an ∼700-kD complex that lacks Tha4. Thylakoid-bound precursor proteins were also associated with an ∼700-kD complex and were coimmunoprecipitated with antibodies to cpTatC or Hcf106. Chemical cross-linking revealed that precursors make direct contact with cpTatC and Hcf106 and confirmed that Tha4 is not associated with precursor, cpTatC, or Hcf106 in the membrane. Precursor binding to the cpTatC–Hcf106 complex required both the twin arginine and the hydrophobic core of the signal peptide. Precursors remained bound to the complex when Tha4 was sequestered by antibody, even in the presence of ΔpH. These results indicate that precursor binding to the cpTatC–Hcf106 complex constitutes the recognition event for this pathway and that subsequent participation by Tha4 leads to translocation

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

Skeletal open bite with amelogenesis imperfecta treated with compression osteogenesis : a case report

Background: We successfully treated a 37-year-old male who had skeletal open bite with severe amelogenesis imperfecta (AI) with orthodontics, compression osteogenesis, and prosthodontics. Case presentation: The patient was diagnosed with severe anterior open bite caused by severe AI. Corticotomy was performed on both buccal and palatal sides of the molar regions, and anchor plates were placed onto the bilateral zygomatic buttress and the center of the hard palate. After corticotomy, posterior maxillary segments were moved 3.5 mm superiorly to correct skeletal open bite with elastic chains. After 8-month, overbite had decreased by 2.0 mm. After further 5 months of prosthodontic preparation, orthodontic appliances were removed, and provisional crowns were set on all teeth. The anterior open bite was corrected, and ideal occlusion with a Class I molar relationship was achieved. The upper first molars were intruded 3.5 mm, resulting in 3.0o counter-clockwise rotation of the mandible. The total active treatment period was 16 months. Acceptable occlusion with a good facial profile was well maintained throughout the 8-year retention period. Conclusions: Our results indicate long-term stability after interdisciplinary treatment combining orthodontics, oral surgery, and prosthodontics in a patient with severe anterior open bite and AI

Pb(Mg1/3Nb2/3)O3 (PMN) Relaxor: Dipole Glass or Nano-Domain Ferroelectric?

Combining our comprehensive investigations of polarization evolution, soft-mode by Raman scattering and microstructure by TEM, and the results reported in the literatures, we show that prototypical relaxor Pb(Mg1/3Nb2/3)O3 (PMN) is essentially ferroelectric for T<Tc~225 K. Its anomalous dielectric behavior over a broad temperature range results from the reorientation of domains in the crystal. A physic picture of the structure evolution in relaxor is also revealed. It is found that nanometric ferroelectric domains (gennerally called as polar nano-region (PNR)) interact cooperatively to form micrometric domain. Such multiscale inhomogeneities of domain structure in addition to the well-known inhomogeneities of chemical composition and local symmetry are considered to play a crucial role in producing the enigmatic phenomena in relaxor system.Comment: 16 pages, 10 figures; http://www.intechopen.com/books/advances-in-ferroelectrics/pb-mg1-3nb2-3-o3-pmn-relaxor-dipole-glass-or-nano-domain-ferroelectric

Synthesis and Physicochemical Properties of 2,7-Disubstituted Phenanthro[2,1-b:7,8-b']dithiophenes

We report the design, synthesis, and physicochemical properties of an array of phenanthro[2,1-b:7,8-b']dithiophene (PDT-2) derivatives by introducing five types of alkyl (CnH2n+1; n = 8, 10, 12, 13, and 14) or two types of decylthienyl groups at 2,7-positions of the PDT-2 core. Systematic investigation revealed that the alkyl length and the type of side chains have a great effect on the physicochemical properties. For alkylated PDT-2, the solubility was gradually decreased as the chain length was increased. For instance, C-8-PDT-2 exhibited the highest solubility (5.0 g/L) in chloroform. Additionally, substitution with 5-decylthienyl groups showed poor solubility in both chloroform and toluene, whereas PDT-2 with 4-decylthienyl groups resulted in higher solubility. Furthermore, UV-vis absorption of PDT-2 derivatives substituted by decylthienyl groups showed a redshift, indicating the extension of their pi-conjugation length. This work reveals that modification of the conjugated core by alkyl or decylthienyl side chains may be an efficient strategy by which to change the physicochemical properties, which might lead to the development of high-performance organic semiconductors