Predictive Coding Based Multiscale Network with Encoder-Decoder LSTM for Video Prediction

We present a multi-scale predictive coding model for future video frames prediction. Drawing inspiration on the ``Predictive Coding" theories in cognitive science, it is updated by a combination of bottom-up and top-down information flows, which can enhance the interaction between different network levels. However, traditional predictive coding models only predict what is happening hierarchically rather than predicting the future. To address the problem, our model employs a multi-scale approach (Coarse to Fine), where the higher level neurons generate coarser predictions (lower resolution), while the lower level generate finer predictions (higher resolution). In terms of network architecture, we directly incorporate the encoder-decoder network within the LSTM module and share the final encoded high-level semantic information across different network levels. This enables comprehensive interaction between the current input and the historical states of LSTM compared with the traditional Encoder-LSTM-Decoder architecture, thus learning more believable temporal and spatial dependencies. Furthermore, to tackle the instability in adversarial training and mitigate the accumulation of prediction errors in long-term prediction, we propose several improvements to the training strategy. Our approach achieves good performance on datasets such as KTH, Moving MNIST and Caltech Pedestrian. Code is available at https://github.com/Ling-CF/MSPN

A brief review of neural networks based learning and control and their applications for robots

As an imitation of the biological nervous systems, neural networks (NN), which are characterized with powerful learning ability, have been employed in a wide range of applications, such as control of complex nonlinear systems, optimization, system identification and patterns recognition etc. This article aims to bring a brief review of the state-of-art NN for the complex nonlinear systems. Recent progresses of NNs in both theoretical developments and practical applications are investigated and surveyed. Specifically, NN based robot learning and control applications were further reviewed, including NN based robot manipulator control, NN based human robot interaction and NN based behavior recognition and generation

A multimodal human-robot sign language interaction framework applied in social robots

Deaf-mutes face many difficulties in daily interactions with hearing people through spoken language. Sign language is an important way of expression and communication for deaf-mutes. Therefore, breaking the communication barrier between the deaf-mute and hearing communities is significant for facilitating their integration into society. To help them integrate into social life better, we propose a multimodal Chinese sign language (CSL) gesture interaction framework based on social robots. The CSL gesture information including both static and dynamic gestures is captured from two different modal sensors. A wearable Myo armband and a Leap Motion sensor are used to collect human arm surface electromyography (sEMG) signals and hand 3D vectors, respectively. Two modalities of gesture datasets are preprocessed and fused to improve the recognition accuracy and to reduce the processing time cost of the network before sending it to the classifier. Since the input datasets of the proposed framework are temporal sequence gestures, the long-short term memory recurrent neural network is used to classify these input sequences. Comparative experiments are performed on an NAO robot to test our method. Moreover, our method can effectively improve CSL gesture recognition accuracy, which has potential applications in a variety of gesture interaction scenarios not only in social robots

Adsorption characteristics of bovine serum albumin onto alumina with a specific crystalline structure

Bone cement containing alumina particles with a specific crystalline structure exhibits the ability to bond with bone. These particles (AL-P) are mainly composed of delta-type alumina (δ-Al2O3). It is likely that some of the proteins present in the body environment are adsorbed onto the cement and influence the expression of its bioactivity. However, the effect that this adsorption of proteins has on the bone-bonding mechanism of bone cement has not yet been elucidated. In this study, we investigated the characteristics of the adsorption of bovine serum albumin (BSA) onto AL-P and compared them with those of its adsorption onto hydroxyapatite (HA), which also exhibits bone-bonding ability, as well as with those of adsorption onto alpha-type alumina (α-Al2O3), which does not bond with bone. The adsorption characteristics of BSA onto AL-P were very different from those onto α-Al2O3 but quite similar to those onto HA. It is speculated that BSA is adsorbed onto AL-P and HA by interionic interactions, while it is adsorbed onto α-Al2O3 by electrostatic attraction. The results suggest that the specific adsorption of albumin onto implant materials might play a role in the expression of the bone-bonding abilities of the materials

A receptor-like kinase mutant with absent endodermal diffusion barrier displays selective nutrient homeostasis defects

We thank the Genomic Technologies Facility (GTF) and the Central Imaging Facility (CIF) of the University of Lausanne for expert technical support. We thank Valérie Dénervaud Tendon, Guillaume Germion, Deborah Mühlemann, and Kayo Konishi for technical assistance and John Danku and Véronique Vacchina for ICP-MS analysis. This work was funded by grants from the Swiss National Science Foundation (SNSF), the European Research Council (ERC) to NG and a Human Frontiers Science Program (HFSP) grant to JT and NG. GL and CM were supported by the Agropolis foundation (Rhizopolis) and the Agence Nationale de la Recherche (HydroRoot; ANR-11-BSV6-018). MB was supported by a EMBO long-term postdoctoral fellowship, JEMV by a Marie Curie IEF fellowship and TK by the Japan Society for the Promotion of Sciences (JSPS).Peer reviewedPublisher PD

Iterative learning control based on stretch and compression mapping for trajectory tracking in human-robot collaboration

This paper presents a novel iterative learning control (ILC) scheme based on stretch and compression mapping for a robotic manipulator to learn its human partner’s desired trajectory, which is a typical task in the field of human-robot interaction. The proposed scheme is used to reduce the interaction force between the robot and the human partner in repetitive learning process. Thus, the robot can track the human partner’s repetitive trajectory with a small interaction force, leading to little control effort from the human. As the human is involved in the control loop, there are various uncertainties in the system, including variable iteration period in the task under study. The stretch and compression mapping is applied to this problem. In the simulation, the proposed scheme is implemented in the human-robot interaction scenario. Results confirm the effectiveness of the proposed scheme and also illustrate better performance of the proposed ILC compared with other ILC methods with variable periods

Carousel Personalization in Music Streaming Apps with Contextual Bandits

Media services providers, such as music streaming platforms, frequently leverage swipeable carousels to recommend personalized content to their users. However, selecting the most relevant items (albums, artists, playlists...) to display in these carousels is a challenging task, as items are numerous and as users have different preferences. In this paper, we model carousel personalization as a contextual multi-armed bandit problem with multiple plays, cascade-based updates and delayed batch feedback. We empirically show the effectiveness of our framework at capturing characteristics of real-world carousels by addressing a large-scale playlist recommendation task on a global music streaming mobile app. Along with this paper, we publicly release industrial data from our experiments, as well as an open-source environment to simulate comparable carousel personalization learning problems.Comment: 14th ACM Conference on Recommender Systems (RecSys 2020, Best Short Paper Candidate