Collaborative Deep Reinforcement Learning for Joint Object Search

We examine the problem of joint top-down active search of multiple objects under interaction, e.g., person riding a bicycle, cups held by the table, etc.. Such objects under interaction often can provide contextual cues to each other to facilitate more efficient search. By treating each detector as an agent, we present the first collaborative multi-agent deep reinforcement learning algorithm to learn the optimal policy for joint active object localization, which effectively exploits such beneficial contextual information. We learn inter-agent communication through cross connections with gates between the Q-networks, which is facilitated by a novel multi-agent deep Q-learning algorithm with joint exploitation sampling. We verify our proposed method on multiple object detection benchmarks. Not only does our model help to improve the performance of state-of-the-art active localization models, it also reveals interesting co-detection patterns that are intuitively interpretable

Hybrid Lifi And Wifi Communication System

A system and method are proposed that may transfer information between devices using both LiFi and the WiFi technologies. The hybrid system may use LiFi for downlink traffic and WiFi for uplink traffic. The system may include a LiFi receiver/transmitter and also a WiFi receiver/transmitter in the devices. When a device tries to communicate with a network, the data is uploaded from the device to the router connected to the network through WiFi. The device receives response from the network via the router through LiFi. The disclosed hybrid system incorporates the high download speed of LiFi (up to 4 Gbps), while maintaining the reliability of WiFi (upload speed up to 0.443 Gbps)

Dissecting the dynamics of signaling events in the BMP, WNT, and NODAL cascade during self-organized fate patterning in human gastruloids.

During gastrulation, the pluripotent epiblast self-organizes into the 3 germ layers-endoderm, mesoderm and ectoderm, which eventually form the entire embryo. Decades of research in the mouse embryo have revealed that a signaling cascade involving the Bone Morphogenic Protein (BMP), WNT, and NODAL pathways is necessary for gastrulation. In vivo, WNT and NODAL ligands are expressed near the site of gastrulation in the posterior of the embryo, and knockout of these ligands leads to a failure to gastrulate. These data have led to the prevailing view that a signaling gradient in WNT and NODAL underlies patterning during gastrulation; however, the activities of these pathways in space and time have never been directly observed. In this study, we quantify BMP, WNT, and NODAL signaling dynamics in an in vitro model of human gastrulation. Our data suggest that BMP signaling initiates waves of WNT and NODAL signaling activity that move toward the colony center at a constant rate. Using a simple mathematical model, we show that this wave-like behavior is inconsistent with a reaction-diffusion-based Turing system, indicating that there is no stable signaling gradient of WNT/NODAL. Instead, the final signaling state is homogeneous, and spatial differences arise only from boundary effects. We further show that the durations of WNT and NODAL signaling control mesoderm differentiation, while the duration of BMP signaling controls differentiation of CDX2-positive extra-embryonic cells. The identity of these extra-embryonic cells has been controversial, and we use RNA sequencing (RNA-seq) to obtain their transcriptomes and show that they closely resemble human trophoblast cells in vivo. The domain of BMP signaling is identical to the domain of differentiation of these trophoblast-like cells; however, neither WNT nor NODAL forms a spatial pattern that maps directly to the mesodermal region, suggesting that mesoderm differentiation is controlled dynamically by the combinatorial effect of multiple signals. We synthesize our data into a mathematical model that accurately recapitulates signaling dynamics and predicts cell fate patterning upon chemical and physical perturbations. Taken together, our study shows that the dynamics of signaling events in the BMP, WNT, and NODAL cascade in the absence of a stable signaling gradient control fate patterning of human gastruloids.R01 GM126122 - NIGMS NIH HHSPublished versio

Automatic generation of human machine interface screens from component-based reconfigurable virtual manufacturing cell

Increasing complexity and decreasing time-tomarket require changes in the traditional way of building automation systems. The paper describes a novel approach to automatically generate the Human Machine Interface (HMI) screens for component-based manufacturing cells based on their corresponding virtual models. Manufacturing cells are first prototyped and commissioned within a virtual engineering environment to validate and optimise the control behaviour. A framework for reusing the embedded control information in the virtual models to automatically generate the HMI screens is proposed. Finally, for proof of concept, the proposed solution is implemented and tested on a test rig

Realising the open virtual commissioning of modular automation systems

To address the challenges in the automotive industry posed by the need to rapidly manufacture more product variants, and the resultant need for more adaptable production systems, radical changes are now required in the way in which such systems are developed and implemented. In this context, two enabling approaches for achieving more agile manufacturing, namely modular automation systems and virtual commissioning, are briefly reviewed in this contribution. Ongoing research conducted at Loughborough University which aims to provide a modular approach to automation systems design coupled with a virtual engineering toolset for the (re)configuration of such manufacturing automation systems is reported. The problems faced in the virtual commissioning of modular automation systems are outlined. AutomationML - an emerging neutral data format which has potential to address integration problems is discussed. The paper proposes and illustrates a collaborative framework in which AutomationML is adopted for the data exchange and data representation of related models to enable efficient open virtual prototype construction and virtual commissioning of modular automation systems. A case study is provided to show how to create the data model based on AutomationML for describing a modular automation system

Bacteria derived extracellular vesicles in the pathogenesis and treatment of gastrointestinal tumours

Extracellular vesicles are fundamentally significant in the communication between cells. Outer Membrane Vesicles(OMVs) are a special kind of EVs produced by Gram-negative bacteria, which are minute exosome-like particles budding from the outer membrane, which have been found to play essential roles in diverse bacterial life events, including regulation of microbial interactions, pathogenesis promotion, stress responses and biofilm formation. Recently, and more researches have explored the substantial potentials of EVs as natural functional nanoparticles in the bioengineering applications in infectious diseases, cardiovascular diseases, autoimmune diseases and neurological diseases, such as antibacterial therapy, cancer drugs and immunoadjuvants, with several candidates in clinical trials showing promising efficacy. However, due to the poor understanding of sources, membrane structures and biogenesis mechanisms of EVs, progress in clinical applications still remains timid. In this review, we summarize the latest findings of EVs, especially in gastrointestinal tract tumours, to provide a comprehensive introduction of EVs in tumorigenesis and therapeutics