MetaVIM: Meta Variationally Intrinsic Motivated Reinforcement Learning for Decentralized Traffic Signal Control

Traffic signal control aims to coordinate traffic signals across intersections to improve the traffic efficiency of a district or a city. Deep reinforcement learning (RL) has been applied to traffic signal control recently and demonstrated promising performance where each traffic signal is regarded as an agent. However, there are still several challenges that may limit its large-scale application in the real world. To make the policy learned from a training scenario generalizable to new unseen scenarios, a novel Meta Variationally Intrinsic Motivated (MetaVIM) RL method is proposed to learn the decentralized policy for each intersection that considers neighbor information in a latent way. Specifically, we formulate the policy learning as a meta-learning problem over a set of related tasks, where each task corresponds to traffic signal control at an intersection whose neighbors are regarded as the unobserved part of the state. Then, a learned latent variable is introduced to represent the task's specific information and is further brought into the policy for learning. In addition, to make the policy learning stable, a novel intrinsic reward is designed to encourage each agent's received rewards and observation transition to be predictable only conditioned on its own history. Extensive experiments conducted on CityFlow demonstrate that the proposed method substantially outperforms existing approaches and shows superior generalizability

Task-Oriented Over-the-Air Computation for Multi-Device Edge AI

Departing from the classic paradigm of data-centric designs, the 6G networks for supporting edge AI features task-oriented techniques that focus on effective and efficient execution of AI task. Targeting end-to-end system performance, such techniques are sophisticated as they aim to seamlessly integrate sensing (data acquisition), communication (data transmission), and computation (data processing). Aligned with the paradigm shift, a task-oriented over-the-air computation (AirComp) scheme is proposed in this paper for multi-device split-inference system. In the considered system, local feature vectors, which are extracted from the real-time noisy sensory data on devices, are aggregated over-the-air by exploiting the waveform superposition in a multiuser channel. Then the aggregated features as received at a server are fed into an inference model with the result used for decision making or control of actuators. To design inference-oriented AirComp, the transmit precoders at edge devices and receive beamforming at edge server are jointly optimized to rein in the aggregation error and maximize the inference accuracy. The problem is made tractable by measuring the inference accuracy using a surrogate metric called discriminant gain, which measures the discernibility of two object classes in the application of object/event classification. It is discovered that the conventional AirComp beamforming design for minimizing the mean square error in generic AirComp with respect to the noiseless case may not lead to the optimal classification accuracy. The reason is due to the overlooking of the fact that feature dimensions have different sensitivity towards aggregation errors and are thus of different importance levels for classification. This issue is addressed in this work via a new task-oriented AirComp scheme designed by directly maximizing the derived discriminant gain

Pushing AI to Wireless Network Edge: An Overview on Integrated Sensing, Communication, and Computation towards 6G

Pushing artificial intelligence (AI) from central cloud to network edge has reached board consensus in both industry and academia for materializing the vision of artificial intelligence of things (AIoT) in the sixth-generation (6G) era. This gives rise to an emerging research area known as edge intelligence, which concerns the distillation of human-like intelligence from the huge amount of data scattered at wireless network edge. In general, realizing edge intelligence corresponds to the process of sensing, communication, and computation, which are coupled ingredients for data generation, exchanging, and processing, respectively. However, conventional wireless networks design the sensing, communication, and computation separately in a task-agnostic manner, which encounters difficulties in accommodating the stringent demands of ultra-low latency, ultra-high reliability, and high capacity in emerging AI applications such as auto-driving. This thus prompts a new design paradigm of seamless integrated sensing, communication, and computation (ISCC) in a task-oriented manner, which comprehensively accounts for the use of the data in the downstream AI applications. In view of its growing interest, this article provides a timely overview of ISCC for edge intelligence by introducing its basic concept, design challenges, and enabling techniques, surveying the state-of-the-art development, and shedding light on the road ahead

The Effect of Myosin Light Chain Kinase on the Occurrence and Development of Intracranial Aneurysm

Myosin light chain kinase is a key enzyme in smooth muscle cell contraction. However, whether myosin light chain kinase plays a role in the occurrence or development of intracranial aneurysms is not clear. The present study explored the function of myosin light chain kinase in human intracranial aneurysm tissues. Five aneurysm samples and five control samples were collected, and smooth muscle cells (SMCs) were dissociated and cultured. A label-free proteomic analysis was performed to screen the differentially expressed proteins between aneurysm and control samples. The expression and function of myosin light chain kinase in aneurysms were examined. We found that 180 proteins were differentially expressed between the aneurysm and control samples, among which 88 were increased and 92 (including myosin light chain kinase) were decreased in aneurysms compared to control tissues. In a model of the inflammatory environment, contractility was weakened and apoptosis was increased in aneurysm SMCs compared to human brain SMCs (p < 0.05). The knock down of myosin light chain kinase in human brain SMCs caused effects similar to those observed in aneurysm SMCs. These results indicated that myosin light chain kinase plays an important role in maintaining smooth muscle contractility, cell survival and inflammation tolerance

On-Site Biolayer Interferometry-Based Biosensing of Carbamazepine in Whole Blood of Epileptic Patients

On-site monitoring of carbamazepine (CBZ) that allows rapid, sensitive, automatic, and high-throughput detection directly from whole blood is of urgent demand in current clinical practice for precision medicine. Herein, we developed two types (being indirect vs. direct) of fiber-optic biolayer interferometry (FO-BLI) biosensors for on-site CBZ monitoring. The indirect FO-BLI biosensor preincubated samples with monoclonal antibodies towards CBZ (MA-CBZ), and the mixture competes with immobilized CBZ to bind towards MA-CBZ. The direct FO-BLI biosensor used sample CBZ and CBZ-horseradish peroxidase (CBZ-HRP) conjugate to directly compete for binding with immobilized MA-CBZ, followed by a metal precipitate 3,3′-diaminobenzidine to amplify the signals. Indirect FO-BLI detected CBZ within its therapeutic range and was regenerated up to 12 times with negligible baseline drift, but reported results in 25 min. However, Direct FO-BLI achieved CBZ detection in approximately 7.5 min, down to as low as 10 ng/mL, with good accuracy, specificity and negligible matric interference using a high-salt buffer. Validation of Direct FO-BLI using six paired sera and whole blood from epileptic patients showed excellent agreement with ultra-performance liquid chromatography. Being automated and able to achieve high throughput, Direct FO-BLI proved itself to be more effective for integration into the clinic by delivering CBZ values from whole blood within minutes

Development and validation of a liquid chromatographic method for the analysis of squaric acid dibutyl ester and its impurities

A simple, fast and selective stability indicating liquid chromatographic method has been described for the simultaneous determination of squaric acid dibutyl ester and its impurities. The chromatographic separation was achieved on a C2 column (250 mm × 4.6 mm i.d., 5 μm) using a mobile phase consisting of 0.15 % phosphoric acid – acetonitrile – methanol (30:60:10, v/v/v). Isocratic elution was performed at a flow rate of 1.0 mL min-1. The analytes were detected by UV at 252 nm. The method was validated according to the ICH guidelines and satisfactory results were obtained. The specificity of the developed method was tested using forced degradation solutions of the drug substance. Characterization of squaric acid dibutyl ester and its forced degradation products was achieved by coupling mass spectrometry (MS) to the liquid chromatographic (LC) system. The method was successfully applied for quality control purposes including assay and determination of related compounds as required by regulatory guidelines to ensure its safety and efficacy since no monograph is available in official compendia.publisher: Elsevier articletitle: Development and validation of a liquid chromatographic method for the analysis of squaric acid dibutyl ester and its impurities journaltitle: Journal of Pharmaceutical and Biomedical Analysis articlelink: http://dx.doi.org/10.1016/j.jpba.2017.04.022 content_type: article copyright: © 2017 Elsevier B.V. All rights reserved.status: publishe

Vertical Federated Edge Learning with Distributed Integrated Sensing and Communication

This letter studies a vertical federated edge learning (FEEL) system for collaborative objects/human motion recognition by exploiting the distributed integrated sensing and communication (ISAC). In this system, distributed edge devices first send wireless signals to sense targeted objects/human, and then exchange intermediate computed vectors (instead of raw sensing data) for collaborative recognition while preserving data privacy. To boost the spectrum and hardware utilization efficiency for FEEL, we exploit ISAC for both target sensing and data exchange, by employing dedicated frequency-modulated continuous-wave (FMCW) signals at each edge device. Under this setup, we propose a vertical FEEL framework for realizing the recognition based on the collected multi-view wireless sensing data. In this framework, each edge device owns an individual local L-model to transform its sensing data into an intermediate vector with relatively low dimensions, which is then transmitted to a coordinating edge device for final output via a common downstream S-model. By considering a human motion recognition task, experimental results show that our vertical FEEL based approach achieves recognition accuracy up to 98\% with an improvement up to 8\% compared to the benchmarks, including on-device training and horizontal FEEL.Comment: 5 pages, 7 figures, accepted by IEEE Communications Letter

Diastereomer recognition of oxytetracycline and its 4-epimer by electrospray ionization mass spectrometry and mechanistic investigation

status: publishe