Wall Matters: Rethinking the Effect of Wall for Wireless Sensing

Wireless sensing has demonstrated its potential of utilizing radio frequency (RF) signals to sense individuals and objects. Among different wireless signals, LoRa signal is particularly promising for through-wall sensing owing to its strong penetration capability. However, existing works view walls as a bad thing as they attenuate signal power and decrease the sensing coverage. In this paper, we show a counter-intuitive observation, i.e., walls can be used to increase the sensing coverage if the RF devices are placed properly with respect to walls. To fully understand the underlying principle behind this observation, we develop a through-wall sensing model to mathematically quantify the effect of walls. We further show that besides increasing the sensing coverage, we can also use the wall to help mitigate interference, which is one well-known issue in wireless sensing. We demonstrate the effect of wall through two representative applications, i.e., macro-level human walking sensing and micro-level human respiration monitoring. Comprehensive experiments show that by properly deploying the transmitter and receiver with respect to the wall, the coverage of human walking detection can be expanded by more than 160%. By leveraging the effect of wall to mitigate interference, we can sense the tiny respiration of target even in the presence of three interferers walking nearby

The Acceptability and Influencing Factors of an Internet-Based Tinnitus Multivariate Integrated Sound Therapy for Patients With Tinnitus

Objective: To explore the acceptability and influencing factors of an Internet-based Tinnitus Multivariate Integrated Sound Therapy (iT-MIST). The individually tailored sound therapy used narrowband noise centered on the patient’s tinnitus frequency in combination with natural sounds and relaxing music. Design: Patients with tinnitus were given a 1-week trial of iT-MIST. Semistructured interviews were then carried out and a thematic analysis used to analyze, identify, organize, and report factors discovered in the data. Study Sample: Semistructured interviews were carried out with 11 participants, 2 women and 9 men, mean age 39.82 years. Results: The first theme identified from patient interview analysis was their motivation to undertake and expectations of iT-MIST. Nearly half of the participants indicated that advice from the physician was considered very important and professional. Benefits acknowledged by most participants from their iT-MIST experience were accessibility, convenience, time- and cost-effectiveness, and emotional benefit. However, a few participants with poor understanding of tinnitus and iT-MIST showed a negative acceptability with doubtful thoughts and complaints about technical issues such as being easily interrupted by messages and phone calls. Conclusion: Patients with tinnitus in this study were not universally accepting of the iT-MIST therapy. Concerns about their tinnitus and ability to comply with doctor’s recommendations were the main influencing factors. Attitude or willingness to explore new therapies facilitated its use. Emotional benefits, for example, relaxation and comfort, were seen to sustain motivation, while doubtful thoughts and technical problems negatively affected acceptability

Rethinking Closed-loop Training for Autonomous Driving

Recent advances in high-fidelity simulators have enabled closed-loop training of autonomous driving agents, potentially solving the distribution shift in training v.s. deployment and allowing training to be scaled both safely and cheaply. However, there is a lack of understanding of how to build effective training benchmarks for closed-loop training. In this work, we present the first empirical study which analyzes the effects of different training benchmark designs on the success of learning agents, such as how to design traffic scenarios and scale training environments. Furthermore, we show that many popular RL algorithms cannot achieve satisfactory performance in the context of autonomous driving, as they lack long-term planning and take an extremely long time to train. To address these issues, we propose trajectory value learning (TRAVL), an RL-based driving agent that performs planning with multistep look-ahead and exploits cheaply generated imagined data for efficient learning. Our experiments show that TRAVL can learn much faster and produce safer maneuvers compared to all the baselines. For more information, visit the project website: https://waabi.ai/research/travlComment: ECCV 202

A Joint Extraction System Based on Conditional Layer Normalization for Health Monitoring

Natural language processing (NLP) technology has played a pivotal role in health monitoring as an important artificial intelligence method. As a key technology in NLP, relation triplet extraction is closely related to the performance of health monitoring. In this paper, a novel model is proposed for joint extraction of entities and relations, combining conditional layer normalization with the talking-head attention mechanism to strengthen the interaction between entity recognition and relation extraction. In addition, the proposed model utilizes position information to enhance the extraction accuracy of overlapping triplets. Experiments on the Baidu2019 and CHIP2020 datasets demonstrate that the proposed model can effectively extract overlapping triplets, which leads to significant performance improvements compared with baselines.publishedVersio

Methyltransferases of gentamicin biosynthesis

Gentamicin C complex from Micromonospora echinospora remains a globally important antibiotic, and there is revived interest in the semisynthesis of analogs that might show improved therapeutic properties. The complex consists of five components differing in their methylation pattern at one or more sites in the molecule. We show here, using specific gene deletion and chemical complementation, that the gentamicin pathway up to the branch point is defined by the selectivity of the methyltransferases GenN, GenD1, and GenK. Unexpectedly, they comprise a methylation network in which early intermediates are ectopically modified. Using whole-genome sequence, we have also discovered the terminal 6'-N-methyltransfer required to produce gentamicin C2b from C1a or gentamicin C1 from C2, an example of an essential biosynthetic enzyme being located not in the biosynthetic gene cluster but far removed on the chromosome. These findings fully account for the methylation pattern in gentamicins and open the way to production of individual gentamicins by fermentation, as starting materials for semisynthesis.This work was supported by National Natural Science Foundation of China Grant 31470186; by the 973 Program Grant 2012CB721005 from the Ministry of Science and Technology of China; by Open Project Grant MMLKF15-12 from the State Key Laboratory of Microbial Metabolism, Shanghai Jiao Tong University (to Y.S.); by Medical Research Council (MRC) Grants G1001687 and MR/M019020/1 (to P.F.L.); and by an MRC postgraduate studentship (1343325) (to A.R.)

SEPTIN2 suppresses an IFN-γ-independent, proinflammatory macrophage activation pathway

Interferon-gamma (IFN-γ) signaling is necessary for the proinflammatory activation of macrophages but IFN-γ-independent pathways, for which the initiating stimuli and downstream mechanisms are lesser known, also contribute. Here we identify, by high-content screening, SEPTIN2 (SEPT2) as a negative regulation of IFN-γ-independent macrophage autoactivation. Mechanistically, endoplasmic reticulum (ER) stress induces the expression of SEPT2, which balances the competition between acetylation and ubiquitination of heat shock protein 5 at position Lysine 327, thereby alleviating ER stress and constraining M1-like polarization and proinflammatory cytokine release. Disruption of this negative feedback regulation leads to the accumulation of unfolded proteins, resulting in accelerated M1-like polarization, excessive inflammation and tissue damage. Our study thus uncovers an IFN-γ-independent macrophage proinflammatory autoactivation pathway and suggests that SEPT2 may play a role in the prevention or resolution of inflammation during infection

LiFS: Low human-effort, device-free localization with fine-grained subcarrier information

Device-free localization of people and objects indoors not equipped with radios is playing a critical role in many emerging applications. This paper presents an accurate model-based device-free localization system LiFS, implemented on cheap commercial off-the-shelf (COTS) Wi-Fi devices. Unlike previous COTS device-based work, LiFS is able to localize a target accurately without offline training. The basic idea is simple: channel state information (CSI) is sensitive to a target's location and by modelling the CSI measurements of multiple wireless links as a set of power fading based equations, the target location can be determined. However, due to rich multipath propagation indoors, the received signal strength (RSS) or even the fine-grained CSI can not be easily modelled. We observe that even in a rich multipath environment, not all subcarriers are affected equally by multipath reflections. Our pre-processing scheme tries to identify the subcarriers not affected by multipath. Thus, CSIs on the "clean" subcarriers can be utilized for accurate localization. We design, implement and evaluate LiFS with extensive experiments in three different environments. Without knowing the majority transceivers' locations, LiFS achieves a median accuracy of 0.5 m and 1.1 m in line-of-sight (LoS) and non-line-of-sight (NLoS) scenarios respectively, outperforming the state-of-the-art systems. Besides single target localization, LiFS is able to differentiate two sparsely-located targets and localize each of them at a high accuracy