Interpretable rumor detection in microblogs by attending to user interactions

We address rumor detection by learning to differentiate between the community's response to real and fake claims in microblogs. Existing state-of-the-art models are based on tree models that model conversational trees. However, in social media, a user posting a reply might be replying to the entire thread rather than to a specific user. We propose a post-level attention model (PLAN) to model long distance interactions between tweets with the multi-head attention mechanism in a transformer network. We investigated variants of this model: (1) a structure aware self-attention model (StA-PLAN) that incorporates tree structure information in the transformer network, and (2) a hierarchical token and post-level attention model (StA-HiTPLAN) that learns a sentence representation with token-level self-attention. To the best of our knowledge, we are the first to evaluate our models on two rumor detection data sets: the PHEME data set as well as the Twitter15 and Twitter16 data sets. We show that our best models outperform current state-of-the-art models for both data sets. Moreover, the attention mechanism allows us to explain rumor detection predictions at both token-level and post-level

A high frame rate wearable EIT system using active electrode ASICs for lung respiration and heart rate monitoring

A high specification, wearable, electrical impedance tomography (EIT) system with 32 active electrodes is presented. Each electrode has an application specific integrated circuit (ASIC) mounted on a flexible printed circuit board, which is then wrapped inside a disposable fabric cover containing silver-coated electrodes to form the wearable belt. It is connected to a central hub that operates all the 32 ASICs. Each ASIC comprises a high- performance current driver capable of up to 6 mAp−p output, a voltage buffer for EIT and heart rate signal recording as well as contact impedance monitoring, and a sensor buffer that provides multi-parameter sensing. The ASIC was designed in a CMOS 0.35-μm high-voltage process technology. It operates from ±9-V power supplies and occupies a total die area of 3.9 mm2. The EIT system has a bandwidth of 500 kHz and employs two parallel data acquisition channels to achieve a frame rate of 107 frames/s, the fastest wearable EIT system reported to date. Measured results show that the system has a measurement accuracy of 98.88% and a minimum EIT detectability of 0.86 Q/frame. Its successful operation in capturing EIT lung respiration and heart rate biosignals from a volunteer is demonstrated

Ypt1/Rab1 regulates Hrr25/CK1δ kinase activity in ER-Golgi traffic and macroautophagy.

ER-derived COPII-coated vesicles are conventionally targeted to the Golgi. However, during cell stress these vesicles also become a membrane source for autophagosomes, distinct organelles that target cellular components for degradation. How the itinerary of COPII vesicles is coordinated on these pathways remains unknown. Phosphorylation of the COPII coat by casein kinase 1 (CK1), Hrr25, contributes to the directional delivery of ER-derived vesicles to the Golgi. CK1 family members are thought to be constitutively active kinases that are regulated through their subcellular localization. Instead, we show here that the Rab GTPase Ypt1/Rab1 binds and activates Hrr25/CK1δ to spatially regulate its kinase activity. Consistent with a role for COPII vesicles and Hrr25 in membrane traffic and autophagosome biogenesis, hrr25 mutants were defective in ER–Golgi traffic and macroautophagy. These studies are likely to serve as a paradigm for how CK1 kinases act in membrane traffic

Precise Temporal Action Localization by Evolving Temporal Proposals

Locating actions in long untrimmed videos has been a challenging problem in video content analysis. The performances of existing action localization approaches remain unsatisfactory in precisely determining the beginning and the end of an action. Imitating the human perception procedure with observations and refinements, we propose a novel three-phase action localization framework. Our framework is embedded with an Actionness Network to generate initial proposals through frame-wise similarity grouping, and then a Refinement Network to conduct boundary adjustment on these proposals. Finally, the refined proposals are sent to a Localization Network for further fine-grained location regression. The whole process can be deemed as multi-stage refinement using a novel non-local pyramid feature under various temporal granularities. We evaluate our framework on THUMOS14 benchmark and obtain a significant improvement over the state-of-the-arts approaches. Specifically, the performance gain is remarkable under precise localization with high IoU thresholds. Our proposed framework achieves mAP@IoU=0.5 of 34.2%