35 research outputs found
Vehicular Edge Cloud Computing: Depressurize the Intelligent Vehicles Onboard Computational Power
Recently, with the rapid development of autonomous vehicles and connected
vehicles, the demands of vehicular computing keep continuously growing. We
notice a constant and limited onboard computational ability can hardly keep up
with the rising requirements of the vehicular system and software application
during their long-term lifetime, and also at the same time, the vehicles
onboard computation causes an increasingly higher vehicular energy consumption.
Therefore, we suppose to build a vehicular edge cloud computing (VECC)
framework to resolve such a vehicular computing dilemma. In this framework,
potential vehicular computing tasks can be executed remotely in an edge cloud
within their time latency constraints. Simultaneously, an effective wireless
network resources allocation scheme is one of the essential and fundamental
factors for the QoS (quality of Service) on the VECC. In this paper, we adopted
a stochastic fair allocation (SFA) algorithm to randomly allocate minimum
required resource blocks to admitted vehicular users. The numerical results
show great effectiveness of energy efficiency in VECC.Comment: 2018 IEEE 21st International Conference on Intelligent Transportation
Systems (ITSC
LightSAGE: Graph Neural Networks for Large Scale Item Retrieval in Shopee's Advertisement Recommendation
Graph Neural Network (GNN) is the trending solution for item retrieval in
recommendation problems. Most recent reports, however, focus heavily on new
model architectures. This may bring some gaps when applying GNN in the
industrial setup, where, besides the model, constructing the graph and handling
data sparsity also play critical roles in the overall success of the project.
In this work, we report how GNN is applied for large-scale e-commerce item
retrieval at Shopee. We introduce our simple yet novel and impactful techniques
in graph construction, modeling, and handling data skewness. Specifically, we
construct high-quality item graphs by combining strong-signal user behaviors
with high-precision collaborative filtering (CF) algorithm. We then develop a
new GNN architecture named LightSAGE to produce high-quality items' embeddings
for vector search. Finally, we design multiple strategies to handle cold-start
and long-tail items, which are critical in an advertisement (ads) system. Our
models bring improvement in offline evaluations, online A/B tests, and are
deployed to the main traffic of Shopee's Recommendation Advertisement system
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Cryo-EM Studies of TMEM16F Calcium-Activated Ion Channel Suggest Features Important for Lipid Scrambling.
As a Ca2+-activated lipid scramblase and ion channel that mediates Ca2+ influx, TMEM16F relies on both functions to facilitate extracellular vesicle generation, blood coagulation, and bone formation. How a bona fide ion channel scrambles lipids remains elusive. Our structural analyses revealed the coexistence of an intact channel pore and PIP2-dependent protein conformation changes leading to membrane distortion. Correlated to the extent of membrane distortion, many tightly bound lipids are slanted. Structure-based mutagenesis studies further reveal that neutralization of some lipid-binding residues or those near membrane distortion specifically alters the onset of lipid scrambling, but not Ca2+ influx, thus identifying features outside of channel pore that are important for lipid scrambling. Together, our studies demonstrate that membrane distortion does not require open hydrophilic grooves facing the membrane interior and provide further evidence to suggest separate pathways for lipid scrambling and ion permeation
Electromagnetic Spatiotemporal Differentiators
Spatiotemporal optical computing devices which could perform mathematical
operations in both spatial and temporal domains can provide unprecedented
measures to build efficient and real-time information processing systems. It is
particularly important to realize the comprehensive functions in a compact
design for better integration with electronic components. In this work, we
experimentally demonstrated an analogue spatiotemporal differentiator in
microwaves based on an asymmetrical metasurface which has a phase singularity
in the spatiotemporal domain. We showed that this structure could give rise to
a spatiotemporal transfer function required by an ideal first-order
differentiator in both spatial and temporal domains by tailoring the
unidirectional excitation of spoof surface plasmon polaritons (SSPPs). The
spatial edge detection was performed utilizing a metallic slit and the temporal
differentiation capability of the device was examined by Gaussian-like temporal
pulses of different width. We further confirmed the differentiator demonstrated
here could detect sharp changes of spatiotemporal pulses even with intricate
profiles and theoretically estimated the resolution limits of the spatial and
temporal edge detection. We also show that the pulse input after passing the
spatiotemporal differentiator implemented here could carry a transverse orbital
angular momentum (OAM) with a fractal topology charge which further increases
the information quantity.Comment: 6 figure
Aggregation-Induced Emission (AIE), Life and Health
Light has profoundly impacted modern medicine and healthcare, with numerous luminescent agents and imaging techniques currently being used to assess health and treat diseases. As an emerging concept in luminescence, aggregation-induced emission (AIE) has shown great potential in biological applications due to its advantages in terms of brightness, biocompatibility, photostability, and positive correlation with concentration. This review provides a comprehensive summary of AIE luminogens applied in imaging of biological structure and dynamic physiological processes, disease diagnosis and treatment, and detection and monitoring of specific analytes, followed by representative works. Discussions on critical issues and perspectives on future directions are also included. This review aims to stimulate the interest of researchers from different fields, including chemistry, biology, materials science, medicine, etc., thus promoting the development of AIE in the fields of life and health
A community challenge to evaluate RNA-seq, fusion detection, and isoform quantification methods for cancer discovery
The accurate identification and quantitation of RNA isoforms present in the cancer transcriptome is key for analyses ranging from the inference of the impacts of somatic variants to pathway analysis to biomarker development and subtype discovery. The ICGC-TCGA DREAM Somatic Mutation Calling in RNA (SMC-RNA) challenge was a crowd-sourced effort to benchmark methods for RNA isoform quantification and fusion detection from bulk cancer RNA sequencing (RNA-seq) data. It concluded in 2018 with a comparison of 77 fusion detection entries and 65 isoform quantification entries on 51 synthetic tumors and 32 cell lines with spiked-in fusion constructs. We report the entries used to build this benchmark, the leaderboard results, and the experimental features associated with the accurate prediction of RNA species. This challenge required submissions to be in the form of containerized workflows, meaning each of the entries described is easily reusable through CWL and Docker containers at https://github.com/SMC-RNA-challenge. A record of this paper's transparent peer review process is included in the supplemental information
Tethering-induced destabilization and ATP-binding for tandem RRM domains of ALS-causing TDP-43 and hnRNPA1
Abstract TDP-43 and hnRNPA1 contain tandemly-tethered RNA-recognition-motif (RRM) domains, which not only functionally bind an array of nucleic acids, but also participate in aggregation/fibrillation, a pathological hallmark of various human diseases including amyotrophic lateral sclerosis (ALS), frontotemporal dementia (FTD), alzheimer's disease (AD) and Multisystem proteinopathy (MSP). Here, by DSF, NMR and MD simulations we systematically characterized stability, ATP-binding and conformational dynamics of TDP-43 and hnRNPA1 RRM domains in both tethered and isolated forms. The results reveal three key findings: (1) upon tethering TDP-43 RRM domains become dramatically coupled and destabilized with Tm reduced to only 49 °C. (2) ATP specifically binds TDP-43 and hnRNPA1 RRM domains, in which ATP occupies the similar pockets within the conserved nucleic-acid-binding surfaces, with the affinity slightly higher to the tethered than isolated forms. (3) MD simulations indicate that the tethered RRM domains of TDP-43 and hnRNPA1 have higher conformational dynamics than the isolated forms. Two RRM domains become coupled as shown by NMR characterization and analysis of inter-domain correlation motions. The study explains the long-standing puzzle that the tethered TDP-43 RRM1–RRM2 is particularly prone to aggregation/fibrillation, and underscores the general role of ATP in inhibiting aggregation/fibrillation of RRM-containing proteins. The results also rationalize the observation that the risk of aggregation-causing diseases increases with aging