53 research outputs found
Model-based State-of-energy Estimation of Lithium-ion Batteries in Electric Vehicles
AbstractWith the increasing application of lithium-ion batteries, the function of battery management system (BMS) comes to be more sophisticated. The state-of-energy (SOE) of lithium-ion batteries is a critical index for energy optimization and management in electric vehicles. The conventional power integral methods are easy to cause accumulated error due to current or voltage drift of sensors. Therefore the EKF method is employed in this study. A data-driven model is established to describe the relationship between the open-circuit voltage (OCV) and SOE based on the experimental data of a Li(Ni1/3Co1/3Mn1/3)O2 battery. The dynamic urban driving schedule of Wuhui city in China has been conducted on the lithium-ion battery to verify the accuracy of the proposed method. The results show that accurate SOE estimation results can be obtained by the proposed method
PEPNet: Parameter and Embedding Personalized Network for Infusing with Personalized Prior Information
With the increase of content pages and interactive buttons in online services
such as online-shopping and video-watching websites, industrial-scale
recommender systems face challenges in multi-domain and multi-task
recommendations. The core of multi-task and multi-domain recommendation is to
accurately capture user interests in multiple scenarios given multiple user
behaviors. In this paper, we propose a plug-and-play \textit{\textbf{P}arameter
and \textbf{E}mbedding \textbf{P}ersonalized \textbf{Net}work
(\textbf{PEPNet})} for multi-domain and multi-task recommendation. PEPNet takes
personalized prior information as input and dynamically scales the bottom-level
Embedding and top-level DNN hidden units through gate mechanisms.
\textit{Embedding Personalized Network (EPNet)} performs personalized selection
on Embedding to fuse features with different importance for different users in
multiple domains. \textit{Parameter Personalized Network (PPNet)} executes
personalized modification on DNN parameters to balance targets with different
sparsity for different users in multiple tasks. We have made a series of
special engineering optimizations combining the Kuaishou training framework and
the online deployment environment. By infusing personalized selection of
Embedding and personalized modification of DNN parameters, PEPNet tailored to
the interests of each individual obtains significant performance gains, with
online improvements exceeding 1\% in multiple task metrics across multiple
domains. We have deployed PEPNet in Kuaishou apps, serving over 300 million
users every day.Comment: Accepted by KDD 202
BLAC: A Blockchain-based Lightweight Access Control Scheme in Vehicular Social Networks
Vehicular Social Networks (VSNs) rely on data shared by users to provide convenient services. Data is outsourced to the cloud server and the distributed roadside unit in VSNs. However, roadside unit has limited resources, so that data sharing process is inefficient and is vulnerable to security threats, such as illegal access, tampering attack and collusion attack. In this article, to overcome the shortcomings of security, we define a chain tolerance semi-trusted model to describe the credibility of distributed group based on the anti tampering feature of blockchain. We further propose a Blockchain-based Lightweight Access Control scheme in VSNs that resist tampering and collusion attacks, called BLAC. To overcome the shortcomings of efficiency, we design a ciphertext piece storage algorithm and a recovery one to achieve lightweight storage cost. In the decryption operation, we separate a pre-decryption algorithm based on outsourcing to achieve lightweight decryption computation cost on the user side. Finally, we present the formal security analyses and the simulation experiments for BLAC, and compare the results of experiments with existing relevant schemes. The security analyses show that our scheme is secure, and the results of experiments show that our scheme is lightweight and practical
TWIN: TWo-stage Interest Network for Lifelong User Behavior Modeling in CTR Prediction at Kuaishou
Life-long user behavior modeling, i.e., extracting a user's hidden interests
from rich historical behaviors in months or even years, plays a central role in
modern CTR prediction systems. Conventional algorithms mostly follow two
cascading stages: a simple General Search Unit (GSU) for fast and coarse search
over tens of thousands of long-term behaviors and an Exact Search Unit (ESU)
for effective Target Attention (TA) over the small number of finalists from
GSU. Although efficient, existing algorithms mostly suffer from a crucial
limitation: the \textit{inconsistent} target-behavior relevance metrics between
GSU and ESU. As a result, their GSU usually misses highly relevant behaviors
but retrieves ones considered irrelevant by ESU. In such case, the TA in ESU,
no matter how attention is allocated, mostly deviates from the real user
interests and thus degrades the overall CTR prediction accuracy. To address
such inconsistency, we propose \textbf{TWo-stage Interest Network (TWIN)},
where our Consistency-Preserved GSU (CP-GSU) adopts the identical
target-behavior relevance metric as the TA in ESU, making the two stages twins.
Specifically, to break TA's computational bottleneck and extend it from ESU to
GSU, or namely from behavior length to length , we build a
novel attention mechanism by behavior feature splitting. For the video inherent
features of a behavior, we calculate their linear projection by efficient
pre-computing \& caching strategies. And for the user-item cross features, we
compress each into a one-dimentional bias term in the attention score
calculation to save the computational cost. The consistency between two stages,
together with the effective TA-based relevance metric in CP-GSU, contributes to
significant performance gain in CTR prediction.Comment: Accepted by KDD 202
Locally advanced head and neck squamous cell carcinoma treatment efficacy and safety: a systematic review and network meta-analysis
Head and neck squamous cell carcinoma (HNSCC) accounts for approximately 3% of new cancer cases and 3% of all deaths worldwide. Most HNSCC patients are locally advanced (LA) at diagnosis. The combination of radiotherapy (RT), chemotherapy, targeted therapy, and immunotherapy are the primary LA-HNSCC treatment options. Nevertheless, the choice of optimal LA-HNSCC treatment remains controversial. We systematically searched public databases for LA-HNSCC-related studies and assess treatment effectiveness and safety by assessing the objective response rate (ORR), ≥3 adverse events (AEs), overall survival (OS), progression-free survival (PFS), disease-free survival (DFS), local-region control (LRC), and disease-specific survival (DSS). 126 randomized controlled clinical trials (RCTs) were included in this study. We show that concurrent RT with nimotuzumab or conventional concurrent chemo-radiotherapy (CCRT) had significantly better efficacy and long-term survival without increasing AEs than RT alone. Accelerated fractionated radiotherapy (AFRT) showed better efficiency than conventional fractionated RT, although it had higher AEs. In addition, concurrent cetuximab combined with RT failed to show a significant advantage over RT alone.Trial registration: PROSPERO CRD42022352127
Inferring prototypes for multi-label few-shot image classification with word vector guided attention
Artificial General Intelligence for Radiation Oncology
The emergence of artificial general intelligence (AGI) is transforming
radiation oncology. As prominent vanguards of AGI, large language models (LLMs)
such as GPT-4 and PaLM 2 can process extensive texts and large vision models
(LVMs) such as the Segment Anything Model (SAM) can process extensive imaging
data to enhance the efficiency and precision of radiation therapy. This paper
explores full-spectrum applications of AGI across radiation oncology including
initial consultation, simulation, treatment planning, treatment delivery,
treatment verification, and patient follow-up. The fusion of vision data with
LLMs also creates powerful multimodal models that elucidate nuanced clinical
patterns. Together, AGI promises to catalyze a shift towards data-driven,
personalized radiation therapy. However, these models should complement human
expertise and care. This paper provides an overview of how AGI can transform
radiation oncology to elevate the standard of patient care in radiation
oncology, with the key insight being AGI's ability to exploit multimodal
clinical data at scale
RadOnc-GPT: A Large Language Model for Radiation Oncology
This paper presents RadOnc-GPT, a large language model specialized for
radiation oncology through advanced tuning methods. RadOnc-GPT was finetuned on
a large dataset of radiation oncology patient records and clinical notes from
the Mayo Clinic in Arizona. The model employs instruction tuning on three key
tasks - generating radiotherapy treatment regimens, determining optimal
radiation modalities, and providing diagnostic descriptions/ICD codes based on
patient diagnostic details. Evaluations conducted by comparing RadOnc-GPT
outputs to general large language model outputs showed that RadOnc-GPT
generated outputs with significantly improved clarity, specificity, and
clinical relevance. The study demonstrated the potential of using large
language models fine-tuned using domain-specific knowledge like RadOnc-GPT to
achieve transformational capabilities in highly specialized healthcare fields
such as radiation oncology
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