198 research outputs found
An Efficient Source Model Selection Framework in Model Databases
With the explosive increase of big data, training a Machine Learning (ML)
model becomes a computation-intensive workload, which would take days or even
weeks. Thus, reusing an already trained model has received attention, which is
called transfer learning. Transfer learning avoids training a new model from
scratch by transferring knowledge from a source task to a target task. Existing
transfer learning methods mostly focus on how to improve the performance of the
target task through a specific source model, and assume that the source model
is given. Although many source models are available, it is difficult for data
scientists to select the best source model for the target task manually. Hence,
how to efficiently select a suitable source model in a model database for model
reuse is an interesting but unsolved problem. In this paper, we propose SMS, an
effective, efficient, and flexible source model selection framework. SMS is
effective even when the source and target datasets have significantly different
data labels, and is flexible to support source models with any type of
structure, and is efficient to avoid any training process. For each source
model, SMS first vectorizes the samples in the target dataset into soft labels
by directly applying this model to the target dataset, then uses Gaussian
distributions to fit for clusters of soft labels, and finally measures the
distinguishing ability of the source model using Gaussian mixture-based metric.
Moreover, we present an improved SMS (I-SMS), which decreases the output number
of the source model. I-SMS can significantly reduce the selection time while
retaining the selection performance of SMS. Extensive experiments on a range of
practical model reuse workloads demonstrate the effectiveness and efficiency of
SMS
Secured and Cooperative Publish/Subscribe Scheme in Autonomous Vehicular Networks
In order to save computing power yet enhance safety, there is a strong
intention for autonomous vehicles (AVs) in future to drive collaboratively by
sharing sensory data and computing results among neighbors. However, the
intense collaborative computing and data transmissions among unknown others
will inevitably introduce severe security concerns. Aiming at addressing
security concerns in future AVs, in this paper, we develop SPAD, a secured
framework to forbid free-riders and {promote trustworthy data dissemination} in
collaborative autonomous driving. Specifically, we first introduce a
publish/subscribe framework for inter-vehicle data transmissions{. To defend
against free-riding attacks,} we formulate the interactions between publisher
AVs and subscriber AVs as a vehicular publish/subscribe game, {and incentivize
AVs to deliver high-quality data by analyzing the Stackelberg equilibrium of
the game. We also design a reputation evaluation mechanism in the game} to
identify malicious AVs {in disseminating fake information}. {Furthermore, for}
lack of sufficient knowledge on parameters of {the} network model and user cost
model {in dynamic game scenarios}, a two-tier reinforcement learning based
algorithm with hotbooting is developed to obtain the optimal {strategies of
subscriber AVs and publisher AVs with free-rider prevention}. Extensive
simulations are conducted, and the results validate that our SPAD can
effectively {prevent free-riders and enhance the dependability of disseminated
contents,} compared with conventional schemes
Knowledge-refined Denoising Network for Robust Recommendation
Knowledge graph (KG), which contains rich side information, becomes an
essential part to boost the recommendation performance and improve its
explainability. However, existing knowledge-aware recommendation methods
directly perform information propagation on KG and user-item bipartite graph,
ignoring the impacts of \textit{task-irrelevant knowledge propagation} and
\textit{vulnerability to interaction noise}, which limits their performance. To
solve these issues, we propose a robust knowledge-aware recommendation
framework, called \textit{Knowledge-refined Denoising Network} (KRDN), to prune
the task-irrelevant knowledge associations and noisy implicit feedback
simultaneously. KRDN consists of an adaptive knowledge refining strategy and a
contrastive denoising mechanism, which are able to automatically distill
high-quality KG triplets for aggregation and prune noisy implicit feedback
respectively. Besides, we also design the self-adapted loss function and the
gradient estimator for model optimization. The experimental results on three
benchmark datasets demonstrate the effectiveness and robustness of KRDN over
the state-of-the-art knowledge-aware methods like KGIN, MCCLK, and KGCL, and
also outperform robust recommendation models like SGL and SimGCL
Endoscopic rhizotomy for chronic lumbar zygapophysial joint pain.
BACKGROUND: Chronic lumbar zygapophysial joint pain is a common cause of chronic low back pain. Percutaneous radiofrequency ablation (RFA) is one of the effective management options; however, the results from the traditional RFA need to be improved in certain cases. The aim of this study is to investigate the effect of percutaneous radiofrequency ablation under endoscopic guidance (ERFA) for chronic low back pain secondary to facet joint arthritis.
METHODS: This is a prospective study enrolled 60 patients. The cases were randomized into two groups: 30 patients in the control group underwent traditional percutaneous radiofrequency ablation, others underwent ERFA. The lumbar visual analog scale (VAS), MacNab score, and postoperative complications were used to evaluate the outcomes. All outcome assessments were performed at postoperative 1 day, 1 month, 3 months, 6 months, and 12 months.
RESULTS: There was no difference between the two groups in preoperative VAS (P \u3e 0.05). VAS scores, except the postoperative first day, in all other postoperative time points were significantly lower than preoperative values each in both groups (P \u3c 0.05). There was no significant difference between the two groups in VAS at 1 day, 1 month, and 3 months after surgery (P \u3e 0.05). However, the EFRA demonstrated significant benefits at the time points of 3 months and 6 months (P \u3e 0.05). The MacNab scores of 1-year follow-up in the ERFA group were higher than that in the control group (P \u3c 0.05). The incidence of complications in the ERFA group was significantly less than that in the control group (P \u3c 0.05).
CONCLUSIONS: ERFA may achieve more accurate and definite denervation on the nerves, which leads to longer lasting pain relief
Ghost in the Minecraft: Generally Capable Agents for Open-World Enviroments via Large Language Models with Text-based Knowledge and Memory
The captivating realm of Minecraft has attracted substantial research
interest in recent years, serving as a rich platform for developing intelligent
agents capable of functioning in open-world environments. However, the current
research landscape predominantly focuses on specific objectives, such as the
popular "ObtainDiamond" task, and has not yet shown effective generalization to
a broader spectrum of tasks. Furthermore, the current leading success rate for
the "ObtainDiamond" task stands at around 20%, highlighting the limitations of
Reinforcement Learning (RL) based controllers used in existing methods. To
tackle these challenges, we introduce Ghost in the Minecraft (GITM), a novel
framework integrates Large Language Models (LLMs) with text-based knowledge and
memory, aiming to create Generally Capable Agents (GCAs) in Minecraft. These
agents, equipped with the logic and common sense capabilities of LLMs, can
skillfully navigate complex, sparse-reward environments with text-based
interactions. We develop a set of structured actions and leverage LLMs to
generate action plans for the agents to execute. The resulting LLM-based agent
markedly surpasses previous methods, achieving a remarkable improvement of
+47.5% in success rate on the "ObtainDiamond" task, demonstrating superior
robustness compared to traditional RL-based controllers. Notably, our agent is
the first to procure all items in the Minecraft Overworld technology tree,
demonstrating its extensive capabilities. GITM does not need any GPU for
training, but a single CPU node with 32 CPU cores is enough. This research
shows the potential of LLMs in developing capable agents for handling
long-horizon, complex tasks and adapting to uncertainties in open-world
environments. See the project website at https://github.com/OpenGVLab/GITM
Bubble Trajectory Tracking Based on ORB Algorithm
The system of gas-liquid two-phase bubbly flows is widely found in many industrial fields, such as nuclear energy, chemical, petroleum, and refrigeration. Bubbly two-phase flows measuring including detection and tracking affects the specific engineering problem solving to a great extent. The particle tracking velocity (PTV) algorithm is generally used for the tracking of the particles in the flow field. However, it does not take the shape change of particles into account in the process of flow. In this paper, a kind of bubble feature matching method based on ORB algorithm is proposed, and the edge detection method of findContours in OpenCV is used to extract the bubble contour in the image. The proposed algorithm implements the trajectory tracking of the bubbles with shape change when moving up in liquid. The feasibility of bubble trajectory tracking is shown by displaying of different bubble tracks in the plan, 3D plots and contour changing plots
Dual Semantic Fusion Network for Video Object Detection
Video object detection is a tough task due to the deteriorated quality of
video sequences captured under complex environments. Currently, this area is
dominated by a series of feature enhancement based methods, which distill
beneficial semantic information from multiple frames and generate enhanced
features through fusing the distilled information. However, the distillation
and fusion operations are usually performed at either frame level or instance
level with external guidance using additional information, such as optical flow
and feature memory. In this work, we propose a dual semantic fusion network
(abbreviated as DSFNet) to fully exploit both frame-level and instance-level
semantics in a unified fusion framework without external guidance. Moreover, we
introduce a geometric similarity measure into the fusion process to alleviate
the influence of information distortion caused by noise. As a result, the
proposed DSFNet can generate more robust features through the multi-granularity
fusion and avoid being affected by the instability of external guidance. To
evaluate the proposed DSFNet, we conduct extensive experiments on the ImageNet
VID dataset. Notably, the proposed dual semantic fusion network achieves, to
the best of our knowledge, the best performance of 84.1\% mAP among the current
state-of-the-art video object detectors with ResNet-101 and 85.4\% mAP with
ResNeXt-101 without using any post-processing steps.Comment: 9 pages,6 figure
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