60 research outputs found
A Reduction of the Elastic Net to Support Vector Machines with an Application to GPU Computing
The past years have witnessed many dedicated open-source projects that built
and maintain implementations of Support Vector Machines (SVM), parallelized for
GPU, multi-core CPUs and distributed systems. Up to this point, no comparable
effort has been made to parallelize the Elastic Net, despite its popularity in
many high impact applications, including genetics, neuroscience and systems
biology. The first contribution in this paper is of theoretical nature. We
establish a tight link between two seemingly different algorithms and prove
that Elastic Net regression can be reduced to SVM with squared hinge loss
classification. Our second contribution is to derive a practical algorithm
based on this reduction. The reduction enables us to utilize prior efforts in
speeding up and parallelizing SVMs to obtain a highly optimized and parallel
solver for the Elastic Net and Lasso. With a simple wrapper, consisting of only
11 lines of MATLAB code, we obtain an Elastic Net implementation that naturally
utilizes GPU and multi-core CPUs. We demonstrate on twelve real world data
sets, that our algorithm yields identical results as the popular (and highly
optimized) glmnet implementation but is one or several orders of magnitude
faster.Comment: 10 page
Collaborative Edge Caching: a Meta Reinforcement Learning Approach with Edge Sampling
Current learning-based edge caching schemes usually suffer from dynamic
content popularity, e.g., in the emerging short video platforms, users' request
patterns shift significantly over time and across different edges. An intuitive
solution for a specific local edge cache is to collect more request histories
from other edge caches. However, uniformly merging these request histories may
not perform satisfactorily due to heterogeneous content distributions on
different edges. To solve this problem, we propose a collaborative edge caching
framework. First, we design a meta-learning-based collaborative strategy to
guarantee that the local model can timely meet the continually changing content
popularity. Then, we design an edge sampling method to select more "valuable"
neighbor edges to participate in the local training. To evaluate the proposed
framework, we conduct trace-driven experiments to demonstrate the effectiveness
of our design: it improves the average cache hit rate by up to
(normalized) compared with other baselines.Comment: Published on IEEE International Conference on Multimedia and Expo
2023 (ICME2023
Ultrasound-targeted microbubble destruction mediated herpes simplex virus-thymidine kinase gene treats hepatoma in mice
<p>Abstract</p> <p>Objective</p> <p>The purpose of the study was to explore the anti-tumor effect of ultrasound -targeted microbubble destruction mediated herpes simplex virus thymidine kinase (HSV-TK) suicide gene system on mice hepatoma.</p> <p>Methods</p> <p>Forty mice were randomly divided into four groups after the models of subcutaneous transplantation tumors were estabilished: (1) PBS; (2) HSV-TK (3) HSV-TK+ ultrasound (HSV-TK+US); (4) HSV-TK+ultrasound+microbubbles (HSV-TK+US+MB). The TK protein expression in liver cancer was detected by western-blot. Applying TUNEL staining detected tumor cell apoptosis. At last, the inhibition rates and survival time of the animals were compared among all groups.</p> <p>Results</p> <p>The TK protein expression of HSV-TK+MB+US group in tumor-bearing mice tissues were significantly higher than those in other groups. The tumor inhibitory effect of ultrasound-targeted microbubble destruction mediated HSV-TK on mice transplantable tumor was significantly higher than those in other groups (p < 0.05), and can significantly improve the survival time of tumor-bearing mice.</p> <p>Conclusion</p> <p>Ultrasound-targeted microbubble destruction can effectively transfect HSV-TK gene into target tissues and play a significant inhibition effect on tumors, which provides a new strategy for gene therapy in liver cancer.</p
Robust K-Median and K-Means Clustering Algorithms for Incomplete Data
Incomplete data with missing feature values are prevalent in clustering problems. Traditional clustering methods first estimate the missing values by imputation and then apply the classical clustering algorithms for complete data, such as K-median and Kmeans. However, in practice, it is often hard to obtain accurate estimation of the missing values, which deteriorates the performance of clustering. To enhance the robustness of clustering algorithms, this paper represents the missing values by interval data and introduces the concept of robust cluster objective function. A minimax robust optimization (RO) formulation is presented to provide clustering results, which are insensitive to estimation errors. To solve the proposed RO problem, we propose robust K-median and K-means clustering algorithms with low time and space complexity. Comparisons and analysis of experimental results on both artificially generated and real-world incomplete data sets validate the robustness and effectiveness of the proposed algorithms
Robust K-Median and K-Means Clustering Algorithms for Incomplete Data
Incomplete data with missing feature values are prevalent in clustering problems. Traditional clustering methods first estimate the missing values by imputation and then apply the classical clustering algorithms for complete data, such as K-median and K-means. However, in practice, it is often hard to obtain accurate estimation of the missing values, which deteriorates the performance of clustering. To enhance the robustness of clustering algorithms, this paper represents the missing values by interval data and introduces the concept of robust cluster objective function. A minimax robust optimization (RO) formulation is presented to provide clustering results, which are insensitive to estimation errors. To solve the proposed RO problem, we propose robust K-median and K-means clustering algorithms with low time and space complexity. Comparisons and analysis of experimental results on both artificially generated and real-world incomplete data sets validate the robustness and effectiveness of the proposed algorithms
A Multilayer Perceptron-based Fast Sunlight Assessment for the Conceptual Design of Residential Neighborhoods under Chinese Policy
In Chinese building codes, it is required that residential buildings receive
a minimum number of hours of natural, direct sunlight on a specified winter
day, which represents the worst sunlight condition in a year. This requirement
is a prerequisite for obtaining a building permit during the conceptual design
of a residential project. Thus, officially sanctioned software is usually used
to assess the sunlight performance of buildings. These software programs
predict sunlight hours based on repeated shading calculations, which is
time-consuming. This paper proposed a multilayer perceptron-based method, a
one-stage prediction approach, which outputs a shading time interval caused by
the inputted cuboid-form building. The sunlight hours of a site can be obtained
by calculating the union of the sunlight time intervals (complement of shading
time interval) of all the buildings. Three numerical experiments, i.e.,
horizontal level and slope analysis, and simulation-based optimization are
carried out; the results show that the method reduces the computation time to
1/84~1/50 with 96.5%~98% accuracies. A residential neighborhood layout planning
plug-in for Rhino 7/Grasshopper is also developed based on the proposed model.
This paper indicates that deep learning techniques can be adopted to accelerate
sunlight hour simulations at the conceptual design phase
Revisiting Adversarial Attacks on Graph Neural Networks for Graph Classification
Graph neural networks (GNNs) have achieved tremendous success in the task of
graph classification and its diverse downstream real-world applications.
Despite the huge success in learning graph representations, current GNN models
have demonstrated their vulnerability to potentially existent adversarial
examples on graph-structured data. Existing approaches are either limited to
structure attacks or restricted to local information, urging for the design of
a more general attack framework on graph classification, which faces
significant challenges due to the complexity of generating local-node-level
adversarial examples using the global-graph-level information. To address this
"global-to-local" attack challenge, we present a novel and general framework to
generate adversarial examples via manipulating graph structure and node
features. Specifically, we make use of Graph Class Activation Mapping and its
variant to produce node-level importance corresponding to the graph
classification task. Then through a heuristic design of algorithms, we can
perform both feature and structure attacks under unnoticeable perturbation
budgets with the help of both node-level and subgraph-level importance.
Experiments towards attacking four state-of-the-art graph classification models
on six real-world benchmarks verify the flexibility and effectiveness of our
framework.Comment: 13 pages, 7 figure
Marketing Budget Allocation with Offline Constrained Deep Reinforcement Learning
We study the budget allocation problem in online marketing campaigns that
utilize previously collected offline data. We first discuss the long-term
effect of optimizing marketing budget allocation decisions in the offline
setting. To overcome the challenge, we propose a novel game-theoretic offline
value-based reinforcement learning method using mixed policies. The proposed
method reduces the need to store infinitely many policies in previous methods
to only constantly many policies, which achieves nearly optimal policy
efficiency, making it practical and favorable for industrial usage. We further
show that this method is guaranteed to converge to the optimal policy, which
cannot be achieved by previous value-based reinforcement learning methods for
marketing budget allocation. Our experiments on a large-scale marketing
campaign with tens-of-millions users and more than one billion budget verify
the theoretical results and show that the proposed method outperforms various
baseline methods. The proposed method has been successfully deployed to serve
all the traffic of this marketing campaign.Comment: WSDM 23, Best Paper Candidat
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