391 research outputs found
Keyword Search in Large-Scale Databases with Topic Cluster Units
To solve the inefficiency of the existing keyword search methods in large databases, this paper proposes TCU-based query, an offline query method based on topic cluster units. First, topic cluster units (TCUs) are constructed through vertical grouping and horizontal grouping on tables and tuples. In contrast to traditional keyword query methods, this offline method cannot only reduce the query response time, but also return results comprising richer and more complete semantic information. In order to further improve the efficiency of data preprocessing, an optimized solution for table join ordering based on the genetic algorithm is presented. Second, we select index terms using the association rule, and then we build an index on every topic cluster; by doing so we can improve the query speed significantly. Finally, we conduct extensive experiments to demonstrate that our approach greatly improves the performance of keyword search
Sub-national locations and FDI spillovers : theory and evidence
The welfare-enhancing role of spillovers from foreign direct investment (FDI) in a host country generates significant interests and debates among policymakers, long after a wide range of regulatory changes in favour of FDI in the late 1980s and the 1990s. The expectation of positive spillovers reinforces the development of government policies to attract multinational enterprises (MNEs) to the host country. However, as is documented in surveys of the literature on FDI spillovers (Görg and Strobl, 2001; Havránek and Irsová, 2012; Meyer and Sinani, 2009; Wooster and Diebel, 2010), the empirical evidence on FDI spillovers is rather mixed. The surveys highlight two important factors that might offer the explanations of mixed findings. First, the degree of foreign ownership is a primary factor in determining the strength of linkages between domestic and foreign firms and thereby affects spillovers (Javorcik and Spatareanu, 2008). As argued by Görg and Greenaway (2004), MNEs may be effective at preventing spillover effects of firm-specific assets. This is connected to the ownership strategies of MNEs that often use wholly owned subsidiaries (WOS) to better control the technologies they transfer to their foreign locations. Second, absorptive capacity of domestic firms and the strength of linkages between domestic and foreign firms are critical for spillovers. However, studies taking these factors into consideration are sparse. According to Havránek and Irsová (2012), among 1205 horizontal spillover estimates from 52 studies, only 5.7 per cent and 7.8 per cent control for absorptive capacity of domestic firms and the strength of linkages between domestic and foreign firms, respectively
Vital Sign Monitoring in Dynamic Environment via mmWave Radar and Camera Fusion
Contact-free vital sign monitoring, which uses wireless signals for
recognizing human vital signs (i.e, breath and heartbeat), is an attractive
solution to health and security. However, the subject's body movement and the
change in actual environments can result in inaccurate frequency estimation of
heartbeat and respiratory. In this paper, we propose a robust mmWave radar and
camera fusion system for monitoring vital signs, which can perform consistently
well in dynamic scenarios, e.g., when some people move around the subject to be
tracked, or a subject waves his/her arms and marches on the spot. Three major
processing modules are developed in the system, to enable robust sensing.
Firstly, we utilize a camera to assist a mmWave radar to accurately localize
the subjects of interest. Secondly, we exploit the calculated subject position
to form transmitting and receiving beamformers, which can improve the reflected
power from the targets and weaken the impact of dynamic interference. Thirdly,
we propose a weighted multi-channel Variational Mode Decomposition (WMC-VMD)
algorithm to separate the weak vital sign signals from the dynamic ones due to
subject's body movement. Experimental results show that, the 90
percentile errors in respiration rate (RR) and heartbeat rate (HR) are less
than 0.5 RPM (respirations per minute) and 6 BPM (beats per minute),
respectively
Distributed Optimal Vehicle Grid Integration Strategy with User Behavior Prediction
With the increasing of electric vehicle (EV) adoption in recent years, the
impact of EV charging activities to the power grid becomes more and more
significant. In this article, an optimal scheduling algorithm which combines
smart EV charging and V2G gird service is developed to integrate EVs into power
grid as distributed energy resources, with improved system cost performance.
Specifically, an optimization problem is formulated and solved at each EV
charging station according to control signal from aggregated control center and
user charging behavior prediction by mean estimation and linear regression. The
control center collects distributed optimization results and updates the
control signal, periodically. The iteration continues until it converges to
optimal scheduling. Experimental result shows this algorithm helps fill the
valley and shave the peak in electric load profiles within a microgrid, while
the energy demand of individual driver can be satisfied.Comment: IEEE PES General Meeting 201
Dynamics-aware Adversarial Attack of Adaptive Neural Networks
In this paper, we investigate the dynamics-aware adversarial attack problem
of adaptive neural networks. Most existing adversarial attack algorithms are
designed under a basic assumption -- the network architecture is fixed
throughout the attack process. However, this assumption does not hold for many
recently proposed adaptive neural networks, which adaptively deactivate
unnecessary execution units based on inputs to improve computational
efficiency. It results in a serious issue of lagged gradient, making the
learned attack at the current step ineffective due to the architecture change
afterward. To address this issue, we propose a Leaded Gradient Method (LGM) and
show the significant effects of the lagged gradient. More specifically, we
reformulate the gradients to be aware of the potential dynamic changes of
network architectures, so that the learned attack better "leads" the next step
than the dynamics-unaware methods when network architecture changes
dynamically. Extensive experiments on representative types of adaptive neural
networks for both 2D images and 3D point clouds show that our LGM achieves
impressive adversarial attack performance compared with the dynamic-unaware
attack methods
Synthesis and Characterization of an Amphiphilic Linoleic Acid-g-Quaternary Chitosan with Low Toxicity
A novel amphiphilic derivative of chitosan, namely, a linoleic acid-g-quaternary chitosan (LA-g-QC), was designed and synthesized as low toxic material for biomedical applications in this study. The chemical structure of LA-g-QC was characterized by Fourier transform infrared spectroscopy (FTIR), 1H nuclear magnetic resonance (1H-NMR), and elemental analysis. LA-g-QC could form nanosized micelles with self-assembly, which was confirmed by the results of critical micelle concentration (CMC) via fluorescence spectroscopy. The average size of LA-g-QC was 140 nm and its zeta potential was approximately +35.50 mV. CMC value was 31.00 mg/mL. Furthermore, LA-g-QC micelles, at final concentrations between 0.94 μg/mL and 30 μg/mL, did not inhibit the proliferation of HepG2 or SMMC 7721 cell lines. Taken together, LA-g-QC has low cytotoxicity and high potential for the preparation of novel drug-delivery micelles
Towards the Flatter Landscape and Better Generalization in Federated Learning under Client-level Differential Privacy
To defend the inference attacks and mitigate the sensitive information
leakages in Federated Learning (FL), client-level Differentially Private FL
(DPFL) is the de-facto standard for privacy protection by clipping local
updates and adding random noise. However, existing DPFL methods tend to make a
sharp loss landscape and have poor weight perturbation robustness, resulting in
severe performance degradation. To alleviate these issues, we propose a novel
DPFL algorithm named DP-FedSAM, which leverages gradient perturbation to
mitigate the negative impact of DP. Specifically, DP-FedSAM integrates
Sharpness Aware Minimization (SAM) optimizer to generate local flatness models
with improved stability and weight perturbation robustness, which results in
the small norm of local updates and robustness to DP noise, thereby improving
the performance. To further reduce the magnitude of random noise while
achieving better performance, we propose DP-FedSAM- by adopting the
local update sparsification technique. From the theoretical perspective, we
present the convergence analysis to investigate how our algorithms mitigate the
performance degradation induced by DP. Meanwhile, we give rigorous privacy
guarantees with R\'enyi DP, the sensitivity analysis of local updates, and
generalization analysis. At last, we empirically confirm that our algorithms
achieve state-of-the-art (SOTA) performance compared with existing SOTA
baselines in DPFL.Comment: 20 pages. arXiv admin note: substantial text overlap with
arXiv:2303.1124
Towards More Suitable Personalization in Federated Learning via Decentralized Partial Model Training
Personalized federated learning (PFL) aims to produce the greatest
personalized model for each client to face an insurmountable problem--data
heterogeneity in real FL systems. However, almost all existing works have to
face large communication burdens and the risk of disruption if the central
server fails. Only limited efforts have been used in a decentralized way but
still suffers from inferior representation ability due to sharing the full
model with its neighbors. Therefore, in this paper, we propose a personalized
FL framework with a decentralized partial model training called DFedAlt. It
personalizes the "right" components in the modern deep models by alternately
updating the shared and personal parameters to train partially personalized
models in a peer-to-peer manner. To further promote the shared parameters
aggregation process, we propose DFedSalt integrating the local Sharpness Aware
Minimization (SAM) optimizer to update the shared parameters. It adds proper
perturbation in the direction of the gradient to overcome the shared model
inconsistency across clients. Theoretically, we provide convergence analysis of
both algorithms in the general non-convex setting for decentralized partial
model training in PFL. Our experiments on several real-world data with various
data partition settings demonstrate that (i) decentralized training is more
suitable for partial personalization, which results in state-of-the-art (SOTA)
accuracy compared with the SOTA PFL baselines; (ii) the shared parameters with
proper perturbation make partial personalized FL more suitable for
decentralized training, where DFedSalt achieves most competitive performance.Comment: 26 page
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