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${^{th}}$ 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

A new perspective from hypertournaments to tournaments

A $k$-tournament $H$ on $n$ vertices is a pair $(V, A)$ for $2\leq k\leq n$, where $V(H)$ is a set of vertices, and $A(H)$ is a set of all possible $k$-tuples of vertices, such that for any $k$-subset $S$ of $V$, $A(H)$ contains exactly one of the $k!$ possible permutations of $S$. In this paper, we investigate the relationship between a hyperdigraph and its corresponding normal digraph. Particularly, drawing on a result from Gutin and Yeo, we establish an intrinsic relationship between a strong $k$-tournament and a strong tournament, which enables us to provide an alternative (more straightforward and concise) proof for some previously known results and get some new results.Comment: 10 page

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

Finding emergence in data by maximizing effective information

Quantifying emergence and modeling emergent dynamics in a data-driven manner for complex dynamical systems is challenging due to the lack of direct observations at the micro-level. Thus, it's crucial to develop a framework to identify emergent phenomena and capture emergent dynamics at the macro-level using available data. Inspired by the theory of causal emergence (CE), this paper introduces a machine learning framework to learn macro-dynamics in an emergent latent space and quantify the degree of CE. The framework maximizes effective information, resulting in a macro-dynamics model with enhanced causal effects. Experimental results on simulated and real data demonstrate the effectiveness of the proposed framework. It quantifies degrees of CE effectively under various conditions and reveals distinct influences of different noise types. It can learn a one-dimensional coarse-grained macro-state from fMRI data, to represent complex neural activities during movie clip viewing. Furthermore, improved generalization to different test environments is observed across all simulation data