40,853 research outputs found
Who gets caught for corruption when corruption is pervasive? Evidence from China’s anti-bribery blacklist
© 2016 Informa UK Limited, trading as Taylor & Francis Group. This article empirically investigates why in a corruption-pervasive country only a minority of the firms get caught for bribery while the majority get away with it. By matching manufacturing firms to a blacklist of bribers in the healthcare sector of a province in China, we show that the government-led blacklisting is selective: while economically more visible firms are slightly more likely to be blacklisted, state-controlled firms are the most protected compared to their private and foreign competitors. Our finding points to the fact that a government can use regulations to impose its preferences when the rule of law is weak and the rule of government is strong
Recommended from our members
Optimal coverage multi-path scheduling scheme with multiple mobile sinks for WSNs
Wireless Sensor Networks (WSNs) are usually formed with many tiny sensors which are randomly deployed within sensing field for target monitoring. These sensors can transmit their monitored data to the sink in a multi-hop communication manner. However, the ‘hot spots’ problem will be caused since nodes near sink will consume more energy during forwarding. Recently, mobile sink based technology provides an alternative solution for the long-distance communication and sensor nodes only need to use single hop communication to the mobile sink during data transmission. Even though it is difficult to consider many network metrics such as sensor position, residual energy and coverage rate etc., it is still very important to schedule a reasonable moving trajectory for the mobile sink. In this paper, a novel trajectory scheduling method based on coverage rate for multiple mobile sinks (TSCR-M) is presented especially for large-scale WSNs. An improved particle swarm optimization (PSO) combined with mutation operator is introduced to search the parking positions with optimal coverage rate. Then the genetic algorithm (GA) is adopted to schedule the moving trajectory for multiple mobile sinks. Extensive simulations are performed to validate the performance of our proposed method
Frequency Recognition in SSVEP-based BCI using Multiset Canonical Correlation Analysis
Canonical correlation analysis (CCA) has been one of the most popular methods
for frequency recognition in steady-state visual evoked potential (SSVEP)-based
brain-computer interfaces (BCIs). Despite its efficiency, a potential problem
is that using pre-constructed sine-cosine waves as the required reference
signals in the CCA method often does not result in the optimal recognition
accuracy due to their lack of features from the real EEG data. To address this
problem, this study proposes a novel method based on multiset canonical
correlation analysis (MsetCCA) to optimize the reference signals used in the
CCA method for SSVEP frequency recognition. The MsetCCA method learns multiple
linear transforms that implement joint spatial filtering to maximize the
overall correlation among canonical variates, and hence extracts SSVEP common
features from multiple sets of EEG data recorded at the same stimulus
frequency. The optimized reference signals are formed by combination of the
common features and completely based on training data. Experimental study with
EEG data from ten healthy subjects demonstrates that the MsetCCA method
improves the recognition accuracy of SSVEP frequency in comparison with the CCA
method and other two competing methods (multiway CCA (MwayCCA) and phase
constrained CCA (PCCA)), especially for a small number of channels and a short
time window length. The superiority indicates that the proposed MsetCCA method
is a new promising candidate for frequency recognition in SSVEP-based BCIs
Regional economic status inference from information flow and talent mobility
Novel data has been leveraged to estimate socioeconomic status in a timely
manner, however, direct comparison on the use of social relations and talent
movements remains rare. In this letter, we estimate the regional economic
status based on the structural features of the two networks. One is the online
information flow network built on the following relations on social media, and
the other is the offline talent mobility network built on the anonymized resume
data of job seekers with higher education. We find that while the structural
features of both networks are relevant to economic status, the talent mobility
network in a relatively smaller size exhibits a stronger predictive power for
the gross domestic product (GDP). In particular, a composite index of
structural features can explain up to about 84% of the variance in GDP. The
result suggests future socioeconomic studies to pay more attention to the
cost-effective talent mobility data.Comment: 7 pages, 5 figures, 2 table
- …