[DE]construction and [RE]construction : a promenade theater

REFRAME THE COMMONPLACE I see corners, ceilings, openings and steps. I see the whole through gathering the spatial fragmentations. I see the extraordinary phenomenon within the frame of domestic life. I see the potential for being anomaly from what we usually take for granted. Spaces are entities propelled by the phenomenological, the conceptual and the physical. Built and spatial forms serves as functional and structural vessel to capture, balance and rearrange the multiple forces between the common and the abnormal, between the real and the abstract. Capturing the domestic moments in daily life and reframe the fragmentations into certain from as a whole. Through strategic but simple maneuvers of the architectural language, simple but significant manipulations could lead to a extracted but powerful existence

The costs of socializing with government officials: A new measure of corporate political connections

AbstractWe construct a new measure to capture corporate political connections, which is based on the amount a corporation spends on socializing with government officials. We examine the validity of this measure using the exogenous turnover of top local officials and find that firms increase their political networking expenditures when top local officials are replaced. Compared to state-owned enterprises (SOEs), non-state-owned enterprises (non-SOEs) react more aggressively to changes in local officials, which implies that non-SOEs have stronger incentives to build political connections with officials through social intercourse. We also find that firms located in regions with low levels of marketization react more aggressively to the turnover of local politicians. In addition, we find a positive effect of corporate political socializing expenditures on corporate performance and valuation, which suggests that political connections built through social intercourse benefit corporations

Long Short-Term Memory with Dynamic Skip Connections

In recent years, long short-term memory (LSTM) has been successfully used to model sequential data of variable length. However, LSTM can still experience difficulty in capturing long-term dependencies. In this work, we tried to alleviate this problem by introducing a dynamic skip connection, which can learn to directly connect two dependent words. Since there is no dependency information in the training data, we propose a novel reinforcement learning-based method to model the dependency relationship and connect dependent words. The proposed model computes the recurrent transition functions based on the skip connections, which provides a dynamic skipping advantage over RNNs that always tackle entire sentences sequentially. Our experimental results on three natural language processing tasks demonstrate that the proposed method can achieve better performance than existing methods. In the number prediction experiment, the proposed model outperformed LSTM with respect to accuracy by nearly 20%

An Efficient LS-SVM-Based Method for Fuzzy System Construction

This paper proposes an efficient learning mechanism to build fuzzy rule-based systems through the construction of sparse least-squares support vector machines (LS-SVMs). In addition to the significantly reduced computational complexity in model training, the resultant LS-SVM-based fuzzy system is sparser while offers satisfactory generalization capability over unseen data. It is well known that the LS-SVMs have their computational advantage over conventional SVMs in the model training process; however, the model sparseness is lost, which is the main drawback of LS-SVMs. This is an open problem for the LS-SVMs. To tackle the nonsparseness issue, a new regression alternative to the Lagrangian solution for the LS-SVM is first presented. A novel efficient learning mechanism is then proposed in this paper to extract a sparse set of support vectors for generating fuzzy if-then rules. This novel mechanism works in a stepwise subset selection manner, including a forward expansion phase and a backward exclusion phase in each selection step. The implementation of the algorithm is computationally very efficient due to the introduction of a few key techniques to avoid the matrix inverse operations to accelerate the training process. The computational efficiency is also confirmed by detailed computational complexity analysis. As a result, the proposed approach is not only able to achieve the sparseness of the resultant LS-SVM-based fuzzy systems but significantly reduces the amount of computational effort in model training as well. Three experimental examples are presented to demonstrate the effectiveness and efficiency of the proposed learning mechanism and the sparseness of the obtained LS-SVM-based fuzzy systems, in comparison with other SVM-based learning techniques

Security enhancement for NOMA-UAV networks

Owing to its distinctive merits, non-orthogonal multiple access (NOMA) techniques have been utilized in unmanned aerial vehicle (UAV) enabled wireless base stations to provide effective coverage for terrestrial users. However, the security of NOMA-UAV systems remains a challenge due to the line-of-sight air-to-ground channels and higher transmission power of weaker users in NOMA. In this paper, we propose two schemes to guarantee the secure transmission in UAV-NOMA networks. When only one user requires secure transmission, we derive the hovering position for the UAV and the power allocation to meet rate threshold of the secure user while maximizing the sum rate of remaining users. This disrupts the eavesdropping towards the secure user effectively. When multiple users require secure transmission, we further take the advantage of beamforming via multiple antennas at the UAV to guarantee their secure transmission. Due to the non-convexity of this problem, we convert it into a convex one for an iterative solution by using the second order cone programming. Finally, simulation results are provided to show the effectiveness of the proposed scheme

In Vitro Selection of Aptamers Against Avian Influenza Virus H5N1

Over $10 billion losses in the poultry industry were caused by avian influenza (AI) so far. Rapid and specific detection of avian influenza virus is urgently needed with the concerns over the outbreaks of highly pathogenic H5N1 influenza virus and cases of animal and human infection. Aptamers are oligonucleic acid or peptide molecules that bind a specific target molecule with good affinity. They show better thermal stability than antibodies. The goal of this research was to select DNA-aptamers as the specific recognition element of AI H5N1virus to be used in detection assays specific for field application. In this study, Systematic Evolution of Ligands by EXponential enrichment (SELEX) was used to select DNA aptamers targeted to hemagglutinin (HA) and neuraminidase (NA) proteins of AI H5N1 virus. In the first four cycles of selection, aptamers were selected by incubating HA proteins with a DNA library starting from 1014 molecules randomized at central 74 nt and subsequent nitrocellulose filtration. Then aptamers were eluted from filters and amplified by PCR. Single stranded DNA aptamers were derived from these double stranded DNAs by &lambda digest and were used as input for the next selecting cycle. In the following 9 cycles of selection, H5N1 virus was incubated as a substitute of NA proteins with aptamers pool in SELEX process. After 13 cycles of isolation, 115 bp DNA-aptamers were screened out and three apatmer sequences were obtained after cloning. Results of Dot ELISA and Dot Blot showed that these DNA-aptamers have stronger binding specificity and affinity to AI H5N1 subtype compared with their binding to H5N2, H5N3, H5N9, H2N2, H7N2 and H9N2. SPR test detected binding affinity of aptamer O16 to HA protein KD is 4.65×10-9 M and a linear equation between y (SPR signal) in RU and x (virus titer) in HAU was described as: y=208.39x +2.2347 (R2=0.99). But SPR results showed aptamers had weak cross-reaction with H5N2. Theses selected aptamers could be applied to detection of AI H5N1 virus in the future

Resource Allocation for D2D Communications Based on Matching Theory

PhDDevice-to-device (D2D) communications underlaying a cellular infrastructure takes advantage of the physical proximity of communicating devices and increasing resource utilisation. However, adopting D2D communications in complex scenarios poses substantial challenges for the resource allocation design. Meanwhile, matching theory has emerged as a promising framework for wireless resource allocation which can overcome some limitations of game theory and optimisation. This thesis focuses on the resource allocation optimisation for D2D communications based on matching theory. First, resource allocation policy is designed for D2D communications underlaying cellular networks. A novel spectrum allocation algorithm based on many-to-many matching is proposed to improve system sum rate. Additionally, considering the quality-of-service (QoS) requirements and priorities of di erent applications, a context-aware resource allocation algorithm based on many-to-one matching is proposed, which is capable of providing remarkable performance enhancement in terms of improved data rate, decreased packet error rate (PER) and reduced delay. Second, to improve resource utilisation, joint subchannel and power allocation problem for D2D communications with non-orthogonal multiple access (NOMA) is studied. For the subchannel allocation, a novel algorithm based on the many-to-one matching is proposed for obtaining a suboptimal solution. Since the power allocation problem is non-convex, sequential convex programming is adopted to transform the original power allocation problem to a convex one. The proposed algorithm is shown to enhance the network sum rate and number of accessed users. Third, driven by the trend of heterogeneity of cells, the resource allocation problem for NOMA-enhanced D2D communications in heterogeneous networks (HetNets) is investigated. In such a scenario, the proposed resource allocation algorithm is able to closely approach the optimal solution within a limited number of iterations and achieves higher sum rate compared to traditional HetNets schemes. Thorough theoretical analysis is conducted in the development of all proposed algorithms, and performance of proposed algorithm is evaluated via comprehensive simulations. This thesis concludes that matching theory based resource allocation for D2D communications achieves near-optimal performance with acceptable complexity. In addition, the application of D2D communications in NOMA and HetNets can improve system performance in terms of sum rate and users connectivity