A two-dimensional Neighborhood Preserving Projection for appearance-based face recognition

This paper presents a two-dimensional Neighborhood Preserving Projection (2DNPP) for appearance-based face representation and recognition. 2DNPP enables us to directly use a feature input of 2D image matrices rather than 1D vectors. We use the same neighborhood weighting procedure that is involved in NPP to form the nearest neighbor affinity graph. Theoretical analysis of the connection between 2DNPP and other 2D methods is presented as well. We conduct extensive experimental verifications to evaluate the performance of 2DNPP on three face image datasets, i.e. ORL, UMIST, and AR face datasets. The results corroborate that 2DNPP outperforms the standard NPP approach across all experiments with respect to recognition rate and training time. 2DNPP delivers consistently promising results compared with other competing methods such as 2DLPP, 2DLDA, 2DPCA, ONPP, OLPP, LPP, LDA, and PCA.     

Properties of the DGS-Auction Algorithm

This paper investigates algorithmic properties and overall performance of the exact auction algorithm in Demange, Gale and Sotomayor (J. Polit. Economy 94: 863–872, 1986) or DGS for short. This task is achieved by interpreting DGS as a graph and by conducting a large number of computer simulations. The crucial step in DGS is when the auctioneer selects a so-called minimal overdemanded set of items because the specific selection may affect a number of performance measures such as the number of iterations and the ratio of elicited preferences. The computational results show that (i) DGS graphs are typically large even for relatively small numbers of bidders and items, (ii) DGS converges slowly and (iii) DGS performs well in terms of preference elicitation. The paper also demonstrates that the modification to DGS based on the Ford–Fulkerson algorithm outperforms all investigated rules for selecting a minimal overdemanded set of items in DGS both in terms of termination speed and preference elicitation.     

The future of the private rental sector in sweden

The private rental sector in Sweden is competing on a less-regulated and less-subsidized housing market. The paper analyses the prospects of this sector. The private sector has the dual advantage of an older (more attractive and centrally located) stock and a slightly beter-off tenant profile than the municipal sector. Thus, the private sector will be less affected by the withdrawal of interest subsidies, as these were given to new stock. The rent-to-income ratio will surpass 30 per cent in all sectors of the housing market in the year 2002, according to a recent projection. The private sector suffers less from vacancies than the municipal sector. The economic situation is, however, characterized by high LTV ratios, due to a price and borrowing boom in the late 1980s. This has led to bankruptcies and a weak economic situation for many private landlords. The boom was followed by a bust in 1990–93. Both the upswing and the downturn can be explained by fundamentals such as changes in GDP, unemployment, housing subsidy and tax rules. In the present recession, the private rental sector will benefit from its large share of small dwellings, which will also be advantagenous in the longer run, in view of the demographic trend towards more small households. There is fierce competition between the private and the municipal housing sector. In the short run, the private sector will benefit from the removal of interest subsidies. The effects will be felt in the younger municipal sector. All in all, both short-run and long-run considerations point at a housing market in Sweden which will keep its dualistic character of free competition between the private and the municipal sector in the future. Bengt Turner is Professor of Economics and Chair of the Institute for Housing Research at Uppsala University. He is also chairman of the European Network for Housing Research and coordinates a working group on housing finance. His research area is housing policy, microeconomic analysis of the housing market, as well as housing finance. He has also analysed the housing situation in transitory (East European) countries. In Sweden he is actively involved in housing policy formation as an expert in Governmental commissions and as a member of a number of research boards. Tommy Berger is an economist at the Institute of Housing Research, Uppsala University. His main interest is in housing finance and taxation policy.     

On compressing data in wireless sensor networks for energy efficiency and real time delivery

Wireless sensor networks possess significant limitations in storage, bandwidth, processing, and energy. Additionally, real-time sensor network applications such as monitoring poisonous gas leaks cannot tolerate high latency. While some good data compression algorithms exist specific to sensor networks, in this paper we present TinyPack, a suite of energy-efficient methods with high-compression ratios that reduce latency, storage, and bandwidth usage further in comparison with some other recently proposed algorithms. Our Huffman style compression schemes exploit temporal locality and delta compression to provide better bandwidth utilization important in the wireless sensor network, thus reducing latency for real time sensor-based monitoring applications. Our performance evaluations over many different real data sets using a simulation platform as well as a hardware implementation show comparable compression ratios and energy savings with a significant decrease in latency compared to some other existing approaches. We have also discussed robust error correction and recovery methods to address packet loss and corruption common in sensor network environments.     

Anomaly detection of cooling fan and fault classification of induction motor using Mahalanobis–Taguchi system

A health index, Mahalanobis distance (MD), is proposed to indicate the health condition of cooling fan and induction motor based on vibration signal. Anomaly detection and fault classification are accomplished by comparing MDs, which are calculated based on the feature data set extracted from the vibration signals under normal and abnormal conditions. Since MD is a non-negative and non-Gaussian distributed variable, Box–Cox transformation is used to convert the MDs into normal distributed variables, such that the properties of normal distribution can be employed to determine the ranges of MDs corresponding to different health conditions. Experimental data of cooling fan and induction motor are used to validate the proposed approach. The results show that the early stage failure of cooling fan caused by bearing generalized-roughness faults can be detected successfully, and the different unbalanced electrical faults of induction motor can be classified with a higher accuracy by Mahalanobis–Taguchi system. Such works could aid in the reliable operation of the machines, the reduction of the unexpected failures, and the improvement of the maintenance plan.     

Recognition of word collocation habits using frequency rank ratio and inter-term intimacy

An effective algorithm for extracting two useful features from text documents for analyzing word collocation habits, “Frequency Rank Ratio” (FRR) and “Intimacy”, is proposed. FRR is derived from a ranking index of a word according to its word frequency. Intimacy, computed by a compact language model called Influence Language Model (ILM), measures how close a word is to others within the same sentence. Using the proposed features, a visualization framework is developed for word collocation analysis. To evaluate our proposed framework, two corpora are designed and collected from the real-life data covering diverse topics and genres. Extensive simulations are conducted to illustrate the feasibility and effectiveness of our visualization framework. Our results demonstrate that the proposed features and algorithm are able to conduct reliable text analysis efficiently.     

Multicell power allocation method based on game theory for inter-cell interference coordination

As a new technology, coordinated multipoint (CoMP) transmission is included in LTE-Advanced study item. Moreover, the network architecture in LTE-Advanced system is modified to take into account coordinated transmission. Under this background, a novel power allocation game model is established to mitigate inter-cell interference with cellular coordination. In the light of cellular cooperation relationship and centralized control in eNodeB, the power allocation in each served antenna unit aims to make signal to interference plus noise ratio (SINR) balanced among inter-cells. Through the proposed power allocation game algorithm, the users’ SINR can reach the Nash equilibrium, making it feasible to reduce the co-frequency interference by decreasing the transmitted power. Numerical results show that the proposed power allocation algorithm improves the throughput both in cell-center and cell-edge. Moreover, the blocking rate in cell-edge is reduced too.     

Docking to flexible nicotinic acetylcholine receptors: a validation study using the acetylcholine binding protein

Computational docking to nicotinic acetylcholine receptors (nAChRs) and other members of the Cys-loop receptor family is complicated by the flexibility of the so-called C-loop. As observed in the large number of published crystal structures of the acetylcholine binding protein (AChBP), a structural surrogate and homology modeling template for the nAChRs, the conformation of this loop is controlled by the ligand present in the binding pocket. As part of the development of a protocol for unbiased docking to the nAChRs, we here present the results of docking of ligands with known binding modes to an AChBP ensemble with systematic variations in C-loop closure generated via a series of targeted geometry optimizations. We demonstrate the ability to correctly predict binding modes for 12 out of 15 ligands and induced degrees of C-loop closure for 14 out of 15 ligands. Our approach holds a promising potential for structure based drug discovery within nAChRs and related receptors.     

Neighborhood field for cooperative optimization

Inspired by the biological evolution, local cooperation behaviors have been modeled in function optimizations for providing effective search methods. This paper proposes a new meta-heuristic algorithm named Neighborhood Field Optimization algorithm (NFO), which totally utilizes the local cooperation of individuals. This paper also analyzes how the local cooperation helps optimization, which is modeled as the neighborhood field. The proposed NFO is compared with other widely used evolutionary algorithms in intensive simulation under different benchmark functions. The presented results show that NFO is able to solve multimodal problems globally, and thus the cooperation behavior is proven its significance to model a search method.     

Human Cell Encapsulation in Gel Microbeads with Cosynthesized Concentric Nanoporous Solid Shells

Encapsulation of therapeutic cells in core–shell microparticles has great promise for the treatment of a range of health conditions. Unresolved challenges related to control of the particle morphology, mechanical stability, and immunogenicity hinder dissemination of this promising approach. Here, a novel polymer material for cell encapsulation and a combined novel, easy to control, synthesis method are introduced. Core–shell cell encapsulation is demonstrated with a concentric core–shell morphology formed during a single UV exposure, resulting in particles that consist of a synthetic hydrogel core of polyethylene glycol diacrylate and a solid, but porous, shell of off‐stoichiometric thiol‐ene. The encapsulated human cells in 100 µm diameter particles have >90% viability. The average shell thickness is controlled between 7 and 13 µm by varying the UV exposure, and the shell is measured to be permeable to low molecular weight species (<180 Da) but impermeable to higher molecular weight species (>480 Da). The unique material properties and the orthogonal control of the microparticle core size, shell thickness, shell permeability, and shell surface properties address the key unresolved challenges in the field, and are expected to enable faster translation of novel cell therapy concepts from research to clinical practice.