Who’s Next? An Analysis of Lodging Industry Acquisitions

The years 2004 through 2007 witnessed a rush of takeover deals in the lodging industry, in which numerous publicly traded hotel companies and hotel real estate investment trusts (REITs) were acquired—mostly by private equity firms, in many cases, Blackstone Group. Notwithstanding the suspension of such activities in the past two years, this article analyzes what factors determine the choice of the targets during that period in the lodging industry. An examination of these takeover deals determined that targets were most likely to: (1) be either a large hotel company or a relatively small REIT; (2) have a high percentage of fixed assets and a low level of debt; (3) have a mismatch between growth prospects and available resources; and (4) be in their middle age as publicly traded firms. Conditions that permit acquisitions, including availability of credit, will eventually return, making this analysis useful to current and future owners, investors, and executives in the lodging industry. Those who want to be acquired, for instance, can adjust their corporate profile to be more attractive, and those who wish to discourage acquisition can take on debt and spin off assets to be less attractive

Stein Variational Gradient Descent with Multiple Kernel

Stein variational gradient descent (SVGD) and its variants have shown promising successes in approximate inference for complex distributions. In practice, we notice that the kernel used in SVGD-based methods has a decisive effect on the empirical performance. Radial basis function (RBF) kernel with median heuristics is a common choice in previous approaches, but unfortunately this has proven to be sub-optimal. Inspired by the paradigm of Multiple Kernel Learning (MKL), our solution to this flaw is using a combination of multiple kernels to approximate the optimal kernel, rather than a single one which may limit the performance and flexibility. Specifically, we first extend Kernelized Stein Discrepancy (KSD) to its multiple kernels view called Multiple Kernelized Stein Discrepancy (MKSD) and then leverage MKSD to construct a general algorithm Multiple Kernel SVGD (MK-SVGD). Further, MKSVGD can automatically assign a weight to each kernel without any other parameters, which means that our method not only gets rid of optimal kernel dependence but also maintains computational efficiency. Experiments on various tasks and models demonstrate that our proposed method consistently matches or outperforms the competing methods

Walk This Way: Footwear Recognition Using Images & Neural Networks

Footwear prints are one of the most commonly recovered in criminal investigations. They can be used to discover a criminal's identity and to connect various crimes. Nowadays, footwear recognition techniques take time to be processed due to the use of current methods to extract the shoe print layout such as platter castings, gel lifting, and 3D-imaging techniques. Traditional techniques are prone to human error and waste valuable investigative time, which can be a problem for timely investigations. In terms of 3D-imaging techniques, one of the issues is that footwear prints can be blurred or missing, which renders their recognition and comparison inaccurate by completely automated approaches. Hence, this research investigates a footwear recognition model based on camera RGB images of the shoe print taken directly from the investigation site to reduce the time and cost required for the investigative process. First, the model extracts the layout information of the evidence shoe print using known image processing techniques. The layout information is then sent to a hierarchical network of neural networks. Each layer of this network is examined in an attempt to process and recognize footwear features to eliminate and narrow down the possible matches until returning the final result to the investigator

“Meta Cloud Discovery” Model: An Approach to Integrity Monitoring for Cloud-Based Disaster Recovery Planning

Article originally published in International Journal of Information and Education TechnologyA structure is required to prevent the malicious code from leaking onto the system. The use of sandboxes has become more advance, allowing for investigators to access malicious code while minimizing the risk of infecting their own machine. This technology is also used to prevent malicious code from compromising vulnerable machines. The use of sandbox technology and techniques can potentially be extended to cloud infrastructures to prevent malicious content from compromising specialized infrastructure such as backups that are used for disaster recovery and business continuity planning. This paper will discuss existing algorithms related to current sandbox technology, and extend the work into the “Meta Cloud Discovery” model, a sandbox integrity-monitoring proposal for disaster recovery. Finally, implementation examples will be discussed as well as future research that would need to be performed to improve the model.SHSU research and sponsored program under an Enhancement Research Grant and the support from the Department of Computer Science

A Method to Detect AAC Audio Forgery

Advanced Audio Coding (AAC), a standardized lossy compression scheme for digital audio, which was designed to be the successor of the MP3 format, generally achieves better sound quality than MP3 at similar bit rates. While AAC is also the default or standard audio format for many devices and AAC audio files may be presented as important digital evidences, the authentication of the audio files is highly needed but relatively missing. In this paper, we propose a scheme to expose tampered AAC audio streams that are encoded at the same encoding bit-rate. Specifically, we design a shift-recompression based method to retrieve the differential features between the re-encoded audio stream at each shifting and original audio stream, learning classifier is employed to recognize different patterns of differential features of the doctored forgery files and original (untouched) audio files. Experimental results show that our approach is very promising and effective to detect the forgery of the same encoding bit-rate on AAC audio streams. Our study also shows that shift recompression-based differential analysis is very effective for detection of the MP3 forgery at the same bit rate

Carbene Triel Bonds Between TrR3 (Tr=B, Al) and N-Heterocyclic Carbenes

The carbene triel bond is predicted and characterized by theoretical calculations. The C lone pair of N‐heterocyclic carbenes (NHCs) is allowed to interact with the central triel atom of TrR3 (Tr = B and Al; R = H, F, Cl, and Br). The ensuing bond is very strong, with an interaction energy of nearly 90 kcal/mol. Replacement of the C lone pair by that of either N or Si weakens the binding. The bond is strengthened by electron‐withdrawing substituents on the triel atom, and the reverse occurs with substitution on the NHC. However, these effects do not strictly follow the typical pattern of F \u3e Cl \u3e Br. The TrR3 molecule suffers a good deal of geometric deformation, requiring on the order of 30 kcal/mol, in forming the complex. The R(C···Tr) bond is quite short, for example, 1.6 Å for Tr = B, and shows other indications of at least a partially covalent bond, such as a high electron density at the bond critical point and a good deal of intermolecular charge transfer

Detecting Differentially Methylated Loci for Multiple Treatments Based on High-Throughput Methylation Data

This article was originally published by BMC BioinformaticsBackground: Because of its important effects, as an epigenetic factor, on gene expression and disease development, DNA methylation has drawn much attention from researchers. Detecting differentially methylated loci is an important but challenging step in studying the regulatory roles of DNA methylation in a broad range of biological processes and diseases. Several statistical approaches have been proposed to detect significant methylated loci; however, most of them were designed specifically for case-control studies. Results: Noticing that the age is associated with methylation level and the methylation data are not normally distributed, in this paper, we propose a nonparametric method to detect differentially methylated loci under multiple conditions with trend for Illumina Array Methylation data. The nonparametric method, Cuzick test is used to detect the differences among treatment groups with trend for each age group; then an overall p-value is calculated based on the method of combining those independent p-values each from one age group. Conclusions: We compare the new approach with other methods using simulated and real data. Our study shows that the proposed method outperforms other methods considered in this paper in term of power: it detected more biological meaningful differentially methylated loci than others.The first author also acknowledges the support from the faculty research funds awarded by the School of Public Health, Indiana University Bloomington