Social and content hybrid image recommender system for mobile social networks

One of the advantages of social networks is the possibility to socialize and personalize the content created or shared by the users. In mobile social networks, where the devices have limited capabilities in terms of screen size and computing power, Multimedia Recommender Systems help to present the most relevant content to the users, depending on their tastes, relationships and profile. Previous recommender systems are not able to cope with the uncertainty of automated tagging and are knowledge domain dependant. In addition, the instantiation of a recommender in this domain should cope with problems arising from the collaborative filtering inherent nature (cold start, banana problem, large number of users to run, etc.). The solution presented in this paper addresses the abovementioned problems by proposing a hybrid image recommender system, which combines collaborative filtering (social techniques) with content-based techniques, leaving the user the liberty to give these processes a personal weight. It takes into account aesthetics and the formal characteristics of the images to overcome the problems of current techniques, improving the performance of existing systems to create a mobile social networks recommender with a high degree of adaptation to any kind of user.

Optimization of Pumping System in Solar Pumped Laser.

The coupling between solar light radiation and laser rod medium in a solar pumped laser affects the efficiency of the laser. To optimize the pumping system, simulation of the two-stage pumping system with a Fresnel lens and conic pumping cavity is carried out with Tracepro software. According to the power density distribution along the axis at focal place of the Fresnel lens, the diameter and position of the pumping cavity window and the distance of the window from the Fresnel lens are optimized. The power density distributions along the laser rod axis of different cavity lengths and different cavity tapers are also analyzed. The optimal structure of taper cavity is obtained. The mirror relecting cavity and ceramic cavity are introduced in detail.

Collaboration of signal transducer and activator of transcription 1 (STAT1) and BRCA1 in differential regulation of IFN-γ target genes

Most of the activities of IFN-γ are the result of STAT1-mediated transcriptional responses. In this study, we show that the BRCA1 tumor suppressor acts in concert with STAT1 to differentially activate transcription of a subset of IFN-γ target genes and mediates growth inhibition by this cytokine. After IFN-γ treatment, induction of the cyclin-dependent kinase inhibitor, p21WAF1, was synergistically activated by BRCA1, whereas the IRF-1 gene was unaffected. Importantly, the differential induction of p21WAF1 was impaired in breast cancer cells homozygous for the mutant BRCA1 5382C allele. Biochemical analysis illustrated that the mechanism of this transcriptional synergy involves interaction between BRCA1 aa 502–802 and the C-terminal transcriptional activation domain of STAT1 including Ser-727 whose phosphorylation is crucial for transcriptional activation. Significantly, STAT1 proteins mutated at Ser-727 bind poorly to BRCA1, reinforcing the importance of Ser-727 in the recruitment of transcriptional coactivators by STAT proteins. These findings reveal a novel mechanism for BRCA1 function in the IFN-γ-dependent tumor surveillance system.

Trinucleotide repeats and long homopeptides in genes and proteins associated with nervous system disease and development.

Several human neurological disorders are associated with proteins containing abnormally long runs of glutamine residues. Strikingly, most of these proteins contain two or more additional long runs of amino acids other than glutamine. We screened the current human, mouse, Drosophila, yeast, and Escherichia coli protein sequence data bases and identified all proteins containing multiple long homopeptides. This search found multiple long homopeptides in about 12% of Drosophila proteins but in only about 1.7% of human, mouse, and yeast proteins and none among E. coli proteins. Most of these sequences show other unusual sequence features, including multiple charge clusters and excessive counts of homopeptides of length > or = two amino acid residues. Intriguingly, a large majority of the identified Drosophila proteins are essential developmental proteins and, in particular, most play a role in central nervous system development. Almost half of the human and mouse proteins identified are homeotic homologs. The role of long homopeptides in fine-tuning protein conformation for multiple functional activities is discussed. The relative contributions of strand slippage and of dynamic mutation are also addressed. Several new experiments are proposed.

A computer simulation of a large scale academic computer system.

Thesis (M.S.)--University of Illinois.

A priori prediction of roundoff error accumulation in the solution of a super-large geodetic normal equation system /

"June 1980."

Towards the preparative and large-scale precision manafacture of virus-like particles

Virus-like particles (VLPs) are of interest in vaccination, gene therapy and drug delivery, but their potential has yet to be fully realized. This is because existing laboratory processes, when scaled, do not easily give a compositionally and architecturally consistent product. Research suggests that new process routes might ultimately be based on chemical processing by self-assembly, involving the precision manufacture of precursor capsomeres followed by in vitro VLP self-assembly and scale-up to required levels. A synergistic interaction of biomolecular design and bioprocess engineering (i.e. biomolecular engineering) is required if these alternative process routes and, thus, the promise of new VLP products, are to be realized.

Segmental body composition determination through a bioelectrical very large scale integration (VLSI) system

The purpose of this investigation was to develop new techniques to generate segmental assessments of body composition based on Segmental Bioelectrical Impedance Analysis (SBIA). An equally important consideration was the design, simulation, development, and the software and hardware integration of the SBIA system. This integration was carried out with a Very Large Scale Integration (VLSI) Field Programmable Gate Array (FPGA) microcontroller that analyzed the measurements obtained from segments of the body, and provided full body and segmental Fat Free Mass (FFM) and Fat Mass (FM) percentages. Also, the issues related to the estimate of the body's composition in persons with spinal cord injury (SCI) were addressed and investigated. This investigation demonstrated that the SBIA methodology provided accurate segmental body composition measurements. Disabled individuals are expected to benefit from these SBIA evaluations, as they are non-invasive methods, suitable for paralyzed individuals. The SBIA VLSI system may replace bulky, non flexible electronic modules attached to human bodies. ^

Surveillance in Cyberspace: Applying Natural and Place Manager Surveillance to System Trespassing

Research on the criminological side of system trespassing (i.e. unlawfully gaining access to a computer system) is relatively rare and has yet to examine the effect of the presence of other users on the system during the trespassing event (i.e. the time of communication between a trespasser’s system and the infiltrated system). This thesis seeks to analyze this relationship drawing on principles of Situational Crime Prevention, Routine Activities Theory, and restrictive deterrence. Data were collected from a randomized control trial of target computers deployed on the Internet network of a large U.S. university. This study examined whether the number (one or multiple) and type (administrative or non-administrative) of computer users present on a system reduced the seriousness and frequency of trespassing. Results indicated that the type of user (administrative) produced a restrictive deterrent effect and significantly reduced the frequency and duration of trespassing events.

Collaborative filtering and deep learning based recommendation system for cold start items

Recommender system is a specific type of intelligent systems, which exploits historical user ratings on items and/or auxiliary information to make recommendations on items to the users. It plays a critical role in a wide range of online shopping, e-commercial services and social networking applications. Collaborative filtering (CF) is the most popular approaches used for recommender systems, but it suffers from complete cold start (CCS) problem where no rating record are available and incomplete cold start (ICS) problem where only a small number of rating records are available for some new items or users in the system. In this paper, we propose two recommendation models to solve the CCS and ICS problems for new items, which are based on a framework of tightly coupled CF approach and deep learning neural network. A specific deep neural network SADE is used to extract the content features of the items. The state of the art CF model, timeSVD++, which models and utilizes temporal dynamics of user preferences and item features, is modified to take the content features into prediction of ratings for cold start items. Extensive experiments on a large Netflix rating dataset of movies are performed, which show that our proposed recommendation models largely outperform the baseline models for rating prediction of cold start items. The two proposed recommendation models are also evaluated and compared on ICS items, and a flexible scheme of model retraining and switching is proposed to deal with the transition of items from cold start to non-cold start status. The experiment results on Netflix movie recommendation show the tight coupling of CF approach and deep learning neural network is feasible and very effective for cold start item recommendation. The design is general and can be applied to many other recommender systems for online shopping and social networking applications. The solution of cold start item problem can largely improve user experience and trust of recommender systems, and effectively promote cold start items.

Comparing the diffusion of online service recovery in small and large organisations

A key concern organisations face is how to incorporate Internet tools into their marketing communications mix. Where and how should companies invest their human, technological and financial resources? This paper explores a subset of this problem, online complaining and electronic customer service. It applies diffusion of innovation as a theoretical framework to investigate organisational implementation of email technology and explain the outcome of annual customer service surveys in 2001, 2002 and 2003. The results add to the small body of research on electronic service recovery by extending diffusion of innovations to email service recovery and underscoring the importance of adoption phases, particularly for SMEs. Larger companies provide more channels for submitting complaints, which represents an early phase of adoption. There was little difference in how large and small companies respond to online complaints, a later phase of adoption.

Why adopt now? Multiple case studies and survey studies comparing small, medium and large firms

While a number of factors have been highlighted in the innovation adoption literature, little is known about the key triggers of innovation adoption across differently-sized firms. This study compares case studies of small, medium and large manufacturing firms who recently decided to adopt a process innovation. We also employ organizational surveys from 134 firms investigating the factors which influence innovation adoption. The quantitative results support the qualitative findings that the external pressures are a trigger among small to medium firms, while the larger firm’s adoption was associated with internal causes.

Effect of representation size and visual ambiguity on RatSLAM system performance

RatSLAM is a vision-based SLAM system based on extended models of the rodent hippocampus. RatSLAM creates environment representations that can be processed by the experience mapping algorithm to produce maps suitable for goal recall. The experience mapping algorithm also allows RatSLAM to map environments many times larger than could be achieved with a one to one correspondence between the map and environment, by reusing the RatSLAM maps to represent multiple sections of the environment. This paper describes experiments investigating the effects of the environment-representation size ratio and visual ambiguity on mapping and goal navigation performance. The experiments demonstrate that system performance is weakly dependent on either parameter in isolation, but strongly dependent on their joint values.