A Study on User Demographic Inference Via Ratings in Recommender Systems

Everyday, millions of people interact with online services that adopt recommender systems, such as personalized movie, news and product recommendation services. Research has shown that the demographic attributes of users such as age and gender can further improve the performance of recommender systems and can be very useful for many other applications such as marketing and social studies. However, users do not always provide those details in their online profiles due to privacy concern. On the other hand, user interactions such as ratings in recommender systems may provide an alternative way to infer demographic information. Most existing approaches can infer user demographics based on sufficient interaction history but could fail for users with few ratings. In this thesis, we study the association between users demographic information and their ratings, and explore the tradeoff between user privacy and the utility of personalization. In particular, we present a novel multi-task preference elicitation method, with which a recommender system asks a new user to rate selected items adaptively and infers the demographics rapidly via a few interactions. Experimental results on real-world datasets demonstrate the performance of the proposed method in terms of the accuracy of both demographics inference and rating prediction

Frequency-limited H∞ model reduction for positive systems

In this paper, the problem of frequency-limited H∞ model reduction for positive linear time-invariant systems is investigated. Specifically, our goal is to find a stable positive reduced-order model for a given positive system such that the H∞ norm of the error system is bounded over a frequency interval of interest. A new condition in terms of matrix inequality is developed for characterizing the frequency-limited H∞ performance. Then an equivalent parametrization of a positive reduced-order model is derived, based on which, an iterative algorithm is constructed for optimizing the reduced-order model. The algorithm utilizes coarse reduced-order models resulting from (generalized) balanced truncation as the initial value. Both continuous- and discrete-time systems are considered in the same framework. Numerical examples clearly show the effectiveness and advantages of the proposed model reduction method

A Statistically Modelling Method for Performance Limits in Sensor Localization

In this paper, we study performance limits of sensor localization from a novel perspective. Specifically, we consider the Cramer-Rao Lower Bound (CRLB) in single-hop sensor localization using measurements from received signal strength (RSS), time of arrival (TOA) and bearing, respectively, but differently from the existing work, we statistically analyze the trace of the associated CRLB matrix (i.e. as a scalar metric for performance limits of sensor localization) by assuming anchor locations are random. By the Central Limit Theorems for $U$-statistics, we show that as the number of the anchors increases, this scalar metric is asymptotically normal in the RSS/bearing case, and converges to a random variable which is an affine transformation of a chi-square random variable of degree 2 in the TOA case. Moreover, we provide formulas quantitatively describing the relationship among the mean and standard deviation of the scalar metric, the number of the anchors, the parameters of communication channels, the noise statistics in measurements and the spatial distribution of the anchors. These formulas, though asymptotic in the number of the anchors, in many cases turn out to be remarkably accurate in predicting performance limits, even if the number is small. Simulations are carried out to confirm our results

Research on the Interaction between Tubeimoside 1 and HepG2 Cells Using the Microscopic Imaging and Fluorescent Spectra Method

The treatment of cancer draws interest from researchers worldwide. Of the different extracts from traditional Chinese medicines, Tubeimoside 1 (TBMS 1) is regarded as an effective treatment for cancer. To determine the mechanism of TBMS 1, the shape/pattern of HepG2 cells based on the microscopic imaging technology was determined to analyze experimental results; then the fluorescent spectra method was designed to investigate whether TBMS 1 affected HepG2 cells. A three-dimensional (3D) fluorescent spectra sweep was performed to determine the characteristic wave peak of HepG2 cells. A 2D fluorescent spectra method was then used to show the florescence change in HepG2 cells following treatment with TBMS 1. Finally, flow cytometry was employed to analyze the cell cycle of HepG2 cells. It was shown that TBMS 1 accelerated the death of HepG2 cells and had a strong dose- and time-dependent growth inhibitory effect on HepG2 cells, especially at the G2/M phase. These results indicate that the fluorescent spectra method is a promising substitute for flow cytometry as it is rapid and cost-effective in HepG2 cells

Non-iterative, fast SE(3) path smoothing

In this paper, we present a fast, non-iterative approach to smooth a noisy input on the Special Euclidean Group, SE(3) manifold. The translational part can be smoothed by a simple Gaussian convolution.We then proposed a novel approach to rotation smoothing. Unlike existing rotation smoothing methods using either iterative optimization methods or stochastic filtering methods, our method allows direct computation of the smoothing result and allows parallelization of the computation. Furthermore, we have done a comparative study on Jia and Evans’s method published in 2014 [1], and shown that our method can better smooth an input rotation sequence, with shorter computational time. The smoothed camera path is then used for video stabilisation, which shows fluid and smooth camera motion.Australian ARC Centre of Excellence for Robotic Vision (CE140100016

PUMA: Policy-Based Unified Multi-radio Architecture for Agile Mesh Networking

This paper presents the design and implementation of PUMA, a declarative constraint-solving platform for policy-based routing and channel selection in multi-radio wireless mesh networks. In PUMA, users formulate channel selection policies as optimization goals and constraints that are concisely declared using the PawLog declarative language. To efficiently execute PawLog programs in a distributed setting, PUMA integrates a high performance constraint solver with a declarative networking engine. We demonstrate the capabilities of PUMA in defining distributed protocols that cross-optimize across channel selection and routing. We have developed a prototype of the PUMA system that we extensively evaluated in simulations and on the ORBIT testbed. Our experimental results demonstrate that PUMA can flexibly and efficiently implement a variety of centralized and distributed channel selection protocols that result in significantly higher throughput compared to single channel and identical channel assignment solutions

Characterization of Sucrose transporter alleles and their association with seed yield-related traits in Brassica napus L

<p>Abstract</p> <p>Background</p> <p>Sucrose is the primary photosynthesis product and the principal translocating form within higher plants. <it>Sucrose transporters </it>(<it>SUC/SUT</it>) play a critical role in phloem loading and unloading. Photoassimilate transport is a major limiting factor for seed yield. Our previous research demonstrated that <it>SUT </it>co-localizes with yield-related quantitative trait loci. This paper reports the isolation of <it>BnA7.SUT1 </it>alleles and their promoters and their association with yield-related traits.</p> <p>Results</p> <p>Two novel <it>BnA7.SUT1 </it>genes were isolated from <it>B. napus </it>lines 'Eagle' and 'S-1300' and designated as <it>BnA7.SUT1.a </it>and <it>BnA7.SUT1.b</it>, respectively. The BnA7.SUT1 protein exhibited typical SUT features and showed high amino acid homology with related species. Promoters of <it>BnA7.SUT1.a </it>and <it>BnA7.SUT1.b </it>were also isolated and classified as <it>pBnA7.SUT1.a </it>and <it>pBnA7.SUT1.b</it>, respectively. Four dominant sequence-characterized amplified region markers were developed to distinguish <it>BnA7.SUT1.a </it>and <it>BnA7.SUT1.b</it>. The two genes were estimated as alleles with two segregating populations (F₂and BC₁) obtained by crossing '3715'×'3769'. <it>BnA7.SUT1 </it>was mapped to the A7 linkage group of the TN doubled haploid population. <it>In silico </it>analysis of 55 segmental <it>BnA7.SUT1 </it>alleles resulted three <it>BnA7.SUT1 </it>clusters: <it>pBnA7.SUT1.a- BnA7.SUT1.a </it>(type I), <it>pBnA7.SUT1.b- BnA7.SUT1.a </it>(type II), and <it>pBnA7.SUT1.b- BnA7.SUT1.b </it>(type III). Association analysis with a diverse panel of 55 rapeseed lines identified single nucleotide polymorphisms (SNPs) in promoter and coding domain sequences of <it>BnA7.SUT1 </it>that were significantly associated with one of three yield-related traits: number of effective first branches (EFB), siliques per plant (SP), and seed weight (n = 1000) (TSW) across all four environments examined. SNPs at other <it>BnA7.SUT1 </it>sites were also significantly associated with at least one of six yield-related traits: EFB, SP, number of seeds per silique, seed yield per plant, block yield, and TSW. Expression levels varied over various tissue/organs at the seed-filling stage, and <it>BnA7.SUT1 </it>expression positively correlated with EFB and TSW.</p> <p>Conclusions</p> <p>Sequence, mapping, association, and expression analyses collectively showed significant diversity between the two <it>BnA7.SUT1 </it>alleles, which control some of the phenotypic variation for branch number and seed weight in <it>B. napus </it>consistent with expression levels. The associations between allelic variation and yield-related traits may facilitate selection of better genotypes in breeding.</p

Carbon trading-based layered operation optimization of the electric–thermal multi-energy-flow coupling system with photothermal power stations

With the increasing development of low-carbon economy, the coupling degree of electric thermal gas system is deepening day by day. The operation mode of traditional energy supply system using electric heating separation mode and hierarchical dispatching of transmission and distribution network has been difficult to mine the whole network resources and realize the global optimal operation strategy. In order to increase the consumption of new energy and solve the multi-energy current coupling system, this paper puts forward a layered optimization operation strategy for the electric-thermal multi-energy current coupling system with Concentrated solar power (CSP) plant and carbon trading mechanism. The upper layer is the solution layer of multi-energy coupled flow system. In order to solve the matrix values of complex multi-energy coupled flow system, Newton's method, improved Newton’s method and improved second-order cone collaborative solution methods are proposed. The lower layer is the multi-energy flow optimization layer. According to the system solution values obtained from the upper layer, the lower layer optimization aims at economic minimization of the total user cost. The carbon trading mechanism model is introduced to optimize the time sequence output of the electric thermal unit under different scenarios, and the mixed integer linear programming method is adopted. Finally, the validity is verified by a simulation example. The results show that this method can optimize the operation of the system, improve the accuracy and rapidity of the system, and “carbon trading and CSP power station” method can better constrain the carbon emission of the multi-energy flow coupling system, reduce the energy pressure of the device, and improve the economy of the total cost of the user.If this method is adopted, it can be used for reference in the future energy management and system decision of multi-energy flow coupling system