538,466 research outputs found
Search Efficient Binary Network Embedding
Traditional network embedding primarily focuses on learning a dense vector
representation for each node, which encodes network structure and/or node
content information, such that off-the-shelf machine learning algorithms can be
easily applied to the vector-format node representations for network analysis.
However, the learned dense vector representations are inefficient for
large-scale similarity search, which requires to find the nearest neighbor
measured by Euclidean distance in a continuous vector space. In this paper, we
propose a search efficient binary network embedding algorithm called BinaryNE
to learn a sparse binary code for each node, by simultaneously modeling node
context relations and node attribute relations through a three-layer neural
network. BinaryNE learns binary node representations efficiently through a
stochastic gradient descent based online learning algorithm. The learned binary
encoding not only reduces memory usage to represent each node, but also allows
fast bit-wise comparisons to support much quicker network node search compared
to Euclidean distance or other distance measures. Our experiments and
comparisons show that BinaryNE not only delivers more than 23 times faster
search speed, but also provides comparable or better search quality than
traditional continuous vector based network embedding methods
TRECVID: evaluating the effectiveness of information retrieval tasks on digital video
TRECVID is an annual exercise which encourages research in information retrieval from digital video by providing a large video test collection, uniform scoring procedures, and a forum for organizations interested in comparing their results. TRECVID benchmarking covers both interactive and manual searching by end users, as well as the benchmarking of some supporting technologies including shot boundary detection, extraction of some semantic features, and the automatic segmentation of TV news broadcasts into non-overlapping news stories. TRECVID has a broad range of over 40 participating groups from across the world and as it is now (2004) in its 4th annual cycle it is opportune to stand back and look at the lessons we have learned from the cumulative activity. In this paper we shall present a brief and high-level overview of the TRECVID activity covering the data, the benchmarked tasks, the overall results obtained by groups to date and an overview of the approaches taken by selective groups in some tasks. While progress from one year to the next cannot be measured directly because of the changing nature of the video data we have been using, we shall present a summary of the lessons we have learned from TRECVID and include some pointers on what we feel are the most important of these lessons
Video information retrieval using objects and ostensive relevance feedback
In this paper, we present a brief overview of current approaches to video information retrieval (IR) and we highlight its limitations and drawbacks in terms of satisfying user needs. We then describe a method of incorporating object-based relevance feedback into video IR which we believe opens up new possibilities for helping users find information in video archives. Following this we describe our own work on shot retrieval from video archives which uses object detection, object-based relevance feedback and a variation of relevance feedback called ostensive RF which is particularly appropriate for this type of retrieval
Adaptive multimodal continuous ant colony optimization
Seeking multiple optima simultaneously, which multimodal optimization aims at, has attracted increasing attention but remains challenging. Taking advantage of ant colony optimization algorithms in preserving high diversity, this paper intends to extend ant colony optimization algorithms to deal with multimodal optimization. First, combined with current niching methods, an adaptive multimodal continuous ant colony optimization algorithm is introduced. In this algorithm, an adaptive parameter adjustment is developed, which takes the difference among niches into consideration. Second, to accelerate convergence, a differential evolution mutation operator is alternatively utilized to build base vectors for ants to construct new solutions. Then, to enhance the exploitation, a local search scheme based on Gaussian distribution is self-adaptively performed around the seeds of niches. Together, the proposed algorithm affords a good balance between exploration and exploitation. Extensive experiments on 20 widely used benchmark multimodal functions are conducted to investigate the influence of each algorithmic component and results are compared with several state-of-the-art multimodal algorithms and winners of competitions on multimodal optimization. These comparisons demonstrate the competitive efficiency and effectiveness of the proposed algorithm, especially in dealing with complex problems with high numbers of local optima
Orthogonal learning particle swarm optimization
Particle swarm optimization (PSO) relies on its
learning strategy to guide its search direction. Traditionally,
each particle utilizes its historical best experience and its neighborhood’s
best experience through linear summation. Such a
learning strategy is easy to use, but is inefficient when searching
in complex problem spaces. Hence, designing learning strategies
that can utilize previous search information (experience) more
efficiently has become one of the most salient and active PSO
research topics. In this paper, we proposes an orthogonal learning
(OL) strategy for PSO to discover more useful information that
lies in the above two experiences via orthogonal experimental
design. We name this PSO as orthogonal learning particle swarm
optimization (OLPSO). The OL strategy can guide particles to
fly in better directions by constructing a much promising and
efficient exemplar. The OL strategy can be applied to PSO with
any topological structure. In this paper, it is applied to both global
and local versions of PSO, yielding the OLPSO-G and OLPSOL
algorithms, respectively. This new learning strategy and the
new algorithms are tested on a set of 16 benchmark functions, and
are compared with other PSO algorithms and some state of the
art evolutionary algorithms. The experimental results illustrate
the effectiveness and efficiency of the proposed learning strategy
and algorithms. The comparisons show that OLPSO significantly
improves the performance of PSO, offering faster global convergence,
higher solution quality, and stronger robustness
Does low-energy sweetener consumption affect energy intake and body weight? A systematic review, including meta-analyses, of the evidence from human and animal studies
By reducing energy density, low-energy sweeteners (LES) might be expected to reduce energy intake (EI) and body weight (BW). To assess the totality of the evidence testing the null hypothesis that LES exposure (versus sugars or unsweetened alternatives) has no effect on EI or BW, we conducted a systematic review of relevant studies in animals and humans consuming LES with ad libitum access to food energy. In 62 of 90 animal studies exposure to LES did not affect or decreased BW. Of 28 reporting increased BW, 19 compared LES with glucose exposure using a specific ‘learning’ paradigm. Twelve prospective cohort studies in humans reported inconsistent associations between LES use and Body Mass Index (-0.002 kg/m2/year, 95%CI -0.009 to 0.005). Meta-analysis of short- term randomized controlled trials (RCTs, 129 comparisons) showed reduced total EI for LES- versus sugar-sweetened food or beverage consumption before an ad libitum meal (-94 kcal, 95%CI -122 to -66), with no difference versus water (-2 kcal, 95%CI -30 to 26). This was consistent with EI results from sustained intervention RCTs (10 comparisons). Meta-analysis of sustained intervention RCTs (4 weeks to 40 months) showed that consumption of LES versus sugar led to relatively reduced BW (nine comparisons; -1.35 kg, 95%CI –2.28 to - 0.42), and a similar relative reduction in BW versus water (three comparisons; -1.24 kg, 95%CI –2.22 to -0.26). Most animal studies did not mimic LES consumption by humans, and reverse causation may influence the results of prospective cohort studies. The preponderance of evidence from all human RCTs indicates that LES do not increase EI or BW, whether compared with caloric or non-caloric (e.g., water) control conditions. Overall, the balance of evidence indicates that use of LES in place of sugar, in children and adults, leads to reduced EI and BW, and possibly also when compared with water
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