Fully Dynamic Connectivity in O(log⁡n(log⁡log⁡n)2)O(\log n(\log\log n)^2) Amortized Expected Time

Dynamic connectivity is one of the most fundamental problems in dynamic graph algorithms. We present a randomized Las Vegas dynamic connectivity data structure with $O(\log n(\log\log n)^2)$ amortized expected update time and $O(\log n/\log\log\log n)$ worst case query time, which comes very close to the cell probe lower bounds of Patrascu and Demaine (2006) and Patrascu and Thorup (2011)

A latent variable model for query expansion using the hidden Markov model

We propose a novel probabilistic method based on the Hidden Markov Model (HMM) to learn the structure of a Latent Variable Model (LVM) for query language modeling. In the proposed LVM, the combinations of query terms are viewed as the latent variables and the segmented chunks from the feedback documents are used as the observations given these latent variables. Our extensive experiments shows that our method significantly outperforms a number of strong base- lines in terms of both effectiveness and robustness

A PatchMatch-based Dense-field Algorithm for Video Copy-Move Detection and Localization

We propose a new algorithm for the reliable detection and localization of video copy-move forgeries. Discovering well crafted video copy-moves may be very difficult, especially when some uniform background is copied to occlude foreground objects. To reliably detect both additive and occlusive copy-moves we use a dense-field approach, with invariant features that guarantee robustness to several post-processing operations. To limit complexity, a suitable video-oriented version of PatchMatch is used, with a multiresolution search strategy, and a focus on volumes of interest. Performance assessment relies on a new dataset, designed ad hoc, with realistic copy-moves and a wide variety of challenging situations. Experimental results show the proposed method to detect and localize video copy-moves with good accuracy even in adverse conditions

Preparation of Amidoxime Polyacrylonitrile Chelating Nanofibers and Their Application for Adsorption of Metal Ions.

Polyacrylonitrile (PAN) nanofibers were prepared by electrospinning and they were modified with hydroxylamine to synthesize amidoxime polyacrylonitrile (AOPAN) chelating nanofibers, which were applied to adsorb copper and iron ions. The conversion of the nitrile group in PAN was calculated by the gravimetric method. The structure and surface morphology of the AOPAN nanofiber were characterized by a Fourier transform infrared spectrometer (FT-IR) and a scanning electron microscope (SEM), respectively. The adsorption abilities of Cu2+ and Fe3+ ions onto the AOPAN nanofiber mats were evaluated. FT-IR spectra showed nitrile groups in the PAN were partly converted into amidoxime groups. SEM examination demonstrated that there were no serious cracks or sign of degradation on the surface of the PAN nanofibers after chemical modification. The adsorption capacities of both copper and iron ions onto the AOPAN nanofiber mats were higher than those into the raw PAN nanofiber mats. The adsorption data of Cu2+ and Fe3+ ions fitted particularly well with the Langmuir isotherm. The maximal adsorption capacities of Cu2+ and Fe3+ ions were 215.18 and 221.37 mg/g, respectively

The Transportation Mode Distribution of Multimodal Transportation in Automotive Logistics

AbstractCurrently, road transportation is still the main part of China's automotive logistics. Railway and waterway traffic are increasing in recent years, but are still small in the proportion of China's automotive logistics. Therefore, the automotive logistics of multimodal transportation and the distribution of the mode of transport are paid more and more attention. This paper focuses on the transport allocation problem of the commodity car. When the route and demand are fixed, considering the transportation cost, transportation time and the mode of transportation capacity constraints, establish a model to solve the road, railway and waterway transportation allocation problem. And design a genetic algorithm to solve the mode of transportation allocation problem to minimize the logistics cost. Finally, a case study is given to verify it

Efficient Algorithms for Large Scale Network Problems

In recent years, the growing scale of data has renewed our understanding of what is an efficient algorithm and poses many essential challenges for the algorithm designers. This thesis aims to improve our understanding of many algorithmic problems in this context. These include problems in communication complexity, matching theory, and approximate query processing for database systems. We first study the fundamental and well-known question of {SetIntersection} in communication complexity. We give a result that incorporates the error probability as an independent parameter into the classical trade-off between round complexity and communication complexity. We show that any $r$-round protocol that errs with error probability $2^{-E}$ requires $Omega(Ek^{1/r})$ bits of communication. We also give several almost matching upper bounds. In matching theory, we first study several generalizations of the ordinary matching problem, namely the $f$-matching and $f$-edge cover problem. We also consider the problem of computing a minimum weight perfect matching in a metric space with moderate expansion. We give almost linear time approximation algorithms for all these problems. Finally, we study the sample-based join problem in approximate query processing. We present a result that improves our understanding of the effectiveness and limitations in using sampling to approximate join queries and provides a guideline for practitioners in building AQP systems from a theory perspective.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/155263/1/hdawei_1.pd

The asymptotic behavior of a limited dependencies language model.

Intuitively, any ‘bag of words’ approach in IR should benefit from taking term dependencies into account. Unfortunately, for years the results of exploiting such dependencies have been mixed or inconclusive. To improve the situation, this paper shows how the natural language properties of the target documents can be used to transform and enrich the term dependencies to more useful statistics. This is done in three steps. First, the term co-occurrence statistics of queries and documents are each represented by a Markov chain. The paper proves that such a chain is ergodic, and therefore its asymptotic behavior is unique, stationary, and independent of the initial state. Second, the stationary distribution is taken to model queries and documents, rather than their initial distributions. Third, ranking is achieved by comparing the Kullback-Leibler divergence between the stationary distributions of query and documents. These steps can be implemented as a simple and computationally inexpensive algorithm. The main contribution of this paper is to argue why the asymptotic behavior of the document model is a better representation of the document than any model that represents the dependencies in the document by its initial distribution. A secondary contribution is to investigate the practical application of this representation. To do so, the algorithm was tested on the AP88-89 and WSJ87-92 collections in a pseudo-relevance feedback setting. Results showed consistent improvements over a standard language model baseline. Moreover, even in its simple form, the algorithm proved already to be on a par with more sophisticated algorithms that depend on choosing sets of parameters or extensive training. Hence, adding such schemes may be expected to improve the the results of the simple algorithm beyond current practice