Online advertising auctions play a critical role in internet commerce, requiring mechanisms that maximize revenue while ensuring incentive compatibility, user experiences, and real-time efficiency. Existing learning-based auction frameworks advance contextual modeling by considering intra-list dependencies among ads, but still face challenges of insufficient global externality modeling and inefficiencies due to sequential processing. In this paper, we propose the Non-autoregressive Generative Auction with global externalities (NGA), a novel end-to-end auction framework for industrial online advertising. NGA explicitly models global externalities by jointly encoding dependencies among ads and the influence of neighboring organic content. To achieve real-time efficiency, NGA employs a non-autoregressive, constraint-based decoding mechanism and a parallel multi-tower evaluator that unifies list-wise reward and payment computation. Extensive offline experiments and large-scale online A/B tests on commercial advertising platforms demonstrate that NGA achieves superior performance in both effectiveness and efficiency compared to the state-of-the-art baselines.

Load balancing, which redistributes dynamic workloads across computing nodes within cloud to improve resource utilization, is one of the main challenges in cloud computing system. Most existing rule-based load balancing algorithms failed to effectively fuse load data of multi-class system resources. The strategies they used for balancing loads were far from optimum since these methods were essentially performed in a combined way according to load state. In this work, a fuzzy clustering method with feature weight preferences is presented to overcome the load balancing problem for multi-class system resources and it can achieve an optimal balancing solution by load data fusion. Feature weight preferences are put forward to establish the relationship between prior knowledge of specific cloud scenario and load balancing procedure. Extensive experiments demonstrate that the proposed method can effectively balance loads consisting of multi-class system resources.

MOTIVATION: Transcriptome sequencing has long been the favored method for quickly and inexpensively obtaining a large number of gene sequences from an organism with no reference genome. Owing to the rapid increase in throughputs and decrease in costs of next- generation sequencing, RNA-Seq in particular has become the method of choice. However, the very short reads (e.g. 2 × 90 bp paired ends) from next generation sequencing makes de novo assembly to recover complete or full-length transcript sequences an algorithmic challenge. RESULTS: Here, we present SOAPdenovo-Trans, a de novo transcriptome assembler designed specifically for RNA-Seq. We evaluated its performance on transcriptome datasets from rice and mouse. Using as our benchmarks the known transcripts from these well-annotated genomes (sequenced a decade ago), we assessed how SOAPdenovo-Trans and two other popular transcriptome assemblers handled such practical issues as alternative splicing and variable expression levels. Our conclusion is that SOAPdenovo-Trans provides higher contiguity, lower redundancy and faster execution. AVAILABILITY AND IMPLEMENTATION: Source code and user manual are available at http://sourceforge.net/projects/soapdenovotrans/.

Recently, cooperative communication is becoming an attractive technology as it can greatly improve the spatial diversity without additional antennas. The previous works have studied the cluster-based cooperative routing to explore the energy efficiency. However, some nodes may use up their energies immediately while they always act as the cluster-heads. Thus, this paper studies the lifetime-optimal transmission scheduling for cluster-based cooperative routing problem in wireless networks. We formalize this problem into an integer programming. Then, this paper presents a polynomial-time algorithm, which mainly uses the improved branch-and-bound method to obtain the transmission scheduling on the clusters. The simulation results show that the proposed algorithm can prolong the network lifetimes about 29% and 84% compared with the previous cluster-based cooperative routing protocols.

Both graphic text and scene text detection in video images with complex background and low resolution is still a challenging and interesting problem for researchers in the field of image processing and computer vision. In this paper, we present a novel technique for detecting both graphic text and scene text in video images by finding segments containing text in an input image and then using statistical features such as vertical and horizontal bars for edges in the segments for detecting true text blocks efficiently. To identify a segment containing text, heuristic rules are formed based on combination of filters and edge analysis. Furthermore, the same rules are extended to grow the boundaries of a candidate segment in order to include complete text in the input image. The experimental results of the proposed method show that the technique performs better than existing methods in terms of a number of metrics.

This paper presents our work on automatically detecting moving rigid text in digital videos. The temporal information is obtained by dividing a video frame into sub-blocks and calculating inter-frame motion vector for each sub-block. Text blocks are then extracted through both intra-frame classification and inter-frame spatial relationship checking. Unlike previous works, our method achieves both detection and tracking of moving text at the same time. The method works very well detecting scrolling text in news clips and movies, and is robust towards low resolution and complex background. The computational efficiency of the method is also discussed.

In this paper, we explore new edge features such as straightness for the elimination of non significant edges from the segmented text portion of a video frame to detect accurate boundary of the text lines in video images. To segment the complete text portions, the method introduces candidate text block selection from a given image. Heuristic rules are formed based on combination of filters and edge analysis for identifying a candidate text block in the image. Furthermore, the same rules are extended to grow boundary of candidate text block in order to segment complete text portions in the image. The experimental results of the proposed method show that the method outperforms an existing method in terms of a number of metrics.

Information graphics, or infographics, are visual representations of information, data or knowledge. Understanding of infographics in documents is a relatively new research problem, which becomes more challenging when infographics appear as raster images. This paper describes technical details and practical applications of the system we built for recognizing and understanding imaged infographics located in document pages. To recognize infographics in raster form, both graphical symbol extraction and text recognition need to be performed. The two kinds of information are then auto-associated to capture and store the semantic information carried by the infographics. Two practical applications of the system are introduced in this paper, including supplement to traditional optical character recognition (OCR) system and providing enriched information for question answering (QA). To test the performance of our system, we conducted experiments using a collection of downloaded and scanned infographic images. Another set of scanned document pages from the University of Washington document image database were used to demonstrate how the system output can be used by other applications. The results obtained confirm the practical value of the system.

Graphical components information extraction is a crucial step in the chart recognition and understanding process. However, existing methods of information extraction from chart images either are type-dependent or rely on certain assumptions. In this paper, we present a general method to extract vectorized graphical information from scientific chart images. Our algorithm firstly constructs a data structure called directional single-connected chains (DSCC). It then employs ellipse-specific fitting and orthogonal diagonalization to calculate the curvatures of the chains and classify the chains into either straight lines or arcs. Finally we combine all straight lines and all arcs accordingly and use linear regression to compute their attributes. The DSCC has a good property in that it is less susceptible to noise. The experiment results show that our algorithm is efficient, robust and accurate.

An important step in chart image understanding is to identify the type of the input image so that corresponding interpretation can be performed. In this paper, we model the chart image classification as a multiple-instance learning problem. A chart image is treated as a bag containing a set of instances that are graphical symbols. For both training and recognition, shape detection is performed and general shape descriptors are used to form feature vectors. For the training images, the correlation factor (CF) of each shape is calculated for each chart type. The learnt CFs are then used to estimate the type of a new input image. Comparing with traditional multiple-instance learning algorithms, we allow negative examples to be less restrictive and hence easier to provide. Using our method, both the type and the data components of the chart image can be obtained in one-pass. The experimental results show that our approach works reasonably well

This paper presents our recent work that aims at associating the recognition results of textual and graphical information contained in the scientific chart images. Text components are first located in the input image and then recognized using OCR. On the other hand, the graphical objects are segmented and form high level symbols. Both logical and semantic correspondence between text and graphical symbols are identified. The association of text and graphics allows us to capture the semantic meaning carried by scientific chart images in a more complete way. The result of scientific chart image understanding is presented using XML documents.

In this paper, we present a novel approach to vectorize elliptic arcs for 3D pie chart recognition and data extraction. As a preprocessing step, a set of straight line segments are obtained first. By estimating the parameters based on the line segments, we are able to generate elliptic arc candidates. Curve fitting is then performed to reject false positives and further refine the parameters of each elliptic arc. The vectorized elliptic arcs are the key components in the 3D pie charts that allow us to determine the chart type and to extract data values embedded. Experiments using a group of testing images show that the proposed method works well and is computationally efficient.

We propose a method for text retrieval from document images without the use of OCR. Documents are segmented into character objects. Image features, namely the vertical traverse density (VTD) and horizontal traverse density (HTD), are extracted. An n-gram-based document vector is constructed for each document based on these features. Text similarity between documents is then measured by calculating the dot product of the document vectors. Testing with seven corpora of imaged textual documents in English and Chinese as well as images from the UW1 (University of Washington 1) database confirms the validity of the proposed method.

A new method for gauging text similarity of image-based documents using word shape recognition is proposed in this paper. Image features are directly extracted instead of using OCR (optical character recognition). The proposed method forms so-called vertical bar patterns by detecting local extrema points in word units extracted by segmenting the document images. These vertical bar patterns form the feature vector of a document. The pair-wise similarity of document images is measured by calculating the scalar product of two document feature vectors. The proposed method is robust to changing fonts and styles, and is less affected by degradation of document qualities. To test the validity of the method, four corpora of document images were used and the ability of the method to retrieve relevant documents is reported.

An approach to word image matching based on weighted Hausdorff distance (WHD) is proposed in this paper to facilitate the detection and location of the user-specified words in the document images. Preprocessing such as eliminating the space between adjacent characters in the word images and scale normalization is first done before the WHD is utilized to measure the distance between the template image and the word image extracted from the document image. Experimental results in the application of detecting the user-specified words from both English and Chinese document images show that it is a promising approach for word image matching.

We propose a word shape recognition method for retrieving image-based documents. Document images are segmented at the word level first. Then the proposed method detects local extrema points in word segments to form so-called vertical bar patterns. These vertical bar patterns form the feature vector of a document. The scalar product of two document feature vectors is calculated to measure the pairwise similarity of document images. The proposed method is robust to changing fonts and styles, and is less affected by degradation of document qualities. Three groups of words in different fonts and image qualities were used to test the validity of our method. Real-life document images were also used to test the method's ability of retrieving relevant documents.

The similarity measure of document images has a crucial role in the area of document image retrieval. A method of measuring the similarity of CCITT Group 4 compressed document images is proposed. The features are extracted directly from the changing elements of the compressed images. Weighted Hausdorff distance is utilized to assign all of the word objects from two document images to corresponding classes by an unsupervised classifier, whereas the possible stop words are excluded. Document vectors are built by the occurrence frequency of the word object classes, and the pair-wise similarity of two document images is represented by the scalar product of the document vectors. Five group articles relating to different domains are used to test the validity of the presented approach.

This paper describes a document image analysis system using multiple agents working on a pyramid structure to separate text from graphics in the image. Text strings appear as different groupings of connected components at different image resolutions. As such, the pyramid structure, which is a multi-resolution image representation, provides a natural means of identifying and grouping of character strings in the document at different levels of resolution. The pyramid structure is also amenable to parallel processing, where multiple agents in the system can individually and concurrently look for groups of connected components at appropriate levels. The agent-based pyramid operations do not require expensive feature analysis among different connected components to detect text strings as found in other existing works.