Recommender systems that learn from implicit feedback often use large volumes of a single type of implicit user feedback, such as clicks, to enhance the prediction of sparse target behavior such as purchases. Using multiple types of implicit user feedback for such target behavior prediction purposes is still an open question. Existing studies that attempted to learn from multiple types of user behavior often fail to: (i) learn universal and accurate user preferences from different behavioral data distributions, and (ii) overcome the noise and bias in observed implicit user feedback.

We present an effective spectral matching method based on a shape association graph for finding region correspondences between two cel animation keyframes. We formulate the correspondence problem as an adapted quadratic assignment problem, which comprehensively considers both the intrinsic geometric and topology of regions to find the globally optimal correspondence. To simultaneously represent the geometric and topological similarities between regions, we propose a shape association graph (SAG), whose node attributes indicate the geometric distance between regions, and whose edge attributes indicate the topological distance between combined region pairs. We convert topological distance to geometric distance between geometric objects with topological features of the pairs, and introduce Kendall shape space to calculate the intrinsic geometric distance. By utilizing the spectral properties of the affinity matrix induced by the SAG, our approach can efficiently extract globally optimal region correspondences, even if shapes have inconsistent topology and severe deformation. It is also robust to shapes undergoing similarity transformations, and compatible with parallel computing techniques.

Image denoising is an important topic in the digital image processing field. This paper theoretically studies the validity of the classical non-local mean filter (NLM) for removing Gaussian noise from a novel statistic perspective. By regarding the restored image as an estimator of the clear image from the statistical view, we gradually analyse the unbiasedness and effectiveness of the restored value obtained by the NLM filter. Then, we propose an improved NLM algorithm called the clustering-based NLM filter (CNLM) that derived from the conditions obtained through the theoretical analysis. The proposed filter attempts to restore an ideal value using the approximately constant intensities obtained by the image clustering process. Here, we adopt a mixed probability model on a prefiltered image to generate an estimator of the ideal clustered components. The experimental results show that our algorithm obtains considerable improvement in peak signal-to-noise ratio (PSNR) values and visual results when removing Gaussian noise. On the other hand, the considerable practical performance of our filter shows that our method is theoretically acceptable as it can effectively estimates ideal images.

DR and next query prediction (NQP) are two core tasks in session search. They are often driven by the same search intent and, hence, it is natural to jointly optimize both tasks. So far, most models proposed for jointly optimizing document reranking (DR) and NQP have focused on users’ short-term intent in an ongoing search session. Because of this limitation, these models fail to account for users’ long-term intent as captured in their historical search sessions. In contrast, we consider a personalized mechanism for learning a user’s profile from their long-term and short-term behavior to simultaneously enhance the performance of DR and NQP in an ongoing search session.

Facial expression editing in face sketch is an important and challenging problem in computer vision community as facial animation and modeling. For criminal investigation and portrait drawing, automatic expression editing tools for face sketch improve work efficiency obviously and reduce professional requirements for users. In this paper, we propose a novel method for facial expression editing in face sketch using shape space theory. The new facial expressions in the sketch images can be regenerated automatically. The method includes two components: 1) face sketch modeling; 2) expression editing. The face sketch modeling constructs 3D face sketch data from 3D facial database to match the 2D face sketch. Using facial landmarks, the "shape" of the face sketch is represented in shape space. The shape space is a manifold space which removes the rigid transform group. In shape space, the accurate 3D face sketch model is obtained which is consistent to the original 2D face sketch. For expression editing, we change the parameters of 3D face sketch model in the shape space to obtain new expressions. The expression transfer in 3D face sketch model can be mapped into the 2D face sketch. The advantages of our method are: full-automatic in modeling process; no requirements of drawing skills to user and friendly interaction; robustness to head poses and different scales. In experiments, we use the 3D facial database, FaceWareHouse, to construct the 3D face sketch model and use face sketch images from database: CUHK Face sketch Database (CUFS) to show the performance of expression editing. Experimental results demonstrate that our method can effectively edit facial expressions in face sketch with high consistency and fidelity.

This paper presents a novel heterogeneous multi-core Digital Signal Processor, named YHFT-QDSP, hosting one RISC CPU core and four VLIW DSP cores. The CPU core is responsible for task scheduling and management, while the DSP cores take charge of speeding up data processing. The YHFT-QDSP provides three kinds of interconnection communication. One is for inner-chip communication between the CPU core and the four DSP cores, the other two for both inner-chip and inter-chip communication amongst DSP cores. The YHFT-QDSP is implemented under SMIC®130nm LVT CMOS technology and can run [email protected] with 114.49 mm2die area.

Current VLSI designs face a serious performance bottleneck due to reverse scaling of global interconnects as CMOS technology scales into VDSM regime. Interconnections techniques which decrease delay, power, and ensure signal integrity, play an important role in the growth of semiconductor industry into future generations. In this paper we present a novel hybrid insertion methodology for on-chip global interconnects. It takes advantage of repeaters and low-swing differential-signaling transceivers on driving long wires in different length, and optimally inserts them along the wires in order to decrease delay, power and gate area cost of interconnects. Simulation results using HSPICE for 0.18mum process showed that delay, power, delay-energy-product (EDP) and gate area cost were considerably decreased compared with other approaches available. Moreover, its computational technique is relatively easy and not limited to a specific low-swing differential-signaling transceiver. Therefore the methodology is very suitable for integration in EDA tool flow and beneficial for the reuse of low-swing differential-signaling transceivers

In this paper, we present a lookup table based model for delay and power estimation of low-swing interconnects: LSIEM. It can be used during high-level design planning, synthesis, and simulation of interconnect-centric VDSM designs. LSIEM is an accurate and efficient high-level estimation model. It has been tested on a wide range of parameters and shown to have over 90% accuracy with respect to HSPICE simulation results.