Selecting linear algebra kernel composition using response time prediction

Numerical linear algebra libraries provide many kernels that can be composed to perform complex computations. For a given computation, there is typically a large number of functionally equivalent kernel compositions. Some of these compositions achieve better response times than others for particular data and when executed on a particular computer architecture. Previous research provides methods to enumerate (a subset of) these kernel compositions. In this work, we study the problem of determining the composition that yields the lowest response time. Our approach is based on a response time prediction for each candidate combination. While this prediction could in principle be obtained using analytical and/or empirical performance models, developing accurate such models is known to be challenging. Instead, we define a feature space that captures salient properties of kernel combinations and predict response time using supervised machine learning. We experiment with a standard set of machine learning algorithms and identify an effective algorithm for our kernel composition selection problem. Using this algorithm, our approach widely outperforms the strategy that would consist in always using the simplest kernel composition and is often close to the fastest kernel compositions among those evaluated. We quantify the potential benefit of our approach if it were to be implemented as part of an interactive computational tool. We find that although the potential benefit is substantial, a limiting factor is the kernel composition enumeration overhead. Copyright © 2014 John Wiley & Sons, Ltd.     

Interacting Socially with the Internet of Things (IoT): Effects of Source Attribution and Specialization in Human–IoT Interaction

This study investigates the theoretical mechanisms by which the variations in source attribution (multiple sources vs. single source) and specialization (multifunctionality vs. single functionality) of Internet of Things (IoT) devices influence the quality of human–IoT interaction. Results from a between‐subjects experiment (N = 100) indicate that IoT devices that elicit the sense of multiple agencies and are specialized in a single function induce greater social presence and perceived expertise, which, in turn, lead individuals to show a more positive attitude toward the devices and to ascribe greater quality to the information transmitted by them. The results also reveal that the effect of multiple source attribution is more pronounced for individuals for whom the content of the information has low personal relevance.     

A timing controller embedded driver IC with 3.24‐Gbps eDP interface for chip‐on‐glass TFT‐LCD applications

This paper presents a timing controller embedded driver (TED) IC with 3.24‐Gbps embedded display port (eDP), which is implemented using a 45‐nm high‐voltage CMOS process for the chip‐on‐glass (COG) TFT‐LCD applications. The proposed TED‐IC employs the input offset calibration scheme, the zero‐adjustable equalizer, and the phase locked loop‐based bang‐bang clock and data recovery to enhance the maximum data rate. Also, the proposed TED‐IC provides efficient power management by supporting advanced link power management feature of eDP standard v1.4. Additionally, the smart charge sharing is proposed to reduce the dynamic power consumption of output buffers. Measured result demonstrates the maximum data rate of 3.24 Gbps from a 1.1 V supply voltage with a 7.9‐inch QXGA 60‐Hz COG‐LCD prototype panel and 44% power saving from the display system.     

Rendering of HDR images to improve brightness discrimination

The impression of quality of images can be enhanced on a high dynamic range (HDR) displays. Generally, a conventional 8‐bit image can be processed to an HDR image by inverse tone mapping operators. Among the operators, brightness discrimination mapping by applying brightness adaptation model attempted to mimic the human visual system. In this paper, we use a brightness adaptation model to derive a brightness discrimination mapping algorithm for HDR displays. The proposed algorithm maximizes a function, which represents the local and global brightness discrimination range by exploiting characteristics of the human visual system. Enhancement of details is verified by visualizing HDR images from dark to bright regions. Improvement of dynamic range is quantified by measuring increased discrimination ratio.     

Scalable digital hardware for a trapped ion quantum computer

Quantum Information Processing - Many of the challenges of scaling quantum computer hardware lie at the interface between the qubits and the classical control signals used to manipulate them....     

A novel part-based approach to mean-shift algorithm for visual tracking

Visual tracking is one of the most important problems considered in computer vision. To improve the performance of the visual tracking, a part-based approach will be a good solution. In this paper, a novel method of visual tracking algorithm named part-based mean-shift (PBMS) algorithm is presented. In the proposed PBMS, unlike the standard mean-shift (MS), the target object is divided into multiple parts and the target is tracked by tracking each individual part and combining the results. For the part-based visual tracking, the objective function in the MS is modified such that the target object is represented as a combination of the parts and iterative optimization solution is presented. Further, the proposed PBMS provides a systematic and analytic way to determine the scale of the bounding box for the target from the perspective of the objective function optimization. Simulation is conducted with several benchmark problems and the result shows that the proposed PBMS outperforms the standard MS.

     

Can 3DTV Create Immersive Environments?

This article has been retracted.     

Asynchronous action-reward learning for nonstationary serial supply chain inventory control

Action-reward learning is a reinforcement learning method. In this machine learning approach, an agent interacts with non-deterministic control domain. The agent selects actions at decision epochs and the control domain gives rise to rewards with which the performance measures of the actions are updated. The objective of the agent is to select the future best actions based on the updated performance measures. In this paper, we develop an asynchronous action-reward learning model which updates the performance measures of actions faster than conventional action-reward learning. This learning model is suitable to apply to nonstationary control domain where the rewards for actions vary over time. Based on the asynchronous action-reward learning, two situation reactive inventory control models (centralized and decentralized models) are proposed for a two-stage serial supply chain with nonstationary customer demand. A simulation based experiment was performed to evaluate the performance of the proposed two models. Chang Ouk Kim received his Ph.D. in industrial engineering from Purdue University in 1996 and his B.S. and M.S. degrees from Korea University, Republic of Korea in 1988 and 1990, respectively. From 1998--2001, he was an assistant professor in the Department of Industrial Systems Engineering at Myongji University, Republic of Korea. In 2002, he joined the Department of Information and Industrial Engineering at Yonsei University, Republic of Korea and is now an associate professor. He has published more than 30 articles at international journals. He is currently working on applications of artificial intelligence and adaptive control theory in supply chain management, RFID based logistics information system design, and advanced process control in semiconductor manufacturing. Ick-Hyun Kwon is a postdoctoral researcher in the Department of Civil and Environmental Engineering at University of Illinois at Urbana-Champaign. Previous to this position, Dr. Kwon was a research assistant professor in the Research Institute for Information and Communication Technology at Korea University, Seoul, Republic of Korea. He received his B.S., M.S., and Ph.D. degrees in Industrial Engineering from Korea University, in 1998, 2000, and 2006, respectively. His current research interests are supply chain management, inventory control, production planning and scheduling. Jun-Geol Baek is an assistant professor in the Department of Business Administration at Kwangwoon University, Seoul, Korea. He received his B.S., M.S., and Ph.D. degrees in Industrial Engineering from Korea University, Seoul, Korea, in 1993, 1995, and 2001 respectively. From March 2002 to February 2007, he was an assistant professor in the Department of Industrial Systems Engineering at Induk Institute of Technology, Seoul, Korea. His research interests include machine learning, data mining, intelligent machine diagnosis, and ubiquitous logistics information systems. An erratum to this article can be found at     

Performance of the MAP/G/1 Queue Under the Dyadic Control of Workload and Server Idleness

This paper studies the steady-state queue length process of the MAP/G/1 queue under the dyadic control of the D-policy and multiple server vacations. We derive the probability generating function of the queue length and the mean queue length. We then present computational experiences and compare the MAP queue with the Poisson queue.

Touch‐screen panel integrated into 12.1‐in. a‐Si:H TFT‐LCD

Abstract— A touch‐screen‐panel (TSP) embedded 12.1‐in. LCD employing a standard existing a‐Si:H TFT‐LCD process has been successfully developed. Compared with conventional external touch‐screen panels, which use additional components to detect touch events, the new internal TSP exhibits a clearer image and improved touch feeling, as well as increased sensing speed using discrete sensing lines to enable higher‐speed sensing functions including handwriting. The new internal digital switching TSP can be fabricated with low cost because it does not require any additional process steps compared to a standard a‐Si:H TFT‐LCD.