Progressive Feature Fusion Network for Enhancing Image Quality Assessment

Image compression has been applied in the fields of image storage and video broadcasting. However, it's formidably tough to distinguish the subtle quality differences between those distorted images generated by different algorithms. In this paper, we propose a new image quality assessment framework to decide which image is better in an image group. To capture the subtle differences, a fine-grained network is adopted to acquire multi-scale features. Subsequently, we design a cross subtract block for separating and gathering the information within positive and negative image pairs. Enabling image comparison in feature space. After that, a progressive feature fusion block is designed, which fuses multi-scale features in a novel progressive way. Hierarchical spatial 2D features can thus be processed gradually. Experimental results show that compared with the current mainstream image quality assessment methods, the proposed network can achieve more accurate image quality assessment and ranks second in the benchmark of CLIC in the image perceptual model track.Comment: Data Compression Conferenc

White Paper from Workshop on Large-scale Parallel Numerical Computing Technology (LSPANC 2020): HPC and Computer Arithmetic toward Minimal-Precision Computing

In numerical computations, precision of floating-point computations is a key factor to determine the performance (speed and energy-efficiency) as well as the reliability (accuracy and reproducibility). However, precision generally plays a contrary role for both. Therefore, the ultimate concept for maximizing both at the same time is the minimal-precision computing through precision-tuning, which adjusts the optimal precision for each operation and data. Several studies have been already conducted for it so far (e.g. Precimoniuos and Verrou), but the scope of those studies is limited to the precision-tuning alone. Hence, we aim to propose a broader concept of the minimal-precision computing system with precision-tuning, involving both hardware and software stack. In 2019, we have started the Minimal-Precision Computing project to propose a more broad concept of the minimal-precision computing system with precision-tuning, involving both hardware and software stack. Specifically, our system combines (1) a precision-tuning method based on Discrete Stochastic Arithmetic (DSA), (2) arbitrary-precision arithmetic libraries, (3) fast and accurate numerical libraries, and (4) Field-Programmable Gate Array (FPGA) with High-Level Synthesis (HLS). In this white paper, we aim to provide an overview of various technologies related to minimal- and mixed-precision, to outline the future direction of the project, as well as to discuss current challenges together with our project members and guest speakers at the LSPANC 2020 workshop; https://www.r-ccs.riken.jp/labs/lpnctrt/lspanc2020jan/

An Analysis of Altitude, Citizen Science and a Convolutional Neural Network Feedback Loop on Object Detection in Unmanned Aerial Systems

Using automated processes to detect wildlife in uncontrolled outdoor imagery in the field of wildlife ecology is a challenging task. In imagery provided by Unmanned Aerial Systems (UAS), this is especially true where individuals are small and visually similar to background substrates. To address these challenges, this work presents an automated feedback loop which can operate on large scale imagery, such as UAS generated orthomosaics, to train convo- lutional neural networks (CNNs) with extremely unbalanced class sizes. This feedback loop was used to help train CNNs using imagery classified by both expert biologists and citizen scientists at the Wildlife@home project. Utilizing the feedback loop dramatically reduced population count error rates from previously published work: from +150% to -3.93% on citizen scientist training data and +88% to +5.24% on expert training data. The system developed was then utilized to investigate the effect of altitude on CNN predictions. The training dataset was split into three subsets depending on the altitude of the imagery (75m, 100m and 120m). While the lowest altitude was shown to provide the best predictions of the three (+11.46%), the aggregate data set still provided the best results (-3.93%) indicating that there is greater benefit to be gained from a large data set at this scale, and there is potential benefit to having training data from multiple altitudes. This article is an extended version of “Detecting Wildlife in Unmanned Aerial Systems Imagery using Convolutional Neural Networks Trained with an Automated Feedback Loop” published in the proceedings of the 18th International Conference of Computational Science (ICCS 2018)

Incorporating Fine-grained Events in Stock Movement Prediction

Considering event structure information has proven helpful in text-based stock movement prediction. However, existing works mainly adopt the coarse-grained events, which loses the specific semantic information of diverse event types. In this work, we propose to incorporate the fine-grained events in stock movement prediction. Firstly, we propose a professional finance event dictionary built by domain experts and use it to extract fine-grained events automatically from finance news. Then we design a neural model to combine finance news with fine-grained event structure and stock trade data to predict the stock movement. Besides, in order to improve the generalizability of the proposed method, we design an advanced model that uses the extracted fine-grained events as the distant supervised label to train a multi-task framework of event extraction and stock prediction. The experimental results show that our method outperforms all the baselines and has good generalizability.Comment: Accepted by 2th ECONLP workshop in EMNLP201

High-Quality Hierarchical Process Mapping

Partitioning graphs into blocks of roughly equal size such that few edges run between blocks is a frequently needed operation when processing graphs on a parallel computer. When a topology of a distributed system is known, an important task is then to map the blocks of the partition onto the processors such that the overall communication cost is reduced. We present novel multilevel algorithms that integrate graph partitioning and process mapping. Important ingredients of our algorithm include fast label propagation, more localized local search, initial partitioning, as well as a compressed data structure to compute processor distances without storing a distance matrix. Moreover, our algorithms are able to exploit a given hierarchical structure of the distributed system under consideration. Experiments indicate that our algorithms speed up the overall mapping process and, due to the integrated multilevel approach, also find much better solutions in practice. For example, one configuration of our algorithm yields similar solution quality as the previous state-of-the-art in terms of mapping quality for large numbers of partitions while being a factor 9.3 faster. Compared to the currently fastest iterated multilevel mapping algorithm Scotch, we obtain 16% better solutions while investing slightly more running time

Providing Metrics-Based Results To Student Pilots For Critical Phases Of General Aviation Flights

This work details the development of the Critical Phase Analysis Tool (CPAT), a tool for analyzing and grading the quality of approach and landing phases of flight for the National General Aviation Flight Information Database (NGAFID). General Aviation (GA) accounts for the highest accident rates in Civil Aviation, and the approach and landing phases are when a majority of these accidents occur. Since GA aircraft typically lack most of the sophisticated technology that exists within Commercial Aviation, detecting phases of flight can be difficult. Moreover, because of the high variability in GA operations and abilities of the pilot, detecting unsafe flight practices is also not trivial. This thesis details the usefulness of an event-driven approach in analyzing the quality and risk level of an approach and landing. In particular, the application uses several parameters from a flight data recorder (FDR) to detect the phases of flight, detect any safety exceedances during the phases, and assign a metrics-based grade based on the accrued number of risk levels. The goal of this work is to improve the post-flight debriefing process for student pilots and Certified Flight Instructors (CFI) by augmenting the currently limited feedback with metrics and visualizations. By improving the feedback available to students, it is believed that it will help to correct unsafe flying habits quicker, which will also help reduce the GA accident rates in the long-term. The data was collected from a Garmin G1000 FDR glass cockpit display on a Cessna C172 fleet. The developed application is able to successfully detect go-arounds, touch-and-goes, and full-stop landings as either stable or unstable with an accuracy of 98.16%. The CPAT can be used to provide post-flight statistics and user-friendly graphs for educational purposes. It is capable of assisting both new and experienced pilots for the safety of themselves, their organization, and GA as a whole

Energy efficiency in short and wide-area IoT technologies—A survey

In the last years, the Internet of Things (IoT) has emerged as a key application context in the design and evolution of technologies in the transition toward a 5G ecosystem. More and more IoT technologies have entered the market and represent important enablers in the deployment of networks of interconnected devices. As network and spatial device densities grow, energy efficiency and consumption are becoming an important aspect in analyzing the performance and suitability of different technologies. In this framework, this survey presents an extensive review of IoT technologies, including both Low-Power Short-Area Networks (LPSANs) and Low-Power Wide-Area Networks (LPWANs), from the perspective of energy efficiency and power consumption. Existing consumption models and energy efficiency mechanisms are categorized, analyzed and discussed, in order to highlight the main trends proposed in literature and standards toward achieving energy-efficient IoT networks. Current limitations and open challenges are also discussed, aiming at highlighting new possible research directions