Classifying, clustering and clumping: defining groups of irrigators in Australia's Namoi Valley

[Extract] This paper explores the non-commercial factors influencing farmers' decision making in the context of the recently implemented Water Sharing Plans (WSP) in the Namoi Valley of New South Wales. In line with the governments water reform goals, the WSP were introduced to rectify an over allocation of groundwater resources. The required amount of entitlement reduction varied across the valley, according to the existing amount of over allocation, and has resulted in some licence holders losing up to 94% of their entitlements. To manage this degree of reduction most licence holders have to make some kind of decision about how to deal with it. This could be by purchasing or selling land or water

Solving the time- and frequency-multiplexed problem of constrained radiofrequency induced hyperthermia

Targeted radiofrequency (RF) heating induced hyperthermia has a wide range of applications, ranging from adjunct anti-cancer treatment to localized release of drugs. Focal RF heating is usually approached using time-consuming nonconvex optimization procedures or approximations, which significantly hampers its application. To address this limitation, this work presents an algorithm that recasts the problem as a semidefinite program and quickly solves it to global optimality, even for very large (human voxel) models. The target region and a desired RF power deposition pattern as well as constraints can be freely defined on a voxel level, and the optimum application RF frequencies and time-multiplexed RF excitations are automatically determined. 2D and 3D example applications conducted for test objects containing pure water (r(target) = 19 mm, frequency range: 500–2000 MHz) and for human brain models including brain tumors of various size (r(1) = 20 mm, r(2) = 30 mm, frequency range 100–1000 MHz) and locations (center, off-center, disjoint) demonstrate the applicability and capabilities of the proposed approach. Due to its high performance, the algorithm can solve typical clinical problems in a few seconds, making the presented approach ideally suited for interactive hyperthermia treatment planning, thermal dose and safety management, and the design, rapid evaluation, and comparison of RF applicator configurations

HMDB: A Large Video Database for Human Motion Recognition

With nearly one billion online videos viewed everyday, an emerging new frontier in computer vision research is recognition and search in video. While much effort has been devoted to the collection and annotation of large scalable static image datasets containing thousands of image categories, human action datasets lag far behind. Current action recognition databases contain on the order of ten different action categories collected under fairly controlled conditions. State-of-the-art performance on these datasets is now near ceiling and thus there is a need for the design and creation of new benchmarks. To address this issue we collected the largest action video database to-date with 51 action categories, which in total contain around 7,000 manually annotated clips extracted from a variety of sources ranging from digitized movies to YouTube. We use this database to evaluate the performance of two representative computer vision systems for action recognition and explore the robustness of these methods under various conditions such as camera motion, viewpoint, video quality and occlusion.United States. Defense Advanced Research Projects Agency. Information Processing Techniques OfficeUnited States. Defense Advanced Research Projects Agency. System Science Division. Defense Sciences OfficeNational Science Foundation (U.S.) (NSF-0640097)National Science Foundation (U.S.) (NSF-0827427)United States. Air Force Office of Scientific Research (FA8650-05- C-7262)Adobe SystemsKing Abdullah University of Science and TechnologyNEC ElectronicsSony CorporationEugene McDermott FoundationBrown University. Center for Computing and VisualizationRobert J. and Nancy D. Carney Fund for Scientific InnovationUnited States. Defense Advanced Research Projects Agency (DARPA-BAA-09-31)United States. Office of Naval Research (ONR-BAA-11-001)Ministry of Science, Research and the Arts of Baden Württemberg, German

Some of the Effects of Domestic Sewage Discharged Into Hickman and Jessamine Creeks in Jessamine County, Kentucky

A 6-week study was made in the summer of 1971 as an initial effort to determine the extent of pollution that the three sewage disposal plants in Jessamine County, Kentucky, are contributing to its streams. With the rapid population increase in Lexington and nearby municipalities, this study should furnish a basis of comparison for future investigations. Eighteen collecting stations were established in riffle areas of Hickman and Jessamine Creeks, and coliform bacteria, macro-invertebrate populations, fish populations and chemical water quality of each riffle area were studied. Hickman Creek\u27s flow was augmented by approximately 3,100,000 gallons/day (11,735 -m3/day) from one of the City of Lexington\u27s sewage disposal plants, and Jessamine Creek\u27s flow by 500,000 gallons/day (1,893 m3/day) from the cities of Nicholasville and Wilmore. The Lexington and Wilmore facilities were greatly overloaded. Chemical analyses were directed toward finding out the fluctuations of phosphates, sulfates, and nitrates. Water disappearing through limestone faults posed investigational problems. Hickman Creek showed evidences of pollution for a greater distance downstream than did Jessamine. Diversity of clean water indicator organisms was higher in lower Jessamine than in lower Hickman; this was particularly true for darters (Etheostoma) and stoneflies (Plecoptera). Jessamine Creek was also supporting limited game fishing

Top-down Attention Recurrent VLAD Encoding for Action Recognition in Videos

Most recent approaches for action recognition from video leverage deep architectures to encode the video clip into a fixed length representation vector that is then used for classification. For this to be successful, the network must be capable of suppressing irrelevant scene background and extract the representation from the most discriminative part of the video. Our contribution builds on the observation that spatio-temporal patterns characterizing actions in videos are highly correlated with objects and their location in the video. We propose Top-down Attention Action VLAD (TA-VLAD), a deep recurrent architecture with built-in spatial attention that performs temporally aggregated VLAD encoding for action recognition from videos. We adopt a top-down approach of attention, by using class specific activation maps obtained from a deep CNN pre-trained for image classification, to weight appearance features before encoding them into a fixed-length video descriptor using Gated Recurrent Units. Our method achieves state of the art recognition accuracy on HMDB51 and UCF101 benchmarks.Comment: Accepted to the 17th International Conference of the Italian Association for Artificial Intelligenc

Single Shot Temporal Action Detection

Temporal action detection is a very important yet challenging problem, since videos in real applications are usually long, untrimmed and contain multiple action instances. This problem requires not only recognizing action categories but also detecting start time and end time of each action instance. Many state-of-the-art methods adopt the "detection by classification" framework: first do proposal, and then classify proposals. The main drawback of this framework is that the boundaries of action instance proposals have been fixed during the classification step. To address this issue, we propose a novel Single Shot Action Detector (SSAD) network based on 1D temporal convolutional layers to skip the proposal generation step via directly detecting action instances in untrimmed video. On pursuit of designing a particular SSAD network that can work effectively for temporal action detection, we empirically search for the best network architecture of SSAD due to lacking existing models that can be directly adopted. Moreover, we investigate into input feature types and fusion strategies to further improve detection accuracy. We conduct extensive experiments on two challenging datasets: THUMOS 2014 and MEXaction2. When setting Intersection-over-Union threshold to 0.5 during evaluation, SSAD significantly outperforms other state-of-the-art systems by increasing mAP from 19.0% to 24.6% on THUMOS 2014 and from 7.4% to 11.0% on MEXaction2.Comment: ACM Multimedia 201