Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve

Detection of the Diffuse Supernova Neutrino Background with JUNO

As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO

Toward Robotic Weed Control: Detection of Nutsedge Weed in Bermudagrass Turf Using Inaccurate and Insufficient Training Data

To enable robotic weed control, we develop algorithms to detect nutsedge weed from Bermudagrass turf. Due to the similarity between the weed and the background turf, it is expensive and error-prone to perform manual data labeling. Consequently, directly applying deep learning methods for object detection cannot generate satisfactory results. Building on an instance detection approach, (i.e. Mask R-CNN), we combine synthetic data with raw data to train the network. We propose an algorithm to generate high fidelity synthetic data, adopting different levels of annotations to reduce labeling cost. Moreover, we construct a nutsedge skeleton-based probabilistic map (NSPM) as the neural network input to reduce the reliance on pixel-wise precise labeling. We also modify loss function from cross entropy to Kullback–Leibler divergence which accommodates uncertainty in the labeling process. We have implemented the proposed algorithm and compare it with Faster R-CNN, a typical object detection approach. The results show that our design can effectively reduce the impact of imprecise and insufficient training sample issues and significantly outperforms the counterpart with a false negative rate of 0.4%, a satisfying result for weed control applications

Toward Robotic Weed Control: Detection of Nutsedge Weed in Bermudagrass Turf Using Inaccurate and Insufficient Training Data

To enable robotic weed control, we develop algorithms to detect nutsedge weed from Bermudagrass turf. Due to the similarity between the weed and the background turf, it is expensive and error-prone to perform manual data labeling. Consequently, directly applying deep learning methods for object detection cannot generate satisfactory results. Building on an instance detection approach, (i.e. Mask R-CNN), we combine synthetic data with raw data to train the network. We propose an algorithm to generate high fidelity synthetic data, adopting different levels of annotations to reduce labeling cost. Moreover, we construct a nutsedge skeleton-based probabilistic map (NSPM) as the neural network input to reduce the reliance on pixel-wise precise labeling. We also modify loss function from cross entropy to Kullback–Leibler divergence which accommodates uncertainty in the labeling process. We have implemented the proposed algorithm and compare it with Faster R-CNN, a typical object detection approach. The results show that our design can effectively reduce the impact of imprecise and insufficient training sample issues and significantly outperforms the counterpart with a false negative rate of 0.4%, a satisfying result for weed control applications

Scheduling Parallel Intrusion Detecting Applications on Hybrid Clouds

Recently, Parallel Intrusion Detection (PID) becomes very popular and its procedure of the parallel processing is called a PID application (PIDA). This PIDA can be regarded as a Bag-of-Tasks (BoT) application, consisting of multiple tasks that can be processed in parallel. Given multiple PIDAs (i.e., BoT applications) to be handled, when the private cloud has insufficiently available resources to afford all tasks, some tasks have to be outsourced to public clouds with resource-used costs. The key challenge here is how to schedule tasks on hybrid clouds to minimize makespan given a limited budget. This problem can be formulated as an Integer Programming model, which is generally NP-Hard. Accordingly, in this paper, we construct an Iterated Local Search (ILS) algorithm, which employs an effective heuristic to obtain the initial task sequence and utilizes an insertion-neighbourhood-based local search method to explore better task sequences with lower makespans. A swap-based perturbation operator is adopted to avoid local optimum. With the objective of improving the proposal’s efficiency without loss of any effectiveness, to calculate task sequences’ objectives, we construct a Fast Task Assignment (FTA) method by integrating an existing Task Assignment (TA) method with an acceleration mechanism designed through theoretical analysis. Accordingly, the proposed ILS is named FILS. Experimental results show that FILS outperforms the existing best algorithm for the considered problem, considerably and significantly. More importantly, compared with TA, FTA achieves a 2.42x speedup, which verifies that the acceleration mechanism employed by FTA is able to remarkably improve the efficiency. Finally, impacts of key factors are also evaluated and analyzed, exhaustively

Behavioral Changes from Stimulating Environmentally Conscious Actions in an Office Without Strong Environmental Consciousness

環境意識の特には高くない通常の事務系オフィスを実験系として, 1995年から2002年までの8年間の系に対する物質収支を環境面から継続実態調査として測定した。その測定時や測定後にいくつかの点について事務所に環境配慮行動をするように促し, どのような行動変容があるかを現場実験として明らかにした。うら紙については事務所の自発的なうら紙ボックスの設置とうら紙の使用という行動変容が起きたが, 再生紙はほとんど購入されることはなく紙ごみもほとんど資源化分別されなかった。 うら紙使用については社員の入れ代わり, 事務所の配置による影響があると考えられた。コピー機, プリンター, デスクトップパソコンについては環境配慮行動の促しにより使用電力量が削減できたが, ノートパソコンについては増加した。コピー機, プリンター, デスクトップパソコンのように間歇的に使用するものは節電に取り組みやすく, ノートパソコンのように半継続的に使用するものは行動変容を起こしにくい傾向のあることがわかった。Actual conditions of environmental balance (input and output) of an office, which was regarded as having no strong environmental consciousness, were measured continuously over a period of eight years (1995-2002) . Following field measurements, we analyzed and clarified effects of some behavioral analysis methods, such as prompting and feedback, on behavioral changes of office members to environmentally conscious actions. By installment of boxes for paper that was printed on a single side, much was used. Nevertheless, recycled paper was seldom purchased. Furthermore, waste paper was seldom classified and was not thought of as a recoverable resource. Instead, it was cast away as a combustible. Effects of installing the boxes for half-used paper on behavioral changes of office members to use single-sided paper disappeared over three years because of the change in office members and the office arrangement. The method of real time feedback together with detailed instructions on environmentally concerned actions caused behavioral changes in use of copy machines, printers, and desktop computers, thereby reducing electricity consumption. On the other hand, the electricity that was consumed by laptop computers increased. Consequently, our findings showed the following tendency: it is easier to reduce electricity consumption by changing the methods of use of infrequently used business machines, such as copy machines, printers and desktop computers than those of frequently used equipment, such as laptop computers