Block Selection Method for Using Feature Norm in Out-of-distribution Detection

Detecting out-of-distribution (OOD) inputs during the inference stage is crucial for deploying neural networks in the real world. Previous methods commonly relied on the output of a network derived from the highly activated feature map. In this study, we first revealed that a norm of the feature map obtained from the other block than the last block can be a better indicator of OOD detection. Motivated by this, we propose a simple framework consisting of FeatureNorm: a norm of the feature map and NormRatio: a ratio of FeatureNorm for ID and OOD to measure the OOD detection performance of each block. In particular, to select the block that provides the largest difference between FeatureNorm of ID and FeatureNorm of OOD, we create Jigsaw puzzle images as pseudo OOD from ID training samples and calculate NormRatio, and the block with the largest value is selected. After the suitable block is selected, OOD detection with the FeatureNorm outperforms other OOD detection methods by reducing FPR95 by up to 52.77% on CIFAR10 benchmark and by up to 48.53% on ImageNet benchmark. We demonstrate that our framework can generalize to various architectures and the importance of block selection, which can improve previous OOD detection methods as well.Comment: 11 pages including reference. 5 figures and 5 table

Uncertainty quantification of granular computing‑neural network model for prediction of pollutant longitudinal dispersion coefficient in aquatic streams

Discharge of pollution loads into natural water systems remains a global challenge that threatens water and food supply, as well as endangering ecosystem services. Natural rehabilitation of contaminated streams is mainly influenced by the longitudinal dispersion coefficient, or the rate of longitudinal dispersion (Dx), a key parameter with large spatiotemporal fluctuations that characterizes pollution transport. The large uncertainty in estimation of Dx in streams limits the water quality assessment in natural streams and design of water quality enhancement strategies. This study develops an artificial intelligence-based predictive model, coupling granular computing and neural network models (GrC-ANN) to provide robust estimation of Dx and its uncertainty for a range of flow-geometric conditions with high spatiotemporal variability. Uncertainty analysis of Dx estimated from the proposed GrC-ANN model was performed by alteration of the training data used to tune the model. Modified bootstrap method was employed to generate different training patterns through resampling from a global database of tracer experiments in streams with 503 datapoints. Comparison between the Dx values estimated by GrC-ANN to those determined from tracer measurements shows the appropriateness and robustness of the proposed method in determining the rate of longitudinal dispersion. The GrC-ANN model with the narrowest bandwidth of estimated uncertainty (bandwidth-factor = 0.56) that brackets the highest percentage of true Dx data (i.e., 100%) is the best model to compute Dx in streams. Considering the significant inherent uncertainty reported in the previous Dx models, the GrC-ANN model developed in this study is shown to have a robust performance for evaluating pollutant mixing (Dx) in turbulent environmental flow systems

Nanomaterials for Neural Interfaces

This review focuses on the application of nanomaterials for neural interfacing. The junction between nanotechnology and neural tissues can be particularly worthy of scientific attention for several reasons: (i) Neural cells are electroactive, and the electronic properties of nanostructures can be tailored to match the charge transport requirements of electrical cellular interfacing. (ii) The unique mechanical and chemical properties of nanomaterials are critical for integration with neural tissue as long-term implants. (iii) Solutions to many critical problems in neural biology/medicine are limited by the availability of specialized materials. (iv) Neuronal stimulation is needed for a variety of common and severe health problems. This confluence of need, accumulated expertise, and potential impact on the well-being of people suggests the potential of nanomaterials to revolutionize the field of neural interfacing. In this review, we begin with foundational topics, such as the current status of neural electrode (NE) technology, the key challenges facing the practical utilization of NEs, and the potential advantages of nanostructures as components of chronic implants. After that the detailed account of toxicology and biocompatibility of nanomaterials in respect to neural tissues is given. Next, we cover a variety of specific applications of nanoengineered devices, including drug delivery, imaging, topographic patterning, electrode design, nanoscale transistors for high-resolution neural interfacing, and photoactivated interfaces. We also critically evaluate the specific properties of particular nanomaterials—including nanoparticles, nanowires, and carbon nanotubes—that can be taken advantage of in neuroprosthetic devices. The most promising future areas of research and practical device engineering are discussed as a conclusion to the review.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/64336/1/3970_ftp.pd

Global Surface Temperature: A New Insight

This paper belongs to our Special Issue “Application of Climate Data in Hydrologic Models” [...

A parsimonious framework of evaluating WSUD features in urban flood mitigation

In this study, a parsimonious framework for supporting Water Sensitive Urban Design (WSUD) was proposed to seek a tradeoff between investment of WSUD features and mitigation of urban flood damage. A two-dimensional (2D) hydrological-hydraulic simulation model, PCSWMM, was adopted to simulate the rainfall-runoff process and inundation scenarios, and the flood damages was evaluated based on inundated water depths and damage curves. The sensitivity of deploying various design features to flood control effects was also tested, which provided useful information for identifying potential design parameters (like conduit sizes and pond locations). The proposed framework was applied to a hypothetical case adapted from an urban district in the tropical region considering various WSUD features (i.e. rainwater storage pond, rain garden, and conduit upgrading). The results showed that when the gross investment of WSUD features increased from 0 to 1.19 million $, the damage cost would decrease from 4.61 to 3.41 million$; a linear relationship (with a R-square fit at 0.9) was found suitable to represent the relationship between the investment and the damage. The proposed framework is effective in helping assess the balance between mitigation of urban flood damage and adoption of WSUD features, and could be used to support urban water managers for a more science-based decision making towards flood risk management.MOE (Min. of Education, S’pore)Published versio

Understanding Conflicting Interests of a Government and a Tobacco Manufacturer: A Game-Theoretic Approach

Rice is the staple food of nearly half of the population of the world, most of whom live in developing countries. Ensuring a domestic supply of rice from outside sources is difficult for developing countries as less than 5% of the total world’s production is available for international trade. Hence, in order to ensure domestic food security, e.g., food availability and access, governments provide subsidies in agriculture. In many occasions, public money used for the subsidy goes toward promoting undesirable crops like tobacco. Although the strategic interaction between governments and manufacturers is critical, it has not been studied in the literature. This study fills this gap by considering a game between a government (of a developing country) and a tobacco manufacturer in which the government decides on a mix of subsidies and the tobacco manufacturer decides on declaring a purchasing price of tobacco. We provide a numerical study to show that controlling the output harvest price is more effective in reaching the desired end result for both the government and the tobacco manufacturer. A subsidy in fertilizer results in the measurable increase in the government spending but does not have significant effect in reaching the production target. The fertilizer subsidy should be provided only when the output price is too high to be affordable for the population

Investigating event-based temporal patterns of design rainfall in a tropical region

This study investigated the temporal rainfall pattern in order to facilitate rainfall design, which normally requires a good understanding of the temporal patterns of rainstorm events. The analysis employed a storm-event-based approach using the concept of inter-event time definition (IETD) and rainfall depth/duration/intensity thresholds. The 5-min rainfall data at three raingauge stations were analysed to determine representative quartiles of a design storm in a tropical city. The temporal characteristics of the design storm could be determined from the rainfall depth ratios of consecutive peak rainfalls for each interval of storm duration, and time to the first peak rainfall depending on each quartile’s rainstorm events. The determination of the quantile distribution of tropical rainfall could help improve the representativeness of design rainfall and facilitate rainfall–runoff modelling for urban flood control in a tropical region.Ministry of Education (MOE)This work was partly supported by Academic Research Fund Tier 1 from the Ministry of Education (MOE), Singapore (2019-T1-001-160), and partly by the Chung-Ang University Research Grants in 2019

Evaluation of Dam Water-Supply Capacity in Korea Using the Water-Shortage Index

This study evaluates the dam water-supply capacity in Korea using the water shortage index. The water-shortage index (SI) and generalized water-shortage index (GSI) used in this study are evaluated and modified slightly by considering both the damage cost due to water-supply failure and the construction cost of water-supply systems in Korea. The modified indices are then applied for performance evaluation of 16 multipurpose dams in Korea, whose results are evaluated using different units: each dam, each river basin, and all dams. In the analysis of the dam level and basin level, water-supply problems are detected in several dams and in some river basins. However, the SI and GSI estimated for all dams are found to be lower than 1. This result indicates that, even though the total amount of storage capacity is enough to satisfy the design supply, water resources are not well spatially distributed in Korea. It is also found that the modified indices are valid to describe the performance of each dam in water deficient regions during occurrence years of major droughts. In conclusion, the SI and GSI can offer alternative ways of evaluating dam water supply under different environmental conditions and potentially help determine optimal water-storage capacity of dams

BattleSound: A Game Sound Benchmark for the Sound-Specific Feedback Generation in a Battle Game

A haptic sensor coupled to a gamepad or headset is frequently used to enhance the sense of immersion for game players. However, providing haptic feedback for appropriate sound effects involves specialized audio engineering techniques to identify target sounds that vary according to the game. We propose a deep learning-based method for sound event detection (SED) to determine the optimal timing of haptic feedback in extremely noisy environments. To accomplish this, we introduce the BattleSound dataset, which contains a large volume of game sound recordings of game effects and other distracting sounds, including voice chats from a PlayerUnknown’s Battlegrounds (PUBG) game. Given the highly noisy and distracting nature of war-game environments, we set the annotation interval to 0.5 s, which is significantly shorter than the existing benchmarks for SED, to increase the likelihood that the annotated label contains sound from a single source. As a baseline, we adopt mobile-sized deep learning models to perform two tasks: weapon sound event detection (WSED) and voice chat activity detection (VCAD). The accuracy of the models trained on BattleSound was greater than 90% for both tasks; thus, BattleSound enables real-time game sound recognition in noisy environments via deep learning. In addition, we demonstrated that performance degraded significantly when the annotation interval was greater than 0.5 s, indicating that the BattleSound with short annotation intervals is advantageous for SED applications that demand real-time inferences