Detecting the Unexpected via Image Resynthesis

Classical semantic segmentation methods, including the recent deep learning ones, assume that all classes observed at test time have been seen during training. In this paper, we tackle the more realistic scenario where unexpected objects of unknown classes can appear at test time. The main trends in this area either leverage the notion of prediction uncertainty to flag the regions with low confidence as unknown, or rely on autoencoders and highlight poorly-decoded regions. Having observed that, in both cases, the detected regions typically do not correspond to unexpected objects, in this paper, we introduce a drastically different strategy: It relies on the intuition that the network will produce spurious labels in regions depicting unexpected objects. Therefore, resynthesizing the image from the resulting semantic map will yield significant appearance differences with respect to the input image. In other words, we translate the problem of detecting unknown classes to one of identifying poorly-resynthesized image regions. We show that this outperforms both uncertainty- and autoencoder-based methods

確率限界法検定に基づく信頼区間・予測区間を導入した水文頻度解析手法および当該区間の構成プログラムの提案

The main purpose of hydrological frequency analysis is to estimate hydrological quantity with design return period. In conventional hydrological frequency analysis, we estimate T-year hydrological quantity by using observed data which were accumulated for several decades. However, observed data of extremes which were accumulated so far is so limited one that the design hydrological quantity includes uncertainty to a large extent. Moreover, one of the difficulties caused by shortage of extreme value data is not to predict catastrophic heavy rainfalls which deviate greatly from adopted probability distribution which is used for river planning. So, it is often impossible to evaluate catastrophic heavy rainfalls and these rainfalls are treated as unexpected. Therefore, we propose new hydrological frequency analysis introducing confidence interval and prediction interval based on probability limit method test. By introducing this confidence interval into hydrological frequency analysis, uncertainty of design hydrological quantity can be quantified. Also, by introducing this prediction interval, it can be possible to predict the scale and occurrence risk of catastrophic heavy rainfalls. In this research, the theoretical frame work of hydrological frequency analysis introducing confidence interval and prediction interval and its construction program are shown

Service Capacity Reserve under Uncertainty by Hospital’s ER Analogies: A Practical Model for Car Services

We define a capacity reserve model to dimension passenger car service installations according to the demographic distribution of the area to be serviced by using hospital?s emergency room analogies. Usually, service facilities are designed applying empirical methods, but customers arrive under uncertain conditions not included in the original estimations, and there is a gap between customer?s real demand and the service?s capacity. Our research establishes a valid methodology and covers the absence of recent researches and the lack of statistical techniques implementation, integrating demand uncertainty in a unique model built in stages by implementing ARIMA forecasting, queuing theory, and Monte Carlo simulation to optimize the service capacity and occupancy, minimizing the implicit cost of the capacity that must be reserved to service unexpected customers. Our model has proved to be a useful tool for optimal decision making under uncertainty integrating the prediction of the cost implicit in the reserve capacity to serve unexpected demand and defining a set of new process indicators, such us capacity, occupancy, and cost of capacity reserve never studied before. The new indicators are intended to optimize the service operation. This set of new indicators could be implemented in the information systems used in the passenger car services

Risk, Unexpected Uncertainty, and Estimation Uncertainty: Bayesian Learning in Unstable Settings

Recently, evidence has emerged that humans approach learning using Bayesian updating rather than (model-free) reinforcement algorithms in a six-arm restless bandit problem. Here, we investigate what this implies for human appreciation of uncertainty. In our task, a Bayesian learner distinguishes three equally salient levels of uncertainty. First, the Bayesian perceives irreducible uncertainty or risk: even knowing the payoff probabilities of a given arm, the outcome remains uncertain. Second, there is (parameter) estimation uncertainty or ambiguity: payoff probabilities are unknown and need to be estimated. Third, the outcome probabilities of the arms change: the sudden jumps are referred to as unexpected uncertainty. We document how the three levels of uncertainty evolved during the course of our experiment and how it affected the learning rate. We then zoom in on estimation uncertainty, which has been suggested to be a driving force in exploration, in spite of evidence of widespread aversion to ambiguity. Our data corroborate the latter. We discuss neural evidence that foreshadowed the ability of humans to distinguish between the three levels of uncertainty. Finally, we investigate the boundaries of human capacity to implement Bayesian learning. We repeat the experiment with different instructions, reflecting varying levels of structural uncertainty. Under this fourth notion of uncertainty, choices were no better explained by Bayesian updating than by (model-free) reinforcement learning. Exit questionnaires revealed that participants remained unaware of the presence of unexpected uncertainty and failed to acquire the right model with which to implement Bayesian updating

Single-occupancy simulator for ambient intelligent environment

In this paper, the simulation of an occupant’s behaviour in a single-occupant ambient intelligent environment is addressed. The algorithm of the simulator is designed flexible enough to accept different environmental profiles including the number of areas and the connections between them along with different occupant’s profiles including expected daily occupancy pattern of him/her and the uncertainty of his/her behaviour to follow this occupancy pattern. The generated occupancy signal by the simulator represents the occupancy of areas by assuming a signal level for the occupancy of each area in a single-occupant environment with the resolution of one minute in a whole day activity of the occupant in the environment. The validity of the simulator will be verified by tuning the simulator’s parameters to occupancy data collected by sensory agents from a real equivalent environment. By applying the generated data from this simulator to the data mining techniques, the ability of different techniques will be investigated

Epilepsy and Ecstatic Experiences: The Role of the Insula.

Ecstatic epilepsy is a rare form of focal epilepsy in which the aura (beginning of the seizures) consists of a blissful state of mental clarity/feeling of certainty. Such a state has also been described as a "religious" or mystical experience. While this form of epilepsy has long been recognized as a temporal lobe epilepsy, we have accumulated evidence converging toward the location of the symptomatogenic zone in the dorsal anterior insula during the 10 last years. The neurocognitive hypothesis for the genesis of a mental clarity is the suppression of the interoceptive prediction errors and of the unexpected surprise associated with any incoming internal or external signal, usually processed by the dorsal anterior insula. This mimics a perfect prediction of the world and induces a feeling of certainty. The ecstatic epilepsy is thus an amazing model for the role of anterior insula in uncertainty and surprise

Design of robust service operations using cybernetic principles and simulation

Information flows in a service organisation allow business units to co-ordinate their response to changes in the operating environment. Processes and interactions can be designed so that the right information flows to the right people, at the right time to make effective decisions regarding job priorities and allocation of limited resource. This paper develops an analysis framework and simulation approach to identify the internal information flows an organisation needs to “tune itself” for changing conditions, thus making itself more robust to uncertainties. The ideas are developed and illustrated through a case study with a major telecoms company.International Design Conference - DESIGN 201