On the Logic of Belief and Propositional Quantification

We consider extending the modal logic KD45, commonly taken as the baseline system for belief, with propositional quantifiers that can be used to formalize natural language sentences such as “everything I believe is true” or “there is some-thing that I neither believe nor disbelieve.” Our main results are axiomatizations of the logics with propositional quantifiers of natural classes of complete Boolean algebras with an operator (BAOs) validating KD45. Among them is the class of complete, atomic, and completely multiplicative BAOs validating KD45. Hence, by duality, we also cover the usual method of adding propositional quantifiers to normal modal logics by considering their classes of Kripke frames. In addition, we obtain decidability for all the concrete logics we discuss

Satellite-based Cloud Remote Sensing: Fast Radiative Transfer Modeling and Inter-Comparison of Single-/Multi-Layer Cloud Retrievals with VIIRS

This dissertation consists of three parts, each of them, progressively, contributing to the problem of great importance that satellite-based remote sensing of clouds. In the first section, we develop a fast radiative transfer model specialized for Visible Infrared Imaging Radiometer Suite (VIIRS), based on the band-average technique. VIIRS, is a passive sensor flying aboard the NOAA’s Suomi National Polar-orbiting Partnership (NPP) spacecraft. This model successfully simulates VIIRS solar and infrared bands, in both moderate (M-bands) and imagery (I-bands) spatial resolutions. Besides, the model is two orders of magnitude faster than Line-by-line & discrete ordinate transfer (DISORT) method with a great accuracy. The second and third parts are going to investigate the retrieval of single-/multi- layer cloud optical properties, especially, cloud optical thickness (τ) and cloud effective particle size (De) with different methods. By presenting the comparison between results derived from VIIRS measurements and benchmark products, potential applications of Bayesian and OE retrieval methods for cloud property retrieval are discussed. It has proved that Bayesian method is more suitable for single-layer scenarios with fewer variables with fast speed, while Optimal Estimation method is superior to Bayesian method for more complicated multi-layer scenarios

On the Logics with Propositional Quantifiers Extending S5Π

Scroggs's theorem on the extensions of S5 is an early landmark in the modern mathematical studies of modal logics. From it, we know that the lattice of normal extensions of S5 is isomorphic to the inverse order of the natural numbers with infinity and that all extensions of S5 are in fact normal. In this paper, we consider extending Scroggs's theorem to modal logics with propositional quantifiers governed by the axioms and rules analogous to the usual ones for ordinary quantifiers. We call them Π-logics. Taking S5Π, the smallest normal Π-logic extending S5, as the natural counterpart to S5 in Scroggs's theorem, we show that all normal Π-logics extending S5Π are complete with respect to their complete simple S5 algebras, that they form a lattice that is isomorphic to the lattice of the open sets of the disjoint union of two copies of the one-point compactification of N, that they have arbitrarily high Turing-degrees, and that there are non-normal Π-logics extending S5Π

Logics of Imprecise Comparative Probability

This paper studies connections between two alternatives to the standard probability calculus for representing and reasoning about uncertainty: imprecise probability andcomparative probability. The goal is to identify complete logics for reasoning about uncertainty in a comparative probabilistic language whose semantics is given in terms of imprecise probability. Comparative probability operators are interpreted as quantifying over a set of probability measures. Modal and dynamic operators are added for reasoning about epistemic possibility and updating sets of probability measures

PMP: Privacy-Aware Matrix Profile against Sensitive Pattern Inference

Recent rapid development of sensor technology has allowed massive fine-grained time series (TS) data to be collected and set the foundation for the development of data-driven services and applications. During the process, data sharing is often involved to allow the third-party modelers to perform specific time series data mining (TSDM) tasks based on the need of data owner. The high resolution of TS brings new challenges in protecting privacy. While meaningful information in high-resolution TS shifts from concrete point values to local shape-based segments, numerous research have found that long shape-based patterns could contain more sensitive information and may potentially be extracted and misused by a malicious third party. However, the privacy issue for TS patterns is surprisingly seldom explored in privacy-preserving literature. In this work, we consider a new privacy-preserving problem: preventing malicious inference on long shape-based patterns while preserving short segment information for the utility task performance. To mitigate the challenge, we investigate an alternative approach by sharing Matrix Profile (MP), which is a non-linear transformation of original data and a versatile data structure that supports many data mining tasks. We found that while MP can prevent concrete shape leakage, the canonical correlation in MP index can still reveal the location of sensitive long pattern. Based on this observation, we design two attacks named Location Attack and Entropy Attack to extract the pattern location from MP. To further protect MP from these two attacks, we propose a Privacy-Aware Matrix Profile (PMP) via perturbing the local correlation and breaking the canonical correlation in MP index vector. We evaluate our proposed PMP against baseline noise-adding methods through quantitative analysis and real-world case studies to show the effectiveness of the proposed method

Conditional Goal-oriented Trajectory Prediction for Interacting Vehicles with Vectorized Representation

This paper aims to tackle the interactive behavior prediction task, and proposes a novel Conditional Goal-oriented Trajectory Prediction (CGTP) framework to jointly generate scene-compliant trajectories of two interacting agents. Our CGTP framework is an end to end and interpretable model, including three main stages: context encoding, goal interactive prediction and trajectory interactive prediction. First, a Goals-of-Interest Network (GoINet) is designed to extract the interactive features between agent-to-agent and agent-to-goals using a graph-based vectorized representation. Further, the Conditional Goal Prediction Network (CGPNet) focuses on goal interactive prediction via a combined form of marginal and conditional goal predictors. Finally, the Goaloriented Trajectory Forecasting Network (GTFNet) is proposed to implement trajectory interactive prediction via the conditional goal-oriented predictors, with the predicted future states of the other interacting agent taken as inputs. In addition, a new goal interactive loss is developed to better learn the joint probability distribution over goal candidates between two interacting agents. In the end, the proposed method is conducted on Argoverse motion forecasting dataset, In-house cut-in dataset, and Waymo open motion dataset. The comparative results demonstrate the superior performance of our proposed CGTP model than the mainstream prediction methods.Comment: 14 pages, 4 figure

Influence of Oil on Heat Transfer Characteristics of R410A Flow Boiling in Conventional and Small Size Microfin Tubes

Compact heat exchangers for refrigeration and air-conditioning systems are beneficial to reduce cost, charge inventory and leakage of refrigerant, and to improve energy efficiency and safety. Using small diameter microfin tubes is one way to decrease the size of heat exchangers. Currently, small diameter micofin tubes with outside diameter (O.D.) of 5.0 mm and 4.0 mm O.D. begin to be applied in newly developed R410A air conditioners instead of conventional size tubes (e.g. 7.0 mm O.D. microfin tubes). With the decrease of the tube diameter, the pressure drop becomes much larger, resulting in the decrease of the heat exchanger performance. In order to avoid such performance decrease, the heat exchanger should be redesign based on clearly understanding the difference of the heat transfer characteristics between conventional size microfin tubes and small diameter micofin tubes. Therefore, the heat transfer characteristics of R410A flow boiling inside both conventional size microfin tubes and small diameter microfin tubes should be known. Under real working conditions of R410A air conditioner, some amount of oil inevitably circulates with the refrigerant and has a significant impact on refrigerant evaporation heat transfer characteristics (Shen and Groll, 2005; Thome, 1996). Therefore, the influence of oil on heat transfer characteristics of R410A flow boiling inside microfin tubes with different diameters covering from conventional size to small size should be investigated. Experiments of R410A-oil mixture flow boiling inside microfin tubes with different outside diameters of 4.0~7.0 mm were performed. The experimental results show that, for 7.0 mm microfin tube, the influence factor of oil on the heat transfer characteristics are larger than 1.0 under the conditions of low vapor qualities (xr,o \u3c 0.4), presenting the enhancement effect of oil on heat transfer characteristics; with the increase of vapor quality, the enhancement becomes smaller, and is smaller than 1.0 under the conditions of low vapor qualities (xr,o \u3e 0.65), showing the deterioration effect of oil on heat transfer characteristics. As the tube diameter decreases from 7.0 mm to 4.0~5.0 mm, the deterioration effect of oil is weakened, especially at intermediate and high vapor qualities; for 4.0-5.0 mm tubes, the presence of oil shows the enhancement effect on heat transfer characteristics under the conditions of intermittent vapor quality (0.4 \u3c xr,o \u3c 0.8), which is not the same as the deterioration effect for 7.0 mm tubes. The comparison of heat transfer coefficient for two 5.0 mm microfin tubes with different fin structures shows that, larger fin height and contact area of liquid with tube wall may enhance the heat transfer for oil-free R410A, but result in smaller enhancement effect of oil at low vapor qualities and smaller deterioration effect of oil at intermediate and high vapor qualities. Based on the experimental data for conventional and small size microfin tubes, a general heat transfer correlation for R410A-oil mixture flow boiling inside microfin tubes was developed, and it agrees with 94% of the experimental data of R410A-oil mixture in 4.0 mm ~ 7.0 mm microfin tubes within a deviation of ±30%