TriPINet: Tripartite Progressive Integration Network for Image Manipulation Localization

Image manipulation localization aims at distinguishing forged regions from the whole test image. Although many outstanding prior arts have been proposed for this task, there are still two issues that need to be further studied: 1) how to fuse diverse types of features with forgery clues; 2) how to progressively integrate multistage features for better localization performance. In this paper, we propose a tripartite progressive integration network (TriPINet) for end-to-end image manipulation localization. First, we extract both visual perception information, e.g., RGB input images, and visual imperceptible features, e.g., frequency and noise traces for forensic feature learning. Second, we develop a guided cross-modality dual-attention (gCMDA) module to fuse different types of forged clues. Third, we design a set of progressive integration squeeze-and-excitation (PI-SE) modules to improve localization performance by appropriately incorporating multiscale features in the decoder. Extensive experiments are conducted to compare our method with state-of-the-art image forensics approaches. The proposed TriPINet obtains competitive results on several benchmark datasets

An Adaptive Phase-Field Method for Structural Topology Optimization

In this work, we develop an adaptive algorithm for the efficient numerical solution of the minimum compliance problem in topology optimization. The algorithm employs the phase field approximation and continuous density field. The adaptive procedure is driven by two residual type a posteriori error estimators, one for the state variable and the other for the objective functional. The adaptive algorithm is provably convergent in the sense that the sequence of numerical approximations generated by the adaptive algorithm contains a subsequence convergent to a solution of the continuous first-order optimality system. We provide several numerical simulations to show the distinct features of the algorithm.Comment: 30 pages, 10 figure

Development and validation of a scale to measure patients’ trust in pharmacists in Singapore

Objective: To develop and validate a scale to measure patients’ trust in pharmacists for use as an outcomes predictor in pharmacoeconomic and pharmaceutical care studies. Methods: Literature review, study team discussion and focus group discussions were conducted to generate items of a candidate version to be pilot-tested for content validity. An amended candidate version was then tested among eligible Singaporeans across different ethnic and age groups. Score distributions were assessed for discriminatory power and item analyses for fi nalizing items. Exploratory factor analysis was used to identify dimensionality and homogeneous items. Cronbach’s alpha was measured for internal consistency and Pearson’s correlation coefficients for convergent validity. Results: Eighteen items were generated with good variability (SD ≻ 1.0) and symmetry (means ranged from −1 to 1) for score distribution. After minor changes to improve content clarity, the amended questionnaire was self-administered among 1196 respondents [mean (SD) age: 38.6 (14.9) years, 51.6% female, 87% ≻6 years of education]. Six items were dropped due to inadequate item-total correlation coefficients, leaving 12-item scale for factor analysis. Three factors (“benevolence”, “technical competence” and “global trust”) were identifi ed, accounting for 55% of the total variance. Cronbach’s alpha was 0.83, indicating high internal consistency. Convergent validity was demonstrated by statistically signifi cant positive correlations between trust and patients’ satisfaction with pharmacists’ service (r = 0.54), returning for care (r = 0.30) and preference of medical decision-making pattern (r = 0.16). Conclusion: The 12-item trust in pharmacists scale demonstrated high reliability and convergent validity. Further studies among other populations are suggested to confi rm the robustness and even improve the current scale

Optimal Batched Best Arm Identification

We study the batched best arm identification (BBAI) problem, where the learner's goal is to identify the best arm while switching the policy as less as possible. In particular, we aim to find the best arm with probability $1-\delta$ for some small constant $\delta>0$ while minimizing both the sample complexity (total number of arm pulls) and the batch complexity (total number of batches). We propose the three-batch best arm identification (Tri-BBAI) algorithm, which is the first batched algorithm that achieves the optimal sample complexity in the asymptotic setting (i.e., $\delta\rightarrow 0$) and runs only in at most $3$ batches. Based on Tri-BBAI, we further propose the almost optimal batched best arm identification (Opt-BBAI) algorithm, which is the first algorithm that achieves the near-optimal sample and batch complexity in the non-asymptotic setting (i.e., $\delta>0$ is arbitrarily fixed), while enjoying the same batch and sample complexity as Tri-BBAI when $\delta$ tends to zero. Moreover, in the non-asymptotic setting, the complexity of previous batch algorithms is usually conditioned on the event that the best arm is returned (with a probability of at least $1-\delta$), which is potentially unbounded in cases where a sub-optimal arm is returned. In contrast, the complexity of Opt-BBAI does not rely on such an event. This is achieved through a novel procedure that we design for checking whether the best arm is eliminated, which is of independent interest.Comment: 32 pages, 1 figure, 3 table