Human Development in East and Southeast Asian Economies: 1990-2010

This report reviews patterns and trends in human development (HD) in East and Southeast Asia (ESA) since 1990, analyzes causes and consequences of this development, highlighting both structural and institutional factors, and identifies the basic principles for durable enhancements in HD. The basic arguments are that most ESA economies have experienced rapid socioeconomic structural changes through industrialization and urbanization in the last two decades. From a HD perspective, these processes offer enormous room for expanding people's capabilities. However, to successfully seize such opportunities, appropriate institutions and public policies are needed, and so is public participation in policy making and implementation. Public policies are also important for equitable distribution of the expanded opportunities, which in turn contribute to the legitimacy of institutions and social cohesion. And while industrialization does often cause more environmental pollution, technological advances also offer the means to reduce such pollution, so long as appropriate environmental policies are implemented to ensure the use of such cleaner technologies. Subject to such appropriate public policies, in net terms industrialization and urbanization should expand people's capabilities and ensure sustainable HD. Six principles are critical to a successful HD strategy-agricultural and rural development to facilitate structural transformation and to increase employment; human capital accumulation to promote continued economic and income growth; inclusive urbanization to reduce dualism and enhance social integration; cleaner industrialization to ensure sustainability; people's participation and empowerment to improve decision making and governance; closer regional and international cooperation to ensure a better future for all on our fragile planet.Human Development, Structural Factors, Public Policy, East and Southeast Asia

A Comparison of Regression Methods in Data Subject to Detection Limits: An Application to Lung Fiber Analysis Among Brake Workers

Objective: This thesis aims to apply and compare selected regression methods with a lung fiber analysis dataset. Final results based on 19 cases will be compared to 2011 Marsh et al.’s analysis based on the first 15 cases. Methods: Two research questions for the lung fiber dataset are: (1) is there a relationship between the lung fiber concentration of TAA and lung fiber concentration of AC? and (2) is there a relationship between the lung fiber concentration of TAA and duration of employment as a brake worker? Besides the substitution method, bivariate normal regression was used in the doubly left-censored situation in question 1, while the censored normal regression and regression modeling with count data were used in the situation with only the dependent variable subject to detection limits in question 2. Result: (1) The estimate of the slopes between the log-scale of two lung concentrations (TAA vs AC) were 0.59, 0.57, 0.59 and 0.54 in the simple linear regression with substitution (DL, 0.5DL, DL/√2) and the bivariate normal regression, respectively. All of the slope estimates were statistically significant different from zero (p-value = 0.001, 0.003, 0.002 and 0.003). (2) The estimate of the slopes between the log-scale of the TAA lung fiber concentrations and DOE were 0.001, 0.014, 0.008, 0.020 and 0.030 in the simple linear regression with substitution (DL, 0.5DL, and DL/√2), censored normal regression and the negative binomial regression, respectively. All of the slope estimates were not statistically significant different from zero (p-value = 0.933, 0.486, 0.675, 0.390 and 0.439). Conclusions: The consistent results from the substitution and other methods provide support for both a positive relationship between the lung concentration of TAA and AC and for no relationship between the lung concentration of TAA and DOE among 19 brake workers with mesothelioma. These findings are consistent with Marsh et al.’s findings in 2011 based on the first 15 cases. The public health significance is that the study results provide additional support for the conclusion that exposure to non-commercial amphibole asbestos, and not chrysotile, is related to the observed mesothelioma in brake workers. However, these conclusions need to be verified with a larger sample size

ALP-KD: Attention-Based Layer Projection for Knowledge Distillation

Knowledge distillation is considered as a training and compression strategy in which two neural networks, namely a teacher and a student, are coupled together during training. The teacher network is supposed to be a trustworthy predictor and the student tries to mimic its predictions. Usually, a student with a lighter architecture is selected so we can achieve compression and yet deliver high-quality results. In such a setting, distillation only happens for final predictions whereas the student could also benefit from teacher's supervision for internal components. Motivated by this, we studied the problem of distillation for intermediate layers. Since there might not be a one-to-one alignment between student and teacher layers, existing techniques skip some teacher layers and only distill from a subset of them. This shortcoming directly impacts quality, so we instead propose a combinatorial technique which relies on attention. Our model fuses teacher-side information and takes each layer's significance into consideration, then performs distillation between combined teacher layers and those of the student. Using our technique, we distilled a 12-layer BERT (Devlin et al. 2019) into 6-, 4-, and 2-layer counterparts and evaluated them on GLUE tasks (Wang et al. 2018). Experimental results show that our combinatorial approach is able to outperform other existing techniques.Comment: AAAI 2021. This work has been done while Peyman Passban was at Huawe

CRC-based Reliable WiFi Backscatter Communiation for Supply Chain Management

Supply chain management is aimed to keep going long-term performance of the supply chain and minimize the costs. Backscatter technology provides a more efficient way of being able to identify items and real-time monitoring. Among the backscatter systems, the ambient backscatter communication (AmBC) system provides a prospect of ultra-low energy consumption and does not require controlled excitation devices. In this paper, we introduce CRCScatter, a CRC reverse algorithm-based AmBC system using a single access point (AP). A CRC reverse decoder is applied to reverse the ambient data from CRC32 sequence in the backscatter packet and realize single-AP decoding. Based on the nature of DBPSK modulation in WiFi signal, the CRCScatter system obtains the tag data by XOR and Differential decoder. Our simulation results verify the effectiveness of our proposed system in the low SNR regime. The average decoding time of CRCScatter system is independent of the length of tag data. Furthermore, our system can append redundant bits in the tag data to improve the decoding accuracy while not increasing the decoding time

To Generate or Not? Safety-Driven Unlearned Diffusion Models Are Still Easy To Generate Unsafe Images ... For Now

The recent advances in diffusion models (DMs) have revolutionized the generation of complex and diverse images. However, these models also introduce potential safety hazards, such as the production of harmful content and infringement of data copyrights. Although there have been efforts to create safety-driven unlearning methods to counteract these challenges, doubts remain about their capabilities. To bridge this uncertainty, we propose an evaluation framework built upon adversarial attacks (also referred to as adversarial prompts), in order to discern the trustworthiness of these safety-driven unlearned DMs. Specifically, our research explores the (worst-case) robustness of unlearned DMs in eradicating unwanted concepts, styles, and objects, assessed by the generation of adversarial prompts. We develop a novel adversarial learning approach called UnlearnDiff that leverages the inherent classification capabilities of DMs to streamline the generation of adversarial prompts, making it as simple for DMs as it is for image classification attacks. This technique streamlines the creation of adversarial prompts, making the process as intuitive for generative modeling as it is for image classification assaults. Through comprehensive benchmarking, we assess the unlearning robustness of five prevalent unlearned DMs across multiple tasks. Our results underscore the effectiveness and efficiency of UnlearnDiff when compared to state-of-the-art adversarial prompting methods. Codes are available at https://github.com/OPTML-Group/Diffusion-MU-Attack. WARNING: This paper contains model outputs that may be offensive in nature.Comment: Codes are available at https://github.com/OPTML-Group/Diffusion-MU-Attac

Selectivity Drives Productivity: Efficient Dataset Pruning for Enhanced Transfer Learning

Massive data is often considered essential for deep learning applications, but it also incurs significant computational and infrastructural costs. Therefore, dataset pruning (DP) has emerged as an effective way to improve data efficiency by identifying and removing redundant training samples without sacrificing performance. In this work, we aim to address the problem of DP for transfer learning, i.e., how to prune a source dataset for improved pretraining efficiency and lossless finetuning accuracy on downstream target tasks. To our best knowledge, the problem of DP for transfer learning remains open, as previous studies have primarily addressed DP and transfer learning as separate problems. By contrast, we establish a unified viewpoint to integrate DP with transfer learning and find that existing DP methods are not suitable for the transfer learning paradigm. We then propose two new DP methods, label mapping and feature mapping, for supervised and self-supervised pretraining settings respectively, by revisiting the DP problem through the lens of source-target domain mapping. Furthermore, we demonstrate the effectiveness of our approach on numerous transfer learning tasks. We show that source data classes can be pruned by up to 40% ~ 80% without sacrificing downstream performance, resulting in a significant 2 ~ 5 times speed-up during the pretraining stage. Besides, our proposal exhibits broad applicability and can improve other computationally intensive transfer learning techniques, such as adversarial pretraining. Codes are available at https://github.com/OPTML-Group/DP4TL.Comment: Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS 2023