Rethinking Sampled-Data Control for Unmanned Aircraft Systems

Unmanned aircraft systems are expected to provide both increasingly varied functionalities and outstanding application performances, utilizing the available resources. In this paper, we explore the recent advances and challenges at the intersection of real-time computing and control and show how rethinking sampling strategies can improve performance and resource utilization. We showcase a novel design framework, cyber-physical co-regulation, which can efficiently link together computational and physical characteristics of the system, increasing robust performance and avoiding pitfalls of event-triggered sampling strategies. A comparison experiment of different sampling and control strategies was conducted and analyzed. We demonstrate that co-regulation has resource savings similar to event-triggered sampling, but maintains the robustness of traditional fixed-periodic sampling forming a compelling alternative to traditional vehicle control design

Defining Next Generation Supply Chain Sustainability

The importance of understanding supply chain sustainability is being realized by increasingly more people, including corporate managers, investors, policy makers, customers and other stakeholders. A lot of practitioners and academic researchers have addressed this issue in past few years. However, most of their studies lack systematic thinking and are not quantifiable. Thus, a systematic and quantifiable model which incorporates economic, environmental and social factors is needed. In our study, a systematic and quantifiable risk assessment model based on the concept of “Triple Bottom Line” is developed in order to solve supply chain sustainability problem from risk assessment perspectiveMaster of ScienceNatural Resources and EnvironmentUniversity of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/110983/1/276-Defining Next Generation Supply Chain Sustainability_2015.pd

The Size-Mass Relation of Post-Starburst Galaxies in the Local Universe

We present a study of the size--mass relation for local post-starburst (PSB) galaxies at $z\lesssim0.33$ selected from the Sloan Digital Sky Survey Data Release 8. We find that PSB galaxies with stellar mass ($M_*$) at $10^9~M_{\odot}<M_*<10^{12}~M_{\odot}$ have their galaxy size smaller than or comparable with those of quiescent galaxies (QGs). After controlling redshift and stellar mass, the sizes of PSBs are $\sim 13\%$ smaller on average than those of QGs, such differences become larger and significant towards the low-$M_*$ end, especially at $10^{9.5}~M_{\odot} \lesssim M_*\lesssim 10^{10.5}~M_{\odot}$ where PSBs can be on average $\sim 19\%$ smaller than QGs. In comparison with predictions of possible PSB evolutionary pathways from cosmological simulations, we suggest that a fast quenching of star formation following a short-lived starburst event (might be induced by major merger) should be the dominated pathway of our PSB sample. Furthermore, by cross-matching with group catalogs, we confirm that local PSBs at $M_*\lesssim10^{10}~M_{\odot}$ are more clustered than more massive ones. PSBs resided in groups are found to be slightly larger in galaxy size and more disk-like compared to field PSBs, which is qualitatively consistent with and thus hints the environment-driven fast quenching pathway for group PSBs. Taken together, our results support multiple evolutionary pathways for local PSB galaxies: while massive PSBs are thought of as products of fast quenching following a major merger-induced starburst, environment-induced fast quenching should play a role in the evolution of less massive PSBs, especially at $M_*\lesssim 10^{10}~M_{\odot}$.Comment: 16 pages, 7 figures; accepted for publication in Ap

Integration of residents' experiences into economic planning process of coastal villages: Evidence from the Greater Hangzhou Bay Rim Area

Public value is gaining prominence from both academics and politicians with regards to China's rural development. However, rural planning authorities and practitioners showed limited confidence on public, which manifests as few public perceptions were integrated into the planning documents. This study explores the potential role of residents' experiences in illustrating local economic development within the context of coastal villages in which economic and industries are rapidly transforming. Two case studies from within the locale of the Greater Hangzhou Bay Rim Area are used in this article to examine the gap between residents' experiences and the actual economic development that has occurred. The main findings suggest that rural residents can directly reflect upon both current and historic trends of local economic development. Moreover, household income satisfaction (HIS) is a comprehensive notion of residents' experiences, and indicates social and economic sustainability of industrial transformation, or "thriving business", that have been highlighted in coastal villages. Public experiences could therefore act as a valid and accessible evidence for planners in rural economic planning in China and other developing countries

Learning Domain-Aware Detection Head with Prompt Tuning

Domain adaptive object detection (DAOD) aims to generalize detectors trained on an annotated source domain to an unlabelled target domain. However, existing methods focus on reducing the domain bias of the detection backbone by inferring a discriminative visual encoder, while ignoring the domain bias in the detection head. Inspired by the high generalization of vision-language models (VLMs), applying a VLM as the robust detection backbone following a domain-aware detection head is a reasonable way to learn the discriminative detector for each domain, rather than reducing the domain bias in traditional methods. To achieve the above issue, we thus propose a novel DAOD framework named Domain-Aware detection head with Prompt tuning (DA-Pro), which applies the learnable domain-adaptive prompt to generate the dynamic detection head for each domain. Formally, the domain-adaptive prompt consists of the domain-invariant tokens, domain-specific tokens, and the domain-related textual description along with the class label. Furthermore, two constraints between the source and target domains are applied to ensure that the domain-adaptive prompt can capture the domains-shared and domain-specific knowledge. A prompt ensemble strategy is also proposed to reduce the effect of prompt disturbance. Comprehensive experiments over multiple cross-domain adaptation tasks demonstrate that using the domain-adaptive prompt can produce an effectively domain-related detection head for boosting domain-adaptive object detection

Diffusion Conditional Expectation Model for Efficient and Robust Target Speech Extraction

Target Speech Extraction (TSE) is a crucial task in speech processing that focuses on isolating the clean speech of a specific speaker from complex mixtures. While discriminative methods are commonly used for TSE, they can introduce distortion in terms of speech perception quality. On the other hand, generative approaches, particularly diffusion-based methods, can enhance speech quality perceptually but suffer from slower inference speed. We propose an efficient generative approach named Diffusion Conditional Expectation Model (DCEM) for TSE. It can handle multi- and single-speaker scenarios in both noisy and clean conditions. Additionally, we introduce Regenerate-DCEM (R-DCEM) that can regenerate and optimize speech quality based on pre-processed speech from a discriminative model. Our method outperforms conventional methods in terms of both intrusive and non-intrusive metrics and demonstrates notable strengths in inference efficiency and robustness to unseen tasks. Audio examples are available online (https://vivian556123.github.io/dcem).Comment: Submitted to ICASSP 202