Autonomous Mobility and Energy Service Management in Future Smart Cities: An Overview

With the rise of transportation electrification, autonomous driving and shared mobility in urban mobility systems, and increasing penetrations of distributed energy resources and autonomous demand-side management techniques in energy systems, tremendous opportunities, as well as challenges, are emerging in the forging of a sustainable and converged urban mobility and energy future. This paper is motivated by these disruptive transformations and gives an overview of managing autonomous mobility and energy services in future smart cities. First, we propose a three-layer architecture for the convergence of future mobility and energy systems. For each layer, we give a brief overview of the disruptive transformations that directly contribute to the rise of autonomous mobility-on-demand (AMoD) systems. Second, we propose the concept of autonomous flexibility-on-demand (AFoD), as an energy service platform built directly on existing infrastructures of AMoD systems. In the vision of AFoD, autonomous electric vehicles provide charging flexibilities as a service on demand in energy systems. Third, we analyze and compare AMoD and AFoD, and we identify four key decisions that, if appropriately coordinated, will create a synergy between AMoD and AFoD. Finally, we discuss key challenges towards the success of AMoD and AFoD in future smart cities and present some key research directions regarding the system-wide coordination between AMoD and AFoD.Comment: 19 pages, 4 figure

Threshold Policies with Tight Guarantees for Online Selection with Convex Costs

This paper provides threshold policies with tight guarantees for online selection with convex cost (OSCC). In OSCC, a seller wants to sell some asset to a sequence of buyers with the goal of maximizing her profit. The seller can produce additional units of the asset, but at non-decreasing marginal costs. At each time, a buyer arrives and offers a price. The seller must make an immediate and irrevocable decision in terms of whether to accept the offer and produce/sell one unit of the asset to this buyer. The goal is to develop an online algorithm that selects a subset of buyers to maximize the seller's profit, namely, the total selling revenue minus the total production cost. Our main result is the development of a class of simple threshold policies that are logistically simple and easy to implement, but have provable optimality guarantees among all deterministic algorithms. We also derive a lower bound on competitive ratios of randomized algorithms and prove that the competitive ratio of our threshold policy asymptotically converges to this lower bound when the total production output is sufficiently large. Our results generalize and unify various online search, pricing, and auction problems, and provide a new perspective on the impact of non-decreasing marginal costs on real-world online resource allocation problems

Online Combinatorial Auctions for Resource Allocation with Supply Costs and Capacity Limits

We study a general online combinatorial auction problem in algorithmic mechanism design. A provider allocates multiple types of capacity-limited resources to customers that arrive in a sequential and arbitrary manner. Each customer has a private valuation function on bundles of resources that she can purchase (e.g., a combination of different resources such as CPU and RAM in cloud computing). The provider charges payment from customers who purchase a bundle of resources and incurs an increasing supply cost with respect to the totality of resources allocated. The goal is to maximize the social welfare, namely, the total valuation of customers for their purchased bundles, minus the total supply cost of the provider for all the resources that have been allocated. We adopt the competitive analysis framework and provide posted-price mechanisms with optimal competitive ratios. Our pricing mechanism is optimal in the sense that no other online algorithms can achieve a better competitive ratio. We validate the theoretic results via empirical studies of online resource allocation in cloud computing. Our numerical results demonstrate that the proposed pricing mechanism is competitive and robust against system uncertainties and outperforms existing benchmarks.Comment: arXiv admin note: text overlap with arXiv:2004.0964

Adaptive Semantic Communications: Overfitting the Source and Channel for Profit

Most semantic communication systems leverage deep learning models to provide end-to-end transmission performance surpassing the established source and channel coding approaches. While, so far, research has mainly focused on architecture and model improvements, but such a model trained over a full dataset and ergodic channel responses is unlikely to be optimal for every test instance. Due to limitations on the model capacity and imperfect optimization and generalization, such learned models will be suboptimal especially when the testing data distribution or channel response is different from that in the training phase, as is likely to be the case in practice. To tackle this, in this paper, we propose a novel semantic communication paradigm by leveraging the deep learning model's overfitting property. Our model can for instance be updated after deployment, which can further lead to substantial gains in terms of the transmission rate-distortion (RD) performance. This new system is named adaptive semantic communication (ASC). In our ASC system, the ingredients of wireless transmitted stream include both the semantic representations of source data and the adapted decoder model parameters. Specifically, we take the overfitting concept to the extreme, proposing a series of ingenious methods to adapt the semantic codec or representations to an individual data or channel state instance. The whole ASC system design is formulated as an optimization problem whose goal is to minimize the loss function that is a tripartite tradeoff among the data rate, model rate, and distortion terms. The experiments (including user study) verify the effectiveness and efficiency of our ASC system. Notably, the substantial gain of our overfitted coding paradigm can catalyze semantic communication upgrading to a new era

Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions

Breast cancer has reached the highest incidence rate worldwide among all malignancies since 2020. Breast imaging plays a significant role in early diagnosis and intervention to improve the outcome of breast cancer patients. In the past decade, deep learning has shown remarkable progress in breast cancer imaging analysis, holding great promise in interpreting the rich information and complex context of breast imaging modalities. Considering the rapid improvement in the deep learning technology and the increasing severity of breast cancer, it is critical to summarize past progress and identify future challenges to be addressed. In this paper, we provide an extensive survey of deep learning-based breast cancer imaging research, covering studies on mammogram, ultrasound, magnetic resonance imaging, and digital pathology images over the past decade. The major deep learning methods, publicly available datasets, and applications on imaging-based screening, diagnosis, treatment response prediction, and prognosis are described in detail. Drawn from the findings of this survey, we present a comprehensive discussion of the challenges and potential avenues for future research in deep learning-based breast cancer imaging.Comment: Survey, 41 page

Mechanism Design for Online Resource Allocation: A Unified Approach

This paper concerns the mechanism design for online resource allocation in a strategic setting. In this setting, a single supplier allocates capacity-limited resources to requests that arrive in a sequential and arbitrary manner. Each request is associated with an agent who may act selfishly to misreport the requirement and valuation of her request. The supplier charges payment from agents whose requests are satisfied, but incurs a load-dependent supply cost. The goal is to design an incentive compatible online mechanism, which determines not only the resource allocation of each request, but also the payment of each agent, so as to (approximately) maximize the social welfare (i.e., aggregate valuations minus supply cost). We study this problem under the framework of competitive analysis. The major contribution of this paper is the development of a unified approach that achieves the best-possible competitive ratios for setups with different supply costs. Specifically, we show that when there is no supply cost or the supply cost function is linear, our model is essentially a standard 0-1 knapsack problem, for which our approach achieves logarithmic competitive ratios that match the state-of-the-art (which is optimal). For the more challenging setup when the supply cost is strictly-convex, we provide online mechanisms, for the first time, that lead to the optimal competitive ratios as well. To the best of our knowledge, this is the first approach that unifies the characterization of optimal competitive ratios in online resource allocation for different setups including zero, linear and strictly-convex supply costs.Comment: 49 pages, 5 figure

Variant rs9939609 in the FTO gene is associated with body mass index among Chinese children

<p>Abstract</p> <p>Background</p> <p>Fat-mass and obesity-associated (<it>FTO</it>) gene is a gene located in chromosome region 16q12.2. Genetic variants in <it>FTO </it>are associated with the obesity phenotype in European and Hispanic populations. However, this association still remains controversial in Asian population. We aimed to test the association of <it>FTO </it>genetic variants with obesity and obesity-related metabolic traits among children living in Beijing, China.</p> <p>Methods</p> <p>We genotyped <it>FTO </it>variants rs9939609 in 670 children (332 girls and 338 boys) aged 8-11 years living in Beijing, and analyzed its association with obesity and obesity-related metabolic traits. Overweight and obesity were defined by age- and sex-specific BMI reference for Chinese children. Obesity-related metabolic traits included fasting plasma glucose, lipid profiles, leptin, ghrelin, adiponectin and blood pressures.</p> <p>Results</p> <p>The frequency of rs9939609 A allele was 12.2%, which was 21.9% for the heterozygote and 1.2% for the homozygote of the A allele. The obesity prevalence among the carriers of AA/AT genotypes was significantly higher than that among those with TT genotype (36.4% <it>vs</it>. 22.6%, <it>P </it>= 0.004). Compared to the carrier of TT genotype, the likelihood of obesity was 1.79 (95% confidence interval (95% CI) 1.20-2.67, <it>P </it>= 0.004) for the carrier of AA/AT genotype, after adjustment of sex, age and puberty stages. The BMI Z-score of children with AA/AT genotype were significantly higher than that of their counterparts with the TT genotype (1.1 ± 0.1 <it>vs</it>. 0.8 ± 0.1, <it>P </it>= 0.02). The concentration of triglyceride was 1.03 ± 0.52 mmol/L among TT carrier and 1.13 ± 0.68 mmol/L among AA/AT carrier (<it>P </it>= 0.045). While, the concentrations of adiponectin were 18.0 ± 0.4 μg/ml among carriers of TT and 16.2 ± 0.7 μg/ml among subjects with AA/AT genotype (<it>P </it>= 0.03). The level of glucose marginally increased in the AA/AT genotype subjects (4.67 ± 0.40 mmol/L <it>vs</it>. 4.60 ± 0.35 mmol/L, <it>P </it>= 0.08). The evidence of association was reduced after adjustment for BMI (<it>P </it>= 0.38 for triglyceride, <it>P </it>= 0.20 for adiponectin and glucose). There was weak evidence of association between rs9939609 and other obesity-related metabolic traits including total cholesterol (3.92 ± 0.03 mmol/L <it>vs</it>. 4.02 ± 0.05 mmol/L, <it>P </it>= 0.10), insulin (2.69 ± 1.77 ng/ml <it>vs</it>. 3.12 ± 2.91 ng/ml, <it>P </it>= 0.14), and insulin resistance (HOMA-IR 0.56 ± 0.03 <it>vs</it>. 0.66 ± 0.05, <it>P </it>= 0.10).</p> <p>Conclusions</p> <p>Genetic variation in the <it>FTO </it>gene associates with obesity in Chinese children.</p

Autonomous Mobility and Energy Service Management in Future Smart Cities: An Overview

With the rise of transportation electrification, autonomous driving and shared mobility in urban mobility systems, and increasing penetrations of distributed energy resources and autonomous demand-side management techniques in energy systems, tremendous opportunities, as well as challenges, are emerging in the forging of a sustainable and converged urban mobility and energy future. This paper is motivated by these disruptive transformations and gives an overview of managing autonomous mobility and energy services in future smart cities. First, we propose a three-layer architecture for the convergence of future mobility and energy systems. For each layer, we give a brief overview of the disruptive transformations that directly contribute to the rise of autonomous mobility-on-demand (AMoD) systems. Second, we propose the concept of autonomous flexibility-on-demand (AFoD), as an energy service platform built directly on existing infrastructures of AMoD systems. In the vision of AFoD, autonomous electric vehicles provide charging flexibilities as a service on demand in energy systems. Third, we analyze and compare AMoD and AFoD, and we identify four key decisions that, if appropriately coordinated, will create a synergy between AMoD and AFoD. Finally, we discuss key challenges towards the success of AMoD and AFoD in future smart cities and present some key research directions regarding the system-wide coordination between AMoD and AFoD. Comment: 19 pages, 4 figur