Predictive Visual Tracking: A New Benchmark and Baseline Approach

As a crucial robotic perception capability, visual tracking has been intensively studied recently. In the real-world scenarios, the onboard processing time of the image streams inevitably leads to a discrepancy between the tracking results and the real-world states. However, existing visual tracking benchmarks commonly run the trackers offline and ignore such latency in the evaluation. In this work, we aim to deal with a more realistic problem of latency-aware tracking. The state-of-the-art trackers are evaluated in the aerial scenarios with new metrics jointly assessing the tracking accuracy and efficiency. Moreover, a new predictive visual tracking baseline is developed to compensate for the latency stemming from the onboard computation. Our latency-aware benchmark can provide a more realistic evaluation of the trackers for the robotic applications. Besides, exhaustive experiments have proven the effectiveness of the proposed predictive visual tracking baseline approach.Comment: 7 pages, 5 figure

PVT++: A Simple End-to-End Latency-Aware Visual Tracking Framework

Visual object tracking is an essential capability of intelligent robots. Most existing approaches have ignored the online latency that can cause severe performance degradation during real-world processing. Especially for unmanned aerial vehicle, where robust tracking is more challenging and onboard computation is limited, latency issue could be fatal. In this work, we present a simple framework for end-to-end latency-aware tracking, i.e., end-to-end predictive visual tracking (PVT++). PVT++ is capable of turning most leading-edge trackers into predictive trackers by appending an online predictor. Unlike existing solutions that use model-based approaches, our framework is learnable, such that it can take not only motion information as input but it can also take advantage of visual cues or a combination of both. Moreover, since PVT++ is end-to-end optimizable, it can further boost the latency-aware tracking performance by joint training. Additionally, this work presents an extended latency-aware evaluation benchmark for assessing an any-speed tracker in the online setting. Empirical results on robotic platform from aerial perspective show that PVT++ can achieve up to 60% performance gain on various trackers and exhibit better robustness than prior model-based solution, largely mitigating the degradation brought by latency. Code and models will be made public.Comment: 18 pages, 10 figure

Learning Cooperative Oversubscription for Cloud by Chance-Constrained Multi-Agent Reinforcement Learning

Oversubscription is a common practice for improving cloud resource utilization. It allows the cloud service provider to sell more resources than the physical limit, assuming not all users would fully utilize the resources simultaneously. However, how to design an oversubscription policy that improves utilization while satisfying the some safety constraints remains an open problem. Existing methods and industrial practices are over-conservative, ignoring the coordination of diverse resource usage patterns and probabilistic constraints. To address these two limitations, this paper formulates the oversubscription for cloud as a chance-constrained optimization problem and propose an effective Chance Constrained Multi-Agent Reinforcement Learning (C2MARL) method to solve this problem. Specifically, C2MARL reduces the number of constraints by considering their upper bounds and leverages a multi-agent reinforcement learning paradigm to learn a safe and optimal coordination policy. We evaluate our C2MARL on an internal cloud platform and public cloud datasets. Experiments show that our C2MARL outperforms existing methods in improving utilization ($20\%\sim 86\%$) under different levels of safety constraints

Risk-aware Adaptive Virtual CPU Oversubscription in Microsoft Cloud via Prototypical Human-in-the-loop Imitation Learning

Oversubscription is a prevalent practice in cloud services where the system offers more virtual resources, such as virtual cores in virtual machines, to users or applications than its available physical capacity for reducing revenue loss due to unused/redundant capacity. While oversubscription can potentially lead to significant enhancement in efficient resource utilization, the caveat is that it comes with the risks of overloading and introducing jitter at the level of physical nodes if all the co-located virtual machines have high utilization. Thus suitable oversubscription policies which maximize utilization while mitigating risks are paramount for cost-effective seamless cloud experiences. Most cloud platforms presently rely on static heuristics-driven decisions about oversubscription activation and limits, which either leads to overloading or stranded resources. Designing an intelligent oversubscription policy that can adapt to resource utilization patterns and jointly optimizes benefits and risks is, largely, an unsolved problem. We address this challenge with our proposed novel HuMan-in-the-loop Protoypical Imitation Learning (ProtoHAIL) framework that exploits approximate symmetries in utilization patterns to learn suitable policies. Also, our human-in-the-loop (knowledge-infused) training allows for learning safer policies that are robust to noise and sparsity. Our empirical investigations on real data show orders of magnitude reduction in risk and significant increase in benefits (saving stranded cores) in Microsoft cloud platform for 1st party (internal services).Comment: 9 pages, 3 figure

Double layer composite electrode strategy for efficient perovskite solar cells with excellent reverse-bias stability

Perovskite solar cells (PSCs) have become the representatives of next generation of photovoltaics; nevertheless, their stability is insufficient for large scale deployment, particularly the reverse bias stability. Here, we propose a transparent conducting oxide (TCO) and low-cost metal composite electrode to improve the stability of PSCs without sacrificing the efficiency. The TCO can block ion migrations and chemical reactions between the metal and perovskite, while the metal greatly enhances the conductivity of the composite electrode. As a result, composite electrode-PSCs achieved a power conversion efficiency (PCE) of 23.7% (certified 23.2%) and exhibited excellent stability, maintaining 95% of the initial PCE when applying a reverse bias of 4.0 V for 60 s and over 92% of the initial PCE after 1000 h continuous light soaking. This composite electrode strategy can be extended to different combinations of TCOs and metals. It opens a new avenue for improving the stability of PSCs

Multifunctional succinate additive for flexible perovskite solar cells with more than 23% power-conversion efficiency

Flexible perovskite solar cells (FPSCs) have emerged as power sources in versatile applications owing to their high-efficiency characteristics, excellent flexibility, and relatively low cost. Nevertheless, undesired strain in perovskite films greatly impacts the power-conversion efficiency (PCE) and stability of PSCs, particularly in FPSCs. Herein, a novel multifunctional organic salt, methylammonium succinate, which can alleviate strain and reinforce grain boundaries, was incorporated into the perovskite film, leading to relaxed microstrain and a lower defect concentration. As a result, a PCE of 25.4% for rigid PSCs and a record PCE of 23.6% (certified 22.5%) for FPSCs have been achieved. In addition, the corresponding FPSCs exhibited excellent bending durability, maintaining ∼85% of their initial efficiency after bending at a 6 mm radius for 10 000 cycles

Disease Burden, Risk Factors, and Temporal Trends in Breast Cancer in Low‐ and Middle‐Income Countries: A Global Study

Introduction: Breast cancer poses significant health risks to women and strains healthcare systems extensively. In low‐ and middle‐income countries (LMICs), limited resources and inadequate healthcare infrastructures further exacerbate the challenges of breast cancer prevention, treatment, and awareness. Methods: We examined the prevalence, risk factors, and trends of breast cancer in LMICs. Data on disability‐adjusted life years (DALYs) and breast cancer risk factors were extracted from the Global Burden of Disease (GBD) databases for 203 countries or territories from 1990 to 2019. LMIC DALY rates were examined using joinpoint regression analysis. Results: Among the income groups, the lower middle‐income category had the highest DALYs value, with 1787 years per 100,000 people. LMICs collectively accounted for 74% of the global burden of DALYs lost due to breast cancer in 2019. However, it remained relatively consistent in lower middle income countries (LMCs). In LMCs, the risk associated with metabolic syndromes was higher compared to that with behavioral factors alone. For the past three decades, breast cancer incidences increased significantly in LMCs (average annual percent change [AAPC]: 1.212, confidence intervals [CI]: 1.51–1.87, p < 0.001), upper middle income countries (AAPC: 1.701, CI: 1.12–1.48, p < 0.001), and low‐income countries (AAPC: 1.002, CI: 1.57–1.68, p < 0.001). Conclusion: This research shows how breast cancer in LMICs is aggravated by low resources and healthcare infrastructure. To effectively combat breast cancer in these areas, future strategies must prioritize improvements in healthcare infrastructure, awareness campaigns, and early detection mechanisms