Neural Moving Horizon Estimation for Robust Flight Control

Estimating and reacting to disturbances is crucial for robust flight control of quadrotors. Existing estimators typically require significant tuning for a specific flight scenario or training with extensive ground-truth disturbance data to achieve satisfactory performance. In this paper, we propose a neural moving horizon estimator (NeuroMHE) that can automatically tune the key parameters modeled by a neural network and adapt to different flight scenarios. We achieve this by deriving the analytical gradients of the MHE estimates with respect to the weighting matrices, which enables a seamless embedding of the MHE as a learnable layer into neural networks for highly effective learning. Interestingly, we show that the gradients can be computed efficiently using a Kalman filter in a recursive form. Moreover, we develop a model-based policy gradient algorithm to train NeuroMHE directly from the quadrotor trajectory tracking error without needing the ground-truth disturbance data. The effectiveness of NeuroMHE is verified extensively via both simulations and physical experiments on quadrotors in various challenging flights. Notably, NeuroMHE outperforms a state-of-the-art neural network-based estimator, reducing force estimation errors by up to 76.7%, while using a portable neural network that has only 7.7% of the learnable parameters of the latter. The proposed method is general and can be applied to robust adaptive control of other robotic systems

Advancing Transformer Architecture in Long-Context Large Language Models: A Comprehensive Survey

Transformer-based Large Language Models (LLMs) have been applied in diverse areas such as knowledge bases, human interfaces, and dynamic agents, and marking a stride towards achieving Artificial General Intelligence (AGI). However, current LLMs are predominantly pretrained on short text snippets, which compromises their effectiveness in processing the long-context prompts that are frequently encountered in practical scenarios. This article offers a comprehensive survey of the recent advancement in Transformer-based LLM architectures aimed at enhancing the long-context capabilities of LLMs throughout the entire model lifecycle, from pre-training through to inference. We first delineate and analyze the problems of handling long-context input and output with the current Transformer-based models. We then provide a taxonomy and the landscape of upgrades on Transformer architecture to solve these problems. Afterwards, we provide an investigation on wildly used evaluation necessities tailored for long-context LLMs, including datasets, metrics, and baseline models, as well as optimization toolkits such as libraries, frameworks, and compilers to boost the efficacy of LLMs across different stages in runtime. Finally, we discuss the challenges and potential avenues for future research. A curated repository of relevant literature, continuously updated, is available at https://github.com/Strivin0311/long-llms-learning.Comment: 40 pages, 3 figures, 4 table

Supply chains create global benefits from improved vaccine accessibility

Ensuring a more equitable distribution of vaccines worldwide is an effective strategy to control global pandemics and support economic recovery. We analyze the socioeconomic effects - defined as health gains, lockdown-easing effect, and supply-chain rebuilding benefit - of a set of idealized COVID-19 vaccine distribution scenarios. We find that an equitable vaccine distribution across the world would increase global economic benefits by 11.7% ($950 billion per year), compared to a scenario focusing on vaccinating the entire population within vaccine-producing countries first and then distributing vaccines to non-vaccine-producing countries. With limited doses among low-income countries, prioritizing the elderly who are at high risk of dying, together with the key front-line workforce who are at high risk of exposure is projected to be economically beneficial (e.g., 0.9%~3.4% annual GDP in India). Our results reveal how equitable distributions would cascade more protection of vaccines to people and ways to improve vaccine equity and accessibility globally through international collaboration

Global supply chains amplify economic costs of future extreme heat risk

Evidence shows a continuing increase in the frequency and severity of global heatwaves, raising concerns about the future impacts of climate change and the associated socioeconomic costs. Here we develop a disaster footprint analytical framework by integrating climate, epidemiological and hybrid input–output and computable general equilibrium global trade models to estimate the midcentury socioeconomic impacts of heat stress. We consider health costs related to heat exposure, the value of heat-induced labour productivity loss and indirect losses due to economic disruptions cascading through supply chains. Here we show that the global annual incremental gross domestic product loss increases exponentially from 0.03 ± 0.01 (SSP 245)–0.05 ± 0.03 (SSP 585) percentage points during 2030–2040 to 0.05 ± 0.01–0.15 ± 0.04 percentage points during 2050–2060. By 2060, the expected global economic losses reach a total of 0.6–4.6% with losses attributed to health loss (37–45%), labour productivity loss (18–37%) and indirect loss (12–43%) under different shared socioeconomic pathways. Small- and medium-sized developing countries suffer disproportionately from higher health loss in South-Central Africa (2.1 to 4.0 times above global average) and labour productivity loss in West Africa and Southeast Asia (2.0–3.3 times above global average). The supply-chain disruption effects are much more widespread with strong hit to those manufacturing-heavy countries such as China and the USA, leading to soaring economic losses of 2.7 ± 0.7% and 1.8 ± 0.5%, respectively.

Occurrence of neonicotinoids in Chinese apiculture and a corresponding risk exposure assessment

Neonicotinoids are the most widely used insecticides worldwide, but there is mounting evidence demonstrating that they have adverse effects on nontarget organisms. However, little is known about the extent of environmental neonicotinoids contamination in China. In this study, a total of 693 honey samples from across China, from both Apis melifera and Apis cerana, were analyzed to examine neonicotinoid concentrations and their geographical distribution, and correlation with the primary plant species from which the honey was obtained. Furthermore, chronic and acute exposure risk and risk ranking for humans eating honey were investigated, and risks to bees were also considered. The results revealed that 40.8% of the samples contained at least one of the five neonicotinoids tested. Honeys from commercial crops were found to be more frequently contaminated with neonicotinoids than those from noncommercial crops. Honey samples from Apis mellifera were more frequently contaminated than those from Apis cerana. The concentrations of neonicotinoids found in honey overlapped with those that have been found to have significant adverse effects on honeybee health. The dietary risk assessments indicated that the levels of neonicotinoids detected in honey were likely to be safe for human consumption

Structural and Lipidomic Alterations of Striatal Myelin in 16p11.2 Deletion Mouse Model of Autism Spectrum Disorder

Myelin abnormalities have been observed in autism spectrum disorder (ASD). In this study, we seek to discover myelin-related changes in the striatum, a key brain region responsible for core ASD features, using the 16p11.2 deletion (16p11.2±) mouse model of ASD. We found downregulated expression of multiple myelin genes and decreased myelin thickness in the striatum of 16p11.2± mice versus wild type controls. Moreover, given that myelin is the main reservoir of brain lipids and that increasing evidence has linked dysregulation of lipid metabolism to ASD, we performed lipidomic analysis and discovered decreased levels of certain species of sphingomyelin, hexosyl ceramide and their common precursor, ceramide, in 16p11.2± striatum, all of which are major myelin components. We further identified lack of ceramide synthase 2 as the possible reason behind the decrease in these lipid species. Taken together, our data suggest a role for myelin and myelin lipids in ASD development

Genetic algorithm optimization for storing arbitrary multimode transverse images in thermal atomic vapor

Storing multimode transverse images in atomic media is crucial in constructing large-scale quantum networks. A major obstacle of storing transverse images in the thermal atomic vapor is the distortion of the retrieved images caused by atomic diffusion. Here, we demonstrate the combination of genetic algorithm with the phase-shift lithography method to construct the optimal phase for an arbitrary transverse image that can diminish the effect of diffusion. Theoretical simulations and experimental results manifest that the retrieved images' resolution can be substantially improved when carrying the optimal phases. Our scheme is efficient and straightforward and can be extensively applied in storing complex transverse multimodes in diffusion media