Modeling sea salt and sulfate aerosol over the global oceans to understand the origins of marine cloud condensation nuclei and the impact of pollution on them

Over the oceans, anthropogenic aerosols compete with natural aerosols from sea spray and oceanic phytoplankton-­‐derived sulfate to create cloud condensation nuclei (CCN). To understand the impact of pollution on the marine CCN, we need knowledge of both natural and anthropogenic aerosols. In this research, we model sea salt and sulfate aerosol in a coupled climate and sectional microphysical model, CAM/CARMA. We develop a sea salt source function, CMS, based upon several earlier source functions (Clarke, Monahan, and Smith). The CMS source function is capable of reproducing observed sea salt mass, optical depth and number concentration as well as the size distribution better than other source function choices we tried. However, as we note, it is also important to properly set the removal rate of the particles to reproduce the observed abundances. The simulated non-­‐sea-­‐salt sulfate mass agrees well with the observations. Direct emission of sulfate from sea spray is the largest source of marine sulfate aerosol and depends on the sea salt emission. Non-­‐sea-­‐salt sulfate from gas-­‐ and aqueous-­‐phase conversion, together with sea salt, contributes to the marine CCN over the mid-­‐latitude Northern Hemisphere, while sea salt dominates the CCN over the Southern Ocean. Human impact on marine CCN extends to 45 °S. Anthropogenic sulfur emissions are responsible for about 35% of the surface layer CCN over the global oceans. With doubling the year 2000 anthropogenic sulfur emissions. Surface layer CCN increases by about 22% over the global oceans if sulfur emissions are doubled from. With no or double anthropogenic emissions, the changes in the surface layer CCN number over the Southern Hemisphere oceans are usually less than 10%

Magnetic control of the valley degree of freedom of massive Dirac fermions with application to transition metal dichalcogenides

We study the valley-dependent magnetic and transport properties of massive Dirac fermions in multivalley systems such as the transition metal dichalcogenides. The asymmetry of the zeroth Landau level between valleys and the enhanced magnetic susceptibility can be attributed to the different orbital magnetic moment tied with each valley. This allows the valley polarization to be controlled by tuning the external magnetic field and the doping level. As a result of this magnetic field induced valley polarization, there exists an extra contribution to the ordinary Hall effect. All these effects can be captured by a low energy effective theory with a valley-orbit coupling term.Comment: 9 pages, 6 figure

The current opportunities and challenges of Web 3.0

With recent advancements in AI and 5G technologies,as well as the nascent concepts of blockchain and metaverse,a new revolution of the Internet,known as Web 3.0,is emerging. Given its significant potential impact on the internet landscape and various professional sectors,Web 3.0 has captured considerable attention from both academic and industry circles. This article presents an exploratory analysis of the opportunities and challenges associated with Web 3.0. Firstly, the study evaluates the technical differences between Web 1.0, Web 2.0, and Web 3.0, while also delving into the unique technical architecture of Web 3.0. Secondly, by reviewing current literature, the article highlights the current state of development surrounding Web 3.0 from both economic and technological perspective. Thirdly, the study identifies numerous research and regulatory obstacles that presently confront Web 3.0 initiatives. Finally, the article concludes by providing a forward-looking perspective on the potential future growth and progress of Web 3.0 technology

Unmanned aerial vehicle inspection routing and scheduling for engineering management

Technological advances in unmanned aerial vehicles (UAVs) have enabled the extensive application of UAVs in various industrial domains. For example, UAV-based inspection in engineering management is a more efficient means of searching for hidden dangers in high-risk construction environments than traditional inspections in the artifactual field. Against the above background, this paper investigates the optimization of the UAV inspection routing and scheduling problem. A mixed-integer linear programming model is devised to optimize decisions on the assignment of inspection tasks, the monitoring sequence schedule, and charge decisions. The comprehensive consideration of no-fly zones, monitoring-interval time windows and multiple monitoring rounds distinguish the devised problem from the typical vehicle routing problem and make the mathematical model intractable for a commercial solver in the case of large-scale instances. Thus, a tailored variable neighborhood search metaheuristic is designed to solve the model efficiently. Extensive numerical experiments are conducted to validate the efficiency of the proposed algorithm for large-scale and real-scale instances. In addition, sensitivity experiments and a case study based on an engineering project are conducted to derive insights that will enable an engineering manager to improve the efficiency of inspection works

Projection Robust Wasserstein Distance and Riemannian Optimization

Projection robust Wasserstein (PRW) distance, or Wasserstein projection pursuit (WPP), is a robust variant of the Wasserstein distance. Recent work suggests that this quantity is more robust than the standard Wasserstein distance, in particular when comparing probability measures in high-dimensions. However, it is ruled out for practical application because the optimization model is essentially non-convex and non-smooth which makes the computation intractable. Our contribution in this paper is to revisit the original motivation behind WPP/PRW, but take the hard route of showing that, despite its non-convexity and lack of nonsmoothness, and even despite some hardness results proved by~\citet{Niles-2019-Estimation} in a minimax sense, the original formulation for PRW/WPP \textit{can} be efficiently computed in practice using Riemannian optimization, yielding in relevant cases better behavior than its convex relaxation. More specifically, we provide three simple algorithms with solid theoretical guarantee on their complexity bound (one in the appendix), and demonstrate their effectiveness and efficiency by conducing extensive experiments on synthetic and real data. This paper provides a first step into a computational theory of the PRW distance and provides the links between optimal transport and Riemannian optimization.Comment: Accepted by NeurIPS 2020; The first two authors contributed equally; fix the confusing parts in the proof and refine the algorithms and complexity bound

Iloprost improves running performance at 5,000 m in Han but not in Tibetans

Background: Tibetans experience lose less aerobic exercise capacity in hypoxia compared to lowland Han. We tested if inhalation of iloprost (to counter hypoxic pulmonary vasoconstriction) and furosemide (to decrease afferent vagal traffic from pulmonary receptors) improve performance in hypoxia in Han compared to Tibetans. Methods: 8 Tibetans and 8 Han, living at 2,260 m, did incremental uphill treadmill running to exhaustion at ambient pressure on day 1, followed by three runs at 5,000 m (hypobaric chamber) after inhalation of iloprost (ILO), furosemide (FUR) or placebo (PLA), on different days in a counter-balanced order. Results: In Han the performance decrement from 2,260 m to 5,000 m was greater than in Tibetans (p<0.05). In Han iloprost improved performance at 5,000 m compared to placebo (p<0.05 vs. PLA); furosemide had no effects. In Tibetans there were no treatment effects. Peripheral O2saturations at peak exercise at 5,000 m, were higher by ～8 % in the Tibetans (p<0.05 vs. Han). Maximum heart rate was lowered by 13±6 bpm in Han at 5,000 m regardless of treatment compared to 2,260 m (p<0.05). Tibetans reached similar maximum heart rates ∼200 bpmat 5,000 m and 2,260 m, independent of treatment. Conclusions: The blunting of the exercise impairment in severe hypoxia in Han during maximal exercise after inhalation of iloprost suggests that hypoxic pulmonary vasoconstriction and right ventricular function are potential performance limiting factors in Han in hypoxia

Merging Experts into One: Improving Computational Efficiency of Mixture of Experts

Scaling the size of language models usually leads to remarkable advancements in NLP tasks. But it often comes with a price of growing computational cost. Although a sparse Mixture of Experts (MoE) can reduce the cost by activating a small subset of parameters (e.g., one expert) for each input, its computation escalates significantly if increasing the number of activated experts, limiting its practical utility. Can we retain the advantages of adding more experts without substantially increasing the computational costs? In this paper, we first demonstrate the superiority of selecting multiple experts and then propose a computation-efficient approach called \textbf{\texttt{Merging Experts into One}} (MEO), which reduces the computation cost to that of a single expert. Extensive experiments show that MEO significantly improves computational efficiency, e.g., FLOPS drops from 72.0G of vanilla MoE to 28.6G (MEO). Moreover, we propose a token-level attention block that further enhances the efficiency and performance of token-level MEO, e.g., 83.3\% (MEO) vs. 82.6\% (vanilla MoE) average score on the GLUE benchmark. Our code will be released upon acceptance. Code will be released at: \url{https://github.com/Shwai-He/MEO}.Comment: EMNLP 2023 Main Conference (Oral