Role of Calcium in Cerebellar Learning and Function

The cerebellum, which means little brain in Latin, occupies most of the posterior cranial fossa and connects with the dorsal brainstem (Kandel et al., 2000). The cerebellar cortex is one of the most foliated brain structures, which accounts for 10% of the total volume and over half of the total neurons in the central nervous system of higher vertebrates (Llinas et al., 2004). The unique position and structure of cerebellum has inspired neuroscientists over the past century to dedicate their research and imagination to uncover the function of the cerebellum. In the late 19th century, the cerebellum was suggested to be involved in controlling the spatial accuracy and temporal coordination of motor movement, based on clinical studies on cerebellar specific lesion patients. Further studies suggested that the learning and memory of motor movements may also be stored in cerebellum (for review see Dow and Moruzzi, 1958). Meanwhile, in the late 19th and early 20th century, the Italian scientist Camillo Golgi as well as his Spanish colleague and life-long competitor Santiago Ramón y Cajal (who shared the Nobel Prize for Physiology and Medicine in 1906) carried out their pioneer research on the detailed cellular organization of the cerebellum (and other parts of the central nervous system). Their studies provided the initial description of the cerebellar circuit

Improved Aircraft Environmental Impact Segmentation via Metric Learning

Accurate modeling of aircraft environmental impact is pivotal to the design of operational procedures and policies to mitigate negative aviation environmental impact. Aircraft environmental impact segmentation is a process which clusters aircraft types that have similar environmental impact characteristics based on a set of aircraft features. This practice helps model a large population of aircraft types with insufficient aircraft noise and performance models and contributes to better understanding of aviation environmental impact. Through measuring the similarity between aircraft types, distance metric is the kernel of aircraft segmentation. Traditional ways of aircraft segmentation use plain distance metrics and assign equal weight to all features in an unsupervised clustering process. In this work, we utilize weakly-supervised metric learning and partial information on aircraft fuel burn, emissions, and noise to learn weighted distance metrics for aircraft environmental impact segmentation. We show in a comprehensive case study that the tailored distance metrics can indeed make aircraft segmentation better reflect the actual environmental impact of aircraft. The metric learning approach can help refine a number of similar data-driven analytical studies in aviation.Comment: 32 pages, 11 figure

Heat kernel-based p-energy norms on metric measure spaces

We focus on heat kernel-based p-energy norms (1<p<\infty) on bounded and unbounded metric measure spaces, in particular, weak-monotonicity properties for different types of energies. Such properties are key to related studies, under which we generalise the convergence result of Bourgain-Brezis-Mironescu (BBM) for p\neq2. We establish the equivalence of various p-energy norms and weak-monotonicity properties when there admits a heat kernel satisfying the two-sided estimates. Using these equivalences, we verify various weak-monotonicity properties on nested fractals and their blowups. Immediate consequences are that, many classical results on p-energy norms hold for such bounded and unbounded fractals, including the BBM convergence and Gagliardo-Nirenberg inequality.Comment: 39 pages with 1 figur

Weak monotonicity property of Korevaar-Schoen norms on nested fractals

In this paper, we study the weak monotonicity property of p-energy related Korevaar-Schoen norms on connected nested fractals for $1 < p < \infty$. Such property has many important applications on fractals and other metric measure spaces, such as constructing p-energies (when $p = 2$ this is basically a Dirichlet form), generalizing the classical Sobolev type inequalities and the celebrated Bourgain-Brezis-Mironescu convergence.Comment: 10 pages,1 figur

Numerical Superstition and the Return of Initial Public Offerings: Evidence from Hong Kong IPOs

Abstract This study examines the association between the superstitious belief of ‘Lucky Number 8’ and the initial returns of Initial Public Offerings (IPOs). A sample of 136 Hong Kong IPOs during the period of 2004-2006 indicates that the IPOs with multi 8s in their trading codes had statistically higher returns compared with their peers. The numerical superstition has a significant impact on the performance of IPOs. This study also has new findings in relation to conventional IPOs’ underpricing theories. The measurement of excess demand in retail tranche after ‘claw- back’ is proved to be a better explanatory variable to the IPOs’ underpricing level in the dual-tranche offering mechanism. And the classical signalling effect from secondary offerings is questionable in this study. The findings from this study would be meaningful to the investors and listing firms. Furthermore, the significant effect of superstitious 8 belief also has implications in the behaviour finance theory and Efficient Market Hypothesi

Risk-aware Urban Air Mobility Network Design with Overflow Redundancy

Urban Air Mobility (UAM), as envisioned by researchers and practitioners, will be achieved through the use of highly automated aircraft that operate and transport passengers and cargo at low altitudes within urban and suburban areas. To operate in complex urban environment, precise air traffic management, in particular the management of traffic overflows due to operational disruptions will be critical to ensuring system safety and efficiency. To this end, we propose a methodology for the design of UAM networks with reserve capacity, i.e., a design where alternative landing options and flight corridors are explicitly considered as a means of improving contingency management and reducing risk. Similar redundancy considerations are incorporated in the design of many critical infrastructures, yet remain unexploited in the air transportation literature. In our methodology, we first model how disruptions to a given on-demand UAM network might impact on the nominal traffic flow and how this flow might be re-accommodated on an extended network with reserve capacity. Then, through an optimization problem, we select the locations and capacities for the backup vertiports with the maximal expected throughput of the extended network over all possible disruption scenarios, while the throughput is the maximal amount of flights that the network can accommodate per unit of time. We show that we can obtain the solution for the corresponding bi-level and bi-linear optimization problem by solving a mixed-integer linear program. We demonstrate our methodology in the case study using networks from Milwaukee, Atlanta, and Dallas--Fort Worth metropolitan areas and show how the throughput and flexibility of the UAM networks with reserve capacity can outcompete those without.Comment: 43 pages, 10 figure

Developing 3D Virtual Safety Risk Terrain for UAS Operations in Complex Urban Environments

Unmanned Aerial Systems (UAS), an integral part of the Advanced Air Mobility (AAM) vision, are capable of performing a wide spectrum of tasks in urban environments. The societal integration of UAS is a pivotal challenge, as these systems must operate harmoniously within the constraints imposed by regulations and societal concerns. In complex urban environments, UAS safety has been a perennial obstacle to their large-scale deployment. To mitigate UAS safety risk and facilitate risk-aware UAS operations planning, we propose a novel concept called \textit{3D virtual risk terrain}. This concept converts public risk constraints in an urban environment into 3D exclusion zones that UAS operations should avoid to adequately reduce risk to Entities of Value (EoV). To implement the 3D virtual risk terrain, we develop a conditional probability framework that comprehensively integrates most existing basic models for UAS ground risk. To demonstrate the concept, we build risk terrains on a Chicago downtown model and observe their characteristics under different conditions. We believe that the 3D virtual risk terrain has the potential to become a new routine tool for risk-aware UAS operations planning, urban airspace management, and policy development. The same idea can also be extended to other forms of societal impacts, such as noise, privacy, and perceived risk.Comment: 33 pages, 19 figure

Adaptive and degenerative evolution of the S-Phase Kinase-Associated Protein 1-Like family in Arabidopsis thaliana

Genome sequencing has uncovered tremendous sequence variation within and between species. In plants, in addition to large variations in genome size, a great deal of sequence polymorphism is also evident in several large multi-gene families, including those involved in the ubiquitin-26S proteasome protein degradation system. However, the biological function of this sequence variation is yet not clear. In this work, we explicitly demonstrated a single origin of retroposed Arabidopsis Skp1-Like (ASK) genes using an improved phylogenetic analysis. Taking advantage of the 1,001 genomes project, we here provide several lines of polymorphism evidence showing both adaptive and degenerative evolutionary processes in ASK genes. Yeast two-hybrid quantitative interaction assays further suggested that recent neutral changes in the ASK2 coding sequence weakened its interactions with some F-box proteins. The trend that highly polymorphic upstream regions of ASK1 yield high levels of expression implied negative expression regulation of ASK1 by an as-yet-unknown transcriptional suppression mechanism, which may contribute to the polymorphic roles of Skp1-CUL1-F-box complexes. Taken together, this study provides new evolutionary evidence to guide future functional genomic studies of SCF-mediated protein ubiquitylation