2,201 research outputs found
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 . Such
property has many important applications on fractals and other metric measure
spaces, such as constructing p-energies (when 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
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