6,567 research outputs found
Angular Reconstruction of a Lead Scintillating-Fiber Sandwiched Electromagnetic Calorimeter
A new method called Neighbor Cell Deposited Energy Ratio (NCDER) is proposed
to reconstruct incidence position in a single layer for a 3-dimensional imaging
electromagnetic calorimeter (ECAL).This method was applied to reconstruct the
ECAL test beam data for the Alpha Magnetic Spectrometer-02 (AMS-02). The
results show that this method can achieve an angular resolution of 7.36\pm 0.08
/ \sqrt(E) \oplus 0.28 \pm 0.02 degree in the determination of the photons
direction, which is much more precise than that obtained with the
commonly-adopted Center of Gravity(COG) method (8.4 \pm 0.1 /sqrt(E) \oplus
0.8\pm0.3 degree). Furthermore, since it uses only the properties of
electromagnetic showers, this new method could also be used for other type of
fine grain sampling calorimeters.Comment: 6 pages, 8 figure
An XML-based continuous auditing web services model -An Implementation Study
The concepts of continuous auditing are now more than two decades old, many researchers have issued differ continuous audit system model for applying over internet technology. A continuous audit is an assurance service where the time between the occurrence of events underlying a particular subject matter and the issuance of an auditor‘s opinion on the fairness of a client‘s representation of the subject matter is eliminated. The auditor offer restricted views provided by the continuous audit web services (CAWS) routines on a fee basis to analysts, investors, financial institutions, and other parties interested in obtaining continuous audit (CA) of business performance or other audit objects of interest. In our study proposed not only discuss with how to ensure the integrity and effectiveness of the entire data collection system but also implement the XML web services to enterprise applied for correctness and usefulness well-known the CAWS model. The CAWS design and demonstrate an implementation of continuous audit with the internal auditor data verify for compliance CA domain. The demonstrated CAWS model uses data retrieval layer, data analysis layer and data presentation layer over the internet to continuously monitor by the audit department. The article concludes with suggestion for future research and our implemented experiences
Machine learning for phase ordering dynamics of charge density waves
We present a machine learning (ML) framework for large-scale dynamical
simulations of charge density wave (CDW) states. The charge modulation in a CDW
state is often accompanied by a concomitant structural distortion, and the
adiabatic evolution of a CDW order is governed by the dynamics of the lattice
distortion. Calculation of the electronic contribution to the driving forces,
however, is computationally very expensive for large systems. Assuming the
principle of locality for electron systems, a neural-network model is developed
to accurately and efficiently predict local electronic forces with input from
neighborhood configurations. Importantly, the ML model makes possible a linear
complexity algorithm for dynamical simulations of CDWs. As a demonstration, we
apply our approach to investigate the phase ordering dynamics of the Holstein
model, a canonical system of CDW order. Our large-scale simulations uncover an
intriguing growth of the CDW domains that deviates significantly from the
expected Allen-Cahn law for phase ordering of Ising-type order parameter field.
This anomalous domain-growth could be attributed to the complex structure of
domain-walls in this system. Our work highlights the promising potential of
ML-based force-field models for dynamical simulations of functional electronic
materials.Comment: 16 pages, 9 figure
Anticipating Daily Intention using On-Wrist Motion Triggered Sensing
Anticipating human intention by observing one's actions has many
applications. For instance, picking up a cellphone, then a charger (actions)
implies that one wants to charge the cellphone (intention). By anticipating the
intention, an intelligent system can guide the user to the closest power
outlet. We propose an on-wrist motion triggered sensing system for anticipating
daily intentions, where the on-wrist sensors help us to persistently observe
one's actions. The core of the system is a novel Recurrent Neural Network (RNN)
and Policy Network (PN), where the RNN encodes visual and motion observation to
anticipate intention, and the PN parsimoniously triggers the process of visual
observation to reduce computation requirement. We jointly trained the whole
network using policy gradient and cross-entropy loss. To evaluate, we collect
the first daily "intention" dataset consisting of 2379 videos with 34
intentions and 164 unique action sequences. Our method achieves 92.68%, 90.85%,
97.56% accuracy on three users while processing only 29% of the visual
observation on average
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