2,766 research outputs found
Four-manifolds of Pinched Sectional Curvature
In this paper, we study closed four-dimensional manifolds. In particular, we
show that under various new pinching curvature conditions (for example, the
sectional curvature is no more than 5/6 of the smallest Ricci eigenvalue) then
the manifold is definite. If restricting to a metric with harmonic Weyl tensor,
then it must be self-dual or anti-self-dual under the same conditions.
Similarly, if restricting to an Einstein metric, then it must be either the
complex projective space with its Fubini-Study metric, the round sphere or
their quotients. Furthermore, we also classify Einstein manifolds with positive
intersection form and an upper bound on the sectional curvature.Comment: 20 pages, add a few remarks, references, and acknowledgemen
Combining edge and cloud computing for mobility analytics
Mobility analytics using data generated from the Internet of Mobile Things
(IoMT) is facing many challenges which range from the ingestion of data streams
coming from a vast number of fog nodes and IoMT devices to avoiding overflowing
the cloud with useless massive data streams that can trigger bottlenecks [1].
Managing data flow is becoming an important part of the IoMT because it will
dictate in which platform analytical tasks should run in the future. Data flows
are usually a sequence of out-of-order tuples with a high data input rate, and
mobility analytics requires a real-time flow of data in both directions, from
the edge to the cloud, and vice-versa. Before pulling the data streams to the
cloud, edge data stream processing is needed for detecting missing, broken, and
duplicated tuples in addition to recognize tuples whose arrival time is out of
order. Analytical tasks such as data filtering, data cleaning and low-level
data contextualization can be executed at the edge of a network. In contrast,
more complex analytical tasks such as graph processing can be deployed in the
cloud, and the results of ad-hoc queries and streaming graph analytics can be
pushed to the edge as needed by a user application. Graphs are efficient
representations used in mobility analytics because they unify knowledge about
connectivity, proximity and interaction among moving things. This poster
describes the preliminary results from our experimental prototype developed for
supporting transit systems, in which edge and cloud computing are combined to
process transit data streams forwarded from fog nodes into a cloud. The
motivation of this research is to understand how to perform meaningfulness
mobility analytics on transit feeds by combining cloud and fog computing
architectures in order to improve fleet management, mass transit and remote
asset monitoringComment: Edge Computing, Cloud Computing, Mobility Analytics, Internet of
Mobile Things, Edge Fog Fabri
Enhancing the Fairness and Performance of Edge Cameras with Explainable AI
The rising use of Artificial Intelligence (AI) in human detection on Edge
camera systems has led to accurate but complex models, challenging to interpret
and debug. Our research presents a diagnostic method using Explainable AI (XAI)
for model debugging, with expert-driven problem identification and solution
creation. Validated on the Bytetrack model in a real-world office Edge network,
we found the training dataset as the main bias source and suggested model
augmentation as a solution. Our approach helps identify model biases, essential
for achieving fair and trustworthy models.Comment: IEEE ICCE 202
XAI-Enhanced Semantic Segmentation Models for Visual Quality Inspection
Visual quality inspection systems, crucial in sectors like manufacturing and
logistics, employ computer vision and machine learning for precise, rapid
defect detection. However, their unexplained nature can hinder trust, error
identification, and system improvement. This paper presents a framework to
bolster visual quality inspection by using CAM-based explanations to refine
semantic segmentation models. Our approach consists of 1) Model Training, 2)
XAI-based Model Explanation, 3) XAI Evaluation, and 4) Annotation Augmentation
for Model Enhancement, informed by explanations and expert insights.
Evaluations show XAI-enhanced models surpass original DeepLabv3-ResNet101
models, especially in intricate object segmentation.Comment: IEEE ICCE 202
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