291 research outputs found
The Hierarchical Control Method for Coordinating a Group of Connected Vehicles on Urban Roads
Safety, mobility and environmental impact are the three major challenges in today\u27s transportation system. As the advances in wireless communication and vehicle automation technologies, they have rapidly led to the emergence and development of connected and automated vehicles (CAVs). We can expect fully CAVs by 2030. The CAV technologies offer another solution for the issues we are dealing with in the current transportation system. In the meanwhile, urban roads are one of the most important part in the transportation network. Urban roads are characterized by multiple interconnected intersections. They are more complicated than highway traffic, because the vehicles on the urban roads are moving in multiple directions with higher relative velocity. Most of the traffic accidents happened at intersections and the intersections are the major contribution to the traffic congestions. Our urban road infrastructures are also becoming more intelligent. Sensor-embedded roadways are continuously gathering traffic data from passing vehicles. Our smart vehicles are meeting intelligent roads. However, we have not taken the fully advantages of the data rich traffic environment provided by the connected vehicle technologies and intelligent road infrastructures. The objective of this research is to develop a coordination control strategy for a group of connected vehicles under intelligent traffic environment, which can guide the vehicles passing through the intersections and make smart lane change decisions with the objective of improving overall fuel economy and traffic mobility. The coordination control strategy should also be robust to imperfect connectivity conditions with various connected vehicle penetration rate. This dissertation proposes a hierarchical control method to coordinate a group of connected vehicles travelling on urban roads with intersections. The dissertation includes four parts of the application of our proposed method: First, we focus on the coordination of the connected vehicles on the multiple interconnected unsignalized intersection roads, where the traffic signals are removed and the collision avoidance at the intersection area relays on the communication and cooperation of the connected vehicles and intersection controllers. Second, a fuel efficient hierarchical control method is proposed to control the connected vehicles travel on the signalized intersection roads. With the signal phase and timing (SPAT) information, our proposed approach is able to help the connected vehicles minimize red light idling and improve the fuel economy at the same time. Third, the research is extended form single lane to multiple lane, where the connected vehicle discretionary and cooperative mandatory lane change have been explored. Finally, we have analysis the real-world implementation potential of our proposed algorithm including the communication delay and real-time implementation analysis
Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks
Recent works have cast some light on the mystery of why deep nets fit any
data and generalize despite being very overparametrized. This paper analyzes
training and generalization for a simple 2-layer ReLU net with random
initialization, and provides the following improvements over recent works:
(i) Using a tighter characterization of training speed than recent papers, an
explanation for why training a neural net with random labels leads to slower
training, as originally observed in [Zhang et al. ICLR'17].
(ii) Generalization bound independent of network size, using a data-dependent
complexity measure. Our measure distinguishes clearly between random labels and
true labels on MNIST and CIFAR, as shown by experiments. Moreover, recent
papers require sample complexity to increase (slowly) with the size, while our
sample complexity is completely independent of the network size.
(iii) Learnability of a broad class of smooth functions by 2-layer ReLU nets
trained via gradient descent.
The key idea is to track dynamics of training and generalization via
properties of a related kernel.Comment: In ICML 201
On Exact Computation with an Infinitely Wide Neural Net
How well does a classic deep net architecture like AlexNet or VGG19 classify
on a standard dataset such as CIFAR-10 when its width --- namely, number of
channels in convolutional layers, and number of nodes in fully-connected
internal layers --- is allowed to increase to infinity? Such questions have
come to the forefront in the quest to theoretically understand deep learning
and its mysteries about optimization and generalization. They also connect deep
learning to notions such as Gaussian processes and kernels. A recent paper
[Jacot et al., 2018] introduced the Neural Tangent Kernel (NTK) which captures
the behavior of fully-connected deep nets in the infinite width limit trained
by gradient descent; this object was implicit in some other recent papers. An
attraction of such ideas is that a pure kernel-based method is used to capture
the power of a fully-trained deep net of infinite width.
The current paper gives the first efficient exact algorithm for computing the
extension of NTK to convolutional neural nets, which we call Convolutional NTK
(CNTK), as well as an efficient GPU implementation of this algorithm. This
results in a significant new benchmark for the performance of a pure
kernel-based method on CIFAR-10, being higher than the methods reported
in [Novak et al., 2019], and only lower than the performance of the
corresponding finite deep net architecture (once batch normalization, etc. are
turned off). Theoretically, we also give the first non-asymptotic proof showing
that a fully-trained sufficiently wide net is indeed equivalent to the kernel
regression predictor using NTK.Comment: In NeurIPS 2019. Code available: https://github.com/ruosongwang/cnt
Species diversity patterns of marine plankton and benthos in Chinese bays: Baseline prior to large-scale development
More than 28,000 marine species have been recorded in China, which accounts for approximately 10% of all marine organisms in the world and plays a potentially important role in protecting global marine biodiversity. However, knowledge of marine biodiversity patterns in China is limited, and in particular, no comparative diversity analysis has been carried out for Chinese bays. In this study, national-scale species diversity patterns of coastal bays were examined on the basis of investigations for approximately 81 bays throughout the entire Chinese coastline in the 1980s and the early 1990s, revealing the baseline of diversity patterns prior to large-scale development. Diversity patterns found for coastal bays in China in this study include the following: (1) species richness of benthic macrofauna was larger than that of phytoplankton or zooplankton; (2) spatially, species richness in the subtropical zone was significantly greater than that in the temperate zone; (3) species richness and bay area were significantly correlated and followed power law relationships; and (4) there were significantly positive correlations of species richness among phytoplankton, zooplankton, and benthic macrofauna. The species diversity patterns of marine benthos and plankton for coastal bays in China, in some ways, coincided with general terrestrial patterns. This is the first study to examine national-scale species diversity patterns of coastal bays in China. The findings provide new insights to conservation biology in the marine environment and also are fundamental for future studies of biodiversity and the impact of development on biodiversity
Interdisciplinary Fairness in Imbalanced Research Proposal Topic Inference: A Hierarchical Transformer-based Method with Selective Interpolation
The objective of topic inference in research proposals aims to obtain the
most suitable disciplinary division from the discipline system defined by a
funding agency. The agency will subsequently find appropriate peer review
experts from their database based on this division. Automated topic inference
can reduce human errors caused by manual topic filling, bridge the knowledge
gap between funding agencies and project applicants, and improve system
efficiency. Existing methods focus on modeling this as a hierarchical
multi-label classification problem, using generative models to iteratively
infer the most appropriate topic information. However, these methods overlook
the gap in scale between interdisciplinary research proposals and
non-interdisciplinary ones, leading to an unjust phenomenon where the automated
inference system categorizes interdisciplinary proposals as
non-interdisciplinary, causing unfairness during the expert assignment. How can
we address this data imbalance issue under a complex discipline system and
hence resolve this unfairness? In this paper, we implement a topic label
inference system based on a Transformer encoder-decoder architecture.
Furthermore, we utilize interpolation techniques to create a series of
pseudo-interdisciplinary proposals from non-interdisciplinary ones during
training based on non-parametric indicators such as cross-topic probabilities
and topic occurrence probabilities. This approach aims to reduce the bias of
the system during model training. Finally, we conduct extensive experiments on
a real-world dataset to verify the effectiveness of the proposed method. The
experimental results demonstrate that our training strategy can significantly
mitigate the unfairness generated in the topic inference task.Comment: 19 pages, Under review. arXiv admin note: text overlap with
arXiv:2209.1391
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