287 research outputs found
Paley-Wiener Theorem for Probabilistic Frames
The Paley-Wiener Theorem is a classical result about the stability of basis
in Banach spaces claiming that if a sequence is close to a basis, then this
sequence is a basis. Similar results are also extended to frames in Hilbert
spaces. As the extension of finite frames for , probabilistic
frames are probability measures on with finite second moments
and the support of which span . This paper generalizes the
Paley-Wiener theorem to the probabilistic frame setting. We claim that if a
probability measure is close to a probabilistic frame, then this probability
measure is also a probabilistic frame
Rediscover Climate Change during Global Warming Slowdown via Wasserstein Stability Analysis
Climate change is one of the key topics in climate science. However, previous
research has predominantly concentrated on changes in mean values, and few
research examines changes in Probability Distribution Function (PDF). In this
study, a novel method called Wasserstein Stability Analysis (WSA) is developed
to identify PDF changes, especially the extreme event shift and non-linear
physical value constraint variation in climate change. WSA is applied to
21st-century warming slowdown period and is compared with traditional
mean-value trend analysis. The result indicates that despite no significant
trend, the central-eastern Pacific experienced a decline in hot extremes and an
increase in cold extremes, indicating a La Nina-like temperature shift. Further
analysis at two Arctic locations suggests sea ice severely restricts the hot
extremes of surface air temperature. This impact is diminishing as sea ice
melts. Overall, based on detecting PDF changes, WSA is a useful method for
re-discovering climate change.Comment: 12 pages, 4 figures, and 1 Algorith
Joint Resources and Workflow Scheduling in UAV-Enabled Wirelessly-Powered MEC for IoT Systems
This paper considers a UAV-enabled mobile edge computing (MEC) system, where a UAV first powers the Internet of things device (IoTD) by utilizing Wireless Power Transfer (WPT) technology. Then each IoTD sends the collected data to the UAV for processing by using the energy harvested from the UAV. In order to improve the energy efficiency of the UAV, we propose a new time division multiple access (TDMA) based workflow model, which allows parallel transmissions and executions in the UAV-assisted system. We aim to minimize the total energy consumption of the UAV by jointly optimizing the IoTDs association, computing resources allocation, UAV hovering time, wireless powering duration and the services sequence of the IoTDs. The formulated problem is a mixed-integer non-convex problem, which is very difficult to solve in general. We transform and relax it into a convex problem and apply flow-shop scheduling techniques to address it. Furthermore, an alternative algorithm is developed to set the initial point closer to the optimal solution. Simulation results show that the total energy consumption of the UAV can be effectively reduced by the proposed scheme compared with the conventional systems
A Task Allocation Algorithm for Profit Maximization in NFC-RAN
In this paper, we study a general Near-Far Computing Enhanced C-RAN (NFC-RAN), in which users can offload the tasks to the near edge cloud (NEC) or the far edge cloud (FEC).We aim to propose a profit-aware task allocation model by maximizing the profit of the edge cloud operators. We first prove that this problem can be transformed to a Multiple-Choice Multi-Dimensional 0-1 Knapsack Problem (MMKP), which is NP-hard. Then, we solve it by using a low complexity heuristic algorithm. The simulation results show that the proposed algorithm achieves a good tradeoff between the performance and the complexity compared with the benchmark algorithm
Review helps learn better: Temporal Supervised Knowledge Distillation
Reviewing plays an important role when learning knowledge. The knowledge
acquisition at a certain time point may be strongly inspired with the help of
previous experience. Thus the knowledge growing procedure should show strong
relationship along the temporal dimension. In our research, we find that during
the network training, the evolution of feature map follows temporal sequence
property. A proper temporal supervision may further improve the network
training performance. Inspired by this observation, we propose Temporal
Supervised Knowledge Distillation (TSKD). Specifically, we extract the
spatiotemporal features in the different training phases of student by
convolutional Long Short-term memory network (Conv-LSTM). Then, we train the
student net through a dynamic target, rather than static teacher network
features. This process realizes the refinement of old knowledge in student
network, and utilizes it to assist current learning. Extensive experiments
verify the effectiveness and advantages of our method over existing knowledge
distillation methods, including various network architectures and different
tasks (image classification and object detection) .Comment: Under review in AAAI 202
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