524 research outputs found
ETCH: Efficient Channel Hopping for Communication Rendezvous in Dynamic Spectrum Access Networks
In a dynamic spectrum access (DSA) network, communication rendezvous is the first step for two secondary users to be able to communicate with each other. In this step, the pair of secondary users meet on the same channel, over which they negotiate on the communication parameters, to establish the communication link. This paper presents ETCH, Efficient Channel Hopping based MAC-layer protocols for communication rendezvous in DSA networks. We propose two protocols, SYNC-ETCH and ASYNC-ETCH. Both protocols achieve better time-to-rendezvous and throughput compared to previous work
Theory of the Three-Group Evolutionary Minority Game
Based on the adiabatic theory for the evolutionary minority game (EMG) that
we proposed earlier[1], we perform a detail analysis of the EMG limited to
three groups of agents. We derive a formula for the critical point of the
transition from segregation (into opposing groups) to clustering (towards
cautious behaviors). Particular to the three-group EMG, the strategy switching
in the "extreme" group does not occur at every losing step and is strongly
intermittent. This leads to an correction to the critical value of the number
of agents at the transition, . Our expression for is in agreement
with the results obtained from our numerical simulations.Comment: 4 pages and 2 figure
Responsible Active Learning via Human-in-the-loop Peer Study
Active learning has been proposed to reduce data annotation efforts by only
manually labelling representative data samples for training. Meanwhile, recent
active learning applications have benefited a lot from cloud computing services
with not only sufficient computational resources but also crowdsourcing
frameworks that include many humans in the active learning loop. However,
previous active learning methods that always require passing large-scale
unlabelled data to cloud may potentially raise significant data privacy issues.
To mitigate such a risk, we propose a responsible active learning method,
namely Peer Study Learning (PSL), to simultaneously preserve data privacy and
improve model stability. Specifically, we first introduce a human-in-the-loop
teacher-student architecture to isolate unlabelled data from the task learner
(teacher) on the cloud-side by maintaining an active learner (student) on the
client-side. During training, the task learner instructs the light-weight
active learner which then provides feedback on the active sampling criterion.
To further enhance the active learner via large-scale unlabelled data, we
introduce multiple peer students into the active learner which is trained by a
novel learning paradigm, including the In-Class Peer Study on labelled data and
the Out-of-Class Peer Study on unlabelled data. Lastly, we devise a
discrepancy-based active sampling criterion, Peer Study Feedback, that exploits
the variability of peer students to select the most informative data to improve
model stability. Extensive experiments demonstrate the superiority of the
proposed PSL over a wide range of active learning methods in both standard and
sensitive protection settings.Comment: 15 pages, 8 figure
Long-term Navigation Optimal Operation of Cascaded Reservoirs
Water Resources Planning and Managemen
Interaction of Avelox with Bovine Serum Albumin and Effect of the Coexistent Drugs on the Reaction
The interaction between Avelox and bovine serum albumin (BSA) was investigated at different temperatures by fluorescence spectroscopy. Results showed that Avelox could quench the intrinsic fluorescence of BSA strongly, and the quenching mechanism was a static quenching process with Förester spectroscopy energy transfer. The electrostatic force played an important role on the conjugation reaction between BSA and Avelox. The order of magnitude of binding constants (Ka) was 104, and the number of binding site (n) in the binary system was approximately equal to 1. The binding distance (r) was less than 3 nm and the primary binding site for Avelox was located in subdomain IIA of BSA. Synchronous fluorescence spectra clearly revealed that the microenvironment of amino acid residues and the conformation of BSA were changed during the binding reaction. In addition, the effect of some antibiotics on the binding constant of Avelox with BSA was also studied
Knowledge-Aware Federated Active Learning with Non-IID Data
Federated learning enables multiple decentralized clients to learn
collaboratively without sharing the local training data. However, the expensive
annotation cost to acquire data labels on local clients remains an obstacle in
utilizing local data. In this paper, we propose a federated active learning
paradigm to efficiently learn a global model with limited annotation budget
while protecting data privacy in a decentralized learning way. The main
challenge faced by federated active learning is the mismatch between the active
sampling goal of the global model on the server and that of the asynchronous
local clients. This becomes even more significant when data is distributed
non-IID across local clients. To address the aforementioned challenge, we
propose Knowledge-Aware Federated Active Learning (KAFAL), which consists of
Knowledge-Specialized Active Sampling (KSAS) and Knowledge-Compensatory
Federated Update (KCFU). KSAS is a novel active sampling method tailored for
the federated active learning problem. It deals with the mismatch challenge by
sampling actively based on the discrepancies between local and global models.
KSAS intensifies specialized knowledge in local clients, ensuring the sampled
data to be informative for both the local clients and the global model. KCFU,
in the meantime, deals with the client heterogeneity caused by limited data and
non-IID data distributions. It compensates for each client's ability in weak
classes by the assistance of the global model. Extensive experiments and
analyses are conducted to show the superiority of KSAS over the
state-of-the-art active learning methods and the efficiency of KCFU under the
federated active learning framework.Comment: 14 pages, 12 figure
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