1,219 research outputs found
Real-Time Misbehavior Detection in IEEE 802.11e Based WLANs
The Enhanced Distributed Channel Access (EDCA) specification in the IEEE
802.11e standard supports heterogeneous backoff parameters and arbitration
inter-frame space (AIFS), which makes a selfish node easy to manipulate these
parameters and misbehave. In this case, the network-wide fairness cannot be
achieved any longer. Many existing misbehavior detectors, primarily designed
for legacy IEEE 802.11 networks, become inapplicable in such a heterogeneous
network configuration. In this paper, we propose a novel real-time hybrid-share
(HS) misbehavior detector for IEEE 802.11e based wireless local area networks
(WLANs). The detector keeps updating its state based on every successful
transmission and makes detection decisions by comparing its state with a
threshold. We develop mathematical analysis of the detector performance in
terms of both false positive rate and average detection rate. Numerical results
show that the proposed detector can effectively detect both contention window
based and AIFS based misbehavior with only a short detection window.Comment: Accepted to IEEE Globecom 201
ACTS in Need: Automatic Configuration Tuning with Scalability Guarantees
To support the variety of Big Data use cases, many Big Data related systems
expose a large number of user-specifiable configuration parameters. Highlighted
in our experiments, a MySQL deployment with well-tuned configuration parameters
achieves a peak throughput as 12 times much as one with the default setting.
However, finding the best setting for the tens or hundreds of configuration
parameters is mission impossible for ordinary users. Worse still, many Big Data
applications require the support of multiple systems co-deployed in the same
cluster. As these co-deployed systems can interact to affect the overall
performance, they must be tuned together. Automatic configuration tuning with
scalability guarantees (ACTS) is in need to help system users. Solutions to
ACTS must scale to various systems, workloads, deployments, parameters and
resource limits. Proposing and implementing an ACTS solution, we demonstrate
that ACTS can benefit users not only in improving system performance and
resource utilization, but also in saving costs and enabling fairer
benchmarking
THE INFLUENCE OF IDEOLOGICAL AND POLITICAL EDUCATION IDEAS IN COLLEGE COURSES ON THE HEALTHY DEVELOPMENT OF STUDENTS’ PSYCHOLOGICAL QUALITY
THE INFLUENCE OF IDEOLOGICAL AND POLITICAL EDUCATION IDEAS IN COLLEGE COURSES ON THE HEALTHY DEVELOPMENT OF STUDENTS’ PSYCHOLOGICAL QUALITY
BestConfig: Tapping the Performance Potential of Systems via Automatic Configuration Tuning
An ever increasing number of configuration parameters are provided to system
users. But many users have used one configuration setting across different
workloads, leaving untapped the performance potential of systems. A good
configuration setting can greatly improve the performance of a deployed system
under certain workloads. But with tens or hundreds of parameters, it becomes a
highly costly task to decide which configuration setting leads to the best
performance. While such task requires the strong expertise in both the system
and the application, users commonly lack such expertise.
To help users tap the performance potential of systems, we present
BestConfig, a system for automatically finding a best configuration setting
within a resource limit for a deployed system under a given application
workload. BestConfig is designed with an extensible architecture to automate
the configuration tuning for general systems. To tune system configurations
within a resource limit, we propose the divide-and-diverge sampling method and
the recursive bound-and-search algorithm. BestConfig can improve the throughput
of Tomcat by 75%, that of Cassandra by 63%, that of MySQL by 430%, and reduce
the running time of Hive join job by about 50% and that of Spark join job by
about 80%, solely by configuration adjustment
Topological Photonic Phase in Chiral Hyperbolic Metamaterials
Recently the possibility of achieving one-way backscatter immune
transportation of light by mimicking the topological order present within
certain solid state systems, such as topological insulators, has received much
attention. Thus far however, demonstrations of non-trivial topology in
photonics have relied on photonic crystals with precisely engineered lattice
structures, periodic on the scale of the operational wavelength and composed of
finely tuned, complex materials. Here we propose a novel effective medium
approach towards achieving topologically protected photonic surface states
robust against disorder on all length scales and for a wide range of material
parameters. Remarkably, the non-trivial topology of our metamaterial design
results from the Berry curvature arising from the transversality of
electromagnetic waves in a homogeneous medium. Our investigation therefore acts
to bridge the gap between the advancing field of topological band theory and
classical optical phenomena such as the Spin Hall effect of light. The
effective medium route to topological phases will pave the way for highly
compact one-way transportation of electromagnetic waves in integrated photonic
circuits.Comment: 11 pages, 3 figures. To appear in PR
tSF: Transformer-based Semantic Filter for Few-Shot Learning
Few-Shot Learning (FSL) alleviates the data shortage challenge via embedding
discriminative target-aware features among plenty seen (base) and few unseen
(novel) labeled samples. Most feature embedding modules in recent FSL methods
are specially designed for corresponding learning tasks (e.g., classification,
segmentation, and object detection), which limits the utility of embedding
features. To this end, we propose a light and universal module named
transformer-based Semantic Filter (tSF), which can be applied for different FSL
tasks. The proposed tSF redesigns the inputs of a transformer-based structure
by a semantic filter, which not only embeds the knowledge from whole base set
to novel set but also filters semantic features for target category.
Furthermore, the parameters of tSF is equal to half of a standard transformer
block (less than 1M). In the experiments, our tSF is able to boost the
performances in different classic few-shot learning tasks (about 2%
improvement), especially outperforms the state-of-the-arts on multiple
benchmark datasets in few-shot classification task
Three dimensional photonic Dirac points in metamaterials
Topological semimetals, representing a new topological phase that lacks a
full bandgap in bulk states and exhibiting nontrivial topological orders,
recently have been extended to photonic systems, predominantly in photonic
crystals and to a lesser extent, metamaterials. Photonic crystal realizations
of Dirac degeneracies are protected by various space symmetries, where Bloch
modes span the spin and orbital subspaces. Here, we theoretically show that
Dirac points can also be realized in effective media through the intrinsic
degrees of freedom in electromagnetism under electromagnetic duality. A pair of
spin polarized Fermi arc like surface states is observed at the interface
between air and the Dirac metamaterials. These surface states show linear
k-space dispersion relation, resulting in nearly diffraction-less propagation.
Furthermore, eigen reflection fields show the decomposition from a Dirac point
to two Weyl points. We also find the topological correlation between a Dirac
point and vortex/vector beams in classic photonics. The theoretical proposal of
photonic Dirac point lays foundation for unveiling the connection between
intrinsic physics and global topology in electromagnetism.Comment: 15 pages, 5 figure
A Likelihood Approach to Incorporating Self-Report Data in HIV Recency Classification
Estimating new HIV infections is significant yet challenging due to the
difficulty in distinguishing between recent and long-term infections. We
demonstrate that HIV recency status (recent v.s. long-term) could be determined
from the combination of self-report testing history and biomarkers, which are
increasingly available in bio-behavioral surveys. HIV recency status is
partially observed, given the self-report testing history. For example, people
who tested positive for HIV over one year ago should have a long-term
infection. Based on the nationally representative samples collected by the
Population-based HIV Impact Assessment (PHIA) Project, we propose a
likelihood-based probabilistic model for HIV recency classification. The model
incorporates both labeled and unlabeled data and integrates the mechanism of
how HIV recency status depends on biomarkers and the mechanism of how HIV
recency status, together with the self-report time of the most recent HIV test,
impacts the test results, via a set of logistic regression models. We compare
our method to logistic regression and the binary classification tree (current
practice) on Malawi, Zimbabwe, and Zambia PHIA data, as well as on simulated
data. Our model obtains more efficient and less biased parameter estimates and
is relatively robust to potential reporting error and model misspecification
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