8,766 research outputs found
Characterizing Transgender Health Issues in Twitter
Although there are millions of transgender people in the world, a lack of
information exists about their health issues. This issue has consequences for
the medical field, which only has a nascent understanding of how to identify
and meet this population's health-related needs. Social media sites like
Twitter provide new opportunities for transgender people to overcome these
barriers by sharing their personal health experiences. Our research employs a
computational framework to collect tweets from self-identified transgender
users, detect those that are health-related, and identify their information
needs. This framework is significant because it provides a macro-scale
perspective on an issue that lacks investigation at national or demographic
levels. Our findings identified 54 distinct health-related topics that we
grouped into 7 broader categories. Further, we found both linguistic and
topical differences in the health-related information shared by transgender men
(TM) as com-pared to transgender women (TW). These findings can help inform
medical and policy-based strategies for health interventions within transgender
communities. Also, our proposed approach can inform the development of
computational strategies to identify the health-related information needs of
other marginalized populations
A robust machine learning method for cell-load approximation in wireless networks
We propose a learning algorithm for cell-load approximation in wireless
networks. The proposed algorithm is robust in the sense that it is designed to
cope with the uncertainty arising from a small number of training samples. This
scenario is highly relevant in wireless networks where training has to be
performed on short time scales because of a fast time-varying communication
environment. The first part of this work studies the set of feasible rates and
shows that this set is compact. We then prove that the mapping relating a
feasible rate vector to the unique fixed point of the non-linear cell-load
mapping is monotone and uniformly continuous. Utilizing these properties, we
apply an approximation framework that achieves the best worst-case performance.
Furthermore, the approximation preserves the monotonicity and continuity
properties. Simulations show that the proposed method exhibits better
robustness and accuracy for small training sets in comparison with standard
approximation techniques for multivariate data.Comment: Shorter version accepted at ICASSP 201
Use of domperidone to increase breast milk supply: further consideration of the benefit-risk ratio is required
Letter to the EditorLuke E. Grzeskowiak, Lisa H. Ami
Detection for 5G-NOMA: An Online Adaptive Machine Learning Approach
Non-orthogonal multiple access (NOMA) has emerged as a promising radio access
technique for enabling the performance enhancements promised by the
fifth-generation (5G) networks in terms of connectivity, low latency, and high
spectrum efficiency. In the NOMA uplink, successive interference cancellation
(SIC) based detection with device clustering has been suggested. In the case of
multiple receive antennas, SIC can be combined with the minimum mean-squared
error (MMSE) beamforming. However, there exists a tradeoff between the NOMA
cluster size and the incurred SIC error. Larger clusters lead to larger errors
but they are desirable from the spectrum efficiency and connectivity point of
view. We propose a novel online learning based detection for the NOMA uplink.
In particular, we design an online adaptive filter in the sum space of linear
and Gaussian reproducing kernel Hilbert spaces (RKHSs). Such a sum space design
is robust against variations of a dynamic wireless network that can deteriorate
the performance of a purely nonlinear adaptive filter. We demonstrate by
simulations that the proposed method outperforms the MMSE-SIC based detection
for large cluster sizes.Comment: Accepted at ICC 201
The S66 noncovalent interactions benchmark reconsidered using explicitly correlated methods near the basis set limit
The S66 benchmark for noncovalent interactions has been re-evaluated using
explicitly correlated methods with basis sets near the one-particle basis set
limit. It is found that post-MP2 "high-level corrections" are treated
adequately well using a combination of CCSD(F12*) with (aug-)cc-pVTZ-F12 basis
sets on the one hand, and (T) extrapolated from conventional
CCSD(T)/heavy-aug-cc-pV{D,T}Z on the other hand. Implications for earlier
benchmarks on the larger S66x8 problem set in particular, and for accurate
calculations on noncovalent interactions in general, are discussed. At a slight
cost in accuracy, (T) can be considerably accelerated by using sano-V{D,T}Z+
basis sets, while half-counterpoise CCSD(F12*)(T)/cc-pVDZ-F12 offers the best
compromise between accuracy and computational cost.Comment: Australian Journal of Chemistry, in press [Graham S. Chandler special
issue
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