460 research outputs found
2D Proactive Uplink Resource Allocation Algorithm for Event Based MTC Applications
We propose a two dimension (2D) proactive uplink resource allocation
(2D-PURA) algorithm that aims to reduce the delay/latency in event-based
machine-type communications (MTC) applications. Specifically, when an event of
interest occurs at a device, it tends to spread to the neighboring devices.
Consequently, when a device has data to send to the base station (BS), its
neighbors later are highly likely to transmit. Thus, we propose to cluster
devices in the neighborhood around the event, also referred to as the
disturbance region, into rings based on the distance from the original event.
To reduce the uplink latency, we then proactively allocate resources for these
rings. To evaluate the proposed algorithm, we analytically derive the mean
uplink delay, the proportion of resource conservation due to successful
allocations, and the proportion of uplink resource wastage due to unsuccessful
allocations for 2D-PURA algorithm. Numerical results demonstrate that the
proposed method can save over 16.5 and 27 percent of mean uplink delay,
compared with the 1D algorithm and the standard method, respectively.Comment: 6 pages, 6 figures, Published in 2018 IEEE Wireless Communications
and Networking Conference (WCNC
A profiling analysis of contributions of cigarette smoking, dietary calcium intakes, and physical activity to fragility fracture in the elderly
Fragility fracture and bone mineral density (BMD) are influenced by common and modifiable lifestyle factors. In this study, we sought to define the contribution of lifestyle factors to fracture risk by using a profiling approach. The study involved 1683 women and 1010 men (50+ years old, followed up for up to 20 years). The incidence of new fractures was ascertained by X-ray reports. A “lifestyle risk score” (LRS) was derived as the weighted sum of effects of dietary calcium intake, physical activity index, and cigarette smoking. Each individual had a unique LRS, with higher scores being associated with a healthier lifestyle. Baseline values of lifestyle factors were assessed. In either men or women, individuals with a fracture had a significantly lower age-adjusted LRS than those without a fracture. In men, each unit lower in LRS was associated with a 66% increase in the risk of total fracture (non-adjusted hazard ratio [HR] 1.66; 95% CI, 1.26 to 2.20) and still significant after adjusting for age, weight or BMD. However, in women, the association was uncertain (HR 1.30; 95% CI, 1.11 to 1.53). These data suggest that unhealthy lifestyle habits are associated with an increased risk of fracture in men, but not in women, and that the association is mediated by BMD
Calculating energy derivatives for quantum chemistry on a quantum computer
Modeling chemical reactions and complicated molecular systems has been
proposed as the `killer application' of a future quantum computer. Accurate
calculations of derivatives of molecular eigenenergies are essential towards
this end, allowing for geometry optimization, transition state searches,
predictions of the response to an applied electric or magnetic field, and
molecular dynamics simulations. In this work, we survey methods to calculate
energy derivatives, and present two new methods: one based on quantum phase
estimation, the other on a low-order response approximation. We calculate
asymptotic error bounds and approximate computational scalings for the methods
presented. Implementing these methods, we perform the world's first geometry
optimization on an experimental quantum processor, estimating the equilibrium
bond length of the dihydrogen molecule to within 0.014 Angstrom of the full
configuration interaction value. Within the same experiment, we estimate the
polarizability of the H2 molecule, finding agreement at the equilibrium bond
length to within 0.06 a.u. (2% relative error).Comment: 19 pages, 1 page supplemental, 7 figures. v2 - tidied up and added
example to appendice
Subject-Independent ERP-Based Brain-Computer Interfaces
© 2001-2011 IEEE. Brain-computer interfaces (BCIs) are desirable for people to express their thoughts, especially those with profound disabilities in communication. The classification of brain patterns for each different subject requires an extensively time-consuming learning stage specific to that person, in order to reach satisfactory accuracy performance. The training session could also be infeasible for disabled patients as they may not fully understand the training instructions. In this paper, we propose a unified classification scheme based on ensemble classifier, dynamic stopping, and adaptive learning. We apply this scheme on the P300-based BCI, with the subject-independent manner, where no learning session is required for new experimental users. According to our theoretical analysis and empirical results, the harmonized integration of these three methods can significantly boost up the average accuracy from 75.00% to 91.26%, while at the same time reduce the average spelling time from 12.62 to 6.78 iterations, approximately to two-fold faster. The experiments were conducted on a large public dataset which had been used in other related studies. Direct comparisons between our work with the others' are also reported in details
Marine Phytoplankton Functional Types Exhibit Diverse Responses to Thermal Change
Marine phytoplankton generate half of global primary production, making them essential to ecosystem functioning and biogeochemical cycling. Though phytoplankton are phylogenetically diverse, studies rarely designate unique thermal traits to different taxa, resulting in coarse representations of phytoplankton thermal responses. Here we assessed phytoplankton functional responses to temperature using empirically derived thermal growth rates from four principal contributors to marine productivity: diatoms, dinoflagellates, cyanobacteria, and coccolithophores. Using modeled sea surface temperatures for 1950-1970 and 2080-2100, we explored potential alterations to each group\u27s growth rates and geographical distribution under a future climate change scenario. Contrary to the commonly applied Eppley formulation, our data suggest phytoplankton functional types may be characterized by different temperature coefficients (Q(10)), growth maxima thermal dependencies, and thermal ranges which would drive dissimilar responses to each degree of temperature change. These differences, when applied in response to global simulations of future temperature, result in taxon-specific projections of growth and geographic distribution, with low-latitude coccolithophores facing considerable decreases and cyanobacteria substantial increases in growth rates. These results suggest that the singular effect of changing temperature may alter phytoplankton global community structure, owing to the significant variability in thermal response between phytoplankton functional types. Phytoplankton communities are important players in biogeochemical processes, but are sensitive to global warming. Here, a meta-analysis shows how the varied responses of phytoplankton to rising temperatures could potentially alter growth dynamics and community structure in a future ocean
Temporal and Spatial Scales of Correlation in Marine Phytoplankton Communities
Ocean circulation shapes marine phytoplankton communities by setting environmental conditions and dispersing organisms. In addition, processes acting on the water column (e.g., heat fluxes and mixing) affect the community structure by modulating environmental variables that determine in situ growth and loss rates. Understanding the scales over which phytoplankton communities vary in time and space is key to elucidate the relative contributions of local processes and ocean circulation on phytoplankton distributions. Using a global ocean ecosystem model, we quantify temporal and spatial correlation scales for phytoplankton phenotypes with diverse functional traits and cell sizes. Through this analysis, we address these questions: (1) Over what timescales do perturbations in phytoplankton populations persist? and (2) over what distances are variations in phytoplankton populations synchronous? We find that correlation timescales are short in regions of strong currents, such as the Gulf Stream and Antarctic Circumpolar Current. Conversely, in the subtropical gyres, phytoplankton population anomalies persist for relatively long periods. Spatial correlation length scales are elongated near ocean fronts and narrow boundary currents, reflecting flow paths and frontal patterns. In contrast, we find nearly isotropic spatial correlation fields where current speeds are small, or where mixing acts roughly equally in all directions. Phytoplankton timescales and length scales also vary coherently with phytoplankton body size. In addition to aiding understanding of phytoplankton population dynamics, our results provide global insights to guide the design of biological ocean observing networks and to better interpret data collected at long-term monitoring stations
Countering Eavesdroppers with Meta-learning-based Cooperative Ambient Backscatter Communications
This article introduces a novel lightweight framework using ambient
backscattering communications to counter eavesdroppers. In particular, our
framework divides an original message into two parts: (i) the active-transmit
message transmitted by the transmitter using conventional RF signals and (ii)
the backscatter message transmitted by an ambient backscatter tag that
backscatters upon the active signals emitted by the transmitter. Notably, the
backscatter tag does not generate its own signal, making it difficult for an
eavesdropper to detect the backscattered signals unless they have prior
knowledge of the system. Here, we assume that without decoding/knowing the
backscatter message, the eavesdropper is unable to decode the original message.
Even in scenarios where the eavesdropper can capture both messages,
reconstructing the original message is a complex task without understanding the
intricacies of the message-splitting mechanism. A challenge in our proposed
framework is to effectively decode the backscattered signals at the receiver,
often accomplished using the maximum likelihood (MLK) approach. However, such a
method may require a complex mathematical model together with perfect channel
state information (CSI). To address this issue, we develop a novel deep
meta-learning-based signal detector that can not only effectively decode the
weak backscattered signals without requiring perfect CSI but also quickly adapt
to a new wireless environment with very little knowledge. Simulation results
show that our proposed learning approach, without requiring perfect CSI and
complex mathematical model, can achieve a bit error ratio close to that of the
MLK-based approach. They also clearly show the efficiency of the proposed
approach in dealing with eavesdropping attacks and the lack of training data
for deep learning models in practical scenarios
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