3,238 research outputs found
Stable Throughput Region of Cognitive-Relay Networks with Imperfect Sensing and Finite Relaying Buffer
In this letter, we obtain the stable throughput region for a cognitive
relaying scheme with a finite relaying buffer and imperfect sensing. The
analysis investigates the effect of the secondary user's finite relaying
capabilities under different scenarios of primary, secondary and relaying links
outages. Furthermore, we demonstrate the effect of miss detection and false
alarm probabilities on the achievable throughput for the primary and secondary
users
Collective Classification of Textual Documents by Guided Self-Organization in T-Cell Cross-Regulation Dynamics
We present and study an agent-based model of T-Cell cross-regulation in the
adaptive immune system, which we apply to binary classification. Our method
expands an existing analytical model of T-cell cross-regulation (Carneiro et
al. in Immunol Rev 216(1):48-68, 2007) that was used to study the
self-organizing dynamics of a single population of T-Cells in interaction with
an idealized antigen presenting cell capable of presenting a single antigen.
With agent-based modeling we are able to study the self-organizing dynamics of
multiple populations of distinct T-cells which interact via antigen presenting
cells that present hundreds of distinct antigens. Moreover, we show that such
self-organizing dynamics can be guided to produce an effective binary
classification of antigens, which is competitive with existing machine learning
methods when applied to biomedical text classification. More specifically, here
we test our model on a dataset of publicly available full-text biomedical
articles provided by the BioCreative challenge (Krallinger in The biocreative
ii. 5 challenge overview, p 19, 2009). We study the robustness of our model's
parameter configurations, and show that it leads to encouraging results
comparable to state-of-the-art classifiers. Our results help us understand both
T-cell cross-regulation as a general principle of guided self-organization, as
well as its applicability to document classification. Therefore, we show that
our bio-inspired algorithm is a promising novel method for biomedical article
classification and for binary document classification in general
Globally Optimal Cooperation in Dense Cognitive Radio Networks
The problem of calculating the local and global decision thresholds in hard
decisions based cooperative spectrum sensing is well known for its mathematical
intractability. Previous work relied on simple suboptimal counting rules for
decision fusion in order to avoid the exhaustive numerical search required for
obtaining the optimal thresholds. However, these simple rules are not globally
optimal as they do not maximize the overall global detection probability by
jointly selecting local and global thresholds. Instead, they maximize the
detection probability for a specific global threshold. In this paper, a
globally optimal decision fusion rule for Primary User signal detection based
on the Neyman- Pearson (NP) criterion is derived. The algorithm is based on a
novel representation for the global performance metrics in terms of the
regularized incomplete beta function. Based on this mathematical
representation, it is shown that the globally optimal NP hard decision fusion
test can be put in the form of a conventional one dimensional convex
optimization problem. A binary search for the global threshold can be applied
yielding a complexity of O(log2(N)), where N represents the number of
cooperating users. The logarithmic complexity is appreciated because we are
concerned with dense networks, and thus N is expected to be large. The proposed
optimal scheme outperforms conventional counting rules, such as the OR, AND,
and MAJORITY rules. It is shown via simulations that, although the optimal rule
tends to the simple OR rule when the number of cooperating secondary users is
small, it offers significant SNR gain in dense cognitive radio networks with
large number of cooperating users
On the Capacity of the Underwater Acoustic Channel with Dominant Noise Sources
This paper provides an upper-bound for the capacity of the underwater
acoustic (UWA) channel with dominant noise sources and generalized fading
environments. Previous works have shown that UWA channel noise statistics are
not necessary Gaussian, especially in a shallow water environment which is
dominated by impulsive noise sources. In this case, noise is best represented
by the Generalized Gaussian (GG) noise model with a shaping parameter .
On the other hand, fading in the UWA channel is generally represented using an
- distribution, which is a generalization of a wide range of well
known fading distributions. We show that the Additive White Generalized
Gaussian Noise (AWGGN) channel capacity is upper bounded by the AWGN capacity
in addition to a constant gap of bits. The same gap also exists when characterizing the ergodic
capacity of AWGGN channels with - fading compared to the faded
AWGN channel capacity. We justify our results by revisiting the sphere-packing
problem, which represents a geometric interpertation of the channel capacity.
Moreover, UWA channel secrecy rates are characterized and the dependency of UWA
channel secrecy on the shaping parameters of the legitimate and eavesdropper
channels is highlighted
Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processes
Predicated on the increasing abundance of electronic health records, we
investi- gate the problem of inferring individualized treatment effects using
observational data. Stemming from the potential outcomes model, we propose a
novel multi- task learning framework in which factual and counterfactual
outcomes are mod- eled as the outputs of a function in a vector-valued
reproducing kernel Hilbert space (vvRKHS). We develop a nonparametric Bayesian
method for learning the treatment effects using a multi-task Gaussian process
(GP) with a linear coregion- alization kernel as a prior over the vvRKHS. The
Bayesian approach allows us to compute individualized measures of confidence in
our estimates via pointwise credible intervals, which are crucial for realizing
the full potential of precision medicine. The impact of selection bias is
alleviated via a risk-based empirical Bayes method for adapting the multi-task
GP prior, which jointly minimizes the empirical error in factual outcomes and
the uncertainty in (unobserved) counter- factual outcomes. We conduct
experiments on observational datasets for an inter- ventional social program
applied to premature infants, and a left ventricular assist device applied to
cardiac patients wait-listed for a heart transplant. In both experi- ments, we
show that our method significantly outperforms the state-of-the-art
Forecasting Individualized Disease Trajectories using Interpretable Deep Learning
Disease progression models are instrumental in predicting individual-level
health trajectories and understanding disease dynamics. Existing models are
capable of providing either accurate predictions of patients prognoses or
clinically interpretable representations of disease pathophysiology, but not
both. In this paper, we develop the phased attentive state space (PASS) model
of disease progression, a deep probabilistic model that captures complex
representations for disease progression while maintaining clinical
interpretability. Unlike Markovian state space models which assume memoryless
dynamics, PASS uses an attention mechanism to induce "memoryful" state
transitions, whereby repeatedly updated attention weights are used to focus on
past state realizations that best predict future states. This gives rise to
complex, non-stationary state dynamics that remain interpretable through the
generated attention weights, which designate the relationships between the
realized state variables for individual patients. PASS uses phased LSTM units
(with time gates controlled by parametrized oscillations) to generate the
attention weights in continuous time, which enables handling
irregularly-sampled and potentially missing medical observations. Experiments
on data from a realworld cohort of patients show that PASS successfully
balances the tradeoff between accuracy and interpretability: it demonstrates
superior predictive accuracy and learns insightful individual-level
representations of disease progression
Bayesian Nonparametric Causal Inference: Information Rates and Learning Algorithms
We investigate the problem of estimating the causal effect of a treatment on
individual subjects from observational data, this is a central problem in
various application domains, including healthcare, social sciences, and online
advertising. Within the Neyman Rubin potential outcomes model, we use the
Kullback Leibler (KL) divergence between the estimated and true distributions
as a measure of accuracy of the estimate, and we define the information rate of
the Bayesian causal inference procedure as the (asymptotic equivalence class of
the) expected value of the KL divergence between the estimated and true
distributions as a function of the number of samples. Using Fano method, we
establish a fundamental limit on the information rate that can be achieved by
any Bayesian estimator, and show that this fundamental limit is independent of
the selection bias in the observational data. We characterize the Bayesian
priors on the potential (factual and counterfactual) outcomes that achieve the
optimal information rate. As a consequence, we show that a particular class of
priors that have been widely used in the causal inference literature cannot
achieve the optimal information rate. On the other hand, a broader class of
priors can achieve the optimal information rate. We go on to propose a prior
adaptation procedure (which we call the information based empirical Bayes
procedure) that optimizes the Bayesian prior by maximizing an information
theoretic criterion on the recovered causal effects rather than maximizing the
marginal likelihood of the observed (factual) data. Building on our analysis,
we construct an information optimal Bayesian causal inference algorithm
Encoding Distortion Modeling For DWT-Based Wireless EEG Monitoring System
Recent advances in wireless body area sensor net- works leverage wireless and
mobile communication technologies to facilitate development of innovative
medical applications that can significantly enhance healthcare services and
improve quality of life. Specifically, Electroencephalography (EEG)-based
applications lie at the heart of these promising technologies. However, the
design and operation of such applications is challenging. Power consumption
requirements of the sensor nodes may turn some of these applications
impractical. Hence, implementing efficient encoding schemes are essential to
reduce power consumption in such applications. In this paper, we propose an
analytical distortion model for the EEG-based encoding systems. Using this
model, the encoder can effectively reconfigure its complexity by adjusting its
control parameters to satisfy application constraints while maintaining
reconstruction accuracy at the receiver side. The simulation results illustrate
that the main parameters that affect the distortion are compression ratio and
filter length of the considered DWT-based encoder. Furthermore, it is found
that the wireless channel variations have a significant influence on the
estimated distortion at the receiver side
Random Aerial Beamforming for Underlay Cognitive Radio with Exposed Secondary Users
In this paper, we introduce the exposed secondary users problem in underlay
cognitive radio systems, where both the secondary-to-primary and
primary-to-secondary channels have a Line-of-Sight (LoS) component. Based on a
Rician model for the LoS channels, we show, analytically and numerically, that
LoS interference hinders the achievable secondary user capacity when
interference constraints are imposed at the primary user receiver. This is
caused by the poor dynamic range of the interference channels fluctuations when
a dominant LoS component exists. In order to improve the capacity of such
system, we propose the usage of an Electronically Steerable Parasitic Array
Radiator (ESPAR) antennas at the secondary terminals. An ESPAR antenna involves
a single RF chain and has a reconfigurable radiation pattern that is controlled
by assigning arbitrary weights to M orthonormal basis radiation patterns via
altering a set of reactive loads. By viewing the orthonormal patterns as
multiple virtual dumb antennas, we randomly vary their weights over time
creating artificial channel fluctuations that can perfectly eliminate the
undesired impact of LoS interference. This scheme is termed as Random Aerial
Beamforming (RAB), and is well suited for compact and low cost mobile terminals
as it uses a single RF chain. Moreover, we investigate the exposed secondary
users problem in a multiuser setting, showing that LoS interference hinders
multiuser interference diversity and affects the growth rate of the SU capacity
as a function of the number of users. Using RAB, we show that LoS interference
can actually be exploited to improve multiuser diversity via opportunistic
nulling
Some Characterizations on the Normalized Lommel, Struve and Bessel Functions of the First Kind
In this paper, we introduce new technique for determining some necessary and
sufficient conditions of the normalized Bessel functions , normalized
Struve functions and normalized Lommel functions of the
first kind, to be in the subclasses of starlike and convex functions of order
and type .Comment: arXiv admin note: text overlap with arXiv:1610.03233 by other author
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