256 research outputs found
Application of Artificial Intelligence in Medical Imaging Diagnosis
Both the treatment of cancer and other serious diseases often depends on the diagnosis of artificial complexity and heavy experience. The introduction of artificial intelligence in medical imaging has injected vitality into the diagnosis of images. Artificial intelligence uses deep learning, image segmentation, neural networks and other algorithms flexibly in image recognition through learning data sets to extract features for accurate diagnosis of clinical diseases. At the same time, it also plays a special role in controlling the spread of infectious diseases such as new coronary pneumonia
The -weighted dual programming of the linear Chebyshev approximation and an interior-point method
Given samples of a real or complex-valued function on a set of distinct
nodes, the traditional linear Chebyshev approximation is to compute the best
minimax approximation on a prescribed linear functional space. Lawson's
iteration is a classical and well-known method for that task. However, Lawson's
iteration converges linearly and in many cases, the convergence is very slow.
In this paper, by the duality theory of linear programming, we first provide an
elementary and self-contained proof for the well-known Alternation Theorem in
the real case. Also, relying upon the Lagrange duality, we further establish an
-weighted dual programming for the linear Chebyshev approximation. In this
framework, we revisit the convergence of Lawson's iteration, and moreover,
propose a Newton type iteration, the interior-point method, to solve the
-weighted dual programming. Numerical experiments are reported to
demonstrate its fast convergence and its capability in finding the reference
points that characterize the unique minimax approximation.Comment: 29 pages, 8 figure
A convex dual programming for the rational minimax approximation and Lawson's iteration
Computing the discrete rational minimax approximation in the complex plane is
challenging. Apart from Ruttan's sufficient condition, there are few other
sufficient conditions for global optimality. The state-of-the-art rational
approximation algorithms, such as the adaptive Antoulas-Anderson (AAA),
AAA-Lawson, and the rational Krylov fitting (RKFIT) method, perform highly
efficiently, but the computed rational approximants may be near-best. In this
paper, we propose a convex programming approach, the solution of which is
guaranteed to be the rational minimax approximation under Ruttan's sufficient
condition. Furthermore, we present a new version of Lawson's iteration for
solving this convex programming problem. The computed solution can be easily
verified as the rational minimax approximant. Our numerical experiments
demonstrate that this updated version of Lawson's iteration generally converges
monotonically with respect to the objective function of the convex programming.
It is an effective competitive approach for the rational minimax problem,
compared to the highly efficient AAA, AAA-Lawson, and the stabilized
Sanathanan-Koerner iteration.Comment: 38 pages, 10 figure
CoMP Transmission in Downlink NOMA-Based Cellular-Connected UAV Networks
In this paper, we study the integration between the coordinated multipoint
(CoMP) transmission and the non-orthogonal multiple access (NOMA) in the
downlink cellular-connected UAV networks with the coexistence of aerial users
(AUs) and terrestrial users (TUs). Based on the comparison of the desired
signal strength to the dominant interference strength, the AUs are classified
into CoMP-AUs and Non-CoMP AUs, where the former receives transmissions from
two cooperative BSs, and constructs two exclusive NOMA clusters with two TUs,
respectively. A Non-CoMP AU constructs a NOMA cluster with a TU served by the
same BS. By leveraging the tools from stochastic geometry, we propose a novel
analytical framework to evaluate the performance of the CoMP-NOMA based
cellular-connected UAV network in terms of coverage probability, and average
ergodic rate. We reveal the superiority of the proposed CoMP-NOMA scheme by
comparing with three benchmark schemes, and further quantify the impacts of key
system parameters on the network performance. By harvesting the benefits of
both CoMP and NOMA, we prove that the proposed framework can provide reliable
connection for AUs by using CoMP and enhance the average ergodic rate through
NOMA technique as well.Comment: 29 pages,10 figures, submitted to a transaction journa
Reliability Assurance for Deep Neural Network Architectures Against Numerical Defects
With the widespread deployment of deep neural networks (DNNs), ensuring the
reliability of DNN-based systems is of great importance. Serious reliability
issues such as system failures can be caused by numerical defects, one of the
most frequent defects in DNNs. To assure high reliability against numerical
defects, in this paper, we propose the RANUM approach including novel
techniques for three reliability assurance tasks: detection of potential
numerical defects, confirmation of potential-defect feasibility, and suggestion
of defect fixes. To the best of our knowledge, RANUM is the first approach that
confirms potential-defect feasibility with failure-exhibiting tests and
suggests fixes automatically. Extensive experiments on the benchmarks of 63
real-world DNN architectures show that RANUM outperforms state-of-the-art
approaches across the three reliability assurance tasks. In addition, when the
RANUM-generated fixes are compared with developers' fixes on open-source
projects, in 37 out of 40 cases, RANUM-generated fixes are equivalent to or
even better than human fixes.Comment: To appear at 45th International Conference on Software Engineering
(ICSE 2023), camera-ready versio
Certifying Out-of-Domain Generalization for Blackbox Functions
Certifying the robustness of model performance under bounded data
distribution drifts has recently attracted intensive interest under the
umbrella of distributional robustness. However, existing techniques either make
strong assumptions on the model class and loss functions that can be certified,
such as smoothness expressed via Lipschitz continuity of gradients, or require
to solve complex optimization problems. As a result, the wider application of
these techniques is currently limited by its scalability and flexibility --
these techniques often do not scale to large-scale datasets with modern deep
neural networks or cannot handle loss functions which may be non-smooth such as
the 0-1 loss. In this paper, we focus on the problem of certifying
distributional robustness for blackbox models and bounded loss functions, and
propose a novel certification framework based on the Hellinger distance. Our
certification technique scales to ImageNet-scale datasets, complex models, and
a diverse set of loss functions. We then focus on one specific application
enabled by such scalability and flexibility, i.e., certifying out-of-domain
generalization for large neural networks and loss functions such as accuracy
and AUC. We experimentally validate our certification method on a number of
datasets, ranging from ImageNet, where we provide the first non-vacuous
certified out-of-domain generalization, to smaller classification tasks where
we are able to compare with the state-of-the-art and show that our method
performs considerably better.Comment: 39th International Conference on Machine Learning (ICML) 202
Coverage Analysis for Cellular-Connected Random 3D Mobile UAVs with Directional Antennas
This letter proposes an analytical framework to evaluate the coverage
performance of a cellular-connected unmanned aerial vehicle (UAV) network in
which UAV user equipments (UAV-UEs) are equipped with directional antennas and
move according to a three-dimensional (3D) mobility model. The ground base
stations (GBSs) equipped with practical down-tilted antennas are distributed
according to a Poisson point process (PPP). With tools from stochastic
geometry, we derive the handover probability and coverage probability of a
random UAV-UE under the strongest average received signal strength (RSS)
association strategy. The proposed analytical framework allows to investigate
the effect of UAV-UE antenna beamwidth, mobility speed, cell association, and
vertical motions on both the handover probability and coverage probability. We
conclude that the optimal UAV-UE antenna beamwidth decreases with the GBS
density, and the omnidirectional antenna model is preferred in the sparse
network scenario. What's more, the superiority of the strongest average RSS
association over the nearest association diminishes with the increment of GBS
density.Comment: 5 pages, 5 figures, submitted to IEEE Wireless Communications Letter
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