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 LqL_q-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 $L_q$-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 $L_2$-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

Involvement of the vacuolar proton-translocating ATPase in multiple steps of the endo-lysosomal system and in the contractile vacuole system of Dictyostelium discoideum

We have investigated the effects of Concanamycin A (CMA), a specific inhibitor of vacuolar type H(+)-ATPases, on acidification and function of the endo-lysosomal and contractile vacuole (CV) systems of D. discoideum. This drug inhibited acidification and increased the pH of endo-lysosomal vesicles both in vivo and in vitro in a dose dependent manner. Treatment also inhibited endocytosis and exocytosis of fluid phase, and phagocytosis of latex beads. This report also confirms our previous conclusions (Cardelli et al. (1989) J. Biol. Chem. 264, 3454–3463) that maintenance of acidic pH in lumenal compartments is required for efficient processing and targeting of a lysosomal enzyme, alpha-mannosidase. CMA treatment compromised the function of the contractile vacuole complex as amoebae exposed to a hypo-osmotic environment in the presence of CMA, swelled rapidly and ruptured. Fluorescence microscopy revealed that CMA treatment induced gross morphological changes in D. discoideum cells, characterized by the formation of large intracellular vacuoles containing fluid phase. The reticular membranes of the CV system were also no longer as apparent in drug treated cells. Finally, this is the first report describing cells that can adapt in the presence of CMA; in nutrient medium, D. discoideum overcame the effects of CMA after one hour of drug treatment even in the absence of protein synthesis. Upon adaptation to CMA, normal sized endo-lysosomal vesicles reappeared, endo-lysosomal pH decreased, and the rate of endocytosis, exocytosis and phagocytosis returned to normal. This study demonstrates that the V-H(+)-ATPase plays an important role in maintaining the integrity and function of the endo-lysosomal and CV systems and that D. discoideum can compensate for the loss of a functional V-H(+)-ATPase