Discrete-Time Recurrent Neural Network and Its Application to Compression of Infra-Red Spectrum

We study the discrete-time recurrent neural network that derived from the Leaky-integrator model and its application to compression of infra-red spec- trum. Our results show that the discrete-time Leaky-integrator recurrent neural network (RNN) model can be used to approximate the continuous-time model and inherit its dynamical characters if a proper step size is chosen. Moreover, the discrete-time Leaky-integrator RNN model is absolutely stable. By developing the double discrete integral method and employing the state space search algorithm for the discrete-time recurrent neural network model, we demonstrate with quality spectra regenerated from the compressed data how to compress the infra-red spectrum effectively. The information we stored is the parameters of the system and its initial states. The method offers an ideal setting to carry out the recurrent neural network approach to chaotic cases of data compression

Discrete-Time Recurrent Neural Network and Its Application to Compression of Infra-Red Spectrum

We study the discrete-time recurrent neural network that derived from the Leaky-integrator model and its application to compression of infra-red spec- trum. Our results show that the discrete-time Leaky-integrator recurrent neural network (RNN) model can be used to approximate the continuous-time model and inherit its dynamical characters if a proper step size is chosen. Moreover, the discrete-time Leaky-integrator RNN model is absolutely stable. By developing the double discrete integral method and employing the state space search algorithm for the discrete-time recurrent neural network model, we demonstrate with quality spectra regenerated from the compressed data how to compress the infra-red spectrum effectively. The information we stored is the parameters of the system and its initial states. The method offers an ideal setting to carry out the recurrent neural network approach to chaotic cases of data compression

A New Optimization Algorithm for Single Hidden Layer Feedforward Neural Networks

Feedforward neural networks are the most commonly used function approximation techniques in neural networks. By the universal approximation theorem, it is clear that a single-hidden layer feedforward neural network (FNN) is sufficient to approximate the corresponding desired outputs arbitrarily close. Some researchers use genetic algorithms (GAs) to explore the global optimal solution of the FNN structure. However, it is rather time consuming to use GA for the training of FNN. In this paper, we propose a new optimization algorithm for a single-hidden layer FNN. The method is based on the convex combination algorithm for massaging information in the hidden layer. In fact, this technique explores a continuum idea which combines the classic mutation and crossover strategies in GA together. The proposed method has the advantage over GA which requires a lot of preprocessing works in breaking down the data into a sequence of binary codes before learning or mutation can apply. Also, we set up a new error function to measure the performance of the FNN and obtain the optimal choice of the connection weights and thus the nonlinear optimization problem can be solved directly. Several computational experiments are used to illustrate the proposed algorithm, which has good exploration and exploitation capabilities in search of the optimal weight for single hidden layer FNNs

A State Space Search Algorithm and its Application to Learn the Short-Term Foreign Exchange Rates

Abstract We propose the use of a state space search algorithm of the discretetime recurrent neural network to learn the short-term foreign exchange rates. By searching in the neighborhood of the target trajectory in the state space, the algorithm performs nonlinear optimization learning process to provide the best feasible solution for the nonlinear least square problem. The convergence analysis shows that the convergence of the algorithm to the desired solution is guaranteed. The stability properties of the algorithm are also discussed. The empirical results show that our method is simple and effectively in learning the short-term foreign exchange rates and is applicable to other applications. Mathematics Subject Classification: 68T05, 68Q32, 91B2

Factors determining the survival of nasopharyngeal carcinoma with lung metastasis alone: does combined modality treatment benefit?

<p>Abstract</p> <p>Background</p> <p>Nasopharyngeal carcinoma (NPC) with lung metastasis alone has been reported as a relatively favorable prognostic group, and combined modality treatment might be indicated for selected cases. However, the prognostic factors determining survival of this group and the indication of combined therapy have not been thoroughly studied.</p> <p>Methods</p> <p>We retrospectively reviewed 246 patients of NPC with lung metastasis(es) alone presented at diagnosis or as the first failure after primary treatment from 1993 to 2008 in an academic tertiary hospital. Univariate and multivariate survival analyses of post-metastasis survival (PMS) and overall survival (OS) were carried out to determine the prognostic factors.</p> <p>Results</p> <p>The 3-year, 5-year, and 10-year of PMS and OS for the whole cohort were 34.3%, 17.0%, 8.6% and 67.8%, 45.4%, 18.5%, respectively. The median PMS (45.6 months <it>vs</it>. 23.7 months) and OS (73.7 months <it>vs</it>. 46.2 months) of patients treated with combined therapy was significantly longer than that of those treated with chemotherapy alone (<it>P </it>< 0.001). Age, disease-free interval (DFI) and treatment modality were evaluated as independent prognostic factors of OS, while only age and treatment modality retain their independent significance in PMS analysis. In stratified survival analysis, compared to chemotherapy alone, combined therapy could benefit the patients with DFI > 1 year, but not those with DFI ≤ 1 year.</p> <p>Conclusions</p> <p>Age ≤ 45 years, DFI > 1 year, and the combined therapy were good prognostic factors for NPC patients with lung metastasis(es) alone. The combination of local therapy and the basic chemotherapy should be considered for these patients with DFI > 1 year.</p

Atrasentan and renal events in patients with type 2 diabetes and chronic kidney disease (SONAR): a double-blind, randomised, placebo-controlled trial

Background: Short-term treatment for people with type 2 diabetes using a low dose of the selective endothelin A receptor antagonist atrasentan reduces albuminuria without causing significant sodium retention. We report the long-term effects of treatment with atrasentan on major renal outcomes. Methods: We did this double-blind, randomised, placebo-controlled trial at 689 sites in 41 countries. We enrolled adults aged 18–85 years with type 2 diabetes, estimated glomerular filtration rate (eGFR)25–75 mL/min per 1·73 m 2 of body surface area, and a urine albumin-to-creatinine ratio (UACR)of 300–5000 mg/g who had received maximum labelled or tolerated renin–angiotensin system inhibition for at least 4 weeks. Participants were given atrasentan 0·75 mg orally daily during an enrichment period before random group assignment. Those with a UACR decrease of at least 30% with no substantial fluid retention during the enrichment period (responders)were included in the double-blind treatment period. Responders were randomly assigned to receive either atrasentan 0·75 mg orally daily or placebo. All patients and investigators were masked to treatment assignment. The primary endpoint was a composite of doubling of serum creatinine (sustained for ≥30 days)or end-stage kidney disease (eGFR <15 mL/min per 1·73 m 2 sustained for ≥90 days, chronic dialysis for ≥90 days, kidney transplantation, or death from kidney failure)in the intention-to-treat population of all responders. Safety was assessed in all patients who received at least one dose of their assigned study treatment. The study is registered with ClinicalTrials.gov, number NCT01858532. Findings: Between May 17, 2013, and July 13, 2017, 11 087 patients were screened; 5117 entered the enrichment period, and 4711 completed the enrichment period. Of these, 2648 patients were responders and were randomly assigned to the atrasentan group (n=1325)or placebo group (n=1323). Median follow-up was 2·2 years (IQR 1·4–2·9). 79 (6·0%)of 1325 patients in the atrasentan group and 105 (7·9%)of 1323 in the placebo group had a primary composite renal endpoint event (hazard ratio [HR]0·65 [95% CI 0·49–0·88]; p=0·0047). Fluid retention and anaemia adverse events, which have been previously attributed to endothelin receptor antagonists, were more frequent in the atrasentan group than in the placebo group. Hospital admission for heart failure occurred in 47 (3·5%)of 1325 patients in the atrasentan group and 34 (2·6%)of 1323 patients in the placebo group (HR 1·33 [95% CI 0·85–2·07]; p=0·208). 58 (4·4%)patients in the atrasentan group and 52 (3·9%)in the placebo group died (HR 1·09 [95% CI 0·75–1·59]; p=0·65). Interpretation: Atrasentan reduced the risk of renal events in patients with diabetes and chronic kidney disease who were selected to optimise efficacy and safety. These data support a potential role for selective endothelin receptor antagonists in protecting renal function in patients with type 2 diabetes at high risk of developing end-stage kidney disease. Funding: AbbVie

Effects of hospital facilities on patient outcomes after cancer surgery: an international, prospective, observational study

Background Early death after cancer surgery is higher in low-income and middle-income countries (LMICs) compared with in high-income countries, yet the impact of facility characteristics on early postoperative outcomes is unknown. The aim of this study was to examine the association between hospital infrastructure, resource availability, and processes on early outcomes after cancer surgery worldwide.Methods A multimethods analysis was performed as part of the GlobalSurg 3 study-a multicentre, international, prospective cohort study of patients who had surgery for breast, colorectal, or gastric cancer. The primary outcomes were 30-day mortality and 30-day major complication rates. Potentially beneficial hospital facilities were identified by variable selection to select those associated with 30-day mortality. Adjusted outcomes were determined using generalised estimating equations to account for patient characteristics and country-income group, with population stratification by hospital.Findings Between April 1, 2018, and April 23, 2019, facility-level data were collected for 9685 patients across 238 hospitals in 66 countries (91 hospitals in 20 high-income countries; 57 hospitals in 19 upper-middle-income countries; and 90 hospitals in 27 low-income to lower-middle-income countries). The availability of five hospital facilities was inversely associated with mortality: ultrasound, CT scanner, critical care unit, opioid analgesia, and oncologist. After adjustment for case-mix and country income group, hospitals with three or fewer of these facilities (62 hospitals, 1294 patients) had higher mortality compared with those with four or five (adjusted odds ratio [OR] 3.85 [95% CI 2.58-5.75]; p&lt;0.0001), with excess mortality predominantly explained by a limited capacity to rescue following the development of major complications (63.0% vs 82.7%; OR 0.35 [0.23-0.53]; p&lt;0.0001). Across LMICs, improvements in hospital facilities would prevent one to three deaths for every 100 patients undergoing surgery for cancer.Interpretation Hospitals with higher levels of infrastructure and resources have better outcomes after cancer surgery, independent of country income. Without urgent strengthening of hospital infrastructure and resources, the reductions in cancer-associated mortality associated with improved access will not be realised

Learning fixed point patterns by recurrent networks

Several learning algorithms have been derived for equilibrium points in recurrent neural networks. In this paper, we also consider learning the equilibrium points of such dynamical systems. We derive a structurally simple learning algorithm for recurrent networks which does not involve computing the trajectories of the system and we prove convergence and give examples. We also discuss solving for the connection weight matrix by iterative learning algorithms or direct solving

Convergence Analysis of the Weighted State Space Search Algorithm for Recurrent Neural Networks

American Institute of Mathematical Sciences. All rights reserved. Recurrent neural networks (RNNs) have emerged as a promising tool in modeling nonlinear dynamical systems. The convergence is one of the most important issues of concern among the dynamical properties for the RNNs in practical applications. The reason is that the viability of many applications of RNNs depends on their convergence properties. We study in this paper the convergence properties of the weighted state space search algorithm (WSSSA) -- a derivative-free and non-random learning algorithm which searches the neighborhood of the target trajectory in the state space instead of the parameter space. Because there is no computation of partial derivatives involved, the WSSSA has a couple of salient features such as simple, fast and cost effective. In this study we provide a necessary and sufficient condition that required for the convergence of the WSSSA. Restrictions are offered that may help assure convergence of the of the WSSSA to the desired solution. The asymptotic rate of convergence is also analyzed. Our study gives insights into the problem and provides useful information for the actual design of the RNNs. A numerical example is given to support the theoretical analysis and to demonstrate that it is applicable to many applications

A Neural Network Approach for Global Optimization with Applications

We propose a neural network approach for global optimization with applications to nonlinear least square problems. The center idea is defined by the algorithm that is developed from neural network learning. By searching in the neighborhood of the target trajectory in the state space, the algorithm provides the best feasible solution to the optimization problem. The convergence analysis shows that the convergence of the algorithm to the desired solution is guaranteed. Our examples show that the method is effective and accurate. The simplicity of this new approach would provide a good alternative in addition to statistics methods for power regression models with large data