Reduced pattern training based on task decomposition using pattern distributor

Task Decomposition with Pattern Distributor (PD) is a new task decomposition method for multilayered feedforward neural networks. Pattern distributor network is proposed that implements this new task decomposition method. We propose a theoretical model to analyze the performance of pattern distributor network. A method named Reduced Pattern Training is also introduced, aiming to improve the performance of pattern distribution. Our analysis and the experimental results show that reduced pattern training improves the performance of pattern distributor network significantly. The distributor module’s classification accuracy dominates the whole network’s performance. Two combination methods, namely Cross-talk based combination and Genetic Algorithm based combination, are presented to find suitable grouping for the distributor module. Experimental results show that this new method can reduce training time and improve network generalization accuracy when compared to a conventional method such as constructive backpropagation or a task decomposition method such as Output Parallelism

Task decomposition using pattern distributor

In this paper, we propose a new task decomposition method for multilayered feedforward neural networks, namely Task Decomposition with Pattern Distributor in order to shorten the training time and improve the generalization accuracy of a network under training. This new method uses the combination of modules (small-size feedforward network) in parallel and series, to produce the overall solution for a complex problem. Based on a “divide-and-conquer” technique, the original problem is decomposed into several simpler sub-problems by a pattern distributor module in the network, where each sub-problem is composed of the whole input vector and a fraction of the output vector of the original problem. These sub-problems are then solved by the corresponding groups of modules, where each group of modules is connected in series with the pattern distributor module and the modules in each group are connected in parallel. The design details and implementation of this new method are introduced in this paper. Several benchmark classification problems are used to test this new method. The analysis and experimental results show that this new method could reduce training time and improve generalization accuracy

PEVD-based speech enhancement in reverberant environments

The enhancement of noisy speech is important for applications involving human-to-human interactions, such as telecommunications and hearing aids, as well as human-to-machine interactions, such as voice-controlled systems and robot audition. In this work, we focus on reverberant environments. It is shown that, by exploiting the lack of correlation between speech and the late reflections, further noise reduction can be achieved. This is verified using simulations involving actual acoustic impulse responses and noise from the ACE corpus. The simulations show that even without using a noise estimator, our proposed method simultaneously achieves noise reduction, and enhancement of speech quality and intelligibility, in reverberant environments over a wide range of SNRs. Furthermore, informal listening examples highlight that our approach does not introduce any significant processing artefacts such as musical noise

The Role of Frailty Scores to Predict the Outcomes in Non Operative Treatment of Valgus Impacted Neck of Femur Fractures

\ua9 2024 The Authors. Objectives: The management of valgus-impacted neck of femur fracture is controversial between operative and conservative treatments. This study aimed to investigate the usefulness of the Clinical frailty Score for predicting the prognosis of patients who underwent non-operative treatment for the valgus-impacted neck of femur fracture (NOF). Methods: A single-centred retrospective review of patients admitted with valgus impacted NOF. Data were collected from patients’ records, including demographics, Clinical Frailty Score (CFS), Nottingham Hip Fracture Score (NHFS) and Abbreviated Mental Test Score (AMTS). Patients were followed up to 24 months postoperatively. Results: Fifty-eight patients who were treated non-operatively with a mean follow-up of 2.6 years met our inclusion criteria. Twenty-nine patients failed the non-operative treatment and required replacement surgeries, while 29 had successful outcomes (50%). There were no differences between the two groups’ mean age and gender distributions (P 0.527 and 0.139, respectively). The successful group had significantly higher CFS (P 0.013), worse AMTS and higher mortality risk based on the NHFS (P 0.006 and P < 0.001, respectively). Conclusion: This study demonstrates that CFS, AMTS and NHFS can be used as predictors when considering non-operative treatment for the valgus-impacted neck of femur fracture. Patients who are frail, demented and high risk based on the NHFS have higher success rates with non-operative treatment

Sustaining Economic Exploitation of Complex Ecosystems in Computational Models of Coupled Human-Natural Networks

Understanding ecological complexity has stymied scientists for decades. Recent elucidation of the famously coined "devious strategies for stability in enduring natural systems" has opened up a new field of computational analyses of complex ecological networks where the nonlinear dynamics of many interacting species can be more realistically mod-eled and understood. Here, we describe the first extension of this field to include coupled human-natural systems. This extension elucidates new strategies for sustaining extraction of biomass (e.g., fish, forests, fiber) from ecosystems that account for ecological complexity and can pursue multiple goals such as maximizing economic profit, employment and carbon sequestration by ecosystems. Our more realistic modeling of ecosystems helps explain why simpler "maxi-mum sustainable yield" bioeconomic models underpinning much natural resource extraction policy leads to less profit, biomass, and biodiversity than predicted by those simple models. Current research directions of this integrated natu-ral and social science include applying artificial intelligence, cloud computing, and multiplayer online games

Deep Learning Mental Health Dialogue System

Mental health counseling remains a major challenge in modern society due to cost, stigma, fear, and unavailability. We posit that generative artificial intelligence (AI) models designed for mental health counseling could help improve outcomes by lowering barriers to access. To this end, we have developed a deep learning (DL) dialogue system called Serena. The system consists of a core generative model and post-processing algorithms. The core generative model is a 2.7 billion parameter Seq2Seq Transformer fine-tuned on thousands of transcripts of person-centered-therapy (PCT) sessions. The series of post-processing algorithms detects contradictions, improves coherency, and removes repetitive answers. Serena is implemented and deployed on \url{https://serena.chat}, which currently offers limited free services. While the dialogue system is capable of responding in a qualitatively empathetic and engaging manner, occasionally it displays hallucination and long-term incoherence. Overall, we demonstrate that a deep learning mental health dialogue system has the potential to provide a low-cost and effective complement to traditional human counselors with less barriers to access

Synthesis of Ultrafine Β′-Alumina Powders via Flame Spray Pyrolysis of Polymeric Precursors

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/66387/1/j.1151-2916.1998.tb02506.x.pd