An Adaptive Penalty-Based Learning Extension for Backpropagation and its Variants

金沢大学理工研究域　電子情報学系階層型ニュートラルネットワークの学習法としてよく用いられるバックプロパゲーション(BP)アルゴリズムに対して，学習の収束性を改善する多くの方法が提案されている.　本稿では，BPアルゴリズムやBPROP法などの類似する学習法を対象として，新しい適応形ペナルティに基づく学習法を提案する.学習法で用いられる出力誤差をペナルティにより増減する.ペナルティは出力が目標値と同じ放物線上にあれば小さく，そうでなければ大きく制御される.　これにより，局所解に陥ることを防ぐことが出来，最適解への収束性を高めることが出来る. 多くの例を用いてシュミュレーションを行った結果，提案方法の有効性が確認できた

Neural networks following a binary approach applied to the integer prime-factorization problem

Nowadays, the integer prime-factorization problem finds its application often in modern cryptography. Artificial Neural Networks (ANNs) have been applied to the integer prime-factorization problem. A composed number N is applied to the ANNs, and one of its prime factors p is obtained as the output. Previously, neural networks dealing with the input and output data in a decimal format have been proposed. However, accuracy is not sufficient. In this paper, a neural network following a binary approach is proposed. The input N as well as the desired output p were expressed in a binary form. The proposed neural network is expected to be more stable, i.e. less sensitive to small errors in the network outputs. Simulations have been performed and the results are compared with the results reported in the previous study. The number of required search times for the true prime number can be well reduced. Furthermore, the probability density function of the training patterns is investigated and the need for different data creation and/or selection techniques is shown. © 2005 IEEE

An adaptive penalty-based learning extension for backpropagation and its variants

Over the years, many improvements and refinements of the backpropagation learning algorithm have been reported. In this paper, a new adaptive penalty-based learning extension for the backpropagation learning algorithm and its variants is proposed. The new method initially puts pressure on artificial neural networks in order to get all outputs for all training patterns into the correct half of the output range, instead of mainly focusing on minimizing the difference between the target and actual output values. The technique is easy to implement and computationally inexpensive. In this study, the new approach has been applied to the backpropagation learning algorithm as well as the RPROP learning algorithm and simulations have been performed. The superiority of the new proposed method is demonstrated. By applying the extension, the number of successful runs can be greatly increased and the average number of epochs to convergence can be well reduced on various problem instances. Furthermore, the change of the penalty values during training has been studied and its observation shows the active role the penalties play within the learning process. © 2006 IEEE

An adaptive penalty-based learning extension for backpropagation and its variants

金沢大学理工研究域　電子情報学系Over the years, many improvements and refinements of the backpropagation learning algorithm have been reported. In this paper, a new adaptive penalty-based learning extension for the backpropagation learning algorithm and its variants is proposed. The new method initially puts pressure on artificial neural networks in order to get all outputs for all training patterns into the correct half of the output range, instead of mainly focusing on minimizing the difference between the target and actual output values. The technique is easy to implement and computationally inexpensive. In this study, the new approach has been applied to the backpropagation learning algorithm as well as the RPROP learning algorithm and simulations have been performed. The superiority of the new proposed method is demonstrated. By applying the extension, the number of successful runs can be greatly increased and the average number of epochs to convergence can be well reduced on various problem instances. Furthermore, the change of the penalty values during training has been studied and its observation shows the active role the penalties play within the learning process. © 2006 IEEE

ニューラルネットワークにおける適応ペナルティに基づく学習法と素因数分解への応用

取得学位：博士(工学)，学位授与番号：博甲第857号，学位授与年月日：平成18年9月28

Psoas abscess secondary to retroperitoneal distant metastases from squamous cell carcinoma of the cervix with thrombosis of the inferior vena cava and duodenal infiltration treated by Whipple procedure: a case report and review of the literature

Background: Psoas abscess is a rare clinical disease of various origins. Most common causes include hematogenous spread of bacteria from a different primary source, spondylodiscitis or perforated intestinal organs. But rarely some abscesses are related to malignant metastatic disease. Case presentation: In this case report we present the case of a patient with known squamous cell carcinoma of the cervix treated with radio-chemotherapy three years prior. She now presented with a psoas abscess and subsequent complete inferior vena cava thrombosis, as well as duodenal and vertebral infiltration. The abscess was drained over a prolonged period of time and later was found to be a complication caused by metastases of the cervical carcinoma. Due to the massive extent of the metastases a Whipple procedure was performed to successfully control the local progress of the metastasis. Conclusion: As psoas abscess is an unspecific disease which presents with non-specific symptoms adequate therapy may be delayed due to lack of early diagnostic results. This case report highlights the difficulties of managing a malignant abscess and demonstrates some diagnostic pitfalls that might be encountered. It stresses the necessity of adequate diagnostics to initiate successful therapy. Reports on psoas abscesses that are related to cervix carcinoma are scarce, probably due to the rarity of this event, and are limited to very few case reports. We are the first to report a case in which an extensive and complex abdominal procedure was needed for local control to improve quality of life

Uncertainty Analysis and Order-by-Order Optimization of Chiral Nuclear Interactions

Chiral effective field theory (chi EFT) provides a systematic approach to describe low-energy nuclear forces. Moreover, chi EFT is able to provide well-founded estimates of statistical and systematic uncertainties-although this unique advantage has not yet been fully exploited. We fill this gap by performing an optimization and statistical analysis of all the low-energy constants (LECs) up to next-to-next-to-leading order. Our optimization protocol corresponds to a simultaneous fit to scattering and bound-state observables in the pion-nucleon, nucleon-nucleon, and few-nucleon sectors, thereby utilizing the full model capabilities of chi EFT. Finally, we study the effect on other observables by demonstrating forward-error-propagation methods that can easily be adopted by future works. We employ mathematical optimization and implement automatic differentiation to attain efficient and machine-precise first-and second-order derivatives of the objective function with respect to the LECs. This is also vital for the regression analysis. We use power-counting arguments to estimate the systematic uncertainty that is inherent to chi EFT, and we construct chiral interactions at different orders with quantified uncertainties. Statistical error propagation is compared with Monte Carlo sampling, showing that statistical errors are, in general, small compared to systematic ones. In conclusion, we find that a simultaneous fit to different sets of data is critical to (i) identify the optimal set of LECs, (ii) capture all relevant correlations, (iii) reduce the statistical uncertainty, and (iv) attain order-by-order convergence in chi EFT. Furthermore, certain systematic uncertainties in the few-nucleon sector are shown to get substantially magnified in the many-body sector, in particular when varying the cutoff in the chiral potentials. The methodology and results presented in this paper open a new frontier for uncertainty quantification in ab initio nuclear theory