Unsupervised Feature Learning through Divergent Discriminative Feature Accumulation

Unlike unsupervised approaches such as autoencoders that learn to reconstruct their inputs, this paper introduces an alternative approach to unsupervised feature learning called divergent discriminative feature accumulation (DDFA) that instead continually accumulates features that make novel discriminations among the training set. Thus DDFA features are inherently discriminative from the start even though they are trained without knowledge of the ultimate classification problem. Interestingly, DDFA also continues to add new features indefinitely (so it does not depend on a hidden layer size), is not based on minimizing error, and is inherently divergent instead of convergent, thereby providing a unique direction of research for unsupervised feature learning. In this paper the quality of its learned features is demonstrated on the MNIST dataset, where its performance confirms that indeed DDFA is a viable technique for learning useful features.Comment: Corrected citation formattin

Competitive Coevolution through Evolutionary Complexification

Two major goals in machine learning are the discovery and improvement of solutions to complex problems. In this paper, we argue that complexification, i.e. the incremental elaboration of solutions through adding new structure, achieves both these goals. We demonstrate the power of complexification through the NeuroEvolution of Augmenting Topologies (NEAT) method, which evolves increasingly complex neural network architectures. NEAT is applied to an open-ended coevolutionary robot duel domain where robot controllers compete head to head. Because the robot duel domain supports a wide range of strategies, and because coevolution benefits from an escalating arms race, it serves as a suitable testbed for studying complexification. When compared to the evolution of networks with fixed structure, complexifying evolution discovers significantly more sophisticated strategies. The results suggest that in order to discover and improve complex solutions, evolution, and search in general, should be allowed to complexify as well as optimize

Epeirogenic and Climatic Controls of Early Pleistocene Fluvial Sediment Dispersal in Nebraska

The change from Pliocene to Pleistocene fluvial sedimentation in Nebraska is denoted by gravel with relative enrichment of mechanically weak rock species and a two-fold increase in largest clast size. These changes in fluvial sediments suggest modification in degradational energy affecting detritus apparently related to deterioration of climate in the early Pleistocene. Cooler Pleistocene climates with increased moisture resulted in greater discharge and carrying capacity for streams headed in the Rocky Mountains and flowing across Nebraska. These streams carried granitic detritus eastward toward the continental glacier margin in easternmost Nebraska. There, streams flowing off ice sheets carrying sedimentary and metamorphic detritus derived from the ice joined the east-flowing streams from the mountains. Detritus derived from continental glaciers in easternmost Nebraska, therefore, was not transported westward, but instead, was mixed with Rocky Mountain-derived detritus and transported southward along the ice-front margin. Even though the drainage basin of the Platte River system came into existence in the Tertiary, the present course of the Platte River dates only from mid-Pleistocene time. Widespread occurrence of lower Pleistocene braided channel deposits east of the Chadron-Cambridge Arch that contain Laramie Range-derived anorthosite indicates repeated channel switching and meandering during times of aggradation over surfaces of minimal relief. Relations of these gravels to the Chadron-Cambridge and Siouxana Arches suggest that uplift on these structures was sufficient to deflect and control the course of streams headed in the Laramie Range and flowing across the plains. Activity on the Chadron-Cambridge Arch also is suggested by the distribution of earthquake epicenters, modern drainage patterns, and the relation of the pre-Pleistocene bedrock surface to the arch and the profile of the Platte River. The presence of knickpoints on rivers crossing the arch suggests that these rivers are maintaining a course antecedent to a spasmodically rising arch. Rivers are entrenched now, but during the early Pleistocene when streams carried a heavy load of sand and gravel, similar activity along the Chadron-Cambridge Arch would have been adequate to spill the aggrading stream over its fan and divert it southward where it could follow a new course. Eastward-flowing streams heading in the Rocky Mountains were controlled by changes in discharge of streams and movement on epeirogenic structures during the early Pleistocene

The Application of Portfolio Concepts to Credit Analysis

Thomas O. Stanley is an Assistant Professor of Finance at Southern Illinois University at Carbondale. John K. Ford is an Associate Professor of Finance at the University of Maine at Orono

The Evolution of Neural Network-Based Chart Patterns: A Preliminary Study

A neural network-based chart pattern represents adaptive parametric features, including non-linear transformations, and a template that can be applied in the feature space. The search of neural network-based chart patterns has been unexplored despite its potential expressiveness. In this paper, we formulate a general chart pattern search problem to enable cross-representational quantitative comparison of various search schemes. We suggest a HyperNEAT framework applying state-of-the-art deep neural network techniques to find attractive neural network-based chart patterns; These techniques enable a fast evaluation and search of robust patterns, as well as bringing a performance gain. The proposed framework successfully found attractive patterns on the Korean stock market. We compared newly found patterns with those found by different search schemes, showing the proposed approach has potential.Comment: 8 pages, In proceedings of Genetic and Evolutionary Computation Conference (GECCO 2017), Berlin, German

Impact of health management information systems on service delivery among healthcare workers at Iten County Referral Hospital

Background: Decision-making process and effective planning within the health sector relies majorly on availability of reliable, accurate and prompt information. Most referral healthcare facilities in Kenya utilize health management information systems (HMIS) yet delivery of effective services remains relatively challenging. Against this backdrop, we assessed the impact of use of HMIS on service delivery among healthcare workers at Iten County Referral Hospital (ICRH).Methods: This study used a cross-sectional study design target population was all healthcare workers at ICRH. Closed and open-ended questionnaires were used to obtain data. Purposive and stratified sampling techniques were used to select study site and participants respectively. The sample size was 185 healthcare workers but 142 participants filled out the questionnaire.Results: Most respondents were nurses (37.4%). Least cohort were pharmacists (1.40%). About 62.7% were diploma-holders, 26.8% had a bachelor’s degree. Further, 66.9% of participants had <10 years working experience, 22.5% had 11-20 years while; 10.6% had 21-30 years. Participants (26.1-47.2%) agreed that using HMIS is efficient and effective for managing hospital data. Majority were undecided whether HMIS can be used for managing financial imperatives and providing epidemiological data. Participants (77.5%) rated HMIS as being user-friendly. Remarkably, 22.5% rated the HMIS as suitable for use as a centralized planning system for the hospital. Data also showed that HMIS is yet to be fully integrated into the hospital system.Conclusions: The use of HMIS has positively impacted service-delivery at ICRH. We therefore recommend that healthcare facilities integrate the use of HMIS in management of hospital data.

Analisis Laporan Keuangan dalam Mengukur Kinerja Keuangan pada PT. Bank Artha Graha Internasional, Tbk

Bank sebagai badan usaha di bidang keuangan yang memiliki berbagai jasa yang dapat digunakan oleh pengguna jasa. Bank secara periodik wajib membuat laporan keuangan untuk dapat mengetahui tingkat keuangan yang dimiliki dan kondisi keuangan Bank tersebut. Tujuan penelitian ini untuk menganalisis laporan keuangan dalam mengukur kinerja keuangan pada PT. Bank Artha Graha Internasional, Tbk Jakarta. Metode analisis data adalah analisis deskriptif komparatif. Dari laporan keuangan Bank kemudian dianalisis likuiditas, solvabilitas, dan profitabilitas untuk mendapatkan perbandingan hasil pada tiap tahun dan akan disesuaikan dengan standar Bank Indonesia (BI) yang dapat dianalisis untuk mengukur seperti apa tingkat keuangan dan kinerja bank tersebut. Hasil penelitian menunjukan, likuiditas Bank Artha Graha mampu memenuhi kewajiban jangka pendek yang dimiliki. Hasil solvabilitas memperlihatkan kemampuan bank dalam permodalan yang dimiliki mampu untuk menutupi penurunan maupun kerugian. Hasil profitabilitas memperlihatkan bank memiliki hasil rasio yang terus meningkat. Ketiga rasio keuangan sesuai dengan standar yang ditentukan BI. Kondisi keuangan Bank Artha Graha masih dalam keadaan baik dan dapat memenuhi kewajiban terhadap pihak ketiga. Manajemen Bank Artha Graha sebaiknya terus melakukan pengelolaan keuangan dengan baik, agar tidak terjadi penurunan yang dapat menyebabkan bank menjadi tidak sanggup untuk menyelesaikan masalah keuangan yang ada nantinya. Kata kunci: analisis, laporan keuangan, kinerja keuanga

Real-time hebbian learning from autoencoder features for control tasks

Neural plasticity and in particular Hebbian learning play an important role in many research areas related to artficial life. By allowing artificial neural networks (ANNs) to adjust their weights in real time, Hebbian ANNs can adapt over their lifetime. However, even as researchers improve and extend Hebbian learning, a fundamental limitation of such systems is that they learn correlations between preexisting static features and network outputs. A Hebbian ANN could in principle achieve significantly more if it could accumulate new features over its lifetime from which to learn correlations. Interestingly, autoencoders, which have recently gained prominence in deep learning, are themselves in effect a kind of feature accumulator that extract meaningful features from their inputs. The insight in this paper is that if an autoencoder is connected to a Hebbian learning layer, then the resulting Realtime Autoencoder-Augmented Hebbian Network (RAAHN) can actually learn new features (with the autoencoder) while simultaneously learning control policies from those new features (with the Hebbian layer) in real time as an agent experiences its environment. In this paper, the RAAHN is shown in a simulated robot maze navigation experiment to enable a controller to learn the perfect navigation strategy significantly more often than several Hebbian-based variant approaches that lack the autoencoder. In the long run, this approach opens up the intriguing possibility of real-time deep learning for control

Coevolution of Generative Adversarial Networks

Generative adversarial networks (GAN) became a hot topic, presenting impressive results in the field of computer vision. However, there are still open problems with the GAN model, such as the training stability and the hand-design of architectures. Neuroevolution is a technique that can be used to provide the automatic design of network architectures even in large search spaces as in deep neural networks. Therefore, this project proposes COEGAN, a model that combines neuroevolution and coevolution in the coordination of the GAN training algorithm. The proposal uses the adversarial characteristic between the generator and discriminator components to design an algorithm using coevolution techniques. Our proposal was evaluated in the MNIST dataset. The results suggest the improvement of the training stability and the automatic discovery of efficient network architectures for GANs. Our model also partially solves the mode collapse problem.Comment: Published in EvoApplications 201

Phase Transitions in Two-Dimensional Traffic Flow Models

We introduce two simple two-dimensional lattice models to study traffic flow in cities. We have found that a few basic elements give rise to the characteristic phase diagram of a first-order phase transition from a freely moving phase to a jammed state, with a critical point. The jammed phase presents new transitions corresponding to structural transformations of the jam. We discuss their relevance in the infinite size limit.Comment: RevTeX 3.0 file. Figures available upon request to e-address [email protected] (or 'dopico' or 'molera' or 'anxo', same node