Applications of the Sufficiency Principle in Acceleration of Neural Networks Trainig

One of the problems in AI tasks solving by neurocomputing methods is a considerable training time. This problem especially appears when it is needed to reach high quality in forecast reliability or pattern recognition. Some formalised ways for increasing of networks’ training speed without loosing of precision are proposed here. The offered approaches are based on the Sufficiency Principle, which is formal representation of the aim of a concrete task and conditions (limitations) of their solving [1]. This is development of the concept that includes the formal aims’ description to the context of such AI tasks as classification, pattern recognition, estimation etc.

A study of the regulatory role of retinoic acid receptor gamma in Zebrafish development

Retinoic acid (RA) is thought to signal through retinoic acid receptors (RARs), i.e. RARα, β, and γ to play important roles in embryonic development and tissue regeneration. In this thesis, the zebrafish (Danio rario) was used as a vertebrate model organism to examine the role of RARγ. Treatment of zebrafish embryos with a RARγ specific agonist reduced the axial length of developing embryos, associated with reduced somite number and loss of hoxb13a expression. There were no clear alterations in hoxc11a or myoD expression. Treatment with the RARγ agonist disrupted the formation of anterior structures of the head, the cranial bones and the anterior lateral line ganglia, associated with a loss of sox9 immunopositive cells in the same regions. Pectoral fin outgrowth was blocked by treatment with the RARγ agonist; however, this was not associated with loss of tbx5a immunopositive lateral plate cells and was reversed by wash out of the RARγ agonist or co-treatment with a RARγ antagonist. Regeneration of the transected caudal fin was also blocked by RARγ agonist treatment and restored by agonist washout or antagonist co-treatment; this phenotype was associated with a localised reduction in canonical Wnt signalling. Conversely, elevated canonical Wnt signalling after RARγ treatment was seen in other tissues, including ectopically in the notochord. Furthermore, some phenotypes seen in the RARγ treated embryos were present in mutant zebrafish embryos in which canonical Wnt signalling was constitutively increased. These data suggest that RARγ plays an essential role in maintaining neural crest and mesodermal stem/progenitor cells during normal embryonic development and tissue regeneration when the receptor is in its non-ligated state. In addition, this work has provided evidence that the activation status of RARγ may regulate hoxb13a gene expression and canonical Wnt signalling. Further research is required to confirm such novel regulatory roles.

Neural Networks in Bankruptcy Prediction - A Comparative Study on the Basis of the First Hungarian Bankruptcy Model

The article attempts to answer the question whether or not the latest bankruptcy prediction techniques are more reliable than traditional mathematical–statistical ones in Hungary. Simulation experiments carried out on the database of the first Hungarian bankruptcy prediction model clearly prove that bankruptcy models built using artificial neural networks have higher classification accuracy than models created in the 1990s based on discriminant analysis and logistic regression analysis. The article presents the main results, analyses the reasons for the differences and presents constructive proposals concerning the further development of Hungarian bankruptcy prediction.

Prenatal augmented auditory stimulation and visual system development: Effects of timing on intersensory organization

This study investigated the effects of augmented prenatal auditory stimulation on postnatal visual responsivity and neural organization in bobwhite quail (Colinus virginianus). I delivered conspecific embryonic vocalizations before, during, or after the development of a multisensory, midbrain audiovisual area, the optic tectum. Postnatal simultaneous choice tests revealed that hatchlings receiving augmented auditory stimulation during optic tectum development as embryos failed to show species-typical visual preferences for a conspecific maternal hen 72 hours after hatching. Auditory simultaneous choice tests showed no hatchlings had deficits in auditory function in any of the groups, indicating deficits were specific to visual function. ZENK protein expression confirmed differences in the amount of neural plasticity in multiple neuroanatomical regions of birds receiving stimulation during optic tecturn development, compared to unmanipulated birds. The results of these experiments support the notion that the timing of augmented prenatal auditory stimulation relative to optic tectum development can impact postnatal perceptual organization in an enduring way.^

Design strategies for an artificial neural network based algorithm for automatic incident detection on major arterial streets

Traffic incidents are non-recurring events that can cause a temporary reduction in roadway capacity. They have been recognized as a major contributor to traffic congestion on our nation’s highway systems. To alleviate their impacts on capacity, automatic incident detection (AID) has been applied as an incident management strategy to reduce the total incident duration. AID relies on an algorithm to identify the occurrence of incidents by analyzing real-time traffic data collected from surveillance detectors. Significant research has been performed to develop AID algorithms for incident detection on freeways; however, similar research on major arterial streets remains largely at the initial stage of development and testing. This dissertation research aims to identify design strategies for the deployment of an Artificial Neural Network (ANN) based AID algorithm for major arterial streets. A section of the US-1 corridor in Miami-Dade County, Florida was coded in the CORSIM microscopic simulation model to generate data for both model calibration and validation. To better capture the relationship between the traffic data and the corresponding incident status, Discrete Wavelet Transform (DWT) and data normalization were applied to the simulated data. Multiple ANN models were then developed for different detector configurations, historical data usage, and the selection of traffic flow parameters. To assess the performance of different design alternatives, the model outputs were compared based on both detection rate (DR) and false alarm rate (FAR). The results show that the best models were able to achieve a high DR of between 90% and 95%, a mean time to detect (MTTD) of 55-85 seconds, and a FAR below 4%. The results also show that a detector configuration including only the mid-block and upstream detectors performs almost as well as one that also includes a downstream detector. In addition, DWT was found to be able to improve model performance, and the use of historical data from previous time cycles improved the detection rate. Speed was found to have the most significant impact on the detection rate, while volume was found to contribute the least. The results from this research provide useful insights on the design of AID for arterial street applications.

The role of the transcription factor Ets1 in melanocyte development

Melanocytes, pigment-producing cells, derive from the neural crest (NC), a population of pluripotent cells that arise from the dorsal aspect of the neural tube during embryogenesis. Many genes required for melanocyte development were identified using mouse pigmentation mutants. The deletion of the transcription factor Ets1 in mice results in hypopigmentation; nevertheless, the function of Ets1 in melanocyte development is unknown. The goal of the present study was to establish the temporal requirement and role of Ets1 in murine melanocyte development. In the mouse, Ets1 is widely expressed in developing organs and tissues, including the NC. In the chick cranial NC, Ets1 is required for the expression of Sox10, a transcription factor critical for the development of melanocytes, enteric ganglia, and other NC derivatives. ^ Using a combination of immunofluorescence and cell survival assays Ets1 was found to be required between embryonic days 10 and 11, when it regulates NC cell and melanocyte precursor (melanoblast) survival. Given the requirement of Ets1 for Sox10 expression in the chick cranial NC, a potential interaction between these genes was investigated. Using genetic crosses, a synergistic genetic interaction between Ets1 and Sox10 in melanocyte development was found. Since Sox10 is essential for enteric ganglia formation, the importance of Ets1 on gut innervation was also examined. In mice, Ets1 deletion led to decreased gut innervation, which was exacerbated by Sox10 heterozygosity. ^ At the molecular level, Ets1 was found to activate a Sox10 enhancer critical for Sox10 expression in melanoblasts. Furthermore, mutating Ets1 at a site I characterized in the spontaneous variable spotting mouse pigmentation mutant, led to a 2-fold decrease in enhancer activation. Overexpression and knockdown of Ets1 did not affect Sox10 expression; nonetheless, Ets1 knockdown led to a 6-fold upregulation of the transcription factor Sox9, a gene required for melanocyte and chondrocyte development, but which impairs melanocyte development when its expression is prolonged. Together, these results suggest that Ets1 is required early during melanocyte development for NC cell and melanoblast survival, possibly acting upstream of Sox10. The transcription factor Ets1 may also act indirectly in melanocyte fate specification by repressing Sox9 expression, and consequently cartilage fate.^

Development of New Structural Health Monitoring Techniques

During the past two decades, many researchers have developed methods for the detection of structural defects at the early stages to operate the aerospace vehicles safely and to reduce the operating costs. The Surface Response to Excitation (SuRE) method is one of these approaches developed at FIU to reduce the cost and size of the equipment. The SuRE method excites the surface at a series of frequencies and monitors the propagation characteristics of the generated waves. The amplitude of the waves reaching to any point on the surface varies with frequency; however, it remains consistent as long as the integrity and strain distribution on the part is consistent. These spectral characteristics change when cracks develop or the strain distribution changes. The SHM methods may be used for many applications, from the detection of loose screws to the monitoring of manufacturing operations. A scanning laser vibrometer was used in this study to investigate the characteristics of the spectral changes at different points on the parts. The study started with detecting a load on a plate and estimating its location. The modifications on the part with manufacturing operations were detected and the Part-Based Manufacturing Process Performance Monitoring (PbPPM) method was developed. Hardware was prepared to demonstrate the feasibility of the proposed methods in real time. Using low-cost piezoelectric elements and the non-contact scanning laser vibrometer successfully, the data was collected for the SuRE and PbPPM methods. Locational force, loose bolts and material loss could be easily detected by comparing the spectral characteristics of the arriving waves. On-line methods used fast computational methods for estimating the spectrum and detecting the changing operational conditions from sum of the squares of the variations. Neural networks classified the spectrums when the desktop – DSP combination was used. The results demonstrated the feasibility of the SuRE and PbPPM methods.

Development of New Structural Health Monitoring Techniques

During the past two decades, many researchers have developed methods for the detection of structural defects at the early stages to operate the aerospace vehicles safely and to reduce the operating costs. The Surface Response to Excitation (SuRE) method is one of these approaches developed at FIU to reduce the cost and size of the equipment. The SuRE method excites the surface at a series of frequencies and monitors the propagation characteristics of the generated waves. The amplitude of the waves reaching to any point on the surface varies with frequency; however, it remains consistent as long as the integrity and strain distribution on the part is consistent. These spectral characteristics change when cracks develop or the strain distribution changes. The SHM methods may be used for many applications, from the detection of loose screws to the monitoring of manufacturing operations. A scanning laser vibrometer was used in this study to investigate the characteristics of the spectral changes at different points on the parts. The study started with detecting a load on a plate and estimating its location. The modifications on the part with manufacturing operations were detected and the Part-Based Manufacturing Process Performance Monitoring (PbPPM) method was developed. Hardware was prepared to demonstrate the feasibility of the proposed methods in real time. Using low-cost piezoelectric elements and the non-contact scanning laser vibrometer successfully, the data was collected for the SuRE and PbPPM methods. Locational force, loose bolts and material loss could be easily detected by comparing the spectral characteristics of the arriving waves. On-line methods used fast computational methods for estimating the spectrum and detecting the changing operational conditions from sum of the squares of the variations. Neural networks classified the spectrums when the desktop – DSP combination was used. The results demonstrated the feasibility of the SuRE and PbPPM methods.

Design Strategies for an Artificial Neural Network Based Algorithm for Automatic Incident Detection on Major Arterial Streets

Traffic incidents are non-recurring events that can cause a temporary reduction in roadway capacity. They have been recognized as a major contributor to traffic congestion on our national highway systems. To alleviate their impacts on capacity, automatic incident detection (AID) has been applied as an incident management strategy to reduce the total incident duration. AID relies on an algorithm to identify the occurrence of incidents by analyzing real-time traffic data collected from surveillance detectors. Significant research has been performed to develop AID algorithms for incident detection on freeways; however, similar research on major arterial streets remains largely at the initial stage of development and testing. This dissertation research aims to identify design strategies for the deployment of an Artificial Neural Network (ANN) based AID algorithm for major arterial streets. A section of the US-1 corridor in Miami-Dade County, Florida was coded in the CORSIM microscopic simulation model to generate data for both model calibration and validation. To better capture the relationship between the traffic data and the corresponding incident status, Discrete Wavelet Transform (DWT) and data normalization were applied to the simulated data. Multiple ANN models were then developed for different detector configurations, historical data usage, and the selection of traffic flow parameters. To assess the performance of different design alternatives, the model outputs were compared based on both detection rate (DR) and false alarm rate (FAR). The results show that the best models were able to achieve a high DR of between 90% and 95%, a mean time to detect (MTTD) of 55-85 seconds, and a FAR below 4%. The results also show that a detector configuration including only the mid-block and upstream detectors performs almost as well as one that also includes a downstream detector. In addition, DWT was found to be able to improve model performance, and the use of historical data from previous time cycles improved the detection rate. Speed was found to have the most significant impact on the detection rate, while volume was found to contribute the least. The results from this research provide useful insights on the design of AID for arterial street applications.

Cascade artificial neural networks technique for solving ellipsometry problems

Ellipsometry is a well known optical technique used for the characterization of reflective surfaces in study and films between two media. It is based on measuring the change in the state of polarization that occurs as a beam of polarized light is reflected from or transmitted through the film. Measuring this change can be used to calculate parameters of a single layer film such as the thickness and the refractive index. However, extracting these parameters of interest requires significant numerical processing due to the noninvertible equations. Typically, this is done using least squares solving methods which are slow and adversely affected by local minima in the solvable surface. This thesis describes the development and implementation of a new technique using only Artificial Neural Networks (ANN) to calculate thin film parameters. The new method offers a speed in the orders of magnitude faster than preceding methods and convergence to local minima is completely eliminated.

Implementação de uma arquitetura fuzzy neural em hardware com treinamento online

Computational Intelligence Methods have been expanding to industrial applications motivated by their ability to solve problems in engineering. Therefore, the embedded systems follow the same idea of using computational intelligence tools embedded on machines. There are several works in the area of embedded systems and intelligent systems. However, there are a few papers that have joined both areas. The aim of this study was to implement an adaptive fuzzy neural hardware with online training embedded on Field Programmable Gate Array – FPGA. The system adaptation can occur during the execution of a given application, aiming online performance improvement. The proposed system architecture is modular, allowing different configurations of fuzzy neural network topologies with online training. The proposed system was applied to: mathematical function interpolation, pattern classification and selfcompensation of industrial sensors. The proposed system achieves satisfactory performance in both tasks. The experiments results shows the advantages and disadvantages of online training in hardware when performed in parallel and sequentially ways. The sequentially training method provides economy in FPGA area, however, increases the complexity of architecture actions. The parallel training method achieves high performance and reduced processing time, the pipeline technique is used to increase the proposed architecture performance. The study development was based on available tools for FPGA circuits.

A experiência do usuário idoso no design de jogo digital para a reabilitação neural de pacientes com quadro de acidente vascular cerebral (AVC)

Stroke is nowadays one of the main causes of death in Brazil and worldwide. During the rehabilitation process, patients undergo physioterapic exercises based on repetition, which may cause them to feel little progress is being made. Focusing on themes from the areas of Human-Computer Interaction and Motor Imagery, the present work describes the development of a digital game concept aimed at motor rehabilitation to the neural rehabilitation of patients who have suffered a stroke in a playful and engaging way. The research hypothesizes that an interactive digital game based on Motor Imagery contributes to patients' raised commitment in the stroke sequel rehabilitation process. The research process entailed the investigation of 10 subjects who live with sequels caused by stroke - it was further established that subjects were over 60 years old. Using as foundation an initial survey regarding target-users' specificities, where an investigation on subjectrelated aspects was carried out through Focus Group (n=9) and Contextual Analysis (n=3), having as subjects elderly individuals, a list with the necessary requirements for the conceptualization of a digital game was fleshed out. The initial survey also enabled the establishment of preliminary interactions for the formulation of game prototypes. At first, low-resolution prototypes were used, with two distinct interaction models for the game - one with a direct approach to the Motor Imagery concept, and another using a narrative with characters and scene settings. The goal was to verify participants' receptivity regarding the addition of playful activities into game dynamics. Prototypes were analyzed while being used by five patients, through the Cooperative Evaluation technique. The tests indicated a preference for option with elements in a playful narrative. Based on these results high fidelity prototypes were created, where concepts close to the game's final version were elaborated. The High Fidelity prototype was also evaluated with four patients through the Cooperative Evaluation technique. It was concluded that elderly individuals and patients were receptive to the idea of a digital game for the rehabilitation from sequels caused by stroke; that, for the success of devices aimed at these cohorts, their contexts, needs and expectations must be respected above all; and that user-centered design is an essential approach in that regard.

The Role of TET Proteins in the Epigenetic Regulation of Neural Gene Expression and Behavior

Understanding how genes affect behavior is critical to develop precise therapies for human behavioral disorders. The ability to investigate the relationship between genes and behavior has been greatly advanced over the last few decades due to progress in gene-targeting technology. Recently, the Tet gene family was discovered and implicated in epigenetic modification of DNA methylation by converting 5-methylcytosine to 5-hydroxymethylcytosine (5hmC). 5hmC and its catalysts, the TET proteins, are highly abundant in the postnatal brain but with unclear functions. To investigate their neural functions, we generated new lines of Tet1 and Tet3 mutant mice using a gene targeting approach. We designed both mutations to cause a frameshift by deleting the largest coding exon of Tet1 (Tet1Δe4) and the catalytic domain of Tet3 (Tet3Δe7-9). As Tet1 is also highly expressed in embryonic stem cells (ESCs), we generated Tet1 homozygous deleted ESCs through sequential targeting to compare the function of Tet1 in the brain to its role in ESCs. To test our hypothesis that TET proteins epigenetically regulate transcription of key neural genes important for normal brain function, we examined transcriptional and epigenetic differences in the Tet1Δe4 mouse brain. The oxytocin receptor (OXTR), a neural gene implicated in social behaviors, is suggested to be epigenetically regulated by an unknown mechanism. Interestingly, several human studies have found associations between OXTR DNA hypermethylation and a wide spectrum of behavioral traits and neuropsychiatric disorders including autism spectrum disorders. Here we report the first evidence for an epigenetic mechanism of Oxtr transcription as expression of Oxtr is reduced in the brains of Tet1Δe4-/- mice. Likewise, the CpG island overlapping the promoter of Oxtr is hypermethylated during early embryonic development and persists into adulthood. We also discovered altered histone modifications at the hypermethylated regions, indicating the loss of TET1 has broad effects on the chromatin structure at Oxtr. Unexpectedly, we discovered an array of novel mRNA isoforms of Oxtr that are selectively reduced in Tet1Δe4-/- mice. Additionally, Tet1Δe4-/- mice display increased agonistic behaviors and impaired maternal care and short-term memory. Our findings support a novel role for TET1 in regulating Oxtr expression by preventing DNA hypermethylation and implicate TET1 in social behaviors, offering novel insight into Oxtr epigenetic regulation and its role in neuropsychiatric disorders.

Neural Mechanisms of Young Adult Sexual Decision-Making and Risk Behavior

Sexual risk behavior among young adults is a serious public health concern; 50% will contract a sexually transmitted infection (STI) before the age of 25. The current study collected self-report personality and sexual history data, as well as neuroimaging, experimental behavioral (e.g., real-time hypothetical sexual decision making data), and self-report sexual arousal data from 120 heterosexual young adults ages 18-26. In addition, longitudinal changes in self-reported sexual behavior were collected from a subset (n = 70) of the participants. The primary aims of the study were (1) to predict differences in self-report sexual behavior and hypothetical sexual decision-making (in response to sexually explicit audio-visual cues) as a function of ventral striatum (VS) and amygdala activity, (2) test whether the association between sexual behavior/decision-making and brain function is moderated by gender, self-reported sexual arousal, and/or trait-level personality factors (i.e., self-control, impulsivity, and sensation seeking) and (3) to examine how the main effects of neural function and interaction effects predict sexual risk behavior over time. Our hypotheses were mostly supported across the sexual behavior and decision-making outcome variables, such that neural risk phenotypes (heightened reward-related ventral striatum activity coupled with decreased threat-related amygdala activity) were associated with greater lifetime sexual partners at baseline measured and over time (longitudinal analyses). Impulsivity moderated the relationship between neural function and self-reported number of sexual partners at baseline and follow up measures, as well as experimental condom use decision-making. Sexual arousal and sensation seeking moderated the relationship between neural function and baseline and follow up self-reports of number of sexual partners. Finally, unique gender differences were observed in the relationship between threat and reward-related neural reactivity and self-reported sexual risk behavior. The results of this study provide initial evidence for the potential role for neurobiological approaches to understanding sexual decision-making and risk behavior. With continued research, establishing biomarkers for sexual risk behavior could help inform the development of novel and more effective individually tailored sexual health prevention and intervention efforts.

Individual Differences in the Neural Response to Personal Goal Pursuit Success and Failure: A Developmental Analysis

Regulatory focus theory (RFT) proposes two different social-cognitive motivational systems for goal pursuit: a promotion system, which is organized around strategic approach behaviors and "making good things happen," and a prevention system, which is organized around strategic avoidance and "keeping bad things from happening." The promotion and prevention systems have been extensively studied in behavioral paradigms, and RFT posits that prolonged perceived failure to make progress in pursuing promotion or prevention goals can lead to ineffective goal pursuit and chronic distress (Higgins, 1997).

Research has begun to focus on uncovering the neural correlates of the promotion and prevention systems in an attempt to differentiate them at the neurobiological level. Preliminary research suggests that the promotion and prevention systems have both distinct and overlapping neural correlates (Eddington, Dolcos, Cabeza, Krishnan, & Strauman, 2007; Strauman et al., 2013). However, little research has examined how individual differences in regulatory focus develop and manifest. The development of individual differences in regulatory focus is particularly salient during adolescence, a crucial topic to explore given the dramatic neurodevelopmental and psychosocial changes that take place during this time, especially with regard to self-regulatory abilities. A number of questions remain unexplored, including the potential for goal-related neural activation to be modulated by (a) perceived proximity to goal attainment, (b) individual differences in regulatory orientation, specifically general beliefs about one's success or failure in attaining the two kinds of goals, (c) age, with a particular focus on adolescence, and (d) homozygosity for the Met allele of the catechol-O-methyltransferase (COMT) Val158Met polymorphism, a naturally occurring genotype which has been shown to impact prefrontal cortex activation patterns associated with goal pursuit behaviors.

This study explored the neural correlates of the promotion and prevention systems through the use of a priming paradigm involving rapid, brief, masked presentation of individually selected promotion and prevention goals to each participant while being scanned. The goals used as priming stimuli varied with regard to whether participants reported that they were close to or far away from achieving them (i.e. a "match" versus a "mismatch" representing perceived success or failure in personal goal pursuit). The study also assessed participants' overall beliefs regarding their relative success or failure in attaining promotion and prevention goals, and all participants were genotyped for the COMT Val158Met polymorphism.

A number of significant findings emerged. Both promotion and prevention priming were associated with activation in regions associated with self-referential cognition, including the left medial prefrontal cortex, cuneus, and lingual gyrus. Promotion and prevention priming were also associated with distinct patterns of neural activation; specifically, left middle temporal gyrus activation was found to be significantly greater during prevention priming. Activation in response to promotion and prevention goals was found to be modulated by self-reports of both perceived proximity to goal achievement and goal orientation. Age also had a significant effect on activation, such that activation in response to goal priming became more robust in the prefrontal cortex and in default mode network regions as a function of increasing age. Finally, COMT genotype also modulated the neural response to goal priming both alone and through interactions with regulatory focus and age. Overall, these findings provide further clarification of the neural underpinnings of the promotion and prevention systems as well as provide information about the role of development and individual differences at the personality and genetic level on activity in these neural systems.