Ali Minai | University of Cincinnati (original) (raw)

Papers by Ali Minai

Springer eBooks, 2006

The field of collective robotics exploits the use of technologically simple robots, deployed in l... more The field of collective robotics exploits the use of technologically simple robots, deployed in large numbers, to collectively perform complex tasks. Here, the real challenge is in developing simple algorithms which the robots can execute autonomously, based on data from their vicinity, to achieve global behavior. One such global task that many researchers (including the authors) have developed algorithms for is the formation of a circle. In this chapter, we discuss how the circle formation algorithm can be used as a means for solving other formation and organization problems in multi-robot systems. The idea behind this approach is that circle formation can be seen as a method of organizing the robots in a regular formation which can then be exploited. This involves identifying specific robots to achieve different geometric patterns like lines, semicircles, triangles and squares, and dividing the robots into subgroups, which can then perform specific group-wise tasks. The algorithms that achieve these tasks are entirely distributed and do not need any manual intervention. The results from these studies are presented here. 2 Collective Robotics Collective robotics is the study of groups of relatively simple robots that are capable of moving around and accomplishing tasks collaboratively. The number of robots used can vary from tens to tens of thousands, based on the application. The goal is to use robots that are as simple-and therefore, as cheap-as possible, deploy them in large numbers and coordinate them to achieve complex tasks. Groups of robots create a very complex coordination and control problem because of the extremely large configuration space created by numerous interacting agents. Centralized control methods are not feasible in this situation,

Frontiers in Neuroscience, Aug 21, 2017

The whole-brain functional connectivity (FC) pattern obtained from resting-state functional magne... more The whole-brain functional connectivity (FC) pattern obtained from resting-state functional magnetic resonance imaging data are commonly applied to study neuropsychiatric conditions such as autism spectrum disorder (ASD) by using different machine learning models. Recent studies indicate that both hyper-and hypoaberrant ASD-associated FCs were widely distributed throughout the entire brain rather than only in some specific brain regions. Deep neural networks (DNN) with multiple hidden layers have shown the ability to systematically extract lower-tohigher level information from high dimensional data across a series of neural hidden layers, significantly improving classification accuracy for such data. In this study, a DNN with a novel feature selection method (DNN-FS) is developed for the high dimensional whole-brain resting-state FC pattern classification of ASD patients vs. typical development (TD) controls. The feature selection method is able to help the DNN generate low dimensional high-quality representations of the whole-brain FC patterns by selecting features with high discriminating power from multiple trained sparse auto-encoders. For the comparison, a DNN without the feature selection method (DNN-woFS) is developed, and both of them are tested with different architectures (i.e., with different numbers of hidden layers/nodes). Results show that the best classification accuracy of 86.36% is generated by the DNN-FS approach with 3 hidden layers and 150 hidden nodes (3/150). Remarkably, DNN-FS outperforms DNN-woFS for all architectures studied. The most significant accuracy improvement was 9.09% with the 3/150 architecture. The method also outperforms other feature selection methods, e.g., two sample t-test and elastic net. In addition to improving the classification accuracy, a Fisher's score-based biomarker identification method based on the DNN is also developed, and used to identify 32 FCs related to ASD. These FCs come from or cross different pre-defined brain networks including the default-mode, cingulo-opercular, frontal-parietal, and cerebellum. Thirteen of them are statically significant between ASD and TD groups (two sample t-test p < 0.05) while 19 of them are Guo et al. A Model for Diagnosing Autism not. The relationship between the statically significant FCs and the corresponding ASD behavior symptoms is discussed based on the literature and clinician's expert knowledge. Meanwhile, the potential reason of obtaining 19 FCs which are not statistically significant is also provided.

MAICS, 2012

The media today bombards us with massive amounts of news about events ranging from the mundane to... more The media today bombards us with massive amounts of news about events ranging from the mundane to the memorable. This growing cacophony places an ever greater premium on being able to identify significant stories and to capture their salient features. In this paper, we consider the problem of mining on-line news over a certain period to identify what the major stories were in that time. Major stories are defined as those that were widely reported, persisted for significant duration or had a lasting influence on subsequent stories. Recently, some statistical methods have been proposed to extract important information from large corpora, but most of them do not consider the full richness of language or variations in its use across multiple reporting sources. We propose a method to extract major stories from large news corpora using a combination Latent Dirichlet Allocation and with n-gram analysis.

While recurrent neural networks can store pattern sequences through incremental learning, there c... more While recurrent neural networks can store pattern sequences through incremental learning, there could be a trade-off between network capacity and the speed of learning. The brain may solve this problem by using a two-stage system: a compact, low-capacity subsystem for rapid temporary storage of a few sequences, and a larger, high-capacity, slow learning subsystem for long-term storage of all sequences. In this study, we evaluate the ability of sparsely connected networks to learn pattern sequences in a single exposure using very high learning rates. The key factor is the amount of recurrent inhibition in the system. Our results indicate that post-synaptic gating in the learning rule enhances the rapid learning ability of networks. We also suggest how such rapid learning networks could transfer their memories to long-term storage.

Creativity is an important attribute of the human mind, and shows itself in all aspects of its fu... more Creativity is an important attribute of the human mind, and shows itself in all aspects of its function. However, its neural basis remains poorly understood. In this paper, we explore two issues with regard to creativity in the semantic domain: 1) What neural mechanism enable the brain to construct context-specific semantic spaces to facilitate the generation of relevant ideas? and 2) Can these mechanisms support greater creativity simply by exploring unusual semantic spaces? We use a variant of our previously developed neural model of ideation to show that a dynamical modular neural system can, indeed, learn to configure context-appropriate semantic domains based on experience, and that exploratory dynamics within this system can lead to the unmasking of novel emergent ideas.

Cognitive Computation, Mar 6, 2023

IEEE Transactions on Cognitive and Developmental Systems

Central Pattern Generators (CPGs) form the neural basis of the observed rhythmic behaviors for lo... more Central Pattern Generators (CPGs) form the neural basis of the observed rhythmic behaviors for locomotion in legged animals. The CPG dynamics organized into networks allow the emergence of complex locomotor behaviors. In this work, we take this inspiration for developing walking behaviors in multi-legged robots. We present novel DeepCPG policies that embed CPGs as a layer in a larger neural network and facilitate end-to-end learning of locomotion behaviors in deep reinforcement learning (DRL) setup. We demonstrate the effectiveness of this approach on physics engine-based insectoid robots. We show that, compared to traditional approaches, DeepCPG policies allow sample-efficient end-to-end learning of effective locomotion strategies even in the case of high-dimensional sensor spaces (vision). We scale the DeepCPG policies using a modular robot configuration and multiagent DRL. Our results suggest that gradual complexification with embedded priors of these policies in a modular fashion could achieve non-trivial sensor and motor integration on a robot platform. These results also indicate the efficacy of bootstrapping more complex intelligent systems from simpler ones based on biological principles. Finally, we present the experimental results for a proof-of-concept insectoid robot system for which DeepCPG learned policies initially using the simulation engine and these were afterwards transferred to real-world robots without any additional fine-tuning.

Creativity Research Journal

2022 International Joint Conference on Neural Networks (IJCNN)

2022 International Joint Conference on Neural Networks (IJCNN)

2022 International Joint Conference on Neural Networks (IJCNN)

Creativity and Innovation, 2021

2018 International Joint Conference on Neural Networks (IJCNN), 2018

Semantic analysis of text corpora is of broad utility, including for data from conversations, on-... more Semantic analysis of text corpora is of broad utility, including for data from conversations, on-line chats, brainstorming sessions, comments on blogs, etc. – all of which are potentially interesting sources of information and ideas. In the present paper, we look at data from a large group brainstorming experiment that generated thousands of mostly brief statements. The ultimate goal is to detect which statements are semantically atypical within the overall corpus. In contexts such as spam detection or detection of on-line intrusions, autoencoders have been used successfully to separate typical from atypical data, and we consider this approach in the present paper. Texts are embedded in a semantic space obtained through topic analysis, and an autoencoder network is used to reconstruct each embedded text. The results show that, while difficulty of reconstruction is related to quantitative measures of atypicality in the embedding vector space, it is not well correlated with novelty assignments made by a human rater. However, this is not the case when the data is first clustered in the embedding space: The reconstruction error for each data cluster indicates that some clusters represent more novel data than others, and that the inverse size of the cluster and the mean reconstruction error of the texts in the cluster capture this well. In particular, autoencoders that enforce dimensionality reduction improve discrimination. The results also show that, in the reconstruction process, the autoencoder implicitly discovers the same clusters in the data that are discovered explicitly by an optimized k-means approach.

Unifying Themes in Complex Systems IX, 2018

Thinking is a self-organized dynamical process and, as such, interesting to characterize. However... more Thinking is a self-organized dynamical process and, as such, interesting to characterize. However, direct, real-time access to thought at the semantic level is still very limited. The best that can be done is to look at spoken or written expression. The question we address in this research is the following: Is there a characteristic pitch of thought? To begin answering this complex question, we look at text documents from several large corpora at the sentence level – i.e., using sentences as the units of meaning – and considering each document to be the result of a random process in semantic space. Given a large corpus of multi-sentence documents, we build a lexical association network representing associations between words in the corpus. This network is used to induce a semantic similarity metric between sentences, and each document is segmented into multi-sentence semantically coherent blocks (SCBs) with occasional connecting text between the blocks. Based on this segmentation, the process of document generation is modeled as a sticky Markov chain at the sentence level. We show that most documents across all the corpora are sequences of blocks with a very consistent mean length of 6.4 sentences across the corpora. This consistency suggests that a value of 6-7 sentences may be the typical mean length for single coherent thoughts in texts. We have also described several ways of visualizing the semantic structure of documents in space and time.

2019 International Joint Conference on Neural Networks (IJCNN), 2019

Politics is an area of broad interest to policy-makers, researchers, and the general public. The ... more Politics is an area of broad interest to policy-makers, researchers, and the general public. The recent explosion in the availability of electronic data and advances in data analysis methods – including techniques from machine learning – have led to many studies attempting to extract political insight from this data. Speeches in the U.S. Congress represent an exceptionally rich dataset for this purpose, and these have been analyzed by many researchers using statistical and machine learning methods. In this paper, we analyze House of Representatives floor speeches from the 1981 - 2016 period, with the goal of inferring the partisan affiliation of the speakers from their use of words. Previous studies with sophisticated machine learning models has suggested that this task can be accomplished with an accuracy in the 55 to 80% range, depending on the year. In this paper, we show that, in fact, very comparable results can be obtained using a much simpler linear classifier in word space, indicating that the use of words in partisan ways is not particularly complicated. Our results also confirm that, over the period of study, it has become steadily easier to infer partisan affiliation from political speeches in the United States. Finally, we make some observations about specific terms that Republicans and Democrats have favored over the years in service of partisan expression.

Bioinspiration & biomimetics, Nov 11, 2017

Biomimetic robots have gained attention recently for various applications ranging from resource h... more Biomimetic robots have gained attention recently for various applications ranging from resource hunting to search and rescue operations during disasters. Biological species are known to intuitively learn from the environment, gather and process data, and make appropriate decisions. Such sophisticated computing capabilities in robots are difficult to achieve, especially if done in real-time with ultra- low energy consumption. Here, we present a novel memristive device based learning architecture for robots. Two terminal memristive devices with resistive switching of oxide layer are modeled in a crossbar array to develop a neuromorphic platform that can impart active real-time learning capabilities in a robot. This approach is validated by navigating a robot vehicle in an unknown environment with randomly placed obstacles. Further, the proposed scheme is compared with Reinforcement Learning based algorithms using local and global knowledge of the environment. The simulation as well as...

Unifying Themes in Complex Systems IX, 2018

Preventing suicide requires early identification of suicidal ideation. In this research, we propo... more Preventing suicide requires early identification of suicidal ideation. In this research, we propose an approach to evaluate whether an individual’s statements during a clinical interview can be classified as coming from a suicidal or non-suicidal mindset. To do so, we compare the statements with distinct lexical associative networks constructed from corpora of suicidal and control texts. Each node in these networks is a word, and the weight of the edge between every word pair indicates how strongly the words are associated in that corpus. Several metrics of association are evaluated in this work. Preliminary results show good classification performance with above 75% accuracy on novel test data.

Springer eBooks, Feb 24, 2012

Springer eBooks, 2015

Most of the earliest work in both experimental and theoretical/computational systems neuroscience... more Most of the earliest work in both experimental and theoretical/computational systems neuroscience focused on sensory systems and the peripheral (spinal) control of movement. However, over the last three decades, attention has turned increasingly towards "higher" functions related to cognition, decision-making and voluntary behavior. Experimental studies have shown that specific brain structuresthe prefrontal cortex, the premotor and motor cortices, and the basal gangliaplay a central role in these functions, as does the dopamine system that signals reward during reinforcement learning. Because of the complexity of the issues involved and the difficulty of direct observation in deep brain structures, computational modeling has been crucial in elucidating the neural basis of cognitive control, decision making, reinforcement learning, working memory and motor control. The resulting computational models are also very useful in engineering domains such as robotics, intelligents agents and adaptive control. While it is impossible to encompass the totality of such modeling work, this chapter provides an overview of significant efforts in the last 20 years. It also outlines many of the theoretical issues underlying this work, and discusses significant experimental results that motivated the computational models.

Springer eBooks, 2006

The field of collective robotics exploits the use of technologically simple robots, deployed in l... more The field of collective robotics exploits the use of technologically simple robots, deployed in large numbers, to collectively perform complex tasks. Here, the real challenge is in developing simple algorithms which the robots can execute autonomously, based on data from their vicinity, to achieve global behavior. One such global task that many researchers (including the authors) have developed algorithms for is the formation of a circle. In this chapter, we discuss how the circle formation algorithm can be used as a means for solving other formation and organization problems in multi-robot systems. The idea behind this approach is that circle formation can be seen as a method of organizing the robots in a regular formation which can then be exploited. This involves identifying specific robots to achieve different geometric patterns like lines, semicircles, triangles and squares, and dividing the robots into subgroups, which can then perform specific group-wise tasks. The algorithms that achieve these tasks are entirely distributed and do not need any manual intervention. The results from these studies are presented here. 2 Collective Robotics Collective robotics is the study of groups of relatively simple robots that are capable of moving around and accomplishing tasks collaboratively. The number of robots used can vary from tens to tens of thousands, based on the application. The goal is to use robots that are as simple-and therefore, as cheap-as possible, deploy them in large numbers and coordinate them to achieve complex tasks. Groups of robots create a very complex coordination and control problem because of the extremely large configuration space created by numerous interacting agents. Centralized control methods are not feasible in this situation,

Frontiers in Neuroscience, Aug 21, 2017

The whole-brain functional connectivity (FC) pattern obtained from resting-state functional magne... more The whole-brain functional connectivity (FC) pattern obtained from resting-state functional magnetic resonance imaging data are commonly applied to study neuropsychiatric conditions such as autism spectrum disorder (ASD) by using different machine learning models. Recent studies indicate that both hyper-and hypoaberrant ASD-associated FCs were widely distributed throughout the entire brain rather than only in some specific brain regions. Deep neural networks (DNN) with multiple hidden layers have shown the ability to systematically extract lower-tohigher level information from high dimensional data across a series of neural hidden layers, significantly improving classification accuracy for such data. In this study, a DNN with a novel feature selection method (DNN-FS) is developed for the high dimensional whole-brain resting-state FC pattern classification of ASD patients vs. typical development (TD) controls. The feature selection method is able to help the DNN generate low dimensional high-quality representations of the whole-brain FC patterns by selecting features with high discriminating power from multiple trained sparse auto-encoders. For the comparison, a DNN without the feature selection method (DNN-woFS) is developed, and both of them are tested with different architectures (i.e., with different numbers of hidden layers/nodes). Results show that the best classification accuracy of 86.36% is generated by the DNN-FS approach with 3 hidden layers and 150 hidden nodes (3/150). Remarkably, DNN-FS outperforms DNN-woFS for all architectures studied. The most significant accuracy improvement was 9.09% with the 3/150 architecture. The method also outperforms other feature selection methods, e.g., two sample t-test and elastic net. In addition to improving the classification accuracy, a Fisher's score-based biomarker identification method based on the DNN is also developed, and used to identify 32 FCs related to ASD. These FCs come from or cross different pre-defined brain networks including the default-mode, cingulo-opercular, frontal-parietal, and cerebellum. Thirteen of them are statically significant between ASD and TD groups (two sample t-test p < 0.05) while 19 of them are Guo et al. A Model for Diagnosing Autism not. The relationship between the statically significant FCs and the corresponding ASD behavior symptoms is discussed based on the literature and clinician's expert knowledge. Meanwhile, the potential reason of obtaining 19 FCs which are not statistically significant is also provided.

MAICS, 2012

The media today bombards us with massive amounts of news about events ranging from the mundane to... more The media today bombards us with massive amounts of news about events ranging from the mundane to the memorable. This growing cacophony places an ever greater premium on being able to identify significant stories and to capture their salient features. In this paper, we consider the problem of mining on-line news over a certain period to identify what the major stories were in that time. Major stories are defined as those that were widely reported, persisted for significant duration or had a lasting influence on subsequent stories. Recently, some statistical methods have been proposed to extract important information from large corpora, but most of them do not consider the full richness of language or variations in its use across multiple reporting sources. We propose a method to extract major stories from large news corpora using a combination Latent Dirichlet Allocation and with n-gram analysis.

While recurrent neural networks can store pattern sequences through incremental learning, there c... more While recurrent neural networks can store pattern sequences through incremental learning, there could be a trade-off between network capacity and the speed of learning. The brain may solve this problem by using a two-stage system: a compact, low-capacity subsystem for rapid temporary storage of a few sequences, and a larger, high-capacity, slow learning subsystem for long-term storage of all sequences. In this study, we evaluate the ability of sparsely connected networks to learn pattern sequences in a single exposure using very high learning rates. The key factor is the amount of recurrent inhibition in the system. Our results indicate that post-synaptic gating in the learning rule enhances the rapid learning ability of networks. We also suggest how such rapid learning networks could transfer their memories to long-term storage.

Creativity is an important attribute of the human mind, and shows itself in all aspects of its fu... more Creativity is an important attribute of the human mind, and shows itself in all aspects of its function. However, its neural basis remains poorly understood. In this paper, we explore two issues with regard to creativity in the semantic domain: 1) What neural mechanism enable the brain to construct context-specific semantic spaces to facilitate the generation of relevant ideas? and 2) Can these mechanisms support greater creativity simply by exploring unusual semantic spaces? We use a variant of our previously developed neural model of ideation to show that a dynamical modular neural system can, indeed, learn to configure context-appropriate semantic domains based on experience, and that exploratory dynamics within this system can lead to the unmasking of novel emergent ideas.

Cognitive Computation, Mar 6, 2023

IEEE Transactions on Cognitive and Developmental Systems

Central Pattern Generators (CPGs) form the neural basis of the observed rhythmic behaviors for lo... more Central Pattern Generators (CPGs) form the neural basis of the observed rhythmic behaviors for locomotion in legged animals. The CPG dynamics organized into networks allow the emergence of complex locomotor behaviors. In this work, we take this inspiration for developing walking behaviors in multi-legged robots. We present novel DeepCPG policies that embed CPGs as a layer in a larger neural network and facilitate end-to-end learning of locomotion behaviors in deep reinforcement learning (DRL) setup. We demonstrate the effectiveness of this approach on physics engine-based insectoid robots. We show that, compared to traditional approaches, DeepCPG policies allow sample-efficient end-to-end learning of effective locomotion strategies even in the case of high-dimensional sensor spaces (vision). We scale the DeepCPG policies using a modular robot configuration and multiagent DRL. Our results suggest that gradual complexification with embedded priors of these policies in a modular fashion could achieve non-trivial sensor and motor integration on a robot platform. These results also indicate the efficacy of bootstrapping more complex intelligent systems from simpler ones based on biological principles. Finally, we present the experimental results for a proof-of-concept insectoid robot system for which DeepCPG learned policies initially using the simulation engine and these were afterwards transferred to real-world robots without any additional fine-tuning.

Creativity Research Journal

2022 International Joint Conference on Neural Networks (IJCNN)

2022 International Joint Conference on Neural Networks (IJCNN)

2022 International Joint Conference on Neural Networks (IJCNN)

Creativity and Innovation, 2021

2018 International Joint Conference on Neural Networks (IJCNN), 2018

Semantic analysis of text corpora is of broad utility, including for data from conversations, on-... more Semantic analysis of text corpora is of broad utility, including for data from conversations, on-line chats, brainstorming sessions, comments on blogs, etc. – all of which are potentially interesting sources of information and ideas. In the present paper, we look at data from a large group brainstorming experiment that generated thousands of mostly brief statements. The ultimate goal is to detect which statements are semantically atypical within the overall corpus. In contexts such as spam detection or detection of on-line intrusions, autoencoders have been used successfully to separate typical from atypical data, and we consider this approach in the present paper. Texts are embedded in a semantic space obtained through topic analysis, and an autoencoder network is used to reconstruct each embedded text. The results show that, while difficulty of reconstruction is related to quantitative measures of atypicality in the embedding vector space, it is not well correlated with novelty assignments made by a human rater. However, this is not the case when the data is first clustered in the embedding space: The reconstruction error for each data cluster indicates that some clusters represent more novel data than others, and that the inverse size of the cluster and the mean reconstruction error of the texts in the cluster capture this well. In particular, autoencoders that enforce dimensionality reduction improve discrimination. The results also show that, in the reconstruction process, the autoencoder implicitly discovers the same clusters in the data that are discovered explicitly by an optimized k-means approach.

Unifying Themes in Complex Systems IX, 2018

Thinking is a self-organized dynamical process and, as such, interesting to characterize. However... more Thinking is a self-organized dynamical process and, as such, interesting to characterize. However, direct, real-time access to thought at the semantic level is still very limited. The best that can be done is to look at spoken or written expression. The question we address in this research is the following: Is there a characteristic pitch of thought? To begin answering this complex question, we look at text documents from several large corpora at the sentence level – i.e., using sentences as the units of meaning – and considering each document to be the result of a random process in semantic space. Given a large corpus of multi-sentence documents, we build a lexical association network representing associations between words in the corpus. This network is used to induce a semantic similarity metric between sentences, and each document is segmented into multi-sentence semantically coherent blocks (SCBs) with occasional connecting text between the blocks. Based on this segmentation, the process of document generation is modeled as a sticky Markov chain at the sentence level. We show that most documents across all the corpora are sequences of blocks with a very consistent mean length of 6.4 sentences across the corpora. This consistency suggests that a value of 6-7 sentences may be the typical mean length for single coherent thoughts in texts. We have also described several ways of visualizing the semantic structure of documents in space and time.

2019 International Joint Conference on Neural Networks (IJCNN), 2019

Politics is an area of broad interest to policy-makers, researchers, and the general public. The ... more Politics is an area of broad interest to policy-makers, researchers, and the general public. The recent explosion in the availability of electronic data and advances in data analysis methods – including techniques from machine learning – have led to many studies attempting to extract political insight from this data. Speeches in the U.S. Congress represent an exceptionally rich dataset for this purpose, and these have been analyzed by many researchers using statistical and machine learning methods. In this paper, we analyze House of Representatives floor speeches from the 1981 - 2016 period, with the goal of inferring the partisan affiliation of the speakers from their use of words. Previous studies with sophisticated machine learning models has suggested that this task can be accomplished with an accuracy in the 55 to 80% range, depending on the year. In this paper, we show that, in fact, very comparable results can be obtained using a much simpler linear classifier in word space, indicating that the use of words in partisan ways is not particularly complicated. Our results also confirm that, over the period of study, it has become steadily easier to infer partisan affiliation from political speeches in the United States. Finally, we make some observations about specific terms that Republicans and Democrats have favored over the years in service of partisan expression.

Bioinspiration & biomimetics, Nov 11, 2017

Biomimetic robots have gained attention recently for various applications ranging from resource h... more Biomimetic robots have gained attention recently for various applications ranging from resource hunting to search and rescue operations during disasters. Biological species are known to intuitively learn from the environment, gather and process data, and make appropriate decisions. Such sophisticated computing capabilities in robots are difficult to achieve, especially if done in real-time with ultra- low energy consumption. Here, we present a novel memristive device based learning architecture for robots. Two terminal memristive devices with resistive switching of oxide layer are modeled in a crossbar array to develop a neuromorphic platform that can impart active real-time learning capabilities in a robot. This approach is validated by navigating a robot vehicle in an unknown environment with randomly placed obstacles. Further, the proposed scheme is compared with Reinforcement Learning based algorithms using local and global knowledge of the environment. The simulation as well as...

Unifying Themes in Complex Systems IX, 2018

Preventing suicide requires early identification of suicidal ideation. In this research, we propo... more Preventing suicide requires early identification of suicidal ideation. In this research, we propose an approach to evaluate whether an individual’s statements during a clinical interview can be classified as coming from a suicidal or non-suicidal mindset. To do so, we compare the statements with distinct lexical associative networks constructed from corpora of suicidal and control texts. Each node in these networks is a word, and the weight of the edge between every word pair indicates how strongly the words are associated in that corpus. Several metrics of association are evaluated in this work. Preliminary results show good classification performance with above 75% accuracy on novel test data.

Springer eBooks, Feb 24, 2012

Springer eBooks, 2015

Most of the earliest work in both experimental and theoretical/computational systems neuroscience... more Most of the earliest work in both experimental and theoretical/computational systems neuroscience focused on sensory systems and the peripheral (spinal) control of movement. However, over the last three decades, attention has turned increasingly towards "higher" functions related to cognition, decision-making and voluntary behavior. Experimental studies have shown that specific brain structuresthe prefrontal cortex, the premotor and motor cortices, and the basal gangliaplay a central role in these functions, as does the dopamine system that signals reward during reinforcement learning. Because of the complexity of the issues involved and the difficulty of direct observation in deep brain structures, computational modeling has been crucial in elucidating the neural basis of cognitive control, decision making, reinforcement learning, working memory and motor control. The resulting computational models are also very useful in engineering domains such as robotics, intelligents agents and adaptive control. While it is impossible to encompass the totality of such modeling work, this chapter provides an overview of significant efforts in the last 20 years. It also outlines many of the theoretical issues underlying this work, and discusses significant experimental results that motivated the computational models.

This volume presents a complex systems approach to analyzing, modeling, understanding, and comba... more This volume presents a complex systems approach to analyzing, modeling, understanding, and combating terrorism and conflict, and is a unique and timely contribution to a topic of critical importance. Much of the effort in this area has used—and continues to use—classical methods based on intelligence, statistical and game theoretic modeling, and military operations. The need for other methods has become increasingly clear. Recognizing and conflict, at least in part, as a social phenomenon suggests that methods that have succeeded in analyzing other social systems may also work well in this case. This has led to the application of network modeling and analysis to terrorism and conflict. Other complex systems concepts such as chaotic dynamics, self- organization, emergent patterns, and fractals have also been applied, generating important insights. This book reviews and discusses these efforts.