User:Newton Howard/sandbox

Prof. Newton Howard is a brain and cognitive scientist and former Director of the MIT Mind Machine Project at the Massachusetts Institute of Technology (MIT). He is currently the Professor of Computational Neuroscience and Functional Neurosurgery at the University of Oxford, where he directs the Oxford Computational Neuroscience Laboratory. He is also the Director of the Synthetic Intelligence Lab at MIT, the founder of the Center for Advanced Defense Studies and the Chairman of the Brain Sciences Foundation.

His research areas include Cognition, Memory, Trauma, Machine Learning, Comprehensive Brain Modeling, Natural Language Processing, Nanotech, Medical Devices and Artificial Intelligence.

Education
Dr. Howard earned his B.A. from Concordia University, then obtained an M.A. in Technology from Eastern Michigan University. He went on to continue his studies at the University of Oxford where as a graduate member of the Faculty of Mathematical Sciences he proposed the Theory of Intention Awareness (IA), which made a significant impact on the design of command & control systems and information exchange systems at tactical, operational and strategic levels. He further developed this theory at the University of Paris-Sorbonne, where he received a Doctorate in Cognitive Informatics and Mathematics and was awarded the prestigious Habilitation a Diriger des Recherches for his work on the Physics of Cognition (PoC).

Career
Professor Howard is an author and national security advisor to several U.S. Government organizations. He has directed leading international cooperation programs on emerging trends in global security and information assurance and has been instrumental in the creation of National Centers of Excellence in medicine, psychiatry, and computation at multiple research institutions in the US. His work has contributed to more than 30 U.S. patents and over 90 publications, fully half of which are in the areas of cognitive and computational theory.

In 2009, Prof. Howard founded the Brain Sciences Foundation (BSF), a nonprofit 501(c)3 organization to help fund research and promote awareness for combating neurological and neurodegenerative diseases. The foundation provides grants and scholarships to support the development and promotion of several highly promising new diagnostic and therapeutic technologies. The organization includes a multitude of fellows from various leading research institutions, including Oxford University, Massachusetts Institute of Technology, La Sorbonne University of Paris and INSERM. The goal of the Foundation is to improve the quality of life for those suffering from neurological disorders.

Research
Prof. Howard's initial work focused on intentionality within the individual and various formal and informal command structures. His Theory of Intention Awareness (IA) is grounded in the systematic design and construction of naturalistic systems and serves as a possible model for explaining volition in human intelligence, recursively throughout all layers of biological organization. Conceptually, IA informs intelligent action planning; being both the process and product of planning and becomes the intelligent architecture to which we can credit purposeful action. This work on Intention Awareness led him to expand his research into signal processing, linguistic analysis and Artificial Intelligence (AI), eventually bringing him to MIT.

While at MIT, he developed the Mood State Indicator (MSI) a system that can model and explain the mental processes involved in human speech and writing to predict emotional states (2011). He next developed the Language Axiological Input/Output system (LXIO) as a practical application of the Mood Sate Indicator technology. LXIO is a system capable of detecting both sentiment and mood states based on the analysis of written or verbal discourse, representing a patient’s mood state by the sum of values generated by a given sentence or word string. LXIO parses sentences into words that are processed through modules to evaluate cognitive states. Words are then processed through time orientation, contextual-prediction and consequent modules and each word's context and grammatical function is computed with the use of a Mind Default Axiology (MDA). Within the LXIO, a learning algorithm tracks patients’ word analysis histories to further enhance accuracy. The significance of LXIO is its ability to incorporate conscious thought and bodily expression (linguistic or otherwise) into a uniform code schema.

While at the University of Oxford, Professor Howard turned his focus to neurology, neurosurgery, nanoscale medical devices and biological coprocessors. In 2012, he published and patented the Fundamental Code Unit (FCU) theory, which uses unitary mathematics (ON/OFF +/-) to correlate networks of neurophysiological processes and map them to higher order function. The FCU provides the underlying foundation for the Brain Code (BC) theory, also proposed by Prof. Howard, and is used to map entire circuits of neurological activity to behavior and response, effectively decoding the language of the brain.

Professor Howard has worked on various nanoscale Deep Brain Stimulation and optogenetic technologies and in 2014 discovered a functional endogenous optical network within the brain, mediated by neuropsin (OPN5). Within this sub-neural network, photonic activity is transduced into synaptic membrane potential changes within neocortical networks via a cGMP-dependent mechanism, accompanied by a photostimulation-catalyzed G protein/cGMP phosphodiesterase activation, which regulates membrane potential by closing cGMP-gated ion channels. This self-regulating cycle of photon-mediated events in the neocortex involves sequential interactions among 3 mitochondrial sources of endogenously-generated photons during periods of increased neural spiking activity: (a) near-UV photons (~380 nm), a free radical reaction byproduct; (b) blue photons (~470 nm) emitted by NAD(P)H upon absorption of near-UV photons; and (c) green photons (~530 nm) generated by NAD(P)H oxidases, upon NAD(P)H-generated blue photon absorption. Neuropsin (OPN5) has two switchable conformations (~380nm-absorbing and ~470nm-absorbing), allowing it to serve as an effective regulatory signaling mechanism. The bistable nature of this nanoscale quantum process provides evidence for an on/off (UNARY +/-) coding system existing at the most fundamental level of brain operation and provides a solid neurophysiological basis for the FCU to build from.

Several of these theories have been recently brought to clinical trial and use, beginning with a wearable neurodiagnostic system that uses synchronous multimodal data capture techniques in combination with the FCU to establish a highly comprehensive patient profile that can be securely analyzed against millions of other profiles to find disease biomarkers. The system has demonstrated the ability to diagnose a number of formerly undiagnosable neurodegenerative conditions, including Alzheimer's Disease (AD) and Parkinson's Disease (PD).

Selected Works

 * Howard, N., Argamon, S. (Eds.) (2009). Computational Methods For Counterterrorism. Berlin: Springer-Verlag.
 * Howard, N. (1999) The Logic of Uncertainty and Situational Understanding. Published by Center for Advanced Defense Studies (CADS)/Institute for the Mathematical Complexity & Cognition (MC) Centre de Recherche en Informatique, Université Paris Sorbonne
 * Howard, N. (1999) Multidimensional Time Understanding Combinatorial Manifold. Published by Center for Advanced Defense Studies (CADS)/Institute for the Mathematical Complexity & Cognition (MC) Centre de Recherche en Informatique, Université Paris Sorbonne
 * Neuman, Y., Howard, N., (2015). The Embodied Nature of Abstract Words: A Proposed Neuro-Mechanism. Neuroscience and Biobehavioral Reviews, in review.
 * Nave, O., Lehavi, Y., Ajadi, S., Howard, N. & Goldshtein, V. (2015). Analysis of Polydisperse Fuel Spray Flame. Springer Plus, in press.
 * Poria, S., Cambria, E., Hussain, A. & Howard, N. (2015). Extending Text based Sentiment Analysis to Multimodal Sentiment Analysis. Cognitive Computation, in press.
 * Poria, S., Cambria, E., Howard, N. (2015). Fusing Audio, Visual and Textual Clues for Big Social Data Analysis. Neurocomputing, in press.
 * Howard, N. (2015). The Case for Intention Awareness in Security Systems., Journal of Cyber-Security and Digital Forensics, 1(1), in press.
 * Cambria, E., White, B., Durrani, T., Howard, N. (2014) Computational Intelligence for Natural Language Processing.  IEEE Computational Intelligence Magazine, 9(1)
 * Poria, S., Agarwal, Basant., Gelbukh, A., Hussain, A., Howard, N. (2014) Dependency-Based Semantic Parsing for Concept-Level Text Analysis. Computational Linguistics and Intelligent Text Processing. Lecture Notes in Computer Science, 8403, 113-127
 * Hussain, A., Cambria, E., Schuller, B., Howard, N. (2014). Affective Neural Networks and Cognitive Learning Systems for Big Data Analysis, Neural Networks, Special Issue, 58, 1-3.
 * Cambria, E., Howard, N., Song, Y. & Wang, H. (2014). Semantic Multidimeensional Scaling for Open Domain Sentiment Analysis. IEEE Intelligent Systems, 29 March/April.
 * Bermann, J., Langdon, P., Mayagoita, R. & Howard, N. (2014). Exploring the use of sensors to measure behavioral interactions: An experimental evaluation of using hand trajectories. PLoS ONE, 9, e88080.
 * Nave, O., Neuman, Y., Perlovsky, L. & Howard, N. (2014) How Much Information Should We Drop to Become Intelligent? Applied Mathematics and Computation, 245: 261-264.
 * Poria, S., Gelbukh, A., Hussain, A., Bandyopadhyay, S. & Howard, N. (2013). Music Genre Classification: A Semi-supervised Approach. Pattern Recognition. Springer Berlin Heidelberg,
 * Poria, S., Gelbukh, A., Agarwal, B., Cambria, E. & Howard, N. (2013). Common Sense Knowledge Based Personality Recognition from Text. Advances in Soft Computing and Its Applications,. Lecture Notes in Computer Science, Springer, 8266:2013, 484-496.
 * Howard, N., Cambria, E. (2013). Development of a Diplomatic, Information, Military, Health, and Economic Effects Modeling System. International Journal of Privacy and Health Information 1:1, pp. 1-1.
 * Cambria, E., Mazzocco, T., Hussain, A., Howard, N. (2013). Sentic Neurons: A Biologically inspired cognitive architecture for affective common sense reasoning. In Procedia Computer Science, 00, 1-6.
 * Neuman, Y., Assaf, D., Cohen, Y., Last, M., Argamon, S., Howard, N. & Frieder, O. (2013). Metaphor identification in large texts corpora. PLoS One, 8.
 * Howard, N., Bergmann, J. & Stein, J. (2013). Combined Modality of the Brain Code Approach for Early Detection and the Long-term Monitoring of Neurodegenerative Processes. Frontiers Special Issue INCF Course Imaging the Brain at Different Scales.
 * Bergmann, J., Graham, S., Howard, N. & Mcgregor, A. (2013). Comparison of Median Frequency Between Traditional and Functional Sensor Placements During Activity Monitoring. Measurement, 46, 2193-2200.
 * Howard, N., Stein, J. & Aziz, T. (2013). Early Detection of Parkinson's Disease from Speech and Movement Recordings. Oxford Parkinson's Disease Center Research Day 2013.
 * Howard, N. & Bergmann, J. (2012). Combining Computational Neuroscience and Body Sensor Networks to Investigate Alzheimer’s Disease. Journal of Functional Neurology, Rehabilitation and Ergonomics, 2(1), 29-38
 * Bergmann, J. & Howard, N. (2012). Combining computational neuroscience and body sensor networks to investigate Alzheimer’s disease. BMC Neuroscience, 13(supp), 178.
 * Cambria, E., White, B., Durrani, T., & Howard, N. (2012). Computational Intelligence for Natural Language Processing. IEEE Computational Intelligence Magazine, 9(1), 19-63.
 * Howard, N. (2012). Brain Language: The Fundamental Code Unit. The Brain Sciences Journal, 1(1), 4-45.
 * Howard, N. (2012). Energy Paradox of the Brain. The Brain Sciences Journal, 1(1), 46-61.
 * Howard, N., Lieberman, H. (2012). Brain Space: Automated Brain Understanding and Machine Constructed Analytics in Neuroscience. Brain Sciences Journal, 1(1), 85-97.
 * Howard, N., Guidere, M. (2012). LXIO The Mood Detection Robopsych. The Brain Sciences Journal, 1(1), 98-109.
 * Howard, N., Kanareykin, S. (2012) Transcranial Ultrasound Application Methods: Low-frequency ultrasound as a treatment for brain dysfunction. The Brain Sciences Journal, 1(1), 110-124.
 * Dunn, J., Heredia, J. B. D., Burke, M., Gandy, L., Kanareykin, S., Kapah, O., Taylor, M., Hines, D., Freieder, O., Grossman, D., Howard, N., Koppel, M., Morris, S., Ortony, A. & Argamon, S. *Howard, N., Jehel, L. & Arnal, R. (2014). Towards a Differential Diagnostic of PTSD Using Cognitive Computing Methods. International Conference on Cognitive Informatics and Cognitive Computing ICCI CC. London, UK: IEEE.
 * Howard, N., Bergmann, J. & Fahlstrom, R. (2013). Exploring the Relationship Between Everyday Speech and Motor Symptoms of Parkinson's Disease as Prerequisite Analysis for Tool Development. Lecture Notes in Computer Science, MICAI, November 24-30, 2013, Mexico City, Mexico.
 * Howard, N. (2013). Approach Towards a Natural Language Analysis for Diagnosing Mood Disorders and Comorbid Conditions. Lecture Notes in Computer Science, MICAI, November 24-30, 2013, Mexico City, Mexico.
 * Howard, N. & Cambria, E. (2013). Intention awareness: improving upon situation awareness in human-centric environments. Human-centric Computing and Information Sciences, 3;9, 1-17.
 * Howard, N. (2013). The Twin Hypotheses: Brain Code and the Fundamental Code Unit: Towards Understanding the Computational Primitive Elements of Cortical Computing. Lecture Notes in Artificial Intelligence, MICAI, November 24-30, 2013, Mexico City, Mexico.
 * Howard, N., Pollock, R., Prinold, J., Sinha, J., Newham, D., Bergmann, J. (2013). Effect of Impairment on upper limb performance in an Ageing Population. The Human Computer Interaction International 2013 Conference (HCI) July 21-26, 2013, Las Vegas, Nevada.
 * Howard, N. (2013). Toward Understanding Analogical Mapping and Ideological Cataloguing in the Brain. Research Challenges in Information Science Series Conference (RCIS) May 29-31, 2013, Paris, France.
 * Last, M., Assaf, D., Neuman, Y., Cohen, Y., Argamon, S., Howard, N., Frieder, O., Koppel, M. (2013) Towards Metaphor Analysis for Natural Language Understanding. The 35th Annual Conference on Information Retrieval, March 24-27, 2013, Moscow, Russia.
 * Assaf, D., Howard, N., Neuman, Y., Last, M., Cohen, Y., Frieder, O., Argamon, S., Koppel (2013). Identifying -Noun Metaphors. 2013 IEEE Symposium Series on Computational Intelligence April 15-19, 2013, Singapore.
 * Bergmann, J., Howard, N. (2013). Design Considerations for a Wearable Sensor Network that Measures Accelerations during Water-Ski Jumping. IEEE Body Sensor Network Conference, May 6-9, 2013, Cambridge, Massachusetts.
 * Howard, N., Rao, D., Fahlstrom, R. & Stein, J. (2013). The Fundamental Code Unit: A Framework for Biomarker Analysis. . The 2nd Neurological Biomarkers Conference at the 2013 Biomarker Summit. San Francisco, California..
 * Howard, N. & Bergmann, J. (2012). Combining Computational Neuroscience and Body Sensor Networks to Investigate Alzheimer's Disease. Organization for Computational Neurosciences, July 21-26, 2012, Atlanta/Decatur.
 * Howard, N. (2012). Intention Awareness in Human-Machine Interaction Sensemaking in Joint Cognitive Systems. International Conference on Information Sciences and Interaction Science, June 26-28, 2012, Jeju Island, Korea, 293-299.
 * Howard, N. Global Defense Policy System of Laws: Graph Theory Approach to Balance of Power Theory. European Intelligence and Security Informatics Conference (EISIC), 2011a. IEEE, 248-258.

Patents (US)

 * S. Patent: US 7058355 B2 - Propagation of a wireless network through commercial outlets
 * S. Patent: US 7177643 B2 - Wireless network for routing a signal without using a tower
 * S. Patent: US 8380902 B2 - Situation Understanding and Intent-Based Analysis for Dynamic Information Exchange
 * S. Patent: US 8407281 B2 - Intent-Based Automated Conflict Prediction and Notification System
 * S. Patent Application: 13/747448 - System, Method, and Applications of Using the Fundamental Code Unit and Brain Language
 * S. Patent Application: 12/880042 - Medical Co-Processor for signaling pattern decoding and manipulation of cellular structures via direct interface
 * S. Patent Application: 13/083352 - Method for Cognitive Computing
 * S. Patent Application: 60/223813 - Wireless Communications System and Method
 * S. Patent Application: 60/221231 - System and Method for Command, Control and Communication for Personnel and Weaponry
 * S. Patent Application: 14/336679 - Intent-Based Ontology for Grid Computing Using Autonomous Mobile Agents
 * S. Patent Application: 60/907520 - The Glass Office
 * S. Patent Application: 60/907523 - XML Threats: Intent-aware Intrusion Detection System or Web Services Applications
 * S. Patent Application: 60/907522 - Intent-centric Paradigms Cognitive Computing and Cognitive Engines

Patents (International)

 * International Patent Application: WO/2011/127424 A1 (PCT) - Method for Cognitive Computing
 * International Patent Application: WO/2002/019290 A3 (PCT) - Intention-Based Automated Conflict Prediction and Notification System
 * International Patent Application: WO/2002/013415 A3 (PCT) - Wireless Network
 * International Patent Application: WO/2008/070101 A2 (PCT) - Situation Understanding and Intent-Based Analysis for Dynamic Information Sharing