316 research outputs found
Cognitive Computation sans Representation
The Computational Theory of Mind (CTM) holds that cognitive processes are essentially computational, and hence computation provides the scientific key to explaining mentality. The Representational Theory of Mind (RTM) holds that representational content is the key feature in distinguishing mental from non-mental systems. I argue that there is a deep incompatibility between these two theoretical frameworks, and that the acceptance of CTM provides strong grounds for rejecting RTM. The focal point of the incompatibility is the fact that representational content is extrinsic to formal procedures as such, and the intended interpretation of syntax makes no difference to the execution of an algorithm. So the unique 'content' postulated by RTM is superfluous to the formal procedures of CTM. And once these procedures are implemented in a physical mechanism, it is exclusively the causal properties of the physical mechanism that are responsible for all aspects of the system's behaviour. So once again, postulated content is rendered superfluous. To the extent that semantic content may appear to play a role in behaviour, it must be syntactically encoded within the system, and just as in a standard computational artefact, so too with the human mind/brain - it's pure syntax all the way down to the level of physical implementation. Hence 'content' is at most a convenient meta-level gloss, projected from the outside by human theorists, which itself can play no role in cognitive processing
A Theory of Cheap Control in Embodied Systems
We present a framework for designing cheap control architectures for embodied
agents. Our derivation is guided by the classical problem of universal
approximation, whereby we explore the possibility of exploiting the agent's
embodiment for a new and more efficient universal approximation of behaviors
generated by sensorimotor control. This embodied universal approximation is
compared with the classical non-embodied universal approximation. To exemplify
our approach, we present a detailed quantitative case study for policy models
defined in terms of conditional restricted Boltzmann machines. In contrast to
non-embodied universal approximation, which requires an exponential number of
parameters, in the embodied setting we are able to generate all possible
behaviors with a drastically smaller model, thus obtaining cheap universal
approximation. We test and corroborate the theory experimentally with a
six-legged walking machine. The experiments show that the sufficient controller
complexity predicted by our theory is tight, which means that the theory has
direct practical implications. Keywords: cheap design, embodiment, sensorimotor
loop, universal approximation, conditional restricted Boltzmann machineComment: 27 pages, 10 figure
"Open Innovation" and "Triple Helix" Models of Innovation: Can Synergy in Innovation Systems Be Measured?
The model of "Open Innovations" (OI) can be compared with the "Triple Helix
of University-Industry-Government Relations" (TH) as attempts to find surplus
value in bringing industrial innovation closer to public R&D. Whereas the firm
is central in the model of OI, the TH adds multi-centeredness: in addition to
firms, universities and (e.g., regional) governments can take leading roles in
innovation eco-systems. In addition to the (transversal) technology transfer at
each moment of time, one can focus on the dynamics in the feedback loops. Under
specifiable conditions, feedback loops can be turned into feedforward ones that
drive innovation eco-systems towards self-organization and the auto-catalytic
generation of new options. The generation of options can be more important than
historical realizations ("best practices") for the longer-term viability of
knowledge-based innovation systems. A system without sufficient options, for
example, is locked-in. The generation of redundancy -- the Triple Helix
indicator -- can be used as a measure of unrealized but technologically
feasible options given a historical configuration. Different coordination
mechanisms (markets, policies, knowledge) provide different perspectives on the
same information and thus generate redundancy. Increased redundancy not only
stimulates innovation in an eco-system by reducing the prevailing uncertainty;
it also enhances the synergy in and innovativeness of an innovation system.Comment: Journal of Open Innovations: Technology, Market and Complexity, 2(1)
(2016) 1-12; doi:10.1186/s40852-016-0039-
Sociological and Communication-Theoretical Perspectives on the Commercialization of the Sciences
Both self-organization and organization are important for the further
development of the sciences: the two dynamics condition and enable each other.
Commercial and public considerations can interact and "interpenetrate" in
historical organization; different codes of communication are then
"recombined." However, self-organization in the symbolically generalized codes
of communication can be expected to operate at the global level. The Triple
Helix model allows for both a neo-institutional appreciation in terms of
historical networks of university-industry-government relations and a
neo-evolutionary interpretation in terms of three functions: (i) novelty
production, (i) wealth generation, and (iii) political control. Using this
model, one can appreciate both subdynamics. The mutual information in three
dimensions enables us to measure the trade-off between organization and
self-organization as a possible synergy. The question of optimization between
commercial and public interests in the different sciences can thus be made
empirical.Comment: Science & Education (forthcoming
Shannon Information Theory and Molecular Biology
The role and the contribution of Shannon Information Theory to the development of Molecular Biology has been the object of stimulating debates during the last thirty years. This seems to be connected with some semantic charms associated with the use of the word \u201cinformation\u201d in the biological context. Furthermore information itself, if viewed in a broader perspective, is far from being completely defined in a fashion that overcomes the technical level at which the classical Information Theory has been conceived. This review aims at building on the acknowledged contribution of Shannon Information Theory to Molecular Biology, so as to discover if it is only a technical tool to analyze DNA and proteinic sequences, or if it can rise, at least in perspective, to a higher role that exerts an influence on the construction of a suitable model for handling the genetic information in Molecular Biology
An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics
For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types
Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context
Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts
Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas
Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN
Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas
This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing
molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin
Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images
Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images
of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL
maps are derived through computational staining using a convolutional neural network trained to
classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and
correlation with overall survival. TIL map structural patterns were grouped using standard
histopathological parameters. These patterns are enriched in particular T cell subpopulations
derived from molecular measures. TIL densities and spatial structure were differentially enriched
among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial
infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic
patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for
the TCGA image archives with insights into the tumor-immune microenvironment
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