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A new local structure called a second-order structure was proposed to generate a new class of nets called synchronized choice nets (SNC). SNC covers well-behaved free choice nets. Reachability is no longer P-Space hard problem, but can be solved with polynomial time complexity. How to extend them to non-SNC and its application to deadlock detection in flexible manufacturing systems are discussed.
Though it has been possible in the past to learn to predict DNA hydration patterns from crystallographic data, there is ambiguity in the choice of training data (both in terms of the relevant set of cases and the features needed to represent them), which limits the usefulness of standard learning techniques. Thus, we have developed a knowledge-based system to generate machine learning experiments for inducing DNA hydration pattern classifiers. The system takes as input (1) a set of classified training examples described by a large set of attributes and (2) information about a set of learning experiments that have already been run. It outputs a new learning experiment, namely a (not necessarily proper) subset of the input examples represented by a new set of features. Domain specific and domain independent knowledge is used to suggest subsets of training examples from suspected subpopulations, transform attributes in the training data or generate new ones, and choose interesting ways to substitute one experiment's set of attributes with another. Automatic hydration pattern predictors are of both theoretical and practical interest to DNA crystallographers, because they can speed up a labor intensive process, and because the extracted rules add to the knowledge of what determines DNA hydration.
Written protocols are often employed to guide patient care. For treatment within a clinical trial, compliance with the trial protocol may be critical in ensuring efficacy and safety. Previous empirical work has established generic safety principles for reasoning about adverse events in clinical trials and their formalisation has been applied in a decision support system for managing treatment plans in oncology. The same generic knowledge can be reused to generate specific safety clauses when designing new treatment plans. Typically, clinicians devise trial protocols relatively infrequently and so software aids, especially those assisting with regulatory/safety conformance, will encourage more effective use of their time. A similar approach to the formalisation of safety knowledge in the control of hazardous industrial processes is discussed.
Most biologists and some cognitive scientists have independently reached the conclusion that there is no such thing as learning in the traditional "instructive" sense. This is, admittedly, a somewhat extreme thesis, but I defend it here in the light of data and theories jointly extracted from biology, especially from evolutionary theory and immunology, and from modern generative grammar. I also point out that the general demise of learning is uncontroversial in the biological sciences, while a similar consensus has not yet been reached in psychology and in linguistics at large. Since many arguments presently offered in defense of learning and in defense of "general intelligence" are often based on a distorted picture of human biological evolution, I devote some sections of this paper to a critique of "adaptationism," providing also a sketch of a better evolutionary theory (one based on "exaptation"). Moreover, since certain standard arguments presented today as "knock-down" in psychology, in linguistics and in artificial intelligence are a perfect replica of those once voiced by biologists in favor of instruction and against selection, I capitalize on these errors of the past to draw some lessons for the present and for the future.
Corneal topography is used to measure the anterior surface of the cornea. It is conventionally represented as radial slope, radial curvature, and elevation. In this paper, we introduce the application of mean curvature mapping as an alternative representation of the corneal topography. The purpose is to improve the detection of keratoconus and other diseases characterized by local increase in corneal curvature. Both simulated keratoconic cornea and real keratoconus data exported from the corneal topography system were analyzed. Four representations of corneal topography were generated and compared. It was found that mean curvature mapping provided the most precise cone location in simulated keratoconus. In both actual and simulated keratoconus cases, the appearance of the cone-like distortion is more consistent on mean curvature maps. Mean curvature mapping may improve the detection and localization of corneal shape abnormalities.
Evolvable hardware (EHW) refers to self-reconfiguration hardware design, where the configuration is under the control of an evolutionary algorithm (EA). One of the main difficulties in using EHW to solve real-world problems is scalability, which limits the size of the circuit that may be evolved. This paper outlines a new type of decomposition strategy for EHW, the "generalized disjunction decomposition" (GDD), which allows the evolution of large circuits. The proposed method has been extensively tested, not only with multipliers and parity bit problems traditionally used in the EHW community, but also with logic circuits taken from the Microelectronics Center of North Carolina (MCNC) benchmark library and randomly generated circuits. In order to achieve statistically relevant results, each analyzed logic circuit has been evolved 100 times, and the average of these results is presented and compared with other EHW techniques. This approach is necessary because of the probabilistic nature of EA; the same logic circuit may not be solved in the same way if tested several times. The proposed method has been examined in an extrinsic EHW system using the (1 + lambda) evolution strategy. The results obtained demonstrate that GDD significantly improves the evolution of logic circuits in terms of the number of generations, reduces computational time as it is able to reduce the required time for a single iteration of the EA, and enables the evolution of larger circuits never before evolved. In addition to the proposed method, a short overview of EHW systems together with the most recent applications in electrical circuit design is provided.
Although several methods for generating the centerline of a colon from CT colonographic scans have been proposed, in general they are time-consuming and do not take into account that the images of the colon may be of nonoptimal quality, with collapsed regions, and stool within the colon. Furthermore, the colonic lumen or wall, which is often used as a basis for computation of a centerline, is not always precisely segmented. In this study, we have developed an algorithm for computation of a colon centerline that is fast compared to the centerline algorithms presented in the reviewed literature, and that relies little on a complete colon segments identification. The proposed algorithm first extracts local maxima in a distance map of a segmented colonic lumen. The maxima are considered to be nodes in a set of graphs, and are iteratively linked together, based on a set of connection criteria, giving a minimum distance spanning tree. The connection criteria are computed from the distance from object boundary, the Euclidean distance between nodes and the voxel values on the pathway between pairs of nodes. After the last iteration, redundant branches are removed and end segments are recovered for each remaining graph. A subset of the initial maxima is used for distinguishing between the colon and noncolonic centerline segments among the set of graphs, giving the final centerline representation. A phantom study showed that, with respect to phantom variations, the algorithm achieved nearly constant computation time (2.3-2.9 s) except for the most extreme setting (20.2 s). The algorithm successfully found all, or most of, the centerline (93% - 100%). Displacement from optimum varied with colon diameter (1.2-6.6 mm). By use of 40 CT colonographic scans, the computer-generated centerlines were compared with the centerlines generated by three radiologists. The similarity was measured based on percent coverage and average displacement. The computer-generated centerlines, when compared with human-generated centerlines, had approximately the same displacement as when the human-generated centerlines were compared among each other (3.8 mm versus 4.0 mm). The coverage of the computer-generated centerlines was slightly less than that of the human-generated centerlines (92% versus 94%). The 40 centerlines were, on average, computed in 10.5 seconds, including computation time for the distance transform, with an Intel Pentium-based 800 MHz computer, as compared with 12-17 seconds or more (excluding computation time for the distance transform needed) per centerline as reported in other studies.
The authors describe a methodology for helping computational biologists diagnose discrepancies they encounter between experimental data and the predictions of scientific models. The authors call these discrepancies data-model conflicts. They have built a prototype system to help scientists resolve these conflicts in a more systematic, evidence-based manner. In computational biology, data-model conflicts are the result of complex computations in which data and models are transformed and evaluated. Increasingly, the data, models, and tools employed in these computations come from diverse and distributed resources, contributing to a widening gap between the scientist and the original context in which these resources were produced. This contextual rift can contribute to the misuse of scientific data or tools and amplifies the problem of diagnosing data-model conflicts. The authors' hypothesis is that systematic collection of metadata about a computational process can help bridge the contextual rift and provide information for supporting automated diagnosis of these conflicts. The methodology involves three major steps. First, the authors decompose the data-model evaluation process into abstract functional components. Next, they use this process decomposition to enumerate the possible causes of the data-model conflict and direct the acquisition of diagnostically relevant metadata. Finally, they use evidence statically and dynamically generated from the metadata collected to identify the most likely causes of the given conflict. They describe how these methods are implemented in a knowledge-based system called GRENDEL and show how GRENDEL can be used to help diagnose conflicts between experimental data and computationally built structural models of the 30S ribosomal subunit.
An approach to the deformable registration of three-dimensional brain tumor images to a normal brain atlas is presented. The approach involves the integration of three components: a biomechanical model of tumor mass-effect, a statistical approach to estimate the model's parameters, and a deformable image registration method. Statistical properties of the sought deformation map from the atlas to the image of a tumor patient are first obtained through tumor mass-effect simulations on normal brain images. This map is decomposed into the sum of two components in orthogonal subspaces, one representing inter-individual differences in brain shape, and the other representing tumor-induced deformation. For a new tumor case, a partial observation of the sought deformation map is obtained via deformable image registration and is decomposed into the aforementioned spaces in order to estimate the mass-effect model parameters. Using this estimate, a simulation of tumor mass-effect is performed on the atlas image in order to generate an image that is similar to tumor patient's image, thereby facilitating the atlas registration process. Results for a real tumor case and a number of simulated tumor cases indicate significant reduction in the registration error due to the presented approach as compared to the direct use of deformable image registration.
This paper presents a method for the discovery of temporal patterns in multivariate time series and their conversion into a linguistic knowledge representation applied to sleep-related breathing disorders. The main idea lies in introducing several abstraction levels that allow a step-wise identification of temporal patterns. Self-organizing neural networks are used to discover elementary patterns in the time series. Machine learning (ML) algorithms use the results of the neural networks to automatically generate a rule-based description. At the next levels, temporal grammatical rules are inferred. This method covers one of the main "bottlenecks" in the design of knowledge-based systems, namely, the knowledge acquisition problem. An evaluation of the rules lead to an overall sensitivity of 0.762, and a specificity of 0.758.
In this paper, we present a method for the decomposition of a volumetric image into its most relevant visual patterns, which we define as features associated to local energy maxima of the image. The method involves the clustering of a set of predefined bandpass energy filters according to their ability to segregate the different features in the image, thus generating a set of composite-feature detectors tuned to the specific visual patterns present in the data. Clustering is based on a measure of statistical dependence between pairs of frequency features. We will illustrate the applicability of the method to the initialization of a three-dimensional geodesic active model.
The high compression efficiency and various features provided by JPEG2000 make it attractive for image transmission purposes. A novel joint source/channel coding scheme tailored for JPEG2000 is proposed in this paper to minimize the end-to-end image distortion within a given total transmission rate through memoryless channels. It provides unequal error protection by combining the forward error correction capability from channel codes and the error detection/localization functionality from JPEG2000 in an effective way. The proposed scheme generates quality scalable and error-resilient codestreams. It gives competitive performance with other existing schemes for JPEG2000 in the matched channel condition case and provides more graceful quality degradation for mismatched cases. Furthermore, both fixed-length source packets and fixed-length channel packets can be efficiently formed with the same algorithm.
With the fast development of visual noise-shaping related applications (visual compression, error resilience, watermarking, encryption, and display), there is an increasingly significant demand on incorporating perceptual characteristics into these applications for improved performance. In this paper, a very important mechanism of the human brain, visual attention, is introduced for visual sensitivity and visual quality evaluation. Based upon the analysis, a new numerical measure for visual attention's modulatory aftereffects, perceptual quality significance map (PQSM), is proposed. To a certain extent, the PQSM reflects the processing ability of the human brain on local visual contents statistically. The PQSM is generated with the integration of local perceptual stimuli from color contrast, texture contrast, motion, as well as cognitive features (skin color and face in this study). Experimental results with subjective viewing demonstrate the performance improvement on two PQSM-modulated visual sensitivity models and two PQSM-based visual quality metrics.
We describe an ontology engineering methodology by which conceptual knowledge is extracted from an informal medical thesaurus (UMLS) and automatically converted into a formal description logics system (LOOM). Our approach consists of four steps: concept definitions are automatically generated from the UMLS, integrity checking of taxonomic and partonomic hierarchies is performed by LOOM's terminological classifier, cycles and inconsistencies are eliminated, as well as incremental refinement of the evolving knowledge base is performed by a domain expert. We report on experiments with a very large knowledge base composed of 164,000 concepts and 76,000 relations.
While many trading strategies are based on price prediction, traders in financial markets are typically interested in optimizing risk-adjusted performance such as the Sharpe Ratio, rather than the price predictions themselves. This paper introduces an approach which generates a nonlinear strategy that explicitly maximizes the Sharpe Ratio. It is expressed as a neural network model whose output is the position size between a risky and a risk-free asset. The iterative parameter update rules are derived and compared to alternative approaches. The resulting trading strategy is evaluated and analyzed on both computer-generated data and real world data (DAX, the daily German equity index). Trading based on Sharpe Ratio maximization compares favorably to both profit optimization and probability matching (through cross-entropy optimization). The results show that the goal of optimizing out-of-sample risk-adjusted profit can indeed be achieved with this nonlinear approach.
As the costs associated with clinical research and new drug development increase, it is incumbent upon us to develop alternative research methodologies. A new Computer Intuition (CI) program identifies literature-based evidence with the potential to generate a hypothesis that is most likely to be clinically confirmed regarding the cause of a disease or the questions being posed. The objective of this study was to demonstrate whether computer intuition can be used to guide scientific research in solving important biomedical questions. The establishment of CI as an alternative research methodology has the potential to accelerate the translation of basic science findings into clinical practice. The specific aim of this study was to develop a CI-driven hypothesis related to the controversial issue debating which radiographic imaging technology is most suitable for diagnostic purposes prior to the placement of oral implants. This hypothesis was then compared, in retrospect, with a known opinion established following a recent literature review on this issue. In his review of the literature, Frederiksen (1995) suggests that multiplanar reformatted computed tomography (CT) is the diagnostic imaging modality of choice prior to implant placement. To compare the CI hypothesis with Frederiksen's (1995) opinion, we subjected 34 relevant papers to CI analysis. The output consisted of clusters of important statements, phrases, and thought processes from the given dataset rated with the greatest potential to generate testable hypotheses. Both CI's output and Frederiksen (1995) indicated that CT scanning is the diagnostic imaging modality of choice prior to implant placement. Although the ultimate utility of CI is dependent on the successful testing of its derived hypothesis, this preliminary retrospective study suggests that CI might be useful in guiding scientific research.
MOTIVATION: Models deployed for genomic prediction of diseases perform unevenly across populations, limiting clinical utility. Two factors drive this limitation: large imbalances in sample availability across ancestry groups and non-stationarity of genotype-phenotype effect sizes across the ancestry continuum. While tabular foundation models with in-context learning (ICL) have shown strong sample efficiency in other domains, their effectiveness for genotype-to-phenotype prediction and their robustness to ancestry-driven effect heterogeneity remain unclear. RESULTS: Using large, ancestrally diverse biobank data, we show that ICL-capable tabular foundation models reduce performance degradation in under-sampled ancestry groups compared to conventional supervised approaches. However, we find that prevailing models trained on existing synthetic tabular tasks fail when allele effect sizes vary across ancestry space. Treating genetic ancestry as a continuous variable, we introduce an instruction-tuning framework that exposes models to synthetic tasks with ancestry-dependent non-stationary effects. Instruction-tuned models achieve improved and more stable predictive performance across the genetic ancestry continuum, including for individuals distant from in-context exemplars in ancestry space. AVAILABILITY AND IMPLEMENTATION: All code for instruction-tuning models, synthetic task generation, data wrangling, and model evaluation, is publicly available at https://github.com/ai4pm/Bridging-Ancestry-Gaps-in-Genomic-Risk-Prediction-with-Tabular-Foundation-Models. The final instruction-tuned model (ICL-NS-G2P-proto) is also released in this repository. Detailed documentation is provided, including environment setup instructions and guidelines for running various parts. The instruction-tuning task datasets are available at https://zenodo.org/records/18309187.
The evaluation of algorithms' developed for computer assisted diagnosis in digital mammography requires image databases that allow relative comparisons and assessment of algorithms clinical value. A review of the literature indicates that there is no consensus on the guidelines of how databases should be established. Image selection is usually done based on subjective criteria or availability. The generation of common database(s) available to the research community makes relative evaluations of algorithms with similar properties easier. However, questions regarding the "right database size," the "right image resolution," and the "right contents" remain. In this paper, database issues are reviewed and discussed and possible remedies to the various problems are proposed.