4,934 research outputs found
Computational strategies for dissecting the high-dimensional complexity of adaptive immune repertoires
The adaptive immune system recognizes antigens via an immense array of
antigen-binding antibodies and T-cell receptors, the immune repertoire. The
interrogation of immune repertoires is of high relevance for understanding the
adaptive immune response in disease and infection (e.g., autoimmunity, cancer,
HIV). Adaptive immune receptor repertoire sequencing (AIRR-seq) has driven the
quantitative and molecular-level profiling of immune repertoires thereby
revealing the high-dimensional complexity of the immune receptor sequence
landscape. Several methods for the computational and statistical analysis of
large-scale AIRR-seq data have been developed to resolve immune repertoire
complexity in order to understand the dynamics of adaptive immunity. Here, we
review the current research on (i) diversity, (ii) clustering and network,
(iii) phylogenetic and (iv) machine learning methods applied to dissect,
quantify and compare the architecture, evolution, and specificity of immune
repertoires. We summarize outstanding questions in computational immunology and
propose future directions for systems immunology towards coupling AIRR-seq with
the computational discovery of immunotherapeutics, vaccines, and
immunodiagnostics.Comment: 27 pages, 2 figure
Training Passive Photonic Reservoirs with Integrated Optical Readout
As Moore's law comes to an end, neuromorphic approaches to computing are on
the rise. One of these, passive photonic reservoir computing, is a strong
candidate for computing at high bitrates (> 10 Gbps) and with low energy
consumption. Currently though, both benefits are limited by the necessity to
perform training and readout operations in the electrical domain. Thus, efforts
are currently underway in the photonic community to design an integrated
optical readout, which allows to perform all operations in the optical domain.
In addition to the technological challenge of designing such a readout, new
algorithms have to be designed in order to train it. Foremost, suitable
algorithms need to be able to deal with the fact that the actual on-chip
reservoir states are not directly observable. In this work, we investigate
several options for such a training algorithm and propose a solution in which
the complex states of the reservoir can be observed by appropriately setting
the readout weights, while iterating over a predefined input sequence. We
perform numerical simulations in order to compare our method with an ideal
baseline requiring full observability as well as with an established black-box
optimization approach (CMA-ES).Comment: Accepted for publication in IEEE Transactions on Neural Networks and
Learning Systems (TNNLS-2017-P-8539.R1), copyright 2018 IEEE. This research
was funded by the EU Horizon 2020 PHRESCO Grant (Grant No. 688579) and the
BELSPO IAP P7-35 program Photonics@be. 11 pages, 9 figure
Collective Values, Behavioural Norms, and Rules: Building Institutions for Economic Growth and Poverty Reduction
Growth, Poverty, Rules, Institutions, Human behaviour
Supporting students with learning disabilities to explore linear relationships using online learning objects
The study of linear relationships is foundational for mathematics teaching and learning. However, studentsâ abilities connect different representations of linear relationships have proven to be challenging. In response, a computer-based instructional sequence was designed to support studentsâ understanding of the connections among representations. In this paper we report on the affordances of this dynamic mode of representation specifically for students with learning disabilities. We outline four results identified by teachers as they implemented the online lessons
Literature Review of PID Controller based on Various Soft Computing Techniques
This paper profound the various soft computing techniques like fuzzy logic, genetic algorithm, ant colony optimization, particle swarm optimization used in controlling the parameters of PID Controller. Its widespread use and universal acceptability is allocated to its elementary operating algorithm, the relative ease with the controller effects can be adjusted, the broad range of applications where it has truly developed excellent control performances, and the familiarity with which it is deduced among researchers. In spite of its wide spread use, one of its short-comings is that there is no efficient tuning method for PID controller. Given this background, the main objective of this is to develop a tuning methodology that would be universally applicable to a range of well-liked process that occurs in the process control industry
Understanding Leadership A Coordination Theory
Important aspects of leadership behavior can be rendered intelligible through a focus on coordination games. The concept of common knowledge is shown to be particularly important to understanding leadership. Thus, leaders may establish common knowledge conditions and assist the coordination of strategies in this way, or make decisions in situations where coordination problems persist in spite of common knowledge.Game theory, management, organization
A CASE STUDY INVESTIGATING RULE BASED DESIGN IN AN INDUSTRIAL SETTING
This thesis presents a case study on the implementation of a rule based design (RBD) process for an engineer-to-order (ETO) company. The time taken for programming and challenges associated with this process are documented in order to understand the benefits and limitations of RBD. These times are obtained while developing RBD programs for grid assemblies of bottle packaging machines that are designed and manufactured by Hartness International (HI). In this project, commercially available computer-aided design (CAD) and RBD software are integrated to capture the design and manufacturing knowledge used to automate the grid design process of HI. The stages involved in RBD automation are identified as CAD modeling, knowledge acquisition, capturing parameters, RBD programming, debugging, and testing, and production deployment. The stages and associated times in RBD program development process are recorded for eighteen different grid products. Empirical models are developed to predict development times of RBD program, specifically enabling HI to estimate their return on investment. The models are demonstrated for an additional grid product where the predicted time is compared to actual RBD program time, falling within 20% of each other. This builds confidence in the accuracy of the models. Modeling guidelines for preparing CAD models are also presented to help in RBD program development. An important observation from this case study is that a majority of the time is spent capturing information about product during the knowledge acquisition stage, where the programmer\u27s development of a RBD program is dependent upon the designer\u27s product knowledge. Finally, refining these models to include other factors such as time for building CAD models, programmers experience with the RBD software (learning curve), and finally extending these models to other product domains are identified possible areas of future work
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