SDSS-RASS: Next Generation of Cluster-Finding Algorithms

We outline here the next generation of cluster-finding algorithms. We show how advances in Computer Science and Statistics have helped develop robust, fast algorithms for finding clusters of galaxies in large multi-dimensional astronomical databases like the Sloan Digital Sky Survey (SDSS). Specifically, this paper presents four new advances: (1) A new semi-parametric algorithm - nicknamed ``C4'' - for jointly finding clusters of galaxies in the SDSS and ROSAT All-Sky Survey databases; (2) The introduction of the False Discovery Rate into Astronomy; (3) The role of kernel shape in optimizing cluster detection; (4) A new determination of the X-ray Cluster Luminosity Function which has bearing on the existence of a ``deficit'' of high redshift, high luminosity clusters. This research is part of our ``Computational AstroStatistics'' collaboration (see Nichol et al. 2000) and the algorithms and techniques discussed herein will form part of the ``Virtual Observatory'' analysis toolkit.Comment: To appear in Proceedings of MPA/MPE/ESO Conference "Mining the Sky", July 31 - August 4, 2000, Garching, German

Performance Study of Software AER-Based Convolutions on a Parallel Supercomputer

This paper is based on the simulation of a convolution model for bioinspired neuromorphic systems using the Address-Event-Representation (AER) philosophy and implemented in the supercomputer CRS of the University of Cadiz (UCA). In this work we improve the runtime of the simulation, by dividing an image into smaller parts before AER convolution and running each operation in a node of the cluster. This research involves a test cases design in which the optimal parameters are set to run the AER convolution in parallel processors. These cases consist on running the convolution taking an image divided in different number of parts, applying to each part a Sobel filter for edge detection, and based on the AER-TOOL simulator. Execution times are compared for all cases and the optimal configuration of the system is discussed. In general, CRS obtain better performances when the image is divided than for the whole image.Ministerio de Ciencia e Innovación TEC2009-10639-C04-0

Exploring Systems Performance Using Modeling and Simulation – Project-based Study and Teaching

Modeling and Simulation (M&S) provides a risk-free environment allowing the users to experiment in a computer-generated virtual platform and analyze the what-if scenarios for effective decision support systems. Due to its pervasive usefulness, the concept of M&S is widely used across many sectors, including manufacturing, warehouse operations, supply chain, logistics, transportation, mining, and many more. The field of M&S requires computer-intensive and software-based training, which is very different from teaching in a regular classroom setting. Hence, we develop a three-stage (mimic-guide-scaffold) project-based teaching strategy to enhance students learning experience in M&S education. Here, students first follow the instructor to understand basics of simulation and become familiar with AnyLogic software. Second, the students work on a group project under the passive supervision of the instructor to enhance their problem-solving capability. In the third step, students work independently on a similar but extensive project to scaffold their knowledge. The project was designed to answer three high-level key research questions for a hospital system including systems throughput, resource utilization, and patients’ length of stay reduction. We performed a thorough evaluation using an anonymous survey, where thirty-one students participated to provide their feedback. This paper provides a detailed description of the projects including problem statements, learning objectives, evaluation rubrics, data collection criteria, and evaluation outcomes with detailed discussion

Program evaluation of a school district\u27s multisensory reading initiative

The purpose of this study was to conduct a formative program evaluation of a school district\u27s multisensory reading initiative. The mixed methods study involved semi-structured interviews, online survey, focus groups, document review, and analysis of extant special education student reading achievement data. Participants included elementary special education teachers of high incidence students with disabilities, elementary assistant principals, central office special education leaders, and contracted training partners. Facilitating conditions that supported multisensory reading instruction included supportive school administrators, professional learning communities, intensive initial professional development, plentiful instructional materials, and supportive central office personnel. Constraints included school master schedules, limited time for small group specialized reading instruction, inconsistent frequency and duration of multisensory instruction, reading instruction not aligned to student needs, inconsistent progress monitoring, isolation of multisensory skills without application, and inconsistent levels of administrative support. A correlation between hours of multisensory instruction and gain scores on the Developmental Reading Assessment (DRA2) showed no statistically significant relationship. Recommendations to strengthen the implementation of multisensory reading instruction included: providing additional and effective follow-up professional development, developing required progress monitoring tools, exploring assessments more aligned with multisensory instruction, fostering school-based reading PLCs, building accountability procedures that assist school administrators in supervising teacher implementation, and developing a comprehensive curriculum with more detailed lessons and pacing guides. Recommendations for continued program evaluation are included with an annual process of review, including formal summative evaluation

Program evaluation of a school district\u27s multisensory reading initiative

The purpose of this study was to conduct a formative program evaluation of a school district\u27s multisensory reading initiative. The mixed methods study involved semi-structured interviews, online survey, focus groups, document review, and analysis of extant special education student reading achievement data. Participants included elementary special education teachers of high incidence students with disabilities, elementary assistant principals, central office special education leaders, and contracted training partners. Facilitating conditions that supported multisensory reading instruction included supportive school administrators, professional learning communities, intensive initial professional development, plentiful instructional materials, and supportive central office personnel. Constraints included school master schedules, limited time for small group specialized reading instruction, inconsistent frequency and duration of multisensory instruction, reading instruction not aligned to student needs, inconsistent progress monitoring, isolation of multisensory skills without application, and inconsistent levels of administrative support. A correlation between hours of multisensory instruction and gain scores on the Developmental Reading Assessment (DRA2) showed no statistically significant relationship. Recommendations to strengthen the implementation of multisensory reading instruction included: providing additional and effective follow-up professional development, developing required progress monitoring tools, exploring assessments more aligned with multisensory instruction, fostering school-based reading PLCs, building accountability procedures that assist school administrators in supervising teacher implementation, and developing a comprehensive curriculum with more detailed lessons and pacing guides. Recommendations for continued program evaluation are included with an annual process of review, including formal summative evaluation

Kriging metamodeling for simulation

Many scientific disciplines use mathematical models to describe complicated real systems. Often, analytical methods are inadequate, so simulation is applied. This thesis focuses on computer intensive simulation experiments in Operations Research/Management Science. For such experiments it is necessary to apply interpolation. In this thesis, Kriging interpolation for random simulation is proposed and a novel type of Kriging - called Detrended Kriging - is developed. Kriging turns out to give better predictions in random simulation than classic low-order polynomial regression. Kriging is not sensitive to variance heterogeneity: i.e. Kriging is a robust method. Moreover, the thesis develops a novel method to select experimental designs for expensive simulation. This method is sequential, and accounts for the specific input/output function implied by the underlying simulation model. For deterministic simulation the designs are constructed through cross-validation and jackknifing, whereas for random simulation the customization is achieved through bootstrapping. The novel method simulates relatively more input combinations in the interesting parts of the input/output function, and gives better predictions than traditional Latin Hypercube Sample designs with prefixed sample sizes.

On Experimental Designs for Derivative Random Fields

Es werden differenzierbare zufällige Felder zweiter Ordnung untersucht und Vorschläge zur Versuchsplanung von Beobachtungen der abgeleiteten Felder unterbreitet. Von einem gewissen Standpunkt aus werden die folgenden Fragen beantwortet: Wie viele Informationen liefern Beobachtungen von Ableitungen für die Vorhersage des zugrunde liegenden Stochastischen Feldes? Wie beeinflusst eine a priori Wahl der Kovarianzfunktion das Informationsverhältnis zwischen verschiedenen abgeleiteten Feldern im Hinblick auf die Vorhersage? Als Zielfunktion wird das so genannte "imse-update" für den besten linearen Prädiktor betrachtet. Den zentralen Teil stellt die Untersuchung von Versuchsplänen mit (asymptotisch) verschwindenden Korrelationen dar. Hier wird insbesondere der Einfluss der Maternschen Klasse und J-Besselschen Klassen von Kovarianzfuntionen untersucht. Ferner wird der Einfluss gleichzeitiger Beobachtung von verschiedenen Ableitungen untersucht. Schließlich werden einige empirische Studien durchgeführt, aus denen einige praktische Ratschläge abgeleitet werden

A Tracking Algorithm For Cell Motility Assays in CMOS Systems

This work proposes a method for the study and real-time monitoring of a single cell on a 2D electrode matrix, of great interest in cell motility assays and in the characterization of cancer cell metastasis. A CMOS system proposal for cell location based on occupation maps data generated from Electrical Cell-substrate Impedance Spectroscopy (ECIS) has been developed. From experimental assays data, an algorithm based on the analysis of the eight nearest neighbours has been implemented to find the cell center of mass. The path followed by a cell, proposing a Brownian route, has been simulated with the proposed algorithm. The presented results give an accuracy over 95% in the determination of the coordinates (x, y) from the expected cell center of mass.Ministerio de Economía y Competitividad TEC2013-46242-C3-1-

Engineering-Driven Learning Approaches for Bio-Manufacturing and Personalized Medicine

Healthcare problems have tremendous impact on human life. The past two decades have witnessed various biomedical research advances and clinical therapeutic effectiveness, including minimally invasive surgery, regenerative medicine, and immune therapy. However, the development of new treatment methods relies heavily on heuristic approaches and the experience of well-trained healthcare professionals. Therefore, it is often hindered by patient-specific genotypes and phenotypes, operator-dependent post-surgical outcomes, and exorbitant cost. Towards clinically effective and in-expensive treatments, this thesis develops analytics-based methodologies that integrate statistics, machine learning, and advanced manufacturing. Chapter 1 of my thesis introduces a novel function-on-function surrogate model with application to tissue-mimicking of 3D-printed medical prototypes. Using synthetic metamaterials to mimic biological tissue, 3D-printed medical prototypes are becoming increasingly important in improving surgery success rates. Here, the objective is to model mechanical response curves via functional metamaterial structures, and then conduct a tissue-mimicking optimization to find the best metamaterial structure. The proposed function-on-function surrogate model utilizes a Gaussian process for efficient emulation and optimization. For functional inputs, we propose a spectral-distance correlation function, which captures important spectral differences between two functional inputs. Dependencies for functional outputs are then modeled via a co-kriging framework. We further adopt shrinkage priors to learn and incorporate important physics. Finally, we demonstrate the effectiveness of the proposed emulator in a real-world study on heart surgery. Chapter 2 proposes an adaptive design method for experimentation under response censoring, often encountered in biomedical experiments. Censoring would result in a significant loss of information, and thereby a poor predictive model over an input domain. For such problems, experimental design is paramount for maximizing predictive power with a limited budget for expensive experimental runs. We propose an integrated censored mean-squared error (ICMSE) design method, which first estimates the posterior probability of a new observation being censored and then adaptively chooses design points that minimize predictive uncertainty under censoring. Adopting a Gaussian process model with product correlation functions, our ICMSE criterion has an easy-to-evaluate expression for efficient design optimization. We demonstrate the effectiveness of the ICMSE method in an application of medical device testing. Chapter 3 develops an active image synthesis method for efficient labeling (AISEL) to improve the learning performance in healthcare and medicine tasks. This is because the limited availability of data and the high costs of data collection are the key challenges when applying deep neural networks to healthcare applications. Our AISEL can generate a complementary dataset, with labels actively acquired to incorporate underlying physical knowledge at hand. AISEL framework first leverages a bidirectional generative invertible network (GIN) to extract interpretable features from training images and generate physically meaningful virtual ones. It then efficiently samples virtual images to exploit uncertain regions and explore the entire image space. We demonstrate the effectiveness of AISEL on a heart surgery study, where it lowers the labeling cost by 90% while achieving a 15% improvement in prediction accuracy. Chapter 4 presents a calibration-free statistical framework for the promising chimeric antigen receptor T cell therapy in fighting cancers. The objective is to effectively recover critical quality attributes under the intrinsic patient-to-patient variability, and therefore lower the cost of cell therapy. Our calibration-free approach models the patient-to-patient variability via a patient-specific calibration parameter. We adopt multiple biosensors to construct a patient-invariance statistic and alleviate the effect of the calibration parameter. Using the patient-invariance statistic, we can then recover the critical quality attribute during cell culture, free from the calibration parameter. In a T cell therapy study, our method effectively recovers viable cell concentration for cell culture monitoring and scale-up.Ph.D

Solving optimisation problems in metal forming using FEM: A metamodel based optimisation algorithm

During the last decades, Finite Element (FEM) simulations of metal forming processes have\ud become important tools for designing feasible production processes. In more recent years,\ud several authors recognised the potential of coupling FEM simulations to mathematical opti-\ud misation algorithms to design optimal metal forming processes instead of only feasible ones.\ud This report describes the selection, development and implementation of an optimisa-\ud tion algorithm for solving optimisation problems for metal forming processes using time\ud consuming FEM simulations. A Sequential Approximate Optimisation algorithm is pro-\ud posed, which incorporates metamodelling techniques and sequential improvement strate-\ud gies for enhancing the e±ciency of the algorithm. The algorithm has been implemented in\ud MATLABr and can be used in combination with any Finite Element code for simulating\ud metal forming processes.\ud The good applicability of the proposed optimisation algorithm within the ¯eld of metal\ud forming has been demonstrated by applying it to optimise the internal pressure and ax-\ud ial feeding load paths for manufacturing a simple hydroformed product. Resulting was\ud a constantly distributed wall thickness throughout the ¯nal product. Subsequently, the\ud algorithm was compared to other optimisation algorithms for optimising metal forming\ud by applying it to two more complicated forging examples. In both cases, the geometry of\ud the preform was optimised. For one forging application, the algorithm managed to solve\ud a folding defect. For the other application both the folding susceptibility and the energy\ud consumption required for forging the part were reduced by 10% w.r.t. the forging process\ud proposed by the forging company. The algorithm proposed in this report yielded better\ud results than the optimisation algorithms it was compared to