Genome-Wide Cell-Free DNA Methylation Profiling in Advanced Stage Ovarian Cancer. Are We Looking at the Tumor or the Patient's Immune Response to the Tumor?
Caroline B. van den BergGatske M. Nieuwenhuyzen-de BoerIngrid BoereRuben BoersJoachim BoersWilfred F. J. van IJckenMaurice P.H.M. JansenTugce Senturk KirmizitasJoost GribnauHeleen J. van Beekhuizen
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Profiling (computer programming)
Profiling (computer programming)
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We are currently reviewing our method of profiling. We are keen to hear from any other areas who have successfully implemented a profiling format to be used by district nurses and health visitors as a resource for needs assessment.
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We propose a learner profiling system that uses the multidimensional characteristic analysis of learners. For this purpose, a real-time monitoring system for learners was developed and then used to extract information of the characteristics of learners that appear in learning environments. The extracted information on the characteristics of learners is automatically constructed into personalized learner profiles through the learner profiling system. The contents of learner profiles consist of the cognitive ability of learners, the learning condition, and teacher assessment. To verify the effectiveness of the proposed study method, profiles of actual learners were constructed, with 210 elementary school students as subjects. As a result, the group that learned using the learner profiling showed a significant level of satisfaction in comparison to the group that did not use learner profiling. From this, it was demonstrated that the learner profiling system proposed in this paper could enhance learning effectiveness.
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Application profiling is an important step in the design and optimization of embedded systems. Accurately identifying and analyzing the execution of frequently executed computational kernels is needed to effectively optimize the system implementation, at both design time and runtime. Most previous profiling approaches are software based, which can incur significant overhead and may be prohibitive or impractical for profiling embedded systems at runtime. In addition, profiling methods typically focus on profiling the execution of specific tasks executing on a single core, but do not consider accurate and holistic profiling across multiple processor cores. Directly utilizing and naively combining isolated profiles from multiple processor cores can lead to significant profile inaccuracy. In this paper, we present a hardware-based dynamic application profiler for non-intrusively and accurately profiling software applications in multicore embedded systems. The profiler provides a detailed execution profile for computational kernels and maintains profile accuracy across multiple processor cores. The hardware-based profiler achieves an average error of less than 0.5% for the percentage execution time of profiled applications.
Profiling (computer programming)
Multi-core processor
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Designing a system usually acquires lots of instincts and knowledge to harmonize the computing resources under the paradigm of application specific heterogeneous systems. This paper presents a phase-based profiling mechanism to speed up the process of learning how application behaviors perform on the hardware and vice versa. By analyzing program phases, performance information can be gathered in a way that highlights the performance of high-level tasks in an application running on different hardware settings. We evaluated our phase-based profiling framework using QEMU, employing approximate timing models and mechanisms to track functions/events in programs and operating systems of the guest system. Furthermore, by using timing simulations, it is possible to escape the confined boundaries of real-world machine based systems, and to rapidly explore the impact of hardware parameters on the system performance. In our experimental results, phase-based profiling yields useful information of the runtime behaviors and performance of a program, allowing developers to discover program bottlenecks, and predicts the performance of optimization ideas on the software and/or underlying hardware. Our results suggest that incorporating phase profiling with the timing approximate simulator helps to facilitate hardware and software co-design.
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With the growing amount of information being digitized, users find it difficult to obtain the most relevant information that is hidden in the deluge of information returned to them by search engines. In this paper, we describe a conceptual model using a hybrid approach based on user profiling approaches and information filtering techniques that can alleviate the problem of information overload. We discuss the concepts, namely static content profiling, static collaborative profiling, dynamic content profiling and dynamic collaborative profiling, followed by the design and implementation of a library search facility, which has employed these concepts. Preliminary experiments conducted have shown that users have benefited from our prototype system. It appears that there is value in employing these new concepts that we have proposed in the design and implementation of future information retrieval system for better retrieval.
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We advocate that simulation based on offline profiling is a promising approach to better understand and improve the complex ML systems. Our approach uses operation-level profiling and dataflow based simulation to ensure it offers a unified and automated solution for all frameworks and ML models, and is also accurate by considering the various parallelization strategies in a real system.
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