Improving average ranking precision in user searches for biomedical research datasets

Availability of research datasets is keystone for health and life science study reproducibility and scientific progress. Due to the heterogeneity and complexity of these data, a main challenge to be overcome by research data management systems is to provide users with the best answers for their search queries. In the context of the 2016 bioCADDIE Dataset Retrieval Challenge, we investigate a novel ranking pipeline to improve the search of datasets used in biomedical experiments. Our system comprises a query expansion model based on word embeddings, a similarity measure algorithm that takes into consideration the relevance of the query terms, and a dataset categorisation method that boosts the rank of datasets matching query constraints. The system was evaluated using a corpus with 800k datasets and 21 annotated user queries. Our system provides competitive results when compared to the other challenge participants. In the official run, it achieved the highest infAP among the participants, being +22.3% higher than the median infAP of the participant's best submissions. Overall, it is ranked at top 2 if an aggregated metric using the best official measures per participant is considered. The query expansion method showed positive impact on the system's performance increasing our baseline up to +5.0% and +3.4% for the infAP and infNDCG metrics, respectively. Our similarity measure algorithm seems to be robust, in particular compared to Divergence From Randomness framework, having smaller performance variations under different training conditions. Finally, the result categorization did not have significant impact on the system's performance. We believe that our solution could be used to enhance biomedical dataset management systems. In particular, the use of data driven query expansion methods could be an alternative to the complexity of biomedical terminologies

Real-Time Power-Efficient Integration of Multi-Sensor Occupancy Grid on Many-Core

International audienceSafe Autonomous Vehicles (AVs) will emerge when comprehensive perception systems will be successfully integrated into vehicles. Advanced perception algorithms, estimating the position and speed of every obstacle in the environment by using data fusion from multiple sensors, were developed for AV prototypes. Computational requirements of such application prevent their integration into AVs on current low-power embedded hardware. However, recent emerging many-core architectures offer opportunities to fulfill the automotive market constraints and efficiently support advanced perception applications. This paper, explores the integration of the occupancy grid multi-sensor fusion algorithm into low power many-core architectures. The parallel properties of this function are used to achieve real-time performance at low-power consumption. The proposed implementation achieves an execution time of 6.26ms, 6× faster than typical sensor output rates and 9× faster than previous embedded prototypes

Power filters for gravitational wave bursts: network operation for source position estimation

A method is presented to generalize the power detectors for short bursts of gravitational waves that have been developed for single interferometers so that they can optimally process data from a network of interferometers. The performances of this method for the estimation of the position of the source are studied using numerical simulations.Comment: To appear in the proceedings of GWDAW 2002 (Classical and Quantum Gravity, Special issue

A Power Consumption Estimation Approach for Embedded Software Design using Trace Analysis

International audienceWith the explosion of advanced power control knobs such as dynamic voltage frequency scaling, mastering energy constraints in embedded systems is becoming challenging for software developers. Several power estimation techniques have been proposed over the past years, from electrical level to more abstract models such as SystemC/TLM. They offer various trade-offs between performance and accuracy, but suffer from a number of shortcomings. They are expensive and time-consuming, requiring intricate models of the architecture and finally, fail to be applied from the software developer perspective. In this paper, we propose a lightweight and cost-effective approach suitable for software developers. It relies on trace analysis and high-level modeling of architectures to perform quick and efficient power consumption estimations without loosing accuracy. This approach is fully supported by a tool and is validated using a simple thermal mitigation case study and checked against physical measurements. We show that, for our case study, the relative error between our tool and real values is 8% in average

Energy Management via PI Control for Data Parallel Applications with Throughput Constraints

International audienceThis paper presents a new proportional-integral (PI) controller that sets the operating point of computing tiles in a system on chip (SoC). We address data-parallel applications with throughput constraints. The controller settings are investigated for application configurations with different QoS levels and different buffer sizes. The control method is evaluated on a test chip with four tiles executing a realistic HMAX object recognition application. Experimental results suggest that the proposed controller outperforms the state-of-the-art results: it attains, on average, 25% less number of frequency switches and has slightly higher energy savings. The reduction in number of frequency switches is important because it decreases the involved overhead. In addition, the PI controller meets the throughput constraint in cases where other approaches fail

A simulation framework for rapid prototyping and evaluation of thermal mitigation techniques in many-core architectures

International audienceModern SoCs are characterized by increasing power density and consequently increasing temperature, that directly impacts performances, reliability and cost of a device through its packaging. Thermal issues need to be predicted and mitigated as early as possible in the design flow, when the optimization opportunities are the highest. In this paper, we present an efficient framework for the design of dynamic thermal mitigation schemes based on a high-level SystemC virtual prototype tightly coupled with efficient power and thermal simulation tools. We demonstrate the benefit of our approach through silicon comparison with the SThorm 64-core architecture and provide simulation speed results making it a sound solution for the design of thermal mitigation early in the flow

System-Level Modeling, Analysis and Code Generation: Object Recognition Case Study

International audienceOne of the most important challenges in complex embedded systems design is developing methods and tools for modeling and analyzing the behavior of application software running on multi-processor platforms. We propose a tool-supported flow for systematic and compositional construction of mixed software/hardware system models. These models are intended to represent, in an operational way, the set of timed executions of parallel application software statically mapped on a multi-processor platform. As such, system models will be used for performance analysis using simulation-based techniques as well as for code generation on specific platforms. The construction of the system model proceeds in two steps. In the first step, an abstract system model is obtained by composition and specific transformations of (1) the (untimed) model of the application software, (2) the model of the platform and (3) the mapping between them. In the second step, the abstract system model is refined into concrete system model, by including specific timing constraints for execution of the application software, according to chosen mapping on the platform. We illustrate the system model construction method and its use for performance analysis and code generation on an object recognition application provided by Hellenic Airspace Industry. This case study is build upon the HMAX models algorithm [RP99] and is looking at significant speedup factors. This paper reports results obtained on different system model configurations and used to determine the optimal implementation strategy in accordance to hardware resources

DRC 2 : Dynamically Reconfigurable Computing Circuit based on Memory Architecture

International audienceThis paper presents a novel energy-efficient and Dynamically Reconfigurable Computing Circuit (DRC²) concept based on memory architecture for data-intensive (imaging, …) and secure (cryptography, …) applications. The proposed computing circuit is based on a 10-Transistor (10T) 3-Port SRAM bitcell array driven by a peripheral circuitry enabling all basic operations that can be traditionally performed by an ALU. As a result, logic and arithmetic operations can be entirely executed within the memory unit leading to a significant reduction in power consumption related to the data transfer between memories and computing units. Moreover, the proposed computing circuit can perform extremely-parallel operations enabling the processing of large volume of data. A test case based on image processing application and using the saturating increment function is analytically modeled to compare conventional and DRC²-based approaches. It is demonstrated that DRC²-based approach provides a reduction of clock cycle number of up to 2x. Finally, potential applications and must-be-considered changes at different design levels are discussed

Multilingual RECIST classification of radiology reports using supervised learning.

OBJECTIVES The objective of this study is the exploration of Artificial Intelligence and Natural Language Processing techniques to support the automatic assignment of the four Response Evaluation Criteria in Solid Tumors (RECIST) scales based on radiology reports. We also aim at evaluating how languages and institutional specificities of Swiss teaching hospitals are likely to affect the quality of the classification in French and German languages. METHODS In our approach, 7 machine learning methods were evaluated to establish a strong baseline. Then, robust models were built, fine-tuned according to the language (French and German), and compared with the expert annotation. RESULTS The best strategies yield average F1-scores of 90% and 86% respectively for the 2-classes (Progressive/Non-progressive) and the 4-classes (Progressive Disease, Stable Disease, Partial Response, Complete Response) RECIST classification tasks. CONCLUSIONS These results are competitive with the manual labeling as measured by Matthew's correlation coefficient and Cohen's Kappa (79% and 76%). On this basis, we confirm the capacity of specific models to generalize on new unseen data and we assess the impact of using Pre-trained Language Models (PLMs) on the accuracy of the classifiers