Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhanced dual-stage correlated diffusion imaging

Prostate cancer is the most common form of cancer and third leading cause of cancer death in Canadian men. Multi-parametric magnetic resonance imaging (mpMRI) has become a powerful non-invasive diagnostic tool for the detection of prostate cancer. Among mpMRI imaging modalities, diffusion-weighed imaging has shown the most promising results in accurate detection of prostate cancer. Introduced recently, correlated diffusion imaging (CDI) is a new form of diffusion imaging which accounts for the joint correlation of diffusion signal attenuation across multiple gradient pulse strengths and timings to improve the separability of cancerous and healthy tissues. Dual-stage CDI (D-CDI) is a newer generation of CDI where in contrast to CDI that does not capture anatomical information, an additional signal mixing stage between the correlated diffusion signal from the first signal mixing stage (CDI) and an auxiliary diffusion signal is performed to incorporate anatomical context. The core of D-CDI is a signal mixing algorithm that combines diffusion images at different b values to construct a single image. In this paper, we enhance the signal mixing algorithm to optimize the contribution of each single b-value image to maximize the separability of cancerous and healthy tissues. We evaluated the enhanced D-CDI (eD-CDI) using area under the ROC curve for datasets of 17 patient cases with confirmed prostate cancer and the results show that eD-CDI outperforms the original D-CDI as well as T2 weighted images and diffusion-weighed images used in the form of apparent diffusion coefficient maps.
Wireless brain computer interfaces enabling synchronized optogenetics and electrophysiology

This paper presents different miniature wireless brain computer interfaces (BCI) enabling synchronized optogenetics and electrophysiology recording for various experimental conditions. These devices, which are entirely built using commercial off-the-shelf components, are validated in-vivo with small transgenic mice. First, a system including 32 electrophysiological recording channels and up to 32 high-power optical stimulation channels is presented. It can process 32 neuronal signals in parallel with high compression ratio using an embedded digital field-programmable gate array (FPGA) signal processor performing spike detection and data compression in-situ. Then, an advanced version featuring equivalent characteristics, but having the third of the size and the weight is presented. The design of a third system dedicated to small freely moving animals is presented. It includes 4 electrophysiological recording channels and 1 high-power optical stimulation channel. In-vivo result obtained in freely moving mice with this system are reported. This latter system performs aggressive data reduction using a resource optimized spike detector running on a low-power microcontroller unit. Finally, a multichannel optical stimulator operating within a network of up to 6 active devices is presented for enabling optogenetic behavioral experiments with several mice at once. These different wireless BCI all provide a multimodal access to brain activity through optogenetics and large-scale electrophysiology.
BEAR: Techniques for mitigating bandwidth bloat in gigascale DRAM caches

Die stacking memory technology can enable gigascale DRAM caches that can operate at 4x-8x higher bandwidth than commodity DRAM. Such caches can improve system performance by servicing data at a faster rate when the requested data is found in the cache, potentially increasing the memory bandwidth of the system by 4x-8x. Unfortunately, a DRAM cache uses the available memory bandwidth not only for data transfer on cache hits, but also for other secondary operations such as cache miss detection, fill on cache miss, and writeback lookup and content update on dirty evictions from the last-level on-chip cache. Ideally, we want the bandwidth consumed for such secondary operations to be negligible, and have almost all the bandwidth be available for transfer of useful data from the DRAM cache to the processor. We evaluate a 1GB DRAM cache, architected as Alloy Cache, and show that even the most bandwidth-efficient proposal for DRAM cache consumes 3.8x bandwidth compared to an idealized DRAM cache that does not consume any bandwidth for secondary operations. We also show that redesigning the DRAM cache to minimize the bandwidth consumed by secondary operations can potentially improve system performance by 22%. To that end, this paper proposes Bandwidth Efficient ARchitecture (BEAR) for DRAM caches. BEAR integrates three components, one each for reducing the bandwidth consumed by miss detection, miss fill, and writeback probes. BEAR reduces the bandwidth consumption of DRAM cache by 32%, which reduces cache hit latency by 24% and increases overall system performance by 10%. BEAR, with negligible overhead, outperforms an idealized SRAM Tag-Store design that incurs an unacceptable overhead of 64 megabytes, as well as Sector Cache designs that incur an SRAM storage overhead of 6 megabytes.
An approach to identify commonalities in web application engineering for a web content management system

The process of Web applications engineering can be complex and time consuming. We argue that Web engineering based on a standardized platform with reusable components is a logical next step in the evolution of Web application development. One popular platform to create Web applications is called a Web Content Management Systems (WCMS) which allows organizations to develop Web applications in a time and resource efficient way. This paper presents a method to identify software commonalities in WCMS-based Web applications to improve the software product for future implementations based on feature modeling and e-business models. The resulting method provides insight in relevant e-business models and their corresponding functionalities. Moreover, this paper shows how these commonalities can be identified and how that could influence the software product line. The approach has been applied in a practical case study of a series of Web application engineering projects within the publishing vertical market. We have validated the approach with experts within the case study company and found that the approach is useful in aiding requirements engineers in the Web application engineering process and product managers in the software product management process.
A UML-based meta-framework for system design in public health informatics

UNLABELLED The National Agenda for Public Health Informatics calls for standards in data and knowledge representation within public health, which requires a multi-level framework that links all aspects of public health. METHOD The literature of public health informatics and public health informatics application were reviewed. A UML-based systems analysis was performed. Face validity of results was evaluated in analyzing the public health domain of lead poisoning. RESULTS The core class of the UML-based system of public health is the Public Health Domain, which is associated with multiple Problems, for which Actors provide Perspectives. Actors take Actions that define, generate, utilize and/or evaluate Data Sources. The life cycle of the domain is a sequence of activities attributed to its problems that spirals through multiple iterations and realizations within a domain. CONCLUSION The proposed Public Health Informatics Meta-Framework broadens efforts in applying informatics principles to the field of public health
Stem Trace: an interactive visual tool for comparative RNA structure analysis

MOTIVATION Stem Trace is one of the latest tools available in STRUCTURELAB, an RNA structure analysis computer workbench. The paradigm used in STRUCTURELAB views RNA structure determination as a problem of dealing with a database of a large number of computationally generated structures. Stem Trace provides the capability to analyze this data set in a novel, visually driven, interactive and exploratory way. In addition to providing graphs at a high level of ion, it is also connected with complementary visualization tools which provide orthogonal views of the same data, as well as drawing of structures represented by a stem trace. Thus, on top of being an analysis tool, Stem Trace is a graphical user interface to an RNA structural information database. RESULTS We illustrate Stem Trace's capabilities with several examples of the analysis of RNA folding data performed on 24 strains of HIV-1, HIV-2 and SIV sequences around the HIV dimerization region. This dimer linkage site has been found to play a role in encapsidation, reverse transcription, recombination, and inhibition of translation. Our examples show how Stem Trace elucidates preservation of structures in this region across the various strains of HIV. AVAILABILITY The program can be made available upon request. It runs on SUN, SGI and DEC (Compaq) Unix workstations.
Distributed Fusion Parameters Extraction for Integrated System Health Management to Space Avionics

This paper addresses a parameter extraction problem for space avionics integrated system health management. A distributed fusion-based parameter extraction scheme is developed by fusing the preprocessed results of the space avionics multi-sensor module. Because of the complex environment in space avionics, the feature parameters of electronic circuit in avionics systems are often corrupted by temporally correlated noise. For these correlated noise parameter systems, a distributed fusion parameters extraction algorithm is presented, in which the traditional method of dealing with correlated noise by augmenting the systems state vector is avoided. The proposed distributed fusion parameters extraction algorithm guarantees optimality in the sense of being equivalent to the optimal centralized estimated fusion. Numerical examples are provided to demonstrate the performance of the new algorithm.
A new hybrid system identification algorithm with automatic tuning

Hybrid system identification is composed of two subproblems: estimate the discrete state or mode for each data point, and estimate the submodel governing the dynamics of the continuous state for each mode. For linear hybrid systems, the paper proposes to tackle these problems in a single step by simultaneously approximating each submodel while associating data points to each of these. The method borrows ideas from bounded-error approaches and Support Vector Regression to extend the algebraic procedure. The algorithm can easily deal with noise by fixing a predefined accuracy threshold. This bound on the error can be different for each mode when the noise level is considered to switch with the system. An extension of the algorithm to automatically tune itself in accordance with the noise level of each mode is also provided. The method can be seen as an an extension of the algebraic approach, but also as an alternative to the bounded error approach when a predefined or preferred model structure is given or when the noise level is unknown. An extension of the method to nonlinear submodels is provided in the companion paper (Lauer and Bloch, 2008).
Selective Acquisition Techniques for Enculturation-Based Melodic Phrase Segmentation

Automatic melody segmentation is an important yet un-solved problem in Music Information Retrieval. Research in the ﬁeld of Music Cognition suggests that previous listening experience plays a considerable role in the perception of melodic segment structure. At present automatic melody segmenters that model listening experience commonly do so using unsupervised statistical learning with ‘non-selective’ information acquisition techniques, i.e. the learners gather and store information indiscriminately into memory. In this paper we investigate techniques for ‘selective’ information acquisition, i.e. our learning model uses a goal-oriented approach to select what to store in memory. We test the usefulness of the segmentations produced using selective acquisition learning in a melody classiﬁcation ex-periment involving melodies of different cultures. Our re-sults show that the segments produced by our selective learner segmenters substantially improve classiﬁcation accuracy when compared to segments produced by a non-selective learner segmenter, two local segmentation meth-ods, and two na¨ıve baselines.
Heterotrophic Production of Omega-3 Long-Chain Polyunsaturated Fatty Acids by Trophically Converted Marine Diatom Phaeodactylum tricornutum

We have created via metabolic engineering a heterotrophic strain of Phaeodactylum tricornutum that accumulates enhanced levels of the high value omega-3 long chain polyunsaturated fatty acid (LC-PUFAs) docosahexaenoic acid (DHA). This was achieved by generation of transgenic strains in which the Δ5-elongase from Ostreococcus tauri was co-expressed with a glucose transporter from the moss Physcomitrella patens. This double transformant has the capacity to grow in the dark in liquid medium supplemented with glucose and accumulate substantial levels of omega-3 LC-PUFAs. The effects of glucose concentrations on growth and LC-PUFA production of wild type and transformed strains cultivated in the light and dark were studied. The highest omega-3 LC-PUFAs accumulation was observed in cultures grown under mixotrophic conditions in the presence of 1% glucose (up to 32.2% of total fatty acids, TFA). Both DHA and EPA are detected at high levels in the neutral lipids of transgenic cells grown under phototrophic conditions, averaging 36.5% and 23.6% of TFA, respectively. This study demonstrates the potential for P. tricornutum to be developed as a viable commercial strain for both EPA and DHA production under mixo- and heterotrophic conditions.
Stochastic Analyses of Electric Vehicle Charging Impacts on Distribution Network

A stochastic modeling and simulation technique for analyzing impacts of electric vehicles charging demands on distribution network is proposed in this paper. Different from the previous deterministic approaches, the feeder daily load models, electric vehicle start charging time, and battery state of charge used in the impact study are derived from actual measurements and survey data. Distribution operation security risk information, such as over-current and under-voltage, is obtained from three-phase distribution load flow studies that use stochastic parameters drawn from Roulette wheel selection. Voltage and congestion impact indicators are defined and a comparison of the deterministic and stochastic analytical approaches in providing information required in distribution network reinforcement planning is presented. Numerical results illustrate the capability of the proposed stochastic models in reflecting system losses and security impacts due to electric vehicle integrations. The effectiveness of a controlled charging algorithm aimed at relieving the system operation problem is also presented.
A reliable and efficient procedure for oscillator PPV computation, with phase noise macromodeling applications

The main effort in oscillator phase noise calculation and macromodeling lies in computing a vector function called the perturbation projection vector (PPV). Current techniques for PPV calculation use time-domain numerics to generate the system's monodromy matrix, followed by full or partial eigenanalysis. We present superior methods that find the PPV using only a single linear solution of the oscillator's time- or frequency-domain steady-state Jacobian matrix. The new methods are better suited for implementation in existing tools with harmonic balance or shooting capabilities (especially those incorporating "fast" variants), and can also be more accurate than explicit eigenanalysis. A key advantage is that they dispense with the need to select the correct one eigenfunction from amongst a potentially large set of choices, an issue that explicit eigencalculation-based methods have to face. We illustrate the new methods in detail using LC and ring oscillators.
Bassuk et al . : Ground-penetrating Radar Locates Tree Roots in Two Soil Media Under Pavement

This study involved locating tree roots with a ground-penetrating radar (GPR) system and then examining excavated roots in the same soil volume to compare the accuracy of the GPR system with true root location. In 2003, Acer platanoides ‘Emerald Queen’ Norway maples were planted in trenches containing two compacted soils (native silt loam and CU-Structural Soil). The trenches were paved with 10 cm of concrete. In 2008, a GPR system consisting of a 900 MHz antenna mounted on a root-scanning cart was used to conduct linear scans on top of the concrete. Immediately after scanning, the concrete was removed for selected trees and whole root systems were excavated (as an entire system attached to the tree trunk) using an air excavation tool. Regression analysis using mixed effect models showed that the radar reliably predicted root presence in both the native and structural soils. The root count correlations were r = 0.76 and r = 0.81 for the native and structural soils, respectively. In the compacted native soil under concrete, the radar output overestimated the presence of roots at the minimum detection diameter but did provide a signal associated with root presence at this detection level. In the structural soil under concrete, the radar output reliably predicted roots with only slight overestimation. This study showed that GPR data reliably predicted the presence and locations of roots under the concrete pavement in two compacted soils.
Environmental statistics and the trade-off between model-based and TD learning in humans

There is much evidence that humans and other animals utilize a combination of model-based and model-free RL methods. Although it has been proposed that these systems may dominate according to their relative statistical efficiency in different circumstances, there is little specific evidence — especially in humans — as to the details of this trade-off. Accordingly, we examine the relative performance of different RL approaches under situations in which the statistics of reward are differentially noisy and volatile. Using theory and simulation, we show that model-free TD learning is relatively most disadvantaged in cases of high volatility and low noise. We present data from a decision-making experiment manipulating these parameters, showing that humans shift learning strategies in accord with these predictions. The statistical circumstances favoring model-based RL are also those that promote a high learning rate, which helps explain why, in psychology, the distinction between these strategies is traditionally conceived in terms of rule-based vs. incremental learning.
Multi-GPU Distributed Parallel Bayesian Differential Topic Modelling

There is an explosion of data, documents, and other content, and people require tools to analyze and interpret these, tools to turn the content into information and knowledge. Topic modeling have been developed to solve these problems. Topic models such as LDA [Blei et. al. 2003] allow salient patterns in data to be extracted automatically. When analyzing texts, these patterns are called topics. Among numerous extensions of LDA, few of them can reliably analyze multiple groups of documents and extract topic similarities. Recently, the introduction of differential topic modeling (SPDP) [Chen et. al. 2012] performs uniformly better than many topic models in a discriminative setting. There is also a need to improve the sampling speed for topic models. While some effort has been made for distributed algorithms, there is no work currently done using graphical processing units (GPU). Note the GPU framework has already become the most cost-efficient platform for many problems. In this thesis, I propose and implement a scalable multi-GPU distributed parallel framework which approximates SPDP. Through experiments, I have shown my algorithms have a gain in speed of about 50 times while being almost as accurate, with only one single cheap laptop GPU. Furthermore, I have shown the speed improvement is sublinearly scalable when multiple GPUs are used, while fairly maintaining the accuracy. Therefore on a medium-sized GPU cluster, the speed improvement could potentially reach a factor of a thousand. Note SPDP is just a representative of other extensions of LDA. Although my algorithm is implemented to work with SPDP, it is designed to be a general enough to work with other topic models. The speed-up on smaller collections (i.e., 1000s of documents), means that these more complex LDA extensions could now be done in real-time, thus opening up a new way of using these LDA models in industry.
Spread spectrum techniques for indoor wireless IR communications

Multipath dispersion and fluorescent light interference are two major problems in indoor wireless infrared communications systems. Multipath dispersion introduces intersymbol interference at data rates above 10 Mb/s, while fluorescent light induces severe narrowband interference to baseband modulation schemes commonly used such as OOK and PPM. This article reviews the research into the application of direct sequence spread spectrum techniques to ameliorate these key channel impairments without having to resort to complex signal Processing techniques. The inherent properties of a spreading sequence are exploited in order to combat the ISI and narrowband interference. In addition, to reducing the impact of these impairments, the DSSS modulation schemes have strived to be bandwidth-efficient and simple to implement. Three main DSSS waveform techniques have been developed and investigated. These are sequence inverse keying, complementary sequence inverse keying, and M-ary biorthogonal keying (MBOK). The operations of the three systems are explained; their performances were evaluated through simulations and experiments for a number of system parameters, including spreading sequence type and length. By comparison with OOK, our results show that SIK, CSIK, and MBOK are effective against multipath dispersion and fluorescent light interference because the penalties incurred on the DSSS schemes are between 0-7 dB, while the penalty on OOK in the same environment is more than 17 dB. The DSSS solution for IR wireless transmission demonstrates that a transmission waveform can be designed to remove the key channel impairments in a wireless IR system.
A New Approach for Detecting Urban Centers and Their Spatial Structure With Nighttime Light Remote Sensing

Urban spatial structure affects many aspects of urban functions and has implications for accessibility, environmental sustainability, and public expenditures. During the urbanization process, a careful and efficient examination of the urban spatial structure is crucial. Different from the traditional approach that relies on population or employment census data, this research exploits the nighttime light (NTL) intensity of the earth surface recorded by satellite sensors. The NTL intensity is represented as a continuous mathematical surface of human activities, and the elemental features of urban structures are identified by analogy with earth’s topography. We use a topographical metaphor of a mount to identify an urban center or subcenter and the surface slope to indicate an urban land-use intensity gradient. An urban center can be defined as a continuous area with higher concentration or density of employments and human activities. We successfully identified 33 urban centers, delimited their corresponding boundaries, and determined their spatial relations for Shanghai metropolitan area, by developing a localized contour tree method. In addition, several useful properties of the urban centers have been derived, such as 9% of Shanghai administrative area has become urban centers. We believe that this method is applicable to other metropolitan regions at different spatial scales.