Miracle-dz/UniFAR-FTUs huggingface dataset:
Triassic palaeosols in the upper narrabeen group of New South Wales

Abstract Detailed study of palaeosols in the sea cliffs between Long Reef and Palm Beach, north of Sydney, has given a much clearer idea of the environment and climate of the area during the late Scythian to early Anisian portion of Triassic time. At the base of the exposed succession, kaolinitic clayey soils with red B horizons were formed on volcanogenic sediments derived from the old Gerringong volcanic ridge to the east. The red Bald Hill Claystone consists largely of soil material, both in place and resorted. These were grey‐brown podzolic soils (ferrods) formed on well‐drained sites under coniferous forest. Humic gleys (fibrists) within the overlying Garie Formation were probably lowland catenary equivalents formed under Dicroidium, Voltziopsis, and equisetalean swamp woodland. These soils were drowned by the relative rise of the Narrabeen lagoonal complex and the concomitant southward growth of sediments of the Gosford Delta. These sediments formed the lower Newport Formation, of clean quartz‐lithi.
Wall-to-wall tree type classification using airborne lidar data and CIR images

Extensive ground surveys of forest resources are expensive, and remote sensing is commonly used to extend surveys to large areas for which no ground data are available to provide more accurate estimates for forest management decisions. Remote-sensing data for tree type classification are usually analysed at the individual tree level (object-based). However, due to computational challenges, most object-based studies cover only smaller areas and experience of larger areas is lacking. We present an approach for an object-based, unsupervised classification of trees into broadleaf or conifer using airborne light detection and ranging (lidar) data and colour infrared (CIR) images on a countrywide scale. We adjusted the classification procedure using field data from countrywide tree species trial (TST) plots, and verified it on data from the National Forest Inventory (NFI). Results of the object-based classification of the TST plots showed an overall accuracy of 84% and a kappa coefficient () of 0.61 when using all plots, and 92% and 0.79, respectively, when leaving out plots with larch. NFI plots were assigned to conifer- or broadleaf-dominated or mixed depending on the area covered by the segments of the two tree types. In areas where lidar data were collected specifically during leaf-off conditions, 71% of the NFI plots were assigned correctly into the three categories with = 0.53. Using only NFI plots dominated by one type (broadleaf or conifer), 78% were categorized correctly with = 0.61. These results demonstrate that using wall-to-wall remote-sensing data, unsupervised classification of forest stands into broadleaf, conifer, or mixed is possible with an accuracy comparable to that of limited area studies. However, challenges and restrictions of using countrywide airborne remote sensing data lie in the costs associated with data collection and the data processing time.
Induction of Mean Output Prediction Trees from Continuous Temporal Meteorological Data

In this paper, we present a novel method for fast data-driven construction of regression trees from temporal datasets including continuous data streams. The proposed Mean Output Prediction Tree (MOPT) algorithm transforms continuous temporal data into two statistical moments according to a user-specified time resolution and builds a regression tree for estimating the prediction interval of the output (dependent) variable. Results on two benchmark data sets show that the MOPT algorithm produces more accurate and easily interpretable prediction models than other state-of-the-art regression tree methods.
Artifacts and pitfalls in diffusion MRI

Although over the last 20 years diffusion MRI has become an established technique with a great impact on health care and neurosciences, like any other MRI technique it remains subject to artifacts and pitfalls. In addition to common MRI artifacts, there are specific problems that one may encounter when using MRI scanner gradient hardware for diffusion MRI, especially in terms of eddy currents and sensitivity to motion. In this article we review those artifacts and pitfalls on a qualitative basis, and introduce possible strategies that have been developed to mitigate or overcome them. J. Magn. Reson. Imaging 2006. © 2006 Wiley‐Liss, Inc.
Application profiling in hierarchical Hadoop for geo-distributed computing environments

In the past two decades there has been a growing interest over the definition of new distributed computational paradigms capable to serve the need of manipulating and analyzing huge amounts of data. Among the others, the MapReduce outstands for popularity. Its open-source implementation Hadoop is widely used in academic environments and is also greatly supported by huge IT players. There are many application scenarios where the data to be manipulated resides on data centers which are heterogeneous in term of computing capacity and are geographically distant from each other's. Unfortunately, in this contexts Hadoop performs very poorly. In this paper we propose to leverage on a hierarchical computing framework to boost the Hadoop performance in geo-distributed computing environments. The framework we propose drains fresh information from the distributed computing context and exploits it to carry out a smart job scheduling strategy. In this work, the focus is put on the study and definition of the application profile of the jobs. We implemented a software prototype of the proposed hierarchical Hadoop framework. Tests run on the prototype proved the capability of the job scheduling system to compute the job's execution path and estimate its completion time.
In Vivo Evaluation of Activities and Expression of Antioxidant Enzymes in Wistar Rats Exposed for 90 Days to a Modified Clay

Although clays are wildly used in a range of applications, the toxicity assessment of these new materials is still scarce. In the present study, oxidative stress induced by Clay 1, a novel clay, was determined in rats after 90 d of oral exposure. The activities of antioxidant enzymes, namely, superoxide dismutase (SOD), catalase (CAT), glutathione peroxidase (GPx), and glutathione S-transferase (GST), were examined. In addition, genetic expressions of SOD and CAT and relative protein abundance of CAT were also determined. Data showed that most of the biomarkers assayed remained unaltered. Only CAT activity, as well as its genetic and protein expressions, appeared enhanced in the kidney. Therefore, further studies are needed to clarify the relevance and consequences of these findings to ensure the safety of this clay.
NLHB: A non-linear Hopper-Blum protocol

The Hopper-Blum (HB) protocol, which uses noised linear parities of a shared key for authentication, has been proposed for light-weight applications such as RFID. Recently, algorithms for decoding linear codes have been specially designed for use in passive attacks on the HB protocol. These linear coding attacks have resulted in the need for long keys in the HB protocol, making the protocol too complex for RFID in some cases. In this work, we propose the NLHB protocol, which is a non-linear variant of the HB protocol. The non-linearity is such that passive attacks on the NLHB protocol continue to be provably hard by reduction. However, the linear coding attacks cannot be directly adapted to the proposed NLHB protocol because of the non-linearity. Hence, smaller key sizes appear to be sufficient in the NLHB protocol for the same level of security as the HB protocol. We construct specific instances of the NLHB protocol and show that they can be significantly less complex for implementation than the HB protocol, in spite of the non-linearity. Further, we propose an extension, called the NLHB+ protocol, that is provably secure against a class of active attack models.
A method for hand vein recognition based on Curvelet Transform phase feature

A new approach to palm-dorsal vein recognition based on the second generation Curvelet Transform is presented in this paper. After palm-dorsal image preprocessing and ROI (Region Of Interest) extraction, we use UDCT (Uniform Discrete Curvelet Transform) of the Curvelet Transform on ROI, and encode the Curvelet coefficients phase variance, and evaluate the Chi-square distance of coding histogram for vein recognition. Experiments are carried on our low-resolution palm-dorsal vein image database including 400 images. Experimental results show that UDCT phase feature has higher recognition rate (EER is 2.17%) compared with FDCT phase feature, PDFB phase feature, Gabor phase feature [17], and Triangulation and Knuckle Shape [9]. UDCT phase feature consumes less time than FDCT phase feature, Gabor phase feature, and Triangulation and Knuckle Shape.
Do Executive Stock Options Encourage Risk-Taking?

Executive stock options create incentives for executives to manage firms in ways that maximize firm market value. Since options increase in value with the volatility of the underlying stock, executive stock options provide managers with incentives to take actions that increase firm risk. We find that executives respond to these incentives. There is a statistically significant relationship between increases in option holdings by executives and subsequent increases in firm risk. This relationship is robust to the inclusion of fixed effects, year effects, and a variety of other controls and does not seem to be driven by reverse causality. However, the estimated effect on risk-taking is small and we do not find a negative (or positive) market response to option-induced risk-taking. In sum, although options appear to increase firm risk, there is no evidence that this effect is either large or damaging to shareholders.
Trace cache sampling filter

This paper presents a new technique for efficient usage of small trace caches. A trace cache can significantly increase the performance of wide out-of-order processors, but to be effective, the size of the trace cache should be large. Power and timing considerations indicate that a small trace cache is desirable, with special mechanisms to increase its effectiveness despite the limited size. Hence several authors have proposed various filtering methods to select "good traces" for keeping in the trace cache, from among the general population of traces. This paper presents a new filtering technique, which is based on sampling. Our new technique suggests that instead of building all the traces and trying to select the good ones among them, it is more efficient to make a preliminary selection of traces. This selection is based on a random sampling approach. We show that the sampling filter improves trace cache and overall system performance, while reducing power dissipation. The sampling filter reduces admission of traces that are not used prior to their eviction from the cache, and prolongs the percentage of time a trace is in its live phase during its stay in the cache. Moreover, the sampling filter reduces duplication between the trace cache and the instruction cache and thus reduces the overall misses in the first level of cache hierarchy.
The effect of interstimulus interval on somatosensory point localization

Somatosensory point localization is a clinical test evaluating spatial accuracy of the somatosensory system. Possible effects of the interstimulus interval (ISI) on point localization threshold have not been previously examined. In the present set of experiments the effect of time delay on somatosensory point localization was studied using ISIs of 1, 3, 5, 7, and 9 s, and applying a newly developed computer-controlled application method of a Semmes–Weinstein monofilament. It was found that the point localization threshold was not significantly affected by the ISI length. However, the response time was shorter and response accuracy better at the shorter (1 and 3 s) than at the longer (5, 7, and 9 s) ISIs, suggesting a change in the mechanism underlying point localization decision criteria in ISIs longer than 3 s.
Artificial olfaction system with hardware on-chip learning neural networks

This paper presents a hardware implementation of a multilayer feed-forward neural network based on back-propagation. The implementation is assumed to design and implement modules that emulate FF-BP functions with computing blocks of the predefined System Generator library and user defined blocks integrated in the System Generator library. The main application of the developed structure is an artificial olfactory system used to recognize the type of coffee presented in a test chamber. Data acquisition was achieved through the PC-MIO-16E-1 acquisition card and a virtual instrument, developed in Labview, for signal pre-processing and data logging into text files. The patterns presented (the type of coffee) have been recognized through neural networks. In order to select the RNA with the highest accuracy in recognising the coffee type, several different RNAs were simulated.
A New Recursive Partitioning Multicast Routing Algorithm for 3D Network-on-Chip

Network on chip (NoC) provides a fast and scalable interconnect for communication between many IP cores and System on Chips (SoCs). As the number of on chip elements increase to fulfill the demand of high performance computing, scalable and efficient communication infrastructure is required for higher levels of integration, for which 3D NoCs have evolved. Multicast communication provides a better solution for many cache coherence protocols and parallel algorithms. This paper proposes a New Recursive Partitioning Multicast Routing Algorithm (3D-RPM) along with its optimized version, a New Optimized Recursive Partitioning Multicast Routing Algorithm (3D-ORPM) for 3D mesh networks. Simulation results show around 8-13% reduction in percentage link utilization and link power consumed for the proposed approach compared to the tree based 3D-XYZ multicast routing algorithm. Results show that the approach is scalable for larger networks, as well as large number of multicast destinations.
Tubular Graphite Cones

We report the synthesis of tubular graphite cones using a chemical vapor deposition method. The cones have nanometer-sized tips, micrometer-sized roots, and hollow interiors with a diameter ranging from about 2 to several tens of nanometers. The cones are composed of cylindrical graphite sheets; a continuous shortening of the graphite layers from the interior to the exterior makes them cone-shaped. All of the tubular graphite cones have a faceted morphology. The constituent graphite sheets have identical chiralities of a zigzag type across the entire diameter, imparting structural control to tubular-based carbon structures. The tubular graphite cones have potential for use as tips for scanning probe microscopy, but with greater rigidity and easier mounting than currently used carbon nanotubes.
Single-channel speech separation by using a sparse decomposition with periodic structure

In this paper, we propose single-channel speech separation by using a sparse decomposition method. First, the model for the periodic signals with time-varying amplitude is introduced to approximate speech signals. The sparse decomposition is proposed with this signal model and a sparsity measure. The sparsity measure is defined as a sum of the l2 norms of the resultant periodic subsignals to find the shortest path to the approximation. By this penalty of the sparsity, the proposed decomposition extracts significant periodic components from a mixture and has ability of the source estimation for mixtures of periodic signals. In experiments, we apply the proposed decomposition to speech mixtures and demonstrate speech separation with codebooks of the speakers. In additionally, comparison with MaxVQ method that performs separation on the frequency spectrum domain is also demonstrated. Comparing with the MaxVQ, our method is less sensitive to the codebook design and requires less computational costs.
Characterization of temperature dependent mechanical behavior of cartilage

Few quantitative studies have investigated the temperature dependent viscoelastic properties of cartilage tissue. Cartilage softens and can be reshaped when heated using laser, RF, or contact heating sources. The objectives of this study were to: (1) measure temperature dependent flexural storage moduli and mechanical relaxation in cartilage, (2) determine the impact of tissue water content and orientation on these mechanical properties, and (3) use these measurements to estimate the activation energy associated with the mechanical relaxation process.
Bayesian reinforcement learning in continuous POMDPs with gaussian processes

Partially Observable Markov Decision Processes (POMDPs) provide a rich mathematical model to handle real-world sequential decision processes but require a known model to be solved by most approaches. However, mainstream POMDP research focuses on the discrete case and this complicates its application to most realistic problems that are naturally modeled using continuous state spaces. In this paper, we consider the problem of optimal control in continuous and partially observable environments when the parameters of the model are unknown. We advocate the use of Gaussian Process Dynamical Models (GPDMs) so that we can learn the model through experience with the environment. Our results on the blimp problem show that the approach can learn good models of the sensors and actuators in order to maximize long-term rewards.
Performance and Robustness of Control Charting Methods for Autocorrelated Data

With the proliferation of in-process measurement technology, autocorrelated data are increasingly common in industrial SPC applications. A number of high performance control charting techniques that take into account the specific characteristics of the autocorrelation through time series modeling have been proposed over the past decade. We present a survey of such methods and analyze and compare their performances for a range of typical autocorrelated process models. One practical concern with these methods is that their performances are often strongly affected by errors in the time series models used to represent the autocorrelation. We also provide some analytical results comparing the robustness of the various methods with respect to time series modeling errors.
A Robotic Agent in a Virtual Environment that Performs Situated Incremental Understanding of Navigational Utterances

We demonstrate a robotic agent in a 3D virtual environment that understands human navigational instructions. Such an agent needs to select actions based on not only instructions but also situations. It is also expected to immediately react to the instructions. Our agent incrementally understands spoken instructions and immediately controls a mobile robot based on the incremental understanding results and situation information such as the locations of obstacles and moving history. It can be used as an experimental system for collecting human-robot interactions in dynamically changing situations.
Spanish Adaptation and Validation of the Outcome Questionnaire OQ-30.2

This study assessed the psychometric properties of a Spanish version of the Shortened Outcome Questionnaire (OQ-30.2, Lambert et al., 2004) validated with a sample of 546 patients in an outpatient mental health clinic and 100 non-clinical adults in Chile. Our results show that this measure has similar normative data to the original measure, with a cutoff score for the Chilean population set at 43.36, and the reliable change index at 14. This Spanish OQ-30.2 has good internal consistency (α = 0.90), has concurrent validity with the Depressive, Anxious, and Somatoform disorders measuring scale (Alvarado and Vera, 1991), and is sensitive to change during psychotherapy. Consistent with previous studies, factorial analyses showed that both, the one-factor solution for a general scale and the three-factor solution containing three theoretical scales yielded poor fit estimates. Overall, our results are similar to past research on the OQ-45 and the OQ-30. The short version has adequate psychometric properties, comparable to those of the OQ-45, but provides a gain in application time that could be relevant in the setting of psychotherapy research with large samples, frequent assessments over time, and/or samples that may require more assistance completing items (e.g., low-literacy). We conclude that this measure will be a valuable instrument for research and clinical practice.
Two reentrant pathways in the hippocampal‐entorhinal system

The entorhinal cortex has long been recognized as an important interface between the hippocampal formation and the neocortex. The notion of bidirectional connections between the entorhinal cortex and the hippocampal formation have led to the suggestion that hippocampal output originating in CA1 and subiculum may reenter hippocampal subfields via the entorhinal cortex. To investigate this, we used simultaneous multi‐site field potential recordings and current source density analysis in the entorhinal cortex and hippocampal formation of the rat in vivo. Under ketamine/xylazine anesthesia, we found that repetitive stimulation of subiculum or Schaffer collaterals facilitated entorhinal responses, such that a population spike appeared in layer III. In addition, a current sink in stratum lacunosum‐moleculare of area CA1 was found, that followed responses in the entorhinal cortex, indicating reentrance into this area. Responses indicating reentrance in the dentate gyrus were not found under ketamine/xylazine anesthesia, but were readily evoked under urethane anesthesia. Reentrance into CA1 was also encountered under urethane anesthesia. These results suggest that parallel, but possibly functionally distinct, connections are present between the output of the hippocampal formation and cells in layers III and II of the entorhinal cortex that project to area CA1 and the dentate gyrus, respectively. © 2004 Wiley‐Liss, Inc.
Detecting Blood-Based Biomarkers in Metastatic Breast Cancer: A Systematic Review of Their Current Status and Clinical Utility

Reviews on circulating biomarkers in breast cancer usually focus on one single biomarker or a selective group of biomarkers. An overview summarizing the discovery and evaluation of all blood-based biomarkers in metastatic breast cancer is lacking. This systematic review aims to identify the available evidence of known blood-based biomarkers in metastatic breast cancer, regarding their clinical utility and state-of-the-art position in the validation process. The initial search yielded 1078 original studies, of which 420 were assessed for eligibility. A total of 320 studies were included in the final synthesis. A Development, Evaluation and Application Chart (DEAC) of all biomarkers was developed. Most studies focus on identifying new biomarkers and search for relations between these biomarkers and traditional molecular characteristics. Biomarkers are usually investigated in only one study (68.8%). Only 9.8% of all biomarkers was investigated in more than five studies. Circulating tumor cells, gene expression within tumor cells and the concentration of secreted proteins are the most frequently investigated biomarkers in liquid biopsies. However, there is a lack of studies focusing on identifying the clinical utility of these biomarkers, by which the additional value still seems to be limited according to the investigated evidence.