Miracle-dz/UniFAR-FTUs huggingface dataset:
Current Trends in Time Series Representation

Time series data generation has been exploded in almost every domain such as in business, industry, medicine, science or entertainment. Consequently, there is an increasing need for analysing efficiently the huge amount of this information either online or offline. The inherent characteristics of time series data, specifically, the high dimensionality, the high feature correlation and the large amounts of noise led researchers to focus mostly on proposing novel representation schemes for this type of data in order to address the resulting problems. In addition to that, representation schemes have also been proposed for the purpose of exploiting specific methods and algorithms that require different type of data, such as Markov models. Concurrently, since most of the data mining tasks require searching for similar patterns, such as query by content, clustering or classification, many papers have been published that propose a generic or representation-based similarity measure. The objective of this paper is to serve as an overview of the recent approaches and trends in the field of time series data mining representations. It seems that the main interest has been gradually drawn in manipulating streaming data and / or multivariate time series. Although a general overview is included, the literature review is focused mainly on papers of the last four years.
SKI-606, an Src Inhibitor, Reduces Tumor Growth, Invasion, and Distant Metastasis in a Mouse Model of Thyroid Cancer

Purpose: Src is overexpressed or hyperactivated in a variety of human cancers, including thyroid carcinoma. Src is a central mediator in multiple signaling pathways that are important in oncogenesis and cancer progression. In this study, we evaluated the effects of an Src inhibitor, SKI-606 (bosutinib), in a spontaneous metastatic thyroid cancer model with constitutively activated Src (ThrbPV/PVPten+/− mice). Experimental Design: ThrbPV/PVPten+/− mice were treated with SKI-606 or vehicle controls, beginning at 6 weeks of age until the mice succumbed to thyroid cancer. We assessed the effects of SKI-606 on thyroid cancer progression and analyzed the impact of SKI-606 on aberrant Src-mediated signaling. Results: SKI-606 effectively inhibited aberrant activation of Src and its downstream targets to markedly inhibit the growth of thyroid tumor, thereby prolonging the survival of treated mice. While Src inhibition did not induce cell apoptosis, it decreased cell proliferation by affecting the expression of key regulators of cell-cycle progression. Importantly, SKI-606 dramatically prevented dedifferentiation, vascular invasion, and lung metastasis of thyroid cancer cells. These responses were meditated by downregulation of mitogen-activated protein kinase pathways and inhibition of the epithelial–mesenchymal transition. Conclusions: Our findings suggest that Src is critical in the progression of thyroid cancer, making oral SKI-606 a promising treatment strategy for refractory thyroid cancer. Clin Cancer Res; 18(5); 1281–90. ©2012 AACR.
The age of transformation: the Triassic Period and the rise of today's land vertebrate fauna

The Triassic Period (about 251 to 200 million years ago on the most recent geological timescales) was revolutionary in two senses. First, faunal turnover among terrestrial tetrapods was greater, by some measures, than at any other time in history. In addition, independent advances in locomotion, growth rates, and associated physiological features in two major lineages of tetrapods, derived nonmammalian synapsids (therapsids) and the ornithodiran archosaurs, were more profound than at any other time during the evolutionary history of continental tetrapods. (Only the emergence of tetrapods onto land during the Devonian Period is comparable.) Here we describe the principal changes in communities of continental tetrapods during the Triassic and how they unfolded over some 50 million years. Different groups dominated different global regions, changes in the tetrapod faunas through time were not in lockstep, and several waves of faunal replacement took place (Padian 1986, 2013; Fraser 2006; Sues and Fraser 2010; Irmis 2011; Irmis et al. 2007). Although we want to avoid overgeneralizing, we stress that these observations and inferences are based on a fossil record that is necessarily incomplete, but is surprisingly good in many respects.
The Anatomy of a Context-Aware Application

We describe a sensor-driven, or sentient, platform for context-aware computing that enables applications to follow mobile users as they move around a building. The platform is particularly suitable for richly equipped, networked environments. The only item a user is required to carry is a small sensor tag, which identifies them to the system and locates them accurately in three dimensions. The platform builds a dynamic model of the environment using these location sensors and resource information gathered by telemetry software, and presents it in a form suitable for application programmers. Use of the platform is illustrated through a practical example, which allows a user's current working desktop to follow them as they move around the environment.
Combining Character-Based and Subsequence-Based Tagging for Chinese Word Segmentation

Chinese word segmentation is the initial step for Chinese information processing. The performance of Chinese word segmentation has been greatly improved by character-based approaches in recent years. This approach treats Chinese word segmentation as a character-word-position-tagging problem. With the help of powerful sequence tagging model, character-based method quickly rose as a mainstream technique in this field. This paper presents our segmentation system for evaluation of CIPS-SIGHAN 2010 in which method combining char-acter-based and subsequence-based tagging is applied and conditional random fields (CRFs) is taken as sequence tagging model. We evaluated our system in closed and open tracks on four corpuses, namely Literary , Computer science , Medicine and Finance, and reported our evaluation results.
Hierarchical spatial features learning with deep CNNs for very high-resolution remote sensing image classification

ABSTRACT Recently, very high resolution (VHR) remote sensing (RS) images have become widely available. While a VHR RS image provides more detailed information of the imaged scene, inter-class variation is enhanced as well, which makes it a challenging task to accurately classify the image at pixel level. It has been found that spatial features of a VHR image derived from the spectral measurements can improve the accuracy of image classification. Recent studies have shown that deep convolutional neural networks (CNNs) are powerful to extract high-level spatial features for object recognition and image retrieval. However, few CNNs have been developed for the pixel-wise classification of RS image. Moreover, in order to obtain large-scale spatial features, CNNs can be unmanageably complicated. In this paper, we propose a novel deep CNNs based method for VHR image classification. Firstly, pixel level CNNs are applied to multiscale images generated from the original VHR RS image using the Gaussian pyramid method. The hierarchical multiscale spatial features are then resampled to the size of the raw image. Finally, feature fusion is conducted with the original spectral bands for spectral-spatial based classification, where a support vector machine (SVM) classifier can be employed. Experiments were conducted on the image of VHR aerial image in Vaihingen, Germany. Compared to using spectral features only or with extended morphological profiles (EMPs) for image classification, the proposed method performs better, and the best improvement of classification accuracy is as high as 13%.
Low regret bounds for Bandits with Knapsacks

Achievable regret bounds for Multi-Armed Bandit problems are now well-documented. They can be classified into two categories based on the dependence on the time horizon $T$: (1) small, distribution-dependent, bounds of order of magnitude $\ln(T)$ and (2) robust, distribution-free, bounds of order of magnitude $\sqrt{T}$. The Bandits with Knapsacks theory, an extension to the framework allowing to model resource consumption, lacks this duality. While several algorithms have been shown to yield asymptotically optimal distribution-free bounds on regret, there has been little progress toward the development of small distribution-dependent regret bounds. We partially bridge the gap by designing a general purpose algorithm which we show enjoy asymptotically optimal regret bounds in several cases that encompass many practical applications including dynamic pricing with limited supply and online bidding in ad auctions.
Dynamic Modeling and Evaluation of a Robotic Exoskeleton for Upper-Limb Rehabilitation

Proper functioning of the shoulder, elbow, and wrist movements play a vital role in the performance of essential daily activities. To assist physically disabled people with impaired upper-limb function, we have been developing an exoskeleton robot (ExoRob) to rehabilitate and to ease upper limb motion. The proposed ExoRob will be comprised of seven degrees of freedom (DOFs) to enable natural movements of the human upper-limb. This paper focuses on the kinematic and dynamic modeling of the proposed ExoRob that corresponds to human upper-limbs. For this purpose, a nonlinear computed torque control technique was employed. In simulations, trajectory tracking corresponding to typical rehabilitation exercises were carried out to evaluate the performances of the developed model and controller. For the experimental part, only 3DOFs (elbow, wrist flexion/extension, wrist abduction/adduction) were considered. Simulated and experimental results show that the controller was able to maneuver the proposed ExoRob efficiently in order to track the desired trajectories, which in this case consisted in passive arm movements. Such movements are widely used in therapy and were performed efficiently with the developed ExoRob and the controller.
Partitioning of Minimotifs Based on Function with Improved Prediction Accuracy

Background Minimotifs are short contiguous peptide sequences in proteins that are known to have a function in at least one other protein. One of the principal limitations in minimotif prediction is that false positives limit the usefulness of this approach. As a step toward resolving this problem we have built, implemented, and tested a new data-driven algorithm that reduces false-positive predictions. Methodology/Principal Findings Certain domains and minimotifs are known to be strongly associated with a known cellular process or molecular function. Therefore, we hypothesized that by restricting minimotif predictions to those where the minimotif containing protein and target protein have a related cellular or molecular function, the prediction is more likely to be accurate. This filter was implemented in Minimotif Miner using function annotations from the Gene Ontology. We have also combined two filters that are based on entirely different principles and this combined filter has a better predictability than the individual components. Conclusions/Significance Testing these functional filters on known and random minimotifs has revealed that they are capable of separating true motifs from false positives. In particular, for the cellular function filter, the percentage of known minimotifs that are not removed by the filter is ∼4.6 times that of random minimotifs. For the molecular function filter this ratio is ∼2.9. These results, together with the comparison with the published frequency score filter, strongly suggest that the new filters differentiate true motifs from random background with good confidence. A combination of the function filters and the frequency score filter performs better than these two individual filters.
Caudal Epidural of Pulsed Radiofrequency in Post Herpetic Neuralgia (PHN); Report of Three Cases

Introduction: Postherpetic neuralgia (PHN) is a frequently occurring neuropathic pain, its pathophysiology is not fully understood. There are only few evidence based therapeutic options; sympathetic nerve block can be considered for patients with PHN refractory to conservative treatment, but long-term effects are poor. Application of pulsed radiofrequency was effective to treat a variety of pain syndromes without neurological complications or other sequelae. Case Presentation: We observed a remarkable long-lasting pain relief in patients with post herpetic neuralgia (PHN) treated with caudal epidural PRF. We described the technique of caudal epidural PRF and three case reports. Conclusions: The mode of action of PRF is far from being completely elucidated. The high frequency current induces an electric field that in turn seems to influence the immunity, the inflammation and other pain conducting mechanisms. Our findings suggest an effect distal from the application of the current. It reaches targets that are difficultly attainable by any other means of current application. The observations of pain relief in the difficult to treat patients with PHN justifies further investigation.
Concomitant determination of folar nitrogen loss, net carbon dioxide uptake, and transpiration.

A closed system was designed for concomitant determination of net CO(2) uptake, transpiration, and foliar nitrogen (N) loss in soybeans (Glycine max [L.] Merr.). The CO(2) uptake was monitored by infrared analysis of system air removed periodically in aliquots. Leaf vapors were trapped in calibrated tubes at Dry Ice temperature, and transpiration rate was determined from the quantity of condensate. Subsequent pyrochemiluminescent analysis of this condensate revealed reduced N forms, although a [ill] percentage (4 to 15%) of oxidized forms was found.Maximum CO(2) uptake (22.3 milligrams per square decimeter per hour), transpiration (2.5 grams H(2)O per square decimeter per hour), and the total (4.9 micrograms per square decimeter per hour), reduced (4.1 micrograms per square decimeter per hour), and oxidized (0.7 microgram per square decimeter per hour) N loss rates were measured between 6.5 and 10.5 hours (30 C) of a simulated 13-hour photoperiod in leaves of V4 to V5 (three to four trifoliolate stage) soybeans. During a temperature study (20, 25, 30, and 35 C) with these plants and several leaf positions of older vegetative soybeans (V9 to V11), total and reduced N loss were maximum at 30 C (V4 to V5: 9.8 and 9.3 micrograms per square decimeter per hour; V9 to V11: 3.3 and 2.9 micrograms per square decimeter per hour); transpiration was maximal at 30 C in V4 to V5 plants (2.6 grams H(2)O per square decimeter per hour) and at 35 C in V9 to V11 plants (2.4 grams H(2)O per square decimeter per hour); and CO(2) uptake was maximal at 25 and 30 C during both sampling dates (V4 to V5: 22.7 to 23.5 milligrams CO(2) per square decimeter per hour: V9 to V11: 14.1 to 14.5 milligrams CO(2) per square decimeter per hour). At 30 and 35 C these parameters were highest in the youngest tissue of V9 to V11 plants.In V9 to V11 plants at all four temperatures, reduced N loss was correlated with total N loss (r = 0.99 at 20, 25, and 30 C and 0.97 at 35 C), and CO(2) uptake with transpiration (r = 0.47 at 20 C, 0.75 at 25 C, 0.85 at 30 C, and 0.81 at 35 C). Transpiration was correlated with both total and reduced N loss at 30 C (total N: r = 0.69; reduced N: r = 0.70) and 35 C (total N: r = 0.58; reduced N: r = 0.54). In addition, CO(2) uptake was directly related to total (r = 0.69 at 30 C and 0.56 at 35 C) and reduced (r = 0.67 at 30 C and 0.56 at 35 C) N loss at these two temperatures.
Using Gamma and Quantile Regressions to Explore the Association between Job Strain and Adiposity in the ELSA-Brasil Study: Does Gender Matter?

This paper explores the association between job strain and adiposity, using two statistical analysis approaches and considering the role of gender. The research evaluated 11,960 active baseline participants (2008–2010) in the ELSA-Brasil study. Job strain was evaluated through a demand–control questionnaire, while body mass index (BMI) and waist circumference (WC) were evaluated in continuous form. The associations were estimated using gamma regression models with an identity link function. Quantile regression models were also estimated from the final set of co-variables established by gamma regression. The relationship that was found varied by analytical approach and gender. Among the women, no association was observed between job strain and adiposity in the fitted gamma models. In the quantile models, a pattern of increasing effects of high strain was observed at higher BMI and WC distribution quantiles. Among the men, high strain was associated with adiposity in the gamma regression models. However, when quantile regression was used, that association was found not to be homogeneous across outcome distributions. In addition, in the quantile models an association was observed between active jobs and BMI. Our results point to an association between job strain and adiposity, which follows a heterogeneous pattern. Modelling strategies can produce different results and should, accordingly, be used to complement one another.
Mapping High-Resolution Soil Moisture over Heterogeneous Cropland Using Multi-Resource Remote Sensing and Ground Observations

High spatial resolution soil moisture (SM) data are crucial in agricultural applications, river-basin management, and understanding hydrological processes. Merging multi-resource observations is one of the ways to improve the accuracy of high spatial resolution SM data in the heterogeneous cropland. In this paper, the Bayesian Maximum Entropy (BME) methodology is implemented to merge the following four types of observed data to obtain the spatial distribution of SM at 100 m scale: soil moisture observed by wireless sensor network (WSN), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)-derived soil evaporative efficiency (SEE), irrigation statistics, and Polarimetric L-band Multi-beam Radiometer (PLMR)-derived SM products (~700 m). From the poor BME predictions obtained by merging only WSN and SEE data, we observed that the SM heterogeneity caused by irrigation and the attenuating sensitivity of the SEE data to SM caused by the canopies result in BME prediction errors. By adding irrigation statistics to the merged datasets, the overall RMSD of the BME predictions during the low-vegetated periods can be successively reduced from 0.052 m3·m−3 to 0.033 m3·m−3. The coefficient of determination (R2) and slope between the predicted and in situ measured SM data increased from 0.32 to 0.64 and from 0.38 to 0.82, respectively, but large estimation errors occurred during the moderately vegetated periods (RMSD = 0.041 m3·m−3, R = 0.43 and the slope = 0.41). Further adding the downscaled SM information from PLMR SM products to the merged datasets, the predictions were satisfactorily accurate with an RMSD of 0.034 m3·m−3, R2 of 0.4 and a slope of 0.69 during moderately vegetated periods. Overall, the results demonstrated that merging multi-resource observations into SM estimations can yield improved accuracy in heterogeneous cropland.
Fast Multi-Objective Algorithmic Design Co-Exploration for FPGA-based Accelerators

The reconfigurability of Field Programmable Gate Arrays (FPGAs) makes them an attractive platform for accelerating algorithms. Accelerating a particular algorithm is a challenging task as the large number of possible algorithmic and hardware design parameters lead to different accelerator variant implementations, each with its own metrics such as performance, area, power, and arithmetic accuracy characteristics. To identify these parameters that optimize the accelerator for certain metrics, we propose techniques for fast design space exploration and non-linear multi-objective optimization (e.g., minimize power under arithmetic inaccuracy bounds). Our methodology samples a small part of the design space and uses measurements from the sampled implementations to train mathematical models for the different metrics. To automate and improve the model generation process, we propose the use of L1-regularized least squares regression techniques. To demonstrate the effectiveness of our approach, we implement a high-throughput real-time accelerator for image debluring. We demonstrate the accuracy (e.g., within 8% for power modeling) of our modeling techniques and their ability to identify the optimal accelerator designs with large speed-ups (340×) in comparison to brute-force enumeration.
Spatial variation of reference evapotranspiration and its influence on the hydrology of Luvuvhu River Catchment

This study estimated spatial variation of reference evapotranspiration (ETo) and its influence on the hydrology of Luvuvhu River Catchment.The ETo plays a key role in irrigation systems design, water resource management under irrigated and rainfed production.Simulation of ETo was performed using the Penman-Monteith method in CROPWAT 8.0 software. Results showed the spatial and temporal distribution of ETo with peaks in summer months. Areas with higher ETo values were near rivers and streams, which generally have more abundant vegetation. Areas with low ETo values were relatively dry, where pasture and grasslands dominated the landscape. Correlation results showed that no relationship exists between streamflow and ETo (r = 0.36) in the study area, hence, a significant relationship exists between rainfall and ETo (r = 0.86). The study recommends the use ofCROPWATmodel for computing ETo under arid and semi-arid climatic conditions for water resource management and planning.
Support tucker machines

In this paper we address the two-class classification problem within the tensor-based framework, by formulating the Support Tucker Machines (STuMs). More precisely, in the proposed STuMs the weights parameters are regarded to be a tensor, calculated according to the Tucker tensor decomposition as the multiplication of a core tensor with a set of matrices, one along each mode. We further extend the proposed STuMs to the Σ/Σw STuMs, in order to fully exploit the information offered by the total or the within-class covariance matrix and whiten the data, thus providing in-variance to affine transformations in the feature space. We formulate the two above mentioned problems in such a way that they can be solved in an iterative manner, where at each iteration the parameters corresponding to the projections along a single tensor mode are estimated by solving a typical Support Vector Machine-type problem. The superiority of the proposed methods in terms of classification accuracy is illustrated on the problems of gait and action recognition.