Miracle-dz/UniFAR-FTUs huggingface dataset:
Reflectivity modeling : a tool for testing processing algorithms

The reflectivity modeling technique provides a fast and flexible tool for seismic modeling. It allows investigation of different aspects of specific wavefields. With the possibility of selectively switching on various wave types wave propagation aspects (conversions, multiples) can be investigated with ease. The method enables the calculation of synthetic sections for different source and receiver configurations, e.g VSP. Furthermore, the use of receiver arrays over a certain x z area is possible, such that time slices or arbitrary cuts through the wave field can be obtained. Computation of p sections is a byproduct of the modeling technique. Its flexibility allows to easily test assumptions on which processing algorithms are based. In contrast to other modeling techniques, such as Finite Difference modeling, the reflectivity method has the advantage of being fast, although restricted to laterally homogeneous media.
Fast Model-Based Contact Patch and Pose Estimation for Highly Deformable Dense-Geometry Tactile Sensors

Modeling deformable contact is a well-known problem in soft robotics and is particularly challenging for compliant interfaces that permit large deformations. We present a model for the behavior of a highly deformable dense geometry sensor in its interaction with objects; the forward model predicts the elastic deformation of a mesh given the pose and geometry of a contacting rigid object. We use this model to develop a fast approximation to solve the inverse problem: estimating the contact patch when the sensor is deformed by arbitrary objects. This inverse model can be easily identified through experiments and is formulated as a sparse Quadratic Program (QP) that can be solved efficiently online. The proposed model serves as the first stage of a pose estimation pipeline for robot manipulation. We demonstrate the proposed inverse model through real-time estimation of contact patches on a contact-rich manipulation problem in which oversized fingers screw a nut onto a bolt, and as part of a complete pipeline for pose-estimation and tracking based on the Iterative Closest Point (ICP) algorithm. Our results demonstrate a path towards realizing soft robots with highly compliant surfaces that perform complex real-world manipulation tasks.
reveals promoter and enhancer architectures elegans C . The landscape of RNA polymerase II transcription initiation in Material Supplemental

Material Supplemental http://genome.cshlp. org/content/suppl/2013/04/16/gr.153668. 112.DC1. html References http://genome.cshlp. org/content/23/8/1339.full. html#ref-list-1 This article cites 78 articles, 28 of which can be accessed free at: Open Access Open Access option. Genome Research Freely available online through the License Commons Creative . http://creativecommons.org/licenses/by-nc/3.0/ License (Attribution-NonCommercial 3.0 Unported), as described at , is available under a Creative Commons Genome Research This article, published in
TILE FOR AUTONOMOUS AND PASSIVE PHYSIOLOGICAL MONITORING : PRELIMINARY DESIGN AND RESULTS

Independent living for older adults gives them more freedom and a more fulfilling lifestyle. Independence, however, must be accompanied by protection from potential causes of adverse events (e.g. falls, forgetting to turn off house appliances, etc.) and sufficient assistance as the physical and/or cognitive abilities of older adults are often compromised. The typical problems in older adults living independently are categorized as: medical, functional, psychosocial, and environmental problems [1].
THE INTERNATIONAL BATHYMETRIC CHART OF THE ARCTIC OCEAN (IBCAO) - A NEW DIRECTION FOR OCEAN MAPMAKING

The project to develop the International Bathymetric Chart of the Arctic Ocean (IBCAO) featured innovations that could be usefully applied to the development of future editions of GEBCO and associated digital products. These innovations include: the use of digital data and methodologies in virtually all stages of the operation; the establishment of a single team to work across the entire Arctic region; and liberal policies for the public release of selected data products and images. By emulating some of these practices and through judicious marketing of the resulting outputs, GEBCO may be able to attract a wider range of users while generating revenue that would support future operations.
A New Family of Maximally Regular T 2 R 1-type Spatial Parallel Manipulators with Unlimited Rotation of the Moving Platform

This paper presents a new family of maximally regular T2R1-type spatial parallel manipulators (PMs) with three degrees of freedom. The mobile platform performs two independent translations (T2) and one independent rotation (R1) whose axis lies in the plane of translations. This family is called spatial T2R1-type to distinguish it from the planar T2R1-type PMs in which the rotation axis is perpendicular to the plane of translation. A method is proposed for structural synthesis of maximally regular T2R1-type spatial PMs based on the theory of linear transformations. A one-to-one correspondence exists between the actuated joint velocity space and the external velocity space of the moving platform. The Jacobian matrix mapping the two vector spaces of maximally regular T2R1type PMs presented in this paper is the 3×3 identity matrix throughout the entire workspace. The condition number and the determinant of the Jacobian matrix being equal to one, the manipulator performs very well with regard to force and motion transmission capabilities. Moreover, the moving platform has unlimited rotational capabilities. To the best knowledge of the author, this paper presents for the first time solutions of maximally regular T2R1-type PMs with unlimited rotation of the moving platform.
Using Ensembles for Improving Fall Detection 1

Technology can highly enhance the quality of life of elderly people when living autonomously. Actually, fall detection is one of the possible improvements: having an oracle to request help in case of falling may succed in provining confidence to adults who have already fallen, for instance. In a recent study, a wrist worn wearable solution has been proposed based on threshold for peak detection and several low computational models for classifying the peak events as related with falls. It was found that ensembles could lead to a better performance of the system. In this research, a preliminary study in ensembling the classifiers is proposed, analysing the outcome in each case for each of the different scenarios initially proposed. Although a very simple ensemble solution is used, results suggest this may be the solution when a robust fall detection system is needed.
Web Semantics: Science, Services and Agents on the World Wide Web

, businesses and citizens. To dissolve the boundaries between the data silos, the Flemish government applied Linked Data design principles in an operational public sector context. This paper discusses the trends we have identified while ‘rewiring’ the Authentic Source for addresses to a Linked Base Registry. We observed the impact on multiple interoperability levels; namely on the legal, organisational, semantic and technical level. In conclusion Linked Data can increase semantic and technical interoperability and lead to a better adoption of government information in the public and private sector. We strongly believe that the insights from the past thirteen years in the region of Flanders could speed up processes in other countries that are facing the complexity of raising technical and semantic interoperability.
Providing A Model for Supply Chain Agility of Ground-Based Military Products and Its Impact on Supply Chain Performance

The most important goal of manufacturing military products in Iran is self-reliance and defensive deterrence against threats. These two goals have led to international competition of Iranian military industries with pioneer countries in this field. The rapid manufacturing of diverse and new military products by advanced countries enforced Iran's military industries supply chain to produce diverse products rapidly. Manufacturing military products by such features needs an agile supply chain, which can produce diverse military products rapidly and meet different volumes of demand. Military products are categorized into three groups: ground-based, air-based, and sea-based. Although airbased and sea-based military products are known as strategic military products in the world, ultimate success has not yet been achieved in any global military event without the help of ground-based military products. This paper aims to provide a model that shows the relations between supply chain agility practices for ground-based military products and their impact on chain performance. To this end, first, we identified the most important supply chain agility practices by expert’s questionnaire. Then, using factor analysis, practices are categorized. Finally, the final model is represented by using interpretative structure model (ISM). Research findings indicate that there are 41 effective practices in the agility of military products supply chain within 8 groups including supplier’s relationship, workshop level management, improving organizational structure, improving human resources, product design, process integration and improvement, IT utilization, and customer’s relation. The final model of this research indicates that using such practices in the format of hierarchical relations will lead to the proper responsiveness of the military products supply chain to its customers.
Emplacement of the Kodiak Batholith : A consequence of slab-window migration

The Kodiak batholith is one of the largest, most elongate intrusive bodies in the forearc Sanak-Baranof plutonic belt located along the southern margin of Alaska. These plutons are interpreted to have formed during the subduction of an oceanic spreadingcenter and the associated migration of a slab-window. Individual plutons of the Kodiak batholith track the location and evolution of the underlying slab-window. Six U-Pb zircon ages from the axis of the batholith show a northeastward decreasing age progression of 59.2±0.2 Ma at the southwest end to 58.4±0.2 Ma at the northeast tip. The trench-parallel rate of age progression is within error of the average slab-window migration rate for the entire Sanak-Baranof belt (ca. 19 cm/yr). Structural relationships, U-Pb ages and a model of new gravity data indicate magma from the Kodiak batholith ascended 5-10 km as a northeastward younging series of 18 km diameter visco-elastic diapirs. Individual plutons ascended by multiple emplacement mechanisms including downward flow, collapse of wall rock, stoping and diking. Stokes flow xenolith calculations suggest ascent rates of 50-100 m/yr and an effective magmatic viscosity of 10 Pa s. Pre-existing structural or lithologic
Baseline Assisted Classification of Heart Rate Variability

Recently, among various analysis methods of physiological signals, automatic analysis of Electrocardiogram (ECG) signals, especially heart rate variability (HRV) has received significant attention in the field of machine learning. Heart rate variability is an important indicator of health prediction and it is applicable to various fields of scientific research. Heart rate variability is based on measuring the differences in time between consecutive heartbeats (also known as RR interval), and the most common measuring techniques are divided into the time domain and frequency domain. In this research study, a classifier based on analysis of HRV signal is developed to classify different activities including sleep, exam, and exercise. The performance of the classifier is improved using a novel feature construction approach named as baseline assisted classifier. ECG data are collected from 39 subjects and RR intervals are derived from ECG data using Firstbeat analysis software to compute HRV metrics. These metrics are utilized as features in a logistic regression, SVM, decision tree, random forest classifiers. Performance of all classifiers is assessed by leave one person out cross-validation technique. Features are derived by statistical time domain method from HRV segmentation during 5-minutes recording. Using a combination of 5-min segmentation feature vector and 5-min segmentation feature vector of sleep record results in a median area under the receiver operating curve (AUC) of 88% for sleep and 74% for the exam on leave one person out cross-validation test set data by SVM classifier. These results demonstrate that adding a baseline feature vector of sleep data improves the classification accuracy and classification AUC accuracy of almost all classifiers from HRV measures, and tracking of activity can be achieved by measuring the HRV signal.
SGVB Topic Modeling

Latent Dirichlet Allocation (LDA), and more recently Deep Exponential Families (DEF), are effective methods for performing inference on bag-of-words data with a generative model. However, the dependencies in their respective graphical Models are all linear, limiting the power of such a model. Recent developments in efficient large-scale variational inference have made it possible to train generative models with non-linear dependencies such as neural networks on large amounts of data. In this thesis we investigate if such methods can be applied to bag-of-words data effectively. Specifically, we develop and test how to best use Stochastic Gradient Variational Bayes for bag-of-words topic modeling. We define a Topic Variational Autoencoder, which closely resembles a typical SGVB approach, and develop additional methods to complement or improve upon this approach. These methods include the use of stick-breaking priors on the latent variables, and the use of dimensionality reduction methods on large vocabulary sizes. In our experiments on the resulting models, we first characterize the behavior of a Topic VAE during optimization. Next, we experiment with different architectures and examine its performance when trained to different amounts of data. Furthermore, we test the efficacy and feasibility of batch normalization, stick-breaking priors, and dimensionality reduction techniques. For all these experiments we use the small KOS blog post dataset and a large collection of New York Times articles. Lastly, we compare our results to LDA and DEF, showing that an approach like ours is a very appealing alternative to current methods.
Neurofusion : Fusing MEG and EEG Data

Understanding the human brain requires advancing noninvasive human neuroimaging monitoring and analysis capabilities. Currently, two dominant non-invasive brain activity collection methods are electroand magnetoencephalography (EEG and MEG). They focus, respectively, on recording the electrical and magnetic fields generated by the flow of electrical current through neurons and head tissue [1]. This project is part of a year-long neuroimaging side project that explores different ways of fusing MEG and EEG data together and examines whether that fusion increases our ability to predict a user’s intention from raw MEG/EEG signals. The CS 231n portion of this project was spent attempting to develop a neural network architecture that could use EEG data alone (64 data streams x 198 time steps per stream) to classify the examples into three categories based on whether the subject was shown a familiar face, an unfamiliar face, or a control. We implement an SVM baseline and describe how we can use convolutional neural networks, recurrent neural networks, and optimally convolutional recurrent neural networks for the task. We experiment with those architectures and with transforming the EEG data into a spectrogram using a Fast Fourier Transform. Although we see disappointing accuracy, we believe this is the result of poor hyperparameter choice, and that future work will demonstrate that a recurrent convolutional neural network is the best way to perform classification over EEG data. Future work will then extend this into a multimodal model to examine whether an EEG+MEG fusion increases our ability to predict a user’s intention compared to either modality alone.
Combining Visual and Textual Attention in Neural Models for Enhanced Visual Question Answering

While visual information is essential for humans as it models our environment, language is our main method of communication and reasoning. Moreover, these two human capabilities interact in complex ways, therefore problems involving both visual and natural language data became widely explored in recent years. Thus, visual question answering aims at building systems able to process questions expressed in natural language about images or even videos. This would significantly ease the quality of life for visually impaired people by allowing them to get real-time answers about their surroundings. Unfortunately, the relations between images and questions are complex and the current solutions that exploit recent advanced in deep learning for text and image representation are not reliable enough. To improve these results, the visual and text representations must be fused into the same multimodal space. In this paper we present two different solutions for solving this problem. The first performs reasoning on the image by using soft attention mechanisms computed given the question. The second uses soft attention not just on the image, but the text as well. Although our models are more lightweight than state of the art solutions for this task, we achieve near top performance with the proposed combination of visual and textual representations.
Perfluorooctane Sulfonate-Choline Ion Pair Formation : A Potential Mechanism Modulating Hepatic Steatosis and Oxidative Stress in Mice

The mechanisms underlying perfluorooctane sulfonate (PFOS)-induced steatosis remain unclear. The hypothesis that PFOS causes steatosis and other hepatic effects by forming an ion pair with choline was examined. C57BL/6 mice were fed either a control diet or a marginal methionine/choline-deficient (mMCD) diet, with and without 0.003, 0.006, or 0.012% potassium PFOS. Dietary PFOS caused a dose-dependent decrease in body weight, and increases in the relative liver weight, hepatic triglyceride concentration and serum markers of liver toxicity and oxidative stress. Some of these effects were exacerbated in mice fed the mMCD diet supplemented with 0.012% PFOS compared with those fed the control diet supplemented with 0.012% PFOS. Surprisingly, serum PFOS concentrations were higher while liver PFOS concentrations were lower in mMCDfed mice compared with corresponding control-fed mice. To determine if supplemental dietary choline could prevent PFOSinduced hepatic effects, C57BL/6 mice were fed a control diet, or a choline supplemental diet (1.2%) with or without 0.003% PFOS. Lipidomic analysis demonstrated that PFOS caused alterations in hepatic lipid metabolism in the PFOS-fed mice compared with controls, and supplemental dietary choline prevented these PFOS-induced changes. Interestingly, dietary choline supplementation also prevented PFOS-induced oxidative damage. These studies are the first to suggest that PFOS may cause hepatic steatosis and oxidative stress by effectively reducing the choline required for hepatic VLDL production and export by forming an ion pair with choline, and suggest that choline supplementation may prevent and/or treat PFOSinduced hepatic steatosis and oxidative stress.
The Role of Rejection within the Trust Calibration Process : Insights from a Mixed-Methods Human-Robot Teaming Experiment

An important influence on the appropriate exploitation of robotic and autonomous systems will be the human-RAS trust calibration process in the context of transitioning between different modes of control. Understanding the dynamic interaction of factors that influence calibration in such use contexts is an important research question. This paper sheds light on these constructs through a simulated experiment in which participants (n = 11) alternated between teleoperation (TO) and supervisory control (SC) whilst monitoring the robot’s performance and locating objects concurrently. Applying an integrative mixed-methods approach involving semi-structured interview questions, behavioural observations, and quantitative data from a parallel study, a narrative account of the trust calibration process and the primacy of rejection relating to trust calibration was extracted. Implications for the design of robotic systems and the training of human-robot teams are discussed. Keywords—human-robot trust; mixed-methods research; autonomous systems; naturalistic decision-making; rejection
Force control for electro-hydraulic servo actuator used for a humanoid robotic arm

Due to the great power of hydraulic system actuators, the control of electro-hydraulic actuators has been an important and ever developing point of research in the field of humanoid robots. But due to the non-linearities of the hydraulic systems, It is very difficult to implement either motion or force controllers on the hydraulic actuators experimentally before the mechanism is modeled and simulated. This is due safety reasons and complex control issues. So, taking into account the humanoid robot HYDROïD, this paper presents the design of PID position and force control algorithms, tested on an electro-hydraulic servo actuator. This actuator is one of the actuators present on the robotic arm of HYDROïD. The virtual model of the system is built through co-simulation between ADAMS [1] and EASY5 [2] software. The output results were later validated using hardware implementation.
APPLICATION OF 2 D ACOUSTIC FULL WAVEFORM INVERSION TO OBC-DATA

In exploration geophysics it is important to characterise the subsurface – in particular oil and gas deposits – as good as possible. Full waveform inversion (FWI) exploits the full content of seismic data in order to reconstruct parameter models at sub-wavelength scales. It helps to improve the seismic and petrophysical interpretation by assigning physical parameters to structural images obtained by classical methods. In this work we applied 2D acoustic FWI in the time domain to a marine seismic data set acquired in a river delta area using ocean bottom cables. Based on particular circumstances, such as the occurrence of guided waves in shallow water and a simple starting model for the P-wave velocity, we developed a comprehensive inversion strategy involving data windowing (with focus on refracted waves), data filtering and an alternating inversion for model parameter and source signature. This complex FWI workflow has been successfully applied to the field data set, resulting in the reconstruction of a velocity model showing small-scale structures. In particular, we identified low-velocity areas as potential fault zones or gas “pockets”. The comparison of the FWI model with the result of a standard reflection seismic imaging method shows a high similarity. On the one hand, this can be considered as a validation of the FWI strategy. On the other hand, the availability of structural information and physical parameters improves petrophysical characterisation of the subsurface.
Focused Attention for Action Recognition

Current state-of-the art approaches to action recognition emphasize learning ConvNets on large amounts of training data, using 3D convolutions to process the temporal dimension. This approach is expensive in terms of memory usage and constitutes a major performance bottleneck of existing approaches. Further, video input data points typically include irrelevant information, along with useful features, which limits the level of detail that networks can process, regardless of the quality of the original video. Hence, models that can focus computational resources on relevant training signal are desirable.To address this problem, we rely on network-specific saliency outputs to drive an attention model that provides tighter crops around relevant video regions. We experimentally validate this approach and show how this strategy improves performance for the action recognition task.