Miracle-dz/UniFAR-FTUs huggingface dataset:
2D and 3D virtual environment for human robot interaction: From virtual perception to real localization

The ability for tele-operators to derive remote robots' location is crucial: it allows them to develop strategies to perform high level interactions and mainly navigation. In normal conditions, remote robots can perform autonomously such a task by comparing an priori knowledge and local sensed data. For some typical situations, this approach is impossible. Indeed, when remote environments are changing or when inherent sensors uncertainty and errors are too high for correct localization, the robots are unable to derive locations and contexts. Following that, the intervention of tele-operators is needed to compensate automatic localization failures and misestimations: they use a map and available remote sensed data to estimate robot's position and orientation. The quality of this intervention is intimately linked to four factors: the pertinence of the remote data, the way these data are displayed to operators, the nature of the map the operators use and their ability to derive robot's position. This paper describes our investigations in quantifying the last two factors and their effects localization accuracy. We present preliminary results concerning a comparison between two localization methods. A tele-operator is wearing a helmet displaying a video stream coming from the robot. He or she can move the head freely to move the remote camera allowing him/her to discover the remote environment. Using a 2D map (a top view) or an interactive (the user can move within the virtual representation) 3D partial model of the remote world, the tele-operator has to specify the exact position/orientation of the robot.
Event-Based Instantaneous Fundamental Frequency Estimation From Speech Signals

Exploiting the impulse-like nature of excitation in the sequence of glottal cycles, a method is proposed to derive the instantaneous fundamental frequency from speech signals. The method involves passing the speech signal through two ideal resonators located at zero frequency. A filtered signal is derived from the output of the resonators by subtracting the local mean computed over an interval corresponding to the average pitch period. The positive zero crossings in the filtered signal correspond to the locations of the strong impulses in each glottal cycle. Then the instantaneous fundamental frequency is obtained by taking the reciprocal of the interval between successive positive zero crossings. Due to filtering by zero-frequency resonator, the effects of noise and vocal-tract variations are practically eliminated. For the same reason, the method is also robust to degradation in speech due to additive noise. The accuracy of the fundamental frequency estimation by the proposed method is comparable or even better than many existing methods. Moreover, the proposed method is also robust against rapid variation of the pitch period or vocal-tract changes. The method works well even when the glottal cycles are not periodic or when the speech signals are not correlated in successive glottal cycles.
Content Aware Media Retargeting for still images using Seam Carving

When changing height and width of image traditional techniques for image resizing are oblivious to the content of image. A simple operator seam carving is used for image and video retargeting. This seam carving operator is used for content aware image resizing to reduce or expand image size. According to seam carving method every object in the image must be scaled down proportionally. Pixels in image which are belonging to most interesting parts of image are preserving and pixels which are belonging to most uninteresting parts of image are removed. By repeatedly inserting seams in vertical and horizontal directions we can increase size of image or the aspect ratio of image can be changed. Seam carving technique can also be used for enhancing the content of image and removal of object from the image by applying successive operation of the seam carving operator. This paper discuss problem in object removal with seam carving and distortion of image contents when inserting excessive seams in image. General Terms
MultiML: a general purpose representation language for multimodal human utterances

We present MultiML, a markup language for the annotation of multimodal human utterances. MultiML is able to represent input from several modalities, as well as the relationships between these modalities. Since MultiML separates general parts of representation from more context-specific aspects, it can easily be adapted for use in a wide range of contexts. This paper demonstrates how speech and gestures are described with MultiML, showing the principles - including hierarchy and underspecification - that ensure the quality and extensibility of MultiML. As a proof of concept, we show how MultiML is used to annotate a sample human-robot interaction in the domain of a multimodal joint-action scenario.
Being a professional: Three perspectives on design thinking, acting, and being

The purpose of this paper is to present three perspectives for interpreting design thinking: (1) an alternative framework on learning to become a professional, and (2) two interpretations of this framework that speak broadly to a topic of “design thinking”. The first perspective draws on a framework for “an embodied understanding of professional practice” that focuses on the ways professionals form and organize their knowledge and skills into a particular “professional-way-of-being”. The second and third perspectives provide examples of using this framework as a lens for interpreting existing results from phenomenographic studies on ways of experiencing design and ways of experiencing cross-disciplinary practice. We conclude with a discussion of how these three perspectives contribute to conceptualizing a working synthesis of design thinking.
Compressive sensing for sparse arrays

We assess the usefulness of compressive sensing (CS) for processing signals from sparse or highly thinned receive arrays. Compressive sensing consists of a theory and related algorithms for solving under determined linear equations with the assumption that solutions have only a few non-zero or dominant values and the remaining values are zero or small. Sparse arrays are a natural application of CS when (a) there are only a few array observations with each expressed as a linear combination of the received plane wave signal amplitudes and (b) there are a large number of possible angles of arrival of signals but only a few signals are actually present. Our approach employs linear beamforming of the sparse array signals to create data for compressive sensing that are a linear combination of the plane wave signal amplitudes. The beamforming matrix serves to create coefficients in this linear combination that nearly orthonormal, a requirement for successful application of CS. We evaluate CS for the problem of a five element sparse array located on a 90° sector of a circular arc with large radius of curvature. Elements of the sparse array are themselves digitally beamformed phased arrays with main beams steered to a common direction. We found that compressive sensing works well for large signal to noise, reliably indicating the direction of arrival of a plane wave within the beamwidth of the phased array element and potentially reliable resolution of two equal intensity plane waves within one tenth of the beamwidth of the sparse array. False plane wave indications occur with significant probability when the signal to noise is small. However, an n or more of m indication algorithm with m statistically independent noise samples reduces significantly the probability of false indication.
Cloud detection from satellite images based on Haar wavelet and clustering

Cloud covers normally present in satellite images which is one of the major obstacle in the processing of satellite images. Remote sensing images from tropical regions are partially unusable especially because of the presence of clouds. For improving the accuracy of land cover classification in satellite images cloud detection is useful. The major obstacle in cloud detection approach is the misclassification rate of cloud pixels. In this paper novel cloud detection from satellite image based on stationary Haar wavelet is presented. The proposed approach uses wavelet transform, histogram analysis and clustering for cloud detection. Remote sensing satellite images can be considered as a signal. Clouds are low frequency components in satellite image. So wavelet analysis of satellite images is helpful to extract low frequency components and detection of clouds. The result analysis shows that the proposed method can detect clouds automatically from satellite images with high accuracy. Experimental analysis is performed on Landsat-7 Enhanced Thematic Mapper Plus (ETM+) sensor images and results are obtained.
Molecular Patterns of Neurodevelopmental Preconditioning: A Study of the Effects of Antenatal Steroid Therapy in a Protein-Restriction Mouse Model

Introduction. Prenatal programming secondary to maternal protein restriction renders an inherent susceptibility to neural compromise in neonates and any addition of glucocorticosteroids results in further damage. This is an investigation of consequent global gene activity due to effects of antenatal steroid therapy on a protein restriction mouse model. Methods. C57BL/6N pregnant mice were administered control or protein restricted diets and subjected to either 100 μg/Kg of dexamethasone sodium phosphate with normosaline or normosaline alone during late gestation (E10–E17). Nontreatment groups were also included. Brain samples were collected on embryonic day 17 and analyzed by mRNA microarray analysis. Results. Microarray analyses presented 332 significantly regulated genes. Overall, neurodevelopmental genes were overrepresented and a subset of 8 genes allowed treatment segregation through the hierarchical clustering method. The addition of stress or steroids greatly affected gene regulation through glucocorticoid receptor and stress signaling pathways. Furthermore, differences between dexamethasone-administered treatments implied a harmful effect during conditions of high stress. Microarray analysis was validated using qPCR. Conclusion. The effects of antenatal steroid therapy vary in fetuses according to maternal-fetal factors and environmental stimuli. Defining the key regulatory networks that signal either beneficial or damaging corticosteroid action would result in valuable adjustments to current treatment protocols.
Thermal parameter identification of an electrical machine using inverse modelling and non-collocated thermal sensors

The performance of electrical machines is highly dependent on the temperature. Overheating for instance leads to failure of the machine. Heating of the critical components sets a limit to the maximum allowed power and torque density. Having a high fidelity thermal model is essential for safe operation safeguarding maximum efficiency. This paper shows how the thermal models, based on the finite element method, of an electric machine can be improved using inverse modeling techniques. We studied in depth an axial permanent magnet motor and improved the thermal model using inverse modeling techniques via non-collocated thermal sensors. Model order reduction enabled the significant reduction of the computational effort that is required to recover the actual thermal parameters characterizing the thermal properties of an electric machine. These techniques were as well employed for determining the power losses and the air gap convection within the axial flux permanent magnet motor. The results of the developed techniques show that the material properties can deviate from the theoretical ones, and that in this way the material imperfections can be revealed.
A Prediction of SICK Scapula Syndrome Score from Scapular Stabilizer Muscle Activation Analysis in Overhead Athletes

Sarah Lynn Vizza A Prediction of SICK Scapula Syndrome Score from Muscle Activation and Kinematic Analysis in Overhead Athletes (Under the direction of Dr. Joseph B. Myers, Sakiko Oyama, Dr. Steven M. Zinder, and Dr. William E. Prentice) Objective: To determine if subject score on the SICK Scapula, Static Measurements, 0 to 20 Point Rating Scale can be predicted from scapular stabilizer muscle activation. Design: Quasi-experimental, one group design with a counterbalancing of two functional tasks. Subjects: NCAA Division I and/or recreational club overhead athletes (n = 40). Measurements: The SICK Scapula, Static Measurements, 0 to 20 Point Rating Scale was used for assessing the severity of scapular malposition. Muscle activation was recorded for the upper trapezius, middle trapezius, lower trapezius, and serratus anterior muscles. Results: Regression analyses revealed that scapular stabilizer muscle activation amplitude did not significantly predict the subject score on the SICK Scapula, Static Measurements, 0 to 20 Point Rating Scale. Conclusion: In overhead athletes, mean muscle activation amplitude of the scapular stabilizers was not found to be valid predictor of subject score on the SICK Scapula, Static Measurements, 0 to 20 Point Rating Scale.
The Improved Research of Fault Diagnosis Algorithm in Intelligent BIT Design

This paper expatiates the realization procession and key technology of neural network in fault diagnosis platform in detail, which combined with the realization of fault diagnosis platform based on neural network. Then we have carried on amiss control coding of the output cells in the deferent communication. So we have carried out mapping class once more of type and type of coding and carried out mapping class in reverse during the period of decoding. Using the theory and realization of intelligent BIT, we have gotten the development trend of fault diagnosis system, the use of neural network in intelligent BIT and the idiographic configuration of neural network in intelligent BIT, under the environment of hardware and software platform.
Impact of Surrogate Assessments on High-Recall Retrieval

We are concerned with the effect of using a surrogate assessor to train a passive (i.e., batch) supervised-learning method to rank documents for subsequent review, where the effectiveness of the ranking will be evaluated using a different assessor deemed to be authoritative. Previous studies suggest that surrogate assessments may be a reasonable proxy for authoritative assessments for this task. Nonetheless, concern persists in some application domains---such as electronic discovery---that errors in surrogate training assessments will be amplified by the learning method, materially degrading performance. We demonstrate, through a re-analysis of data used in previous studies, that, with passive supervised-learning methods, using surrogate assessments for training can substantially impair classifier performance, relative to using the same deemed-authoritative assessor for both training and assessment. In particular, using a single surrogate to replace the authoritative assessor for training often yields a ranking that must be traversed much lower to achieve the same level of recall as the ranking that would have resulted had the authoritative assessor been used for training. We also show that steps can be taken to mitigate, and sometimes overcome, the impact of surrogate assessments for training: relevance assessments may be diversified through the use of multiple surrogates; and, a more liberal view of relevance can be adopted by having the surrogate label borderline documents as relevant. By taking these steps, rankings derived from surrogate assessments can match, and sometimes exceed, the performance of the ranking that would have been achieved, had the authority been used for training. Finally, we show that our results still hold when the role of surrogate and authority are interchanged, indicating that the results may simply reflect differing conceptions of relevance between surrogate and authority, as opposed to the authority having special skill or knowledge lacked by the surrogate.
Speeding-up Phase-Locked Loops based on Adaptive Loop Bandwidth

paper describes three techniques for controlling the loop filter of the PLL for high operating speed. The proposed fast- locking PLL reduces the pull-in time and enhances the switching speed, while maintaining better noise bandwidth. Extended loop bandwidth enhancement is achieved by the adaptive control on the loop filter resistances. This work differs from previously published results in that it presents a comprehensive study for modeling, circuit simulation and practical circuit implementation of a 2nd order PLL with loop filter control for speeding-up the PLL. The overall improvement in performance of the proposed PLL is evaluated and compared with the conventional PLL. An industrial CMOS IC is used to implement the PLL.
Complexity and Algorithms for the Discrete Fréchet Distance Upper Bound with Imprecise Input

We study the problem of computing the upper bound of the discrete Fr\'{e}chet distance for imprecise input, and prove that the problem is NP-hard. This solves an open problem posed in 2010 by Ahn \emph{et al}. If shortcuts are allowed, we show that the upper bound of the discrete Fr\'{e}chet distance with shortcuts for imprecise input can be computed in polynomial time and we present several efficient algorithms.
Nonlinear electrical transport in porous silicon.

We present a study of the electrical transport in porous Si layers prepared by anodic etching of two different kinds of (100) p-type Si substrates. It is shown that by choosing a sufficiently thick layer, the problem of injection from the contacts can be eliminated. In this way we measure the intrinsic transport properties. The results suggest that a Poole-Frenkel type of mechanism accounts for the observed electric-field-enhanced conduction.
Nursing effort and quality of care for nursing home residents.

PURPOSE The purpose of this study was to determine the relationship between nursing home staffing level, care received by individual residents, and resident quality-related care processes and functional outcomes. DESIGN AND METHODS Nurses recorded resident care time for 5,314 residents on 156 units in 105 facilities in four states (Colorado, Indiana, Minnesota, and Mississippi). We linked residents' care times to their measures of health and functioning from Minimum Data Set assessments. Major variables were unit- and resident-specific minutes of care per day, process measures (physical restraints, range of motion, toileting program, and training in activities of daily living [ADLs]), outcome measures (ADL decline, mobility decline, and worsening behavior between the time study and 90-day follow-up), and covariates such as unit type and resident health status. We used multilevel analysis to examine staffing and quality relationships. RESULTS Residents with toileting programs, range of motion or ADL training, and restraints received significantly more care from unlicensed but not from licensed staff. However, functional outcomes were not significantly related to care received from licensed or unlicensed staff, except for ADL decline, which was greatest for residents receiving more unlicensed minutes of care. Unit staffing level (licensed and unlicensed) was unrelated to any of the care processes or outcome measures, although higher overall staffing was associated with more time devoted to direct resident care. IMPLICATIONS Future research into nursing home quality should focus on organization and delivery rather than simply the amount of care available.
Stable and Compact Inductance Modeling of 3-D Interconnect Structures

Recent successful techniques for the efficient simulation of large-scale interconnect models rely on the sparsification of the inverse of the inductance matrix L. While there are several techniques for sparsifying L-1, the stability of these approximations for general interconnect structures has not been established, i.e., the sparsified reluctance and inductance matrices are not guaranteed to be positive-definite. In this paper, we present a novel technique for reluctance sparsification for general interconnect structures that enjoys several advantages: first, the resulting sparse approximation is guaranteed to be positive definite; second, the approximation is optimal, in a certain well-defined sense; third, owing to its computational efficiency and numerical stability, the algorithm is applicable for very large problem sizes; and finally our approach yields a compact representation of both inductance and reluctance matrices for general cases
Sleep‐disordered breathing in major depressive disorder

Individuals with major depressive disorder often experience obstructive sleep apnea. However, the relationship between depression and less severe sleep‐disordered breathing is unclear. This study examined the rate of sleep‐disordered breathing in depression after excluding those who had clinically significant sleep apnea (>5 apneas∙h−1). Archival data collected between 1991 and 2005 were used to assess the prevalence of sleep‐disordered breathing events in 60 (31 depressed; 29 healthy controls) unmedicated participants. Respiratory events were automatically detected using a program developed in‐house measuring thermal nasal air‐flow and chest pressure. Results show that even after excluding participants with clinically significant sleep‐disordered breathing, individuals with depression continue to exhibit higher rates of sleep‐disordered breathing compared with healthy controls (depressed group: apnea–hypopnea index mean = 0.524, SE = 0.105; healthy group: apnea–hypopnea index mean = 0.179, SE = 0.108). Exploratory analyses were also conducted to assess for rates of exclusion in depression studies due to sleep‐disordered breathing. Study exclusion of sleep‐disordered breathing was quantified based on self‐report during telephone screening, and via first night polysomnography. Results from phone screening data reveal that individuals reporting depression were 5.86 times more likely to report a diagnosis of obstructive sleep apnea than presumptive control participants. Furthermore, all of the participants excluded for severe sleep‐disordered breathing detected on the first night were participants with depression. These findings illustrate the importance of understanding the relationship between sleep‐disordered breathing and depression, and suggest that screening and quantification of sleep‐disordered breathing should be considered in depression research.
Potentially pathogenic bacteria cultured from the sputum of stable asthmatics are associated with increased 8-isoprostane and airway neutrophilia

Abstract Potential bacterial pathogens are found in the airways in several diseases that are associated with neutrophilic inflammation. The aim of this study was to characterize subjects with stable asthma, with no symptoms of respiratory infection, to assess whether key potentially pathogenic bacteria were present in significant quantities in the airways and to correlate this with the pattern of airway inflammation and oxidative stress. Subjects with stable asthma (n = 115) and healthy controls (n = 8) underwent clinical assessment, including hypertonic saline challenge combined with sputum induction. A significant load of potentially pathogenic bacteria (> 106 cfu/mL) was cultured from the sputum of 17 (15%) subjects with stable asthma and was associated with higher total cell counts, proportion and number of neutrophils, sputum IL-8 and 8-isoprostane concentrations. The role of bacteria in potentiating neutrophilic asthma warrants further investigation. Therapies such as antibiotic and antioxidant treatment may be most effective in this sub-group of patients.