Miracle-dz/UniFAR-FTUs huggingface dataset:
Geospatial data aggregation and reduction in vehicular sensing applications: The case of road surface monitoring

Mobile devices present several features which make them attractive as enabling technology for crowdsensing systems. In particular, their spectrum of sensing capabilities, together with consolidated diffusion and ease of use contribute to an increasing adoption in different mobility-based sensing scenarios. On the other hand, the availability of massive volumes of geospatial data provided by large-scale distributed sensing systems prompts the need for innovative approaches to efficient data gathering and processing. Data reduction strategies are often necessary in order to cope with challenges posed by these volumes, for instance when dealing with real-time visualization of query results. In this paper we present a data reduction and aggregation approach for mitigating the impact of data size in a vehicular sensing application aimed at monitoring the roughness of road surfaces. Data collected by smartphones on board of vehicles is progressively thinned at different levels of the proposed architecture through sampling and spatial/temporal aggregation. Preliminary results show that the proposed methodology provides substantial benefits in terms of reduced impact of data while, at the same time, it enables full exploitation of statistical error compensation.
A Novel Multi-hop B3G Architecture for Adaptive Gateway Management in Heterogeneous Wireless Networks

In this paper, a Multi-hop B3G architecture, which is a Heterogeneous Wireless Network (HWN) architecture, integrating MANET, Infrastructure WLAN with 3G UMTS network, is considered for the purpose of providing anytime, anywhere data access as it utilizes the higher bandwidth rates of MANET and WLAN networks and wider range of communication of 3G network. The issues of Gateways in HWN, which serve as a liaison between the MANET/WLAN and UMTS, are analyzed in this paper. A multi-metric Gateway Selection mechanism using Simple Additive Weighting (SAW) of MANET node metrics such as residual energy, 3G signal strength and mobility speed, is used to select an optimal Gateway from the dual-interface-enabled Gateway Candidates. As transaction progresses, when the above-mentioned metrics of the Gateway reach their respective pre-defined threshold values, thereby, affecting optimality of the current Gateway, a Multi-metric Adaptive Gateway Migration Mechanism (MAGMM) is proposed, which migrates the responsibility of the current Gateway to a newly-selected optimal Gateway, in order to sustain connectivity of the integrated network. This MAGMM is incorporated in the considered multi-hop B3G architecture and hence it is termed as Adaptive Gateway Management-based Multi-hop B3G Network (AGMMB3G) Architecture. In addition to MAGMM, this paper also compares AGMMB3G with the other existing HWN architectures. Simulations are carried out in HWN environment using NS2 and the results show how MAGMM enhances the overall HWN performance in terms of Transaction Duration, Packet Delivery Ratio and Control Packet Overhead parameters.
Face recognition based on the feature fusion of 2DLDA and LBP

To study the robustness of face recognition algorithms on conditions of complex illumination, facial expression and posture, three subset databases (Illumination, Expression and Posture subsets) are constructed by selecting images from several existing face databases. Advantages and disadvantages of seven typical algorithms on extracting global and local features are discussed respectively through the experiments on ORL and the three databases mentioned above. To improve the recognition rate, an algorithm of face recognition based on the feature fusion of Two-Dimensional Linear Discriminant Analysis (2DLDA) and Local Binary Pattern (LBP) is proposed in this paper. The experimental results verify both the complementarities of the two kinds of feature and the effectiveness of the proposed feature fusion algorithm. Keywords-face recognition; global feature; local feature; 2DLDA; LBP; feature fusion;
Kinematic and kinetic comparison of running in standard and minimalist shoes.

PURPOSE The purpose of this study was to determine whether running in a minimalist shoe results in a reduction in ground reaction forces and alters kinematics over standard shoe running. The secondary purpose of this study was to determine whether within-session accommodation to a novel minimalist shoe occurs. METHODS Subjects were 14 male, rearfoot striking runners who had never run in a minimalist shoe. Subjects were tested while running 3.35 m·s(-1) for 10 min on an instrumented treadmill in a minimalist and a standard shoe as three-dimensional lower extremity kinematics and kinetics were evaluated. Data were collected at minute 1 and then again after 10 min of running in both shoe conditions to evaluate accommodation to the shoe conditions. RESULTS Shoe-time interactions were not found for any of the variables of interest. Minimalist shoe running resulted in no changes in step length (P = 0.967) or in step rate (P = 0.230). At footstrike, greater knee flexion (P = 0.001) and greater dorsiflexion angle (P = 0.025) were noted in the minimalist shoe. Vertical impact peak (P = 0.017) and average vertical loading rate (P < 0.000) were greater during minimalist shoe running. There were main effects of time as dorsiflexion angle decreased (P = 0.035), foot inclination at footstrike decreased (P = 0.048), and knee flexion at footstrike increased (P = 0.002), yet the vertical impact peak (P = 0.002) and average vertical loading rate (P < 0.000) increased. CONCLUSIONS Running in a minimalist shoe appears to, at least in the short term, increase loading of the lower extremity over standard shoe running. The accommodation period resulted in less favorable landing mechanics in both shoes. These findings bring into question whether minimal shoes will provide enough feedback to induce an alteration that is similar to barefoot running.
Quantitative Tests of General Models for the Evolution of Development

Comparative developmental biologists have proposed models to describe patterns of conserved features in vertebrate ontogeny. The hourglass model suggests evolutionary change is most difficult at an intermediate “phylotypic” stage, the adaptive penetrance model suggests change is easiest at an intermediate stage, and the early conservation model suggests change is easier later in ontogeny. Although versions of some of these models have been discussed since the nineteenth century, quantitative approaches have been proposed only recently. Here we present quantitative phylogenetic approaches to evaluating trends in the evolution of ontogeny. We apply these approaches to the proposed models and demonstrate that an existing approach to assessing these models is biased. We show that the hourglass, adaptive penetrance, and early conservation models are unnecessarily complex explanations of the patterns observed in developmental event data for 14 species of vertebrates. Rather, a simpler model that postulates that evolutionary change is easier between ontogenetically adjacent events is adequate.
A robot uses its own microphone to synchronize its steps to musical beats while scatting and singing

Musical beat tracking is one of the effective technologies for human-robot interaction such as musical sessions. Since such interaction should be performed in various environments in a natural way, musical beat tracking for a robot should cope with noise sources such as environmental noise, its own motor noises, and self voices, by using its own microphone. This paper addresses a musical beat tracking robot which can step, scat and sing according to musical beats by using its own microphone. To realize such a robot, we propose a robust beat tracking method by introducing two key techniques, that is, spectro-temporal pattern matching and echo cancellation. The former realizes robust tempo estimation with a shorter window length, thus, it can quickly adapt to tempo changes. The latter is effective to cancel self noises such as stepping, scatting, and singing. We implemented the proposed beat tracking method for Honda ASIMO. Experimental results showed ten times faster adaptation to tempo changes and high robustness in beat tracking for stepping, scatting and singing noises. We also demonstrated the robot times its steps while scatting or singing to musical beats.
Research on the Stability Problem of Hydroelectric Station Penstock under External Pressure

The process of losing stability and breach for reinforcing ring steel penstock under external pressure is very complex and a lot of factors work on this process, so the research about this problem is very difficult. At present, various calculating methods of this problem have their limitations, sometimes even the results have great differences. In this paper, the problem is simulated by artificial neural network and simulated annealing. Test results show that the method is feasible and its validity is verified. This paper provides an efficient solving path for stability problem of reinforcing ring steel Penstock under external pressure.
Polluted insulator optimization using neural network combined with genetic algorithms

A number of investigations were undertaken to enhance the behavior of high voltage outdoor insulators by adopting numerical methods of optimization, but no work is performed to account for the presence of pollution. In this paper, a shape optimization of a high voltage insulator is achieved with the objective of reducing the tangential electric field along its polluted surface by means of numerical methods, namely neural networks and genetic algorithms.
Evidence for Surface Cooling Emission in the XMM-Newton Spectrum of the X-Ray Pulsar PSR B2334+61

We report on the first XMM-Newton observation of the Vela-like pulsar PSR B2334+61. Spectral analysis reveals soft X-ray emission, with the bulk of the photons emitted at energies below ~1.5 keV. We find that the spectrum has a thermal origin and is well-fitted with either a blackbody or a magnetized, pure H atmospheric model. In the latter case, for a neutron star with a radius of 13 km and a magnetic field of 1013 G, the best fit gives a hydrogen column density NH = 0.33 × 1022 cm-2 and an effective temperature T = 0.65 × 106 K, as measured at Earth. A comparison of the surface temperature of PSR B2334+61 obtained from this fit with cooling curves favors a medium-mass neutron star with M ~ 1.45 M☉ or M ~ 1.6 M☉, depending on which of two different models of proton superfluidity in the interior is used. We do not detect any pulsed emission from the source and determine an upper limit of 5% for the modulation amplitude of the emission on the pulsar's radio frequency.
Cheap eagerness: speculative evaluation in a lazy functional language

Cheap eagerness is an optimization where cheap and safe expressions are evaluated before it is known that their values are needed. Many compilers for lazy functional languages implement this optimization, but they are limited by a lack of information about the global flow of control and about which variables are already evaluated. Without this information, even a variable reference is a potentially unsafe expression! In this paper we show that significant speedups are achievable by cheap eagerness. Our cheapness analysis uses the results of a program-wide data and control flow analysis to find out which variables may be unevaluated and which variables may be bound to functions which are dangerous to call.
Deconstructing psychosis and misperception symptoms in Parkinson’s disease

Objective Patients with Lewy body disease develop a variety of psychotic and misperception symptoms, including visual hallucinations and delusions, as well as ‘minor hallucinations’, that is, a sense of presence, passage hallucinations and visual illusions. Although these symptoms have been suggested to have common underlying mechanisms, the commonalities and differences among them have not been systematically investigated at the neural level. Methods Sixty-seven patients with Parkinson’s disease underwent neuropsychological and behavioural assessments, volumetric MRI and 18F-fluorodeoxyglucose-positron emission tomography (FDG-PET). A factor analysis was performed to discover correlations among psychotic and misperception symptoms, other behavioural symptoms and neuropsychological performances. Partial least-squares correlation analysis was used to investigate the relationship between these symptoms and the joint features of MRI and FDG-PET. Results A sense of presence, passage hallucinations and visual illusions constituted a single behavioural factor (minor hallucinations/illusions). Visual hallucinations formed another behavioural factor along with delusions, depression and fluctuating cognition (psychosis/dysphoria). Three distinct brain–behaviour correlation patterns were identified: (1) posterior cortical atrophy/hypometabolism associated with minor hallucinations/illusions and visuospatial impairment; (2) upper brainstem and thalamic atrophy/hypometabolism associated with psychosis/dysphoria and (3) frontal cortical atrophy/hypometabolism associated with non-visual cognition. No significant differences in neuroimaging findings were identified between patients who had minor hallucinations/illusions alone and patients who also had visual hallucinations. Conclusions Our findings suggest that combined damage to the upper brainstem/thalamus and the posterior neocortex underlies both minor hallucinations/illusions and visual hallucinations and that the former pathology is more associated with visual hallucinations/frank psychosis and the latter is more associated with minor hallucinations/illusions.
Serum and urinary markers of bone remodeling: assessment of bone turnover.

It appears that at present, serum BGP is the one bone protein that has the most promise for assisting in the diagnosis and management of high turnover metabolic bone disease states. If further studies confirm its usefulness in osteoporosis as a predictor of rapid bone loss without the need for bone biopsy, this serum marker will then not only allow early detection but also an appropriate choice of therapy in osteoporosis, i.e. the use of specific inhibitors of high turnover states such as estrogen, calcitonin, or bisphosphonates. In addition, it may also permit more accurate follow-up of patients suffering from diseases such as primary hyperparathyroidism after surgery. In low turnover osteoporosis, it may also serve a useful function to observe whether the osteoblast can be stimulated to enhance bone formation with therapies such as fluoride, anabolic steroids, PTH, etc. As yet, additional measurements, such as bone histomorphometry and other bone mineral markers, are required for definitive diagnosis. Hopefully, the availability of specific well-characterized antibodies against BGP may define its role more accurately. Recently, several other new bone proteins have been identified but at present they have very limited clinical application. Future studies into the structure-function relationship of these bone proteins may identify those markers which will be most relevant to the diagnosis and treatment of metabolic bone disease.
An Automatically Aligned Corpus of Child-Directed Speech

Forced alignment would enable phonetic analyses of child directed speech (CDS) corpora which have existing transcriptions. But existing alignment systems are inaccurate due to the atypical phonetics of CDS. We adapt a Kaldi forced alignment system to CDS by extending the dictionary and providing it with heuristically-derived hints for vowel locations. Using this system, we present a new time-aligned CDS corpus with a million aligned segments. We manually correct a subset of the corpus and demonstrate that our system is 70% accurate. Both our automatic and manually corrected alignments are publically available at osf.io/ke44q.
An Autistic Endophenotype and Testosterone Are Involved in an Atypical Decline in Selective Attention and Visuospatial Processing in Middle-Aged Women

Mothers of offspring with autism spectrum disorders (ASD) could present mild forms of their children’s cognitive characteristics, resulting from prenatal brain exposure and sensitivity to testosterone (T). Indeed, their cognition is frequently characterized by hyper-systemizing, outperforming in tests that assess cognitive domains such as selective attention, and fine motor and visuospatial skills. In the general population, all these start to decline around the mid-forties. This study aimed to characterize whether middle-aged women who are biological mothers of individuals with ASD had better performance in the aforementioned cognitive skills than mothers of normative children (in both groups n = 22; mean age = 45), using the standardized Stroop and mirror-drawing tests. We also examined the role of T in their performance in the aforementioned tests. ASD mothers outperformed controls in both tests, giving more correct answers and making fewer mistakes. In addition, they presented higher T levels, which have been associated with better cognitive performance. Cognitive decline in specific skills with aging could be delayed in these middle-aged women, corresponding to a cognitive endophenotype, T playing an important role in this process.
Assistive Technology for Students with Learning Disabilities: Perceptions of Students and their Parents

Assistive technology refers to “any item, piece of equipment or product system, whether acquired commercially off the shelf, modified, or customized, that is used to increase, maintain, or improve the functional capabilities of a child with a disability” (Individuals with Disabilities Education Act, 2004). Relevant assistive technology for students with learning disabilities includes, but is not limited to, computer programs that provide textto-speech (Kurzweil 3000), speech-to-text (Dragon Naturally Speaking), word prediction capabilities (WordQ), and graphic organizers (Inspiration).
ECG coding by wavelet-based linear prediction

Presents a novel coding scheme for the electrocardiogram (ECG). Following beat delineation, the periods of the beats are normalized by multirate processing. After amplitude normalization, a discrete wavelet transform is applied to each beat. Due to the period and amplitude normalization, the wavelet transform coefficients bear a high correlation across beats at identical locations. To increase the compression ratio, the residual sequence obtained after linear prediction of the significant wavelet coefficients is transmitted to the decoder. The difference between the actual period and the mean beat period, and that between the actual scale factor and the average amplitude scale factor are also transmitted for each beat. At the decoder, the inverse wavelet transform is computed from the reconstructed wavelet transform coefficients. The original amplitude and period of each beat are then recovered. The approximation achieved, at an average rate of 180 b/s, is of high quality. The authors have evaluated the normalized maximum amplitude error and its position in each cycle, in addition to the normalized root mean square error. The significant feature of the proposed technique is that, while the error is nearly uniform throughout the cycle, the diagnostically crucial QRS region is kept free of maximal reconstruction error.
Judgment analysis : theory, methods, and applications

Theoretical Foundations of Judgment Analysis. Designing Judgment Analysis Research. Constructing Judgment Analysis Tasks. Capturing Judgment Policies. Comparing Systems: The Lens Model Equation. Aggregating and Comparing Judgment Policies. Special Topics and Issues in Judgment Analysis. Future Directions for Judgment Analysis Research. Appendices: Computer Programs for Supporting Judgment Analysis Research. Glossary of Important Terms. References. Subject Index.
orphan a nesthesia 1 Anaesthesia recommendations for patients suffering from Catecholaminergic polymorphic ventricular tachycardia

Catecholaminergic polymorphic ventricular tachycardia (CPVT) is a rare genetic disease with an incidence of approximately 1:10,000 in European population and a very high mortality rate if left untreated, reaching 31% by the age of 30 years [1]. It is characterized by bidirectional or polymorphic ventricular tachycardias (VT) induced by an adrenergic triggering factor, such as emotional stress or physical activity, while some patients may also develop episodes of atrial fibrillation [1,2]. Stress-related syncope is the most common symptom in otherwise healthy children or adolescents, while sudden cardiac death due to degeneration of VT to ventricular fibrillation (VF) may also occur. Palpitations and dizziness during exercise represent more benign manifestations of the disorder [3].
What can tracer observations in the continental boundary layer tell us about surface-atmosphere fluxes?

Abstract. We analyze the potential for inferring spatially resolved surface fluxes from atmospheric tracer observations within the mixed layer, such as from monitoring towers, using a receptor oriented transport model (Stochastic Time-Inverted Lagrangian Transport model - STILT) coupled to a simple biosphere in which CO2 fluxes are represented as functional responses to environmental drivers (radiation and temperature). Transport and biospheric fluxes are coupled on a dynamic grid using a polar projection with high horizontal resolution (~20 km) in near field, and low resolution far away (as coarse as 2000 km), reducing the number of surface pixels without significant loss of information. To test the system, and to evaluate the errors associated with the retrieval of fluxes from atmospheric observations, a pseudo data experiment was performed. A large number of realizations of measurements (pseudo data) and a priori fluxes were generated, and for each case spatially resolved fluxes were retrieved. Results indicate strong potential for high resolution retrievals based on a network of tall towers, subject to the requirement of correctly specifying the a priori uncertainty covariance, especially the off diagonal elements that control spatial correlations. False assumptions about the degree to which the uncertainties in the a priori fluxes are spatially correlated may lead to a strong underestimation of uncertainties in the retrieved fluxes, or, equivalently, to biased retrievals. The framework presented here, however, allows a conservative choice of the off diagonal elements that avoids biasing the retrievals.
Ligand Pose Optimization with Atomic Grid-Based Convolutional Neural Networks

Docking is an important tool in computational drug discovery that aims to predict the binding pose of a ligand to a target protein through a combination of pose scoring and optimization. A scoring function that is differentiable with respect to atom positions can be used for both scoring and gradient-based optimization of poses for docking. Using a differentiable grid-based atomic representation as input, we demonstrate that a scoring function learned by training a convolutional neural network (CNN) to identify binding poses can also be applied to pose optimization. We also show that an iteratively-trained CNN that includes poses optimized by the first CNN in its training set performs even better at optimizing randomly initialized poses than either the first CNN scoring function or AutoDock Vina.
Hybrid cooperative co-evolution for the CEC15 benchmarks

In this paper, we test the performance of hybrid cooperative co-evolution (hCC) on the CEC15 benchmarks. In its initial stage, the method applies the recently introduced differential grouping to learn the problem variables' inter-dependencies and separate the variables into groups of separable and non-separable ones. In its second stage, the method adopts different algorithms within the cooperative co-evolution (CC) framework to simultaneously optimize the generated groups. Results are reported for all required problem sizes.