Miracle-dz/UniFAR-FTUs huggingface dataset:
Invariants of Finite Reflection Groups and the Mean Value Problem for Polytopes

Let P be an n‐dimensional polytope admitting a finite reflection group G as its symmetry group. Consider the set HP(k) of all continuous functions on Rn satisfying the mean value property with respect to the k‐skeleton P(k) of P, as well as the set HG of all G‐harmonic functions. Then a necessary and sufficient condition for the equality HP(k) = HG is given in terms of a distinguished invariant basis, called the canonical invariant basis, of G. 1991 Mathematics Subject Classification 20F55, 52B15.
Profile-based View Composition in Development Dashboards

Continuous Integration (CI) environments cope with the repeated integration of source code changes and provide rapid feedback about the status of a software project. However, as the integration cycles become shorter, the amount of data increases, and the effort to find information in statically composed CI dashboards becomes substantial. We want to address the shortcoming of static views with a so-called Software Quality Assessment (SQA) mashup and profiles. SQA-Mashup describes an approach to enable the integration and presentation of data generated in CI environments. In addition, SQA-Profiles describe sets of rules to enable a dynamic composition of CI dashboards based on stakeholder activities in tools of a CI environment (e.g., version control system).
Neural-Network-Based Photometric Stereo for 3D Surface Reconstruction

This paper proposes a novel neural-network-based photometric stereo approach for 3D surface reconstruction. The neural network inputs are the pixel values of the 2D images to be reconstructed. The normal vectors of the surface can then be obtained from the weights of the neural network after supervised learning, where the illuminant direction does not have to be known in advance. Finally, the obtained normal vectors are applied to enforce integrability when reconstructing 3D objects. The experimental results demonstrate that the proposed neural-network-based photometric stereo approach can be successfully applied to objects generally, and perform 3D surface reconstruction better than some existing approaches.
Adverse Intrauterine Conditions Diminish the Fetal Defense Against Acute Hypoxia by Increasing Nitric Oxide Activity

Background—The incidence of perinatal morbidity arising from birth hypoxia or asphyxia has not changed significantly in recent years despite marked improvements in labor management. Perinatal mortality in these circumstances may therefore reflect antenatal compromise and subsequent alteration of the fetal capacity to respond to episodes of hypoxia that may occur during labor. Hence, we have investigated the effects of fetal pre-exposure to a period of adverse intrauterine conditions on the mechanisms mediating the fetal defense response to a subsequent episode of acute hypoxia in sheep. Methods and Results—Sixteen fetal sheep were chronically instrumented at 118±2 days for recording of blood pressure, heart rate, and femoral and umbilical blood flows. In 8 of these fetuses, umbilical blood flow was reduced by 30% for 3 days (between days 125 and 128). The remaining 8 fetuses acted as sham-operated controls. Between 2 and 7 days after umbilical cord/sham compression, all fetuses were exposed to 2 episodes of acute hypoxemia on separate days during infusion with either saline or treatment with a combination of NG-nitro-l-arginine methyl ester and sodium nitroprusside. We show that previous fetal exposure to a period of adverse intrauterine conditions, such as that induced by compression of the umbilical cord, elevates nitric oxide activity and results in a markedly diminished cardiovascular defense response to subsequent acute hypoxia. Conclusions—The data imply that pre-exposure to adverse antenatal conditions may render the fetus more susceptible to the acute hypoxia or asphyxia that can accompany relatively uncomplicated labor and delivery.
A novel rollover warning method for ground vehicles based on smartphone built in GIS/GPS

In recent years, it has been shown that damages of casualties and property losses caused by vehicle rollover accidents are severe. Vehicle rollovers can easily cause secondary accidents such as chain collisions on freeways. Ground vehicles with relatively higher center of gravity on sharp curves are prone to rollover accidents. Therefore, it is of vital significance to design a rollover warning system for this type of vehicles. After summarizing current research achievements about vehicle rollover warning methods, the paper presents a novel rollover warning method for ground vehicles based on GIS/GPS. It firstly elaborates the method for road curve identification and curve radius estimation. Then, a curve speed model is built based on vehicle dynamics and road environment conditions. Lastly, it presents the architecture and field test verification of the rollover warning system. The main contributions of this paper are organized as follows: 1) Curve speed model is built by analyzing the correlation between the front wheel angle and the curve radius. Moreover, rollover limiting condition is analyzed based on vehicle roll dynamics modeling; 2) Curve road recognition based on GIS/GPS using smartphone platform. It presents a curve recognition algorithm based on acquiring road and vehicle parameters such as curve entry, driving direction and vehicle lateral acceleration; 3) Curve radius estimation based on road curve fitting and radius calculation. After obtaining location of each circular section on the curve through the digital map, the center of each circular section is determined and then the average radius of the curve is calculated.
Security for wireless sensor networks in military operations

The communication in military is vital for distribution of commands, logistical information and proper functionality of all units. It is must to have a secure channel through which critical information is exchanged in real time and privacy of information is maintained. Wireless sensor network can be used in military application for monitoring militant activities like tracking enemies and force protection. Wireless sensor network has set of distributes sensors nodes which are connected to each other. These sensor nodes are low powered, low cost, small in size and can do limited amount of computation. But wireless sensor network is vulnerable to various kinds of attacks like node capturing, eavesdropping in communication links and man in the middle or reply to a message. To ensure security, messages from wireless sensor networks must be encrypted. Many key agreement schemes have been proposed to ensure security but most of them are quite complex. In this paper we are proposing pre key distribution scheme for public key cryptography in military communication by establishing secure key arrangement between set of nodes in sensor network. This will improve performance in term of memory usage, resource consumption, resiliency against node capturing, scalability, resistant against node replication and security in information sharing.
Smooth pursuit and antisaccade eye movements as endophenotypes in schizophrenia spectrum research.

Smooth pursuit eye movement (SPEM) and antisaccade deficits have been proposed as schizophrenia spectrum endophenotypes. An endophenotype is a behavioural or biological deficit thought to represent, more closely than the disease phenotype, the effects of an underlying disease gene. Oculomotor endophenotypes possess phenotypic homogeneity, well-understood neural correlates and objective assessment and may thus be used as phenotypes in linkage studies. This thesis investigated a number of issues concerning the reliability and validity of the SPEM and antisaccade tasks as schizophrenia spectrum endophenotypes (and two tasks thought to be unimpaired in the schizophrenia spectrum, visual fixation and prosaccades). The schizophrenia spectrum encompasses not only people with schizophrenia but any population with an increased frequency of schizophrenia-related phenotypes or genotypes, such as schizotypal individuals or first-degree relatives of schizophrenia patients. A valid endophenotype should thus be detected in these populations. Study I investigated reliability, namely internal consistency and temporal stability, of eye movements in healthy individuals. Study II utilised first-episode psychosis patients and healthy controls, aiming to detect behavioural oculomotor deficits in the absence of secondary confounds that may be encountered in chronic schizophrenia. Study III assessed performance in siblings discordant for schizophrenia. Study IV explored the relationship between psychometric schizotypy and oculomotor performance. Study V . examined possible state effects of procyclidine, an anticholinergic compound often administered to schizophrenia patients, on performance in a patient group. The results generally confirmed the validity of the SPEM and antisaccade deficits as schizophrenia spectrum endophenotypes: Oculomotor performance was mostly stable both within and between assessments. SPEM and antisaccade impairments were observed in firstepisode psychosis patients and schizophrenia patients and their healthy siblings. Antisaccade, but not SPEM, impairments were associated with high levels of schizotypy. State effects of procyclidine on SPEM and antisaccade performance were observed, suggesting the need to consider the influence of pharmacological treatment in future patient studies. These findings suggest that SPEM and antisaccade deficits may be studied profitably as endophenotypes in schizophrenia spectrum research.
Mechanisms controlling primary and new production in a global ecosystem model – Part I: Validation of the biological simulation

A global general circulation model coupled to a simple six-compartment ecosystem model is used to study the extent to which global variability in primary and export production can be realistically predicted on the basis of advanced parameterizations of upper mixed layer physics, without recourse to introducing extra complexity in model biology. The “K profile parameterization” (KPP) scheme employed, combined with 6-hourly external forcing, is able to capture short-term periodic and episodic events such as diurnal cycling and storm-induced deepening. The model realistically reproduces various features of global ecosystem dynamics that have been problematic in previous global modelling studies, using a single generic parameter set. The realistic simulation of deep convection in the North Atlantic, and lack of it in the North Pacific and Southern Oceans, leads to good predictions of chlorophyll and primary production in these contrasting areas. Realistic levels of primary production are predicted in the oligotrophic gyres due to high frequency external forcing of the upper mixed layer (accompanying paper Popova et al., 2006) and novel parameterizations of zooplankton excretion. Good agreement is shown between model and observations at various JGOFS time series sites: BATS, KERFIX, Papa and HOT. One exception is the northern North Atlantic where lower grazing rates are needed, perhaps related to the dominance of mesozooplankton there. The model is therefore not globally robust in the sense that additional parameterizations are needed to realistically simulate ecosystem dynamics in the North Atlantic. Nevertheless, the work emphasises the need to pay particular attention to the parameterization of mixed layer physics in global ocean ecosystem modelling as a prerequisite to increasing the complexity of ecosystem models.
An Improved Co-evolutionary Particle Swarm Optimization for Wireless Sensor Networks with Dynamic Deployment

The effectiveness of wireless sensor networks (WSNs) depends on the coverage and target detection probability provided by dynamic deployment, which is usually supported by the virtual force (VF) algorithm. However, in the VF algorithm, the virtual force exerted by stationary sensor nodes will hinder the movement of mobile sensor nodes. Particle swarm optimization (PSO) is introduced as another dynamic deployment algorithm, but in this case the computation time required is the big bottleneck. This paper proposes a dynamic deployment algorithm which is named “virtual force directed co-evolutionary particle swarm optimization” (VFCPSO), since this algorithm combines the co-evolutionary particle swarm optimization (CPSO) with the VF algorithm, whereby the CPSO uses multiple swarms to optimize different components of the solution vectors for dynamic deployment cooperatively and the velocity of each particle is updated according to not only the historical local and global optimal solutions, but also the virtual forces of sensor nodes. Simulation results demonstrate that the proposed VFCPSO is competent for dynamic deployment in WSNs and has better performance with respect to computation time and effectiveness than the VF, PSO and VFPSO algorithms.
Compact tunable Ku-band Schiffman phase shifter for beam-steering applications

This study focuses on the design of a tunable compact phase shifter intended for Ku applications. The adopted topology is based on the basic Schiffman model where the coupled microstrip transmission lines have been modified and loaded by Varactor diodes. Various models of phase shifters can be obtained by moving the location of these diodes and optimized using ADS software from Agilent Technologies. The modified Schiffman phase shifter is able to create a continuously tunable phase shift by varying the voltage of the diodes while ensuring good performance over the entire operating band.
Variability in the Thermal Emission from Accreting Neutron Star Transients

The composition of the outer 100 m of a neutron star sets the heat flux that flows outward from the core. For an accreting neutron star in an X-ray transient, the thermal quiescent flux depends sensitively on the amount of hydrogen and helium remaining on the surface after an accretion outburst and on the composition of the underlying ashes of previous H/4He burning. Because H/4He has a higher thermal conductivity, a larger mass of H/4He implies a shallower thermal gradient through the low-density envelope and hence a higher effective temperature for a given core temperature. The mass of residual H and 4He varies from outburst to outburst, so the thermal quiescent flux is variable even though the core temperature is constant for timescales ≲104 yr. Heavy elements settle from an H/4He envelope in a few hours; we therefore model the quiescent envelope as two distinct layers, H/4He over heavier elements, and treat the mass of H/4He as a free parameter. We find that the emergent thermal quiescent flux can vary by a factor of 2-3 between different quiescent epochs. The variation is more pronounced at lower interior temperatures, making systems with low quiescent luminosities and frequent outbursts, such as SAX J1808.4-3658, ideal candidates from which to observe this effect. Because the ashes of H/4He burning are heavier than 56Fe, their thermal conductivity is greatly reduced. This increases the inferred crust temperature beyond previous estimates for a given effective temperature. We survey this effect for different ash compositions and apply our calculations to Cen X-4, Aql X-1, and SAX J1808.4-3658. In the case of Aql X-1, the inferred high interior temperature suggests that neutrino cooling contributes to the neutron star's thermal balance.
Policymakers' Revealed Preferences and the Output-Inflation Variability Trade-Off: Implications for the European System of Central Banks

This paper explores two aspects of the conduct of monetary policy under a monetary union. First, even if the preferences of policymakers over inflation and output variability are identical across member countries, differences in economic structure will mean different desired policy responses to even a common shock. Second, policymakers may be forced to make important concessions in their preferences over inflation and output variability. To examine these issues, in this paper we estimate the objective functions that the European national central banks were implicitly maximizing over the 15 or so years prior to monetary union, as well as the slopes of the inflation-output variability trade-off in each country. While the slopes of the trade-offs vary dramatically across countries, the objective functions are quite similar, with most countries having weights in excess of three-quarters on inflation variability and less than one-quarter on output variability. Our findings suggest that the concessions (in terms of preferences over output and inflation variability) that current inflation-targeting countries such as the UK and Sweden would have to make on accession to the European Monetary Union (EMU) are likely to be minimal. On the other hand, the differences in economic structure across the Eurosystem countries might make it difficult to formulate a common policy even in the face of common goals, suggesting that there may still be significant costs to joining for countries currently outside the EMU.
Evaluating the effectiveness of time-domain features for motor imagery movements using SVM

Motor imagery electroencephalogram signals are the only bio-signals that enable locked-in patients, who have lost control over every motor output, to communicate with and control their surroundings. Brain Machine Interface is collaboration between a human and machines, which translates brain waves to desired, understandable commands for a machine. Classification of motor imagery tasks for BMIs is the crucial part. Classification accuracy not only depends on how accurate and robust the classifier is; it is also about data. For well separated data, classifiers such as kernel SVM can handle classification and deliver acceptable results. If a feature provides large interclass difference for different classes, immunity to random noise and chaotic behavior of EEG signal is rationally conformed, which means the applied feature is suitable for classifying EEG signals. In this work, in order to have less computational complexity, time-domain algorithms are employed to motor imagery signals. Extracted features are: Mean Absolute Value, Maximum peak value, Simple Square Integral, Willison Amplitude, and Waveform Length. Support Vector Machine with polynomial kernel is applied for classification of four different classes of data. The obtained results show that these features have acceptable, distinct values for different these four motor imagery tasks. Maximum classification accuracy belongs to contribution of Willison amplitude as feature and SVM as classifier, with 95.1 percentages accuracy. Where, the lowest is the contribution of Waveform Length and SVM with 31.67 percentages classification accuracy.
Size-Dictionary Interpolation for Robot’s Adjustment

This paper describes the classification and size-dictionary interpolation of the three-dimensional data obtained by a laser scanner to be used in a realistic virtual fitting room, where automatic activation of the chosen mannequin robot, while several mannequin robots of different genders and sizes are simultaneously connected to the same computer, is also considered to make it mimic the body shapes and sizes instantly. The classification process consists of two layers, dealing, respectively, with gender and size. The interpolation procedure tries to find out which set of the positions of the biologically inspired actuators for activation of the mannequin robots could lead to the closest possible resemblance of the shape of the body of the person having been scanned, through linearly mapping the distances between the subsequent size-templates and the corresponding position set of the bioengineered actuators, and subsequently, calculating the control measures that could maintain the same distance proportions, where minimizing the Euclidean distance between the size-dictionary template vectors and that of the desired body sizes determines the mathematical description. In this research work, the experimental results of the implementation of the proposed method on Fits.me’s mannequin robots are visually illustrated, and explanation of the remaining steps toward completion of the whole realistic online fitting package is provided.
Enabling Cloud Connectivity for Mobile Internet of Things Applications

The number of small embedded devices connected to the Internet is increasing. This growth is mostly due to the large number of Internet of Things (IoT) deployments, with applications such as: industrial monitoring, home automation, and others. One common aspect with the majority of application areas is the lack of mobility. Most IoT devices are stationary and often use IEEE 802.15. 4/6LoWPAN solutions. When a high level of mobility is required, the use of IEEE 802.15. 4 is not possible without adding additional hardware for the user to carry. In this article, a holistic network architecture consisting of heterogeneous devices is presented. The architecture is composed of Embedded Internet Systems (EIS) and uses standard communication protocols. One important feature is the use of the Service-oriented architecture (SOA) paradigm. The use of SOA, by utilization of the CoAP protocol and standard services, enables the proposed architecture to exchange sensor- and actuator data with an Internet-based cloud as well as a user's local cloud consisting of sensor IoT devices, smart phones and laptops. Another component of the architecture is a web-based human-machine interface for configuration, monitoring and visualization of sensor and actuator data using emerging web technologies for structured data processing. Results from experiments and real-world tests show that the proposed architecture can support sample rates of up to several kHz while enabling sensor data to be transmitted to SOA services in real time. This proves that the use of SOA, and Restful web services in particular, is feasible on resource-constrained platforms while supporting true mobility.
Mechanisms and predictors of chronic facial pain in lateral medullary infarction

The purpose of this study was to identify clinical predictors and anatomical structures involved in patients with pain after dorsolateral medullary infarction. Eight out of 12 patients (67%) developed poststroke pain within 12 days to 24 months after infarction. The pain occurred in the ipsilateral face (6 patients) and/or the contralateral limbs and trunk (5 patients, 3 of whom also had facial pain). Ipsilateral facial pain was significantly correlated with lower medullary lesions, including those of the spinal trigeminal tract and/or nucleus, as documented by magnetic resonance imaging. The R2 blink reflex component was abnormal only in patients with facial pain. Likewise, pain and temperature sensation in the ipsilateral face was decreased in all patients with facial pain but not in patients without pain. Ipsilateral touch sensation in the face was also decreased in all patients with facial pain, but the lesions revealed on magnetic resonance imaging did not involve the principal sensory nucleus of the fifth cranial nerve, and the R1 blink reflex latencies were normal. Although facial pain was correlated with lesions of the spinal trigeminal tract and/or nucleus, none of the lesions involved the subnucleus caudalis, which contains most nociceptive neurons. These findings suggest that facial pain after medullary infarction is due to lesions of the lower spinal trigeminal tract (axons of primary afferent neurons), leading to deafferentation of spinal trigeminal nucleus neurons. Ann Neurol 2001;49:493–500