Miracle-dz/UniFAR-FTUs huggingface dataset:
Energy efficiency design in MC-CDMA cooperative networks

The energy efficiency (EE) maximization in multi-carrier code division multiple access (MC-CDMA)1 cooperative wireless networks is a NP-hard optimization problem of great interest for future networks systems as well as wireless sensor networks. This paper presents a game theoretic approach for EE maximization in MC-CDMA wireless cooperative networks considering receiver single-user or multi-user design, as well as distributed implementation approach in order to solve the EE design problem. Moreover, the iterative water-filling algorithm (IWFA) and the Verhulst distributed power control algorithm (V-DPCA) are employed to solve the inner loop of the proposed EE maximization algorithm. A study over the quasi-concavity of the utility function is presented while numerical results are offered to corroborate the mathematical model, as well as to verify the better performance of the IWFA over the V-DPCA. Indeed, the superiority of the IWFA over V-DPCA in terms of both EE and SE is evident. This may be explained through the fact that the IWFA optimizes the power allocation of each user through all sub-carriers at the same time, while Verhulst-based DPCA performs the power control on each sub-channel considering all users.
Chronic arthritis induced in rats by cell wall fragments of Eubacterium species from the human intestinal flora

To investigate arthritis-inducing properties of Eubacterium species, which are major residents of the human intestinal flora, cell wall fragments (CWF) of several Eubacterium strains were prepared and tested in an animal model. After a single intraperitoneal injection in the rat, CWF of E. aerofaciens, E. contortum, and E. lentum induced a chronic polyarthritis. E. limosum and E. tortuosum CWF induced an acute self-limiting joint inflammation, whereas E. rectale CWF failed to do so. The rhamnose contents of the isolated CWF were not related to their arthritis-inducing properties. Paradoxically, the sensitivity of CWF to lysozyme digestion, which is regarded as a parameter for the clearance of CWF in tissues, appeared to be positively correlated with the ability of Eubacterium CWF to induce chronic joint inflammation. Our findings show the diversity in arthritis-inducing properties among different species of the anaerobic genus Eubacterium and underline the importance of the anaerobic intestinal flora in the induction of joint inflammation.
Simulation on the Thixocasting Process of the Turbocharger Impellers and Selection of the Viscosity Models

The thixocasting technology is chosen to produce the turbocharger impellers as it is capable of producing castings with high precision dimension, free of oxide and shrinkage porosity defects and therefore long service life. The thixocasting turbocharger impellers have the similar mechanical property to those produced by forging and machine processes, but much less costs. In the thixocasting process, the semisolid slurry with certain solid fraction is injected into mould at a high speed. Since high viscosity and thixotropic characteristics, the semisolid slurry reveals unique flow condition and characteristics in the filling process. So, its very desirable for the die design, process optimization, and defect control to visualize the high-speed filling process of semisolid slurry by numerical simulation. In this paper, several commonly used viscosity models for semisolid slurry are analysed. The Power law cut-off model (PLCO) model is selected to simulate the filling process in the thixocasting process of the impellers of 319s alloy, compared with actual results by partial filling testing. The causes of the casting defects are also analysed, indicating that the simulation results can accurately reproduce the filling process of semisolid slurry, and PLCO model is a successful choice for simulating the filling process of semisolid slurry with high solid fraction.
Transition-Based Deep Input Linearization

Traditional methods for deep NLG adopt pipeline approaches comprising stages such as constructing syntactic input, predicting function words, linearizing the syntactic input and generating the surface forms. Though easier to visualize, pipeline approaches suffer from error propagation. In addition, information available across modules cannot be leveraged by all modules. We construct a transition-based model to jointly perform linearization, function word prediction and morphological generation, which considerably improves upon the accuracy compared to a pipelined baseline system. On a standard deep input linearization shared task, our system achieves the best results reported so far.
Spectral Modeling and Relighting of Reflective-Fluorescent Scenes

Hyper spectral reflectance data allows for highly accurate spectral relighting under arbitrary illumination, which is invaluable to applications ranging from archiving cultural e-heritage to consumer product design. Past methods for capturing the spectral reflectance of scenes has proven successful in relighting but they all share a common assumption. All the methods do not consider the effects of fluorescence despite fluorescence being found in many everyday objects. In this paper, we describe the very different ways that reflectance and fluorescence interact with illuminants and show the need to explicitly consider fluorescence in the relighting problem. We then propose a robust method based on well established theories of reflectance and fluorescence for imaging each of these components. Finally, we show that we can relight real scenes of reflective-fluorescent surfaces with much higher accuracy in comparison to only considering the reflective component.
The effectiveness of a supervised physical training model tailored to the individual needs of patients with whiplash-associated disorders - a randomized controlled trial

Objective: To evaluate the effects of a physical training programme which is supervised and tailored to meet the needs of patients with subacute whiplash-associated disorders. Design: A randomized controlled trial with follow-up at three and nine months after randomization. Setting: An interdisciplinary rehabilitation centre. Subjects: Forty-seven patients with subacute disorders following a whiplash trauma. Interventions: Patients were randomized to a supervised training group or a self-administered home training group. Main measures: Primary outcome measures were the Self-Efficacy Scale, the Tampa Scale for Kinesiophobia and the Pain Disability Index. Secondary outcome measures were neck pain intensity, sensory and affective dimensions of pain, pain location and duration, muscle tenderness, grip strength, cervical mobility, sick leave and analgesic consumption. Results: Forty patients (85%) completed the intervention period, and the drop-outs were followed up by intention-to-treat. The results showed that supervised training was significantly more favourable than home training, with a more rapid improvement in self-efficacy (P=0.03), fear of movement/(re)injury (P=0.03) and pain disability (P=0.03) at three months. Further, supervised training significantly reduced the frequency of analgesic consumption (P=0.03). The improvements were partly maintained at nine months, even though there was no amelioration in pain and physical disorders. Despite the favourable outcome, supervised intervention did not reduce sick leave. Conclusions: The findings indicate a treatment approach that is feasible in the rehabilitation of patients with subacute whiplash-associated disorders in the short term, but additional research is needed to extend these findings and elucidate treatment strategies that also are cost effective.
Developing energy efficient filtering systems

Processing large volumes of information generally requires massive amounts of computational power, which consumes a significant amount of energy. An emerging challenge is the development of ``environmentally friendly'' systems that are not only efficient in terms of time, but also energy efficient. In this poster, we outline our initial efforts at developing greener filtering systems by employing Field Programmable Gate Arrays (FPGA) to perform the core information processing task. FPGAs enable code to be executed in parallel at a chip level, while consuming only a fraction of the power of a standard (von Neuman style) processor. On a number of test collections, we demonstrate that the FPGA filtering system performs 10-20 times faster than the Itanium based implementation, resulting in considerable energy savings.
Electrode for Force Sensor of Conductive Rubber

Tactile sensors are believed to be a key element in order to realize robotic fingers to catch a fragile object without damage. Force sensitive conductive rubber is a low-cost material and then attractive for the application to tactile sensors. We have studied the effect of electrodes attached to the rubber sheets. We have tried four kinds of electrodes: vacuum deposited Al, adhesive Cu tape, Al thin film sheet and silver paste. It can be concluded that vacuum deposited Al has the highest potential from the practical point of view; it has the widest dynamic range and good precision at the same time.
Data Mining and Machine Learning Techniques for the Identification of Mutagenicity Inducing Substructures and Structure Activity Relationships of Noncongeneric Compounds

This paper explores the utility of data mining and machine learning algorithms for the induction of mutagenicity structure-activity relationships (SARs) from noncongeneric data sets. We compare (i) a newly developed algorithm (MOLFEA) for the generation of descriptors (molecular fragments) for noncongeneric compounds with traditional SAR approaches (molecular properties) and (ii) different machine learning algorithms for the induction of SARs from these descriptors. In addition we investigate the optimal parameter settings for these programs and give an exemplary interpretation of the derived models. The predictive accuracies of models using MOLFEA derived descriptors is approximately 10-15%age points higher than those using molecular properties alone. Using both types of descriptors together does not improve the derived models. From the applied machine learning techniques the rule learner PART and support vector machines gave the best results, although the differences between the learning algorithms are only marginal. We were able to achieve predictive accuracies up to 78% for 10-fold cross-validation. The resulting models are relatively easy to interpret and usable for predictive as well as for explanatory purposes.
Genetic Analysis of Histamine Signaling in Larval Zebrafish Sleep

Abstract Pharmacological studies in mammals and zebrafish suggest that histamine plays an important role in promoting arousal. However, genetic studies using rodents with disrupted histamine synthesis or signaling have revealed only subtle or no sleep/wake phenotypes. Studies of histamine function in mammalian arousal are complicated by its production in cells of the immune system and its roles in humoral and cellular immunity, which can have profound effects on sleep/wake states. To avoid this potential confound, we used genetics to explore the role of histamine in regulating sleep in zebrafish, a diurnal vertebrate in which histamine production is restricted to neurons in the brain. Similar to rodent genetic studies, we found that zebrafish that lack histamine due to mutation of histidine decarboxylase (hdc) exhibit largely normal sleep/wake behaviors. Zebrafish containing predicted null mutations in several histamine receptors also lack robust sleep/wake phenotypes, although we are unable to verify that these mutants are completely nonfunctional. Consistent with some rodent studies, we found that arousal induced by overexpression of the neuropeptide hypocretin (Hcrt) or by stimulation of hcrt-expressing neurons is not blocked in hdc or hrh1 mutants. We also found that the number of hcrt-expressing or histaminergic neurons is unaffected in animals that lack histamine or Hcrt signaling, respectively. Thus, while acute pharmacological manipulation of histamine signaling has been shown to have profound effects on zebrafish and mammalian sleep, our results suggest that chronic loss of histamine signaling due to genetic mutations has only subtle effects on sleep in zebrafish, similar to rodents.
Modelling of Distributed Energy Resources in Industrial Context Using Service Curves of Network Calculus

In this paper, we consider an optimized consumption of energy produced by distributed energy resources (e.g., wind, solar, storage, .) to match an energy demand in industrial microgrid context. In order to match the load with the supply, we need to optimize the selection of available resources based on some constraints such as energy costs and weather conditions. When an imbalance occurs, we consider buying energy from the main grid or spot market if there is a shortage. In case of surplus energy, we consider selling the excess on the spot market. To achieve our goal, we utilize a network calculus approach (specifically the concept of service curves) to model the energy resources. In this context, service curves provide a lower bound on the amount of energy production of the resources. Then, we propose different strategies and compare their performances against each other to minimize energy procurement costs. To apply our model, we consider a real case study of an industrial site located in France.
High Performance Inverted Pyramidal Texture for Silicon Photovoltaics

An inverted pyramidal grating texture is known to reduce both surface reflection and to promote light trapping in crystalline silicon (c-Si) solar cells. However, these textures are not used in commercial solar cells mainly because of high fabrication costs, limited scalability of conventional fabrication techniques to thin wafers, and insufficient knowledge of the optimum grating parameters for silicon of different thicknesses. These issues become even more important as industry makes a transition to thinner Si wafers to reduce device cost. The objective of this this thesis is to address all of these issues. Firstly, a new process for inverted pyramidal texturing of c-Si has been developed that is compatible with thin silicon wafers and foils. Secondly, a theoretical study of the optimum inverted pyramidal grating parameters has been done for a wide range of Si wafer thicknesses. Finally, the optical performance of the optimal textures has been experimentally verified. The laser assisted texturing method produces high quality inverted pyramids in a noncleanroom environment and is potentially scalable to mass production. Because of the contactless fabrication, the method can be used to texture fragile, ultra-thin Si foils. This iii approach also offers precise control of the patterned areas, which can exclude areas for front surface contacts on PV devices. The wave-optical study of the size dependent performance of inverted pyramidal textures identifies a 1000 nm period as being universally optimal for silicon thicknesses ranging from 2 – 400 m. As a point of comparison, inverted pyramidal textures were also fabricated by electron beam lithography. The measured reflectances show that textures with micron scale periodicity outperform a submicron periodic texture, in agreement with trends predicted by the wave-optical simulations. Finally, a novel phenomenon of internal structuring within an optically transparent thin film is described that was discovered in the course of optimizing the laser processing parameters for making apertures in the hard mask layer for PV surface texturization. This phenomenon shows promise for varied applications such as marking of surfaces, and the production of buried channels within a thin film for lab on chip architectures.
K-Reach: Who is in Your Small World

We study the problem of answering k-hop reachability queries in a directed graph, i.e., whether there exists a directed path of length k, from a source query vertex to a target query vertex in the input graph. The problem of k-hop reachability is a general problem of the classic reachability (where k = ∞). Existing indexes for processing classic reachability queries, as well as for processing shortest path queries, are not applicable or not efficient for processing k-hop reachability queries. We propose an index for processing k-hop reachability queries, which is simple in design and efficient to construct. Our experimental results on a wide range of real datasets show that our index is more efficient than the state-of-the-art indexes even for processing classic reachability queries, for which these indexes are primarily designed. We also show that our index is efficient in answering k-hop reachability queries.
Folksonomy-based adaptive query expansion

Adaptive query expansion (QE) allows users to better define their search domain by supplementing the original query with additional terms related to their preferences and info rmation needs. The system we present is an extension of the traditional QE techniques, which rely on the computation of two-dimensional co-occurrence matrices. Our system makes use of three-dimensional co-occurrence matrices, where the added dimension is represented by semantic classes (i.e., categories comprising all the terms that share a semantic property) related to the folksonomy extracted from social bookmarking services such as delicious, Digg, and StumbleUpon. The generation of the user profile occurs through the creatio n of a model that is dynamically updated using the information gleaned from the searches (visited pages and corresponding search queries). The system analyzes the input queries and, if they actually reflect the interests already shown by the us er in previous searches, it returns different QEs involving di fferent semantic fields. The output of the system is structure d in different blocks categorized through keywords, thus helping the user judge which result is most relevant to him. The results of an experimental evaluation involving real users are reported.
Using Postural Control System Measures to Detect Hypovigilance

Posturography is a method to assess the postural control system quantitatively providing the possibility for testing vigilance. In this paper we present pilot experiments and analysis investigating the discriminatory abilities of posturography. A total of 10 young adults participated in postural assessment within a study with extended time awake. Acquired measurements were assigned to two classes “vigilant” and “hypovigilant” according to subject’s continuous time-since-sleep (TSS). Two features sets were extracted from posturographical recordings. In time domain 7 kinds of features from other authors were extracted, including measures of sway velocity and sway area. In spectral domain features were extracted by estimating power spectral densities and subsequent averaging in equidistant spectral bands. In addition to static feature extraction this paper introduces analysis of temporal dynamics within the features of both domains. The ability to discriminate between both classes “vigilant” and “hypovigilant” was evaluated in terms of mean test set errors estimated using 25-fold delete-d cross validation. Different algorithms of computational intelligence including artificial neural networks (ANN) and SupportVector Machines (SVM) were applied. SVM using Gaussian kernel functions performed best with achieved mean test set error rates of 9.0 ± 4.2 %. Author Keywords
Geographic and Opportunistic Routing for Underwater Sensor Networks

Underwater wireless sensor networks (UWSNs) have been showed as a promising technology to monitor and explore the oceans in lieu of traditional undersea wireline instruments. Nevertheless, the data gathering of UWSNs is still severely limited because of the acoustic channel communication characteristics. One way to improve the data collection in UWSNs is through the design of routing protocols considering the unique characteristics of the underwater acoustic communication and the highly dynamic network topology. In this paper, we propose the GEDAR routing protocol for UWSNs. GEDAR is an anycast, geographic and opportunistic routing protocol that routes data packets from sensor nodes to multiple sonobuoys (sinks) at the sea's surface. When the node is in a communication void region, GEDAR switches to the recovery mode procedure which is based on topology control through the depth adjustment of the void nodes, instead of the traditional approaches using control messages to discover and maintain routing paths along void regions. Simulation results show that GEDAR significantly improves the network performance when compared with the baseline solutions, even in hard and difficult mobile scenarios of very sparse and very dense networks and for high network traffic loads.
Hybrid Skeletal-Surface Motion Graphs for Character Animation from 4D Performance Capture

We present a novel hybrid representation for character animation from 4D Performance Capture (4DPC) data which combines skeletal control with surface motion graphs. 4DPC data are temporally aligned 3D mesh sequence reconstructions of the dynamic surface shape and associated appearance from multiple-view video. The hybrid representation supports the production of novel surface sequences which satisfy constraints from user-specified key-frames or a target skeletal motion. Motion graph path optimisation concatenates fragments of 4DPC data to satisfy the constraints while maintaining plausible surface motion at transitions between sequences. Space-time editing of the mesh sequence using a learned part-based Laplacian surface deformation model is performed to match the target skeletal motion and transition between sequences. The approach is quantitatively evaluated for three 4DPC datasets with a variety of clothing styles. Results for key-frame animation demonstrate production of novel sequences that satisfy constraints on timing and position of less than 1% of the sequence duration and path length. Evaluation of motion-capture-driven animation over a corpus of 130 sequences shows that the synthesised motion accurately matches the target skeletal motion. The combination of skeletal control with the surface motion graph extends the range and style of motion which can be produced while maintaining the natural dynamics of shape and appearance from the captured performance.
Promising Direct-Drive Generator System for Large Wind Turbines

The aim of this paper is to review direct-drive generators and to suggest promising direct-drive generator concepts for large wind turbines. Different large direct-drive generators are compared based on the mass and the torque. Features of different PM machines are investigated to find promising machine type for direct-drive. The requirements and alternative solutions are suggested for both electromagnetic and mechanical design.
Approximate Analysis of Power Offset over Spatially Correlated MIMO Channels

Power offset is zero-order term in the capacity versus signal-to-noise ratio curve. In this paper, approximate analysis of power offset is presented to describe MIMO system with uniform linear antenna arrays of fixed length. It is assumed that the number of receive antenna is larger than that of transmit antenna. Spatially Correlated MIMO Channel is approximated by tri-diagonal toeplitz matrix. The determinant of tri-diagonal toeplitz matrix, which is fitted by elementary curve, is one of the key factors related to power offset. Based on the curve fitting, the determinant of tri-diagonal toeplitz matrix is mathematically tractable. Consequently, the expression of local extreme points can be derived to optimize power offset. The simulation results show that approximation above is accurate in local extreme points of power offset. The proposed expression of local extreme points is helpful to approach optimal power offset.