Miracle-dz/UniFAR-FTUs huggingface dataset:
A Nose Gesture Interface Device: Extending Virtual Realities

This paper reports on the1 development of a nose-machine interface device that provides real-time gesture, position, smell and facial expression information. The DATANOSE™2—Data AtomaTa CORNUCOPIA pNeumatic Olfactory 1/O-deviSE Tactile Manipulation (Olsen, 1986; Myers, 1991)—allows novice users without any formal nose training to perform complex interactive tasks.
HIT Approaches to Entity Linking at TAC 2011

This paper describes the system of HIT at the 2011 Text Analysis Conference (TAC) Knowledge Base Population (KBP) track English Entity Linking task. Based on structured and unstructured information extracted from Wikipedia, this system predicts the most probable entity that a query mention might refer to. A similarity score is assigned to the candidate entity by computing the the relatedness between the query and the entity, and augmented by the popularity of the entity. We model the query context as a graph of the entities and utilize the referential relationship between the context entities and the candidate entities in Wikipedia to measure the relatedness between the query context and the candidate entity. Evaluation results show the performance of our system reaches the median value of all the participating systems.
Classification of cognitive load from speech using an i-vector framework

The goal in this work is to automatically classify speakers’ level of cognitive load (low, medium, high) from a standard battery of reading tasks requiring varying levels of working memory. This is a challenging machine learning problem because of the inherent difficulty in defining/measuring cognitive load and due to intra-/inter-speaker differences in how their effects are manifested in behavioral cues. We experimented with a number of static and dynamic features extracted directly from the audio signal (prosodic, spectral, voice quality) and from automatic speech recognition hypotheses (lexical information, speaking rate). Our approach to classification addressed the wide variability and heterogeneity through speaker normalization and by adopting an i-vector framework that affords a systematic way to factorize the multiple sources of variability. Index Terms: computational paralinguistics, behavioral signal processing (BSP), prosody, ASR, i-vector, cognitive load
Collective Monte Carlo updating for spin systems.

A Monte Carlo algorithm is presented that updates large clusters of spins simultaneously in systems at and near criticality. We demonstrate its efficiency in the two-dimensional $\mathrm{O}(n)$ $\ensuremath{\sigma}$ models for $n=1$ (Ising) and $n=2$ ($x\ensuremath{-}y$) at their critical temperatures, and for $n=3$ (Heisenberg) with correlation lengths around 10 and 20. On lattices up to ${128}^{2}$ no sign of critical slowing down is visible with autocorrelation times of 1-2 steps per spin for estimators of long-range quantities.
Alpha-shapes for visualizing irregular-shaped class clusters in 3D feature space for classification of remotely sensed imagery

In this study, we present a geovisualization tool using Alpha-shapes to visualize class clusters in a remotely sensed image classification. An Alpha-shape is an accurate representation of the shape of a cluster of points in a 2D or 3D feature space. Traditionally, spheres and ellipsoids are used to represent class clusters in a classification. These shapes, however, are rough approximations of irregular shaped class clusters. In remote sensing classification we often have to deal with these irregular clusters (e.g. concavities, pockets and voids) and Alpha-shapes will improve visualization of these classes. We argue that Alpha-shapes will also improve insight into a classification process, and related uncertainty. Uncertainty can arise from ambiguity in the attribution of class labels to pixels. This ambiguity is often caused by overlapping classes. Visualization is helpful in communicating this ambiguity as Alpha-shapes clearly show where classes overlap. In this study, we also propose and implement a novel classification algorithm based on Alpha-shapes. Most classification algorithms cannot cope with irregular and concave cluster shapes in feature space. We apply our algorithm on a Landsat 7 image scene of a study area in Southern France. We show that good classification results can be obtained with Alpha-shapes.
Developmental differences in the expression of childhood anxiety symptoms and fears.

OBJECTIVE To examine age differences in the expression of childhood fears and anxiety symptoms. METHOD A cross-sectional design was used to test recently formulated developmental hypotheses regarding the differential expression of childhood anxiety symptoms and fears in a community sample of youths (N = 145). Three groups of youths were compared: children aged 6-9 years (n = 47), preteens aged 10-13 years (n = 52), and adolescents aged 14-17 years (n = 46). Symptoms of separation anxiety, generalized anxiety, and social anxiety were assessed using a dimensional approach by both parent and child report. Fears were assessed by child report. RESULTS The results indicated that there are systematic age differences in the expression of childhood fears and anxiety symptoms. Results also point toward specific symptoms predominant at certain ages (i.e., separation anxiety symptoms in youths aged 6-9 years, death and danger fears in youths aged 10-13 years, and social anxiety symptoms as well as failure and criticism fears in youths aged 14-17 years) in partial support of predictions. CONCLUSIONS Models of the etiology of childhood anxiety disorders tracing continuity and change over time should consider differences in the developmental expression of childhood fears and anxiety symptoms. Attention to developmental issues concerning symptom expression may be important in the clinical understanding of childhood anxiety.
Multichannel intracortical neurorecording: integration and packaging challenges

This paper covers an overview of multichannel massively parallel biosensing device dedicated for neural recording from the cortex. Attention is paid to describe the design techniques and assembly methods of high reliability Microsystems. These devices are optimized, first from the circuit level, to meet ultra low-power budget and all needed flexibility for efficient neurorecording. Second, at the system level, integration of multichips and their interconnections on top of electrode arrays represent challenging tasks to optimize the building of implantable intracortical sensors. Such device includes electrode arrays, preamplification front-end, data conversion, signal processing for the detection of neural events (spikes). It includes also multiple wireless links, the first one is for affording the energy to power up the whole implant, while data are exchanged bidirectionaly with a base station through two other links for data down and up links. Experimental results are shown.
Statistical modeling and analysis of interference in wireless networks

In current wireless networks, interference is the main performance-limiting factor. The quality of a wireless link depends on the signal and interference power, which is strongly related to the spatial distribution of the concurrently transmitting network nodes, shortly denominated as the network geometry. Motivated by the ongoing revision of wireless network design, this dissertation aims to describe the relation between geometry and network performance. Given the exponential growth of wireless devices, it is meaningful to evaluate how network interference affects signal detection. We propose a unified statistical approach based on the characteristic function of the decision variable to describe the detection performance, accounting for single and multiple interference, as well as different detection schemes and architectures. The proposed framework is able to capture the deployment density of the interferers, transmission power, and fading distribution of the interferers and the signal of interest. In addition, we establish a fundamental limit of the interferer node density beyond which robust energy detection is impossible. This work highlights the crucial role of spatial statistics in the evaluation of signal detection. The capacity gain obtained through the densification of the network architecture comes at the expense of an increase in energy consumption. Although small cell access points consume little energy in comparison with the macrocell base stations, the massive deployment of these additional small cell base stations entails a significant increase in energy consumption. We extend the capacity analysis of small cell networks to include the energy consumption of the small cell tier. Considering a distributed sleep mode strategy for the small cell access points, we cast the trade-off between energy consumption and capacity as a set of optimization problems. We develop an analytical framework, which can be used in practice to correctly set sensing time and sensing probability whilst guaranteeing user quality of service. Furthermore, the analytical tool accounts for the network load and predicts the achievable energy reduction of the small cell tier by means of distributed sleep mode strategies as a function of the user density. Finally, given that current networks are interference-limited, we study how signal processing can improve the signal quality. We present a probabilistic framework to describe the performance gain of successive interference cancellation and show that the benefit is modest when users connect to the base station that provides the highest average signal-to-interference ratio. We extend the analysis to include novel ways to associate users to their access points and demonstrate that the benefits of successive interference cancellation are substantial for these operational scenarios. By systematically incorporating the spatial statistics in the performance analysis, this dissertation presents a methodology and analytical toolset useful to enhance the understanding of the design, operation, and evaluation of future wireless networks.
COMPUTATIONALLY-EFFICIENT METHODS FO R RECOVERING TRANSLATIONAL MOTIO N

ABSTRAC T We have developed robust methods fo r determining the motion of an observer translatin g through a static environment. These methods als o apply to the case of arbitrary observer motio n when the rotational component is known. (The cas e of purely rotational motion is treated elsewher e (1)). Our methods do not rely on establishin g point correspondence, nor do they determin e optical flow. They employ only first derivative s of the image brightness function and do not assume an analytic form for the imaged surface. Our methods are all based on minimizing th e difference between the observed time derivative o f the brightness and that predicted from th e observed spatial brightness gradient given a n estimated motion. The methods exploit the fac t that any imaged surface must lie in front of th e camera. They are robust in that all points in th e image contribute to the final determination o f motion. I. Introductio n In this paper we consider the problem o f determining the motion of a monocular observe r moving with respect to a rigid, unknown world. W e use a "least squares", as opposed to a discret e method of solving for the motion parameters ; ou r method uses all of the points in a two-imag e sequence and does not attempt to establis h correspondence between the images. Hence th e method is fairly robust with respect t o quantization error, noise, illumination gradients , and other effects. We can determine the observer motion in tw o special cases : a) when the motion is pure translation or whe n the rotational component of the motion is known. b) when the motion is pure rotation ; this cas e is discussed in Reference 1. At this writing we have not developed a robus t method which is applicable to arbitrary motion. Three methods of determining observer motio n are considered in this paper : a. Minimization of the Integral of Z 2 Given an estimate of motion, the depth at mos t world points can be calculated. Using a n incorrect value for the motion gives a n estimate of depth which is inaccurate and 2 often grossly so. For scenes with a limite d depth range, an accurate estimate of motion i s obtained by minimizing the sum of the square s of …
Reducing Memory Cost of Exact Diagonalization using Singular Value Decomposition

We present a modified Lanczos algorithm to diagonalize lattice Hamiltonians with dramatically reduced memory requirements, without restricting to variational ansatzes. The lattice of size N is partitioned into two subclusters. At each iteration the Lanczos vector is projected into two sets of n(svd) smaller subcluster vectors using singular value decomposition. For low entanglement entropy S(ee), (satisfied by short-range Hamiltonians), the truncation error is expected to vanish as exp(-n(svd)(1/S(ee))). Convergence is tested for the Heisenberg model on Kagomé clusters of 24, 30, and 36 sites, with no lattice symmetries exploited, using less than 15 GB of dynamical memory. Generalization of the Lanczos-SVD algorithm to multiple partitioning is discussed, and comparisons to other techniques are given.
Benchmarking of Distributed Computing Engines Spark and GraphLab for Big Data Analytics

In this paper we evaluate and compare two representativeand popular distributed processing engines for large scalebig data analytics, Spark and graph based engine GraphLab. Wedesign a benchmark suite including representative algorithmsand datasets to compare the performances of the computingengines, from performance aspects of running time, memory andCPU usage, network and I/O overhead. The benchmark suite istested on both local computer cluster and virtual machines oncloud. By varying the number of computers and memory weexamine the scalability of the computing engines with increasingcomputing resources (such as CPU and memory). We also runcross-evaluation of generic and graph based analytic algorithmsover graph processing and generic platforms to identify thepotential performance degradation if only one processing engineis available. It is observed that both computing engines showgood scalability with increase of computing resources. WhileGraphLab largely outperforms Spark for graph algorithms, ithas close running time performance as Spark for non-graphalgorithms. Additionally the running time with Spark for graphalgorithms over cloud virtual machines is observed to increaseby almost 100% compared to over local computer clusters.
OMERACT/OARSI initiative to define states of severity and indication for joint replacement in hip and knee osteoarthritis.

OBJECTIVE Time to theoretical indication of joint replacement surgery has been proposed as a primary outcome for potential structure-modifying interventions for osteoarthritis (OA). The objectives of this OMERACT/OARSI Working Group were to identify pain, physical function, and structure states that represent the progression from early to late disease for individuals with OA of the hip and knee, and to create a composite measure of these 3 domains to define states of OA severity and a surrogate measure of "need for joint replacement surgery. " METHODS For pain, focus groups and one-on-one interviews were used. For function, Rasch analysis was performed on existing indices the Hip Dysfunction and Osteoarthritis Outcome Score (HOOS) and the Knee injury and Osteoarthritis Outcome Score (KOOS), each of which subsumes the Western Ontario and McMaster University Osteoarthritis Index (WOMAC) questions. For structure, a comparison of existing indices (Kellgren-Lawrence, OARSI stages, and joint space width) was performed for the hip and the knee. RESULTS For pain, key features of pain that are most distressing to people with OA from early to late disease were identified. For function, the reduction of the number of items based on the existing indices continues. For structure, the analysis is also ongoing. CONCLUSION Preliminary results were presented at OMERACT 8; the final objective will be to combine the 3 domains (pain, function, and structure) and to create a composite index that could define states of severity and "need for total joint replacement," which could be used to evaluate treatment response to disease-modifying drugs in OA clinical trials.
LSI vs. Wordnet Ontology in Dimension Reduction for Information Retrieval

In the area of information retrieval, the dimension of doc- ument vectors plays an important role. Firstly, with higher dimensions index structures suer the "curse of dimensionality" and their eciency rapidly decreases. Secondly, we may not use exact words when looking for a document, thus we miss some relevant documents. LSI (Latent Se- mantic Indexing) is a numerical method, which discovers latent semantic in documents by creating concepts from existing terms. However, it is hard to compute LSI. In this article, we oer a replacement of LSI with a projection matrix created from WordNet hierarchy and compare it with LSI.
A combinatorial interpretation of the free-fermion condition of the six-vertex model

The free-fermion condition of the six-vertex model provides a five-parameter sub- manifold on which the Bethe ansatz equations for the wavenumbers that enter into the eigenfunctions of the transfer matrices of the model decouple, hence allowing explicit solutions. Such conditions arose originally in early field-theoretic S-matrix approaches. Here we provide a combinatorial explanation for the condition in terms of a generalized Gessel-Viennot involution. By doing so we extend the use of the Gessel-Viennot theorem, originally devised for non-intersecting walks only, to a special weighted type of intersecting walk, and hence express the partition function of N such walks starting and finishing at fixed endpoints in terms of the single-walk partition functions.
Don’t blame the student, it’s in their mind: helping engineering students to grasp complex concepts

CONTEXT The Thevenin Equivalent Circuit (TEC) concept has been identified as one of the first threshold concepts encountered by students in first year electrical engineering (Harlow, Scott, Peter, and Cowie, 2011). In order to assist students to learn about TEC, it is necessary to reduce the relational complexity of the concepts being taught (Halford, Wilson, and Phillips, 1998). The relational complexity framework reveals that under the traditional teaching method for TEC, nine different concepts are being combined, thus overtaxing the working memory of students. Methods for reducing relational complexity all incorporate chunking of several related cognitive units into more complex wholes, and sequential rather than parallel processing of information (Halford, Wilson, et al., 1998; Miller, 1956). These methods are essential elements of scaffolding (Wood, Bruner, and Ross, 1976).
On rates of convergence of efficient detection criteria in signal processing with white noise

L.C. Zhao et al. (1986) proposed certain information-theoretic criteria for detection of the number of signals under an additive model with white noise when the noise variance is known or unknown. It was shown that these criteria are strongly consistent even when the underlying distribution is not necessarily Gaussian. Upper bounds on the probabilities of error detection are obtained here. >
Magnetic Analysis of Switched Reluctance Actuators in Levitated Linear Transporters

A novel linear magnetic levitated actuator using the switched reluctance principle is addressed in this paper. This actuator can be applied to precise motion control of automation machines. The proposed system has the advantages of a simple and robust structure, and direct drive capability. The contactless structure eliminates mechanical wear, friction, noise, and heat generation. To verify the feasibility of the proposed system, the magnetic levitated force is analyzed by magnetic circuit analysis (MCA) and by finite-element analysis (FEA). A prototype structure is fabricated, and experiments are performed with the actual hardware. Results of the MCA and the FEA are both compared with the results obtained from the hardware experiments. They all agree with the proposed design methodology. This paper can form a useful reference in the design of a mechanical structure for magnetic levitated switched reluctance actuators.
Physical versus virtual pointing

An experiment was conducted to investigate differences in performance between virtual pointing, where a 2-D computer image representing the hand and targets was superimposed on the workspace, and physical pointing with vision of the hand and targets painted on the work surface. A detailed examination of movement kinematics revealed no differences in the initial phase of the movement, but that the final phase of homing in on smaller targets was more difficult in the virtual condition. These differences are summarised by a two-part model of movement time which also captures the effects of scaling distances to, and sizes of, targets. The implications of this model for design, analysis, and classification of pointing devices and positioning tasks are discussed.
Purification and Cloning of Type A/B hnRNP Proteins Involved in Transcriptional Activation from the Rat spi 2 Gene GAGA Box

Abstract The GAGA box of the rat serine protease inhibitor 2 (spi 2) genes not only acts as a basal promoter element, but also mediates transcriptional activation by growth hormone and interleukin-6. The GAGA box is separated from the TATA box by only 12 bp, and this close association is required for efficient transcription. Hence, the GAGA box may influence transcription efficiency through interactions between GAGA box binding proteins and some components of the RNA polymerase II complex. Here we report the cloning of two GAGA box-binding proteins termed p38 and p40, that belong to the type A/B heterogeneous nuclear ribonucleoprotein subgroup. GAGA box mutations that diminish the affinity for p38 and p40 decrease basal and GH-induced reporter gene expression. Furthermore, nuclear extracts depleted of p38 and p40 can no longer support GAGA box-dependent in vitro transcription. Therefore, two polypeptides previously assigned to a family of RNA processing proteins also act as DNA-binding, promoter-specific transcription factors.
constitutive model for tissue adaptation : Necrosis and stress driven growth

This paper presents a general constitutive model for tissue stress driven growth and necrotic remodeling. The general theoretical framework is given by a kinematics formulation in large strain. The model allows the analysis of tissue behaviour considering the coupling of biological and mechanical fields, through the implementation of an internal variable that represents the bioavailability, together with the growth induced by mechanical stimuli. To take into account the tissue necrosis phenomenon a reinterpretation of the classical theory of mixtures is proposed. The proposed formulation is based on the consideration of tress driven growth ule of mixtures two components, normal tissue and necrotic tissue, each one with its own volumetric participation, which is treated in an evolutive way, allowing simulating the phenomena of ischemia. This article also presents a brief description of the numerical implementation of the model as well as the obtained results, which shows the process of necrosis of part of the tissue, its influence on the surrounding healthy tissue, and the interaction between the nutrients availability and the stress driven growth and necrotic remodeling process.