Miracle-dz/UniFAR-FTUs huggingface dataset:
Action understanding using an adaptive Liquid State Machine based on environmental ambiguity

Recently, humans-robots interaction steps aside the traditional master/slave relationship to evolve in a new paradigm of cognitive robotics. Their conception requires the comprehension of human cognitive functions and how they develop. In this paper, we present how an adaptive Liquid State Machine using environment ambiguity may lead to a better emergence of action prediction abilities in a simple robot. The simulation results indicate the efficiency of the proposed method by which a simple robot develop its own prediction capability. These results are promising towards building robots able to develop more complicated capability such as understanding others' intention and further, cooperation with other agents.
A method for passivity analysis of multilateral haptic systems

This paper presents a novel criterion to study the stability of multilateral teleoperation systems based on passivity. Such systems (modeled as n-port networks) have recently found interesting applications in cooperative haptic teleoperation and haptic-assisted training. The criterion provides researchers with an analytical, closed-form, necessary, and sufficient condition useful for both analysis and design of multilateral haptic teleoperation systems. The paper shows that when the proposed conditions reduce to the well-known Raisbeck’s passivity criterion for 2-port networks. The proposed conditions are used to study the passivity (and consequently the stability) of a dual-user haptic system for control of a single teleoperated robot. Simulations and experiments are performed to further test the validity of the proposed criterion. Graphical Abstract
Detecting novel multi-variable associations in big data based on MIC

It is meaningful to discover valuable relationships in big data. The maximal information coefficient (MIC), a new measure of dependence of relationships, was proposed by Reshef et al. in 2011, and an approximate algorithm was designed. But the algorithm designed by Reshef et al. (2011) can only calculate the MIC of two-variable relationships. In this paper, an algorithm (BKM-MIC) is proposed. To our best knowledge, the BKM-MIC algorithm is the first algorithm calculating the MIC of multi-variable relationships. And based on the BKM-MIC algorithm, a matrix iteration algorithm with pruning (MIP) is designed. A simple example shows that MIP algorithm can not only reduce computation workload, but also can precisely identify dependent and independent multi-variable relationships.
Radio Resource Allocation for Mobile MIMO-OFDMA

Radio resource allocation has been widely considered in recent years to further improve communication system performance, while MIMO-OFDMA (multiple input multiple output - orthogonal frequency division multiple access) is one of key technologies in future wireless systems. In this paper we propose an algorithm to adaptively allocate power, bandwidth and antennas among mobile users in MIMO-OFDMA system; the channel equalization was integrated into our signal model to cancel the inter-antenna and inter-carrier interference. By the QR decomposition, we can eliminate the interference and simplified the mobile channel condition to nomadic case. Our numerical results demonstrate that, the proposed algorithm can achieve near-optimal performance while maintain linear complexity simultaneously. Furthermore, the proposed algorithm still can improve system capacity even when we include practical channel estimation and prediction bias into consideration.
Learning from the Web: Recognition method based on object appearance from Internet images

In this work an Object Learning and Recognition method for a Humanoid is presented. This method aims to take advantage of the Cloud Resources, since it is based on image web search in order to build training sets for learning objects' appearance. In case Internet access is unavailable, the robot asks human to show the objects and acquires the images using its camera. Through this technique, our method aims to be a flexible and natural framework for Human-Robot Interaction and to provide as much autonomy as possible to the robot.
Video object segmentation based on mixtures of probabilistic principal component analysis

A novel video object segmentation algorithm is proposed based on mixtures of probabilistic principal component analysis (MPPCA) in this paper. The number of mixture components of MPPCA is estimated and the expectation maximization (EM) algorithm is initialized through segmentation projection after extracting feature. Then the EM algorithm is applied to estimate the distribution of feature vectors. Finally the segmentation is carried out by clustering each pixel into appropriate component according to maximum likelihood criterion. The proposed algorithm can greatly accelerate the convergence of the EM algorithm since the initial value approximates its real value. As a result, the speed of the video object segmentation is improved. Experimental results have demonstrated that the proposed method can extract moving objects from video sequences successfully. At the same time, the algorithm proposed is more stable.
GeoSearch: georeferenced video retrieval system

Conventional video search systems, to find relevant videos, rely on textual data such as video titles, annotations, and text around the video. Nowadays, video recording devices such as ameras, smartphones and car blackboxes are equipped with GPS sensors and able to capture videos with spatiotemporal information such as time, location and camera direction. We call such videos georeferenced videos. This paper presents a georeferenced video retrieval system, geosearch, which efficiently retrieves videos containing a certain point or range in the map. To enable a fast search of georeferenced videos, geosearch adopts a novel data structure MBTR (Minimum Bounding Tilted Rectangle) in the leaf nodes of R-Tree. New algorithms are developed to build MBTRs from georeferenced videos and to efficiently process point and range queries on MBTRs. We demonstrate our system on real georeferenced videos, and show that, compared to previous methods, geosearch substantially reduces the index size and also improves the search speed for georeferenced video data. Our online demo is available at "http://dm.hwanjoyu. org/geosearch".
Minimization Of Inter Symbol Interference Based Error in OFDM System Using Adaptive Decision Mechanism by SNR Estimation

In the wireless multimedia applications OFDM modulation is a promising approach for establishing a high bit rates. Modern OFDM systems uses channel estimation and tracking, instead of differential phase-shift keying (DPSK) it provides a 3-dB SNR loss compared with normal coherent phase-shift keying (PSK). In this article we have improved the performance of OFDM systems in coherent PSK modulation schemes; we implemented a channel estimation technique for OFDM communication. We designed an algorithm based on mean-square-error (MSE) channel estimator by use of calculations based on the time and frequency-domain received signals of the frequency response obtained after passing through time-varying dispersive Rayleigh fading channels distorted with AWGN. We have considered the uncertainty of channel statistics that results in mismatch of the estimator-to- channel statistics and proposed a robust channel estimator that is independent to uncertainty of channel performance statistics. The robust channel estimator response are further used to run a modulation decision scheme logic that can significantly improve the performance of OFDM systems in noisy
An enlargement method of digital images with the prediction of high-frequency components

Enlargement of digital images is one of the most basic processing. Image enlargement corresponds to narrowing the sampling interval of the digital image. Thus, it is necessary to estimate the unknown higher-frequency components in the enlargement process. Recently, several methods, which include the prediction of unknown higher-frequency components, are proposed. We have also proposed the image enlarging method based on the Laplacian pyramid representation. However, we can get only a two times enlarged image by these methods. In this paper, we propose a new image enlarging method, which can realize an arbitrary size enlarging based on a new Laplacian pyramid representation. The effectiveness of the propose method is shown through a lot of experimental results.
Allelic Variation, Alternative Splicing and Expression Analysis of Psy1 Gene in Hordeum chilense Roem. et Schult

Background The wild barley Hordeum chilense Roem. et Schult. is a valuable source of genes for increasing carotenoid content in wheat. Tritordeums, the amphiploids derived from durum or common wheat and H. chilense, systematically show higher values of yellow pigment colour and carotenoid content than durum wheat. Phytoene synthase 1 gene (Psy1) is considered a key step limiting the carotenoid biosynthesis, and the correlation of Psy1 transcripts accumulation and endosperm carotenoid content has been demonstrated in the main grass species. Methodology/Principal findings We analyze the variability of Psy1 alleles in three lines of H. chilense (H1, H7 and H16) representing the three ecotypes described in this species. Moreover, we analyze Psy1 expression in leaves and in two seed developing stages of H1 and H7, showing mRNA accumulation patterns similar to those of wheat. Finally, we identify thirty-six different transcripts forms originated by alternative splicing of the 5′ UTR and/or exons 1 to 5 of Psy1 gene. Transcripts function is tested in a heterologous complementation assay, revealing that from the sixteen different predicted proteins only four types (those of 432, 370, 364 and 271 amino acids), are functional in the bacterial system. Conclusions/Significance The large number of transcripts originated by alternative splicing of Psy1, and the coexistence of functional and non functional forms, suggest a fine regulation of PSY activity in H. chilense. This work is the first analysis of H. chilense Psy1 gene and the results reported here are the bases for its potential use in carotenoid enhancement in durum wheat.
Framework For Co-browsing On Heterogeneous Devices

Efficient co-browsing of Web-pages from devices with different capabilities requires users to have a shared understanding of those Web-pages. This paper presents the concept of shared viewpoint (SVP) and personal viewpoint (PVP) for co-browsing before detailing a framework for implementing these concepts. Finally, the effectiveness of the framework is demonstrated through a perceptual experiment
Let's Discuss: Group Size, Course Performance, and Enjoyability in an Interteaching Class

In 2013, Saville and colleagues examined whether group size affected course performance in an interteaching based classroom, and found there was no significant difference in course performance between the different sizes of groups. In this replication and extension, we increased the larger interteaching group size from four to six, maintained the small group size at two, and included additional measures of social validity. The students rated their groups each class as a group and individually. Additionally, teaching assistants rated their perception of group effectiveness based on responses to end-of-class questions by individual students. The current study used an alternating treatments design to compared weekly test performance and discussion quality between the small and large interteaching discussion groups. The weekly quiz scores did not show a difference between the two group sizes; however, clear preferences for group size emerged. Findings and implications of the social validity data will be discussed.
Smallest Detectable Difference in Outcome Variables Related to Painful Restriction of the Temporomandibular Joint

The smallest detectable difference is the smallest statistically significant change in measurement results. In the field of temporomandibular disorders, the smallest detectable difference is not a commonly used concept. Most outcome studies are based on comparisons of group means, although this does not provide information about individual changes or about the clinical relevance thereof. The smallest detectable difference for maximal mouth opening was calculated from previously published reliability coefficients and the standard deviations of different samples of healthy subjects and patients with complaints of the temporomandibular joint. The smallest detectable difference of pain intensity measured with different visual analogue scales was calculated from the reliability coefficients and standard deviations of a heterogeneous group of pain patients. The smallest detectable difference of function impairment was calculated for a group of patients with complaints of the temporomandibular joint. For maximal mouth opening in healthy subjects, the smallest detectable difference was 5 mm. Repeated measurements improved it to 3 mm. The smallest detectable difference on a visual analogue scale was 28 mm for actual pain intensity and 22 mm for minimal pain as well as for maximal pain intensity. For total function impairment of patients with complaints of the temporomandibular joint, the smallest detectable difference was 8 units on a 0 to 68 scale.
Physics-based modelling of human motion using Kalman filter and collision avoidance algorithm

The paper deals with the problem of computer vision based multi-person motion tracking, which in many cases suffers from lack of discriminating features of observed persons. To solve this problem, a physics based model of human motion is proposed, which includes internal forces of the persons by the means of the Kalman filter, and the cylindrical envelopes, which produce collision avoiding forces when observed persons come to close proximity. We tested the proposed method on two sequences, one from squash match, and the other from the basketball play and found out that the number of tracker mistakes significantly decreased.
IDD In Eastern Europe and Central Asia

■ SALT IODIZATION IN THE PHILIPPINES: WILL THE STALLED PROGRAM RESTART? ■ IN BRIEF ■ ABSTRACTS This article offers the most recent information on the IDD status, control program and salt iodization in 15 countries of Eastern Europe and Central Asia, including 12 countries of the Commonwealth of Independent States (CIS) and the 3 Baltic States (BS), and serves as an update to earlier publications on the same subject (1, 2). The results are presented in similar tables (Table 1 and 2), in a format compatible with ICCIDD’s CIDDS database (3). This update is based on published and unpublished information available as of July 2002, including some country reports, data from agencies, and personal communications.
Spindle orientation in Saccharomyces cerevisiae depends on the transport of microtubule ends along polarized actin cables

Microtubules and actin filaments interact and cooperate in many processes in eukaryotic cells, but the functional implications of such interactions are not well understood. In the yeast Saccharomyces cerevisiae, both cytoplasmic microtubules and actin filaments are needed for spindle orientation. In addition, this process requires the type V myosin protein Myo2, the microtubule end–binding protein Bim1, and Kar9. Here, we show that fusing Bim1 to the tail of the Myo2 is sufficient to orient spindles in the absence of Kar9, suggesting that the role of Kar9 is to link Myo2 to Bim1. In addition, we show that Myo2 localizes to the plus ends of cytoplasmic microtubules, and that the rate of movement of these cytoplasmic microtubules to the bud neck depends on the intrinsic velocity of Myo2 along actin filaments. These results support a model for spindle orientation in which a Myo2–Kar9–Bim1 complex transports microtubule ends along polarized actin cables. We also present data suggesting that a similar process plays a role in orienting cytoplasmic microtubules in mating yeast cells.
Diverse inflammatory responses in transgenic mouse models of Alzheimer's disease and the effect of immunotherapy on these responses

While the presence of an inflammatory response in AD (Alzheimer's disease) is well known, the data on inflammation are conflicting, suggesting that inflammation either attenuates pathology, exacerbates it or has no effect. Our goal was to more fully characterize the inflammatory response in APP (amyloid precursor protein) transgenic mice with and without disease progression. In addition, we have examined how anti-Aβ (amyloid β-peptide) immunotherapy alters this inflammatory response. We have used quantitative RT–PCR (reverse transcription–PCR) and protein analysis to measure inflammatory responses ranging from pro-inflammatory to anti-inflammatory and repair factors in transgenic mice that develop amyloid deposits only (APPSw) and amyloid deposits with progression to tau pathology and neuron loss [APPSw/NOS2−/− (nitric oxide synthase 2−/−)]. We also examined tissues from previously published immunotherapy studies. These studies were a passive immunization study in APPSw mice and an active vaccination study in APPSw/NOS2−/− mice. Both studies have already been shown to lower amyloid load and improve cognition. We have found that amyloid deposition is associated with high expression of alternative activation and acquired deactivation genes and low expression of pro-inflammatory genes, whereas disease progression is associated with a mixed phenotype including increased levels of some classical activation factors. Immunotherapy targeting amyloid deposition in both mouse models resulted in decreased alternative inflammatory markers and, in the case of passive immunization, a transient increase in pro-inflammatory markers. Our results suggest that an alternative immune response favours retention of amyloid deposits in the brain, and switching away from this state by immunotherapy permits removal of amyloid.
Visual feature group matching for autonomous robot localization

The Scale Invariant Feature Transform, SIFT, has been successfully applied to robot vision, object recognition, motion estimation, etc. In this work, we propose a SIFT improvement that makes feature extraction and matching more robust, adding a feature group matching layer, which takes into account mutual spatial relations between features. The feature group matching is very fast to be computed and leads to interesting results, above all for the absence of outliers. Results of vision based robot localization using the proposed approach are presented.
A Standing Tree Image Mosaic Algorithm Based on SIFT

This paper concerns the problem of standing tree image mosaic based on SIFT. In standing tree measurement based on image processing, the first step is to take photo of the measured tree. Due to some special tall trees, or limited by geographical conditions, it is impossible to shoot full image of the tree one time, and need to shoot sequence tree images with overlapped areas and stitch them. In this paper, a tree image mosaic method based on scale invariant feature (SIFT) was put forward. Firstly, the SIFT feature vector of each image was calculated and the feature points were matched using SIFT features. Then the perspective transformation was done to the image which was to be joined by selected four pixels. Lastly, the sequence images were stitched based on weighted factor. The experiment of true tree image sequence stitching achieves the better smooth mosaic effect and show the method is effective.
DETC 2007-35139 FOLKSONOMY AND DESIGNING : AN EXPLORATION

This paper discusses the role of folksonomy in a product design scenario. The focus is on developing a framework for design retrieval using a folksonomic approach and on the emergence of a design taxonomy through user tagging. We analyze the potential uses of a design folksonomy and its relation to current design research. We specifically relate the folksonomy to design representation models, design teams and knowledge sharing and collaboration. Folksonomy is the result of tagging of information objects by a user freely using keywords relevant to the object being tagged. The tags are applied based on the user’s understanding of the information and utilizes the user’s vocabulary. This metadata generated by tagging is shared in a social environment i.e. across a body of users. “The collective metadata in the folksonomy representation is stored in the form of three data points, viz., the person doing the tagging, the object that is tagged, and the tag that has been applied to the object. ” [37] In this paper, the utility of tagging is discussed in a product design context for retrieval of information, locating individuals with similar vocabulary and hence similar interests, and other people utilizing the same objects. We discuss a design information representation model where the folksonomy-based bottom up method and traditional top-down methods of ontology and taxonomy play complementary roles. An approach to implementing a framework for a product design folksonomy is also suggested in this paper.
A constant-factor approximation for stochastic Steiner forest

We consider the stochastic Steiner forest problem: suppose we were given a collection of Steiner forest instances, and were guaranteed that a random one of these instances would appear tomorrow; moreover, the cost of edges tomorrow will be λ times the cost of edges today. Which edges should we buy today so that we can extend it to a solution for the instance arriving tomorrow, to minimize the expected total cost? While very general results have been developed for many problems in stochastic discrete optimization over the past years, the approximation status of the stochastic Steiner Forest problem has remained open, with previous works yielding constant-factor approximations only for special cases. We resolve the status of this problem by giving a constant-factor primal-dual based approximation algorithm.
Continuous adaptive reinforcement learning with the evolution of Self Organizing Classifiers

Learning classifier systems have been solving reinforcement learning problems for some time. However, they face difficulties under multi-step continuous problems. Adaptation may also become harder with time since the convergence of the population decreases its diversity. This article demonstrate that the novel Self Organizing Classifiers method can cope with dynamical multi-step continuous problems. Moreover, adaptation remains the same after convergence.
Spectral Routing in Small-World Networks

The small-world property is one of the most common and well-known features of complex networks: those networks are heavily clustered, yet any two nodes are very often connected by a short number of links. Usually, models that create networks with small-world properties are based on the idea that nodes have some kind of reciprocal a nity. Nodes are placed on a k-dimensional layout, where close nodes are considered a ne and thus more likely to be linked. It is also possible to do routing on the social network, based on the layout information: a node can reach any other node in a short number of steps, just by iteratively jumping at each time towards the node that is closest to the destination. We approach the reverse problem: starting from a given network, is it possible to reconstruct a layout that places nodes so that nodes are able to do e cient routing? Is it possible to do it with a scalable algorithm not requiring any kind of centralization? We take ideas from algorithms for graph drawing and devise a decentralized algorithm that e ciently computes a layout such that short paths are often found between nodes. Such an algorithm can be useful in darknets , that is, anonymous networks where nodes are connected only if there is a mutual feeling of trust; it can also be used as a tool for the analysis of complex networks, for instance in order to associate similar nodes, or performing ranking dependent on the preferences of the user.