Miracle-dz/UniFAR-FTUs huggingface dataset:
The new robotics—towards human‐centered machines

Research in robotics has moved away from its primary focus on industrial applications. The New Robotics is a vision that has been developed in past years by our own university and many other national and international research institutions and addresses how increasingly more human‐like robots can live among us and take over tasks where our current society has shortcomings. Elder care, physical therapy, child education, search and rescue, and general assistance in daily life situations are some of the examples that will benefit from the New Robotics in the near future. With these goals in mind, research for the New Robotics has to embrace a broad interdisciplinary approach, ranging from traditional mathematical issues of robotics to novel issues in psychology, neuroscience, and ethics. This paper outlines some of the important research problems that will need to be resolved to make the New Robotics a reality.
Is That Twitter Hashtag Worth Reading

Online social media such as Twitter, Facebook, Wikis and Linkedin have made a great impact on the way we consume information in our day to day life. Now it has become increasingly important that we come across appropriate content from the social media to avoid information explosion. In case of Twitter, popular information can be tracked using hashtags. Studying the characteristics of tweets containing hashtags becomes important for a number of tasks, such as breaking news detection, personalized message recommendation, friends recommendation, and sentiment analysis among others. In this paper, we have analyzed Twitter data based on trending hashtags, which is widely used nowadays. We have used event based hashtags to know users' thoughts on those events and to decide whether the rest of the users might find it interesting or not. We have used topic modeling, which reveals the hidden thematic structure of the documents (tweets in this case) in addition to sentiment analysis in exploring and summarizing the content of the documents. A technique to find the interestingness of event based twitter hashtag and the associated sentiment has been proposed. The proposed technique helps twitter follower to read, relevant and interesting hashtag.
Estimation of soil microwave effective temperature at L and C bands

The soil microwave effective temperature (T/sub e/) is an important parameter which improves the accuracy of the soil surface moisture derived from low frequency microwave radiometric observations. A new semi-empirical model of T/sub e/ at L- and C-bands is proposed. The model is based on the following inputs: the air temperature (T/sub a/), a deep soil temperature (T/sub d/), and the microwave brightness temperature measured at X-band (/spl lambda//spl sime/3 cm) and V polarization (T/sub BXV/). Unlike other approaches based on the surface temperature (T/sub s/), the proposed model can be implemented without being dependent on the clear sky conditions required to measure T/sub s/ with a spaceborne infrared radiometer. However, the proposed model may also use T/sub s/ when available. The model was designed from a large data set simulated by a physical model for smooth bare soil. The model of T/sub e/ was then successfully validated with experimental data acquired during a ground based experiment with the multifrequency PORTOS radiometer. This model designed from smooth soil data was successfully tested on rough bare soil using experimental data.
Leukemia Associated Antigens: Their Dual Role as Biomarkers and Immunotherapeutic Targets for Acute Myeloid Leukemia

Leukemia associated antigens (LAAs) are being increasingly identified by methods such as cytotoxic T-lymphocyte (CTL) cloning, serological analysis of recombinant cDNA expression libraries (SEREX) and mass spectrometry (MS). In additional, large scale screening techniques such as microarray, single nucleotide polymorphisms (SNPs), serial analysis of gene expression (SAGE) and 2-dimensional gel electrophoresis (2-DE) have expanded our understanding of the role that tumor antigens play in the biological processes which are perturbed in acute myeloid leukemia (AML). It has become increasingly apparent that these antigens play a dual role, not only as targets for immunotherapy, but also as biomarkers of disease state, stage, response to treatment and survival. We need biomarkers to enable the identification of the patients who are most likely to benefit from specific treatments (conventional and/or novel) and to help clinicians and scientists improve clinical end points and treatment design. Here we describe the LAAs identified in AML, to date, which have already been shown to play a dual role as biomarkers of AML disease.
Comparison of Bayesian move prediction systems for Computer Go

Since the early days of research on Computer Go, move prediction systems are an important building block for Go playing programs. Only recently, with the rise of Monte Carlo Tree Search (MCTS) algorithms, the strength of Computer Go programs increased immensely while move prediction remains to be an integral part of state of the art programs. In this paper we review three Bayesian move prediction systems that have been published in recent years and empirically compare them under equal conditions. Our experiments reveal that, given identical input data, the three systems can achieve almost identical prediction rates while differing substantially in their needs for computational and memory resources. From the analysis of our results, we are able to further improve the prediction rates for all three systems.
SOME METHODOLOGICAL PROBLEMS CONCERNING RESEARCH NEGOTIATIONS BETWEEN MONOLINGUALS.

Abstract : In order to obtain some experience with the problems associated with research on negotiation between monolinguals, an experiment was conducted in which American and Greek college students negotiated the best way to raise children. Half of the Greek interpreters were linguistically balanced, while the other half were linguistically dominant in favor of Greek; half the interpreters were in favor of the Greek and half in favor of the American positions on child-rearing in a 2 by 2 design. It was found that the interpreters tended to aid the side with which they were in agreement on the issues under negotiation. Observations of the behavior of the negotiators suggested that there are cultural differences in 'negotiation sophistication. ' Furthermore, the interpreters responded to an English version of an attitude questionnaire in a manner suggesting a greater influence of social desirability factors than when they responded to a Greek version of the same questionnaire. Finally, the issues of negotiation had different degrees of importance for the negotiators, but a procedure for the control of this variable was developed. (Author)
SEQ: A model for sequence databases

This paper presents the SEQ model which is the basis for a system to manage various kinds of sequence data. The model separates the data from the ordering information, and includes operators based on two distinct abstractions of a sequence. The main contributions of the SEQ model are: (a) it can deal with different types of sequence data, (b) it supports an expressive range of sequence queries, (c) it draws from many of the diverse existing approaches to modeling sequence data. <<ETX>>
fMRI Detection via Variational EM Approach

In this paper, we study Gaussian Random Fields (GRFs) as spatial smoothing priors in Functional Magnetic Resonance Imaging (fMRI) detection, and we solve GRFs using the variational Expectation-Maximization (EM) algorithm. Relatively high noise in fMRI images presents a serious challenge for the detection algorithms, creating a need for spatial regularization of the signals. Spatial regularization is usually employed before or after detection, forming a two-process detector. In a two-process detector, defects produced in the first process usually interfere with the performance of the second process. Among all two-process detectors, the Gaussian-smoothing-based detector, which performs detection on spatially smoothed signals, is the most popular. Gaussian filters, traditionally employed to boost the signal-to-noise ratio, often remove small activation regions. This is mainly caused by applying a fixed, but arbitrary, Gaussian filter uniformly over the entire image. In this work, we propose the EM-GRF-based detector that iterates between these two processes, so that parameters of the Gaussian filter are readjusted according to the feedback from detection, and the parameters for detection are readjusted according to the feedback from the filtered results. In addition, we compare the performance of this detector with the Gaussian-smoothing-based detector through ROC analysis on simulated data, and demonstrate their applications in a real fMRI study.
STREAM : Social Networks TITLE : Using Bayesian Agents to Enable Distributed Network Knowledge : A Critique

Resource based theory (RBT) states that there are dynamic relationships between individual-resource interactions, which ultimately determine an organisation’s global behaviour in its environment. When combining in idiosyncratic, functional ways to enable an organisation’s global behaviour, we call them complementary resource combinations (CRCs), and socially complex resource combinations (SRCs) when referring to only the complex web of social interactions of these resources. Casual ambiguity refers to the inherent uncertainty when the global behaviour is both tangibly evident and known, but the way in which the unique local interactions between SRCs amongst themselves and the environment ultimately contribute to the global behaviour is often unclear. Thus, in order to understand social complexity and causal ambiguity of an organization, the SRCs emergent behaviours and the causal local interactions must be observed over time, and the inter-relationships must be identified and made tangible. In our research, we use simple agents to observe the local and global behaviours, to mine the inter-relationships and to model the SRCs. These agents are organized into two types of agencies: Bayesian agencies and competence agencies. The Bayesian agencies are the observers – they collectively implement specialised, distributed Bayesian networks, which enable the agencies to collectively mine relationships between emergent global behaviours and the local interactions that caused them to occur. The competence agencies are the actors – they use the beliefs of selected Bayesian agencies and perform dynamic network analysis. In dynamic network analysis, temporal data is used to predict changes that will occur in the SRCs. Most importantly, the Bayesian agencies observe and mine temporal patterns in various metrics over time, and the competence agencies evolve the SRCs. Relationships discovered and maintained by Bayesian agencies and competence agencies are integrated into cutting-edge, resource-based topic maps (ISO 13250:2002), which provide a way of modelling the SRCs.
Topology-free cut-and-paste editing over meshes

Existing cut-and-paste editing methods over meshes are inapplicable to regions with non-zero genus. To overcome this drawback, we propose a novel method in this paper. Firstly, a base surface passing through the boundary vertices of the selected region is constructed using the boundary triangulation technique. Considering the connectivity between the neighboring vertices, a new detail encoding technique is then presented based on surface parameterization. Finally, the detail representation is transferred onto the target surface via the base surface. This strategy of creating a base surface as a detail carrier allows us to paste features of non-zero genus onto the target surface. By taking the physical relationship of adjacent vertices into account, our detail encoding method produces more natural and less distorted results. Therefore, our elegant method not only can eliminate the dependence on the topology of the selected feature, but also reduces the distortion effectively during pasting.
Clinical DT-MRI estimation, smoothing and fiber tracking with Log-Euclidean metrics

Diffusion tensor MRI is an imaging modality that is gaining importance in clinical applications. However, in a clinical environment, data have to be acquired rapidly, often at the detriment of the image quality. We propose a new variational framework that specifically targets low quality DT-MRI. The Rician nature of the noise on the images leads us to a maximum likelihood strategy to estimate the tensor field. To further reduce the noise, we optimally exploit the spatial correlation by adding to the estimation an anisotropic regularization term. This criterion is easily optimized thanks to the use of recently introduced Log-Euclidean metrics. Results on real clinical data show promising improvements of fiber tracking in the brain and the spinal cord
Design of an anthropomorphic robotic finger system with biomimetic artificial joints

We describe a new robotic finger that is composed of three biomimetic joints whose biomechanics and dynamic properties are close to their human counterparts. By using five pneumatic cylinders, the robotic finger is actuated through a series of simplified antagonistic tendons whose insertion points and moment arms at each finger joint are determined by the anatomy of the human hand. Air dynamic model is empirically solved for controlling the pneumatic system. In order to optimize our future controller for the robotic finger, we use Multi-Joint dynamics with Contact(MuJoCo) - a physics engine custom-developed - to construct a kinematic model for simulating the interaction between the fingers joints and tendons. Experimental data of the tendon excursions are used to validate the efficacy of the robotic finger model. Good fits are observed both in the DIP and PIP flexors (R2 = 0.93 and R2 = 0.83) and central extensor (R2 = 0.96).
Robust Multi-subspace Analysis Using Novel Column L0-norm Constrained Matrix Factorization

We study the underlying structure of data (approximately) generated from a union of independent subspaces. Traditional methods learn only one subspace, failing to discover the multi-subspace structure, while state-of-the-art methods analyze the multi-subspace structure using data themselves as the dictionary, which cannot offer the explicit basis to span each subspace and are sensitive to errors via an indirect representation. Additionally, they also suffer from a high computational complexity, being quadratic or cubic to the sample size. To tackle all these problems, we propose a method, called Matrix Factorization with Column L0-norm constraint (MFC0), that can simultaneously learn the basis for each subspace, generate a direct sparse representation for each data sample, as well as removing errors in the data in an efficient way. Furthermore, we develop a first-order alternating direction algorithm, whose computational complexity is linear to the sample size, to stably and effectively solve the nonconvex objective function and non- smooth l0-norm constraint of MFC0. Experimental results on both synthetic and real-world datasets demonstrate that besides the superiority over traditional and state-of-the-art methods for subspace clustering, data reconstruction, error correction, MFC0 also shows its uniqueness for multi-subspace basis learning and direct sparse representation.
A fuzzy logic based approach for structure-free aggregation in vehicular ad-hoc networks

Information aggregation is used to merge correlated data items from different nodes before redistributing them. Thus, using aggregation the number of transmissions and the communication overhead can be reduced significantly. Especially for applications which require periodic dissemination of information into a large region, aggregation is a prerequisite. Consider for example a traffic information system where each vehicle periodically disseminates information about road conditions. The aim is to provide drivers with accurate information on traffic conditions for a large road section, so drivers can be informed in time and can take alternate routes in case of traffic congestion, for example. Many aggregation approaches for VANETs use a fixed or structured segmentation of roads for these applications. Based on such segments the aggregation function is applied to fuse data. We argue that such fixed bounds contradict the real situation on roads. Thus either the segments are too large, resulting in loss of accuracy or the segments are too small, resulting in high communication load. Therefore, we propose in this work a system for completely structure-free aggregation. We employ fuzzy reasoning to allow for aggregation decisions to be based on a flexible and extensible set of criteria. These criteria can be application specific and enable a dynamic fragmentation of the road according to the applications' requirements.
Endogenous Neurotrophins Are Required for the Induction of GABAergic Long-Term Potentiation in the Neonatal Rat Hippocampus

In the developing rat hippocampus, GABAergic synapses undergo a Ca2+-dependent long-term potentiation (LTPGABA-A); this form of synaptic plasticity is induced in CA3 pyramidal neurons by delivering repetitive depolarizing pulses (DPs) to the recorded neuron, and it is expressed as a long-lasting increase in the frequency and amplitude of spontaneous GABAA receptor-mediated postsynaptic currents. In the present study, we examined the role of endogenous tropomyosin-related kinase receptor B (TrkB) receptor ligands and associated protein tyrosine kinases (PTKs) in the induction of LTPGABA-A. The application of Lavendustin A, a broad spectrum PTK inhibitor, blocked the induction of LTPGABA-A, whereas Lavendustin B, its inactive form, had no effect. Moreover, k-252a and k-252b, two alkaloids that inhibit the kinase activity of the Trk receptor family, also prevented the induction of LTPGABA-A. On hippocampal slices incubated with the soluble form of TrkB receptor IgG (TrkB-IgG), which prevents the activation of TrkB receptors by endogenous ligands, DPs failed to induce LTPGABA-A, whereas the incubation with TrkA-IgG or TrkC-IgG had no such effect. Altogether, these data indicate that endogenous TrkB ligands and associated PTK activity are necessary for the induction of GABAergic LTP in the developing rat hippocampus.
A Temporal Measure of Attention in Schizophrenia and its Clinical Significance

Disturbance of attention has been frequently suggested as one of the mechanisms underlying schizophrenic thought disorder (Weckowicz and Blewett, 1959; Chapman, 1961; Payne, 1966). Much effort has, therefore, been directed toward studying this variable in schizophrenia (McGhie, 1971; Neale and Cromwell, 1972). The approach undertaken has been determined by the psychophysiological and theoretical constructs presumed to be involved. To enumerate a few, disordered attention has been studied with indices of arousal (Venables, 1960; Venables and Wing, 1962), vigilance tasks (Mackworth, 1970), reaction time (Shakow, 1962), size constancy (Weckowicz and Blewett, 1959; Silverman, 1964a), tachistoscopic detection (Kornetsky, 1972), the WAIS Digit Span subtest (Blum et al., 1969), and immediate recall (McGhie et al., 1965). The lack of correspondence between these various procedures has become increasingly apparent (Neale and Cromwell, 1972). In one recent study, for example, the intercorrelations of five purported measures of attention ranged from –. 23 to +. 31 (Kopfstein and Neale, 1972).
Diabetes Mellitus and Outcomes of Cardiac Resynchronization With Implantable Cardioverter-Defibrillator Therapy in Older Patients With Heart Failure

Background— Large-scale data on outcomes with cardiac resynchronization therapy with defibrillator in patients with diabetes mellitus are limited. We compared outcomes after cardiac resynchronization therapy with defibrillator implantation among patients with heart failure who have diabetes mellitus versus those without diabetes mellitus. Methods and Results— Survival curves and covariate adjusted hazard ratio (HR) or odds ratio were used to assess the risks for death, readmission, and device-related complications by diabetes mellitus status among 18 428 patients at least 65 years old receiving cardiac resynchronization therapy with defibrillator from the National Cardiovascular Data Registry, implantable cardioverter-defibrillator registry between 2006 and 2009, with up to 3 years of follow-up. Accounting for differences between groups, compared with those without diabetes mellitus (n=11 345), patients with diabetes mellitus (n=7083) had a higher risk of death both at 1 year (HR, 1.16 [95% confidence interval (CI), 1.05–1.29]; P=0.0037) and 3 years (HR, 1.21 [1.14–1.29]; P<0.001) after device implantation and higher risks of all-cause readmission (sub-HR, 1.16 [1.11–1.21] at 1 year; P<0.0001; sub-HR, 1.15 [1.11–1.19] at 3 years; P<0.0001) and heart failure–related readmission (sub-HR, 1.18 [1.09–1.28] at 1 year; P<0.0001; and sub-HR, 1.22 [1.15–1.30] at 3 years; P<0.0001). Device-related complications within 90 days did not differ between those with and without diabetes mellitus (odds ratio: 0.90 [0.77–1.06]; P=0.37). Interactions of age, sex, ischemic cardiomyopathy, renal failure, or QRS duration were not significant. Conclusions— In older patients with heart failure receiving cardiac resynchronization therapy with defibrillator, diabetes mellitus was independently associated with greater risks of death and rehospitalization, but similar risks of procedural complications.
A method based on continuous spectrum analysis and artificial immune network optimization algorithm for fingerprint image ridge distance estimation

It is important for improving the performance of automatic fingerprint identification system to estimate the ridge distance accurately. The traditional Fourier transform spectral analysis method had the worse redundancy degree in estimating the ridge distance because it was based on the two-dimension discrete Fourier spectrum. The statistical window method cannot obtain the accurate ridge distance because of the noises and the warp of the statistical value. The paper introduces the sampling theorem and artificial immune network into the fingerprint image ridge distance estimation method, transforms the discrete spectrum into the continuous spectrum, acquires the local peak points adopting the artificial immune network optimization algorithm and then obtains the ridge distance in the frequency field. The experimental results indicate that the ridge distance is more accurate and has improved the accuracy rate of automatic fingerprint identification system to a certain extent.