Miracle-dz/UniFAR-FTUs huggingface dataset:
Minimalist plans for interpreting manipulation actions

Humans attribute meaning to actions, and can recognize, imitate, predict, compose from parts, and analyse complex actions performed by other humans. We have built a model of action representation and understanding which takes as input perceptual data of humans performing manipulatory actions and finds a semantic interpretation of it. It achieves this by representing actions as minimal plans based on a few primitives. The motivation for our approach is to have a description, that abstracts away the variations in the way humans perform actions. The model can be used to represent complex activities on the basis of simple actions. The primitives of these minimal plans are embodied in the physicality of the system doing the analysis. The model understands an action under observation by recognising which plan is occurring. Using primitives thus rooted in its own physical structure, the model has a semanticist and causal understanding of what it observes. Using plans, the model considers actions as well as complex activities in terms of causality, compositions, and goal achievement, enabling it to perform complex tasks like prediction of primitives, separation of interleaved actions and filtering of perceptual input. We use our model over an action dataset involving humans using hand tools on objects in a constrained universe to understand an activity it has not seen before in terms of actions whose plans it knows of. The model thus illustrates a novel approach of understanding human actions by a robot.
Clickjacking Revisited A Perceptual View of UI Security

Clickjacking is a powerful attack against modern web applications. While browser primitives like X-Frame-Options provide a rigorous defense for simple applications, mashups such as social media widgets require secure user interaction while embedded in an untrusted web-page. Motivated by these application scenarios, the W3C UI safety speciﬁcation proposes new browser primitives to provide a strong defense against clickjacking attacks on embedded widgets. We investigate whether these proposed primitives provide requisite security against click-jacking. We observe that UI security attacks such as click-jacking are fundamentally attacks on human perception. Revisiting clickjacking from a perceptual perspective, we develop ﬁve novel attacks that completely bypass the proposed UI safety speciﬁcation. Our attacks are powerful with success rates ranging from 20% to 99%. However, they only scratch the surface of possible perceptual attacks on UI security. We discuss possible defenses against our perceptual attacks and ﬁnd that most defenses either have an unacceptable usability cost or do not provide a comprehensive defense. Finally, we posit that a number of attacks are possible with a more comprehensive study of human perception.
Adaptive time-frequency signal analysis and its case study in biomedical ecgwaveform analysis

Traditional time-frequency (TF) signal representations are not appropriate for parametric analysis of most real world signals. In this study, we describe Adaptive time-frequency distribution (TFD) as a robust approach to TF signal decomposition for non-stationary signal processing. This approach has not been utilized for TF signal analysis other than feature extraction. We introduce T wave alternans (TWA) analysis as a new application for this approach. In order to test the robustness of the proposed technique, we challenge it under conditions of non-stationary dynamics that are expected with real world TWA. The results of the numerical simulation support the effectiveness of this approach for TWA estimation.
Sentiment-enhanced multidimensional analysis of online social networks: Perception of the mediterranean refugees crisis

We propose an analytical framework able to investigate discussions about polarized topics in online social networks from many different angles. The framework supports the analysis of social networks along several dimensions: time, space and sentiment. We show that the proposed analytical framework and the methodology can be used to mine knowledge about the perception of complex social phenomena. We selected the refugee crisis discussions over Twitter as a case study. This difficult and controversial topic is an increasingly important issue for the EU. The raw stream of tweets is enriched with space information (user and mentioned locations), and sentiment (positive vs. negative) w.r. t. refugees. Our study shows differences in positive and negative sentiment in EU countries, in particular in UK, and by matching events, locations and perception, it underlines opinion dynamics and common prejudices regarding the refugees.
Scaling in the global spreading patterns of pandemic Influenza A (H1N1) and the role of control: empirical statistics and modeling

Background: The pandemic of influenza A (H1N1) is a serious on-going global public crisis. Understanding its spreading dynamics is of fundamental importance for both public health and scientific researches. Recent studies have focused mainly on evaluation and prediction of on-going spreading, which strongly depends on detailed information about the structure of social contacts, human traveling patterns and biological activity of virus, etc. Methodology/Principal Findings: In this work we analyzed the distributions of confirmed cases of influenza A (H1N1) in different levels and find the Zipf's law and Heaps' law. Similar scaling properties were also observed for severe acute respiratory syndrome (SARS) and bird cases of H5N1. We also found a hierarchical spreading pattern from countries with larger population and GDP to countries with smaller ones. We proposed a model that considers generic control effects on both the local growth and transregional transmission, without the need of the above mentioned detailed information. We studied in detail the impact of control effects and heterogeneity on the spreading dynamics in the model and showed that they are responsible for the scaling and hierarchical spreading properties observed in empirical data. Conclusions/Significance: Our analysis and modeling showed that although strict control measures for interregional travelers could delay the outbreak in the regions without local cases, the focus should be turned to local prevention after the outbreak of local cases. Target control on a few regions with the largest number of active interregional travelers can efficiently prevent the spreading. This work provided not only a deeper understanding of the generic mechanisms underlying the spread of infectious diseases, but also some practical guidelines for decision makers to adopt suitable control strategies.
Hyperresponsiveness on Washout of Volatile Anesthetics from Isolated Spinal Cord Compared to Withdrawal from Ethanol

We performed experiments in spinal cords isolated from neonatal rats to probe the mechanisms responsible for hyperresponsiveness of the population excitatory evoked potential (pEPSP) observed on washout of the volatile anesthetics halothane and isoflurane (1 minimal alveolar anesthetic concentration equivalent, MAC) compared with that observed after an anesthetic concentration of ethanol. After 30 min exposure to each anesthetic and washout, pEPSP area increased to levels significantly more than control (P < 0.01–0.001). Exposure to a very small (0.025 MAC) concentration of isoflurane over the same period itself produced a similarly exaggerated pEPSP (P < 0.05) in the continued presence of the drug, suggesting that the phenomenon is a direct excitatory effect of the small concentrations of anesthetic on washout, unlike the true withdrawal observed with ethanol. Isoflurane, but not halothane, significantly increased the amount of potassium-stimulated release of the excitatory neurotransmitters glutamate, aspartate, and substance P, suggesting the hyperresponsiveness for that drug is the result of a presynaptically mediated increase in transmitter release. A broad spectrum specific protein kinase C inhibitor, GF109203X, blocked ethanol withdrawal hyperresponsiveness but not hyperresponsiveness after halothane. If the behavioral symptoms of emergence from anesthesia are based on excitatory actions similar to those observed in the spinal cord, the results show that they represent direct excitatory actions rather than withdrawal and are attributable to direct actions on ion channels or receptors, rather than indirect effects mediated by protein kinase C.
Vibrational Cooling Effect of Zeolite on Molecular Desorption Studied by Time-Resolved Laser Desorption Ionization Mass Spectrometry

The desorption of the protonated species of 2,4,6-trihydroxyacetophenone (THAP) and 1â€™-hydroxy-2â€™-acetonaphthone (HAN) from their crystals was observed by using a time-resolved mass spectrometer.
A GPS and laser-based localization for urban and non-urban outdoor environments

This paper introduces a localization based on GPS and laser measurements for urban and non-urban outdoor environments. In this approach, the GPS pose is Kalman filtered using wheel odometry and inertial data and tightly integrated into a Monte Carlo localization based on 3D laser range data and a line feature reference map. By applying to this kind of sensor fusion, global localization as well as precise position tracking in close distance to buildings is enabled where only poor GPS observations are available. Following the description of the localization system, real world experiments demonstrate the functionality of the presented approach.
Improved AdaBoost.M1 of decision trees with confidence-rated predictions

This paper proposes an algorithm to integrate AdaBoost.M1 with the decision trees that output confidence-rated predictions, which is done by transforming decision trees from "expert" models to "specialist" models that may abstain when the confidence is less than 1/2. The confidence is used to update the instance weights during the boosting process, and it is also used to determine the vote weights of base classifiers in decision process. This makes the algorithm a "dynamic" one, in that: (1) for a given test instance, only those whose confidences are higher than 1/2 can vote on the decision making; and (2) the vote weight of each base classifier is dependent on the confidence that the classifier has on the target instance. Experimental results with C4.5 decision tree learner as the base learning algorithm have shown that this algorithm has significantly outperformed both the base algorithm and the AdaBoost.M1 of the C4.5 decision trees with simple predictions.
STRUCTURE IN THE DUSTY DEBRIS AROUND VEGA 1

We present images of the Vega system obtained with the IRAM Plateau de Bure interferometer at a 1.3 mm wavelength with submillijansky sensitivity and ∼2 . 5 resolution (about 20 AU). These observations clearly detect the stellar photosphere and two dust emission peaks offset from the star by 9 . 5 and 8 . 0 to the northeast and southwest, respectively. These offset emission peaks are consistent with the barely resolved structure visible in previous submillimeter images, and they account for a large fraction of the dust emission. The presence of two dust concentrations at the observed locations is plausibly explained by the dynamical influence of an unseen planet of a few Jupiter masses in a highly eccentric orbit that traps dust in principal mean motion resonances. Subject headings: celestial mechanics — circumstellar matter — planetary systems: protoplanetary disks — stars: individual (a Lyrae) On-line material: color figures
Topic-based web page recommendation using tags

Collaborative tagging sites allow users to save and annotate their favorite web contents with tags. These tags provide a novel source of information for collaborative filtering. This paper proposes a probabilistic approach to leverage information embedded in tags to improve the effectiveness of Web page recommendation in a social information management context. In our approach, the probability of a Web page visit by a user is estimated by summing up the relevance of this Web page to this user's tags, and then those pages with the highest probabilities are recommended. Experiments using two real-world collaborative tagging datasets show that our algorithms outperform the common collaborative filtering methods.
Immunotoxicological Evaluation of Corn Genetically Modified with Bacillus thuringiensis Cry1Ah Gene by a 30-Day Feeding Study in BALB/c Mice

This study was to investigate the immunotoxicological potential of corn genetically modified (GM) with Bacillus thuringiensis (Bt) Cry1Ah gene in BALB/c mice. Female BALB/c mice were randomly assigned to one of the four groups: the negative control group, the parental corn group, the GM corn group and the positive control group with 10 mice per group. Mice in the GM corn group and the parental corn group were fed with diets containing 70% corresponding corn for 30 days. Mice in the negative control group and the positive control group were fed with AIN93G diet, administered with saline or 200 mg/kg of cyclophosphamide (CY) via intraperitoneal injection 24 h before the termination of the study, respectively. At the end of the study, the immunotoxicological effects of the GM corn were evaluated through immunopathology parameters including body and organ weights, hematology and clinical chemistry parameters, histological examination, peripheral blood lymphocytes phenotype; humoral immunity including antibody plaque-forming cell, serum immunoglobulin, cytokine and half hemolysis value; cellular immunity such as mitogen-induced splenocyte proliferation, cytotoxic T-lymphocyte reaction, delayed-type hypersensitivity reaction; non-specific immunity including phagocytic activities of phagocytes, natural killer cell activity. A single dose of cyclophosphamide (200 mg/kg bw) was found to have significant adverse effects on immunopathology, cellular immunity, and humoral immunity in mice. The corn genetically modified with Bt Cry1Ah gene is considered consistent with the parental corn in terms of immunopathology, humoral immunity, cellular immunity and non-specific immunity. No adverse immunotoxicological effects of GM corn with Bt Cry1Ah gene were found when feeding mice for 30 days.
MODEL-BASED THERAPEUTIC TARGET DISCRIMINATION USING STOCHASTIC SIMULATION AND ROBUSTNESS ANALYSIS IN AN INSULIN SIGNALING PATHWAY

Insulin resistance, which precedes the onset of type 2 diabetes mellitus (T2DM), is a decreased sensitivity of response to normal insulin levels which has been linked to a number of possible changes in the insulin signal transduction network. In this work, we simulate two hypothesized resistance-inducing perturbations using a published differential equation model of insulin signaling, estimate noise in this signaling model with stochastic simulation, and predict sets of optimal multi-drug targets that are robust against the estimated signaling noise using structured singular value analysis.
Selection of the most useful subset of genes for gene expression-based classification

Recently, there has been a growing interest in classification of patient samples based on gene expressions. Here the classification task is made more difficult by the noisy nature of the data, and by the overwhelming number of genes relative to the number of available training samples in the data set. Moreover, many of these genes are irrelevant for classification and have negative effect on the accuracy and on the required learning time for the classifier. We propose a new evolutionary computation method to select the most useful subset of genes for molecular classification. We apply this method to three benchmark data sets and present our unbiased experimental results.
A Prototyping Architecture for Augmented Reality

In this paper we introduce an architecture for rapid development and assessment of advanced 3D visual tracking algorithms. We claim, that no universal tracking approach exists, that fulfills the requirements of all possible application scenarios at the same time. On the contrary, very specific and working solutions can be developed for given situations and uses. Therefore, software for visual tracking must be designed as a highly flexible system that can be quickly re-configured in order to enable the development of optimized solutions in terms of robustness, accuracy, frame rate and delay. To this purpose we designed an architecture that offers many functionalities, which can be combined together to build a new processing chain. The overall system offers numerous advantages, such as interactive programming, run-time access to data and parameters and easy interfacing with other libraries or applications.
Early pregnancy Preconceptional low-dose aspirin for the prevention of hypertensive pregnancy complications and preterm delivery after IVF : a meta-analysis with individual patient data

E. Groeneveld1,*, M.J. Lambers1, C.B. Lambalk1,2, K.A. Broeze3,4, M. Haapsamo5, P. de Sutter2, B.C. Schoot6, R. Schats1, B.W. J. Mol3,4, and P.G. A. Hompes1 Department of Obstetrics and Gynaecology, Centre for Reproductive Medicine, VU University Medical Centre, P.O. Box 7057, Amsterdam 1007 MB, The Netherlands Department of Gynaecology-Obstetrics, Centre for Reproductive Medicine of the University Hospital of Ghent, Ghent, Belgium Department of Obstetrics and Gynaecology, Centre for Reproductive Medicine, Academic Medical Centre, University of Amsterdam, Amsterdam, The Netherlands Department of Clinical Epidemiology and Biostatistics, Academic Medical Centre (AMC), Amsterdam, The Netherlands Department of Obstetrics and Gynecology, University hospital of Oulu, Oulu, Finland Department of Obstetrics and Gynaecology, Catharina Hospital, Eindhoven, The Netherlands
Marine mammals through time: when less is more in studying palaeodiversity

The validity of biological explanations of patterns of palaeodiversity has been called into question owing to an apparent correlation of diversity with the amount of sedimentary rock preserved. However, this claim has largely been based on comprehensive estimates of global marine Phanerozoic diversity, thus raising the question of whether a similar bias applies to the records of smaller, well-defined taxonomic groups. Here, new data on European Caenozoic marine sedimentary rock outcrop area are presented and compared with European occurrences of three groups of marine mammals (cetaceans, pinnipedimorphs and sirenians). Limited evidence was found for a correlation of outcrop area with marine mammal palaeodiversity. In addition, similar patterns were identified in the cetacean and pinnipedimorph diversity data. This may point to the preservation of a genuine biological signal not overwhelmed by geological biases in the marine mammal diversity data, and opens the door to further analyses of both marine mammal evolution and geological bias in other small and well-defined groups of taxa.
Construction and Application of Bayesian Network Model for Spatial Data Mining

The advent of spatial information technologies, such as GIS, GPS and remote sensing, have great enhanced our capabilities to collect and capture spatial data. How to discover potentially useful information and knowledge from massive amounts of spatial data is becoming a crucial project for spatial analysis and spatial decision making. Bayesian networks have a powerful ability for reasoning and semantic representation, which combined with qualitative analysis and quantitative analysis, with prior knowledge and observed data, and provides an effective way to spatial data mining. This paper focuses on construction and learning a Bayesian network model for spatial data mining. Firstly, we introduce the theory of spatial data mining and discuss the characteristics of Bayesian networks. A framework and process of spatial data mining is proposed. Then we construct a Bayesian network model for spatial data mining with the given dataset. The experimental results demonstrate the feasibility and practical of the proposed approach to spatial data mining. Finally, we draw a conclusion and show further avenues for research.
Climbing out of organisational depression: culture change project after a toxic leadership episode

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A novel digitally-controlled varactor for portable delay cell design

In this paper, a novel digitally-controlled varactor (DCV) for portable delay cell design is presented. The proposed varactor uses the gate capacitance differences of NAND/NOR gates under different digital control inputs to build up a digitally-controlled varactor. Then the proposed varactor is applied to design a high resolution delay cell and to achieve a fine delay resolution. Different types of NAND/NOR gates (2-input or 3-input) for DCV design are also investigated in this paper. The proposed DCV can be implemented with standard cells, thus it can be easily ported to different processes in a short time. A test chip fabricated on a standard 0.35 μm CMOS 2P4M process proves that the proposed delay cell has a fine delay resolution about 1.55 ps. As a result, the proposed DCV exhibits finer resolution, better linearity, and better portability than traditional delay elements, and is very suitable for portable delay cell design.
OPTIMIZATION OF MODEL-FREE ADAPTIVE CONTROLLER USING DIFFERENTIAL EVOLUTION METHOD

It is well-known that conventional control theories are widely suited for applications where the proce sses can be reasonably described in advance. However, when t plant’s dynamics are hard to characterize precis ely or are subject to environmental uncertainties, one may enc ou ter difficulties in applying the conventional co ntroller design methodologies. Despite the difficulty in achieving hi h control performance, the fine tuning of contro ller parameters is a tedious task that always requires experts with kn owledge in both control theory and process informat ion. Nowadays, more and more studies have focused on the developme nt of adaptive control algorithms that can be direc tly applied to complex processes whose dynamics are poorly modeled and/or have severe nonlinearities. In this context , the design of a Model-Free Learning Adaptive Control (MFLAC) base d on pseudo-gradient concepts and optimization proc edure by Differential Evolution (DE) is presented in this pa per. DE algorithms are evolutionary algorithms that ve already shown appealing features as efficient methods for t he optimization of continuous space functions. Moti vation for application of DE approach is to overcome the limit ation of the conventional MFLAC design, which canno t guarantee satisfactory control performance when the plant has different gains for the operational range when des igned by trialand-error by user. Numerical results of the MFLAC w ith particle swarm optimization for a nonlinear con tr l valve are showed.