Miracle-dz/UniFAR-FTUs huggingface dataset:
A language for information commerce processes

Automatizing information commerce requires languages to represent the typical information commerce processes. Existing languages and standards cover either only very specific types of business models or are too general to capture in a concise way the specific properties of information commerce processes. We introduce a language that is specifically designed for information commerce. It can be directly used for the implementation of the processes and communication required in information commerce. It allows to cover existing business models that are known either from standard proposals or existing information commerce applications on the Internet. The language has a concise logical semantics. We present the language concepts and an implementation architecture.
Secure Authentication Scheme based on IBE

W ireless sensor networks are confronted with unique challenges. Absence of i nfrastructure s upport , the k ey distribution is one of the most challenging security problems in wireless sensor networks, and the traditional security techniques used in tradition networks cannot be applied directly. We propose an IBE -based authentication protocol as the security scheme, which can achieve the key establishment and security authentication. The security of the authentication protocol is analyzed based on BAN logic. The simulation shows, comparing with previous key-establishment solutions, our approaches need little storage space, and can gain stronger security and reasonable performance efficiency.
Generation of multiple sub-diffraction-limit spots in 3D space with propagating light

This paper reports on diffractive fan-out elements generating multiple sub-diffraction-limit spots in three-dimensional (3D) space. To arrange the sub-diffraction-limit spots, we introduce particular constraints and transfer functions corresponding to the Fresnel diffraction into an iterative Fourier transform algorithm. Experimental results demonstrate that the size of spots generated in 3D space is reduced to 77% of the diffraction limit on average.
Color Feature Extraction Method in the Video Surveillance System

A human color feature extraction method based on the improved Mean shift algorithm and kernel density estimation was proposed with respect to the human feature extraction in the video surveillance system with multi-cameras. It first gets suitable region partition of each person through the improved Mean shift algorithm. Afterwards, kernel density estimation is done to each region’s pixels to attain the color density function. Then, the color feature extraction of each person is realized. The proposed method could attain accurate color model because it automatically gets reasonable region division not by fixed region division fashion, but by human appearance color distribution. Experimental results show the method’s feasibility and robustness. It lays foundation of the human tracking with multi-cameras.
Characterizing the Rate Region of the (4,3,3) Exact-Repair Regenerating Codes

Exact-repair regenerating codes are considered for the case (n,k,d) = (4,3,3), for which a complete characterization of the rate region is provided. This characterization answers in the affirmative the open question whether there exists a non-vanishing gap between the optimal bandwidth-storage tradeoff of the functional-repair regenerating codes (i.e., the cut-set bound) and that of the exact-repair regenerating codes. To obtain an explicit information theoretic converse, a computer-aided proof (CAP) approach based on primal and dual relation is developed. This CAP approach extends Yeung's linear programming (LP) method, which was previously only used on information theoretic problems with a few random variables due to the exponential growth of the number of variables in the corresponding LP problem. The symmetry in the exact-repair regenerating code problem allows an effective reduction of the number of variables, and together with several other problem-specific reductions, the LP problem is reduced to a manageable scale. For the achievability, only one non-trivial corner point of the rate region needs to be addressed in this case, for which an explicit binary code construction is given.
Construction of China's Energy Condition Index

Among the supply shocks on inflation, energy is one of the most important. In order to reflect energy condition’s impact on inflation comprehensively, referring the financial condition index, we construct China’s energy condition index by bring in 3 variables: China’s energy price, consumption and production. The empirical analysis’s result shows that the index has an ideal prediction to China’s inflation.
Monte Carlo Methods for Tempo Tracking and Rhythm Quantization

We present a probabilistic generative model for timing deviations in expressive music performance. The structure of the proposed model is equivalent to a switching state space model. The switch variables correspond to discrete note locations as in a musical score. The continuous hidden variables denote the tempo. We formulate two well known music recognition problems, namely tempo tracking and automatic transcription (rhythm quantization) as filtering and maximum a posteriori (MAP) state estimation tasks. Exact computation of posterior features such as the MAP state is intractable in this model class, so we introduce Monte Carlo methods for integration and optimization. We compare Markov Chain Monte Carlo (MCMC) methods (such as Gibbs sampling, simulated annealing and iterative improvement) and sequential Monte Carlo methods (particle filters). Our simulation results suggest better results with sequential methods. The methods can be applied in both online and batch scenarios such as tempo tracking and transcription and are thus potentially useful in a number of music applications such as adaptive automatic accompaniment, score typesetting and music information retrieval.
Localization using RGB-D cameras orthoimages

3D cameras are a new generation of sensors more and more used in geomatics. The main advantages of 3D cameras are their handiness, their price, and the ability to produce range images or point clouds in real-time. They are used in many areas and the use of this kind of sensors has grown especially as the Kinect (Microsoft) arrived on the market. This paper presents a new localization system based exclusively on the combination of several 3D cameras on a mobile platform. It is planed that the platform moves on sidewalks, acquires the environment and enables the determination of most appropriate routes for disabled persons. The paper will present the key features of our approach as well as promising solutions for the challenging task of localization based on 3D-cameras. We give examples of mobile trajectory estimated exclusively from 3D cameras acquisitions. We evaluate the accuracy of the calculated trajectory, thanks to a reference trajectory obtained by a total station.
Vibration analysis of SCARA type direct drive robot with double link mechanism.

Vibration Analysis of SCARA Type Direct Drive Robot with Double Link Mechanism Hisayoshi SEKIGUCHI and Mitsuo TAKESHITA This paper describes a vibration analysis of a SCARA type Direct Drive Robot (DDR). The arm natural frequency varies depending on the arm configuration, and the bandwidth of the servomechanism for the joint control is restricted by the lowest natural frequency to keep a stable control. Especially in the DDR with one set of parallelogram arm mechanism (single link), the change of the arm natural frequency is remarkably large. In this paper, a mathematical model of the arm mechanism is presented to estimate the arm natural frequency which varies depending on the arm configuration. By the analysis with the model, it is cleared that by using two sets of parallelogram arm mechanism (double link), the lowest natural frequency becomes large and also the change of the natural frequency by the arm configuration can be decreased. It is also shown that there exists an optimum design parameter in which the arm natural frequency becomes almost independent of the change of the arm configuration. Moreover the optimum design parameter is obtained analytically. The analytical results are confirmed by experiments using a developed SCARA type DDR with the double link mechanism.
Sucrose helps regulate cold acclimation of Arabidopsis thaliana

A test was carried out to see if sucrose could regulate cold-acclimation-associated gene expression in Arabidopsis. In plants and excised leaves, sucrose caused an increase in GUS activity, as a reporter for the activity of the cold-responsive COR78 promoter. This increase was transient at 21 °C but lasted for at least 4 d at 4 °C in continuous darkness. However, at 4 °C with a 16 h photoperiod, GUS activity was similarly high with solutions lacking sucrose or with different concentrations of sucrose. In peeled lower epidermis in the cold dark environment, 40 mM sucrose increased COR78 transcript abundance to substantially above that in the controls, but sorbitol had no effect. Similarly to the cold and dark conditions, sucrose increased COR78 transcript abundance in the epidermis in the warm light and warm dark environments, but not in a cold light environment. Sucrose had much less effect on COR78 transcript abundance in leaves without the lower epidermis. Thus sucrose regulates expression of COR78, possibly mainly in the epidermis, at the level of transcription. Furthermore, 40 mM sucrose at 4 °C for 24 h in constant darkness was sufficient to give the same GUS activity as in fully acclimated plants of the same age in a 16 h photoperiod, although by 48 h, GUS activity had become intermediate between control and fully cold-acclimated plants. Thus sucrose has a regulatory role in the acclimation of whole plants to cold and this may be important during diurnal dark periods.
Sample Discriminant Analysis for SAR ATR

Feature extraction is a key step in synthetic-aperture-radar automatic target recognition. In this letter, we propose a novel feature extraction method named sample discriminant analysis (SDA) that is based on the manifold learning theory. The method directly extracts features from 2-D image matrices rather than vectors. Furthermore, SDA preserves the neighborhood information of the original data in dimension reduction. It also makes within-class samples closer and makes between-class samples father away in a low-dimensional space. Meanwhile, a sample discriminant coefficient is employed in the method to give each sample a weight related to its location and similarity to neighboring samples. Thus, the discriminative ability of the method is improved. Experimental results based on the moving and stationary target acquisition and recognition database show that the proposed method can improve recognition performance.
JNK1 Is Required for the Induction of Mkp1 Expression in Macrophages during Proliferation and Lipopolysaccharide-dependent Activation*

Macrophages proliferate in the presence of their growth factor, macrophage colony-stimulating factor (M-CSF), in a process that is dependent on early and short ERK activation. Lipopolysaccharide (LPS) induces macrophage activation, stops proliferation, and delays ERK phosphorylation, thereby triggering an inflammatory response. Proliferating or activating responses are balanced by the kinetics of ERK phosphorylation, the inactivation of which correlates with Mkp1 induction. Here we show that the transcriptional induction of this phosphatase by M-CSF or LPS depends on JNK but not on the other MAPKs, ERK and p38. The lack of Mkp1 induction caused by JNK inhibition prolonged ERK-1/2 and p38 phosphorylation. The two JNK genes, jnk1 and jnk2, are constitutively expressed in macrophages. However, only the JNK1 isoform was phosphorylated and, as determined in single knock-out mice, was necessary for Mkp1 induction by M-CSF or LPS. JNK1 was also required for pro-inflammatory cytokine biosynthesis (tumor necrosis factor-α, interleukin-1β, and interleukin-6) and LPS-induced NO production. This requirement is independent of Mkp1 expression, as shown in Mkp1 knock-out mice. Our results demonstrate a critical role for JNK1 in the regulation of Mkp1 induction and in LPS-dependent macrophage activation.
Quantitative In Vivo Imaging of Breast Tumor Extracellular Matrix

Abstract : SHG intensity combined with the F/B ratio provides unique measures of the local density of ordered collagen, as well as the characteristic length scale of this ordering. In order to fully implement F/B ratio measurements to understand the dynamic ordering of collagen in living tumors, we must be able to measure this property in vivo, in intact tissue, which stops us from using a forwards detector. In this annual report, we described ongoing work developing optical methods to quantify the breast tumor collagen SHG F/B scattering ratio in intact tumors in vivo, i.e. without a forwards detector. In future studies this information will be used to determine how manipulation of gene expression by tumor associated macrophages affects collagen ordering, and to determine if SHG measurement of collagen ordering provides a clinically useful measure of metastatic ability.
Crucial Factors Affecting Decentralized Multirobot Learning in an Object Manipulation Task

Decentralized multirobot learning refers to the use of multiple learning entities to achieve the optimal solution for the overall robot system. We demonstrate that single-robot learning theory can be successfully used with multirobot systems, but with certain conditions. The success and the effectiveness of this method are potentially affected by various factors that we classify into two groups: the nature of the robots and the nature of the learning entities. Incorrect setup of these factors may lead to undesirable results. In this paper, we methodically test the effect of varying four common factors (reward scope, learning algorithms, diversity of robots, and number of robots) in a decentralized multirobot system, first in simulation and then on real robots. The results show that two of these factors, reward scope and learning algorithm, if set up incorrectly, can prevent optimal, cooperative olutions. s
Ethnic Background and Overweight among 5-Year-Old Children: The “Be Active, Eat Right” Study

Introduction. This study investigates the association between ethnic background and overweight (obesity included) among 5 year olds. Methods. We used baseline data from 5 year olds (n = 7801) and their parents collected for the “Be active, eat right” study. A child was considered to be of non-Dutch ethnic background when at least one of the parents was born abroad. Odds ratios (ORs) were adjusted for sociodemographic characteristics. Results. Compared to children of Dutch ethnic background, for children with a Moroccan ethnic background the OR for being overweight (obesity included) was 2.27 (95% CI 1.48–3.47), for Turkish children the OR was 3.63 (95% confidence interval (CI) 2.46–5.35), for Antillean children the OR was 1.97 (95% CI 1.01–3.86), and for Surinamese children the OR was 0.47 (95% CI 0.20–1.06). Addition of parental overweight decreased the ORs for Moroccan and Turkish children by 10.2% and 12.5%, and addition of watching TV and having breakfast by the child decreased the ORs by 7.9% and 12.2%. Conclusion. Already at a young age, children of Moroccan and Turkish ethnic background are at increased risk for being overweight compared to Dutch children. Parental overweight, watching TV, and not having breakfast by the child are contributing factors in this association.
A Unified, Merger-driven Model of the Origin of Starbursts, Quasars, the Cosmic X-Ray Background, Supermassive Black Holes, and Galaxy Spheroids

We present an evolutionary model for starbursts, quasars, and spheroidal galaxies in which mergers between gas-rich galaxies drive nuclear inflows of gas, producing starbursts and feeding the buried growth of supermassive black holes (BHs) until feedback expels gas and renders a briefly visible optical quasar. The quasar lifetime and obscuring column density depend on both the instantaneous and peak quasar luminosity, and we determine this dependence using a large set of galaxy merger simulations varying galaxy properties, orbital geometry, and gas physics. We use these fits to deconvolve observed quasar luminosity functions and obtain the evolution of the formation rate of quasars with peak luminosity, (Lpeak, z). Quasars spend extended periods at luminosities well below peak, so (Lpeak) has a maximum corresponding to the "break" in the observed luminosity function. From (Lpeak) and our simulations, we obtain self-consistent hard and soft X-ray and optical luminosity functions and predict many observables at multiple redshifts, including column density distributions of optical and X-ray samples, the luminosity function of broad-line quasars in X-ray samples and broad-line fraction versus luminosity, active BH mass functions, the distribution of Eddington ratios, the mass function of relic BHs and total BH mass density, and the cosmic X-ray background. In every case, our predictions agree well with observed estimates, without invoking ad hoc assumptions about source properties or distributions. We provide a library of Monte Carlo realizations of our models for comparison with observations.
Learning by Transduction

We describe a method for predicting a classification of an object given classifications of the objects in the training set, assuming that the pairs object/classification are generated by an i.i. d. process from a continuous probability distribution. Our method is a modification of Vapnik's support-vector machine; its main novelty is that it gives not only the prediction itself but also a practicable measure of the evidence found in support of that prediction. We also describe a procedure for assigning degrees of confidence to predictions made by the support vector machine. Some experimental results are presented, and possible extensions of the algorithms are discussed.
Sequences Located within the N-Terminus of the PD-Linked LRRK2 Lead to Increased Aggregation and Attenuation of 6-Hydroxydopamine-Induced Cell Death

Clinical symptoms of Parkinson's disease (PD) arise from the loss of substantia nigra neurons resulting in bradykinesia, rigidity, and tremor. Intracellular protein aggregates are a pathological hallmark of PD, but whether aggregates contribute to disease progression or represent a protective mechanism remains unknown. Mutations in the leucine-rich repeat kinase 2 (LRRK2) gene have been linked to PD in both familial cases and idiopathic cases and aggregates of the LRRK2 protein are present in postmortem PD brain samples. To determine whether LRRK2 contains a region of protein responsible for self-aggregation, two independent, bioinformatic algorithms were used to identify an N-terminal amino acid sequence as being aggregation-prone. Cells subsequently transfected with a construct containing this domain were found to have significantly increased protein aggregation compared to wild type protein or a construct containing only the last half of the molecule. Finally, in support of the hypothesis that aggregates represent a self-protection strategy, aggregated N-terminal LRRK2 constructs significantly attenuated cell death induced by the PD-mimetic, 6-hydroxydopamine (6-OHDA).
Large scale temporal coordination of cortical activity as prerequisite for conscious experience

Phenomenal awareness, the ability to be aware of one’s sensations and feelings, emerges from the capacity of evolved brains to represent their own cognitive processes by iterating and self-reapplying the cortical operations that generate representations of the outer world. Search for the neuronal substrate of awareness therefore converges with the search for the neuronal code through which brains represent their environment. The hypothesis is put forward that the mammalian brain uses two complementary representational strategies. One consists of the generation of neurons responding selectively to particular constellations of features, and is based on selective recombination of inputs in hierarchically structured feed-forward architectures. The other relies on the dynamic association of large numbers of distributed neurons into functionally coherent cell assemblies which as a whole represent a content of cognition. Arguments and data are presented in favor of the second strategy as the one according to which meta-representations that support awareness are established. My hypothesis is that such distributed representations self-organize through transient synchronization of the oscillatory activity. Evidence showing that similar brain states are required both for the occurrence of these synchronization phenomena and for awareness is provided.
Peceptual distortion metric based on wavelet frequency sensitivity and multiple visual fixations

In this paper, we propose a new image quality evaluation method, which is based on wavelet frequency sensitivity model and spatial non-uniform resolution sensitivity model with multiple region-of-interest. The visual quality index is given using probability summation model with weighted visual error factors. Experiments on the video database have shown that the proposed method can achieve very good correlation with subjective evaluation.
Discriminative virtual views for cross-view action recognition

We propose an approach for cross-view action recognition by way of `virtual views' that connect the action descriptors extracted from one (source) view to those extracted from another (target) view. Each virtual view is associated with a linear transformation of the action descriptor, and the sequence of transformations arising from the sequence of virtual views aims at bridging the source and target views while preserving discrimination among action categories. Our approach is capable of operating without access to labeled action samples in the target view and without access to corresponding action instances in the two views, and it also naturally incorporate and exploit corresponding instances or partial labeling in the target view when they are available. The proposed approach achieves improved or competitive performance relative to existing methods when instance correspondences or target labels are available, and it goes beyond the capabilities of these methods by providing some level of discrimination even when neither correspondences nor target labels exist.
From the Cover: Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.