Miracle-dz/UniFAR-FTUs huggingface dataset:
The NEDD8 modification pathway in plants

NEDD8, in plants and yeasts also known as RELATED TO UBIQUITIN (RUB), is an evolutionarily conserved 76 amino acid protein highly related to ubiquitin. Like ubiquitin, NEDD8 can be conjugated to and deconjugated from target proteins, but unlike ubiquitin, NEDD8 has not been reported to form chains similar to the different polymeric ubiquitin chains that have a role in a diverse set of cellular processes. NEDD8-modification is best known as a post-translational modification of the cullin subunits of cullin-RING E3 ubiquitin ligases. In this context, structural analyses have revealed that neddylation induces a conformation change of the cullin that brings the ubiquitylation substrates into proximity of the interacting E2 conjugating enzyme. In turn, NEDD8 deconjugation destabilizes the cullin RING ligase complex allowing for the exchange of substrate recognition subunits via the exchange factor CAND1. In plants, components of the neddylation and deneddylation pathway were identified based on mutants with defects in auxin and light responses and the characterization of these mutants has been instrumental for the elucidation of the neddylation pathway. More recently, there has been evidence from animal and plant systems that NEDD8 conjugation may also regulate the behavior or fate of non-cullin substrates in a number of ways. Here, the current knowledge on NEDD8 processing, conjugation and deconjugation is presented, where applicable, in the context of specific signaling pathways from plants.
Implementation of Workpiece Recognition and Location Based on Opencv

A method of workpiece recognition and location by Hu moment invariants based on Open Source Computer Vision(Opencv) is presented in this paper. Firstly, the methods of image preprocessing including image graying, mean filter, image binaryzation by adaptive threshold segmentation are used. Then, we extract the contours from the binary image. Finally, we can identify the object workpiece by matching the extracted contours with the object contour from the template image by Hu moment invariants. At the same time, the method of confirming the workpiece position and direction is put forward. Experimental results show that the proposed method can recognize the target workpiece and locate the position effectively. The used algorithms are simple and fast in operation.
Aan accurate multi-stage energy detection spectrum sensing

In this paper, we propose a Multi-Stage Energy Detection (ED) algorithm for spectrum sensing. Using an accurate modeling for the energies at different stages, the algorithm can minimize the average required number of samples to achieve the overall target performance, measured in terms of probabilities of detection and false alarm. Specifically, the algorithm determines the required target performances and the number of sensing samples at each stage accurately in order to achieve the overall target performance. Simulation results show a significant decrease in the number of samples needed to achieve the target performance in case of the proposed algorithm compared to the conventional single stage ED. Moreover, unlike other studies in the literature, the simulations show that the proposed algorithm can maintain the target probabilities of detection and false alarm due to the accurate modeling of the problem.
Look before You Leap: Using the Right Hardware Resources to Accelerate Applications

GPUs are widely used to accelerate data-parallel applications. However, while the GPU processing capability is enhanced in each generation, the CPU computing power is also increased by adding more cores and widening vector units. Compared to the rapid development of GPUs and CPUs, the bandwidth of the data transfer between GPUs and the host CPU grows much slower, resulting in a data-transfer wall for using GPUs. In this situation, choosing the right mix of hardware resources - i.e., The right hardware configuration - is critically important for improving application performance, and the right choice is a function of the available hardware resources as well as the application and the dataset to be used. In this paper, we present a systematic approach to determine the hardware configuration that leads to the best performance for a given workload. Our approach captures the variation of hardware capabilities and data-transfer overhead for different applications and datasets, and uses modeling and prediction techniques to determine the best-performing hardware configuration. We have tested our approach on 7 applications with 6 datasets per application. The results show that our approach takes the correct decision in 38 out of 42 test cases, achieving up to 12.6×/6.6× performance improvement compared to an uninformed Only-CPU/Only-GPU decision.
Iterative Deep Learning for Network Topology Extraction

This paper tackles the task of estimating the topology of filamentary networks such as retinal vessels and road networks. Building on top of a global model that performs a dense semantical classification of the pixels of the image, we design a Convolutional Neural Network (CNN) that predicts the local connectivity between the central pixel of an input patch and its border points. By iterating this local connectivity we sweep the whole image and infer the global topology of the filamentary network, inspired by a human delineating a complex network with the tip of their finger. We perform an extensive and comprehensive qualitative and quantitative evaluation on two tasks: retinal veins and arteries topology extraction and road network estimation. In both cases, represented by two publicly available datasets (DRIVE and Massachusetts Roads), we show superior performance to very strong baselines.
Semantic Segmentation and Model Fitting for Triangulated Surfaces using Random Jumps and Graph Cuts

Recent advances in 3D reconstruction allows to acquire highly detailed geometry from a set of images. The outcome of vision-based reconstruction methods is often oversampled, noisy, and without any higher-level information. Further processing such as object recognition, physical measurement, urban modeling, or rendering requires more advanced representations such as computer-aided design (CAD) models. In this paper, we present a global approach that simultaneously decomposes a triangulated surface into meaningful segments and fits a set of bounded geometric primitives. Using the theory of Markov chain Monte Carlo methods (MCMC), a random process is derived to find the solution that most likely explains the measured data. A data-driven approach based on the random sample consensus (RANSAC) paradigm is designed to guide the optimization process with respect to efficiency and robustness. It is shown, that graph cuts allow to incorporate model complexity and spatial regularization into the MCMC process. The algorithm has successfully been implemented and tested on various examples.
Multinational Perspectives on Work Values and Commitment

The aim of this article is to show how different factors influence the commitment of employees in an organization from an international comparative perspective. Commitment is studied as affective and continuance commitment. Personal characteristics, organizational and environmental factors are included as predictors that have an impact on commitment. The role of values and insecurity is also examined. It is proposed that these factors do not have the same impact on the two types of commitment in different countries and that this might have important practical implications. The study, which compared West Germany, East Germany, Japan, Hungary, Slovenia, the UK and the USA, was performed using the data from Work Orientations II, gathered by the International Social Survey Programme group (ISSP). The dataset is from 1997. The study finds that there are some predictors that are universal, but their configurations depend mainly on cultural background.
The Development Push of Refugees: Evidence from Tanzania

We exploit a 1991–2010 Tanzanian household panel to assess the effects of the temporary refugee inflows originating from Burundi (1993) and Rwanda (1994). We find that the refugee presence has had a persistent and positive impact on the welfare of the local population. We investigate the possible channels of transmission, underscoring the importance of a decrease in transport costs as a key driver of this persistent change in welfare. We interpret these findings as the ability of a temporary shock to induce a persistent shift in the equilibrium through subsequent investments rather than a switch to a new equilibrium in a multiple-equilibrium setting.
Using Heteroscedasticity to Identify and Estimate Mismeasured and Endogenous Regressor Models

This article proposes a new method of obtaining identification in mismeasured regressor models, triangular systems, and simultaneous equation systems. The method may be used in applications where other sources of identification, such as instrumental variables or repeated measurements, are not available. Associated estimators take the form of two-stage least squares or generalized method of moments. Identification comes from a heteroscedastic covariance restriction that is shown to be a feature of many models of endogeneity or mismeasurement. Identification is also obtained for semiparametric partly linear models, and associated estimators are provided. Set identification bounds are derived for cases where point-identifying assumptions fail to hold. An empirical application estimating Engel curves is provided.
Handedness throughout the lifespan: cross-sectional view on sex differences as asymmetries change

Manual asymmetries has been studied by many researchers, however contradictory findings still exist as to whether preferred manual asymmetries increases with age or do we become more ambidextrous. Recently it was shown that perhaps there is a third option, that there is no increase or decrease in laterality but rather preferred manual asymmetries remains consistent throughout adulthood. Another related finding is that females appear to have an advantage in some handedness tasks, such as the Grooved Pegboard. When a larger pegboard is used, sex differences may reverse as males may perform better when larger pegs and a larger trajectory are required. However, it is not fully understood if these sex differences arise from an early age and continue throughout life. Therefore, we sought to explore sex differences in preferred hand dominance throughout the lifespan. In order to explore preferred hand dominance during the lifespan we examined 76 children (19.4–5 year olds, 12 female, Mage = 4.73; 34.6–8 year olds, 12 female, Mage = 6.97; 23.9–12 year olds, 14 female, Mage = 10.83) in Experiment 1 and 35 healthy young right-handed adults (15 female, Mage = 20.91) and 37 healthy older right-handed adults (20 female, Mage = 72.3) in Experiment 2. Individuals were tested using a standard size (small) and modified Grooved Pegboard (larger pegboard). Our study demonstrates that hand asymmetries are present early in life (children 4–5 years old) at that these differences attenuate as a function of age until adulthood (Experiment 1). Furthermore, our results demonstrate that as we age (Experiment 2), asymmetries may increase (small and large pegboards), decrease (Annett), or stay the same (finger tapping). As well we demonstrated that the sex differences could not be entirely accounted for by hand size. Therefore, asymmetries as regard to the aging process, seems to be task specific which may account for the conflicting findings in research.
La mise en base de données de matériaux de recherche en botanique et en écologie: Spécimens, données et métadonnées

RÉSUMÉ Le développement de grandes bases de données constitue l’une des évolutions contemporaines les plus marquantes dans les sciences des trente dernières années. Elles constituent de nouveaux supports de production, de représentation et de mise en relation des savoirs et des connaissances. Cet article vise à examiner l’enchaînement d’instruments et de dispositifs dans la configuration matérielle des bases de données et leurs conséquences sur les modalités de production des savoirs, dans deux contextes différents. Nous décrivons, dans le premier cas, le travail nécessaire à la numérisation d’un ensemble de planches d’un herbier destinées à alimenter une base transnationale de données en botanique. Dans le deuxième cas, nous décrivons le travail de documentation d’échantillons de plancton destinés à alimenter une base de données en écologie. Nous analysons les conséquences associées aux processus de mise en base de données sur le plan de la documentation des données, du caractère distribué de la production de la base de données, de l’évolution du statut des données et des enjeux de la mise en base de matériaux de recherche en lien avec l’importance du respect de la procédure et la fragilité des infrastructures. Nos analyses nous conduisent à penser les bases de données non pas comme une fin en soi, mais plutôt comme des éléments qui, de par leur à l’époque) selon les connaissances actuelles. Une fois renommé, il faut le déplacer matériellement dans la collection à l’endroit correspondant au nouveau nom, tout en gardant une trace ( ou fantôme ) à l’ancienne place. Ce travail de vérification du nom et de reclassement est de loin la première servitude matérielle des herbiers bien avant les tâches, pourtant très nécessaires, d’entretien. » (Schäfer, 2005).
Analysis of the Molecular Construction of Xylogalacturonan Isolated from Soluble Soybean Polysaccharides

Soluble soybean polysaccharides (SSPS) extracted from soybean cotyledons are acidic polysaccharides, and exhibited a pectin-like structure. After digesting galacturonan with polygalacturonase, two novel galacturonan (GN) fragments, which were directly linked to xylosyl oligosaccharides, were obtained. One consisted of (β-D-Xyl)7 branched at the C-3 site of 1,4-linked (α-D-GalA)4, and the other consisted of (β-D-Xyl)4 branched at the C-3 site of 1,4-linked (α-D-GalA)3.
Inferring Parameters and Structure of Latent Variable Models by Variational Bayes

Current methods for learning graphical models with latent variables and a fixed structure estimate optimal values for the model parameters. Whereas this approach usually produces overfitting and suboptimal generalization performance, carrying out the Bayesian program of computing the full posterior distributions over the parameters remains a difficult problem. Moreover, learning the structure of models with latent variables, for which the Bayesian approach is crucial, is yet a harder problem. In this paper I present the Variational Bayes framework, which provides a solution to these problems. This approach approximates full posterior distributions over model parameters and structures, as well as latent variables, in an analytical manner without resorting to sampling methods. Unlike in the Laplace approximation, these posteriors are generally non-Gaussian and no Hessian needs to be computed. The resulting algorithm generalizes the standard Expectation Maximization algorithm, and its convergence is guaranteed. I demonstrate that this algorithm can be applied to a large class of models in several domains, including unsupervised clustering and blind source separation.
Analysis of a Fork / Join Synchronization Station with Coxian Inputs from Finite Populations

Fork/join stations are commonly used to model the synchronization constraints in queuing models of computer networks, fabrication/assembly systems and material control strategies for manufacturing systems. In many such applications the fork/join station is fed by two or more inputs from finite populations. This paper presents an exact analysis for the case when the input processes are renewal and the inter-arrival times have two-phase Coxian distributions, allowing us to model a wide range of variability in the input processes. The underlying queue length and departure processes are analyzed to determine performance measures such as throughput, mean queue lengths and distribution of inter-departure times from the fork/join station. The results show that, for certain parameter settings, variability in the arrival processes have a significant impact on the performance of the fork/join station. The model is also used to study the sensitivity of performance measures such as throughput, mean queue lengths, and variability of interdeparture times for a wide class of input processes and customer populations.
Differences in Birthweight Outcomes: A Longitudinal Study Based on Siblings

Objectives: We investigate the differences in birthweight between first- and second-borns, evaluating the impact of changes in pregnancy (e.g., gestational age), demographic (e.g., age), and social (e.g., education level, marital status) maternal characteristics. Data and Methods: All analyses are performed on data collected in Umbria (Italy) taking into account a set of 792 women who delivered twice from 2005 to 2008. Firstly, we use a univariate paired t-test for the comparison between weights of first- and second-borns; Secondly, we use linear and nonlinear regression approaches in order to: (i) evaluate the effect of demographic and social maternal characteristics and (ii) predict the odds-ratio of low and high birthweight infants, respectively. Results: We find that the birthweight of second-borns is significantly higher than that of first-borns. Statistically significant effects are related with a longer gestational age, an increased number of visits during the pregnancy, and the gender of infants. On the other hand, we do not observe any significant effect related with mother’s age and with other characteristics of interest.
A Framework for Efficient Execution of Data Parallel Irregular Applications on Heterogeneous Systems

Exploiting the computing power of the diversity of resources available on heterogeneous systems is mandatory but a very challenging task. The diversity of architectures, execution models and programming tools, together with disjoint address spaces and different computing capabilities, raise a number of challenges that severely impact on application performance and programming productivity. This problem is further compounded in the presence of data parallel irregular applications. This paper presents a framework that addresses development and execution of data parallel irregular applications in heterogeneous systems. A unified task-based programming and execution model is proposed, together with inter and intra-device scheduling, which, coupled with a data management system, aim to achieve performance scalability across multiple devices, while maintaining high programming productivity. Intra-device scheduling on wide SIMD/SIMT architectures resorts to consumer-producer kernels, which, by allowing dynamic generation and rescheduling of new work units, enable balancing irregular workloads and increase resource utilization. Results show that regular and irregular applications scale well with the number of devices, while requiring minimal programming effort. Consumer-producer kernels are able to sustain significant performance gains as long as the workload per basic work unit is enough to compensate overheads associated with intra-device scheduling. This not being the case, consumer kernels can still be used for the irregular application. Comparisons with an alternative framework, StarPU, which targets regular workloads, consistently demonstrate significant speedups. This is, to the best of our knowledge, the first published integrated approach that successfully handles irregular workloads over heterogeneous systems.
Atrial Chamber-specific Expression of the Slow Myosin Heavy Chain 3 Gene in the Embryonic Heart*

The quail slow myosin heavy chain 3 (slow MyHC 3) gene is expressed in the developing heart and in slow muscles of the developing limb. It is first expressed in the pulsatile cardiac tube in the embryo, and as the heart chamberizes its expression becomes restricted to the atria. To identify regulatory elements responsible for atrial-specific expression, the 5′ upstream region of slow MyHC 3 gene was investigated. An atrial regulatory domain (ARD1) between −840 and −680 acts as an atrial cell-specific enhancer in primary cardiocyte cultures. ARD1 also specifies atrial-specific expression in vivo when the ARD1/heterologous promoter was introduced into developing chick embryos by a replication-competent retroviral vector. ARD1 is the first atrial cell-specific enhancer to be identified. Fine deletion and mutation analysis within ARD1 defined a 40-base pair vitamin D3 receptor-like element that controls atrial cell-specific expression of the slow MyHC 3 gene by inhibiting its expression in ventricular cardiocytes.
Highly Reliable Non-Volatile Logic Circuit Technology and Its Application

A ferroelectric-based (FE-based) non-volatile logic is proposed for low-power LSI. Standby currents in a logic circuit can be cut off by using FE-based non-volatile flip-flops(NVFFs), and the standby power can be reduced to zero. The FE capacitor is accessed only when the power turns on/off, performance of the NVFF is almost as same as that of the conventional flip-flop in a logic operation. The use of complementarily stored data in coupled FE capacitors makes it possible to realize wide read voltage margin, which guarantees 10 years retention at 85 degree Celsius under less than 1.5V operation. The low supply voltage and electro-static discharge (ESD) detection technique prevents data destruction caused by illegal access for the FE capacitor during standby state. Applying the proposed circuitry in CPU, the write and read operation for all FE capacitors in 1.6k-bit NVFFs are performed within 7us and 3us with access energy of 23.1nJ and 8.1nJ, respectively, using 130nm CMOS with Pb(Zr, Ti)O3(PZT) thin films.
Object localisation and tracking through subsymbolic classification

This paper presents results of experiments in subsymbolic processing of visual data, to achieve identification and tracking of arbitrary objects, which are intended to be used in autonomous robots for novelty detection and navigation purposes. Artificial neural networks with unsupervised training are used as the classification stage for the vision system, in order to provide the robot the ability to develop its own representations from perceptual data without the need of any external human-provided information. We present an evaluation of the behaviour of the system when using very simple feature extraction techniques, such as horizontal and vertical average histograms, as well as average coarse coding.
Open-minded cognition: The attitude justification effect

Abstract Although open-mindedness is generally valued, people are not equally open-minded in all situations. Open-mindedness is viewed as socially desirable when individuals encounter viewpoints that are compatible with conventional social norms. However, open-mindedness is viewed in less desirable terms when individuals encounter viewpoints that undermine these norms. The perceived desirability of open-mindedness is also influenced by the individual’s personal attitudinal convictions. Individuals ‘inflate’ the normative appropriateness of open-mindedness when it serves to reinforce their convictions, but devalue the normative appropriateness of open-mindedness when it serves to contradict these convictions. Conversely, normative prohibition of closed-mindedness is exaggerated when a closed-minded orientation threatens the individual’s personal attitudinal convictions, but is minimized (or reversed) when a closed-minded orientation reinforces these convictions. Paradoxically, the perceived appropriateness of open-mindedness is engendered (at least in part) by the motivation to confirm one’s prior attitudinal convictions. Evidence of this attitude justification effect is obtained in two experiments.