Miracle-dz/UniFAR-FTUs huggingface dataset:
Abstract Visual Syntax

We propose a separation of visual syntax into concrete and abstract syntax, much like it is often done for textual languages. Here the focus is on visual programming languages; the impact on visual languages in general is not clear by now. We suggest to use unstructured labeled multi-graphs as abstract visual syntax and show how this facilitates semantics definitions and transformations of visual languages. In particular, disregarding structural constraints on the abstract syntax level makes such manipulations simple and powerful. Moreover, we demonstrate that – in contrast to the traditional monolithic graph definition – an inductive view of graphs provides a very convenient way to move in a structured (and declarative) way through graphs. Again this supports the simplicity of descriptions for transformations and semantics. 1. What is Abstract Visual Syntax? To specify (the syntax of) a visual language one can choose among a large variety of formalisms, for a comparison, see [MM96]. All these approaches are concerned with the concrete syntax of visual languages, and there are only a few authors explicitly mentioning abstract visual syntax [AER96, RS95, RS96]. This is surprising since work on textual programming languages has demonstrated that the use of abstract syntax can be very helpful in many different areas, for example, specification of type systems, compiler construction, program transformations, and semantics definition. One reason for this situation might be that the right level of abstractness has not been identified yet: we believe that abstract visual syntax must be “more abstract” than in the textual case. We explain this by a simple example. Consider the following (textual) grammar describing part of a concrete syntax for expressions. expr ::= n-expr | b-expr | if-expr n-expr ::= term | n-expr + term term ::= factor | term * factor factor ::= id | ( n-expr ) b-expr ::= id | b-expr ∨ b-expr | . if-expr ::= if b-expr then expr else expr A corresponding abstract syntax would ignore many details, such as the choice of key
An enhanced nonlocal-means algorithm for image denoising

In this paper, we propose an enhanced nonlocal-means (NL-means) algorithm for reduction of Gaussian (or light-tail distributed) noise of a gray-scale image. In the NL-means approach, for each pixel, the method computes a weighted average of all pixels in the image. More weight is given to a pixel if its neighborhood (surrounding pixels) is more similar to that of the pixel being denoised. The neighborhood similarity measure used in NL-means is a L2 weighted norm. We show that using such similarity measure sometimes gives the same weight to neighborhoods with similar and dissimilar edge pattern to the current neighborhood edge pattern. That is while the neighborhood with a dissimilar edge pattern should receive a lower weight (or removed all together) to preserve the original edge content that is an important indication of perceptual image quality. To this aim, we modified the similarity measure to incorporate the edge pattern similarity of the inputs as well. Superior denoising performance as compared to the traditional NL-means is shown in various simulations both visually and in terms PSNR and SSIM (as a good estimate of subjective image quality).
Of Styles and Methods. A quest for JRC’s identity at times of change

Some reflections are offered on challenges and opportunities for the JRC as a ‘boundary organisation’ between science and policy, at a time of increased complexity and polarisation and decreased trust in scientific enterprise. Some specific threats are identified in relation to the key role played by the JRC in the practice of front-line policy support. JRC is identified as a potentially relevant player to improve the quality of evidence feeding into the policy process. To achieve this potential, the JRC must develop in-house cultures and styles of reflexivity and humility, adopt more participatory styles of science co-production, and become confident enough of its role that it can dare to say ‘the emperor is naked’ whenever EU institutions appear to be in need of such a call.
A fast algorithm for blind separation of non-Gaussian and time-correlated signals

In this article we propose a computationally efficient method (termed FCOMBI) to combine the strengths of non-Gaussianity-based Blind Source Separation (BSS) and cross-correlations-based BSS. This is done by fusing the separation abilities of two well-known BSS algorithms: EFICA and WASOBI. Simulations show that our approach is at least as accurate and often more accurate that other state-of-the-art approaches which also aim to separate simultaneously non-Gaussian and time-correlated components. However, in terms of computational efficiency and stability, FCOMBI is the clear winner which makes it specially suitable for the analysis of very high-dimensional datasets like high-density Electroencephalographic(EEG) or Magnetoencephalographic (MEG) recordings.
Distributional Similarity Model for Multi-modality Clustering in Social Media

User generated content (UGC) has become the fastest growing sector of the WWW. Data mining from UGC presents challenges not typically found in text mining from documents. UGC can be semi-structured and its content can be very short and informal, containing relatively little content similar to a chat or an email conversation. In addition UGC can be viewed as a multi-modality data. These characteristics pose big challenges and research questions for scholars to cope with. To cluster UGC data, we can construct multiple contingency tables of modalities and employ the multi-way distributional clustering (MDC) algorithm. However, by considering a contingency table which summarizes the co-occurrence statistics of two modalities, it is not robust to represent the information entropy between two modalities in UGC data. In this paper, we propose a novel similarity measurement, called distributional similarity model (DSM), to solidify the graph model in the MDC algorithm to deal with the unique characteristics of the UGC data.
Form Follows Sound: Designing Interactions from Sonic Memories

Sonic interaction is the continuous relationship between user actions and sound, mediated by some technology. Because interaction with sound may be task oriented or experience-based it is important to understand the nature of action-sound relationships in order to design rich sonic interactions. We propose a participatory approach to sonic interaction design that first considers the affordances of sounds in order to imagine embodied interaction, and based on this, generates interaction models for interaction designers wishing to work with sound. We describe a series of workshops, called Form Follows Sound, where participants ideate imagined sonic interactions, and then realize working interactive sound prototypes. We introduce the Sonic Incident technique, as a way to recall memorable sound experiences. We identified three interaction models for sonic interaction design: conducting; manipulating; substituting. These three interaction models offer interaction designers and developers a framework on which they can build richer sonic interactions.
Analysis on International Scientific Collaboration and Research Focus on Depression Field

Depression is one of the most prevalent psychiatric diseases and significantly negatively impacts patients’ productivity, quality of life, and cognitive functions. [1] According to a recent survey by the World Health Organization, it has been estimated that depression currently ranks the third in terms of disability-adjusted life years and will be the leading cause of disease and injury burden by 2030, surpassing cancer as well as cardiovascular and respiratory disease. [2] This troubling situation, which has raised concern among the international field of psychiatry, challenges researchers worldwide to investigate the prevention and control of depression and identify the necessary funding to support this research. Given the expansive and interdisciplinary nature of biomedicine, no single researcher or institution can stay current on all developments within depression research. Thus, scientific collaboration becomes indispensable to make progress in biomedicine as it strengthens communication, encourages competency sharing, and facilitates the discovery of new scientific knowledge. Despite this fact, there have been few scientific publications to date regarding collaborative research within the field studying depression. Therefore, this article aimed to measure scientific collaborations at three levels – authors, institutions, and countries. By examining scientific collaboration, this article also identified hot topics in depression research, which represent the areas of focus that numerous researchers have decided to have substantial potential to prevent and cure this pervasive disease.
Cathepsin B & L Are Not Required for Ebola Virus Replication

Ebola virus (EBOV), family Filoviridae, emerged in 1976 on the African continent. Since then it caused several outbreaks of viral hemorrhagic fever in humans with case fatality rates up to 90% and remains a serious Public Health concern and biothreat pathogen. The most pathogenic and best-studied species is Zaire ebolavirus (ZEBOV). EBOV encodes one viral surface glycoprotein (GP), which is essential for replication, a determinant of pathogenicity and an important immunogen. GP mediates viral entry through interaction with cellular surface molecules, which results in the uptake of virus particles via macropinocytosis. Later in this pathway endosomal acidification activates the cysteine proteases Cathepsin B and L (CatB, CatL), which have been shown to cleave ZEBOV-GP leading to subsequent exposure of the putative receptor-binding and fusion domain and productive infection. We studied the effect of CatB and CatL on in vitro and in vivo replication of EBOV. Similar to previous findings, our results show an effect of CatB, but not CatL, on ZEBOV entry into cultured cells. Interestingly, cell entry by other EBOV species (Bundibugyo, Côte d'Ivoire, Reston and Sudan ebolavirus) was independent of CatB or CatL as was EBOV replication in general. To investigate whether CatB and CatL have a role in vivo during infection, we utilized the mouse model for ZEBOV. Wild-type (control), catB−/− and catL−/− mice were equally susceptible to lethal challenge with mouse-adapted ZEBOV with no difference in virus replication and time to death. In conclusion, our results show that CatB and CatL activity is not required for EBOV replication. Furthermore, EBOV glycoprotein cleavage seems to be mediated by an array of proteases making targeted therapeutic approaches difficult.
Application of Neural Networks to the Classification of Pancreatic Intraductal Proliferative Lesions

The aim of the study was to test applycability of neural networks to classification of pancreatic intraductal proliferative lesions basing on nuclear features, especially chromatin texture. Material for the study was obtained from patients operated on for pancreatic cancer, chronic pancreatitis and other tumours requiring pancreatic resection. Intraductal lesions were classified as low and high grade as previously described. The image analysis system consisted of a microscope, CCD camera combined with a PC and AnalySIS v. 2.11 software. The following texture characteristics were measured: variance of grey levels, features extracted from the grey levels correlation matrix and mean values, variance and standard deviation of the energy obtained from Laws matrices. Furthermore we used moments derived invariants and basic geometric data such as surface area, the minimum and maximum diameter and shape factor. The sets of data were randomly divided into training and testing groups. The training of the network using the back‐propagation algorithm, and the final classification of data was carried out with a neural network simulator SNNS v. 4.1. We studied the efficacy of networks containing from one to three hidden layers. Using the best network, containing three hidden layers, the rate of correct classification of nuclei was 73%, and the rate of misdiagnosis was 3%; in 24% the network response was ambiguous. The present findings may serve as a starting point in search for methods facilitating early diagnosis of ductal pancreatic carcinoma.
Using SVD on Clusters to Improve Precision of Interdocument Similarity Measure

Recently, LSI (Latent Semantic Indexing) based on SVD (Singular Value Decomposition) is proposed to overcome the problems of polysemy and homonym in traditional lexical matching. However, it is usually criticized as with low discriminative power for representing documents although it has been validated as with good representative quality. In this paper, SVD on clusters is proposed to improve the discriminative power of LSI. The contribution of this paper is three manifolds. Firstly, we make a survey of existing linear algebra methods for LSI, including both SVD based methods and non-SVD based methods. Secondly, we propose SVD on clusters for LSI and theoretically explain that dimension expansion of document vectors and dimension projection using SVD are the two manipulations involved in SVD on clusters. Moreover, we develop updating processes to fold in new documents and terms in a decomposed matrix by SVD on clusters. Thirdly, two corpora, a Chinese corpus and an English corpus, are used to evaluate the performances of the proposed methods. Experiments demonstrate that, to some extent, SVD on clusters can improve the precision of interdocument similarity measure in comparison with other SVD based LSI methods.
Evolving decision trees using oracle guides

Some data mining problems require predictive models to be not only accurate but also comprehensible. Comprehensibility enables human inspection and understanding of the model, making it possible to trace why individual predictions are made. Since most high-accuracy techniques produce opaque models, accuracy is, in practice, regularly sacrificed for comprehensibility. One frequently studied technique, often able to reduce this accuracy vs. comprehensibility tradeoff, is rule extraction, i.e., the activity where another, transparent, model is generated from the opaque. In this paper, it is argued that techniques producing transparent models, either directly from the dataset, or from an opaque model, could benefit from using an oracle guide. In the experiments, genetic programming is used to evolve decision trees, and a neural network ensemble is used as the oracle guide. More specifically, the datasets used by the genetic programming when evolving the decision trees, consist of several different combinations of the original training data and “oracle data”, i.e., training or test data instances, together with corresponding predictions from the oracle. In total, seven different ways of combining regular training data with oracle data were evaluated, and the results, obtained on 26 UCI datasets, clearly show that the use of an oracle guide improved the performance. As a matter of fact, trees evolved using training data only had the worst test set accuracy of all setups evaluated. Furthermore, statistical tests show that two setups, both using the oracle guide, produced significantly more accurate trees, compared to the setup using training data only.
Large-Scale Gravitational Instability and Star Formation in the Large Magellanic Cloud

Large-scale star formation in disk galaxies is hypothesized to be driven by global gravitational instability. The observed gas surface density is commonly used to compute the strength of gravitational instability, but according to this criterion, star formation often appears to occur in gravitationally stable regions. One possible reason is that the stellar contribution to the instability has been neglected. We have examined the gravitational instability of the Large Magellanic Cloud considering the gas alone, and considering the combination of collisional gas and collisionless stars. We compare the gravitationally unstable regions with the ongoing star formation revealed by Spitzer observations of young stellar objects. Although only 62% of the massive young stellar object candidates are in regions where the gas alone is unstable, some 85% lie in regions unstable due to the combination of gas and stars. The combined stability analysis better describes where star formation occurs. In agreement with other observations and numerical models, a small fraction of the star formation occurs in regions with gravitational stability parameter Q > 1. We further measure the dependence of the star formation timescale on the strength of gravitational instability, and quantitatively compare it to the exponential dependence expected from numerical simulations.
Per subchannel equalization for MIMO FBMC/OQAM systems

In multiple-input multiple-output (MIMO) filter bank multicarrier modulation (FBMC) systems, the frequency selective transmission channel causes intersymbol interference (ISI), and intercarrier interference (ICI), and using multiple antennas creates interantenna interference (IAI). To mitigate these distortions we derive an MMSE equalizer which takes into account all of them. Assuming spatial multiplexing is used, we propose a successive interference cancellation (SIC) and Ordered SIC (OSIC) techniques to extract the transmitted streams. In order to further improve the performance we also introduce a two stage OSIC (TSOSIC) technique. Simulation results confirm the effectiveness of the proposed techniques over the classical one tap equalizer.
AVSER — Active variable stiffness exoskeleton robot system: Design and application for safe active-passive elbow rehabilitation

The paper introduces an active variable stiffness exoskeleton robotic system (AVSER) with the active variable stiffness elastic actuator (AVSEA), which improves the safety for human-robot interaction and produces unique adjustable stiffness capacity to meet the demand for safe active-passive elbow rehabilitation. The AVSEA consists of two DC-motors. One is used to control the position of the joint, and the other is used to adjust the stiffness of the system. The stiffness is generated by a leaf spring. By shortening the effective length of the leaf spring, the AVSEA is able to reduce the stiffness automatically, which makes the AVSER from active (assistive) motion to passive (resistance) rehabilitation during the process of therapy. In the paper, the mechanical design, modeling, and control algorithms are described in details. The capacity of the proposed AVSER with electromyogram (EMG) signal feedback is verified by rehabilitation exercise experiments for the subject to demonstrate the efficacy of the developed system.
Workload-aware power optimization strategy for asymmetric multiprocessors

Asymmetric multi-core architectures, such as the ARM big.LITTLE, are emerging as successful solutions for the embedded and mobile markets due to their capabilities to trade-off performance and power consumption. However, both the Heterogeneous Multi-Processing (HMP) scheduler integrated in the commercial products and the previous research approaches are not able to fully exploit such potentiality. We propose a new runtime resource management policy for the big. LITTLE architecture integrated in Linux aimed at optimizing the power consumption while fulfilling performance requirements specified for the running applications. Experimental results show an improvement of the 11% on the performance and at the same time 8% in peak power consumption w.r. t. the current Linux HMP solution.
Discrete functional analysis tools for Discontinuous Galerkin methods with application to the incompressible Navier-Stokes equations

Two discrete functional analysis tools are established for spaces of piecewise polynomial functions on general meshes: (i) a discrete counterpart of the continuous Sobolev embeddings, in both Hilbertian and non-Hilbertian settings; (ii) a compactness result for bounded sequences in a suitable Discontinuous Galerkin norm, together with a weak convergence property for some discrete gradients. The proofs rely on techniques inspired by the Finite Volume literature, which differ from those commonly used in Finite Element analysis. The discrete functional analysis tools are used to prove the convergence of Discontinuous Galerkin approximations of the steady incompressible Navier--Stokes equations. Two discrete convective trilinear forms are proposed, a non-conservative one relying on Temam's device to control the kinetic energy balance and a conservative one based on a nonstandard modification of the pressure.
Evaluating reliability of WSN with sleep/wake-up interfering nodes

A wireless sensor network (WSN) (singular and plural of acronyms are spelled the same) is a distributed system composed of autonomous sensor nodes wireless connected and randomly scattered into a geographical area to cooperatively monitor physical or environmental conditions. Adequate techniques and strategies are required to manage a WSN so that it works properly, observing specific quantities and metrics to evaluate the WSN operational conditions. Among them, one of the most important is the reliability. Considering a WSN as a system composed of sensor nodes the system reliability approach can be applied, thus expressing the WSN reliability in terms of its nodes' reliability. More specifically, since often standby power management policies are applied at node level and interferences among nodes may arise, a WSN can be considered as a dynamic system. In this article we therefore consider the WSN reliability evaluation problem from the dynamic system reliability perspective. Static–structural interactions are specified by the WSN topology. Sleep/wake-up standby policies and interferences due to wireless communications can be instead considered as dynamic aspects. Thus, in order to represent and to evaluate the WSN reliability, we use dynamic reliability block diagrams and Petri nets. The proposed technique allows to overcome the limits of Markov models when considering non-linear discharge processes, since they cannot adequately represent the aging processes. In order to demonstrate the effectiveness of the technique, we investigate some specific WSN network topologies, providing guidelines for their representation and evaluation.
Visual Issues In The Use Of A Head-Mounted Monocular Display

A miniature display device, recently available commercially, is aimed at providing a portable, inexpensive means of visual information communication. The display is head-mounted in front of one eye with the other eye's view of the environment unobstructed. Various visual phenomena are associated with this design. The consequences of these phenomena for visual safety, comfort, and efficiency of the user were evaluated: (1) The monocular, partially occluded mode of operation interrupts binocular vision. Presenting disparate images to each eye results in binocular rivalry. The two images may appear superimposed, with one image perceived with greater clarity or com letely dominant. Most observers can, use the display comfortably in this rivalrous mode. In many cases, it is easier to use the display in a peripheral position, slightly above or below the line of sight, thus permitting normal binocular vision of the environment. (2) As a head-mounted device, the displayed image is perceived to move during head movements due to the response of the vestibulo-ocular reflex. These movements affect the visibility of small letters during active head rotations and sharp accelerations. Adaptation is likely to reduce this perceived image motion. No evidence for postural instability or motion sickness was noted as a result of these conflicts between vis-ual and vestibular inputs. (3) Small displacements of the image are noted even without head motion, resulting from eye movements and the virtual lack of display persiste ce. These movements are noticed sponta e ously by few observers and are unlikely to interfere with the display use in most tasks.