Miracle-dz/UniFAR-FTUs huggingface dataset:
Learnt Topology Gating Artificial Neural Networks

This work combines several established regression and meta-learning techniques to give a holistic regression model and presents the proposed learnt topology gating artificial neural networks (LTGANN) model in the context of a general architecture previously published by the authors. The applied regression techniques are artificial neural networks, which are on one hand used as local experts for the regression modelling and on the other hand as gating networks. The role of the gating networks is to estimate the prediction error of the local experts dependent on the input data samples. This is achieved by relating the input data space to the performance of the local experts, and thus building a performance map, for each of the local experts. The estimation of the prediction error is then used for the weighting of the local experts predictions. Another advantage of our approach is that the particular neural networks are unconstrained in terms of the number of hidden units. It is only necessary to define the range within which the number of hidden units has to be generated. The model links the topology to the performance, which has been achieved by the network with the given complexity, using a probabilistic approach. As the model was developed in the context of process industry data, it is evaluated using two industrial data sets. The evaluation has shown a clear advantage when using a model combination and meta-learning approach as well as demonstrating the higher performance of LTGANN when compared to a standard combination method.
Transient-fault recovery using simultaneous multithreading

We propose a scheme for transient-fault recovery called Simultaneously, and Redundantly Threaded processors with Recovery (SRTR) that enhances a previously proposed scheme for transient-fault detection, called Simultaneously and Redundantly Threaded (SRT) processors. SRT replicates an application into two communicating threads, one executing ahead of the other. The trailing thread repeats the computation performed by the leading thread, and the values produced by the two threads are compared. In SRT a leading instruction may commit before the check for faults occurs, relying on the trailing thread to trigger detection. In contrast, SRTR must not allow any leading instruction to commit before checking occurs, since a faulty instruction cannot be undone once the instruction commits. To avoid stalling leading instructions at commit while waiting for their trailing counterparts, SRTR exploits the time between the completion and commit of leading instructions. SRTR compares the leading and trailing values as soon as the trailing instruction completes, typically before the leading instruction reaches the commit point. To avoid increasing the bandwidth demand on the register file for checking register values, SRTR uses the register value queue (RVQ) to hold register values for checking. To reduce the bandwidth pressure on the RVQ itself SRTR employs dependence-based checking elision (DBCE). By reasoning that faults propagate through dependent instructions, DBCE exploits register (true) dependence chains so that only the last instruction in a chain uses the RVQ, and has the leading and trailing values checked. SRTR performs within 1% and 7% of SRT for SPEC95 integer and floating-point programs, respectively. While SRTR without DBCE incurs about 18% performance loss when the number of RVQ ports is reduced from four (which is performance-equivalent to an unlimited number) to two ports, with DBCE, a two-ported RVQ performs within 2% of a four-ported RVQ.
Structural Insights into Clostridium perfringens Delta Toxin Pore Formation

Clostridium perfringens Delta toxin is one of the three hemolysin-like proteins produced by C. perfringens type C and possibly type B strains. One of the others, NetB, has been shown to be the major cause of Avian Nectrotic Enteritis, which following the reduction in use of antibiotics as growth promoters, has become an emerging disease of industrial poultry. Delta toxin itself is cytotoxic to the wide range of human and animal macrophages and platelets that present GM2 ganglioside on their membranes. It has sequence similarity with Staphylococcus aureus β-pore forming toxins and is expected to heptamerize and form pores in the lipid bilayer of host cell membranes. Nevertheless, its exact mode of action remains undetermined. Here we report the 2.4 Å crystal structure of monomeric Delta toxin. The superposition of this structure with the structure of the phospholipid-bound F component of S. aureus leucocidin (LukF) revealed that the glycerol molecules bound to Delta toxin and the phospholipids in LukF are accommodated in the same hydrophobic clefts, corresponding to where the toxin is expected to latch onto the membrane, though the binding sites show significant differences. From structure-based sequence alignment with the known structure of staphylococcal α-hemolysin, a model of the Delta toxin pore form has been built. Using electron microscopy, we have validated our model and characterized the Delta toxin pore on liposomes. These results highlight both similarities and differences in the mechanism of Delta toxin (and by extension NetB) cytotoxicity from that of the staphylococcal pore-forming toxins.
Effects of High-Order Co-occurrences on Word Semantic Similarities

Nous presentons un modele informatique de la construction de la signification des mots par l'exposition aux textes, dans le but de simuler, paragraphe apres paragraphe, les effets des valeurs de cooccurrence sur les similarites semantiques intermots. La similarite est ici consideree comme une association semantique. Les resultats montrent que la similarite entre deux mots M1 et M2 augmente fortement avec leur cooccurrence, diminue avec l'occurrence de M1 sans M2 ou de M2 sans M1, et augmente legerement avec des cooccurrences d'ordre superieur. Operationnaliser la similarite par la frequence de cooccurrence introduit donc probablement un biais : tout d'abord, il existe des cas pour lesquels il existe une similarite sans cooccurrence, et d'autre part, la frequence de cooccurrence surestime la similarite.
A Prospective Evaluation of the Association between a Single Nucleotide Polymorphism rs3775291 in Toll-Like Receptor 3 and Breast Cancer Relapse

Background Toll-like receptors (TLRs) regulate the balance between the innate and adaptive immune responses. Missense single nucleotide polymorphisms (SNPs) in TLRs might be functional and thus influence the risks of chronic infection and cancer development. Here, we investigated the association of two missense SNPs, rs3775291 (c.1234G>A) in the TLR3 gene and rs4833095 (c.743T>C) in the TLR1 gene, with relapse-free survival (RFS) in a cohort of prospectively observed breast cancer patients. Methods In this prospective observational study, rs3775291 in TLR3 and rs4833095 in TLR1 were genotyped in 715 patients with primary breast cancer in a Chinese population. Results Univariate analysis revealed that the patients with the AA genotype of rs3775291 had a shorter RFS compared with those carrying the G allele in the recessive model (P<0.01), but this finding was not observed with the dominant model (P = 0.31). The results remained significant after adjusting for the clinical parameters in the recessive model (HR = 3.53, 95% confidence interval [CI]: 1.98–6.31, P<0.01). Further survival analysis indicated that this SNP was significant in the luminal-B, triple-negative breast cancer (TNBC), and human epidermal growth factor receptor 2-positive (HER2+) patients using the recessive model but that it was not significant in the luminal-A patients. The SNP rs4833095 showed a non-significant tendency toward an increased RFS rate in the patients with the TT genotype. Conclusion Our results suggest that the SNP rs3775291 in TLR3 may influence patient outcome. Further studies with larger sample sizes should be conducted to validate our findings.
The Vulnerability to Suicidal Behavior is Associated with Reduced Connectivity Strength

Suicidal behavior constitutes a major public health problem. Based on the stress–diathesis model, biological correlates of a diathesis might help to predict risk after stressor-exposure. Structural changes in cortical and subcortical areas and their connections have increasingly been linked with the diathesis. The current study identified structural network changes associated with a diathesis using a whole-brain approach by examining the structural connectivity between regions in euthymic suicide attempters (SA). In addition, the association between connectivity measures, clinical and genetic characteristics was investigated. We hypothesized that SA showed lower connectivity strength, associated with an increased severity of general clinical characteristics and an elevated expression of short alleles in serotonin polymorphisms. Thirteen euthymic SA were compared with fifteen euthymic non-attempters and seventeen healthy controls (HC). Clinical characteristics and three serotonin-related genetic polymorphisms were assessed. Diffusion MRI together with anatomical scans were administered. Preprocessing was performed using Explore DTI. Whole brain tractography of the diffusion-weighted images was followed by a number of streamlines-weighted network analysis using NBS. The network analysis revealed decreased connectivity strength in SA in the connections between the left olfactory cortex and left anterior cingulate gyrus. Furthermore, SA had increased suicidal ideation, hopelessness and self-reported depression, but did not show any differences for the genetic polymorphisms. Finally, lower connectivity strength between the right calcarine fissure and the left middle occipital gyrus was associated with increased trait anxiety severity (rs = −0.78, p < 0.01) and hopelessness (rs = −0.76, p < 0.01). SA showed differences in white matter network connectivity strength associated with clinical characteristics. Together, these variables could play an important role in predicting suicidal behavior.
An Evaluation of Learning-to-Rank Methods for Lurking Behavior Analysis

Computational approaches to lurking behavior analysis in online social networks (OSNs) have been developed in the last few years. However, the complexity of the problem hints at the opportunity of learning from past lurking experiences as well as of using a variety of behavioral features, including any available information on the activity and interaction of lurkers and active users in an OSN. In this paper, we leverage this opportunity in a principled way, by proposing a machine-learning framework which, once trained on lurking/non-lurking examples from multiple OSNs, allows us to predict the ranking of unseen lurking behaviors, ultimately enabling the prioritization of user engagement tasks.
Proteomic Analysis of Human Neutrophil Granules*S

Stimulated exocytosis of intracellular granules plays a critical role in conversion of inactive, circulating neutrophils to fully activated cells capable of chemotaxis, phagocytosis, and bacterial killing. The functional changes induced by exocytosis of each of the granule subsets, gelatinase (tertiary) granules, specific (secondary) granules, and azurophil (primary) granules, are poorly defined. To improve the understanding of the role of exocytosis of these granule subsets, a proteomic analysis of the azurophil, specific, and gelatinase granules from human neutrophils was performed. Two different methods for granule protein identification were applied. First, two-dimensional (2D) gel electrophoresis followed by MALDI-TOF MS analysis of peptides obtained by in-gel trypsin digestion of proteins was performed. Second, peptides from tryptic digests of granule membrane proteins were separated by two-dimensional microcapillary chromatography using strong cation exchange and reverse phase microcapillary high pressure liquid chromatography and analyzed with electrospray ionization tandem mass spectrometry (2D HLPC ESI-MS/MS). Our analysis identified 286 proteins on the three granule subsets, 87 of which were identified by MALDI MS and 247 were identified by 2D HPLC ESI-MS/MS. The increased sensitivity of 2D HPLC ESI-MS/MS, however, resulted in identification of over 500 proteins from subcellular organelles contaminating isolated granules. Defining the proteome of neutrophil granule subsets provides a basis for understanding the role of exocytosis in neutrophil biology. Additionally, the described methods may be applied to mobilizable compartments of other secretory cells.
Hybrid search for cardinality constrained portfolio optimization

In this paper, we describe how a genetic algorithm approach added to a simulated annealing (SA) process offers a better alternative to find the mean variance frontier in the portfolio selection process. The nonlinear mixed integer quadratic programming model is considerably more difficult to solve than the original model; but some computational experiments have shown that hybrid heuristics offer a good alternative for these types of problems.
Enterprise Architecture Service Provision: Pathways to Value

Enterprise architecture (EA) provides a blueprint of an organisation’s current and desirable future IT infrastructure, systems, data, processes and capabilities, together with a roadmap for achieving the desirable future state. Despite significant interest and investment in EA, organisations still have difficulty articulating the business benefits of EA. In this research in progress paper we conceptualise EA as a service provision function within organisations, rather than an artefact. Using the ResourceBased Theory (RBT), we propose a research model that explains how EA service provision capabilities enhance three types of organisational capability: planning capability, project delivery capability and operating platform capability, to create value. We define the model based on an extensive analysis of the literature and on nineteen interviews with EA experts. The model forms the basis for future empirical work including focus groups, case studies and a survey.
Towards Automated Negotiation Agents that use Chat Interface

To date, a variety of automated negotiation agents have been created. While each of these agents has been shown to be effective in negotiating with people in specific environments, they lack natural language processing (NLP) methods required to enable real-world types of interactions. In this paper we study how existing agents must be modified to address this limitation. After performing an extensive study of agents’ negotiation with human subjects, we found that simply modifying existing agents to include an NLP module is insufficient to create these agents. Instead the agents’ strategies must be modified to address partial agreements and issue-by-issue interactions.
Investigation of skin structures based on infrared wave parameter indirect microscopic imaging

Detailed imaging and analysis of skin structures are becoming increasingly important in modern healthcare and clinic diagnosis. Nanometer resolution imaging techniques such as SEM and AFM can cause harmful damage to the sample and cannot measure the whole skin structure from the very surface through epidermis, dermis to subcutaneous. Conventional optical microscopy has the highest imaging efficiency, flexibility in onsite applications and lowest cost in manufacturing and usage, but its image resolution is too low to be accepted for biomedical analysis. Infrared parameter indirect microscopic imaging (PIMI) uses an infrared laser as the light source due to its high transmission in skins. The polarization of optical wave through the skin sample was modulated while the variation of the optical field was observed at the imaging plane. The intensity variation curve of each pixel was fitted to extract the near field polarization parameters to form indirect images. During the through-skin light modulation and image retrieving process, the curve fitting removes the blurring scattering from neighboring pixels and keeps only the field variations related to local skin structures. By using the infrared PIMI, we can break the diffraction limit, bring the wide field optical image resolution to sub-200nm, in the meantime of taking advantage of high transmission of infrared waves in skin structures.
Evaluation of renal function in intensive care: plasma cystatin C vs. creatinine and derived glomerular filtration rate estimates

Abstract Plasma cystatin C, a new marker of glomerular filtration rate (GFR), was prospectively evaluated in surgical intensive care. Cystatin C was measured (immunonephelometry, Dade-Behring) in 10 patients selected to cover a full range of GFR (phase I) and in 28 unselected consecutive patients followed for 5days post-admission (phase II). Results were compared with 51Cr-EDTA clearance (phase I only), plasma creatinine (kinetic Jaffe, Roche), 24-h or estimated by Cockcroft and Gault (CG) creatinine clearance (CrCl), and modified diet in renal disease (MDRD)-estimated GFR. In phase I, the highest correlation with 51Cr-EDTA clearance (22–198mL/min) was noted for CG CrCl (r 2: 0.883, p<0.001). During phase II follow-up, 24-h CrCl could not be calculated in 25% of daily evaluations. Cystatin C correlated with creatinine (0.856, p<0.0001) and CG CrCl with MDRD GFR (0.926, p<0.0001) in renal failure (10–78mL/min, n=60). There was a +40% (p<0.001) median difference between cystatin C and creatinine (as a % of upper normal cut-off). Sensitivity/specificity to detect a <80mL/min CG CrCl was 88/97% for cystatin C vs. 48/100% for creatinine (laboratory cut-off). In patients with normal and stable renal function (n=14), day-to-day intra-individual variation was 7.4% for cystatin C (vs. 10.6% for creatinine). In intensive care unit surgical adult patients, CG CrCl provides an easy and cost-effective estimate of GFR. Superior to creatinine, plasma cystatin C can be measured in selected patients where CG CrCl is known to be inaccurate.
Plasma-induced adhesion improvement of cotton/polypropylene-laminated fabrics

In this study, improvement in the adhesion strength of plasma-pretreated and laminated cotton/polypropylene (PP) fabrics using acrylic-based adhesive was investigated. Low-temperature, low-pressure oxygen plasma was utilized for surface modification of cotton/PP-laminated fabrics. Water absorption time was measured on plasma-treated cotton fabrics at different plasma power and treatment time conditions. The plasma conditions providing the fastest liquid absorption on the surface were selected and applied during plasma pretreatments. Surface wettability increased with increasing plasma power and plasma exposure time. Plasma-induced surface morphology changes were observed via Scanning Electron Microscope (SEM) images. X-ray Photoelectron Spectroscopy (XPS) analysis showed that oxygen content on the surface increased with plasma treatment, which contributed to the surface polarity and hydrophilicity. Peel bond strength results of untreated and plasma-treated samples were analyzed to determine the effect of plasma pretreatment process. Adhesion strength values of laminated samples, before washing and after 40 wash cycles, were determined by peel bond strength tests. Before washing, adhesion strength of plasma pre-treated, laminated samples was 28–60% higher than that of untreated laminated fabrics. After 40 wash cycles, adhesion strength of plasma pre-treated and laminated samples was about 40–69% higher than the untreated laminated fabrics. Peel bond strength values decreased with the increased number of wash cycles. Plasma pretreatment enhanced both the adhesion strength and washing resistance of laminated samples.
Extracting kinematic background knowledge from interactions using task-sensitive relational learning

To successfully manipulate novel objects, robots must first acquire information about the objects' kinematic structure. We present a method for learning relational kinematic background knowledge from exploratory interactions with the world. As the robot gathers experience, this background knowledge enables the acquisition of kinematic world models with increasing efficiency. Learning such background knowledge, however, proves difficult, especially in complex, feature-rich domains. We present a novel, task-sensitive relational rule learner and demonstrate that it is able to learn accurate kinematic background knowledge in domains where other approaches fail. The resulting background knowledge is more compact and generalizes better than that obtained with existing approaches.
Multiple Representation Databases to Support Visualization on Mobile Devices

This paper presents some applications of mobile map technology utilising an MRDB (multi-resolution/-representation database). An MRDB can be described as a spatial database, which can be used to store the same real world phenomenon at different levels of thematic and geometric detail. Additionally the corresponding objects in the different levels are linked. Case studies of utilising an MRDB in combination with WFS (Web Feature Service) have been implemented and will be presented in this paper. To compensate the limitations of the small display of mobile devices multiscale maps are created. The scale will decrease continuously starting in the centre up to the map border, like a magnifying glass effect. Additionally in the centre of the map the built-up areas are exchanged by buildings. Another use of the MRDB is to emphasise special objects like landmarks or points of interest inside the map. These objects of interest are presented in a higher LoD (Level of Detail) than the other objects to direct the attention of the user to the important facts of the map and to design a clear visualisation. The MRDB can be helpful to support these kinds of presentations and applications as it maintains all the necessary data (levels of detail) and also the necessary links between these levels. This study is part of GiMoDig, a European project that aims at developing methods for spatial data distribution from national primary geodatabases to mobile users.
Introduction Operations on argument structure

Abstract 1. Background This special issue of Linguistics represents the result of a German/ Japanese research project jointly funded by the Deutsche Forschungsgemeinschaft (DFG) and the Japan Society for the Promotion of Sciences (JSPS). The principal investigators on the German side were Ekkehard König (Free University of Berlin) and Peter Siemund (University of Hamburg, formerly Free University of Berlin). On the Japanese side, the research project was conducted by Masayoshi Shibatani (then Kobe University, now Rice University). Affiliated to the research project were Daniel Hole (now University of Munich, then Free University of Berlin), Akio Ogawa (Kansai University, formerly Kobe University) and Mitsunobu Yoshida (Hiroshima University). The funding period started early 2000 and came to an end late in 2002. The codes assigned to the project by the funding agencies were KO 497/8-1 and 446 JAP-113/233/0. We would like to express our gratitude to these agencies for making this cooperation possible.
Numerical study of the force network in a random granular packing during compaction

The granular materials present a large variety of complex behaviours like segregation, dilatancy, avalanche, intermittency. The compaction of granular packings [1, 2] is a particular problem in which both static (contact forces) and dynamical (inelastic collisions) properties of the grains play a role. In order to consider compaction, we propose a numerical study of the complex network of contact forces in dense granular piles when there are slowly driven by an external force. The numerical model [3, 4] is based on the Non Smooth Contact Dynamics [5]. The main difference between this model and the usual Discrete Element Method (DEM) models is the way for the calculation of the contact forces. The model computes a complete force network in the pile at each time step, in opposition to DEMs which condier a force at each contact as a function of the local parameters. The model we consider needs high computing pow but descibes precisely the compaction dynamics at the microscopic (grain) scale.