Miracle-dz/UniFAR-FTUs huggingface dataset:
Contextual relevance feedback in web information retrieval

In this paper, we present an alternative approach to the problem of contextual relevance feedback in web-based information retrieval. Our approach utilises a rich contextual model that exploits a user's implicit and explicit data. Each user's implicit data are gathered from their Internet search histories on their local machine. The user's explicit data are captured from a lexical database, a shared contextual knowledge base and domain-specific concepts using data mining techniques and a relevance feedback approach. This data is later used by our approach to modify queries to more accurately reflect the user's interests as well as to continually build the user's contextual profile and a shared contextual knowledge base. Finally, the approach retrieves personalised or contextual search results from the search engine using the modified/expanded query. Preliminary experiments indicate that our approach has the potential to not only aid in the contextual relevance feedback but also contribute towards the long term goal of intelligent relevance feedback in web-based information retrieval.
Environmental Dimensionality Controls the Interaction of Phagocytes with the Pathogenic Fungi Aspergillus fumigatus and Candida albicans

The fungal pathogens Aspergillus fumigatus and Candida albicans are major health threats for immune-compromised patients. Normally, macrophages and neutrophil granulocytes phagocytose inhaled Aspergillus conidia in the two-dimensional (2-D) environment of the alveolar lumen or Candida growing in tissue microabscesses, which are composed of a three-dimensional (3-D) extracellular matrix. However, neither the cellular dynamics, the per-cell efficiency, the outcome of this interaction, nor the environmental impact on this process are known. Live imaging shows that the interaction of phagocytes with Aspergillus or Candida in 2-D liquid cultures or 3-D collagen environments is a dynamic process that includes phagocytosis, dragging, or the mere touching of fungal elements. Neutrophils and alveolar macrophages efficiently phagocytosed or dragged Aspergillus conidia in 2-D, while in 3-D their function was severely impaired. The reverse was found for phagocytosis of Candida. The phagocytosis rate was very low in 2-D, while in 3-D most neutrophils internalized multiple yeasts. In competitive assays, neutrophils primarily incorporated Aspergillus conidia in 2-D and Candida yeasts in 3-D despite frequent touching of the other pathogen. Thus, phagocytes show activity best in the environment where a pathogen is naturally encountered. This could explain why “delocalized” Aspergillus infections such as hematogeneous spread are almost uncontrollable diseases, even in immunocompetent individuals.
Threshold Saturation via Spatial Coupling: Why Convolutional LDPC Ensembles Perform So Well over the BEC

Convolutional low-density parity-check (LDPC) ensembles, introduced by Felström and Zigangirov, have excellent thresholds and these thresholds are rapidly increasing functions of the average degree. Several variations on the basic theme have been proposed to date, all of which share the good performance characteristics of convolutional LDPC ensembles. We describe the fundamental mechanism that explains why “convolutional-like” or “spatially coupled” codes perform so well. In essence, the spatial coupling of individual codes increases the belief-propagation (BP) threshold of the new ensemble to its maximum possible value, namely the maximum a posteriori (MAP) threshold of the underlying ensemble. For this reason, we call this phenomenon “threshold saturation. ” This gives an entirely new way of approaching capacity. One significant advantage of this construction is that one can create capacity-approaching ensembles with an error correcting radius that is increasing in the blocklength. Although we prove the “threshold saturation” only for a specific ensemble and for the binary erasure channel (BEC), empirically the phenomenon occurs for a wide class of ensembles and channels. More generally, we conjecture that for a large range of graphical systems a similar saturation of the “dynamical” threshold occurs once individual components are coupled sufficiently strongly. This might give rise to improved algorithms and new techniques for analysis.
Age Related Changes in Motion Perception and Perceptual Learning

The following studies examined age-related decrements in motion perception processing. Using a perceptual learning paradigm, perceptual inefficiency was compared between younger and older observers. Perceptual inefficiency was modeled using the Perceptual Template Model (Lu & Dosher, 1998; Lu & Dosher, 1999). Baseline differences and changes over training were assessed for additive internal noise, tolerance to external noise, and internal multiplicative noise in both older and younger observers. Experiments 1 and 2 examined age-related changes in baseline perceptual inefficiency and learning after training. Experiments 1 and 2 trained participants by manipulating contrast in noise embedded sine-wave gratings and Random Dot Cinematograms (RDCs). The results indicate that older observers have higher additive internal noise and lower tolerance to external noise compared to younger observers. Perceptual learning in older observers was found to be at a similar rate to that of younger observers suggesting that cortical plasticity for motion processing is well preserved in advancing age. Experiment 3 examined transfer of learning between sine-wave gratings and RDCs for both older and younger observers. The results indicated that transfer of learning occurred for both age groups. This suggests that older individuals maintain a sufficient degree of plasticity to allow generalization between sine-wave gratings and RDCs. In addition, training with RDCs was found to produce greater perceptual learning than training with sine-wave gratings. Experiment 4 examined if center-surround antagonism could be increased through perceptual training which would provide evidence that neural inhibition is related to internal noise. Center-surround antagonism enables edge detection through an antagonistic interaction between the center and the surround regions of a receptive field. Younger and older participants were trained on duration thresholds with Gabor patches that varied in size and contrast. The results of Experiment 4 indicate that some perceptual learning occurred in both younger and older participants but were inconclusive with regards to inhibition in visual cortex. The present studies provided important findings regarding changes in perceptual efficiency for motion perception in older adults. Cortical plasticity was found to be well preserved in older adults indicating that perceptual training may be an effective way to improve motion perception.
UvA-DARE ( Digital Academic Repository ) Phase-Amplitude Coupling in Rat Orbitofrontal Cortex Discriminates between Correct and Incorrect Decisions during Associative Learning

Disclaimer/Complaints regulations If you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: http://uba.uva. nl/en/contact, or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible.
Feature-space analysis of unstructured meshes

Unstructured meshes are often used in simulations and imaging applications. They provide advanced flexibility in modeling abilities but are more difficult to manipulate and analyze than regular data. This work provides a novel approach for the analysis of unstructured meshes using feature-space clustering and feature-detection. Analyzing and revealing underlying structures in data involve operators on both spatial and functional domains. Slicing concentrates more on the spatial domain, while iso-surfacing or volume rendering concentrate more on the functional domain. Nevertheless, many times it is the combination of the two domains which provides real insight on the structure of the data. In this work, a combined feature-space is defined on top of unstructured meshes in order to search for structure in the data. A point in feature-space includes the spatial coordinates of the point in the mesh domain and all chosen attributes defined on the mesh. A distance measures between points in feature-space is defined enabling the utilization of clustering using the mean shift procedure (previously used for images) on unstructured meshes. Feature space analysis is shown to be useful for feature-extraction, for data exploration and partitioning.
Considering context, content, management, and engagement in design activities with children

In this paper we describe three different design activities carried out for the design of a music device for children. The studies involved researchers from different disciplines as well as children from different schools. We reflected on what happened during the design activities and we looked at the outputs produced by the children in order to understand the feasibility of the activities from two perspectives: whether they contributed to the design of the product and whether they suitably involved children in the process. In relation to the design of the product, information gathered during the activities was associated either to the context or to the content of the design. In relation to the design method, the study enabled us to identify aspects of both children's' engagement and researchers' management that affected the success of the activities. We used these factors to create what we consider a useful framework for meaningful design activities.
Effectively integrating e-assessments in the design of virtual learning scenarios

The design of e-learning oriented assessment poses some technical issues when it has to be designed and integrated with the elements that describe a learning process to be deployed in a virtual learning environment. Current web service-based integration solutions are not completely satisfactory from the authoring perspective, when it comes to provide an integrated approach that seamlessly assembles the design, deployment and enactment stages of a learning process. This work summarizes the integrated authoring approach of LPCEL and explains how its editing environment can be used to design a service-based assessment along with the activities, resources, and user roles in a project-oriented learning course case study.
Sustained complete response of hepatocellular carcinoma with portal vein tumor thrombus following discontinuation of sorafenib: A case report

Hepatocellular carcinoma (HCC) is the third most common cause of cancer-associated mortality worldwide. No effective treatment has been established for unresectable advanced HCC, and the prognosis is poor. Sorafenib is an oral multi-targeted tyrosine kinase inhibitor for unresectable advanced HCC that significantly improves progression-free and overall survival. However, in the two large phase III clinical trials (the SHARP and Asia-Pacific trials), no cases of complete response (CR) were reported. The present study reports the case of a 68-year-old male with hepatitis C virus-related cirrhosis and multiple recurrent HCCs, with a tumor thrombus of the third portal vein following resection. The patient received 400 mg once daily (half the standard dose) of sorafenib for two years and achieved a CR. At the most recent follow-up examination at one year after the cessation of treatment, the patient was observed to be in remission without clinical or imaging evidence of disease recurrence.
Remittances , food security and resilience to rainfall variability : the case of rural Mali

In this paper, we rely on the CFSVA survey of 2005 to assess both the impact of rainfall variability on the food security of rural households in Mali, and the role of remittances in their adaptive capacity. There are two stages to our analysis: first, we design a composite food security index which enables us to divide households into three categories. Then, we estimate a partial proportional odds logistic model (Peterson and Harrell, 1990) in order to find the main determinants leading to a switch from one category to the other. We further show that interannual and seasonal rainfall variability have a negative impact on food security, especially in southern Mali. Agro-ecological conditions in this region are usually more favourable, but it was weakened by the cotton crisis of the 2000s. As for remittances, their impact is positive, though it needs to be qualified: they enable households to solve temporary food security situations, but they have no effect on structural food security issues.
Towards a Dynamic Linked Data Observatory

We describe work-in-progress on the design and methodology of the Dynamic Linked Data Observatory: a framework to monitor Linked Data over an extended period of time. The core goal of our work is to collect frequent, continuous snapshots of a subset of the Web of Data that is interesting for further study and experimentation, with an aim to capture raw data about the dynamics of Linked Data. The resulting corpora will be made openly and continuously available to the Linked Data research community. Herein, we (1) motivate the importance of such a corpus; (2) outline some of the use-cases and requirements for the resulting snapshots; (3) discuss dierent \views" of the Web of Data that aect how we dene a sample to monitor; (4) detail how we select the scope of the monitoring experiment through sampling, (5) discuss the nal design of the monitoring framework that will gather regular snapshots of (subsets of) the Web of Data over the coming months and years.
Dense estimation of surface reflectance properties of objects with interreflections

In augmented virtuality which virtualizes real objects to construct a mixed reality environment, it is important to estimate object surface reflectance properties to render objects under arbitrary illumination conditions. The authors developed a method to estimate reflectance properties of object surfaces densely However, it was difficult to estimate surface reflectance properties faithfully for objects with interreflections. This paper describes a new method of densely estimating nonuniform surface reflectance properties of real objects constructed of convex and concave surfaces with interreflections. We use registered range and surface color texture images obtained by a laser rangefinder The proposed method first determines positions of light to take color images for discriminating diffuse and specular reflection components of surface reflection. Then, surface reflectance parameters are estimated based on radiosity. Experiments show the usefulness of the proposed method.
A flexible recognition engine for complex on-line handwritten character recognition

A major feature of new mobiles terminals using pen-basedinterfaces, such as personal assistants or e-book, istheir personal character, implying that a good interfaceshould be easily customizable in order to meet varioususers' needs. We proposed recently a new recognitionengine with strong adaptation abilities that allowslearning a user's writing style or new symbols easily. Itdealt with characters with simple shape only; we describehere an extension of this recognition system that deals withmore complex graphical symbols. We propose an adaptivelearning scheme that can learn more and more as thewriter gives new samples.
Combined Analyses of 20 Common Obesity Susceptibility Variants

OBJECTIVE Genome-wide association studies and linkage studies have identified 20 validated genetic variants associated with obesity and/or related phenotypes. The variants are common, and they individually exhibit small-to-modest effect sizes. RESEARCH DESIGN AND METHODS In this study we investigate the combined effect of these variants and their ability to discriminate between normal weight and overweight/obese individuals. We applied receiver operating characteristics (ROC) curves, and estimated the area under the ROC curve (AUC) as a measure of the discriminatory ability. The analyses were performed cross-sectionally in the population-based Inter99 cohort where 1,725 normal weight, 1,519 overweight, and 681 obese individuals were successfully genotyped for all 20 variants. RESULTS When combining all variants, the 10% of the study participants who carried more than 22 risk-alleles showed a significant increase in probability of being both overweight with an odds ratio of 2.00 (1.47–2.72), P = 4.0 × 10−5, and obese with an OR of 2.62 (1.76–3.92), P = 6.4 × 10−7, compared with the 10% of the study participants who carried less than 14 risk-alleles. Discrimination ability for overweight and obesity, using the 20 single nucleotide polymorphisms (SNPs), was determined to AUCs of 0.53 and 0.58, respectively. When combining SNP data with conventional nongenetic risk factors of obesity, the discrimination ability increased to 0.64 for overweight and 0.69 for obesity. The latter is significantly higher (P < 0.001) than for the nongenetic factors alone (AUC = 0.67). CONCLUSIONS The discriminative value of the 20 validated common obesity variants is at present time sparse and too weak for clinical utility, however, they add to increase the discrimination ability of conventional nongenetic risk factors.
Deep Contextual Networks for Neuronal Structure Segmentation

The goal of connectomics is to manifest the interconnections of neural system with the Electron Microscopy (EM) images. However, the formidable size of EM image data renders human annotation impractical, as it may take decades to fulfill the whole job. An alternative way to reconstruct the connectome can be attained with the computerized scheme that can automatically segment the neuronal structures. The segmentation of EM images is very challenging as the depicted structures can be very diverse.To address this difficult problem, a deep contextual network is proposed here by leveraging multi-level contextual information from the deep hierarchical structure to achieve better segmentation performance.To further improve the robustness against the vanishing gradients and strengthen the capability of the back-propagation of gradient flow, auxiliary classifiers are incorporated in the architecture of our deep neural network. It will be shown that our method can effectively parse the semantic meaning from the images with the underlying neural network and accurately delineate the structural boundaries with the reference of low-level contextual cues. Experimental results on the benchmark dataset of 2012 ISBI segmentation challenge of neuronal structures suggest that the proposed method can outperform the state-of-the-art methods by a large margin with respect to different evaluation measurements. Our method can potentially facilitate the automatic connectome analysis from EM images with less human intervention effort.
BALL-rapid software prototyping in computational molecular biology

MOTIVATION Rapid software prototyping can significantly reduce development times in the field of computational molecular biology and molecular modeling. Biochemical Algorithms Library (BALL) is an application framework in C++ that has been specifically designed for this purpose. RESULTS BALL provides an extensive set of data structures as well as classes for molecular mechanics, advanced solvation methods, comparison and analysis of protein structures, file import/export, and visualization. BALL has been carefully designed to be robust, easy to use, and open to extensions. Especially its extensibility which results from an object-oriented and generic programming approach distinguishes it from other software packages. BALL is well suited to serve as a public repository for reliable data structures and algorithms. We show in an example that the implementation of complex methods is greatly simplified when using the data structures and functionality provided by BALL.
Marker-Based Estimation of Heritability in Immortal Populations

Heritability is a central parameter in quantitative genetics, from both an evolutionary and a breeding perspective. For plant traits heritability is traditionally estimated by comparing within- and between-genotype variability. This approach estimates broad-sense heritability and does not account for different genetic relatedness. With the availability of high-density markers there is growing interest in marker-based estimates of narrow-sense heritability, using mixed models in which genetic relatedness is estimated from genetic markers. Such estimates have received much attention in human genetics but are rarely reported for plant traits. A major obstacle is that current methodology and software assume a single phenotypic value per genotype, hence requiring genotypic means. An alternative that we propose here is to use mixed models at the individual plant or plot level. Using statistical arguments, simulations, and real data we investigate the feasibility of both approaches and how these affect genomic prediction with the best linear unbiased predictor and genome-wide association studies. Heritability estimates obtained from genotypic means had very large standard errors and were sometimes biologically unrealistic. Mixed models at the individual plant or plot level produced more realistic estimates, and for simulated traits standard errors were up to 13 times smaller. Genomic prediction was also improved by using these mixed models, with up to a 49% increase in accuracy. For genome-wide association studies on simulated traits, the use of individual plant data gave almost no increase in power. The new methodology is applicable to any complex trait where multiple replicates of individual genotypes can be scored. This includes important agronomic crops, as well as bacteria and fungi.