Miracle-dz/UniFAR-FTUs huggingface dataset:
Trilateral Filtering: A Non-linear Noise Reduction Technique for MRI

INTRODUCTION Filtering is a preliminary process in many medical image processing applications, which is aimed at restoring a noise-corrupted image to its noiseless counterpart. Post-processing tasks, e.g., visualization, segmentation and quantification, may benefit from the reduction of noise. Diffusion equations with scalar-valued and tensor-valued diffusivities [1] and non-linear filters [2] have been used to perform smoothing in medical images. In this paper, we present a novel filtering method, integrating geometric, photometric and local structural similarities, to achieve edge-preserving smoothing in medical images. It is simple to implement and is applicable to multi-dimensional signals. The experimental results have shown that this new technique provides greater noise reduction than other denoising techniques. Our method uses a narrow spatial window (3 pixels in each dimension) and takes only a few iterations (3 iterations in the experiments in this work) in the smoothing process. METHODS Bilateral filtering (BF) [3] is a simple, non-iterative and local approach to edge-preserving smoothing. A filtered image is obtained by replacing the intensity value of each pixel with an average value weighted by the geometric and photometric similarities between neighboring pixels within a spatial window. The novel filtering method proposed in this paper, namely trilateral filtering (TF), works along the same lines as BF; it takes the geometric, photometric and local structural similarities to smooth the images with a narrow spatial window while preserving the edges. Local structural information is used to determine inhomogeneity in the images and influence the smoothing process with orientation information. On one hand, low-pass filtering is performed in the homogeneous regions. On the other hand, smoothing along edges is achieved by considering the three similarities between neighborhoods in the inhomogeneous regions. We found that this new approach provides greater noise reduction than that of BF with a 3-pixel-width spatial window. TF is expressed as follows:
On the Relevance of Using Bayesian Belief Networks in Wireless Sensor Networks Situation Recognition

Achieving situation recognition in ubiquitous sensor networks (USNs) is an important issue that has been poorly addressed by both the research and practitioner communities. This paper describes some steps taken to address this issue by effecting USN middleware intelligence using an emerging situation awareness (ESA) technology. We propose a situation recognition framework where temporal probabilistic reasoning is used to derive and emerge situation awareness in ubiquitous sensor networks. Using data collected from an outdoor environment monitoring in the city of Cape Town, we illustrate the use of the ESA technology in terms of sensor system operating conditions and environmental situation recognition.
The effects of ibudilast, a glial activation inhibitor, on opioid withdrawal symptoms in opioid‐dependent volunteers

Glial activation is hypothesized to contribute directly to opioid withdrawal. This study investigated the dose‐dependent effects of a glial cell modulator, ibudilast, on withdrawal symptoms in opioid‐dependent volunteers after abrupt discontinuation of morphine administration. Non‐treatment‐seeking heroin‐dependent volunteers (n = 31) completed the in‐patient, double‐blind, placebo‐controlled, within‐subject and between‐group study. Volunteers were maintained on morphine (30 mg, QID) for 14 days and placebo (0 mg, QID) for the last 7 days of the 3‐week study. Volunteers also received placebo (0 mg, PO, BID) capsules on days 1–7. On days 8–21, volunteers were randomized to receive ibudilast (20 or 40 mg, PO, BID) or placebo capsules. Subjective and clinical ratings of withdrawal symptoms were completed daily using daily using the Subjective Opioid Withdrawal Scale (SOWS) and Clinical Opioid Withdrawal Scale (COWS). Medication side effects were also monitored. Relative to the first 2 weeks, all groups exhibited withdrawal during the third week as assessed by the SOWS and COWS (P ≤ 0.0001). Although overall SOWS scores did not differ between groups, exploratory analyses pooling the two ibudilast groups demonstrated that they had lower ratings of withdrawal symptoms on SOWS items (‘anxious,’ ‘perspiring,’ ‘restless,’ ‘stomach cramps’) during detoxification relative to the placebo group. Ibudilast was well tolerated; no serious adverse events occurred during the study. Pharmacological modulation of glial activity with ibudilast decreased some subjective ratings of opioid withdrawal symptoms. These exploratory findings are the first to demonstrate the potential clinical utility of glial modulators for treating opioid withdrawal in humans.
Progressive Damage Simulation of Laminates in Wind Turbine Blades under Quasistatic and Cyclic Loading.

The overall purpose of this Ph.D. thesis is to develop a framework of methods for progressive delamination modeling and simulation of composite laminates in large scale structures under both quasi-static and fatigue loading. The purpose is motivated by a wish to reduce the cost and consumption of materials while ensuring high a quality of wind turbine blades. The first part of the thesis is an introduction to the project and provides a short introduction to selected basic topics of the theoretical background of the presented papers. The in-depth state-of-the-art review is provided in the papers. The second part of the thesis consists of three refereed journal papers. Paper A is concerned with computational efficiency, ability to converge to a solution and accuracy of the solution when large cohesive elements are applied to quasi-static delamination analyses. It is shown that the accuracy and computational efficiency are improved by reducing the integration error introduced by the numerical integration of the element force vector and stiffness matrix. Paper B presents a review of the available experimental observations, the phenomenological models and the computational simulation methods for the three phases of fatigue-driven delamination (initiation, onset and propagation). The review clarifies which capabilities are required from simulation methods and next review these simulation methods in regards to these capabilities. The overall procedure and characteristics of the methods are described. Paper C presents a new method for simulating fatigue-driven delamination under general mixed mode conditions. The method is capable of simulating both quasi-static and fatigue crack propagation and depend only on easily obtainable material parameters. The experimentally obtained crack growth rate can be reproduced in finite element analyses with practically no error for any given structure and load.
Performance study of a COTS distributed DBMS adapted for multilevel security

Multilevel secure database management system (MLS/DBMS) products no longer enjoy direct commercial-off-the-shelf (COTS) support. Meanwhile, existing users of these MLS/DBMS products continue to rely on them to satisfy their multilevel security requirements. This calls for a new approach to developing MLS/DBMS systems, one that relies on adapting the features of existing COTS database products rather than depending on the traditional custom design products to provide continuing MLS support. We advocate fragmentation as a good basis for implementing multilevel security in the new approach because it is well supported in some current COTS database management systems. We implemented a prototype that utilises the inherent advantages of the distribution scheme in distributed databases for controlling access to single-level fragments; this is achieved by augmenting the distribution module of the host distributed DBMS with MLS code such that the clearance of the user making a request is always compared to the classification of the node containing the fragments referenced; requests to unauthorised nodes are simply dropped. The prototype we implemented was used to instrument a series of experiments to determine the relative performance of the tuple, attribute, and element level fragmentation schemes. Our experiments measured the impact on the front-end and the network when various properties of each scheme, such as the number of tuples, attributes, security levels, and the page size, were varied for a Selection and Join query. We were particularly interested in the relationship between performance degradation and changes in the quantity of these properties. The performance of each scheme was measured in terms of its response time. The response times for the element level fragmentation scheme increased as the numbers of tuples, attributes, security levels, and the page size were increased, more significantly so than when the number of tuples and attributes were increased. The response times for the attribute level fragmentation scheme was the fastest, suggesting that the performance of the attribute level scheme is superior to the tuple and element level fragmentation schemes. In the context of assurance, this research has also shown that the distribution of fragments based on security level is a more natural approach to implementing security in MLS/DBMS systems, because a multilevel database is analogous to a distributed database based on security level. Overall, our study finds that the attribute level fragmentation scheme demonstrates better performance than the tuple and element level schemes. The response times (and hence the performance) of the element level fragmentation scheme exhibited the worst performance degradation compared to
Zinc Supplementation Increases Food Intake and HDL-c and Decreases Platelets in Healthy Children

Zinc Supplementation Increases Food Intake and HDL-c and Decreases Platelets in Healthy Children The aim of the present study was to evaluate the effects of oral zinc supplementation on food intake, and biochemical and hematological parameters in healthy children. Fifty children of both genders, aged 8-9 years, were studied during a three-month period. The study was a randomized, controlled, triple-blind study that used non-probability sampling. The children were randomly assigned to the control (n=25, using placebo) and experimental (n=25, using 10 mg/day elemental zinc) groups.
Semantic context learning with large-scale weakly-labeled image set

There are a large number of images available on the web; meanwhile, only a subset of web images can be labeled by professionals because manual annotation is time-consuming and labor-intensive. Although we can now use the collaborative image tagging system, e.g., Flickr, to get a lot of tagged images provided by Internet users, these labels may be incorrect or incomplete. Furthermore, semantics richness requires more than one label to describe one image in real applications, and multiple labels usually interact with each other in semantic space. It is of significance to learn semantic context with large-scale weakly-labeled image set in the task of multi-label annotation. In this paper, we develop a novel method to learn semantic context and predict the labels of web images in a semi-supervised framework. To address the scalability issue, a small number of exemplar images are first obtained to cover the whole data cloud; then the label vector of each image is estimated as a local combination of the exemplar label vectors. Visual context, semantic context, and neighborhood consistency in both visual and semantic spaces are sufficiently leveraged in the proposed framework. Finally, the semantic context and the label confidence vectors for exemplar images are both learned in an iterative way. Experimental results on the real-world image dataset demonstrate the effectiveness of our method.
Cross-Document Event Ordering through Temporal Relation Inference and Distributional Semantic Models

This paper focuses on the contribution of temporal relations inference and distributional semantic models to the event ordering task. Our system automatically builds ordered timelines of events from different written texts in English by performing first temporal clustering and then semantic clustering. In order to determine temporal compatibility, an inference from the temporal relationships between events –automatically extracted from a Temporal Information Processing system– is applied. Regarding semantic compatibility between events, we analyze two different distributional semantic models: LDA Topic modeling and Word2Vec word embeddings. Both semantic models together with the temporal inference have been evaluated within the framework of SemEval 2015 Task 4 Track B. Experiments show that, using both models, the current State of the Art is improved, showing significant advance in the Cross-Document Event Ordering task.
An efficient, versatile and scalable pattern growth approach to mine frequent patterns in unaligned protein sequences

MOTIVATION Pattern discovery in protein sequences is often based on multiple sequence alignments (MSA). The procedure can be computationally intensive and often requires manual adjustment, which may be particularly difficult for a set of deviating sequences. In contrast, two algorithms, PRATT2 (http//www.ebi.ac.uk/pratt/) and TEIRESIAS (http://cbcsrv.watson.ibm.com/) are used to directly identify frequent patterns from unaligned biological sequences without an attempt to align them. Here we propose a new algorithm with more efficiency and more functionality than both PRATT2 and TEIRESIAS, and discuss some of its applications to G protein-coupled receptors, a protein family of important drug targets. RESULTS In this study, we designed and implemented six algorithms to mine three different pattern types from either one or two datasets using a pattern growth approach. We compared our approach to PRATT2 and TEIRESIAS in efficiency, completeness and the diversity of pattern types. Compared to PRATT2, our approach is faster, capable of processing large datasets and able to identify the so-called type III patterns. Our approach is comparable to TEIRESIAS in the discovery of the so-called type I patterns but has additional functionality such as mining the so-called type II and type III patterns and finding discriminating patterns between two datasets. AVAILABILITY The source code for pattern growth algorithms and their pseudo-code are available at http://www.liacs. nl/home/kosters/pg/.
SOCIAL AND CRITICAL CONSTRUCTION OF REALITY IN EDUCATIONALLY DIFFERENT INDIVIDUALS : A DISCOURSE-BASED ANALYSIS

Considering language as a primary mediational means and setting out with the hypothesis that individuals who stand at the two remote ends of educational ladder in Turkish society will differ from each other in their constructions of social reality and levels of critical thinking, this study analyzes written discoursal data collected from an equal number of primary school and university graduate adults. To find out group-specific characteristics, first, internalized values and value-ordering behaviour of the subjects are identified and compared on the basis of their responses. Next, levels of critical thinking observed in each group’s discourse are analyzed. Comparative results support that educational level is an important social factor affecting also forms of intramental functioning such as reality constructions and critical thinking.
Surface Analysis of Air Exposed Rapidly Solidified Aluminium Powder

Rapidly solidified powders of pure aluminium and aluminium alloy Al5Mn2.5Cr were produced by the ultrasonic gas atomisation method. After exposure to a humid atmosphere the powders were analysed using ESCA and Auger spectroscopy. The powders were exposed at room temperature to a humid atmosphere for 1 h, 1 day and 1 week. The relative humidities (RH) were 60% and 80%. During exposure to conditions of 60% RH for 1 day or 80% RH for 1 h, a thin layer of hydroxide formed on the powder surfaces. Exposure for longer times causes formation on the surface of thicker Al(OH) 3 particles which were formed by hydration of the oxide and corrosion of the aluminium metal. The amount of Al(OH), formed increases with exposure time and relative humidity
Constraint processing in relational database systems: from theory to implementation

Constraint satisfaction problems (CSP) are frequently solved over data residing in relational database systems. In such scenarios, the database is typically just used as a data storage back end. However, there exist important advantages, such as the wide availability of database practices and tools for modeling, to having database systems that are capable of natively modeling and solving CSPs. This paper introduces general concepts and techniques to extend a database system with constraint processing capabilities. Input CSPs are modeled via SQL, augmented with a non-deterministic guess operator as introduced by Cadoli and Mancini (TPLP 2007). Problems are represented with a combination of internal relations and parse trees, and are translated to a flexible intermediate problem representation that is subsequently translated into several common representations for SAT. Benchmarks with a prototype system show the feasibility of the approach and demonstrate the promise of a strong integration of CSP solvers and database systems.
Semantic Learning for Lazy Clause Generation

Lazy clause generation (LCG) combines finite domain (FD) propagation with features from Boolean satisfiability (SAT) solving, notably clause learning. In a LCG solver, finite domain propagators are considered as clause generators that create a SAT description of their behavior for a SAT solver, using some Boolean encoding of the variable domains. When a conflict is triggered by unit propagation on these clauses, SAT conflict analysis learns new (inferred) clauses. In this paper we propose to supplant the SAT component by equivalent methods reasoning semantically on atomic constraints, reducing the roles of SAT to efficient unit propagation. This allows us to reduce the number of literals generated as well as strengthening the conflict analysis. We give experiments exploring the effects of the design choices used in implementing the new solver cpx that results.
BRCA1 haploinsufficiency cell-autonomously activates RANKL expression and generates denosumab-responsive breast cancer-initiating cells

Denosumab, a monoclonal antibody to the receptor activator of nuclear factor-κB ligand (RANKL), might be a novel preventative therapy for BRCA1-mutation carriers at high risk of developing breast cancer. Beyond its well-recognized bone-targeted activity impeding osteoclastogenesis, denosumab has been proposed to interfere with the cross-talk between RANKL-producing sensor cells and cancer-initiating RANK+ responder cells that reside within premalignant tissues of BRCA1-mutation carriers. We herein tested the alternative but not mutually exclusive hypothesis that BRCA1 deficiency might cell-autonomously activate RANKL expression to generate cellular states with cancer stem cell (CSC)-like properties. Using isogenic pairs of normal-like human breast epithelial cells in which the inactivation of a single BRCA1 allele results in genomic instability, we assessed the impact of BRCA1 haploinsufficiency on the expression status of RANK and RANKL. RANK expression remained unaltered but RANKL was dramatically up-regulated in BRCA1mut/+ haploinsufficient cells relative to isogenic BRCA1+/+ parental cells. Neutralizing RANKL with denosumab significantly abrogated the ability of BRCA1 haploinsufficient cells to survive and proliferate as floating microtumors or “mammospheres” under non-adherent/non-differentiating conditions, an accepted surrogate of the relative proportion and survival of CSCs. Intriguingly, CSC-like states driven by epithelial-to-mesenchymal transition or HER2 overexpression traits responded to some extent to denosumab. We propose that breast epithelium-specific mono-allelic inactivation of BRCA1 might suffice to cell-autonomously generate RANKL-addicted, denosumab-responsive CSC-like states. The convergent addiction to a hyperactive RANKL/RANK axis of CSC-like states from genetically diverse breast cancer subtypes might inaugurate a new era of cancer prevention and treatment based on denosumab as a CSC-targeted agent.
Destination-Aided Wireless Power Transfer in Energy-Limited Cognitive Relay Systems

This paper considers an energy-limited cognitive relay network, where a secondary transmitter (ST) assists to forward the traffic from a primary transmitter (PT) to a primary receiver (PR), in exchange for serving its own secondary receiver (SR) in the same frequency. The multiple-antenna ST is assumed to be energy-constrained and powered by both information flow from source (PT) and dedicated energy streams from destinations (PR and SR), which is called a destination-aided wireless power transfer (DWPT) scheme. Then, the relay processing matrix, cognitive beamforming vector, and power splitter are jointly designed to maximize the rate of secondary users under the energy causality constraint and the constraint that the demanded rate of primary users is satisfied. For the perfect channel state information (CSI) case, by adopting the semi-definite relax technique and the Charnes-Cooper transformation, the global optimal solution is given. To reduce the complexity, matrix decomposition, zero forcing scheme, and dual method are jointly employed to derive a suboptimal solution. For the imperfect CSI case, the S-procedure is used to transform the worst case robust problem into a tractable semi-definite program. Simulation results reveal that our proposed DWPT scheme is greatly preferred for both perfect and imperfect CSI cases when ST is close to PR/SR.
A data-driven model for anisotropic heterogeneous subsurface scattering

We present a new BSSRDF representation for editing measured anisotropic heterogeneous translucent materials, such as veined marble, jade, artificial stones with lighting-blocking discontinuities. Our work is inspired by the SubEdit representation introduced in [1]. Our main contribution is to improve the accuracy of the approximation while keeping it compact and efficient for editing. We decompose the local scattering profile into an isotropic term and an anisotropic term. The isotropic term encodes the scattering range and albedo property, and the anisotropic term encodes the spatial-variant subsurface scattering shape profile. We propose a compact model for the scattering profile based on non-negative matrix factorization, which allows user-guided editing. Experimental results have shown that our model can capture more spatial-anisotropic features than the previous work with similar compression rate.
A Generalized Method for Exact Solutions to NLPDE's

Using the idea of the very recently generalized Kudryashov method and based on a generalized Riccati equation we present an alternative method which can be considered as an extension of the well known tanh method. As application of the proposed method, we obtain exact solutions of the Dispersive modied Benjamin-Bona-Mahony equation showing in this way its eectiveness.