Miracle-dz/UniFAR-FTUs huggingface dataset:
Vertebrae segmentation techniques for spinal medical images

Accurate localization and segmentation of the spine from medical images plays an important role in CAD (Computer Aided Diagnoses) as it used for many clinical tasks to diagnose many diseases like degenerative disc disease, kyphosis, scoliosis and spondylolisthesis. Besides it can be used as an input to the classification process. Although the bone structures in medical images have high contrast but the vertebrae identification is considered a challenging task due to many difficulties like the unclear boundaries of the vertebrae, the abnormal spine curves and complex structure of the vertebra. In order to achieve an accurate, efficient and automated spine segmentation and detection from medical images, there are several techniques. This paper analyze and review the different vertebrae segmentation techniques. The spine segmentation task divides into three main stages: initial spine skeleton detection, vertebrae segmentation of the spine while the third stage is designed to enhance the results of the vertebrae segmentation. This paper presents different algorithms for each stage as each algorithm is supported by one or more paper which explain the algorithm.
Using and Interfacing Background Knowledge in Story Understanding

This paper details the use of background knowledge within a story understanding system. The story understanding system is based upon a multi-agent system (MAS). The MAS combines different forms of knowledge into a meaningful structure from which understanding can be demonstrated, i.e., question answering. The system uses two forms of knowledge to accomplish this task: 1) knowledge about objects in the world; and 2) prototypes. The latter represents higher-order knowledge or experience such as repeating a process and forms of thinking like abduction and deduction. The former stores knowledge about objects in the real world and their relationship to each other. The two forms are viewed as two distinct forms of data that might be used in our own understanding process, thus, they are treated separately by the system.
Retinal ganglion cell apoptosis in glaucoma is related to intraocular pressure and IOP-induced effects on extracellular matrix.

PURPOSE To investigate the effect of IOP on retinal ganglion cell (RGC) apoptosis and correlate the effects with IOP-induced changes in extracellular matrix (ECM) in the retina and optic nerve head (ONH) in glaucomatous rat eyes. METHODS Thirty-seven Dark Agouti rats had elevated IOP induced in the left eye by hypertonic saline episcleral vein injections. Eyes were examined at 3 months histologically for RGC apoptosis and expression of specific ECM components. RESULTS RGC apoptosis was significantly related to IOP exposure (integral DeltaIOP P <0.001; peak IOP P <0.01). In the RGC layer, elevated IOP correlated positively to a significant increase in MMP-9 activity (P <0.001), tissue inhibitor of matrix metalloproteinase (TIMP-1) (P <0.05), and collagen I (P <0.01), and negatively correlated to deposition of laminin (P <0.05) and TGF-beta2 (P <0.05). There was a significant correlation between MMP-9 activity and both RGC apoptosis (P <0.001) and loss of laminin (P <0.01). IOP exposure was also associated with increased deposition of TGF-beta2 and collagen I at the ONH (P <0.01). CONCLUSIONS The results demonstrated that RGC apoptosis in glaucoma correlates strongly with elevated IOP and is significantly associated with IOP-induced changes in specific ECM components in the RGC layer. The study shows for the first time a link between MMP-9, laminin degradation, RGC apoptosis, and IOP exposure in glaucoma. The findings suggest that abnormal ECM remodeling in the glaucomatous retina may relate to RGC death and support the notion that the retina is a primary site of injury in glaucoma.
Video motion detection beyond reasonable doubt

We consider the analysis of surveillance video footage containing occasional activities of potential interest interspersed with long periods of no motion. Such evidence is problematic for three reasons: firstly, it takes up a great deal of storage capacity with little evidential value; secondly, human review of such surveillance is extremely time-consuming and subject to errors due to fatigue; and thirdly, there is often a need to prove to the satisfaction of the Court that excised footage contains no images of evidential value. We are therefore concerned with objective, reliable detection of video motion to automate the extraction of activities of interest and to provide simple but reliable measurements to the court to prove that this is a complete record of all activities in the footage. Early results indicate that average luminance-based detection is particularly reliable, and we provide a comparison with other frame-difference techniques.
Towards the didactics of instrumental activities with ICT

In primary and secondary education in France, there is no real place for computer science as such. What is considered is limited to some general software (like word processors and internet browser) with the vision that practice in itself is sufficient for users to acquire their necessary mastery. ICT (Information and communication technology) has to be used for the learning of classical subject matters in a utilitarian and somehow interdisciplinary context. An analysis of the current situation gives interesting results and reveals new stakes that go largely beyond the strictly utilitarian framework. The real competencies of most users are far weaker than might be expected. Users and computer software have changed. Experimentation, modelisation and so on are possible with computer technologies. That leads to new vision of the place of ICT or computer science and therefore of didactics of informatics in connection with computer instrumented activities.
Transcription Regulation of E-Cadherin by Zinc Finger E-Box Binding Homeobox Proteins in Solid Tumors

Downregulation of E-cadherin in solid tumors with regional migration and systematic metastasis is well recognized. In view of its significance in tumorigenesis and solid cancer progression, studies on the regulatory mechanisms are important for the development of target treatment and prediction of clinical behavior for cancer patients. The vertebrate zinc finger E-box binding homeobox (ZEB) protein family comprises 2 major members: ZEB1 and ZEB2. Both contain the motif for specific binding to multiple enhancer boxes (E-boxes) located within the short-range transcription regulatory regions of the E-cadherin gene. Binding of ZEB1 and ZEB2 to the spaced E-cadherin E-boxes has been implicated in the regulation of E-cadherin expression in multiple human cancers. The widespread functions of ZEB proteins in human malignancies indicate their significance. Given the significance of E-cadherin in the solid tumors, a deeper understanding of the functional role of ZEB proteins in solid tumors could provide insights in the design of target therapy against the migratory nature of solid cancers.
Kernel Convergence Estimates for Diffusions with Continuous Coefficients

Bidirectional valuation models are based on numerical methods to obtain kernels of parabolic equations. Here we address the problem of robustness of kernel calculations vis a vis floating point errors from a theoretical standpoint. We are interested in kernels of one-dimensional diffusion equations with continuous coefficients as evaluated by means of explicit discretization schemes of uniform step h > 0 in the limit as h → 0. We consider both semidiscrete triangulations with continuous time and explicit Euler schemes with time step so small that the Courant condition is satisfied. We find uniform bounds for the convergence rate as a function of the degree of smoothness. We conjecture these bounds are indeed sharp. The bounds also apply to the time derivatives of the kernel and its first two space derivatives. The proof is constructive and is based on a new technique of path conditioning for Markov chains and a renormalization group argument. We make the simplifying assumption of time-independence and use longitudinal Fourier transforms in the time direction. Convergence rates depend on the degree of smoothness and Holder differentiability of the coefficients. We find that the fastest convergence rate is of order O(h2) and is achieved if the coefficients have a bounded second derivative. Otherwise, explicit schemes still converge for any degree of Holder differentiability except that the convergence rate is slower. Holder continuity itself is not strictly necessary and can be relaxed by an hypothesis of uniform continuity.
Feasibility analysis of real-time physical modeling using WaveCore processor technology on FPGA

WaveCore is a scalable many-core processor technology. This technology is specifically developed and optimized for real-time acoustical modeling applications. The programmable WaveCore soft-core processor is silicon-technology independent and hence can be targeted to ASIC or FPGA technologies. The WaveCore programming methodology is based on dataflow principles and the abstraction level of the programming language is close to the mathematical structure of for instance finite difference timedomain schemes. The instruction set of the processor inherently supports delay-lines and data-flow graph constructs. Hence, the processor technology is well suitable to capture both digital waveguide as well as finite-difference oriented algorithm descriptions. We have analysed the feasibility of mapping 1D and 2D finite-difference models onto this processor technology, where we took Matlab reference code as a starting point. We analyzed the scalability and mapping characteristics of such models on the WaveCore architecture. Furthermore we investigated the composability of such models, which is an important property to enable the creation and mapping of complete musical instrument models. One part of the composability analysis has been to combine digital waveguide (FDN reverberation model) with finite-difference time-domain models (primitive six-string instrument model). The mapping experiments show a high efficiency in terms of FPGA utilization, combined with a programming methodology that matches in a transparent way with the mathematical abstraction level of the application domain. We used a standard FPGA board to get full-circle confidence of the carried-out analysis. The WaveCore compiler and simulator results have shown the scalability of the processor technology to support large models.
Universal Knowledge Discovery from Big Data: Towards a Paradigm Shift from 'Knowledge Discovery' to 'Wisdom Discovery'

Many people hold a vision that big data will provide big insights and have a big impact in the future, and big-data-assisted scientific discovery is seen as an emerging and promising scientific paradigm. However, how to turn big data into deep insights with tremendous value still remains obscure. To meet the challenge, universal knowledge discovery from big data (UKD) is proposed. The new concept focuses on discovering universal knowledge, which exists in the statistical analyses of big data and provides valuable insights into big data. Universal knowledge comes in different forms, e.g., universal patterns, rules, correlations, models and mechanisms. To accelerate big data assisted universal knowledge discovery, a unified research paradigm should be built based on techniques and paradigms from related research domains, especially big data mining and complex systems science. Therefore, I propose an iBEST@SEE methodology. This study lays a solid foundation for the future development of universal knowledge discovery, and offers a pathway to the discovery of "treasure-trove" hidden in big data.
T3DB: a comprehensively annotated database of common toxins and their targets

In an effort to capture meaningful biological, chemical and mechanistic information about clinically relevant, commonly encountered or important toxins, we have developed the Toxin and Toxin-Target Database (T3DB). The T3DB is a unique bioinformatics resource that compiles comprehensive information about common or ubiquitous toxins and their toxin-targets into a single electronic repository. The database currently contains over 2900 small molecule and peptide toxins, 1300 toxin-targets and more than 33 000 toxin-target associations. Each T3DB record (ToxCard) contains over 80 data fields providing detailed information on chemical properties and descriptors, toxicity values, protein and gene sequences (for both targets and toxins), molecular and cellular interaction data, toxicological data, mechanistic information and references. This information has been manually extracted and manually verified from numerous sources, including other electronic databases, government documents, textbooks and scientific journals. A key focus of the T3DB is on providing ‘depth’ over ‘breadth’ with detailed descriptions, mechanisms of action, and information on toxins and toxin-targets. T3DB is fully searchable and supports extensive text, sequence, chemical structure and relational query searches, similar to those found in the Human Metabolome Database (HMDB) and DrugBank. Potential applications of the T3DB include clinical metabolomics, toxin target prediction, toxicity prediction and toxicology education. The T3DB is available online at http://www.t3db. org.
Adaptive Operator Selection in EAs with Extreme-Dynamic Multi-Armed Bandits

The performance of evolutionary algorithms is highly affected by the selection of the variation operators to solve the problem at hand. This paper presents a brief review of the results that have been recently obtained using the “Extreme Dynamic Multi-Armed Bandit” (Ex-DMAB), a technique used to automatically select the operator to be applied between the available ones, while searching for the solution. Experiments on three well-known unimodal artificial problems of the EC community, namely the OneMax, the Long k-Path and the Royal Road, and on a set of a SAT instances, are briefly presented, demonstrating some improvements over both any choice of a single-operator alone, and the naive uniform choice of one operator at each application. 1 Adaptive Operator Selection Adaptive methods use information from the history of evolution to modify parameters while solving the problem. This paper focuses on the Adaptive Operator Selection (AOS), i.e., the definition of an on-line strategy able to autonomously select between different variation operators each time one needs to be applied. Fig. 1 illustrates the general scheme for achieving this goal, from which we can derive the need of defining two main components: the Credit Assignment how to assess the performance of each operator based on the impact of its application on the progress of the search; and the Operator Selection rule how to select between the different operators based on the rewards that they have received so far. Figure 1: General Adaptive Operator Selection scheme. 2 Extreme Dynamic Multi-Armed Bandit The two ingredients of the Adaptive Operator Selection method proposed by us are: an Operator Selection rule based on the Multi-Armed Bandit paradigm, and a Credit Assignment mechanism based on extreme values. 2.1 Operator Selection: Dynamic Multi-Armed Bandits The explored idea, firstly proposed in [3], is that the selection of an operator can be seen as yet another Exploration vs. Exploitation dilemma, but this time at operator-selection level: there is the need of applying as much as possible the operator known to have brought the best results so far, while nevertheless exploring the other possibilities, in case one of the other operators becomes the best option at some point. Such dilemma has been intensively studied in the context of Game Theory, in the so-called MultiArmed Bandit (MAB) framework. Among the existent MAB variants, the Upper Confidence Bound (UCB) [1] was chosen to be used, for being proved optimal w.r. t. maximization of the cumulative reward. More formally, the UCB algorithm works as follows. Each variation operator is viewed as an arm of a MAB problem. Let ni,t denote the number of times the i arm has been played up to time t, and let p̂i,t denote the average empirical reward received until time t by arm i. At each time step t, the algorithm selects the arm maximizing the following quantity:
Finite Element Study of the Mechanical Response in Spinal Cord during the Thoracolumbar Burst Fracture

Background The mechanical response of the spinal cord during burst fracture was seldom quantitatively addressed and only few studies look into the internal strain of the white and grey matters within the spinal cord during thoracolumbar burst fracture (TLBF). The aim of the study is to investigate the mechanical response of the spinal cord during TLBF and correlate the percent canal compromise (PCC) with the strain in the spinal cord. Methodology/Principal Findings A three-dimensional (3D) finite element (FE) model of human T12-L1 spinal cord with visco-elastic property was generated based on the transverse sections images of spinal cord, and the model was validated against published literatures under static uniaxial tension and compression. With the validated model, a TLBF simulation was performed to compute the mechanical strain in the spinal cord with the PCC. Linear regressions between PCC and strain in the spinal cord show that at the initial stage, with the PCC at 20%, and 45%, the corresponding mechanical strains in ventral grey, dorsal grey, ventral white, dorsal white matters were 0.06, 0.04, 0.12, 0.06, and increased to 0.14, 0.12, 0.23, and 0.13, respectively. At the recoiled stage, when the PCC was decreased from 45% to 20%, the corresponding strains were reduced to 0.03, 0.02, 0.04 and 0.03. The strain was correlated well with PCC. Conclusions/Significance The simulation shows that the strain in the spinal cord correlated well with the PCC, and the mechanical strains in the ventral regions are higher than those in the dorsal regions of spinal cord tissue during burst fracture, suggesting that the ventral regions of the spinal cord may susceptible to injury than the dorsal regions.
Convergence in Homogeneous Random Graphs

For a sequence p = (p(1), p(2), ‥ . ), let G(n, p) denote the random graph with vertex set {1, 2, ‥ . , n} in which two vertices i, j are adjacent with probability p(|i − j|), independently for each pair. We study how the convergence of probabilities of first order properties of G(n, p), can be affected by the behaviour of p and the strength of the language we use. © 1995 Wiley Periodicals, Inc.
About some misconceptions and the discontent with statistical tests in psychology 1

The function and potential importance of statistical tests in examining and evaluating substantive and psychological hypotheses is discussed. Psychological hypotheses are sharply distinguished from statistical hypotheses. Decisions on statistical hypotheses must be separated from decisions on psychological hypotheses. Some differences between both kinds of hypotheses are addressed, and the question is attacked whether they are complementary or not. The answer to this question is negative. The use of the modus tollens in theory corroboration is discussed and it is argued that evaluations of substantive hypotheses always is accompanied by some inductive aspects. A further reason for the discontent with statistical tests is identified: Most of these tests are rather insensitive the differential patterns of predictions and of data, whereas differential patterns of data can be derived from nearly every psychological hypothesis or theory. To test for these differential patterns statistical tests should be applied thoughtfully, not routinely.
Using Multiple-case Studies to Investigate Relationships among Knowledge Management Systems, Business Process and Business Performance: A Task Technology Fit Perspective

In the face of heighted competition, organizations need to continuously improve their competitive advantage. Both knowledge management (KM) and knowledge management systems (KMS) play a pivotal role in helping organizations to stay competitive. There is much research in KMS, however very little is known about how they affect individual and organizational performance. Drawing on task-technology fit theory (Goodhue and Thompson, 1995), this study explores the fit or alignment between business process (task) and KMS (technology) and its impact on KMS utilization based on multiple case studies. Subsequently, the impacts of both the task-technology fit and KMS utilization on individual and business performance are investigated. This paper contributes to the KM literature in several ways. First, it applies task-technology fit theory to an important context, that of KM. Second, it characterizes task as business processes which have the potential to help explain KMS success on business performance. Third, the paper explores the positive impact of task-technology fit on KMS utilization and business performance. Fourth, the study provides insight into the future development of KMS which are better aligned with managerial purposes.
On Minimization of Peak Power for Scan Circuit during Test

Scan circuit generally causes excessive switching activitycompared to normal circuit operation. The higher switchingactivity in turn causes higher peak power supply currentwhich results into supply voltage droop and eventuallyyield loss. This paper proposes an efficient methodologyfor test vector re-ordering to achieve minimum peak powersupported by the given test vector set. The proposed methodologyalso minimizes average power under the minimumpeak power constraint. A methodology to further reduce thepeak power, below the minimum supported peak power, byinclusion of minimum additional vectors is also discussed.The paper defines the lower bound on peak power for agiven test set. The results on several benchmarks shows thatit can reduce peak power by up to 27%.
A Framework to Uncover Multiple Deccorelated Clusterings

Clustering is often referred to as unsupervised learning which aims at uncovering hidden structures from data. Unfortunately, though widely being used as one of the principal tools to understand the data, most conventional clustering techniques are limited in achieving this goal since they only attempt to find a single clustering solution from the data. For many real-world applications, especially those being described in high dimensional data, it is common to see that the data can be grouped into different yet meaningful ways. This gives rise to the recently emerging research area of mining alternative clusterings. In this paper, we propose a framework named MACL that is capable of discovering multiple alternative clusterings from a given dataset. MACL seeks alternative clusterings in sequence and a novel solution is found by conditioning on all previously known clusterings. The framework takes a mathematically appealing approach by combining the maximum likelihood framework and mutual information. Consequently, its resultant clustering quality is achieved by the likelihood maximization over the data whereas the dissimilarity is ensured by the minimization over the information sharing amongst alternatives. We test the proposed algorithm on both synthetic and realworld datasets and the experimental results demonstrate its potential in discovering multiple alternative clusterings from data.