Miracle-dz/UniFAR-FTUs huggingface dataset:
DBA and K-means clustering : Explainability problems for research on behaviors and strategies

: Studying creative problem solving (CPS) activities can enrich our understanding of human learning by facilitating the observation of behaviors related to realistic situations of complex open-ended problem solving . Actions taken by an individual in such activities are situated in time, thus allowing the creation of time sequences of observations. These observations are not purely numerical, and thus can be represented by symbolic time sequences. In particular, the formal analysis of such time sequences leads to a better understanding of the role of temporality in the adoption of CPS strategies. For this, we propose to apply clustering algorithms in order to group sequences in categories that could be associated with emergent behavioral patterns, either at the whole activity level or at the level of subsequences of actions. Clustering requires the deﬁnition of a distance between symbolic data structures representing observations, used to determine the distance (more precisely dissimilarity), and the capacity to interpret such clusters. Our ongoing research uses Dynamic Time Warping (DTW) as an algorithm to compute dissimilarity between sequences. This technical report addresses the issue of computing a K-means clustering algorithm and centroids with DTW for symbolic sequences, using an approximation algorithm named DTW Barycenter Averaging (DBA). It appears that DBA for symbolic sequences is not relevant as it produces non-explainable sequences for the pattern analysis, but this negative result is of interest and allows us to propose an alternative. Clustering algorithms using medoids will be preferred in future research, and we explain why in this report.
A Novel Technique for Fast Detection of Bacillus anthracis Spore by Multiplex PCR

Bacillus anthracis is a spore-forming bacterium that causes anthrax disease. Its spore resists against physical and chemical agents. Molecular detection of B.anthracis spore requaiers the genomic DNA which is embded in the spores. in this study we developed a rapid detection of spor by Multiplex PCR using rapid physical disruption of spore in a short time without genomic DNA extraction. Genome of non-virulent strain of B.anthracis spores was released by MiniBead Beater-8 system. 3 pairs of primers were used in Multiplex PCR. PCR products were analyzed in agarose gels electrophoresis. Detection limit , specificity and accuracy of the test were determined through various process. This method enables to disrupt spores during 3 minutes and perform detection process with specific primers by Multiplex PCR method. Gel electrophoresis analysis showed 3 fragments of PCR products. digested 1083 bp fragment created 696 bp and 386 bp fragments which confirmed the test accuracy. Detection limit was 7680 spores/ml or 7 spore/µl. The results showed that the optimized physical disruption method has a proper velosity and there is no need for DNA extraction. Finally this method is capable to detect B.anthracis spores with high sensitivity and specificity.
Synthesis of new magnetic reusable oil sorbent catalysts based on dodecanol and dodecyl sulfate: their application for oil/water separation

: Magnetic nanoparticles (MNPs) were treated with dodecyl sulfate (DS) and dodecanol (DO) to enhance their affinity for lipids. Various methods, such as X-ray powder diffraction (XRD), scanning electron microscopy (SEM), vibrating sample magnetometery (VSM), thermal gravimetric analysis (TGA), and FTIR spectroscopy were employed to analyze and characterize these modified MNPs. The functionalized MNPs were employed to adsorb different types of oils (edible, motor, and diesel oils). MNP@DO and MNP@DS exhibited oil absorption capacities of up to three and two times their own mass for edible oil, respectively. The complete repulsion of water was evident, and a rapid interaction between lipophilic magnetic catalysts and oil on the water’s surface was observed. These oil-absorbing catalysts could be efficiently removed by employing an external magnet, and they demonstrated excellent reusability. The findings indicate that these novel catalysts exhibit substantial oil-absorbing capabilities and can be used as low-cost and effective technique for oil/solvent-spill cleanups.
An Experimental Study on Intercoronary Reflexes

There is no evidence in the anesthetized dog that occlusion of one of the three coronary arteries leads to vasoconstriction in the vascular areas irrigated by the other two arteries. On the contrary, occlusion of one coronary artery appears to lead to a decrease in the resistance of the two adjacent vascular beds in most experiments. The mechanisms and the implications of this effect are discussed.
USUM: Update Summary Generation System

Huge amount of information is present in the World Wide Web and a large amount is being added to it frequently. A query-specific summary of multiple documents is very helpful to the user in this context. Currently, few systems have been proposed for query-specific, extractive multi-document summarization. If a summary is available for a set of documents on a given query and if a new document is added to the corpus, generating an updated summary from the scratch is time consuming and many a times it is not practical/possible. In this paper we propose a solution to this problem. This is especially useful in a scenario where the source documents are not accessible. We cleverly embed the sentences of the current summary into the new document and then perform query- specific summary generation on that document. Our experimental results show that the performance of the proposed approach is good in terms of both quality and eciency.
Attachment in infancy and personal space regulation in early adolescence

This study longitudinally assessed associations between secure and ambivalent attachment with mothers, fathers and professional caregivers in infancy, and personal space regulation and perceived interpersonal competence in 64 early adolescents (31 boys, 33 girls). Children classified as ambivalently attached to their mothers and/or professional caregivers in infancy displayed significantly larger permeability of personal space as compared with children classified as securely attached. Attachment classifications with fathers were not associated with personal space behavior at 12 years of age. Children who had an insecure attachment relationship with both the mother and the professional caregiver in infancy displayed smaller personal space boundaries, and tolerated larger intrusions into their personal space as compared with children who had two secure attachments in infancy. Finally, perceived interpersonal competence was positively correlated with personal space permeability.
Prediction of peptide bonding affinity: kernel methods for nonlinear modeling

This paper presents regression models obtained from a process of blind prediction of peptide binding affinity from provided descriptors for several distinct datasets as part of the 2006 Comparative Evaluation of Prediction Algorithms (COEPRA) contest. This paper finds that kernel partial least squares, a nonlinear partial least squares (PLS) algorithm, outperforms PLS, and that the incorporation of transferable atom equivalent features improves predictive capability.
A novel semantics-based approach to medical literature search

The goal of Clinical Decision Support (CDS) is to link relevant medical information to meet physicians' needs for taking better care of their patients. An effective CDS system takes medical records as input and returns the most relevant full-text biomedical articles to provide clinical decision supports for the physicians. Existing CDS systems mainly consist of three processing stages i.e. Query Reformulation, Retrieval and Reranking. Most of the existing CDS systems are only implemented based on classical keyword-indexing retrieval models such as BM25, PL2 and BB2, without taking semantics into account. To this end, this paper develops a novel CDS approach based on the semantic vector representations of both documents and queries. Experimental results on the standard Text REtrieval Conference (TREC) CDS track dataset show that the performance of a CDS system can be improved by integrating the semantic information.
Anterior and Posterior types of Neuropsychological Deficits in Anterior and Posterior types of Neuropsychological Deficits in Parkinson’s Disease: A Subgroup Classification of Cognitive Parkinson’s Disease: A Subgroup Classification of Cognitive Outcome Outcome

Individuals with Parkinson’s disease (PD) exhibit cognitive deficits. Recent studies suggest there are distinct cognitive profiles of PD characterized by deficits in abilities that are dependent upon anterior versus posterior areas of the brain. While anterior-based deficits are more prevalent, posterior-based deficits are more predictive of the future occurrence of dementia in PD. The purpose of the current project was to examine these cognitive profiles in more detail. Performance on six tests of anterior function and six tests of posterior function was examined in 34 non-demented PD participants and 27 healthy control participants matched for age and education. Results showed that PD participants performed significantly more poorly than healthy control participants across most measures of cognition. Categorization into profiles was examined using a patient score falling 1.5 standard deviations (SDs) and 2 SDs below the control average. When a deficit was seen on at least two tests within a single domain, the PD participants were identified as having a deficit in that domain. For the 1.5 SD and 2 SD cut-off values, nine of the PD participants exhibited anterior deficits, one had posterior deficits, ten (1.5 cut-off) and five (2 cut-off) had deficits in both domains and 14 (1.5 cut-off) and 19 (2 cut-off) showed no deficits. Further development of this new classification system may allow the prediction of longer-term cognitive outcomes in PD.
Extraction of breast border and removal of pectoral muscle in wavelet domain

Extraction of the breast border and simultaneously exclusion of pectoral muscle are principal steps for diagnosing of breast cancer based on mammogram data. The objective of propose method is to classify the mediolateral oblique fragment of the pectoral muscle. The extraction of breast region is performed using the multilevel wavelet decomposition of mammogram images. Moreover, artifact suppression and pectoral muscle detection is carried out by morphological operator. The efficient extraction with higher accuracy is validated on the set of 322 digital images taken from MIAS dataset.
G2MILP: Learning to Generate Mixed-Integer Linear Programming Instances for MILP Solvers

There have been signiﬁcant eﬀorts devoted to developing advanced mixed-integer linear programming (MILP) solvers, which are powerful tools for solving various real-world optimization problems. Despite the achievements, the limited availability of real-world instances often results in sub-optimal decisions and biased evaluations, which motivates a suite of MILP instance generation techniques. However, these approaches either rely on expert-designed formulations or struggle to capture the rich features of real-world instances. Moreover, the task of generating challenging MILP instances—which are valuable resources for evaluating solvers and motivating more eﬃcient algorithms—remains underexplored. To tackle these problems, we propose G2MILP , which to the best of our knowledge is the ﬁrst deep generative framework for MILP instances. Speciﬁcally, G2MILP represents MILP instances as bipartite graphs and employs a masked variational autoencoder to iteratively corrupt and replace parts of the original graphs to generate new ones. We then propose a hardness-oriented scheme, which iteratively augments the generator by learning from the hardest instances, to enhance G2MILP to construct challenging MILP instances. Experiments demonstrate that
Integrating Intracellular Dynamics Using CompuCell3D and Bionetsolver: Applications to Multiscale Modelling of Cancer Cell Growth and Invasion

In this paper we present a multiscale, individual-based simulation environment that integrates CompuCell3D for lattice-based modelling on the cellular level and Bionetsolver for intracellular modelling. CompuCell3D or CC3D provides an implementation of the lattice-based Cellular Potts Model or CPM (also known as the Glazier-Graner-Hogeweg or GGH model) and a Monte Carlo method based on the metropolis algorithm for system evolution. The integration of CC3D for cellular systems with Bionetsolver for subcellular systems enables us to develop a multiscale mathematical model and to study the evolution of cell behaviour due to the dynamics inside of the cells, capturing aspects of cell behaviour and interaction that is not possible using continuum approaches. We then apply this multiscale modelling technique to a model of cancer growth and invasion, based on a previously published model of Ramis-Conde et al. (2008) where individual cell behaviour is driven by a molecular network describing the dynamics of E-cadherin and -catenin. In this model, which we refer to as the centre-based model, an alternative individual-based modelling technique was used, namely, a lattice-free approach. In many respects, the GGH or CPM methodology and the approach of the centre-based model have the same overall goal, that is to mimic behaviours and interactions of biological cells. Although the mathematical foundations and computational implementations of the two approaches are very different, the results of the presented simulations are compatible with each other, suggesting that by using individual-based approaches we can formulate a natural way of describing complex multi-cell, multiscale models. The ability to easily reproduce results of one modelling approach using an alternative approach is also essential from a model cross-validation standpoint and also helps to identify any modelling artefacts specific to a given computational approach.
A perceptual approach to trimming and tuning unstructured lumigraphs

We present a novel perceptual method to reduce the visual redundancy of unstructured lumigraphs, an image based representation designed for interactive rendering. We combine features of the unstructured lumigraph algorithm and image fidelity metrics to efficiently rank the perceptual impact of the removal of subregions of input views (subviews). We use a greedy approach to estimate the order in which subviews should be pruned to minimize perceptual degradation at each step. Renderings using varying numbers of subviews can then be easily visualized with confidence that the retained subviews are well chosen, thus facilitating the choice of how many to retain. The regions of the input views that are left are repacked into a texture atlas. Our method takes advantage of any scene geometry information available but only requires a very coarse approximation. We perform a user study to validate its behaviour, as well as investigate the impact of the choice of image fidelity metric as well as that of user parameters. The three metrics considered fall in the physical, statistical and perceptual categories. The overall benefit of our method is the semiautomation of the view selection process, resulting in unstructured lumigraphs that are thriftier in texture memory use and faster to render. Using the same framework, we adjust the parameters of the unstructured lumigraph algorithm to optimise it on a scene by scene basis.
RESEARCH OUTPUTS / RÉSULTATS DE RECHERCHE

: Models are used to gain a better understanding of complex systems such as the evolution of a population, the transportation demand, the brain behaviour, elections outcome, the propagation of a disease,. System models should be precise and parsimonious. However, the total variation of the system cannot be precisely captured by the observed variables as there can be unobserved ones inﬂuencing the system output. The unexplained variation caused by unobserved variables is, therefore, considered as a noise in the model. Different models handle that noise in a different way. For instance, a linear regression assumes that the noise follows a normal distribution and explicitly incorporates it into the model formulation. On the other hand, other models, such as a deterministic neural network, do not explicitly incorporate that noise. Several models can then be applied and the selection of the best one can be a challenging question. This research aims to highlight the importance of the unobserved variables on the results of two types of simple yet widely used models: feedforward neural networks (FFNN) and logit discrete choice models (LDCM). The ﬁrst application consists in modelling the divorces in an agent-based microsimulation, the agents being the individuals of a given population. For each couple in the model, the divorce is predicted based on the characteristics of the couple (ex: length of the marriage, age of the individuals). In this application, it is shown that the LDCM outperforms the neural network due to the presence of - possibly many - unobserved variables. The second example is a model deﬁned to predict the level of interaction between groundwater and quarry extensions. In this application, the value of every relevant variable is assumed to be known, i.e. the noise from unobserved variables is minimum. In this case, it is shown that both approaches perform well, but FFNN perform slightly better than LDCM. We then investigate how the model performance evolves when the noise increases by removing variables from the models speciﬁcation. Finally, those two applications will allow us to conclude on the robustness of the discrete choice models and artiﬁcial neural network in presence of unobserved variables.
Fall Detection System using Wearable Sensor Devices and Machine Learning: A Review

Over the past years, Fall Detection System (FDS) has undergone extensive research to improve living risk, especially for the elderly who are vulnerable to these fall events. Devices employing sensors are crucial components of FDS in achieving high accuracy and sensitivity. This article overviews diﬀerent sensor modalities, such as ambient-based and vision-based systems, as well as commonly used wearable devices for fall detection, along with the associated data processing algorithms. The critical elements of fall detection, such as architectures and algorithms for processing sensor data, machine learning and deep learning methodologies, and validation of FDS performance, are considered. The article also delves into safety aspects and presents technical challenges and concerns in FDS for researchers in the ﬁeld to identify areas requiring further improvement. Finally, future research opportunities to improve fall detection for widespread use are outlined.
Analysis of fractional order polynomials using Hermite-Biehler theorem

This paper presents some results for stability analysis of fractional order polynomials using the Hermite-Biehler theorem. The possibilities of the extension of the Hermite-Biehler theorem to fractional order polynomials is investigated and it is observed that the Hermite-Biehler theorem can be an effective tool for the stability analysis of fractional order polynomials. Variable changing has been applied to the fractional order polynomial to transform it into an integer order one. Roots of this polynomial are found and verified with the roots obtained using the Hermite-Biehler theorem. Stability analysis has been done investigating the interlacing property of the polynomial. Results are verified with the Radwan procedure. The method is clarified via illustrative examples.
Spontaneous Intracranial Hypotension Cervical Epidural Blood Patch for Feasibility of Placement of an Anterior

SUMMARY: The dorsal epidural blood patch is a commonly used management technique for spontaneous intracranial hypotension from a dural CSF leak, but it may be less efﬁcacious for cervical or ventral leaks. We report the technique of placing an anterior cervical blood patch for a large cervical ventral leak. To our best knowledge, this approach has not been reported. In the appropriately selected patient, an anterior cervical epidural blood patch may be safely used
Creatininium 2-chloroacetate

In the title compound (systematic name: 2-amino-1-methyl-4-oxo-4,5-dihydro-1H-imidazol-3-ium 2-chloroacetate), C4H8N3O+·C2H2ClO2 −, the molecular aggregations are stabilized through classical (N—H⋯O) and non-classical (C—H⋯O and C—H⋯N) hydrogen-bonding interactions. The cations are linked to the anions, forming ion pairs through two N—H⋯O bonds that produce characteristic R 2 2(8) ring motifs. These cation–anion pairs are connected through another N—H⋯O hydrogen bond, leading to an R 4 2(8) ring motif. Further weak C—H⋯N interactions link the molecules along the a axis, while other C—H⋯O interactions generate zigzag chains extending along b.
Deep Compliant Control for Legged Robots

—Control policies trained using deep reinforcement learning often generate stiff, high-frequency motions in response to unexpected disturbances. To promote more natural and compliant balance recovery strategies, we propose a simple modification to the typical reinforcement learning training process. Our key insight is that stiff responses to perturbations are due to an agent’s incentive to maximize task rewards at all times, even as perturbations are being applied. As an alternative, we introduce an explicit recovery stage where tracking rewards are given irrespective of the motions generated by the control policy. This allows agents a chance to gradually recover from disturbances before attempting to carry out their main tasks. Through an in-depth analysis, we highlight both the compliant nature of the resulting control policies, as well as the benefits that compliance brings to legged locomotion. In our simulation and hardware experiments, the compliant policy achieves more robust, energy-efficient, and safe interactions with the environment.
Development of a flood water level estimation method using satellite images and a digital elevation model for the Mekong floodplain

ABSTRACT We have developed a flood water level estimation method that only employs satellite images and a DEM. The method involves three steps: (1) discriminating flood areas and identifying clumps of each flood area, (2) extracting the edges of the identified flood area using a buffering technique, and (3) performing spatial interpolation to transform the extracted elevation to flood water levels. We compared the estimated flood water levels with the observed ones. The RMSE using the RADARSAT was 1.99 and 1.30 m at river and floodplain points, respectively, whereas the RMSE using the MODIS was 4.33 and 1.33 m at the river and floodplain points, respectively. Given that most errors are attributed to the DEM, the method exhibited good performance. Furthermore, the method reproduced the flow directions and flood water level changes during the flooding period. Thus, we demonstrated that the characteristics of flood inundation can be understood even when ground observation data cannot be obtained.