Miracle-dz/UniFAR-FTUs huggingface dataset:
Modelling of micro tubes hydroforming processes

The wall thickness of formed micro tubes is not uniformly distributed and is difficult to control. The macro mechanics FE modelling can not be used to simulate such effects in micro tubes hydroforming processes. In this paper, a simplified plain strain Crystal Plasticity Finite Element (CPFE) based modelling technique has been developed and used to capture the localized thinning and failure features for hydroforming of micro tubes. Grains within the tube workpiece, their distributions and orientations are generated automatically using the developed VGRAIN system. A set of crystal viscoplasticity model is implemented in ABAQUS/Explicit FE code through the user-defined subroutine, VUMAT. Several cases have been studied and the localised thinning has been analysed for different microstructures of the material using the CPFE modelling technique, and the method is verified by the test result.
A System for Text Summarization Based on Word Importance Measures

This paper presents a text summarization system based on word importance measures. It produces a summary through a part or all of the following four steps. First, we assign local and global scores to the nouns in each sentence according to the functional roles they play and the significancy they assume in the text for summarization. Second, based on the local and global scores, we calculate the importance value of each sentence. Third, the sentences with the importance values big enough for a summary are selected. Fourth and finally, a coherency test is given and we make the final decision for inclusion or exclusion of the selected sentences into the summary. We compare the summaries thus produced with the ones by humans and other systems.
The potential to induce glial differentiation is conserved between Drosophila and mammalian glial cells missing genes

Drosophila glial cells missing (gcm) is a key gene that determines the fate of stem cells within the nervous system. Two mouse gcm homologs have been identified, but their function in the nervous system remains to be elucidated. To investigate their function, we constructed retroviral vectors harboring Drosophila gcm and two mouse Gcm genes. Expression of these genes appeared to influence fibroblast features. In particular, mouse Gcm1 induced the expression of astrocyte-specific Ca2+-binding protein, S100β, in those cells. Introduction of the mouse Gcm1 gene in cultured cells from embryonic brains resulted in the induction of an astrocyte lineage. This effect was also observed by in utero injection of retrovirus harboring mouse Gcm1 into the embryonic brain. However, cultures from mouse Gcm1-deficient mouse brains did not exhibit significant reductions in the number of astrocytes. Furthermore, in situ hybridization analysis of mouse Gcm1 mRNA revealed distinct patterns of expression in comparison with other well-known glial markers. The mammalian homolog of Drosophila gcm, mouse Gcm1, exhibits the potential to induce gliogenesis, but may function in the generation of a minor subpopulation of glial cells.
Minority Stress and Bisexual Women’s Disordered Eating

The purpose of this study was to explore the link between bisexual women’s experiences of anti-bisexual discrimination and disordered eating, while examining potential mediating variables underlying this link: outness/identity concealment and maladaptive coping (i.e., coping via internalization, detachment, and drug and alcohol use). A total of 353 bisexual women participated in this study. The relationship between outness and disordered eating was not significant. Higher levels of anti-bisexual discrimination were related to more disordered eating behaviors, and this relationship was mediated by coping via internalization. However, anti-bisexual discrimination was directly related to more coping via detachment and drug and alcohol use. Findings from the study suggest that attending to bisexual women’s experiences of discrimination in counseling is particularly important. Moreover, assisting bisexual women in resisting internalization of discriminatory experiences may be a potential point of intervention for mental health professionals working with bisexual women experiencing disordered eating.
Predictive reliability using FEA simulations of power stacked ceramic capacitors for aeronautical applications

Viscoplastic finite-element simulation was used to predict reliability of solder joints in a high temperature 4-chips stacked capacitor mounted on a PCB under temperature cycling (-55°C to +125°C, 45min ramps/60min dwells). A three-dimensional (3D) model was built considering the materials properties of a commercial component. Capacitor materials were determined by using scanning electron microscopy (SEM) and energy dispersive x-ray spectroscopy (EDX). Thermomechanical properties, Anand parameters and Darveaux constant of the materials were incorporated into the simulation procedure to evaluate the mechanical strains and the variations in the plastic energy density for the high temperature solder joints of the 4-chips stacked capacitor. The number of cycles before the crack initiation in the solder joint and the number of cycles to failure have been calculated using Darveaux methodology. The obtained results showed that the maximum mechanical strains were localized at the bottom chip. It has been found that the number of cycles to failure exceeded 50,000.
The Role of Dopamine in Temporal Uncertainty

The temporal preparation of motor responses to external events (temporal preparation) relies on internal representations of the accumulated elapsed time (temporal representations) before an event occurs and on estimates about its most likely time of occurrence (temporal expectations). The precision (inverse of uncertainty) of temporal preparation, however, is limited by two sources of uncertainty. One is intrinsic to the nervous system and scales with the length of elapsed time such that temporal representations are least precise for longest time durations. The other is external and arises from temporal variability of events in the outside world. The precision of temporal expectations thus decreases if events become more variable in time. It has long been recognized that the processing of time durations within the range of hundreds of milliseconds (interval timing) strongly depends on dopaminergic (DA) transmission. The role of DA for the precision of temporal preparation in humans, however, remains unclear. This study therefore directly assesses the role of DA in the precision of temporal preparation of motor responses in healthy humans. In a placebo-controlled double-blind design using a selective D2-receptor antagonist (sulpiride) and D1/D2 receptor antagonist (haloperidol), participants performed a variable foreperiod reaching task, under different conditions of internal and external temporal uncertainty. DA blockade produced a striking impairment in the ability of extracting temporal expectations across trials and on the precision of temporal representations within a trial. Large Weber fractions for interval timing, estimated by fitting subjective hazard functions, confirmed that this effect was driven by an increased uncertainty in the way participants were experiencing time. This provides novel evidence that DA regulates the precision with which we process time when preparing for an action.
Very Early Brain Damage Leads to Remodeling of the Working Memory System in Adulthood: A Combined fMRI/Tractography Study

The human brain can adapt to overcome injury even years after an initial insult. One hypothesis states that early brain injury survivors, by taking advantage of critical periods of high plasticity during childhood, should recover more successfully than those who suffer injury later in life. This hypothesis has been challenged by recent studies showing worse cognitive outcome in individuals with early brain injury, compared with individuals with later brain injury, with working memory particularly affected. We invited individuals who suffered perinatal brain injury (PBI) for an fMRI/diffusion MRI tractography study of working memory and hypothesized that, 30 years after the initial injury, working memory deficits in the PBI group would remain, despite compensatory activation in areas outside the typical working memory network. Furthermore we hypothesized that the amount of functional reorganization would be related to the level of injury to the dorsal cingulum tract, which connects medial frontal and parietal working memory structures. We found that adults who suffered PBI did not significantly differ from controls in working memory performance. They exhibited less activation in classic frontoparietal working memory areas and a relative overactivation of bilateral perisylvian cortex compared with controls. Structurally, the dorsal cingulum volume and hindrance-modulated orientational anisotropy was significantly reduced in the PBI group. Furthermore there was uniquely in the PBI group a significant negative correlation between the volume of this tract and activation in the bilateral perisylvian cortex and a positive correlation between this activation and task performance. This provides the first evidence of compensatory plasticity of the working memory network following PBI. SIGNIFICANCE STATEMENT Here we used the example of perinatal brain injury (PBI) associated with very preterm birth to study the brain's ability to adapt to injury sustained early in life. In adulthood, individuals with PBI did not show significant deficits in working memory, but exhibited less activation in typical frontoparietal working memory areas. They also showed a relative overactivation of nontask-specific brain areas (perisylvian cortex) compared with controls, and such activation was negatively correlated with the size of white matter pathways involved in working memory (dorsal cingulum). Furthermore, this “extra” activation was associated with better working memory performance and could represent a novel compensatory mechanism following PBI. Such information could inform the development of neuroscience-based cognitive interventions following PBI.
Combining BOW representation and Appriori algorithm for text mining

The field of text mining seeks to extract useful information from unstructured textual data through the identification and exploration of interesting patterns. The techniques employed usually do not involve deep linguistic analysis or parsing, but rely on simple "Bag-Of-Words" (BPW) text representations based on vector space. In this paper we combine the BOW representation and Appriori algorithm to detect clusters of similar documents and associated rules.
Children's perceptions of disability in the context of elementary school dance education

This study applied a relational ethics lens to investigate the perceptions of disability of elementary school children without disabilities within their dance education contexts. 14 children between the ages 8 and 11 from five elementary schools participated. A qualitative interpretivist approach guided the study. Data collection involved two small group semi-structured interviews, a drawing activity, class observation, and researcher field notes. Data analysis followed thematic analysis guidelines. Findings are conceptualized based on four themes: (a) disability as limited ability; (b) difference as normalized; (c) dance as expression of uniqueness; and (d) classmates as helpers. While understanding disability as a limitation, participation in a dance education environment that encouraged collaborative creative movement exploration contributed to these children’s learning to view difference as ordinary, and appreciate every person’s unique ways to dance.
Clean Energy Consumption Forecast Based on GA-LSSVR Hybrid Learning Paradigm

In order to improve the forecasting accuracy for clean energy consumption with inherently high complexity, a hybrid learning paradigm integrating genetic algorithm (GA) and least squares support vector regression (LSSVR), i.e., GA-LSSVR model, is formulated in this study. In this learning paradigm, LSSVR, as a powerful artificial intelligence tool, is employed to forecast clean energy consumption, furthermore, GA is employed to determine the parameters in LSSVR. Taking the Chinese hydropower energy as sample, empirical results indicate that the GA-LSSVR model significantly outperforms other benchmark models, including Artificial neural network (ANN), Autoregressive integrated moving average (ARIMA) and a set of hybrid models based on LSSVR and other searching methods (e.g., particle swarm optimization (PSO) and simulated annealing (SA)), in both level prediction accuracy and directional forecasting. The GA-LSSVR learning paradigm can be extended as an effective prediction technique for other complex data.
Overconfidence, Insurance, and Paternalism

It is well known that when agents are fully rational, compulsory public insurance may make all agents better off in the Rothschild and Stiglitz (1976) model of insurance markets. We find that when sufficiently many agents underestimate their personal risks, compulsory insurance makes low-risk agents worse off. Hence, behavioral biases may weaken some of the well-established rationales for government intervention based on asymmetric information. (JEL D82, G22)
HDR-VDP-2.2: a calibrated method for objective quality prediction of high-dynamic range and standard images

Abstract. With the emergence of high-dynamic range (HDR) imaging, the existing visual signal processing systems will need to deal with both HDR and standard dynamic range (SDR) signals. In such systems, computing the objective quality is an important aspect in various optimization processes (e.g., video encoding). To that end, we present a newly calibrated objective method that can tackle both HDR and SDR signals. As it is based on the previously proposed HDR-VDP-2 method, we refer to the newly calibrated metric as HDR-VDP-2.2. Our main contribution is toward improving the frequency-based pooling in HDR-VDP-2 to enhance its objective quality prediction accuracy. We achieve this by formulating and solving a constrained optimization problem and thereby finding the optimal pooling weights. We also carried out extensive cross-validation as well as verified the performance of the new method on independent databases. These indicate clear improvement in prediction accuracy as compared with the default pooling weights. The source codes for HDR-VDP-2.2 are publicly available online for free download and use.
A PCA Based Visual DCT Feature Extraction Method for Lip-Reading

This paper proposes a PCA based method to reduce the dimensionality of DCT coefficients for visual only lip-reading systems. A three-stage pixel based visual front end is adopted. First, DCT or block-based DCT features are extracted. Second, Principal Component Analysis is applied for dimension reduction. Finally, all the feature vectors are normalized into a uniform scale. This work investigates this three-stage method, comparing with PCA and two DCT based approaches whose features are selected manually. In the latter manner, PCA coefficients are selected according to energy while the reduction of DCT coefficients leans to the left components in the left-top corner. Experiments prove that the dimension reduction task based on PCA does improve the recognition accuracy when the final dimension is below a certain value. They also show that DCT and block-based DCT work similarly for lip reading task, outperforming PCA slightly.
Statistical descriptors for human actions classification

The objective of this study is to investigate alternative ways for representing suitably, with the fewest possible assumptions, the information derived from video recordings. It proposes a set of statistical descriptors capable of summarizing all the available information from each video frame. A sequence of such features expresses the object motion implicitly without the need for object detection techniques and tedious pre-processing. A video application such as the human action recognition is then tackled as a time-series classification problem. Neural networks are used for the time-series learning; when they are simulated with a new human action video, their predictions constitute the input a typical classifier would require, in order for it to decide which model (from the known time-series) has possibly generated this video.
KEGG for integration and interpretation of large-scale molecular data sets

Kyoto Encyclopedia of Genes and Genomes (KEGG, http://www.genome.jp/kegg/ or http://www.kegg.jp/) is a database resource that integrates genomic, chemical and systemic functional information. In particular, gene catalogs from completely sequenced genomes are linked to higher-level systemic functions of the cell, the organism and the ecosystem. Major efforts have been undertaken to manually create a knowledge base for such systemic functions by capturing and organizing experimental knowledge in computable forms; namely, in the forms of KEGG pathway maps, BRITE functional hierarchies and KEGG modules. Continuous efforts have also been made to develop and improve the cross-species annotation procedure for linking genomes to the molecular networks through the KEGG Orthology system. Here we report KEGG Mapper, a collection of tools for KEGG PATHWAY, BRITE and MODULE mapping, enabling integration and interpretation of large-scale data sets. We also report a variant of the KEGG mapping procedure to extend the knowledge base, where different types of data and knowledge, such as disease genes and drug targets, are integrated as part of the KEGG molecular networks. Finally, we describe recent enhancements to the KEGG content, especially the incorporation of disease and drug information used in practice and in society, to support translational bioinformatics.
A high-robustness and low-cost model for cascading failures

We study numerically the cascading failure problem by using artificially created scale-free networks and the real network structure of the power grid. The capacity for a vertex is assigned as a monotonically increasing function of the load (or the betweenness centrality). Through the use of a simple functional form with two free parameters, it is revealed that it is indeed possible to make networks more robust at a lower cost. We suggest that our method to prevent cascade by protecting less vertices is particularly important for the design of more robust real-world networks to cascading failures.
Sensitive Data Protection of DBaaS Using OPE and FPE

DBaaS (Database as a Service) is a service provided and managed by the cloud provider and supports traditional database functionalities. The DBaaS use multi-tenant architecture to support multiple customers. The biggest problem concerned with DBaaS is the privacy and security of the data contained in the database stored in the cloud environment. The database is stored in a third party data center and it is assumed to be as untrusted. The database is therefore encrypted in order to prevent any data leaks on the third party data center. The result of any query to the database is decrypted at the service provider site before it is sent to the user. The above mentioned solution have two disadvantages. Firstly, the encryption and decryption are done at the server side and hence the cloud owner can extract information from the database. Secondly, the encryption of database does not support range queries on the database. The proposed framework focuses on securing database by supporting range queries and storing sensitive information with protection of memory leak. It performs database encryption, query encryption and also supports range query over encrypted databases. A double layered encryption mechanism is used for sensitive data and a single layer encryption is used for non-sensitive data. Order Preserving Encryption (OPE) is used for single layer encryption. OPE maintains the order in an encrypted database and so range query can be performed over encrypted database using an encrypted query. The drawback associated with OPE is the attacker can guess the value based on the ordering of data and so for sensitive attributes in the database, a double layered encryption using Format Preserving Encryption (FPE) followed by OPE symmetric key encryption algorithm is proposed.
MR tracking of transplanted cells with “positive contrast” using manganese oxide nanoparticles

Rat glioma cells were labeled using electroporation with either manganese oxide (MnO) or superparamagnetic iron oxide (SPIO) nanoparticles. The viability and proliferation of SPIO‐labeled cells (1.9 mg Fe/ml) or cells electroporated with a low dose of MnO (100 μg Mn/ml) was not significantly different from unlabeled cells; a higher MnO dose (785 μg Mn/ml) was found to be toxic. The cellular ion content was 0.1–0.3 pg Mn/cell and 4.4 pg Fe/cell, respectively, with cellular relaxivities of 2.5–4.8 s−1 (R1) and 45–84 s−1 (R2) for MnO‐labeled cells. Labeled cells (SPIO and low‐dose MnO) were each transplanted in contralateral brain hemispheres of rats and imaged in vivo at 9.4T. While SPIO‐labeled cells produced a strong “negative contrast” due to the increase in R2, MnO‐labeled cells produced “positive contrast” with an increased R1. Simultaneous imaging of both transplants with opposite contrast offers a method for MR “double labeling” of different cell populations. Magn Reson Med 60:1–7, 2008. © 2008 Wiley‐Liss, Inc.
Skin-active shape model for face alignment

Active shape model is considered as one of the most useful methods for face alignment. In this paper, we propose using a skin color likelihood transformation method, instead of using a gray level, to model the local profile around each landmark point of active shape model. The goal of using this method is to model the local profile more accurate and to decrease the influence from different races and lighting conditions. To solve the detail losing phenomena problem when using the skin color likelihood transformation method, combination of skin color likelihood and gray-level in multi-resolution is suggested.
A geometric approach to quadratic optimization: an improved method for solving strongly underdetermined systems in CT

The problem of image reconstruction from projections in computerized tomography, when cast as a system of linear equations, leads to an inconsistent system. Hence, a common approach to this problem is to seek a solution that minimizes some optimization criterion, such as minimization of the residual (least-squares solution) by quadratic optimization. The QUAD algorithm is one well-studied method for quadratic optimization which applies the conjugate gradient to a certain so-called “normal equations” system derived from the original system. The concept of a geometric algorithm is introduced and defined as a method that is independent of any particular algebraic representation of the hyperplanes defined by the equations. An example of a geometric algorithm is the well known ART algorithm which, when implemented with a small relaxation parameter, achieves excellent results. Any non-geometric algorithm can be converted to a geometric one by normalizing the equations before applying the algorithm. This concept, when applied to QUAD, results in a geometric algorithm called NQUAD. ART, QUAD, and NQUAD were tested on various phantom reconstructions and evaluated with respect to image quality, convergence measures, and runtime efficiency. The results indicate that NQUAD is always preferable to QUAD, thus proving the usefulness of the geometric approach. Although ART is somewhat better than NQUAD in most standard cases, NQUAD is much better when the system is strongly underdetermined (a situation corresponding to low dose radiation), and this is even more pronounced with low-contrast images. Preliminary results presented at the 8th IASTED Conference on Computer Graphics & Imaging, August 2005.