Miracle-dz/UniFAR-FTUs huggingface dataset:
Toward GPGPU accelerated human electromechanical cardiac simulations

In this paper, we look at the acceleration of weakly coupled electromechanics using the graphics processing unit (GPU). Specifically, we port to the GPU a number of components of C Heart—a CPU‐based finite element code developed for simulating multi‐physics problems. On the basis of a criterion of computational cost, we implemented on the GPU the ODE and PDE solution steps for the electrophysiology problem and the Jacobian and residual evaluation for the mechanics problem. Performance of the GPU implementation is then compared with single core CPU (SC) execution as well as multi‐core CPU (MC) computations with equivalent theoretical performance. Results show that for a human scale left ventricle mesh, GPU acceleration of the electrophysiology problem provided speedups of 164 × compared with SC and 5.5 times compared with MC for the solution of the ODE model. Speedup of up to 72 × compared with SC and 2.6 × compared with MC was also observed for the PDE solve. Using the same human geometry, the GPU implementation of mechanics residual/Jacobian computation provided speedups of up to 44 × compared with SC and 2.0 × compared with MC. © 2013 The Authors. International Journal for Numerical Methods in Biomedical Engineering published by John Wiley & Sons, Ltd.
Robotics in the Home, Office, and Playing Field

Robots are gradually entering into diverse application domains such as home, office, and playing field. This article presents advanced research activities related to these domains. First is RoboCup which is an attempt to promote AI and robotics research by providing a common task for evaluation of various performance, theories, algorithms, and robot architectures. In order for robots (both physical robots and soft agents) to play a soccer game reasonably well, a wide range of technologies need to be integrated and a number of technical breakthrough must be accomplished. The recent results from the last two RoboCups are reviewed and future leagues are introduced. Second, the richer domain of service robotics has also received significant interest recently. The task here is to serve as a human assistant in an office or domestic environment, for tasks like cleaning and delivery. The human-robot interaction is a key issue to success, which poses new challenges in terms of integration of spoken dialogue, gestures, body language, etc. In addition mobile manipulation and safe navigation around humans is essential to success. These two areas integrates many different disciplines including control, perception, natural language processing, hybrid systems and handling of uncertainty, and applied to tour guiding, mail delivery, domestic services, and rescue activities.
The Physical and Psychological Impact of Multiple Sclerosis Using the MSIS-29 via the Web Portal of the UK MS Register

Introduction The MSIS-29 was developed to assess the physical and psychological impact of MS. The aims of this study were to use the responses to the MSIS-29 via the web portal of the UK MS Register to: examine the internal properties of the scale delivered via the internet, profile the cohort, and assess how well the scale measures impact of disability on the potential workforce. Methods Between May 2011 and April 2012, 4558 people with MS completed the MSIS-29(v.1). The responses were collated with basic demographic and descriptive MS data and the resulting dataset was analysed in SPSS(v.20). Results Internal consistency was high (Cronbach's alpha 0.97 MSIS-29-PHYS, 0.92 MSIS-29-PSYCH). The mean MSIS-29-PHYS score was 60.5 (50.6%) with a median of 62 and the mean MSIS-29-PSYCH score was 24.8 (43.8%) with a median of 24. Physical scores increased with age and disease duration (p<0.001, p<0.001), but there was a weak negative relationship between psychological scores and age (p<0.001). The odds of people having an employment status of sick/disabled were 7.2 (CI 5.5, 9.4, p<0.001) for people with a moderate physical score, and 22.3 (CI 17.0, 29.3, p<0.001) for people with a high physical score (relative to having a low physical score). Conclusions This largest known study of its kind has demonstrated how the MSIS-29 can be administered via the internet to characterise a cohort, and to predict the likely impact of disability on taking an active part in the workforce, as a reasonable proxy for the effects of MS on general activities. The findings examining MSIS-29-PHYS and MSIS-29-PSYCH scores against age support the use of two sub-scales, not a combined score. These results underline the importance of using a scale such as this to monitor disability levels regularly in guiding MS care to enable people to be as active as possible.
The Loran Propagation Model : Development , Analysis , Test , and Validation

Propagation phase delays known as additional secondary factors (ASFs) can occur when a Loran signal traverses non-seawater terrain. ASFs are difficult to predict, yet can cause significant errors. Accurate navigation using Loran requires precise timing; hence there is a need to determine ASF values that may be used in a Loran receiver when calculating the position solution. The Loran Propagation Model (LPM) provides predictions of received ASF values by modeling Loran signal propagation, including the effects of ground conductivity and topography. This paper presents and analyzes results from recent validation studies comparing predicted ASFs to ASFs measured during a week of data collection flights in April 2008 over the eastern part of the United States.
Order-Independence of Vector-Based Transition Systems

Semantics of many specification languages, particularly those used in the domain of hardware, is described in terms of vector-based transition systems. In such a transition system, each macro-step transition is labeled by a vector of inputs. When performing a macro-step, several inputs may potentially change. Each macro-step can thus be decomposed in a number of micro-steps, taking one input change at a time into account. This is akin to an interleaving semantics, where a concurrent step is represented by an interleaving of its constituting components. We present criteria on vector-based transition systems, which guarantee that the next state computation is independent of the order in which these micro-steps are executed. If our criteria are satisfied by the semantic definition of a certain specification, then its state-space generation or exploration algorithm needs to only consider one representative among all possible permutations of such micro-steps. We demonstrate the applicability of our criteria to the specification of transistor netlists.
Three models of clinical judgment.

3 simulation models were applied to clinical judgments of "psychotic" vs. "neurotic" for 29 judges across 7 samples of MMPI profiles. A crossvalidated multiple correlation was used as a goodness-of-fit measure for the 3 judgment models. On this basis, each of the 29 clinical judges was characterized as being either "linear" or "configural" in his utilization of MMPI test data, where configurality was operationally defined by the fit of the data to a model employing configural, or patterned, variables. It was shown that configurality was a consistent judgmental characteristic distinguishing 16 of the judges, but this characteristic was unrelated to both accuracy and amount of clinical training. Also, judges, as a group, tended to change their manner of judgment with changes in samples of MMPI profiles.
Groupwise learning for ASR k-best list reranking in spoken language translation

Quality estimation models are used to predict the quality of the output from a spoken language translation (SLT) system. When these scores are used to rerank a k-best list, the rank of the scores is more important than their absolute values. This paper proposes groupwise learning to model this rank. Groupwise features were constructed by grouping pairs, triplets or M-plets among the ASR k-best outputs of the same sentence. Regression and classification models were learnt and a score combination strategy was used to predict the rank among the k-best list. Regression models with pairwise features give a bigger gain over other model and feature constructions. Groupwise learning is robust to sentences with different ASR-confidence. This technique is also complementary to linear discriminant analysis feature projection. An overall BLEU score improvement of 0.80 was achieved on an in-domain English-to-French SLT task.
A Simple Topology Preserving Max-Flow Algorithm for Graph Cut Based Image Segmentation

In this paper, we propose a modification to the Boykov-Kolmogorov maximum flow algorithm in order to make the algorithm preserve the topology of an initial interface. This algorithm is being widely used in computer vision and image processing fields for its efficiency and speed when dealing with problems such as graph cut based image segmentation. Using our modification we are able to incorporate a topology prior into the algorithm allowing us to apply it in situations in which the inherent topological flexibility of graph cuts is inconvenient (e.g., biomedical image segmentation). Our approach exploits the simple point concept from digital geometry and is simpler and more straightforward to implement than previously introduced methods. Due to the NP-completeness of the topology preserving problem our algorithm is only an approximation and is initialization dependent. However, promising results are demonstrated on graph cut based segmentation of both synthetic and real image data.
Developing Imaged KOS with the FRSAD Model: A Conceptual Methodology

This proposal presents the methodology of indexing with images suggested by De Brito and Caribé (2015). The imagetic model is used as a compatible mechanism with FRSAD for a global information share and use of subject data, both within the library sector and beyond. The conceptual model of imagetic indexing shows how images are related to topics and ‘key-images’ are interpreted as nomens to implement the FRSAD model. Indexing with images consists of using images instead of key-words or descriptors, to represent and organize information. Implementing the imaged navigation in OPACs denotes multiple advantages derived from this rethinking the OPAC anew, since we are looking forward to sharing concepts within the subject authority data. Images, carrying linguistic objects, permeate inter-social and cultural concepts. In practice it includes translated metadata, symmetrical multilingual thesaurus, or any traditional indexing tools. iOPAC embodies efforts focused on conceptual levels as expected from librarians. Imaged interfaces are more intuitive since users do not need specific training for information retrieval, offering easier comprehension of indexing codes, larger conceptual portability of descriptors (as images), and a better interoperability between discourse codes and indexing competences affecting positively social and cultural interoperability. The imagetic methodology deploys R&D fields for more suitable interfaces taking into consideration users with specific needs such as deafness and illiteracy. This methodology arouse questions about the paradigms of the primacy of orality in information systems and pave the way to a legitimacy of multiple perspectives in document indexing by suggesting a more universal communication system based on images. Interdisciplinarity in neurosciences, linguistics and information sciences would be desirable competencies for further investigations about he nature of cognitive processes in information organization and classification while developing assistive KOS for individuals with communication problems, such autism and deafness.
On deflection worm routing on meshes

In this paper, we consider the deflection worm routing problem on two dimensional n/spl times/n meshes. Our results include: (i) an off-line algorithm for routing permutations in O(kn) steps, and (ii) a general method to obtain deflection worm routing algorithms from packet routing algorithms. <<ETX>>
Closed-Loop Restoration Approach to Blurry Images Based on Machine Learning and Feedback Optimization

Blind image deconvolution (BID) aims to remove or reduce the degradations that have occurred during the acquisition or processing. It is a challenging ill-posed problem due to a lack of enough information in degraded image for unambiguous recovery of both point spread function (PSF) and clear image. Although recently many powerful algorithms appeared; however, it is still an active research area due to the diversity of degraded images as well as degradations. Closed-loop control systems are characterized with their powerful ability to stabilize the behavior response and overcome external disturbances by designing an effective feedback optimization. In this paper, we employed feedback control to enhance the stability of BID by driving the current estimation quality of PSF to the desired level without manually selecting restoration parameters and using an effective combination of machine learning with feedback optimization. The foremost challenge when designing a feedback structure is to construct or choose a suitable performance metric as a controlled index and a feedback information. Our proposed quality metric is based on the blur assessment of deconvolved patches to identify the best PSF and computing its relative quality. The Kalman filter-based extremum seeking approach is employed to find the optimum value of controlled variable. To find better restoration parameters, learning algorithms, such as multilayer perceptron and bagged decision trees, are used to estimate the generic PSF support size instead of trial and error methods. The problem is modeled as a combination of pattern classification and regression using multiple training features, including noise metrics, blur metrics, and low-level statistics. Multi-objective genetic algorithm is used to find key patches from multiple saliency maps which enhance performance and save extra computation by avoiding ineffectual regions of the image. The proposed scheme is shown to outperform corresponding open-loop schemes, which often fails or needs many assumptions regarding images and thus resulting in sub-optimal results.
The Effects of Inference Instruction on the Reading Comprehension of English Learners With Reading Comprehension Difficulties

Inference skill is one of the most important predictors of reading comprehension. Still, there is little rigorous research investigating the effects of inference instruction on reading comprehension. There is no research investigating the effects of inference instruction on reading comprehension for English learners with reading comprehension difficulties. The current study investigated the effects of small-group inference instruction on the inference generation and reading comprehension of sixth- and seventh-grade students who were below-average readers (M = 86.7, SD = 8.1). Seventy-seven percent of student participants were designated limited English proficient. Participants were randomly assigned to 24, 40-min sessions of the inference instruction intervention (n = 39) or to business-as-usual English language arts instruction (n = 39). Membership in the treatment condition statistically significantly predicted higher outcome score on the Gates-MacGinitie Reading Test Reading Comprehension subtest (d = 0.60, 95% confidence interval [CI] [0.16, 1.03]), but not on the other measures of inference skill.
Utilizing Query Facets for Search Result Navigation

Facets provide an efficient way to analyze and navigate the search result space. However, we believe facet selection has been guided by suboptimal facet and facet term properties. In this paper, we present features that rank facets based on their utility to partition the search result documents. Moreover, we propose a computationally inexpensive facet generation algorithm, provide a novel approach to extract term lists from HTML documents, and analyze facet feedback models using BM25F as baseline retrieval model. We show that the proposed approach outperforms existing algorithms and discuss the significantly reduced computational costs.
Upregulation of miR-760 and miR-186 Is Associated with Replicative Senescence in Human Lung Fibroblast Cells

We have previously shown that microRNAs (miRNAs) miR-760, miR-186, miR-337-3p, and miR-216b stimulate premature senescence through protein kinase CK2 (CK2) down-regulation in human colon cancer cells. Here, we examined whether these four miRNAs are involved in the replicative senescence of human lung fibroblast IMR-90 cells. miR-760 and miR-186 were significantly upregulated in replicatively senescent IMR-90 cells, and their joint action with both miR-337-3p and miR-216b was necessary for efficient downregulation of the α subunit of CK2 (CK2α) in IMR-90 cells. A mutation in any of the four miRNA-binding sequences within the CK2α 3′-untranslated region (UTR) indicated that all four miRNAs should simultaneously bind to the target sites for CK2α downregulation. The four miRNAs increased senescence-associated β-galactosidase (SA-β-gal) staining, p53 and p21Cip1/WAF1 expression, and reactive oxygen species (ROS) production in proliferating IMR-90 cells. CK2α over-expression almost abolished this event. Taken together, the present results suggest that the upregulation of miR-760 and miR-186 is associated with replicative senescence in human lung fibroblast cells, and their cooperative action with miR-337-3p and miR-216b may induce replicative senescence through CK2α downregulation-dependent ROS generation.
Identity as a rights advocate: contextualizing the understanding of mental health user associations in Argentina

Abstract The importance of mental health services users’ participation regarding rights advocacy has been recognized during the last decades. The analysis of the cultural context in which rights advocacy takes place seems to be useful for understanding the different levels of development of users’ advocacy around the world. With the aim to describe the relationship between the perceptions of participants of a mental health rights advocacy association regarding their involvement in advocacy, its effects, and the cultural context, a descriptive qualitative study was carried out in Argentina, based on a ‘what matters most’ framework. Results show that the recovery of the culturally valued identity of militante (advocate) was seen as an important benefit. Social rights such as work and housing were ranked as the most important to advocate for. Based on the Argentinean National Mental Health Law, the relationship between top-down and bottom-up approaches toward users’ participation are discussed
Potential for genomic instability associated with retrotranspositionally-incompetent L1 loci

Expression of the L1 retrotransposon can damage the genome through insertional mutagenesis and the generation of DNA double-strand breaks (DSBs). The majority of L1 loci in the human genome are 5′-truncated and therefore incapable of retrotransposition. While thousands of full-length L1 loci remain, most are retrotranspositionally-incompetent due to inactivating mutations. However, mutations leading to premature stop codons within the L1 ORF2 sequence may yield truncated proteins that retain a functional endonuclease domain. We demonstrate that some truncated ORF2 proteins cause varying levels of toxicity and DNA damage when chronically overexpressed in mammalian cells. Furthermore, transfection of some ORF2 constructs containing premature stop codons supported low levels of Alu retrotransposition, demonstrating the potential for select retrotranspositionally-incompetent L1 loci to generate genomic instability. This result suggests yet another plausible explanation for the relative success of Alu elements in populating the human genome. Our data suggest that a subset of retrotranspositionally-incompetent L1s, previously considered to be harmless to genomic integrity, may have the potential to cause chronic DNA damage by introducing DSBs and mobilizing Alu. These results imply that the number of known L1 loci in the human genome that potentially threaten its stability may not be limited to the retrotranspositionally active loci.
Ecological Succession, Hydrology and Carbon Acquisition of Biological Soil Crusts Measured at the Micro-Scale

The hydrological characteristics of biological soil crusts (BSCs) are not well understood. In particular the relationship between runoff and BSC surfaces at relatively large (>1 m2) scales is ambiguous. Further, there is a dearth of information on small scale (mm to cm) hydrological characterization of crust types which severely limits any interpretation of trends at larger scales. Site differences and broad classifications of BSCs as one soil surface type rather than into functional form exacerbate the problem. This study examines, for the first time, some hydrological characteristics and related surface variables of a range of crust types at one site and at a small scale (sub mm to mm). X-ray tomography and fine scale hydrological measurements were made on intact BSCs, followed by C and C isotopic analyses. A ‘hump’ shaped relationship was found between the successional stage/sensitivity to physical disturbance classification of BSCs and their hydrophobicity, and a similar but ‘inverse hump’ relationship exists with hydraulic conductivity. Several bivariate relationships were found between hydrological variables. Hydraulic conductivity and hydrophobicity of BSCs were closely related but this association was confounded by crust type. The surface coverage of crust and the microporosity 0.5 mm below the crust surface were closely associated irrespective of crust type. The δ 13C signatures of the BSCs were also related to hydraulic conductivity, suggesting that the hydrological characteristics of BSCs alter the chemical processes of their immediate surroundings via the physiological response (C acquisition) of the crust itself. These small scale results illustrate the wide range of hydrological properties associated with BSCs, and suggest associations between the ecological successional stage/functional form of BSCs and their ecohydrological role that needs further examination.
Task assignment strategies for pools of baseband computation units in 4G cellular networks

A promising architecture for future cellular mobile networks is to place remote radio heads on the cell towers and connect those via fibers with a centralized pool of baseband units. Among other things, the centralization of baseband computation facilitates multiplexing gains and can thereby save compute resources. To realize these gains, an efficient and load-balancing assignment of compute jobs to computation units is required. In contrast to approaches in literature, our architecture virtualizes the processing for each UE separately. It thereby provides a finer granularity and allows to newly decide the assignment of a compute job to a processing unit whenever a UE starts transmitting data. In this publication, we present multiple heuristics to decide this assignment. We compare the efficiency of the assignment and the perceived service quality realized by the heuristics with an ideal assignment and the classical static cell-based assignment. Our evaluation shows that by using a good assignment heuristic, about 50% of the hardware resources can be saved.