Miracle-dz/UniFAR-FTUs huggingface dataset:
A suggestive interface for image guided 3D sketching

We present an image guided pen-based suggestive interface for sketching 3D wireframe models. Rather than starting from a blank canvas, existing 2D images of similar objects serve as a guide to the user. Image based filters enable attraction, smoothing, and resampling of input curves, and allows for their selective application using pinning and gluing techniques. New input strokes also invoke suggestions of relevant geometry that can be used, reducing the need to explicitly draw all parts of the new model. All suggestions appear in-place with the model being built, in the user's focal attention space. A curve matching algorithm seamlessly augments basic suggestions with more complex ones from a database populated with previously used geometry. The interface also incorporates gestural command input, and interaction techniques for camera controls that enable smooth transitions between orthographic and perspective views.
Mitochondrial GTP Insensitivity Contributes to Hypoglycemia in Hyperinsulinemia Hyperammonemia by Inhibiting Glucagon Release

Mitochondrial GTP (mtGTP)-insensitive mutations in glutamate dehydrogenase (GDHH454Y) result in fasting and amino acid–induced hypoglycemia in hyperinsulinemia hyperammonemia (HI/HA). Surprisingly, hypoglycemia may occur in this disorder despite appropriately suppressed insulin. To better understand the islet-specific contribution, transgenic mice expressing the human activating mutation in β-cells (H454Y mice) were characterized in vivo. As in the humans with HI/HA, H454Y mice had fasting hypoglycemia, but plasma insulin concentrations were similar to the controls. Paradoxically, both glucose- and glutamine-stimulated insulin secretion were severely impaired in H454Y mice. Instead, lack of a glucagon response during hypoglycemic clamps identified impaired counterregulation. Moreover, both insulin and glucagon secretion were impaired in perifused islets. Acute pharmacologic inhibition of GDH restored both insulin and glucagon secretion and normalized glucose tolerance in vivo. These studies support the presence of an mtGTP-dependent signal generated via β-cell GDH that inhibits α-cells. As such, in children with activating GDH mutations of HI/HA, this insulin-independent glucagon suppression may contribute importantly to symptomatic hypoglycemia. The identification of a human mutation causing congenital hypoglucagonemic hypoglycemia highlights a central role of the mtGTP–GDH–glucagon axis in glucose homeostasis.
Coded beamforming for block fading vector channel

Most recent studies of transmit diversity and space-time coding have focused on random channels which feature rich scattering around both transmitters and receivers. In this paper, we consider a highly structured vector channel in a block fading scenario. Specular propagation and local scattering are assumed at the base-station and mobile station respectively. The channel knowledge required for the transmit design includes: a subspace formed by direction vectors of specular propagation and length of the fading coherent interval. The relatively stationary subspace information reflecting the spatial power spectral distribution is exploited to derive a code design criterion. The optimal design turns out to be a joint coding and beamforming scheme and resistance to fading within the subspace is provided by such code.
Data association in stochastic mapping using the joint compatibility test

In this paper, we address the problem of robust data association for simultaneous vehicle localization and map building. We show that the classical gated nearest neighbor approach, which considers each matching between sensor observations and features independently, ignores the fact that measurement prediction errors are correlated. This leads to easily accepting incorrect matchings when clutter or vehicle errors increase. We propose a new measurement of the joint compatibility of a set of pairings that successfully rejects spurious matchings. We show experimentally that this restrictive criterion can be used to efficiently search for the best solution to data association. Unlike the nearest neighbor, this method provides a robust solution in complex situations, such as cluttered environments or when revisiting previously mapped regions.
Comparative Genomic and Transcriptomic Analyses of LNCaP and C4-2B Prostate Cancer Cell Lines

The LNCaP and C4-2B cell lines form an excellent preclinical model to study the development of metastatic castration-resistant prostate cancer, since C4-2B cells were derived from a bone metastasis that grew in nude mice after inoculation with the LNCaP-derived, castration-resistant C4-2 cells. Exome sequencing detected 2188 and 3840 mutations in LNCaP and C4-2B cells, respectively, of which 1784 were found in both cell lines. Surprisingly, the parental LNCaP cells have over 400 mutations that were not found in the C4-2B genome. More than half of the mutations found in the exomes were confirmed by analyzing the RNA-seq data, and we observed that the expressed genes are more prone to mutations than non-expressed genes. The transcriptomes also revealed that 457 genes show increased expression and 246 genes show decreased expression in C4-2B compared to LNCaP cells. By combining the list of C4-2B-specific mutations with the list of differentially expressed genes, we detected important changes in the focal adhesion and ECM-receptor interaction pathways. Integration of these pathways converges on the myosin light chain kinase gene (MLCK) which might contribute to the metastatic potential of C4-2B cells. In conclusion, we provide extensive databases for mutated genes and differentially expressed genes in the LNCaP and C4-2B prostate cancer cell lines. These can be useful for other researchers using these cell models.
Light Region-based Techniques for Process Discovery

A central problem in the area of Process Mining is to obtain a formal model that represents selected behavior of a system. The theory of regions has been applied to address this problem, enabling the derivation of a Petri net whose language includes a set of traces. However, when dealing with real-life systems, the available tool support for performing such a task is unsatisfactory, due to the complex algorithms that are required. In this paper, the theory of regions is revisited to devise a novel technique that explores the space of regions by combining the elements of a region basis. Due to its light space requirements, the approach can represent an important step for bridging the gap between the theory of regions and its industrial application. Experimental results show that there is improvement in orders of magnitude in comparison with state-of-the-art tools for the same task.
Multi-period traffic engineering of resilient networks for energy efficiency

In this paper we consider the problem of minimizing the energy consumption of IP networks, exploiting the traffic variations over a set of time intervals, while guaranteeing the network QoS and survivability. Energy savings are achieved by putting into sleep mode cards and chassis, when they are not necessary. Network survivability is based on a dedicated protection scheme, wherein a dedicated backup path is assigned to each demand. The multi-period optimization is constrained by inter-period limitations necessary for guaranteeing network stability. Both exact and heuristic methods are proposed. Results obtained with realistic networks operated with flow-based routing protocols (i.e. MPLS) show that up to 60% of the energy savings can be achieved without negatively affecting network resiliency.
Altruists Proliferate Even at a Selective Disadvantage within Their Own Niche

The evolutionary origin of altruism is a long-standing puzzle. Numerous explanations have been proposed, most prominently based on inclusive fitness or group selection. One possibility that has not yet been considered is that new niches will be created disproportionately often when altruism appears, perhaps by chance, causing altruists to be over-represented in such new niches. This effect is a novel variant of group selection in which altruistic groups benefit by discovering unoccupied niches instead of by competing for the limited resources within a single niche. Both an analytical population genetics model and computational simulations support that altruism systematically arises due to this side effect of increased carrying capacity even when it is strongly selected against within any given niche. In fact, even when selection is very strongly negative and altruism does not develop in most populations, it can still be expected to be observed in a consistent fraction of species. The ecological structure provided by niches thereby may be sufficient for altruists to proliferate even if they are always at a disadvantage within each niche considered individually.
Functionalized low-density polyethylene via a novel photografting method and its adhesion properties

A novel photografting, non-vapor and non-liquid phase method was developed to functionalize low-density polyethylene (LDPE) powder. The structure and adhesion properties of LDPE grafted with methacrylic acid (MAA) by this method were studied by Fourier transform infrared (FTIR) spectroscopy, electron spectroscopy for chemical analysis (ESCA), scanning electron microscopy (SEM) and water contact angle, peel strength and graft degree measurements. The results show that the chemically inert LDPE powder can be graft-copolymerized with MAA via this non-vapor and non-liquid phase photografting method. The graft degree increases with the ultraviolet irradiation time. The hydrophilicity of the grafted LDPE powder and the peel strength of high density polyethylene (HDPE)/steel joint with the grafted LDPE powder used as hot melt adhesive are improved considerably as compared to that with the ungrafted LDPE powder.
Kernel Regression for Signals over Graphs

We propose kernel regression for signals over graphs. The optimal regression coefficients are learnt using the assumption that the target vector is a smooth signal over an underlying graph. The condition is imposed using a graph-Laplacian based regularization. We discuss how the proposed kernel regression exhibits a smoothing effect, simultaneously achieving noise-reduction and graph-smoothness. We further extend the kernel regression to simultaneously learn the underlying graph and the regression coefficients. We validate our theory by application to various synthesized and real-world graph signals. Our experiments show that kernel regression over graphs outperforms conventional regression, particularly for small sized training data and under noisy training. We also observe that kernel regression reveals the structure of the underlying graph even with a small number of training samples.
Economically attractive route for the preparation of high quality magnetic nanoparticles by the thermal decomposition of iron(III) acetylacetonate

The thermal decomposition (TD) methods are among the most successful in obtaining magnetic nanoparticles with a high degree of control of size and narrow particle size distribution. Here we investigated the TD of iron(III) acetylacetonate in the presence of oleic acid, oleylamine, and a series of alcohols in order to disclose their role and also investigate economically attractive alternatives for the synthesis of iron oxide nanoparticles without compromising their size and shape control. We have found that some affordable and reasonably less priced alcohols, such as 1,2-octanediol and cyclohexanol, may replace the commonly used and expensive 1,2-hexadecanediol, providing an economically attractive route for the synthesis of high quality magnetic nanoparticles. The relative cost for the preparation of Fe3O4 NPs is reduced to only 21% and 9% of the original cost when using 1,2-octanediol and cyclohexanol, respectively.
DFRN: a new approach for duplication based scheduling for distributed memory multiprocessor systems

Duplication based scheduling (DBS) is a relatively new approach for solving multiprocessor scheduling problems. The problem is defined as finding an optimal schedule which minimizes the parallel execution time of an application on a target system. We classify DBS algorithms into two categories according to the task duplication method used. We then present our new DBS algorithm that extracts the strong features of the two categories of DBS algorithms. Our simulation study shows that the proposed algorithm achieves considerable performance improvement over existing DBS algorithms with equal or less time complexity. We analytically obtain the boundary condition for the worst case behavior of the proposed algorithm and also prove that the algorithm generates an optimal schedule for a tree structured input directed acyclic graph.
Optimal Power Flow of Multiple Energy Carriers

This paper presents an approach for combined optimization of coupled power flows of different energy infrastructures such as electricity, gas, and district heating systems. A steady state power flow model is presented that includes conversion and transmission of an arbitrary number of energy carriers. The couplings between the different infrastructures are explicitly taken into account based on the new concept of energy hubs. With this model, combined economic dispatch and optimal power flow problems are stated covering transmission and conversion of energy. A general optimality condition for optimal dispatch of multiple energy carriers is derived, and the approach is compared with the standard method used for electrical power systems. Finally, the developed tools are demonstrated in examples
Coverage and framing of racial and ethnic health disparities in US newspapers, 1996-2005.

OBJECTIVES We examined how causes of and solutions to racial/ethnic health disparities are covered and framed in newspapers over time. METHODS We used LexisNexis to identify articles on racial/ethnic health disparities published from 1996 through 2005 in 40 US newspapers. We coded articles for diseases and racial/ethnic groups mentioned; whether causes and solutions were framed as genetic, behavioral, health care, or societal responsibility; and whether a social-justice rationale for eliminating racial/ethnic health disparities was invoked. RESULTS We identified 3823 racial/ethnic health disparity articles. Coverage peaked in 1998 and has declined since. Disparities in HIV/AIDS, cardiovascular disease, and cancer generated the most coverage. Articles focused primarily on African Americans. Only 30% of articles provided causal or solution explanations, with academic researchers providing the most causal explanations and advocacy groups providing the most solutions. For both causes and solutions, behavioral explanations dominated the discourse, followed by societal, health care, and genetic explanations. Only 4% of articles invoked a social-justice rationale. CONCLUSIONS The dominance of behavioral explanations may limit public support for policy solutions to eliminate racial/ethnic health disparities. Future research should examine the design and dissemination of effective messages about the social determinants of health.
Distinguishing Subtypes in Primary Progressive Aphasia: Application of the Sydney Language Battery

Background/Aims: Primary progressive aphasia (PPA) comprises three main subtypes, varying in clinical features, patterns of brain atrophy, and underlying pathology. Differentiation of these variants is important for treatment and planning; however, simple, effective cognitive tests to aid diagnosis are lacking. This study introduces a new language battery - the SYDBAT (Sydney Language Battery) - to assist clinicians. Methods: Fifty-seven PPA patients and 54 age- and education-matched healthy controls were compared on naming, repetition, word comprehension, and semantic association subtests. Results: Significant group differences were found for all tasks, reflecting different language profiles for each group. Using discriminative function analysis, 80% of PPA cases were correctly classified from three SYDBAT scores, from which a simple diagnostic algorithm was defined. Conclusion: The SYDBAT is a fast and simple tool which provides a valuable adjunct to clinicians diagnosing PPA.
Adapting lexical representation and OOV handling from written to spoken language with word embedding

Word embeddings have become ubiquitous in NLP, especially when using neural networks. One of the assumptions of such representations is that words with similar properties have similar representation, allowing for better generalization from subsequent models. In the standard setting, two kinds of training corpora are used: a very large unlabeled corpus for learning the word embedding representations; and an in-domain training corpus with gold labels for training classifiers on the target NLP task. Because of the amount of data required to learn embeddings, they are trained on large corpus of written text. This can be an issue when dealing with non-canonical language, such as spontaneous speech: embeddings have to be adapted to fit the particularities of spoken transcriptions. However the adaptation corpus available for a given speech application can be limited, resulting in a high number of words from the embedding space not occurring in the adaptation space. We present in this paper a method for adapting an embedding space trained on written text to a spoken corpus of limited size. In particular we deal with words from the embedding space not occurring in the adaptation data. We report experiments done on a Part-OfSpeech task on spontaneous speech transcriptions collected in a call-centre. We show that our word embedding adaptation approach outperforms state-of-the-art Conditional Random Field approach when little in-domain adaptation data is available.
Microscale Characterization of Mechanical Properties

AbstractLilliputian techniques for measuring the mechanical response of microscale specimens are being developed to characterize the performance and reliability of microelectromechanical systems (MEMS) and other small-scale entities. The challenges associated with the preparation, handling, and testing of small volumes of material have spawned a variety of techniques; this review focuses on uniaxial testing. Results from these experiments provide valuable insight into size-scale effects on the elastic, brittle, and ductile behavior of micron-sized structures. Fundamental elastic interactions show no size effect; in-plane moduli can be predicted from anisotropic elastic constants if crystallographic texture is properly considered. Intrinsic fracture toughness is also size independent, although the fracture strength of brittle MEMS materials is extremely dependent on flaw size and distribution. By contrast, size effects on the strength of ductile materials suggest that the operation of intrinsic dislocation.
SemanticFind: Locating What You Want in a Patient Record, Not Just What You Ask For

We present a new model of patient record search, called SemanticFind, which goes beyond traditional textual and medical synonym matches by locating patient data that a clinician would want to see rather than just what they ask for. The new model is implemented by making extensive use of the UMLS semantic network, distributional semantics, and NLP, to match query terms along several dimensions in a patient record with the returned matches organized accordingly. The new approach finds all clinically related concepts without the user having to ask for them. An evaluation of the accuracy of SemanticFind shows that it found twice as many relevant matches compared to those found by literal (traditional) search alone, along with very high precision and recall. These results suggest potential uses for SemanticFind in clinical practice, retrospective chart reviews, and in automated extraction of quality metrics.
Managing Conflict in System Diagnosis

As systems grow in complexity, the need for reliable testing and diagnosis grows accordingly. The design of complex systems has been facilitated by CAD/CAE tools. Unfortunately, test engineering tools have not kept pace with design tools, and test engineers are having difficulty developing reliable procedures to satisfy the test requirements of modern systems. When testing a complex system, what is the proper response to unexpected and conflicting test results? Should the results be scrapped and the tests rerun? Should the system be replaced or redesigned? Should the test procedures be redeveloped? Determining the best answers to these questions is not easy. In fact, each of these options might be more drastic than necessary for handling conflict in a meaningful way. We discuss two approaches to system diagnosis that apply several tests and interpret the results based on an underlying model of the system being tested. These tests are used to determine if the system is functioning properly and, if not, to explain the faulty performance. By examining potential sources of conflict and the way conflict might become manifest in a reasoning system, we developed an approach to extend diagnosis to handle the conflict and draw more reliable conclusions.
Lossy image compression using multiwavelet transform coding

Multiwavelets are an advance of the well established wavelet theory. The performance of the wavelets in the field of image processing is well known. Multiwavelets take wavelets a step ahead in performance. In this work the performance of the Integer Multiwavelet Transform (IMWT) for Lossy compression has been studied. The IMWT shows good performance with reconstruction of the images. This work analyses the performance of the IMWT for lossy compression of images with Magnitude set coding. The Transform coefficients are coded using the Magnitude set coding and run length Encoding techniques. The Transform coefficient matrix is coded without taking into account of the sign by using the Magnitude Set - Variable Length Integer representation. The sign information of the coefficients is coded as bit plane with zero thresholds. The Bit plane so formed can be used as it is or coded to reduce the bpp. The Simulation was done in Mat lab.
Effects of Activist Shareholding on Corporate Social Responsibility Reporting Practices: An Empirical Study in Spain

New business practices are mainly characteristic of large firms, especially those quoted on the stock market. Listed companies show a higher commitment to corporate social responsibility (CSR) practices because capital markets allow activists to become a firm’s socially oriented shareholders. These actors, although small in number, have a significant influence over other larger block-holders. Recent decades have witnessed a significant increase in societal pressure to control the behavior of companies owing to the risks deriving from the economic, social and environmental effects of their business activity. The aim of this work is to test the effect that CSR activist shareholders have on the decision to disclose corporate social responsibility information in the Spanish context, controlling for the rest of the dimensions in Ullmann’s theoretical framework.