On derivatives and norms of generalized matrix functions and respective symmetric powers

In recent papers, the authors obtained formulas for directional derivatives of all orders, of the immanant and of the m-th xi-symmetric tensor power of an operator and a matrix, when xi is a character of the full symmetric group. The operator norm of these derivatives was also calculated. In this paper, similar results are established for generalized matrix functions and for every symmetric tensor power.

GPU implementation of the simplex identification via split augmented Lagrangian

Hyperspectral imaging can be used for object detection and for discriminating between different objects based on their spectral characteristics. One of the main problems of hyperspectral data analysis is the presence of mixed pixels, due to the low spatial resolution of such images. This means that several spectrally pure signatures (endmembers) are combined into the same mixed pixel. Linear spectral unmixing follows an unsupervised approach which aims at inferring pure spectral signatures and their material fractions at each pixel of the scene. The huge data volumes acquired by such sensors put stringent requirements on processing and unmixing methods. This paper proposes an efficient implementation of a unsupervised linear unmixing method on GPUs using CUDA. The method finds the smallest simplex by solving a sequence of nonsmooth convex subproblems using variable splitting to obtain a constraint formulation, and then applying an augmented Lagrangian technique. The parallel implementation of SISAL presented in this work exploits the GPU architecture at low level, using shared memory and coalesced accesses to memory. The results herein presented indicate that the GPU implementation can significantly accelerate the method's execution over big datasets while maintaining the methods accuracy.

Parallel GPU architecture for hyperspectral unmixing based on augmented Lagrangian method

Hyperspectral imaging has become one of the main topics in remote sensing applications, which comprise hundreds of spectral bands at different (almost contiguous) wavelength channels over the same area generating large data volumes comprising several GBs per flight. This high spectral resolution can be used for object detection and for discriminate between different objects based on their spectral characteristics. One of the main problems involved in hyperspectral analysis is the presence of mixed pixels, which arise when the spacial resolution of the sensor is not able to separate spectrally distinct materials. Spectral unmixing is one of the most important task for hyperspectral data exploitation. However, the unmixing algorithms can be computationally very expensive, and even high power consuming, which compromises the use in applications under on-board constraints. In recent years, graphics processing units (GPUs) have evolved into highly parallel and programmable systems. Specifically, several hyperspectral imaging algorithms have shown to be able to benefit from this hardware taking advantage of the extremely high floating-point processing performance, compact size, huge memory bandwidth, and relatively low cost of these units, which make them appealing for onboard data processing. In this paper, we propose a parallel implementation of an augmented Lagragian based method for unsupervised hyperspectral linear unmixing on GPUs using CUDA. The method called simplex identification via split augmented Lagrangian (SISAL) aims to identify the endmembers of a scene, i.e., is able to unmix hyperspectral data sets in which the pure pixel assumption is violated. The efficient implementation of SISAL method presented in this work exploits the GPU architecture at low level, using shared memory and coalesced accesses to memory.

Estimation of rotational frequency response functions

As it is widely known, in structural dynamic applications, ranging from structural coupling to model updating, the incompatibility between measured and simulated data is inevitable, due to the problem of coordinate incompleteness. Usually, the experimental data from conventional vibration testing is collected at a few translational degrees of freedom (DOF) due to applied forces, using hammer or shaker exciters, over a limited frequency range. Hence, one can only measure a portion of the receptance matrix, few columns, related to the forced DOFs, and rows, related to the measured DOFs. In contrast, by finite element modeling, one can obtain a full data set, both in terms of DOFs and identified modes. Over the years, several model reduction techniques have been proposed, as well as data expansion ones. However, the latter are significantly fewer and the demand for efficient techniques is still an issue. In this work, one proposes a technique for expanding measured frequency response functions (FRF) over the entire set of DOFs. This technique is based upon a modified Kidder's method and the principle of reciprocity, and it avoids the need for modal identification, as it uses the measured FRFs directly. In order to illustrate the performance of the proposed technique, a set of simulated experimental translational FRFs is taken as reference to estimate rotational FRFs, including those that are due to applied moments.

Parallel hyperspectral unmixing method via split augmented lagrangian on GPU

One of the main problems of hyperspectral data analysis is the presence of mixed pixels due to the low spatial resolution of such images. Linear spectral unmixing aims at inferring pure spectral signatures and their fractions at each pixel of the scene. The huge data volumes acquired by hyperspectral sensors put stringent requirements on processing and unmixing methods. This letter proposes an efficient implementation of the method called simplex identification via split augmented Lagrangian (SISAL) which exploits the graphics processing unit (GPU) architecture at low level using Compute Unified Device Architecture. SISAL aims to identify the endmembers of a scene, i.e., is able to unmix hyperspectral data sets in which the pure pixel assumption is violated. The proposed implementation is performed in a pixel-by-pixel fashion using coalesced accesses to memory and exploiting shared memory to store temporary data. Furthermore, the kernels have been optimized to minimize the threads divergence, therefore achieving high GPU occupancy. The experimental results obtained for the simulated and real hyperspectral data sets reveal speedups up to 49 times, which demonstrates that the GPU implementation can significantly accelerate the method's execution over big data sets while maintaining the methods accuracy.

Reducing the number of membership functions in linguistic variables

Dissertation presented at Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia in fulfilment of the requirements for the Masters degree in Mathematics and Applications, specialization in Actuarial Sciences, Statistics and Operations Research

Fractional order describing functions

This paper addresses limit cycles and signal propagation in dynamical systems with backlash. The study follows the describing function (DF) method for approximate analysis of nonlinearities and generalizes it in the perspective of the fractional calculus. The concept of fractional order describing function (FDF) is illustrated and the results for several numerical experiments are analysed. FDF leads to a novel viewpoint for limit cycle signal propagation as time-space waves within system structure.

Effects of dual-task training on balance and executive functions in Parkinson's disease: A pilot study

The aim of this study was to analyze the efficacy of cognitive-motor dual-task training compared with single-task training on balance and executive functions in individuals with Parkinson's disease. Fifteen subjects, aged between 39 and 75 years old, were randomly assigned to the dual-task training group (n = 8) and single-task training group (n = 7). The training was run twice a week for 6 weeks. The single-task group received balance training and the dual-task group performed cognitive tasks simultaneously with the balance training. There were no significant differences between the two groups at baseline. After the intervention, the results for mediolateral sway with eyes closed were significantly better for the dual-task group and anteroposterior sway with eyes closed was significantly better for the single-task group. The results suggest superior outcomes for the dual-task training compared to the single-task training for static postural control, except in anteroposterior sway with eyes closed.

Effects of Exercise on Physical and Mental Health, and Cognitive and Brain Functions in Schizophrenia: Clinical and Experimental Evidence

Exercise promotes several health benefits, such as cardiovascular, musculoskeletal and cardiorespiratory improvements. It is believed that the practice of exercise in individuals with psychiatric disorders, e.g. schizophrenia, can cause significant changes. Schizophrenic patients have problematic lifestyle habits compared with general population; this may cause a high mortality rate, mainly caused by cardiovascular and metabolic diseases. Thus, the aim of this study is to investigate changes in physical and mental health, cognitive and brain functioning due to the practice of exercise in patients with schizophrenia. Although still little is known about the benefits of exercise on mental health, cognitive and brain functioning of schizophrenic patients, exercise training has been shown to be a beneficial intervention in the control and reduction of disease severity. Type of training, form of execution, duration and intensity need to be better studied as the effects on physical and mental health, cognition and brain activity depend exclusively of interconnected factors, such as the combination of exercise and medication. However, one should understand that exercise is not only an effective nondrug alternative, but also acts as a supporting linking up interventions to promote improvements in process performance optimization. In general, the positive effects on mental health, cognition and brain activity as a result of an exercise program are quite evident. Few studies have been published correlating effects of exercise in patients with schizophrenia, but there is increasing evidence that positive and negative symptoms can be improved. Therefore, it is important that further studies be undertaken to expand the knowledge of physical exercise on mental health in people with schizophrenia, as well as its dose-response and the most effective type of exercise.

Crowdsourcing innovation intermediaries functions

Open innovation is a hot topic in innovation management. Its basic premise is open up the innovation process. The innovation process, in general sense, may be seen as the process of designing, developing and commercializing a novel product or service to improve the value added of a company. The development of Web 2.0 tools facilitates this kind of contributions, opening space to the emergence of crowdsourcing innovation initiatives. Crowdsourcing is a form of outsourcing not directed to other companies but to the crowd by means of an open call mostly through an Internet platform. Innovation intermediaries, in general sense, are organizations that work to enable innovation, that just act as brokers or agents between two or more parties. Usually, they are also engaged in other activities like inter-organizational networking and technology development and related activities. A crowdsourcing innovation intermediary is an organization that mediates the communication and relationship between the seekers – companies that aspire to solve some problem or to take advantage of any business opportunity – with a crowd that is prone to give ideas based on their knowledge, experience and wisdom. This paper identifies and analyses the functions to be performed by an intermediary of crowdsourcing innovation through grounded theory analyses from literature. The resulting model is presented and explained. The resulting model summarizes eight main functions that can be performed by a crowdsourcing process, namely, diagnoses, mediation, linking knowledge, community, evaluation, project management, intellectual property governance and marketing and support. These functions are associated with a learning cycle process which covers all the crowdsourcing activities that can be realized by the broker.

Novel functions of the cohesin accessory factor dPDS5 uncover a new meiotic checkpoint

Dissertation presented to obtain the Ph.D degree in Biology.

Manipulation of host cell functions by Salmonella: the role of the bacterial effector protein SteA

Salmonella enterica serovars are Gram-negative facultative intracellular bacterial pathogens that infect a wide variety of animals. Salmonella infections are common in humans, causing usually typhoid fever and gastrointestinal diseases. Salmonella enterica serovar Typhimurium (S. Typhimurium), which is a leading cause of human gastroenteritis, has been extensively used to study the molecular pathogenesis of Salmonella, because of the availability of sophisticated genetic tools, and of suitable animal and tissue culture models mimicking different aspects of Salmonella infections.(...)

Unravelling the role of alpha 2,6 sialic acid on mouse dentritic cells functions

RESUMO: As células dendríticas (CDs) são fundamentais na imunomodulação e iniciação de respostas imunes adaptativas, enquanto os ácidos siálicos (Sias) são potenciais imunomoduladores. Estas células expressam níveis elevados da sialiltransferase ST6Gal-1, que transfere Sias para a posição terminal de oligossacáridos. De facto, a maturação de CDs está associada a uma diminuição da sialilação na sua superfície celular. Apesar de ter função biológica desconhecida, a forma solúvel, extracelular de ST6Gal-1 aumenta em cancros e inflamação. Ainda assim, esta foi recentemente identificada como moduladora da hematopoiese. Considerando o importante papel das CDs na iniciação de respostas anticancerígenas, uma ligação entre a sialilação extrínseca induzida por ST6Gal-1 extracelular e o seu papel na modulação de CDs deve ser identificada. Neste trabalho hipotetizou-se que a sialilação α2,6 extrínseca de CDs diminui o seu perfil de maturação mediante ativação por lipopolissacarídeo (LPS). O objetivo principal foi sialilar extrinsecamente em α2,6 CDs da medula óssea de murganhos, avaliando os seus perfis de maturação e de libertação de citocinas, após estimulação com LPS (por Citometria de Fluxo e ELISA, respetivamente). Ao contrário da hipótese, o perfil celular não foi modulado, usando várias abordagens. Por outro lado, a consequência da falta de α2,6 Sias na maturação de CDs foi avaliada analisando: 1) CDs da medula óssea de murganhos tratadas com sialidase, 2) CDs da medula óssea e 3) CDs das vias aéreas, ambas de murganhos deficientes em ST6Gal-1, comparando com a estirpe selvagem. Estes resultados sugerem que a perta total de α2,6 Sias se relaciona com o aumento da expressão do complexo de histocompatibilidade principal de classe II. Apesar de controverso, é provável existirem mecanismos inerentes à ativação por LPS, reduzindo a eficácia de ST6Gal-1 extracelular. Por outro lado, a modificação no perfil de CDs de murganhos deficientes em ST6Gal-1 poderá relacionar-se com uma predisposição para um estado inflamatório severo. Com isto, o trabalho desenvolvido abriu futuras linhas de investigação, nomeadamente explorar outros fatores envolvidos na (de)sialilação α2,6 de CDs, podendo ter impacto em imunoterapia com uso de CDs.--------------------------ABSTRACT: Dendritic cells (DCs) are vital for immunomodulation and the initiation of adaptive immune responses, whereas sialic acids (Sias) are potential immunomodulators. These cells express high levels of sialyltransferase ST6Gal-1, responsible for transferring Sias to the terminal position of oligosaccharide chains. Indeed, DCs’ maturation is associated with decreased cell surface sialylation. Although its biological significance is unknown, the soluble, extracellular form of ST6Gal-1 increases in cancers and inflammation. However, extracellular ST6Gal-1 was recently identified as modulator of hematopoiesis. Considering that DCs play a crucial role in the initiation of a productive anti-cancer immune response, a link between extrinsic sialylation by the extracellular ST6Gal-1 on DC function needs to be investigated. We hypothesize that extrinsic α2,6 sialylation of DCs diminishes their maturation features upon lipopolysaccharide (LPS) stimulation. The main goal was to extrinsically α2,6 sialylate mice bone marrow derived DCs (BMDCs) and to evaluate their maturation and cytokine profiles upon LPS stimulation (by Flow Cytometry and ELISA, respectively). Unlike the hypothesis, we observed that BMDCs’ profile is not modulated, even using several approaches. In contrast, the consequence of lacking cell surface α2,6 Sias in DC maturation was assessed by analysing: 1) sialidase treated BMDCs, 2) BMDCs from mice lacking ST6Gal-1 and 3) DCs from mice airways, comparing wild type with ST6Gal-1 knockout mice. These results suggest that overall lack in α2,6 Sias is related with increased expression of major histocompatibility class II (MHC-II). Although appearing to be controversial findings, other intracellular mechanisms might be occurring upon LPS-induced BMDC activation, probably reducing extracellular ST6Gal-1 effect. In opposite, the modification observed in DC profile of ST6Gal-1 knockout mice might be related to its predisposition to a more severe inflammatory status. With this, the developed work opened future lines of investigation, namely exploring other factors involved in α2,6 (de)sialylation of DC, which might have influence in immunotherapy using DCs.

Dispersion of fillers in viscoelastic polymer matrices using Eulerian-Lagrangian approaches

Understanding the behavior of c omplex composite materials using mixing procedures is fundamental in several industrial processes. For instance, polymer composites are usually manufactured using dispersion of fillers in polymer melt matrices. The success of the filler dispersion depends both on the complex flow patterns generated and on the polymer melt rheological behavior. Consequently, the availability of a numerical tool that allow to model both fluid and particle would be very useful to increase the process insight. Nowadays there ar e computational tools that allow modeling the behavior of filled systems, taking into account both the behavior of the fluid (Computational Rheology) and the particles (Discrete Element Method). One example is the DPMFoam solver of the OpenFOAM ® framework where the averaged volume fraction momentum and mass conservation equations are used to describe the fluid (continuous phase) rheology, and the Newton’s second law of motion is used to compute the particles (discrete phase) movement. In this work the refer red solver is extended to take into account the elasticity of the polymer melts for the continuous phase. The solver capabilities will be illustrated by studying the effect of the fluid rheology on the filler dispersion, taking into account different fluid types (generalized Newtonian or viscoelastic) and particles volume fraction and size. The results obtained are used to evaluate the relevance of considering the fluid complex rheology for the prediction of the composites morphology