Miracle-dz/UniFAR-FTUs huggingface dataset:
Guidance of Postural Motoneurons Requires MAPK/ERK Signaling Downstream of Fibroblast Growth Factor Receptor 1

Identification of intracellular signaling pathways necessary for appropriate axon guidance is challenging because many CNS populations used to study these events contain multiple cell types. Here, we resolve this issue by using mouse embryonic stem (ES) cells that were directed to differentiate into a population of motoneurons that exclusively innervate epaxial muscles [medial median motor column (MMCm) motoneurons]. These ES cell-derived MMCm motoneurons, like their endogenous counterparts, express fibroblast growth factor receptor 1 (FGFR1) and selectively extend axons toward the epaxial trophin FGF8. Unlike wild-type MMCm motoneurons, FGFR1−/− MMCm motoneurons show guidance defects when transplanted into the neural tube of chick embryos. Furthermore, activation of FGFR1 selectively signals through mitogen-activated protein kinase/extracellular signal-regulated kinase (MAPK/ERK) for appropriate guidance in vitro, whereas overexpression of constitutively active MAPK/ERK in transplanted, or endogenous chick, MMCm cells causes guidance defects in vivo. These results indicate that MAPK/ERK activation downstream of FGFR1 is necessary for MMCm motor axon guidance and that ES cell-derived neurons provide an important tool for dissecting intracellular pathways required for axon guidance.
Finite-temperature chiral transition in QCD with quarks in the fundamental and adjoint representation

We study the nature of the finite-temperature chiral transition in QCD with N_f light quarks in the fundamental and adjoint representation. Universality and renormalization-group (RG) arguments show that the possibility of having a continuous transition is related to the existence of a stable fixed point (FP) in the RG flow of a 3D Landau-Ginzburg-Wilson Phi^4 theory with the same chiral symmetry-breaking pattern. The RG flow of these theories is studied by field-theoretical approaches, computing and analyzing high-order perturbative series, up to six loops. According to this RG analysis, the transition in QCD can be continuous only for N_f=2. In this case it belongs to the 3D O(4) universality class. We also find a stable FP corresponding to a 3D universality class with symmetry breaking U(2)_L x U(2)_R -> U(2)_V, which implies that the transition can be continuous also if the axial-anomaly effects are suppressed at Tc. In the case of quarks in the adjoint representation, we can have a continuous transition for N_f=1,2. For N_f=1 it belongs to the O(3) universality class. For N_f=2 it belongs to a new 3D universality class characterized by the symmetry breaking SU(4)->SO(4).
Quasar-Galaxy Associations from Gravitational Lensing: Revisited

The theoretically expected amplitude of the associations of background quasars with foreground galaxies as a result of gravitational lensing has been updated in this paper. Since the galactic matter alone yields an amplitude of quasar overdensity smaller than that observed, special attention has been paid to the examination or reexamination of the uncertainties in the estimate of the quasar enhancement factor arising from the cosmic evolution of galaxies, the core radius and velocity bias of galactic matter distributions, the clusters of galaxies, the obstruction effect by galactic disks, the nonzero cosmological constant, etc. Unfortunately, none of these factors has been shown to be able to improve the situation significantly, although a combination of some effects may provide a result that marginally agrees with observations. It is concluded that the quasar-galaxy association remains an unsolved puzzle in today's astronomy, if the reported quasar-galaxy associations are not due to the statistical variations and/or the observed quasar number counts as a whole have not been seriously contaminated by gravitational lensing.
Two-Tier Tissue Decomposition for Histopathological Image Representation and Classification

In digital pathology, devising effective image representations is crucial to design robust automated diagnosis systems. To this end, many studies have proposed to develop object-based representations, instead of directly using image pixels, since a histopathological image may contain a considerable amount of noise typically at the pixel-level. These previous studies mostly employ color information to define their objects, which approximately represent histological tissue components in an image, and then use the spatial distribution of these objects for image representation and classification. Thus, object definition has a direct effect on the way of representing the image, which in turn affects classification accuracies. In this paper, our aim is to design a classification system for histopathological images. Towards this end, we present a new model for effective representation of these images that will be used by the classification system. The contributions of this model are twofold. First, it introduces a new two-tier tissue decomposition method for defining a set of multityped objects in an image. Different than the previous studies, these objects are defined combining texture, shape, and size information and they may correspond to individual histological tissue components as well as local tissue subregions of different characteristics. As its second contribution, it defines a new metric, which we call dominant blob scale, to characterize the shape and size of an object with a single scalar value. Our experiments on colon tissue images reveal that this new object definition and characterization provides distinguishing representation of normal and cancerous histopathological images, which is effective to obtain more accurate classification results compared to its counterparts.
Assessment of individual differences in online social networks using machine learning

The services that define our personal and professional lives are increasingly accessed through digital devices, which store extensive records of our behaviour. An individual’s psychological profile can be accurately assessed using offline behaviour, and I investigate if an automated machine learning system can measure the same psychological factors, only from observing the footprints of online behaviour, without observing any offline behaviour or any direct input from the individual. Prior research shows that psychological traits such as personality can be predicted using these digital footprints, although current state-of-the-art accuracy is below psychometric standards of reliability and self-reports consistently outperform machine-ratings in external validity. I introduce a new machine learning system that is capable of doing five-factor personality assessments, as well as other psychological assessments, from online data as accurately as self-report questionnaires in terms of reliability, internal consistency and external and discriminant validity, and demonstrate that passive psychological assessment can be a realistic option in addition to self-report questionnaires for both research and practice. Achieving this goal is not possible using conventional dimensionality reduction and linear regression models. Here I develop a supervised dimensionality reduction method capable of intelligently selecting only useful parts of data for the relevant prediction at hand which also does not lose variance when eliminating redundancies. In the learning stage, instead of linear regression models, I use an ensemble of decision trees which are able to distinguish scenarios where the same observations on digital data can mean different things for different individuals. This work highlights the interesting idea that similar to how a human expert who is able to assess personality from offline behaviour, an expert machine learning system is able to assess personality from online behaviour. It also demonstrates that big-5 personality are predictors of how predictable users are in social media, with neuroticism having the greatest correlation with unpredictability, while openness having the greatest correlation with predictability.
AN ANISOTROPIC DIFFUSION WITH MEANINGFUL SCALE PARAMETER

The well-known anisotropic diffusion (a.k.a. Perona-Malik equation, nonlinear diffusion, or diffusion partial differential equation – PDE) is widely used in image segmentation, filtering and edge detection. The behavior of the diffusion highly depends on the appropriate choice of the gradient thresholding scale parameter K. However, it seems that no clear relationship between the parameter K and the output image has ever been established, and hence the choice of K is a guesswork. This paper establishes an explicit connection between the parameter K and the number ν of edgeelements (edgels) of the filtered image. Let us define that the frontier between two neighboring pixels (p, q) is an edgel if |I(p)−I(q)| > τ , whereI(p) is the image intensity at p and τ is a constant. This connections is valid only for the spatiotemporally discretized diffusion. In our approach, the user specifies the desired ν. From this parameter, an appropriateK is automatically computed in every iteration, so that the final filtered image has ν edgels. Using this approach, the diffusion converges to a nontrivial piecewise constant image, whenever a feasible parameter ν is specified.
The Claims about Test Driven Development

In the traditional software development process, unit-and functional tests are written after the code is implemented. However, recently agile software development methods were introduced which also change traditional testing practice. Test driven development (TDD) is a practice of eXtreme Programming (XP) where unit-and functional tests drive the development of the code. This means that the tests are written before the actual code that is going to be tested. It is claimed, among others, that TDD produces better code quality. The goal of this thesis is to collect all the claims. Evaluation is done in two steps. The first step is studying the literature for supporting or contradictory evidences. The second step is implementing two case studies: a GUI based cinema reservation system and a GUI based shop stock management system. One case study is done using a traditional software process, where the tests are written after the implementation, and the second case study is done using XP and TDD. The results of the case studies are then compared with the results from the literature. This thesis introduces the eXtreme Programming and Test Driven Development. The main focus is on collecting claims about Test Driven Development, and evaluated those claims. I would like to thank my supervisor, the professor Hubert Baumeister. Thanks for Hubert's guidance, correction and advice throughout the whole project.
Sampling and Genealogical Coverage in WALS

WALS was designed with the goal of providing a “systematic answer” to questions about the geographical distribution of language features. In order to achieve this goal, there must be an adequate sample of the world’s languages included in WALS. In this paper we investigate to what extent WALS fulfils its aim of maximizing the genealogical diversity of the samples of languages included. For this we look at the core 200-sample (included on almost all maps) as well as the 1370-sample for the feature OV/VO-word order (the sample with the largest number of languages). The genealogical diversity in these samples is compared against a database of “what could have been done”, i.e., a database of which language families have adequate descriptive resources for the task at hand. In the 200-sample, we find a highly significant overinclusion of Eurasian languages at the expense of South American and Papuan languages. In the 1370-sample, we find a highly significant overinclusion of North American languages at the expense of South American and Papuan languages. It follows that statistics based on these WALS samples cannot be used straightforwardly for sound inferences about the distribution of the features in question.
Architecture-driven modelling and analysis

Over the past 15 years there has been increasing recognition that careful attention to the design of a system's software architecture is critical to satisfying its requirements for quality attributes such as performance, security, and dependability. As a consequence, during this period the field of software architecture has matured significantly. However, current practices of software architecture rely on relatively informal methods, limiting the potential for fully exploiting architectural designs to gain insight and improve the quality of the resulting system. In this paper we draw from a variety of research results to illustrate how formal approaches to software architecture can lead to enhancements in software quality, including improved clarity of design, support for analysis, and assurance that implementations conform to their intended architecture.
An Efficient Method for Diversifying Skylines in SOA Environment

Skyline query processing has recently received a lot of attention in data mining community. In the modern software development process, most of enterprises use a service-oriented architecture (SOA), which is a flexible set of design principles used during the phases of systems development and integration. Motivated by the above fact, in this paper, we focus on the method to diversify the skyline set in SOA environment. Specially, an efficient algorithm, called EDSASE, is proposed to reduce the time cost. We present the extensive experiments that demonstrate our algorithm is both efficient and effective.
One for the Gain, Three for the Loss

I derive indifference curves in mean-standard deviation space for investors with prospect theory preferences when returns are normally distributed. The normality assumption creates a mapping between model parameters and the investment opportunity set. The model is then calibrated to historical return data for various assumptions regarding the set of admissible risky assets. It is found that the parameter for loss aversion must be higher than three for investors to hold finitely leveraged portfolios. For lower rates of loss aversion, in particular those proposed in the earlier experimental literature, the allocation to risky assets is infinite. Numerical simulations produce similar results when the normality assumption is abandoned.
Using a chain of LVQ neural networks for pattern recognition of EEG signals related to intermittent photic-stimulation

This work reports the use of neural networks for pattern recognition in electroencephalographic signals related to intermittent photic-stimulation. Due to the low signal/noise ratio of this kind of signal, it was necessary the use of a spectrogram as a predictor and a chain of LVQ neural networks. The efficiency of this pattern recognition structure was tested for many different configurations of the neural networks parameters and different volunteers. A direct relationship between the dimension of the neural networks and their performance was observed. Results so far encourage new experiments and demonstrate the feasibility of the proposed system for real-time pattern recognition of complex signals.
3D echocardiographic imaging and modeling: Towards the patient-specific virtual mitral valve

Finite element models (FEMs) have been already widely used to analyze mitral valve (MV) biomechanics. Recently, strategies to implement patient-specific, image-based FEMs of the MV were proposed, improving their realism. In particular, we proposed to implement MV FEMs from real time 3D transthoracic echocardiographic imaging. To allow a future clinical use of this approach as a support tool for MV surgical planning, we applied it to three healthy and three regurgitant MVs, to test for its capacity in discriminating between physiological and pathological MV function, in realistically capturing location and extent of the regurgitating areas, and in quantifying the biomechanical anomalies associated to MV regurgitation. MV FEMs proved able to simulate both physiological and pathological MV function, thus confirming the ability of this promising approach in potentially being applied as support tool for surgical planning.
Reevaluating the latency claims of 3D stacked memories

In recent years, 3D technology has been a popular area of study that has allowed researchers to explore a number of novel computer architectures. One of the more popular topics is that of integrating 3D main memory dies below the computing die and connecting them with through-silicon vias (TSVs). This is assumed to reduce off-chip main memory access latencies by roughly 45% to 60%. Our detailed circuit-level models, however, demonstrate that this latency reduction from the TSVs is significantly less. In this paper, we present these models, compare 2D and 3D main memory latencies, and show that the reduction in latency from using 3D main memory to be no more than 2.4 ns. We also show that although the wider I/O bus width enabled by using TSVs increases performance, it may do so with an increase in power consumption. Although TSVs consume less power per bit transfer than off-chip metal interconnects (11.2 times less power per bit transfer), TSVs typically use considerably more bits and may result in a net increase in power due to the large number of bits in the memory I/O bus. Our analysis shows that although a 3D memory hierarchy exploiting a wider memory bus can increase performance, this performance increase may not justify the net increase in power consumption.
Unbound Protein-Protein Docking Selections by the DFIRE-based Statistical Pair Potential

A newly developed statistical pair potential based on Distance-scaled Finite Ideal-gas REference (DFIRE) state is applied to unbound protein-protein docking structure selections. The performance of the DFIRE energy function is compared to those of the well-established ZDOCK energy scores and RosettaDock energy function using the comprehensive decoy sets generated by ZDOCK and RosettaDock. Despite significant difference in the functional forms and complexities of the three energy scores, the differences in overall performance for docking structure selections are small between DFIRE and ZDOCK2.3 and between DFIRE and RosettaDock. This result is remarkable considering that a single-term DFIRE energy function was originally designed for monomer proteins while multiple-term energy functions of ZDOCK and RosettaDock were specifically optimized for docking. This provides hope that the accuracy of the existing energy functions for docking can be improved.
An admission control system for QoS provision on a best-effort GALS interconnect

A central admission control mechanism is introduced in order to provide efficient Quality-of-Service (QoS) support for different types of application over a best-effort Globally-Asynchronous Locally-Synchronous (GALS) interconnect fabric. The mechanism is applied at the ingress edges of the fabric using tokens to allocate dynamic network resources and prevent network saturation. Analysis and simulation results are presented to show the effectiveness of the method. The control provides service guarantees on the network while using a modest physical area because of the simplicity of the control logic. This is a cost-effective way to provide QoS in packet-switched interconnect because of its independence from other network components.
New Developments in the Biodemography of Aging and Longevity

Biodemography is a promising scientific approach based on using demographic data and methods for getting insights into biological mechanisms of observed processes. Recently, new important developments have happened in biodemographic studies of aging and longevity that call into question conventional aging theories and open up novel research directions. Recent studies found that the exponential increase of the mortality risk with age (the famous Gompertz law) continues even at extreme old ages in humans, rats, and mice, thus challenging traditional views about old-age mortality deceleration, mortality leveling-off, and late-life mortality plateaus. This new finding represents a challenge to many aging theories, including the evolutionary theory that explains senescence by a declining force of natural selection with age. Innovative ideas are needed to explain why exactly the same exponential pattern of mortality growth is observed not only at reproductive ages, but also at very-old postreproductive ages (up to 106 years), long after the force of natural selection becomes negligible (when there is no room for its further decline). Another important recent development is the discovery of long-term ‘memory' for early-life experiences in longevity determination. Siblings born to young mothers have significantly higher chances to live up to 100 years, and this new finding, confirmed by two independent research groups, calls for its explanation. As recent studies found, even the place and season of birth matter for human longevity. Beneficial longevity effects of young maternal age are observed only when children of the same parents are compared, while the maternal age effect often could not be detected in across-families studies, presumably being masked by between-family variation. It was also found that male gender of centenarian has a significant positive effect on the survival of adult male biological relatives (brothers and fathers) but not of female relatives. Finally, large gender differences are found in longevity determinants for males and females, suggesting a higher importance of occupation history for male centenarians as well as a higher importance of home environment history for female centenarians.
Scalable Community Discovery of Large Networks

Over the past decade, community structure, a statistical property of networked systems such as social network and World Wide Web, has attracted considerable attention in data mining field because it enables description and prediction of complex networks. Many highly sensitive graph clustering algorithms were developed for identification of communities having dense connections internally and loose connections with others. In this context, Newman and Girvan proposed modularity Q score for quantifying the strength of community structure and measuring the fitness of a division. The Q function has become an important standard recently. In this paper, combining the strengths of the Q score and multilevel paradigm first developed for graph partitioning, we introduced a scalable algorithm MOME (i.e. modularity-based multilevel graph clustering) to efficiently discover communities from a network. The experimental results indicated that MOME ran extremely faster and finally achieved a division with a slightly higher Q score against the latest modularity-based method and its variants, particularly when the network was of a large-scale.