Miracle-dz/UniFAR-FTUs huggingface dataset:
Propagation based X-ray phase microtomography of multi-material objects for simultaneous bone and soft tissue visualisation

We present a method for phase microtomography based on X-ray propagation based phase contrast imaging for multi-material objects. Previously, homogeneous composition assumptions have been in the Radon domain to overcome low sensitivity in the low frequency range. Here, we introduce a prior in the object domain based on multiple, but known, materials in the sample. This is achieved by first reconstructing a tomographic attenuation scan, introducing the prior by segmentation, and finally forward projecting this initial object estimate to yield a priori phase maps. The method is applied to the imaging of a mouse knee where analysis of both soft and hard tissue is of interest, and is shown to perform better than previously proposed methods.
Perceptually based radiance map for realistic composition

The seamless composition of synthetic objects into real-world scenes requires a high level of detail to convince viewers that the rendered object belongs in the scene. These details include illumination properties that perceptually match the real-world environment. To achieve such effects, image based lighting is often used to emulate real-world lighting. This paper explores the role of lighting, and measures effects of local illumination changes. A psychophysical study is presented that captures the thresholds of the human visual systems ability to perceive inconsistencies in illumination in image composites. Further, a set of optimised parameters for image based lighting is proposed. The results show a significant reduction in memory while maintaining the perceived visual quality of a composite render.
Online Learning to Rank with Feedback at the Top

We consider an online learning to rank setting in which, at each round, an oblivious adversary generates a list of $m$ documents, pertaining to a query, and the learner produces scores to rank the documents. The adversary then generates a relevance vector and the learner updates its ranker according to the feedback received. We consider the setting where the feedback is restricted to be the relevance levels of only the top $k$ documents in the ranked list for $k \ll m$. However, the performance of learner is judged based on the unrevealed full relevance vectors, using an appropriate learning to rank loss function. We develop efficient algorithms for well known losses in the pointwise, pairwise and listwise families. We also prove that no online algorithm can have sublinear regret, with top-1 feedback, for any loss that is calibrated with respect to NDCG. We apply our algorithms on benchmark datasets demonstrating efficient online learning of a ranking function from highly restricted feedback.
Lower bounds for the energy in a crumpled elastic sheet - A minimal ridge

We study the linearized Fopl - von Karman theory of a long, thin rectangular elastic membrane that is bent through an angle $2 \alpha$. We prove rigorous bounds for the minimum energy of this configuration in terms of the plate thickness $\sigma$ and the bending angle. We show that the minimum energy scales as $\sigma^{5/3} \alpha^{7/3}$. This scaling is in sharp contrast with previously obtained results for the linearized theory of thin sheets with isotropic compression boundary conditions, where the energy scales as $\sigma$.
El Endomarketing: aplicado al talento clave interno en el sector salud

Este articulo, tiene como objetivo presentar el endomarketing como herramienta gerencial y proceso de gestion, tomando en consideracion el actual contexto de cambios en el sector salud, en especial lo acaecido en las Instituciones prestadoras de salud publicas y privadas, ademas, exige el desarrollo de habilidades que estan directamente relacionadas con la introduccion en las formas de organizacion del trabajo. Nuestra reflexion deja algunos puntos importantes para ser analizados por los gerentes del sector salud, en la busqueda de mejores caminos y alternativas para la formacion de su personal con una mayor satisfaccion en el trabajo, con competencia para gestionar los servicios de salud y con elevado sentido de espiritualidad corporativa que asegure el factor clave en pro de la competitividad y la responsabilidad social organizacional. El gran desafio del endomarkeing es, por lo tanto, conciliar los objetivos e intereses del recurso humano interno a las necesidades y expectativas del publico externo de la organizacion.
A Novel Fast FIR Algorithm for Area-Efficient Parallel FIR Digital Filter Structures Utilizes Symmetric Convolutions

Based on fast finite-impulse response (FIR) algorithms (FFAs), this paper proposes new parallel FIR filter structures, which are beneficial to symmetric coefficients in terms of the hardware cost, under the condition that the number of taps is a multiple of 2 or 3. The proposed parallel FIR structures exploit the inherent nature of symmetric coefficients reducing half the number of multipliers in sub filter section at the expense of additional adders in preprocessing and post processing blocks. Exchanging multipliers with adders is advantageous because adders weigh less than multipliers in terms of silicon area; in addition, the overhead from the additional adders in preprocessing and post processing blocks stay fixed and do not increase along with the length of the FIR filter, whereas the number of reduced multipliers increases along with the length of the FIR filter. For example, for a four-parallel 72-tap filter, the proposed structure saves 27 multipliers at the expense of 11 adders, whereas for a four-parallel 576-tap filter, the proposed structure saves 216 multipliers at the expense of 11 adders still. Overall, the proposed parallel FIR structures can lead to significant hardware savings for symmetric convolutions from the existing FFA parallel FIR filter, especially when the length of the filter is large
Challenges of Decision-Theoretic Planning

Several scientific and engineering disciplines study techniques for automated ecision making. For example, work in control theory, operations research, and management science has produced numerous automated or semi-automated methods for deriving prescribed actions from descriptions of situations and models of the world. The type of techniques studied in these fields is shaped by the primary problems of interest, predominant technical methodologies, and available modeling technologies. Similarly, the shape of research in planningthe decision-oriented discipline of artificial intelligence-has been molded by concerns specific to AI research, and strongly influenced by its technical culture and developmental history.
The promises of computational ethnography: Improving transparency, replicability, and validity for realist approaches to ethnographic analysis

This article argues the advance of computational methods for analyzing, visualizing and disseminating social scientific data can provide substantial tools for ethnographers operating within the broadly realist ‘normal-scientific tradition’ (NST). While computation does not remove the fundamental challenges of method and measurement that are central to social research, new technologies provide resources for leveraging what NST researchers see as ethnography’s strengths (e.g. the production of in situ observations of people over time) while addressing what NST researchers see as ethnography’s weaknesses (e.g. questions of sample size, generalizability and analytical transparency). Specifically, we argue computational tools can help: (1) scale ethnography, (2) improve transparency, (3) allow basic replications, and (4) ultimately address fundamental concerns about internal and external validity. We explore these issues by illustrating the utility of three forms of ethnographic visualization enabled by computational advances – ethnographic heatmaps (ethnoarrays), a combination of participant observation data with techniques from social network analysis (SNA), and text mining. In doing so, we speak to the potential uses and challenges of nascent ‘computational ethnography. ’
Integrated ‘Omics’, Targeted Metabolite and Single-cell Analyses of Arctic Snow Algae Functionality and Adaptability

Snow algae are poly-extremophilic microalgae and important primary colonizers and producers on glaciers and snow fields. Depending on their pigmentation they cause green or red mass blooms during the melt season. This decreases surface albedo and thus further enhances snow and ice melting. Although the phenomenon of snow algal blooms has been known for a long time, large aspects of their physiology and ecology sill remain cryptic. This study provides the first in-depth and multi-omics investigation of two very striking adjacent green and red snow fields on a glacier in Svalbard. We have assessed the algal community composition of green and red snow including their associated microbiota, i.e., bacteria and archaea, their metabolic profiles (targeted and non-targeted metabolites) on the bulk and single-cell level, and assessed the feedbacks between the algae and their physico-chemical environment including liquid water content, pH, albedo, and nutrient availability. We demonstrate that green and red snow clearly vary in their physico-chemical environment, their microbial community composition and their metabolic profiles. For the algae this likely reflects both different stages of their life cycles and their adaptation strategies. Green snow represents a wet, carbon and nutrient rich environment and is dominated by the algae Microglena sp. with a metabolic profile that is characterized by key metabolites involved in growth and proliferation. In contrast, the dry and nutrient poor red snow habitat is colonized by various Chloromonas species with a high abundance of storage and reserve metabolites likely to face upcoming severe conditions. Combining a multitude of techniques we demonstrate the power of such complementary approaches in elucidating the function and ecology of extremophiles such as green and red snow algal blooms, which play crucial roles in glacial ecosystems.
MRI for Crohn's Disease: Present and Future

Crohn's disease (CD) is a chronic inflammatory condition with relapsing-remitting behavior, often causing strictures or penetrating bowel damage. Its lifelong clinical course necessitates frequent assessment of disease activity and complications. Computed tomography (CT) enterography has been used as primary imaging modality; however, the concern for radiation hazard limits its use especially in younger population. Magnetic resonance (MR) imaging has advantages of avoiding radiation exposure, lower incidence of adverse events, ability to obtain dynamic information, and good soft-tissue resolution. MR enterography (MRE) with oral contrast agent has been used as primary MR imaging modality of CD with high sensitivity, specificity, and interobserver agreement. The extent of inflammation as well as transmural ulcers and fibrostenotic diseases can be detected with MRE. Novel MR techniques such as diffusion-weighted MRI (DWI), motility study, PET-MRI, and molecular imaging are currently investigated for further improvement of diagnosis and management of CD. MR spectroscopy is a remarkable molecular imaging tool to analyze metabolic profile of CD with human samples such as plasma, urine, or feces, as well as colonic mucosa itself.
Different dyskinesias in Parkinson's disease and their relationship to levodopa

Levodopa is the principal agent in the treatment of Parkinson’s disease (PD). Unfortunately the therapeutic benefits are optimal only in the early stages of the disease, with long-term use associated with motor complications such as levodopa-induced dyskinesia (LID). Risk factors associated with the development of LID are generally accepted to involve the degree of dopamine (DA) denervation in the nigrostriatal pathway, levodopa dose, and duration of levodopa treatment. Little is known regarding the underlying mechanisms of LID, although it is known that levodopa plasma concentrations are closely associated with the onset of some types of LIDs (peak-dose and biphasic dyskinesias) and it appears that increased DA turnover plays a crucial role in LID development. Recent evidence suggests that other cell types such as serotonin neurons possess the ability to convert levodopa into DA, subsequently storing and releasing it thereby increasing the levels of extracellular DA, exacerbating LID. This review will highlight the evidence to date from in vitro and in vivo studies utilizing both animal models and patients, regarding the relationship between levodopa treatment and the development of LID. Understanding the pathogenesis of LID is a therapeutic priority in tackling motor complications related to levodopa treatment in PD.
Rainfall-Induced Release of Fecal Coliforms and Other Manure Constituents: Comparison and Modeling

ABSTRACT Modeling release of fecal coliforms is an important component of fate and transport simulations related to environmental water quality. Manure constituents other than fecal coliforms may serve as natural tracers of fecal contamination provided that their release from manure to runoff is similar to the fecal coliform release. The objectives of this work were to compare release of fecal coliforms (FC), chloride (Cl−), organic carbon (OC), and water-soluble phosphorus (P) from dissolving manure and to assess the performance of three models in describing the observed release. Bovine manure was applied on 0.5- by 0.3-m bare and vegetated subplots with 20% slope on sandy loam and clay loam soils. Concentrations of Cl−, FC, OC, and P were measured in runoff collected from troughs at the edges of the subplots at 5-min intervals during 1-h rainfall simulations. The one-parametric exponential model and two-parametric Vadas-Kleinman-Sharpley model and Bradford-Schijven model were fitted to the data. The Bradford-Schijven model had uncorrelated parameters, one of which was linearly related to the irrigation rate, and another parameter reflected the presence or the absence of vegetation. Kinetics of the FC release from manure was similar to the release kinetics of P and OC. The Bradford-Schijven model is recommended to simulate the release of manure constituents.
Child implant users' imitation of happy- and sad-sounding speech

Cochlear implants have enabled many congenitally or prelingually deaf children to acquire their native language and communicate successfully on the basis of electrical rather than acoustic input. Nevertheless, degraded spectral input provided by the device reduces the ability to perceive emotion in speech. We compared the vocal imitations of 5- to 7-year-old deaf children who were highly successful bilateral implant users with those of a control sample of children who had normal hearing. First, the children imitated several happy and sad sentences produced by a child model. When adults in Experiment 1 rated the similarity of imitated to model utterances, ratings were significantly higher for the hearing children. Both hearing and deaf children produced poorer imitations of happy than sad utterances because of difficulty matching the greater pitch modulation of the happy versions. When adults in Experiment 2 rated electronically filtered versions of the utterances, which obscured the verbal content, ratings of happy and sad utterances were significantly differentiated for deaf as well as hearing children. The ratings of deaf children, however, were significantly less differentiated. Although deaf children's utterances exhibited culturally typical pitch modulation, their pitch modulation was reduced relative to that of hearing children. One practical implication is that therapeutic interventions for deaf children could expand their focus on suprasegmental aspects of speech perception and production, especially intonation patterns.
Schematized Aspect Maps for Robot Guidance

We present an approach to mobile robot guidance. The proposed architecture grants an abstract interface to robot naviga- tion allowing to bridge from perception to high level control. The approach is based upon a comprehensive map representing metric, configurational, and topological knowledge. Robot instruction and localization is dealt with by communicating and reasoning about cyclic ordering information of shape-features. 1 Motivation Service robots are a growing field of interest and are higly relevant, not only for the research community but also for companies that try to take a share in an emerging market. Within near future, office com- plexes populated with various heterogenous service robots are a very probable scenario resulting in a range of different problems, for ex- ample coordination among the robots and interaction with human users. A key point to instructing robots is to communicate spatial in- formation, for example when commanding a robot to visit a certain place. To provide sufficient control over the various robots, a com- patible method of instruction is required for all of them. As a con- sequence, the spatial representations of the individual robots would need to be compatible with each other. In the presence of different robot systems from various vendors, equipped with different sensors, this is a non-trivial task. Moreover, the chosen method of instruction needs to mediate between the technical abilities of a robot and its often data-driven spatial representation and features desired in a user interaction that are based on more abstract spatial information. To consolidate these different demands we propose a central robot guidance system. A qualitative language, i.e. relational information, is applied for communication with the robots. Allowing for an easy formalization, qualitative information offers a good means for a spec- ification of such a system, which also respects individual robots' ca- pabilities. In particular, a system based on qualitative information can serve as an interface to a human user. Furthermore, with robot guid- ance it is possible to restrict the spatial representation of an individual robot to the needs of its specific task, like cleaning of the floor. There is no need to represent the complete working environment within each single robot. Especially within dynamic environments the man- agement, i.e. construction and maintenance, of a spatial representa- tion is a difficult task that has not been solved thoroughly yet (20). Freeing individual service robots from this burden, our proposed ar- chitecture is a slender one. The robot guidance outlined here does not rely upon a precise or even correct map of the environment. Typically, environments are subject to steady changes. Therefore, handling of unreliable, or even conflicting knowledge is of high importance. The proposed guidance
Genetic Architecture of a Small, Recently Aggregated Aleut Population: Bering Island, Russia

Abstract The fishing community of Bering Island, located in the Russian Commander Islands off the Kamchatka Peninsula, was originally founded by a small number of Russian soldiers and merchants, along with Aleuts forcibly relocated from the western region of the Aleutian archipelago. The purpose of this study is to characterize the genetic variation of Bering Island inhabitants for autosomal, mitochondrial, and Y-chromosome DNA and classic genetic markers and to investigate the genetic impact of the 19th-century founding and subsequent demographic events on this heterogeneous community. Our results show a loss of diversity among maternal lineages in the Bering Aleut population, with fixation of mtDNA haplogroup D, as revealed by median-joining network analysis and mismatch differences. Conversely, paternal haplotypes exhibit an increase in diversity and the presence of a substantial number of non-Native lineages. Admixture results, based on autosomal STR data, indicate that parental contributions to the mixed Aleut population of Bering are approximately 60% Aleut and 40% Russian. Classic genetic markers show affinities between the Bering Island Aleuts and the other historically founded Aleut communities of St. Paul and St. George in the Pribilof Islands, Alaska. This study demonstrates that the opposing evolutionary forces of genetic drift and gene flow acted on the maternal and paternal lineages, respectively, to shape the genetic structure of the present-day inhabitants of Bering Island.
Data-driven Machinery Prognostics Approach using in a Predictive Maintenance Model

Nowadays, more and more manufacturers realize the importance of adopting new maintenance technologies to enable systems to achieve near-zero downtime, so machinery prognostics that enables this paradigm shift from traditional fail-and-fix maintenance to a predict-and-prevent paradigm has arose interests from researchers. Machinery prognostics which could estimate machine condition and degradation strongly support predictive maintenance policy. This paper develops a novel data-driven machine prognostics approach to predict machine’s health condition and describe machine degradation. Based on machine’s prognostics information, a predictive maintenance model is well constructed to decide machine’s optimal maintenance threshold and maintenance cycles. Through a case study, this predictive maintenance model is verified, and the computational results show that this proposed model is efficient and practical.
Experimental enhancement of neurphysiological function

Enhancing brain function entails controlling neuronal function. There are several methods available for this which led to some relevant experimental data. Deca (2011) Since methods for connectome (Briggman et al., 2011; Prevedel et al., 2014) and circuit functional analysis (Marblestone et al., 2013) are advancing rapidly (Deca, 2012), it makes sense to consider only the most convincing neurophysiological data in the context of enhancement and their future development.
Blind source separation based on subspace

The problem of blind source separation consists in recovering mutually statistically independent source signals from mixtures when nothing is known about the sources and the mixture structure. In general, we focus on the real linear instantaneous mixture of real mutually statistically independent sources. When the column vectors of mixing matrix are uncorrelated, it has the possibility that some noise subspaces and signal vectors can be defined. In this way, in this paper, a new simple method of blind source separation is proposed by using higher order statistic, which is based on those signal subspaces and the central limit theorem. This method searches directly the row vector of separating matrix at first, and then revise the cost function to continue to search. In our opinion, this method integrates the characteristics of the like fastICA methods and the like InforMax methods. Illustrative examples, which use different mixing matrices, are utilized to demonstrate the ability of the considered method.
MASTER THESIS Default Reasoning on Top of Ontologies with dl-Programs

We study the usefulness of description logic programs (dl-programs) in implementing Reiter’s default logic on top of a description logic knowledge base (DL-KB). To this end, we investigate transformations from default theories to dlprograms based on different established algorithms for computing default theory extensions, namely the select-defaultsand-check and the select-justifications-and-check algorithms. In each transformation, additional constraints are exploited to prune the search space based on conclusion-conclusion or conclusion-justification relations. The implementation was deployed as a new component in the dl-plugin for dlvhex, and evaluated with various experimental test ontologies, which showed promising results.