Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient Q-Switched Miniature ${\rm Yb{:}Gd}_{3}{\rm Ga}_{5}{\rm O}_{12}$ Laser Generating 180 kW of Peak Power

We report on a very compact, highly efficient, passively Q-switched Yb:Gd3Ga5O12 crystal laser pumped at 935 nm by a diode, from which an average output power of 2.36 W at 1025 nm was produced at a pulse repetition rate of 7.7 kHz with a slope efficiency of 60%; the resulting pulse energy and duration were, respectively, 306 μJ and 1.7 ns, leading to a peak power amounting to 180 kW.
A comprehensive simulation testbench for aerial robot in dynamic scenario using gazebo-ros

Aerial Robot is a new category of intelligent flying machines, which combines smart aerial platform with powerful sensoring and computation capability. Due to the high uncertainty and risk exiting in flight test, simulation is important in the development process. In this paper, we propose a simulation platform with dynamic scenario, based on the Robot Operating System (ROS) and Gazebo. Multiple moving targets and obstacles are installed, while vision and laser sensor are activated as navigation setup.
From EA management patterns towards a prescriptive theory for designing enterprise-specific EA management functions

1 Motivation Today's enterprises find themselves confronted with a changing economic, regulatory, and technical environment that they are forced to adapt to (Ross et al. 2006, Wagter et al. 2005). Performing the necessary and beneficial adaptations is aggravated by the intricate and highly interwoven architecture of the overall enterprise. Therein, local changes to one enterprise artifact, e.g. a business process or a business application, might have unforeseen global consequences at and potentially detrimental impacts on related artifacts. The enterprise architecture (EA) describes the interwoven system of these artifacts and their cause-effect relationships. Therefore, comprehensive knowledge about the EA may support an enterprise to avoid or reduce negative side-effects of adaptations, and may help to leverage the full benefits that can be drawn from an environmental change. Supporting the managed evolution of the enterprise's architecture with a focus on business/IT alignment is the core task of EA management. Driven by the demand from practice, a multitude of approaches to EA management has been developed by practitioners (cf. The research field of EA management is a relatively new but emerging one. One of the first publications concerning the topic EA management was written by Zachman (1987). Since that time the number of publications targeting the topic is increasing (Langenberg and Wegmann 2004). From there, different approaches rooted in various disciplines as e.g. meta-modeling (cf. Frank 2002) or systems
Conceptual object model of a classified electronic documents information system

Business organisations in their everyday operations implement a degree of information classification with regard to the level of secrecy. As a result of that process, a number of classified information in electronic format is created. The information requires a fulfilment of strict security criteria with the aim of preserving its confidentiality, integrity and availability. We believe that the only safe and dependable manner of handling such classified electronic documents is through special information systems used exclusively for managing the above-mentioned type of sensitive data. Thus, this paper analyses the modelling of a conceptual object model of an information system used for managing classified electronic documents.
Activities of Psilostachyin A and Cynaropicrin against Trypanosoma cruzi In Vitro and In Vivo

ABSTRACT In vitro and in vivo activities against Trypanosoma cruzi were evaluated for two sesquiterpene lactones: psilostachyin A and cynaropicrin. Cynaropicrin had previously been shown to potently inhibit African trypanosomes in vivo, and psilostachyin A had been reported to show in vivo effects against T. cruzi, albeit in another test design. In vitro data showed that cynaropicrin was more effective than psilostachyin A. Ultrastructural alterations induced by cynaropicrin included shedding events, detachment of large portions of the plasma membrane, and vesicular bodies and large vacuoles containing membranous structures, suggestive of parasite autophagy. Acute toxicity studies showed that one of two mice died at a cynaropicrin dose of 400 mg/kg of body weight given intraperitoneally (i.p.). Although no major plasma biochemical alterations could be detected, histopathology demonstrated that the liver was the most affected organ in cynaropicrin-treated animals. Although cynaropicrin was as effective as benznidazole against trypomastigotes in vitro, the treatment (once or twice a day) of T. cruzi-infected mice (up to 50 mg/kg/day cynaropicrin) did not suppress parasitemia or protect against mortality induced by the Y and Colombiana strains. Psilostachyin A (0.5 to 50 mg/kg/day given once a day) was not effective in the acute model of T. cruzi infection (Y strain), reaching 100% animal mortality. Our data demonstrate that although it is very promising against African trypanosomes, cynaropicrin does not show efficacy compared to benznidazole in acute mouse models of T. cruzi infection.
Use of Off-line Dynamic Programming for Efficient Image Interpretation

An interpretation system finds the likely mappings from portions of an image to real-world objects. An interpretation policy specifies when to apply which imaging operator, to which portion of the image, during every stage of interpretation. Earlier results compared a number of policies, and demonstrated that policies that select operators which maximize the information gain per cost, worked most effectively. However, those policies are myopic -- they rank the operators based only on their immediate rewards. This can lead to inferior overall results: it may be better to use a relatively expensive operator first, if that operator provides information that will significantly reduce the cost of the subsequent operators. This suggests using some lookahead process to compute the quality for operators non-myopically. Unfortunately, this is prohibitively expensive for most domains, especially for domains that have a large number of complex states. We therefore use ideas from reinforcement learning to compute the utility of each operator sequence. In particular, our system first uses dynamic programming, over abstract simplifications of interpretation states, to precompute the utility of each relevant sequence. It does this off-line, over a training sample of images. At run time, our interpretation system uses these estimates to decide when to use which imaging operator. Our empirical results, in the challenging real-world domain of face recognition, demonstrate that this approach works more effectively than myopic approaches.
Harmonically tuned GaN-HEMT Doherty power amplifier for 6 GHz applications

In this paper a GaN-HEMT Doherty amplifier targeted for vehicular applications at 6 GHz is presented. The harmonic tuning of the selected bare-die transistors was investigated and implemented in order to improve the overall large-signal performance. The Doherty amplifier was studied and optimized on device reference plane level using two equally sized 8-Watt GaN devices for the main class-AB and the peaking class-C amplifiers. The measured maximum output power was 41.5 dBm with 63 % peak drain efficiency (50 % power added efficiency). At 6 dB output power back-off a drain efficiency of 49 % (42 % power added efficiency) was obtained. The linearity of the amplifier was tested by applying modulated signals according to the IEEE 802.11p standard. At 34 dBm average output power a drain efficiency of 43 % was achieved for this high dynamic range OFDM signal. After digital pre-distortion, the related adjacent channel power ratio could be reduced from −31 dBc to values below −43 dBc without significant impact on the efficiency performance.
The Influence of Professional Ethical Beliefs on Academic Dishonest with the Theory of Planned Behavior

To exam the influential factors of cheating intention, we adopted the theory of planned behavior (TPB) by adding professional unethical beliefs to enhance its predictive power. A self-administered survey of 280 business part-time students with full-time profession from a northern university in Taiwan was investigated. Results indicated that perceived behavioral control toward cheating and professional ethical beliefs have greater impact on cheating intention than subjective norm and attitude have.
2 Related Work

Ubuntu’s Internet Relay Chat technical support channel has bots that output specific messages in response to command words from other channel users. These messages can be used to answer frequently-asked questions instead of requiring an expert to (repeatedly) type a lengthy reply. We describe an approach to automatically distinguish bot-answerable questions, which would mitigate this problem. To the best of our knowledge, this is the first work on investigating question answering in a multiparticipant chat domain. Our results indicate that for some types of questions, supervised learning algorithms perform well on this task and, in addition, that character n-grams are a better representation than traditional bag-of-words for this task and domain.
Super-Resolution Image Reconstruction using the ICM Algorithm

Super-resolution image reconstruction is a powerful methodology for resolution enhancement from a set of blurred and noisy low-resolution images. Following a Bayesian framework, we propose a procedure for super-resolution image reconstruction based on Markov random fields (MRF), where a Potts-Strauss model is assumed for the a priori probability density function of the actual image. The first step is given by aligning all the low-resolution observations over a high-resolution grid and then improving the resolution through the iterated conditional modes (ICM) algorithm. The method was analyzed considering a number of simulated low-resolution and globally translated observations and the results demonstrate the effectiveness of the algorithm in reconstructing the desirable high-resolution image.
Who is Mistaken?

Recognizing when people have false beliefs is crucial for understanding their actions. We introduce the novel problem of identifying when people in abstract scenes have incorrect beliefs. We present a dataset of scenes, each visually depicting an 8-frame story in which a character has a mistaken belief. We then create a representation of characters' beliefs for two tasks in human action understanding: predicting who is mistaken, and when they are mistaken. Experiments suggest that our method for identifying mistaken characters performs better on these tasks than simple baselines. Diagnostics on our model suggest it learns important cues for recognizing mistaken beliefs, such as gaze. We believe models of people's beliefs will have many applications in action understanding, robotics, and healthcare.
Sampling the Mouse Hippocampal Dentate Gyrus

Sampling is a critical step in procedures that generate quantitative morphological data in the neurosciences. Samples need to be representative to allow statistical evaluations, and samples need to deliver a precision that makes statistical evaluations not only possible but also meaningful. Sampling generated variability should, e.g., not be able to hide significant group differences from statistical detection if they are present. Estimators of the coefficient of error (CE) have been developed to provide tentative answers to the question if sampling has been “good enough” to provide meaningful statistical outcomes. We tested the performance of the commonly used Gundersen-Jensen CE estimator, using the layers of the mouse hippocampal dentate gyrus as an example (molecular layer, granule cell layer and hilus). We found that this estimator provided useful estimates of the precision that can be expected from samples of different sizes. For all layers, we found that a smoothness factor (m) of 0 generally provided better estimates than an m of 1. Only for the combined layers, i.e., the entire dentate gyrus, better CE estimates could be obtained using an m of 1. The orientation of the sections impacted on CE sizes. Frontal (coronal) sections are typically most efficient by providing the smallest CEs for a given amount of work. Applying the estimator to 3D-reconstructed layers and using very intense sampling, we observed CE size plots with m = 0 to m = 1 transitions that should also be expected but are not often observed in real section series. The data we present also allows the reader to approximate the sampling intervals in frontal, horizontal or sagittal sections that provide CEs of specified sizes for the layers of the mouse dentate gyrus.
Barriers and Facilitators of Digitalization in Organizations

Digitalization has transformed industries and societies in profound manner. Some changes have been documented as increased productivity and reduced costs of communications and information processing, to name a few. However, new technologies are not always fully embraced by organizations. This paper delves into barriers and facilitators surrounding adoption of digital technologies. The study was done as a systematic literature review. The paper introduces the most common technology acceptance models. Even while not being directly facilitators or inhibitors of use of new digital assets in organization, these models help to explain how the technology is accepted in organizations. The paper also introduces main inhibitors of digitalization and use of novel digital solutions in organizations. Moreover, the most significant facilitating factors are presented in the paper.
PLZF Regulates CCR6 and Is Critical for the Acquisition and Maintenance of the Th17 Phenotype in Human Cells

Th17 cells, which express the chemokine receptor CCR6, are implicated in many immune-mediated disorders, such as psoriasis and multiple sclerosis. We found that expression levels of CCR6 on human effector/memory CD4+ T cells reflect a continuum of Th17 differentiation. By evaluating the transcriptome in cells with increasing CCR6, we detected progressive upregulation of ZBTB16, which encodes the broad complex, tramtrack, bric-à-brac–zinc finger transcription factor promyelocytic leukemia zinc finger protein (PLZF). Using chromatin immunoprecipitation for modified histones, p300, and PLZF, we identified enhancer-like sites at −9/−10 and −13/−14 kb from the upstream transcription start site of CCR6 that bind PLZF in CCR6+ cells. For Th cells from adult blood, both in the CCR6+ memory population and in naive cells activated ex vivo, knockdown of ZBTB16 downregulated CCR6 and other Th17-associated genes. ZBTB16 and RORC (which encodes the “master regulator” RORγt) cross-regulate each other, and PLZF binds at the RORC promoter in CCR6+ cells. In naive Th cells from cord blood, ZBTB16 expression was confined to CD161+ cells, which are Th17 cell precursors. ZBTB16 was not expressed in mouse Th17 cells, and Th17 cells could be made from luxoid mice, which harbor an inactivating mutation in Zbtb16. These studies demonstrate a role for PLZF as an activator of transcription important both for Th17 differentiation and the maintenance of the Th17 phenotype in human cells, expand the role of PLZF as a critical regulator in the human adaptive immune system, and identify a novel, essential element in a regulatory network that is of significant therapeutic interest.
An Algorithm of Speaker Clustering Based on Model Distance

An algorithm based on Model Distance (MD) for spectral speaker clustering is proposed to deal with the shortcoming of general spectral clustering algorithm in describing the distribution of signal source. First, an Universal Background Model (UBM) is created with a large quantity of independent speakers; Then, Gaussian Mixture Model (GMM) is trained from the UBM for every speech segment; At last, the probability distance between the GMM of every speech segment is used to build affinity matrix, and speaker spectral clustering is done on the affinity matrix. Experimental results based on news and conference data sets show that an average of 6.38% improvements in F measure is obtained in comparison with algorithm based on the feature vector distance. In addition, the proposed algorithm is 11.72 times faster.
Blending Cr2O3 into a NiO-Ni electrocatalyst for sustained water splitting.

The rising H2 economy demands active and durable electrocatalysts based on low-cost, earth-abundant materials for water electrolysis/photolysis. Here we report nanoscale Ni metal cores over-coated by a Cr2 O3 -blended NiO layer synthesized on metallic foam substrates. The Ni@NiO/Cr2 O3 triphase material exhibits superior activity and stability similar to Pt for the hydrogen-evolution reaction in basic solutions. The chemically stable Cr2 O3 is crucial for preventing oxidation of the Ni core, maintaining abundant NiO/Ni interfaces as catalytically active sites in the heterostructure and thus imparting high stability to the hydrogen-evolution catalyst. The highly active and stable electrocatalyst enables an alkaline electrolyzer operating at 20 mA cm(-2) at a voltage lower than 1.5 V, lasting longer than 3 weeks without decay. The non-precious metal catalysts afford a high efficiency of about 15 % for light-driven water splitting using GaAs solar cells.
Mining Association Rules between Abnormal Health Examination Results and Outpatient Medical Records

Currently, interpretation of health examination reports relies primarily on the physician's own experience. If health screening data could be integrated with outpatient medical records to uncover correlations between disease and abnormal test results, the physician could benefit from having additional reference resources for medical examination report interpretation and clinic diagnosis. This study used the medical database of a regional hospital in Taiwan to illustrate how association rules can be found between abnormal health examination results and outpatient illnesses. The rules can help to build up a disease-prevention knowledge database that assists healthcare providers in follow-up treatment and prevention. Furthermore, this study proposes a new algorithm, the data cutting and sorting method, or DCSM, in place of the traditional Apriori algorithm. DCSM significantly improves the mining performance of Apriori by reducing the time to scan health examination and outpatient medical records, both of which are databases of immense sizes.
EXTRACTION OF PHENOLOGICAL PARAMETERS FROM TEMPORALLY SMOOTHED VEGETATION INDICES

Within the MARS Crop Yield Forecasting System (MCYFS; Royer and Genovese, 2004) of the European Commission vegetation indicators like NDVI, SAVI and fAPAR are operationally derived for daily, decadal and monthly time steps. Besides low resolution sensors as SPOT-VGT and NOAA-AVHRR, medium resolution data from TERRA/AQUA-MODIS or ENVISAT-MERIS are used at pan-European level. In case of available time series, esp. NOAA-AVHRR (since 1981) and SPOT-VGT (since 1998) difference values of the indicators (e.g. relative or absolute differences) and frequency analysis of the indicators (e.g. position in historical range or distribution) are calculated. The exploitation of the data is performed at full resolution, at grid level of the MCYFS or regional unmixed means (C-indicators) are used. Therefore a database has been set-up in order to provide the indicators based on a weighted average for each CORINE land cover class within an area of interest. The current work aims to develop a strategy for an optimal use of the different sensors and thus derived indicators at different aggregation levels for the ingestion into the MCYFS. As a first step smoothing algorithms have to be applied to the time series to diminish noise effects. Therefore, existing methods as simple sliding windows, piecewise linear regression or fitting of polynomial functions are employed and compared. Thereafter, the time series analysis is performed with the aim to establish relationships between indicators profile features and the crop phenology.
Causes of Death and Predictors of 5-Year Mortality in Young Adults After First-Ever Ischemic Stroke: The Helsinki Young Stroke Registry

Background and Purpose— Data on mortality and its prognostic factors after an acute ischemic stroke in young adults are scarce and based on relatively small heterogeneous patient series. Methods— We analyzed 5-year mortality data of all consecutive patients aged 15 to 49 with first-ever ischemic stroke treated at the Department of Neurology, Helsinki University Central Hospital, from January 1994 to September 2003. We followed up the patients using data from the mortality registry of Statistics Finland. We used life table analyses for calculating mortality risks. Kaplan–Meier method allowed comparisons of survival between clinical subgroups. We used the Cox proportional hazard model for identifying predictors of mortality. Stroke severity was measured using the National Institutes of Health Stroke Scale and the Glasgow Coma Scale. Results— Among the 731 patients (mean age, 41.5±7.4 years; 62.8% males) followed, 78 died. Cumulative mortality risks were 2.7% (95% CI, 1.5% to 3.9%) at 1 month, 4.7% (3.1% to 6.3%) at 1 year, and 10.7% (9.9% to 11.5%) at 5 years with no gender difference. Those ≥45 years of age had lower probabilities of survival. Among the 30-day survivors (n=711), stroke caused 21%, cardioaortic and other vascular causes 36%, malignancies 12%, and infections 9% of the deaths. Malignancy, heart failure, heavy drinking, preceding infection, type 1 diabetes, increasing age, and large artery atherosclerosis causing the index stroke independently predicted 5-year mortality adjusted for age, gender, relevant risk factors, stroke severity, and etiologic subtype. Conclusions— Despite the overall low mortality after an ischemic stroke in young adults, several recognizable subgroups had substantially increased risk of death in the long term.
Constitutive promoter methylation of BRCA1 and RAD51C in patients with familial ovarian cancer and early-onset sporadic breast cancer

Genetic defects in breast cancer (BC) susceptibility genes, most importantly BRCA1 and BRCA2, account for ∼40% of hereditary BC and ovarian cancer (OC). Little is known about the contribution of constitutive (soma-wide) epimutations to the remaining cases. We developed bisulfite pyrosequencing assays to screen >600 affected BRCA1/BRCA2 mutation-negative patients from the German Consortium for Hereditary Breast and Ovarian Cancer for constitutive hypermethylation of ATM, BRCA1, BRCA2, RAD51C, PTEN and TP53 in blood cells. In a second step, patients with ≥6% promoter methylation were analyzed by bisulfite plasmid sequencing to demonstrate the presence of hypermethylated alleles (epimutations), indicative of epigenetic gene silencing. Altogether we identified nine (1.4%) patients with constitutive BRCA1 and three (0.5%) with RAD51C hypermethylation. Epimutations were found in both sporadic cases, in particular in 2 (5.5%) of 37 patients with early-onset BC, and familial cases, in particular 4 (10%) of 39 patients with OC. Hypermethylation was always confined to one of the two parental alleles in a subset (12–40%) of the analyzed cells. Because epimutations occurred in cell types from different embryonal layers, they most likely originated in single cells during early somatic development. We propose that analogous to germline genetic mutations constitutive epimutations may serve as the first hit of tumor development. Because the role of constitutive epimutations in cancer development is likely to be largely underestimated, future strategies for effective testing of susceptibility to BC and OC should include an epimutation screen.