Miracle-dz/UniFAR-FTUs huggingface dataset:
Kinematics of our Galaxy from the PMA and TGAS catalogues

Abstract We derive and compare kinematic parameters of the Galaxy using the PMA and Gaia TGAS data. Two methods are used in calculations: evaluation of the Ogorodnikov-Milne model (OMM) parameters by the least square method (LSM) and a decomposition on a set of vector spherical harmonics (VSH). We trace dependencies on the distance of the derived parameters including the Oort constants A and B and the rotational velocity of the Galaxy V rot at the Solar distance for the common sample of stars of mixed spectral composition of the PMA and TGAS catalogues. The distances were obtained from the TGAS parallaxes or from reduced proper motions for fainter stars. The A, B and V rot parameters derived from proper motions of both catalogues used show identical behaviour but the values are systematically shifted by about 0.5 mas/yr. The Oort B parameter derived from the PMA sample of red giants shows gradual decrease with increasing the distance while the Oort A has a minimum at about 2 kpc and then gradually increases. As for models chosen for calculations, first, we confirm conclusions of other authors about the existence of extra-model harmonics in the stellar velocity field. Secondly, not all parameters of the OMM are statistically significant, and the set of parameters depends on the stellar sample used.
CLANet: A Comprehensive Framework for Cross-Batch Cell Line Identification Using Brightfield Images

Cell line authentication plays a crucial role in the biomedical field, ensuring researchers work with accurately identified cells. Supervised deep learning has made remarkable strides in cell line identification by studying cell morphological features through cell imaging. However, biological batch (bio-batch) effects, a significant issue stemming from the different times at which data is generated, lead to substantial shifts in the underlying data distribution, thus complicating reliable differentiation between cell lines from distinct batch cultures. To address this challenge, we introduce CLANet, a pioneering framework for cross-batch cell line identification using brightfield images, specifically designed to tackle three distinct bio-batch effects. We propose a cell cluster-level selection method to efficiently capture cell density variations, and a self-supervised learning strategy to manage image quality variations, thus producing reliable patch representations. Additionally, we adopt multiple instance learning(MIL) for effective aggregation of instance-level features for cell line identification. Our innovative time-series segment sampling module further enhances MIL's feature-learning capabilities, mitigating biases from varying incubation times across batches. We validate CLANet using data from 32 cell lines across 93 experimental bio-batches from the AstraZeneca Global Cell Bank. Our results show that CLANet outperforms related approaches (e.g. domain adaptation, MIL), demonstrating its effectiveness in addressing bio-batch effects in cell line identification.
A MAP blind image deconvolution algorithm with bandwidth over-constrained

We demonstrate a maximum a posteriori (MAP) blind image deconvolution algorithm with bandwidth over-constrained and total variation (TV) regularization to recover a clear image from the AO corrected images. The point spread functions (PSFs) are estimated by bandwidth limited less than the cutoff frequency of the optical system. Our algorithm performs well in avoiding noise magnification. The performance is demonstrated on simulated data.
Society for Neuroscience in Anesthesiology and Critical Care Expert Consensus Statement: Anesthetic Management of Endovascular Treatment for Acute Ischemic Stroke* Endorsed by the Society of NeuroInterventional Surgery and the Neurocritical Care Society

Literature on the anesthetic management of endovascular treatment of acute ischemic stroke (AIS) is limited. Anesthetic management during these procedures is still mostly dependent on individual or institutional preferences. Thus, the Society of Neuroscience in Anesthesiology and Critical Care (SNACC) created a task force to provide expert consensus recommendations on anesthetic management of endovascular treatment of AIS. The task force conducted a systematic literature review (up to August 2012). Because of the limited number of research articles relating to this subject, the task force solicited opinions from experts in this area. The task force created a draft consensus statement based on the available data. Classes of recommendations and levels of evidence were assigned to articles specifically addressing anesthetic management during endovascular treatment of stroke using the standard American Heart Association evidence rating scheme. The draft consensus statement was reviewed by the Task Force, SNACC Executive Committee and representatives of Society of NeuroInterventional Surgery (SNIS) and Neurocritical Care Society (NCS) reaching consensus on the final document. For this consensus statement the anesthetic management of endovascular treatment of AIS was subdivided into 12 topics. Each topic includes a summary of available data followed by recommendations. This consensus statement is intended for use by individuals involved in the care of patients with acute ischemic stroke, such as anesthesiologists, interventional neuroradiologists, neurologists, neurointensivists, and neurosurgeons.
Examining Committee

Recently, Wireless Sensor Networks (WSNs) have been used in many monitoring applications, e.g., environment monitoring. A WSN consists of a set of nodes, each having one or more sensors to measure a phenomena. Nodes are connected to each other using wireless radio communications. Typically, there is at least one base station that functions as an interface between the monitored area and the end user. In many applications, users are interested in statistical summaries of the observed data, e.g., histograms reflecting the distribution of the collected values. In this thesis we propose two main contributions: (1) an efficient algorithm for answering Histogram queries in a WSN, and (2) efficient algorithms for answering snapshot and continuous Median queries in a WSN. While designing applications for the WSN, the main challenge is the battery life time. For WSN nodes, sending a message consumes a significantly higher amount of energy than processing information inside the node. Therefore, we design our proposed algorithms in order to minimize the energy consumption and/or transmission cost, i.e. number of sent bytes, and consequently extend its lifespan. Our experimental results, using synthetic and real datasets, show that our proposed solutions are indeed able to substantially extend the lifespan of the WSN when compared to previously proposed solutions.
The effectiveness and cost evaluation of pain exposure physical therapy and conventional therapy in patients with complex regional pain syndrome type 1 . Rationale and design of a randomized controlled trial

Karlijn J Barnhoorn (karlijnbarnhoorn@gmail.com) Rob AB Oostendorp (R.Oostendorp@iq.umcn.nl) Robert TM van Dongen (R.vanDongen@anes.umcn.nl) Frank P Klomp (F.Klomp@orthop.umcn.nl) Han Samwel (H.Samwel@mps.umcn.nl) Gert Jan van der Wilt (G.vanderWilt@ebh.umcn.nl) Eddy Adang (E.Adang@ebh.umcn.nl) Hans Groenewoud (J.Groenewoud@ebh.umcn.nl) Henk van de Meent (H.vandeMeent@reval.umcn.nl) Jan Paul M Frolke (J.Frolke@chir.umcn.nl)
DIABETES SELF-MANAGEMENT: PATIENT COGNITION AND THE DEVELOPMENT OF EXPERTISE

Lippa, Katherine D. M.S., Department of Psychology, 2006. Diabetes SelfManagement: Patient Cognition and the Development of Expertise Human Factors researchers have explored decision making and the development of expertise in many complex, professional domains. Non-professionals make many equally complex decisions, yet this area has received much less attention. This study uses the case of type II diabetes to explore how Human Factors can help describe the complex decision making demanded by daily life. Type II diabetes is a chronic health condition which can lead to disability and death. While self-management directed at maintaining safe blood glucose levels can reduce these risks, most Americans with diabetes show poor adherence. This study examined how people with diabetes understand their illness and how their understanding affects self-management. Expert-novice differences found in other domains were used to predict the cognitive processes associated with effective self-management. It was hypothesized that participants who showed greater expertise in terms of articulating problem detection strategies, demonstrating functionally structured knowledge, and describing problem solving strategies would have also higher levels of self-management: reporting more selfmanagement activities and lower glucose levels. Cognitive task analysis interviews were conducted with twenty participants with Type II Diabetes. Participants were asked about their knowledge of and experiences with
Blazars at Low Radio Frequencies

Jonas Trüstedt∗ab, Matthias Kadlera, Marcus Brüggenc, Heino Falckeed , George Healdd , John McKeand , Cornelia Müllerab, Eduardo Ros f gh, Robert Schulzab, Jörn Wilmsb a Lehrstuhl für Astronomie, Universität Würzburg, Campus Hubland Nord, Emil-Fischer-Str. 31, D-97074 Würzburg, Germany b Dr, Remeis Sternwarte & ECAP, Universität Erlangen-Nürnberg, Sternwartstr. 7, D-96049, Bamberg, Germany c Universität Hamburg, Hamburger Sternwarte, Gojenbergsweg 112, D-21029, Hamburg, Germany d ASTRON, The Netherlands Institute for Radio Astronomy, Postbus 2, 7990 AA, Dwingeloo, The Netherlands e IMAPP/Department of Astrophysics, Radboud University, PO Box 9010, 6500 GL, Nijmegen, The Netherlands f Max-Planck-Institut für Radioastronomie, Auf dem Hügel 69, D-53121 Bonn, Germany g Observatori Astronòmic, Universitat de València, C. Catedrático José Beltrán 2, E-46980 Paterna, València, Spain h Departament d’Astronomia i Astrofísica, Universitat de València, C. Dr. Moliner 50, E-46100 Burjassot, València, Spain E-mail: jonas.truestedt@astro.uni-wuerzburg.de
Determinantes da detecção de atipias celulares no programa de rastreamento do câncer do colo do útero no Rio de Janeiro, Brasil

OBJECTIVE: To identify the main determinants of cellular atypia detection in the cervical screening program in the state of Rio de Janeiro, Brazil, using data from the Cervical Cancer Information System SISCOLO. METHODS: A random sample of 65 535 Pap smears performed in 2007 was obtained from SISCOLO. This sample was used to produce a logistic regression model to identify variables that impact the process of detecting cellular atypia. A ROC curve was used to define the most suitable cutoff point to classify the presence or absence of atypia. A sensitivity analysis was performed to assess the impact on the model of factors related to the organization of the service model. RESULTS: The variables of impact were "reference laboratory," which reflects laboratory production scale; "presence of cellular elements representative of the transformation zone," which reflects the quality of the sampling; "immature squamous metaplasia," "presence of other benign cellular changes," and "absence of typical vaginal microorganisms. " Each increase of 1 year in age was associated with a 1.7% reduction in the chance of detecting atypia. The ROC curve defined a cutoff of 4.5%, which allowed the maximization of the model's sensitivity (73.0%) and specificity (66.8%). Sensitivity analysis indicated an increase of 46.4% in the frequency of atypia following a simulated increase in the number of samples analyzed in the excellence (42.9%) and in the presence of cellular elements representative of the transformation zone (43.0%). CONCLUSIONS: The model revealed that the detection of atypical cells is strongly influenced by organizational factors such as adequate sample collection and laboratory size. Because these factors can be changed by adequate management practices, the proposed model may be an important tool to improve cervical screening programs.
Compare and Contrast : Learning Prominent Differences in Relative Attributes

Relative attribute models can compare images in terms of all detected properties or attributes, exhaustively predicting which image is fancier, more colorful, more natural, and so on without any regard to ordering. However, when humans are asked to compare images, certain attribute differences will naturally stick out and come to mind first. These most noticeable differences, or prominent differences in relative attributes, are likely to be described first. In addition, certain differences in attributes, although present and true, may not be mentioned at all. In this work, we introduce and model prominent differences, a rich new functionality for comparing images. Using instance-level human annotations of most noticeable differences, we build a model trained using relative attribute features that predicts prominent attributes for new image pairs. We test our model on the challenging UT-Zap50K shoes and LFW-10 faces datasets, and outperform an array of baseline methods. We then demonstrate how our prominence model improves two vision tasks, image search and textual description generation, enabling more natural communication between people and vision systems.
On the evaluation of finite element sensitivities to nodal coordinates.

We present a derivation of the derivative of general systems of finite element equations with respect to the coordinates of the nodes in the underlying finite element mesh. The resulting expressions allow the systematic evaluation of such derivatives without the need to resort to automatic differentiation or the expense associated with finite difference approximations. The principal motivation for this work comes from problems in optimal design, however, other potential applications are also described. The results obtained are validated through numerical examples.
Factors Affecting Job Satisfaction among Academic Employees in Polytechnic

Satisfied employees are crucial in developing a successful organization. The purpose of this study is to identify the factors that affect job satisfaction among polytechnic academic employees in Malaysia. A cross-sectional study was employed and stratified random sampling was used to collect the data. A total of 130 respondents answered the questionnaires. The results revealed that job security, salary and working conditions had significant and positive influence on job satisfaction among polytechnic academic employees. Hence, strengthening the factors of job security, salary and working conditions among the polytechnic academic employees is critically important to ensure the employees are satisfied which will then lead towards a successful polytechnic.
An artificial neural network architecture for non-parametric visual odometry in wireless capsule endoscopy

Wireless capsule endoscopy is a non-invasive screening procedure of the gastrointestinal (GI) tract performed with an ingestible capsule endoscope (CE) of the size of a large vitamin pill. Such endoscopes are equipped with a usually low-frame-rate color camera which enables the visualization of the GI lumen and the detection of pathologies. The localization of the commercially available CEs is performed in the 3D abdominal space using radio-frequency (RF) triangulation from external sensor arrays, in combination with transit time estimation. State-of-the-art approaches, such as magnetic localization, which have been experimentally proved more accurate than the RF approach, are still at an early stage. Recently, we have demonstrated that CE localization is feasible using solely visual cues and geometric models. However, such approaches depend on camera parameters, many of which are unknown. In this paper the authors propose a novel non-parametric visual odometry (VO) approach to CE localization based on a feed-forward neural network architecture. The effectiveness of this approach in comparison to state-of-the-art geometric VO approaches is validated using a robotic-assisted in vitro experimental setup.
N1-Guanyl-1,7-Diaminoheptane Sensitizes Estrogen Receptor Negative Breast Cancer Cells to Doxorubicin by Preventing Epithelial-Mesenchymal Transition through Inhibition of Eukaryotic Translation Initiation Factor 5A2 Activation

Background: Approximately 30% of breast cancer does not express the estrogen receptor (ER), which is necessary for endocrine-based therapy approaches. Many studies demonstrated that eukaryotic translation initiation factor 5A2 (eIF5A2) serves as a proliferation-related oncogene in tumorigenic processes. Methods: The present study used cell viability assays, EdU incorporation assays, western blot, and immunofluorescence to explore whether N1-guanyl-1,7-diaminoheptane (GC7), which inhibits eIF5A2 activation, exerts synergistic cytotoxicity with doxorubicin in breast cancer. Results: We found that GC7 enhanced doxorubicin cytotoxicity in ER-negative HCC1937 cells but had little effect in ER-positive MCF-7 and Bcap-37 cells. Administration of GC7 reversed the doxorubicin-induced epithelial-mesenchymal transition (EMT) in ER-negative breast cancer cells. Knockdown of eIF5A2 by siRNA inhibited the doxorubicin-induced EMT in ER-negative HCC1937 cells. Conclusion: These data demonstrated that GC7 combination therapy may enhance the therapeutic efficacy of doxorubicin in estrogen negative breast cancer cells by preventing EMT through inhibition of eIF5A2 activation.
A POROSITY RESULT FOR A SADDLE POINT PROBLEM

In this paper we consider a complete metric space M of functions f : X × Y → R which satisfy supy∈Y infx∈X f(x, y) = infx∈X supy∈Y f(x, y), where X and Y are complete metric spaces. We establish that the set of all functions from M which have a unique saddle point has a σ-porous complement. AMS (MOS) Subject Classification. 49J35, 54E52
The Relationship between Occupational Stress and Intentions to Quit among Employees at Nkonkobe Municipality , South Africa

The work environment is one of the areas that is very complicated when it comes to human development and behaviour. Most people that are employed tend to follow Maslow Hierarchy of Needs which states that human needs form a five-level hierarchy ranging from physiological needs, safety, belongingness and love, esteem to selfactualization. Based on Maslow’s theory, job satisfaction has been approached by some researchers from the perspective of need fulfilment. However, this approach has become less popular with increasing emphasis on cognitive processes rather than on underlying needs so that the attitudinal perspective has become predominant in the study of job satisfaction by individuals in their work places [1].
Knowledge-Based Systems

Embedding knowledge is a popular and e ective means of increasing the power of sophisticated computer applications. While the intellectual roots of this method go back to the late 1960's, the ideas were rst codi ed, systematized and simpli ed in the 1970's, and have led to a large and successful expert systems industry in the 1980's. Despite these successes, most types of expertise are still extremely di cult to capture, and many fundamental scienti c and engineering challenges remain to this eld. This paper will brie y review the origins and motivations for the eld, indicate the considerable successes it has achieved, outline the many remaining di culties, and highlight a few individual research results that point the way to its future development.
I(1) and I(2) Cointegration Analysis Theory and Applications

This paper considers the likelihood ratio (LR) test for the number of cointegrating and multi-cointegrating relations in the I(2) vector autoregressive (VAR) model. We derive the asymptotic distribution of the LR test for the (multi-) cointegration ranks, and show that this is identical to the asymptotic distribution of the much applied test statistic from the Two-Step procedure, see Johansen (1995), Paruolo (1996), and Rahbek, Kongsted, and Jorgensen (1999). However, conclusions with regards to the cointegration ranks in existing empirical applications change when applying the LR test, and based on Monte Carlo simulations we conclude that the LR test is preferable to the Two-Step based statistic.
Forecasting Cryptocurrencies Financial Time Series

This paper studies the predictability of cryptocurrencies time series. We compare several alternative univariate and multivariate models in point and density forecasting of four of the most capitalized series: Bitcoin, Litecoin, Ripple and Ethereum. We apply a set of crypto–predictors and rely on Dynamic Model Averaging to combine a large set of univariate Dynamic Linear Models and several multivariate Vector Autoregressive models with different forms of time variation. We find statistical significant improvements in point forecasting when using combinations of univariate models and in density forecasting when relying on selection of multivariate models.
Telomerase reverse transcriptase promoter region mutations and the clinical characteristics of pulmonary neuroendocrine tumors

Background: Pulmonary neuroendocrine tumors (NETs) represent approximately 20–25% of all primary lung tumors. It is occasionally difficult to distinguish between the different types of pulmonary NETs. Telomerase reverse transcriptase (TERT) can effectively maintain the structural integrity of telomeres. Alterations in telomere length, telomerase activity, and the expression of hTERT mRNA may be a useful tool for the differential diagnosis among different kinds of NETs. There is no report about TERT promoter mutations in pulmonary NETs in China. This study aimed to clarify the status of TERT promoter region mutations and the clinical characteristics of pulmonary NETs. Methods: A total of 41 surgically resected pulmonary NETs were retrospectively collected from the Zhejiang Cancer Hospital in China between 2008 and 2016, including typical carcinoid (TC) cases, atypical carcinoid (AC) cases, large cell neuroendocrine carcinoma (LCNEC) cases, and small cell lung carcinoma (SCLC) cases. TERT promoter mutation was analyzed by polymerase chain reaction (PCR) ampli cation and Sanger sequencing. The clinical characteristics and treatment data were also collected. The survival time of all patients was followed-up. Results: Most pulmonary NET patients were male. Most SCLC patients were heavy smokers, while most AC/TC patients were non-smokers. No TERT promoter region 124 (C228T) and 146 (C250T) mutations were found in the 41 cases of pulmonary NET. The prognosis of AC/TC was the best, followed by LCNEC, while SCLC was the worst. Overall survival (OS) of the SCLC patients ≤65 years (n=23) and >65 years (n=6) were 35 and 18 months, respectively, P=0.041. SCLC patients who received ≥3 cycles of adjuvant chemotherapy (n=18) had longer OS as compared to those who received ≤2 cycles of adjuvant chemotherapy (n=11) (not reach vs . 22 months, P=0.061). Conclusions: TERT promoter mutations are rare in pulmonary NET. The prognosis of different histological subtypes of pulmonary NET varies. Age and cycles of adjuvant chemotherapy are related to prognosis of resected SCLC.
Exact and Approximation Algortihms for Clustering (extended Abstract)

In this paper we present an n O(k 1? 1=d) time algorithm for solving the k-center problem in R d , under L1 and L2 met-rics. The algorithm extends to other metrics, and to the discrete k-center problem. We also describe a simple (1+)-approximation algorithm for the k-center problem, with running time O(n log k) + (k==) O(k 1? 1=d). Finally, we present a n O(k 1? 1=d) time algorithm for solving the L-capacitated k-center problem, provided that L = (n=k 1? 1=d) or L = O(1). We conclude with a simple approximation algorithm for the L-capacitated k-center problem.