Miracle-dz/UniFAR-FTUs huggingface dataset:
Synergistic Transactivation of the Differentiation-dependent Lung Gene Clara Cell Secretory Protein (Secretoglobin 1a1) by the Basic Region Leucine Zipper Factor CCAAT/Enhancer-binding Protein α and the Homeodomain Factor Nkx2.1/Thyroid Transcription Factor-1*

The basic region-leucine zipper transcription factor CCAAT/enhancer-binding protein α (C/EBPα) and the homeodomain transcription factor Nkx2.1/thyroid transcription factor-1 are essential for normal lung morphogenesis. Nkx2.1 is expressed from the onset of lung development, whereas C/EBPα expression is turned on at later stages. The expression of C/EBPα correlates to the appearance of lung-specific proteins with differentiation-dependent expression patterns, such as the Clara cell secretory protein (secretoglobin 1a1 (Scgb1a1), CCSP). In this study, we demonstrate synergistic transactivation by C/EBPα and Nkx2.1 in the regulation of the CCSP gene. We show that the synergistic activity of C/EBPα and Nkx2.1 originates fromcis-acting elements in the proximal promoter ofCCSP and that the synergism is dependent on NH2-terminal transactivation domains of C/EBPα and Nkx2.1. Our results suggest that the cooperation of C/EBPα and Nkx2.1 is a major determinant for the high level, lung epithelial-specific expression of CCSP during the later stages of lung development and in the adult lung.
Based on Multiple Scales Forecasting Stock Price with a Hybrid Forecasting System

This paper presents an integration prediction method which is called a hybrid forecasting system based on multiple scales. In this method, the original data are decomposed into multiple layers by the wavelet transform and the multiple layers are divided into low-frequency, intermediate-frequency and high-frequency signal layers. Then autoregressive moving average models, Kalman filters and Back Propagation neural network models are employed respectively for predicting the future value of low-frequency, intermediate-frequency and high-frequency signal layers. An effective algorithm for predicting the stock prices is developed. The price data with the Shandong Gold Group of Shanghai stock exchange market from 28th June 2011 to 24th June 2012 are used to illustrate the application of the hybrid forecasting system based on multiple scales in predicting stock price. The result shows that time series forecasting can be produced by forecasting on low-frequency, intermediate-frequency and high-frequency signal layers separately. The actual value and the forecasting results are matching exactly. Therefore, the forecasting result of simulation experiments is excellent.
Delay Tolerant Dynamic Data Collection over a Sensor Network

The work presented in this paper consists in experimenting on the collection of dynamic data distributed over a sensor network, where only neighborhood communication is supported (no routing) and parts of the network can become disconnected (no flooding). This can be used to track physical phenomena in the context of battlefields, crisis management or disaster response. By observing the evolution of the distributed variables that make the phenomenon, it is possible to work out emerging trends and potential threats, and to undertake the required actions, i.e. to support situation management. Our goal in this paper is to describe the framework that we have designed and implemented and that makes it possible to cope with missing measurements. When some data items are missing, either because of interferences or faulty sensors, it still enables to provide a continuous view of the distributed phenomenon by using interpolation or extrapolation techniques. Our focus is on the framework to support the implementation of such features, not on the final applications. Nevertheless we illustrate its use it by means of a temperature collection application.
Evolutionary building layout optimisation

Space layout planning (SLP) is the organisation of functional/living spaces (spatial units-SUs) and corridors/access paths of a building satisfying requirements (e.g. accessibility, adjacency etc.) to achieve design goals (e.g. minimising unutilised space and travelling cost). Out of many ways of arranging SUs, a human designer may consider only a handful of alternatives due to resource limitations (e.g. time and effort). To facilitate this task, decision support for SLP design can be obtained using computer technology. Despite being highly combinatorial, many attempts have been made to automate SLP. However in the majority of these, the SUs are arranged in a fixed building footprint/boundary, which may limit exploration of the entire solution space. Thus, it is aimed to develop a space layout optimisation system that allows SUs to position themselves in a building site to satisfy design goals. The objectives of the research are to: understand architectural SLP and optimisation; assess the need for automation of SLP optimisation; explore methods to formulate the SLP optimisation problem; develop a prototype system to optimise SLP based on building design guidelines, and evaluate performance for its strengths and weaknesses using case studies. As early stages of building design are found to be most e ective in reducing the environmental impact and costs, it is also aimed to make provisions for integrating these aspects in SLP. To address the first three objectives, a literature review was conducted. The main finding of this was the current need for an optimisation tool for SLP. It also revealed that genetic algorithms-GA are widely used and show promise in optimisation. Then, a prototype space layout optimisation system (Sl-Opt) was developed using real-valued GA and was programed in JavaR. Constrained optimisation was employed where adjacency and accessibility needs were modelled as constraints, and the objective was to minimise the spread area of the layout. Following this, using an office layout with 8 SUs, Sl-Opt was evaluated for its performance. Results of the designed experiment and subsequent statistical tests showed that the selected parameters of GA operators influence optimisation collectively. Finally using the best parameter set, strengths and weaknesses of Sl-Opt were evaluated using two case studies: a hospital layout problem with 31 SUs and a problem with 10 non-rectangular SUs. Findings revealed that using the selected GA parameters Sl-Opt can successfully solve small scale problems of about less than 10 SUs. For larger prob- lems, the parameters need to be altered. Case studies also revealed that the system is capable of solving problems with non-rectangular SUs with varied 0rientations. Sl-Opt appear to have potential as a building layout decision support tool, and in addition, integration of other aspects such as energy efficiency and cost is possible.
Adoption of mobile information services: An empirical study

This study investigates the adoption of mobile information services at a Norwegian university. By expanding the Technology Acceptance Model TAM, a new research model, known as the mobile services acceptance model MSAM, is proposed. Based on the research model, seven research hypotheses are presented. The proposed research model and research hypotheses were empirically tested using data collected from a survey of users of a mobile service, extended Mobile Student Information Systems eMSIS, at a Norwegian university. The findings indicate that the fitness of the research model is good. Support was also found for the seven research hypotheses. Among the factors, the personal initiatives and characteristics has the strongest influence on the intention to use eMSIS.
English Compound Versus Non-Compound Noun Phrases in Discourse: An Acoustic and Perceptual Study

The aim of the present paper is to describe, in acoustic and perceptual terms, the prosodic pattern distinguishing English compound and non-compound noun phrases, and to determine how information structure and position affect the production and perception of the two forms. The study is based on the performance of ten English-speaking subjects (five speakers and five listeners). The test utterances were three minimal-pair noun phrases of two constituents, excised from conversational readings. These were analyzed acoustically, and submitted to the listeners for semantic identification. The results indicate that the distinction, when effective, lies primarily in the different prominence pattern: a sequence of an accented constituent followed by an unaccented one in compounds, and of two accented constituents (the second heard as stronger than the first) in non-compounds. It is also based on a different degree of internal cohesion, stronger in compounds and weaker in non-compounds. F 0, associated or trading with intensity, has proved to be the main cue to this distinction — more than duration, the major differentiating parameter in production. When an item is excised from the context, the perception of the intended category depends heavily on the communicative importance it had in the discourse. This means that information structure, through its effects on accentuation, becomes the determining factor in the perception of the distinction. The distinctive accentual pattern weakens or is completely neutralized when the test items convey old information. The degree of deaccentuation also seems to be affected by an immediately following focus, and, to a certain extent, by position. The data are viewed in the framework of speaker-listener interaction, and it is argued that deaccentuation, as well as accentuation, can have a communicative function.
Learnable despeckling framework for optical coherence tomography images

Abstract. Optical coherence tomography (OCT) is a prevalent, interferometric, high-resolution imaging method with broad biomedical applications. Nonetheless, OCT images suffer from an artifact called speckle, which degrades the image quality. Digital filters offer an opportunity for image improvement in clinical OCT devices, where hardware modification to enhance images is expensive. To reduce speckle, a wide variety of digital filters have been proposed; selecting the most appropriate filter for an OCT image/image set is a challenging decision, especially in dermatology applications of OCT where a different variety of tissues are imaged. To tackle this challenge, we propose an expandable learnable despeckling framework, we call LDF. LDF decides which speckle reduction algorithm is most effective on a given image by learning a figure of merit (FOM) as a single quantitative image assessment measure. LDF is learnable, which means when implemented on an OCT machine, each given image/image set is retrained and its performance is improved. Also, LDF is expandable, meaning that any despeckling algorithm can easily be added to it. The architecture of LDF includes two main parts: (i) an autoencoder neural network and (ii) filter classifier. The autoencoder learns the FOM based on several quality assessment measures obtained from the OCT image including signal-to-noise ratio, contrast-to-noise ratio, equivalent number of looks, edge preservation index, and mean structural similarity index. Subsequently, the filter classifier identifies the most efficient filter from the following categories: (a) sliding window filters including median, mean, and symmetric nearest neighborhood, (b) adaptive statistical-based filters including Wiener, homomorphic Lee, and Kuwahara, and (c) edge preserved patch or pixel correlation-based filters including nonlocal mean, total variation, and block matching three-dimensional filtering.
Iterative Closest Conformal Maps between Planar Domains

Conformal maps between planar domains are an important tool in geometry processing, used for shape deformation and image warping. The Riemann mapping theorem guarantees that there exists a conformal map between any two simply connected planar domains, yet computing this map efficiently remains challenging. In practice, one of the main algorithmic questions is the correspondence between the boundaries of the domains. On the one hand, there exist a number of conformal maps between any two domains, thus many potential boundary correspondences, yet on the other, given full boundary prescription a conformal map might not exist. Furthermore, an approximate boundary fitting can be enough for many applications. We therefore propose an alternating minimization algorithm for finding a boundary‐approximating conformal map given only an initial global alignment of the two input domains. We utilize the Cauchy‐Green complex barycentric coordinates to parameterize the space of conformal maps from the source domain, and thus compute a continuous map without requiring the discretization of the domain, and without mapping to intermediate domains. This yields a very efficient method which allows to interactively modify additional user‐provided constraints, such as point‐to‐point and stroke‐to‐stroke correspondences. Furthermore, we show how to easily generalize this setup to quasi‐conformal maps, thus enriching the space of mappings and reducing the area distortion. We compare our algorithm to state‐of‐the‐art methods for mapping between planar domains, and demonstrate that we achieve less distorted maps on the same inputs. Finally, we show applications of our approach to stroke based deformation and constrained texture mapping.
Micromachined Loop Antennas on Low Resistivity Silicon Substrates

The integration of K-band (20-40 GHz) full wavelength square wire- and slot-loop antennas on low resistivity (11-70 Omegacm) silicon substrates is addressed. By the use of polymer or silicon oxide/nitride membranes to support the slot or wire loop over micromachined trenches the efficiency of the antennas is enhanced while the majority of the bulk silicon within the aperture of the antenna is preserved to enable the integration of active devices. A 3.6times3. 6 mm2 large slot loop antenna chip with 200 mum micromachined trench width yields 1.5 dBi gain at 29.5 GHz, while 1.0 dBi gain is obtained at 24 GHz for a wire loop antenna on a 4.5times4. 5 mm2 large chip with 360 mum wide trenches
A comparison of the Oxford and Manus intelligent hand prostheses

Historically, commercial hand prosthesis have adopted a low level of innovation mainly due to the strict conditions such a system must undergo. The difficult feedback to the prosthesis user has limited the functional range of commercial systems. Nevertheless, the use of advanced sensors in combination with performing hand mechanisms and microcontrollers could lead to more natural and functional prototypes. The Oxford and Manus intelligent hand prostheses are examples of innovative approaches. This paper compares and contrasts the technological solutions implemented in both systems to address the design conditions.
On a Theorem of Weyl Concerning Eigenvalues of Linear Transformations I.

* This work was supported in part by the Office of Naval Research. Fan, K. , "On a Theorem of Weyl Concerning Eigenvalues of Linear Transformations. I," these PROCEEDINGS, 35, 652-655 (1949), cited here as I. 2 Weyl, H. , "Inequalities Between the Two Kinds of Eigenvalues of a Linear Trans-formation," Ibid., 35, 408-411 (1949). 3 P6lya, G. , "Remark on Weyl's Note: Inequalities Between the Two Kinds of Eigenvalues of a Linear Transformation," Ibid., 36, 49-51 (1950).
Letter to the Editor Re: Enstrom JE. Fine particulate and total mortality in Cancer Prevention Study cohort reanalysis. Dose-Response. 2017;15(1):1-12.

Enstrom’s article based on data from the American Cancer Society’s Second Cancer Prevention Study (CPS-II) is an important contribution to the literature on long-term relationships between mortality and air pollution exposure. It focuses on exposure uncertainties and regional differences. Neither topic has been explored in the many previous articles or critiques of CPS-II, and Enstrom’s article is the first truly independent analysis of these data. Here I discuss some additional implications of Enstrom’s results, their application to other studies based on CPS-II, and aspects of long-term air pollution studies in general. The CPS-II data are the basis for the US National Ambient Air Quality Standard for ambient particles with median aerodynamic diameters <2.5 mg/m (“fine” particles, PM2.5) and for similar standards worldwide. The CPS-II database includes survival and personal data for about 1 million volunteers, beginning in 1982. Prior to 1999, ambient data for PM2.5 were only available from an experimental and privately operated national network (the Inhalable Particulate Network [IPN]) supported by the US Environmental Protection Agency (US EPA) that measured particulate mass segregated by median aerodynamic diameter from 1979 to 1984. I assembled a national data set from IPN raw data reports such as Suggs et al for use in my 1988 analysis of ecological relationships with mortality that include a listing of the PM2.5 data that I used, as assigned to central cities. These data were adopted by Pope et al in their 1995 analysis of relationships with survival in CPS-II but were assigned to multicounty Standard Metropolitan Statistical Areas (SMSAs). The US EPA then published more formal reports based on the IPN data. Unlike other data used to support ambient air quality regulations, IPN data have never been made available in digital form, thus requiring manual efforts to create the necessary digital databases.
Moving Network Voronoi Diagram

We study the problem of moving network Voronoi diagram: given a network with $n$ nodes and $E$ edges. Suppose there are $m$ sites (cars, postmen, \emph{etc}) moving along the network edges, we design the algorithms to compute the dynamic network Voronoi diagram as sites move such that we can answer the nearest neighbor query efficiently. Furthermore, we extend it to the $k$-order dynamic network Voronoi diagram such that we can answer the $k$ nearest neighbor query efficiently. We also study the problem when the query point is allowed to move at a given speed.
States evolution in Θ(λ)-learning based on logical MDPs with negation

Based on the Logical MDPs with Negation, a model of Relational Reinforcement Learning, proposed in [1], we define the self-loop degree and the common characteristic of abstract states formally, and propose an evolution process collaborated with Theta(lambda)-learning according to the formal definitions. The abstract state space will be self-organized in the evolution process rather than given manually by human. The experiments show that the agent can catch the essence of the given task, and the self-organized states are rational.
Effort analysis in signer-independent sign gestures

A dilemma, caused by data variation, exists in research into signer-independent sign language recognition. An effective way to solve this dilemma, and thereby help push the research forward, is to understand sign language from the perspectives of human kinesics and linguistics. This paper, based on the principles of movement observation science, specifically Laban Movement Analysis (LMA), presents a summary of the factors causing sign language data variation, proposes the definition of, and a method for describing, sign language effort elements, and then provides a strategy for standardizing signer-independent sign language data. The standardized data are to be used for training and recognition. The method presented in this paper has been assessed under different experimental conditions, and the results show that the recognition accuracy is greatly increased.
Development and Validation of the RxDx-Dementia Risk Index to Predict Dementia in Patients with Type 2 Diabetes and Hypertension.

BACKGROUND Elderly patients with type 2 diabetes mellitus and hypertension are at high risk for developing dementia. In addition to comorbid disease conditions (Dx), prescription drugs (Rx) are important risk factors for dementia. OBJECTIVE Develop and validate the RxDx-Dementia risk index by combining diagnosis and prescription information in a single risk index to predict incident dementia, and compare its performance with diagnosis-based Charlson comorbidity score (CCS) and prescription-based chronic disease score (CDS). METHODS Elderly patients diagnosed with type 2 diabetes mellitus and hypertension, and without prior dementia were identified from the Clinical Practice Research Datalink (2003-2012). A Cox proportional hazard model was constructed to model the time to dementia by incorporating age, gender, and 31 RxDx disease conditions as independent variables. Points were assigned to risk factors to obtain summary risk score. Discrimination and calibration of the risk index were evaluated. Different risk indices were compared against RxDx-Dementia risk index using c-statistic, net reclassification improvement (NRI) and integrated discrimination improvement (IDI). RESULTS Of 133,176 patients with type 2 diabetes mellitus and hypertension, 3.42% patients developed dementia.The c-statistics value for RxDx-Dementia risk index was 0.806 (95% CI, 0.799-0.812). Based on the c-statistics, NRI and IDI values, the RxDx-Dementia risk index performed better compared to CCS, CDS, and their combinations. CONCLUSION The RxDx-Dementia risk index can be a useful tool to identify hypertensive and diabetic patients who are at high risk of developing dementia. This has implications for clinical management of patients with multiple comorbid conditions as well as risk adjustment for database studies.
Investigation of Carbon Nanotube-Based Through-Silicon Vias for PDN Applications

This paper presents a comprehensive investigation of the impedance characteristics of power distribution networks (PDNs) made of carbon nanotube through-silicon vias (CNT-TSVs). The equivalent circuit model of the CNT-TSV array is presented and validated through three-dimensional full-wave electromagnetic simulator up to 100 GHz. By virtue of the circuit model, the inductive properties of CNT-TSVs are characterized and compared for various physical parameters. Then, the PDN impedance characteristics of multiple stacked chip-PDNs with CNT-TSVs are captured and evaluated. It is found that the large CNT kinetic inductance may limit the PDN frequency range. Therefore, the fabrication of CNT-TSVs should be improved to increase the CNT density and reduce the contact resistance.
Variable Projection Method and Levenberg-Marquardt Algorithm for Neural Network Training

An optimal solution for single hidden layered feedforward neural network (SLFN) is proposed. SLFN can be considered as separable nonlinear least squares problem and variable projection (VP) method gives the approximation of the Jacobian matrix of the problem. The Jacobian calculation of VP-SLFN is suggested with simplified form. Based on this simplification, Levenberg-Marquardt (LM) algorithm for VP-SLFN is suggested and has faster convergence rate than LM algorithm without VP. Two numerical examples show the superiority than extreme learning machine and LM without variable projection
Assessment of DC servo motor with sliding mode control approach

This paper contracts with the specific type of robust control i.e., sliding mode control for position and speed controlling of a DC servo motor. The paper contributes synthesis and investigation of DC servo motor with sliding mode controller and conventional PI controller. Modeling and simulations are carried out and their enactment is assessed in steady as well as in transient state. Subsequently the DC servo motor drive is sensitive to parameter variation and load disturbance, a robust control methodology based on sliding mode is comprehended. The planned sliding mode controller results are compared with conventional PI controller with no load and on load condition. The usefulness of the projected methodology is demonstrated by performing simulations using MATLAB tool. The simulation results exhibit that the role of sliding mode based scheme is more robust than fixed gain controller.