Miracle-dz/UniFAR-FTUs huggingface dataset:
NOZZLE DESIGN FOR A SMALL, LOW-SPEED, CLOSED-RETURN

Wind tunnels are used to characterize objects aerodynamically (e.g., measure lift and drag as a function of air velocity). They are critical in the design process of many products, including cars and planes. At Houghton College, a wind tunnel is currently being designed and built. One of the most difficult components of a wind tunnel to design is the nozzle, which contracts the air to produce a high-speed flow immediately upstream of the test object. This is difficult because the nozzle must be designed to produce a uniform flow at the exit to get an accurate representation of how air flows over the object. Furthermore, to improve flow uniformity and to increase efficiency, boundary layer separation is to be avoided inside of the nozzle. For the present wind tunnel, several commonly used nozzle designs were tested in order to determine which best fulfills the requirements above. These nozzle designs were tested using viscous, turbulent air flow simulations in ANSYS Fluent. Results and conclusions will be presented and future work highlighted.
Efficient Range Proof Protocols Based on Chinese Cryptographic SM2

Range proof is a special type of zero-knowledge proofs, among which a prover can prove to a verifier that the element of a commitment is within a specified range, but the prover does not need to tell the verifier the concrete information of this hiding element. Due to this special property, the range proof protocols have been widely applied in various scenarios especially those requiring security requirements of identity or data privacy protection (e.g. blockchain, a-nonymous certificates, electronic cash, group or ring signatures). Correspondingly, there are also many design methods of range proof protocols have been proposed recently, such as square decomposition method, signature-based method, inner-product argument method, and so forth, among which the signature-based method (proposed by Camenisch et al. in the conference of ASI-ACRYPT 2008) is one of the most widely used methods at present. However, the range proof
A Whole-Person Function Dictionary for the Mobility, Self-Care and Domestic Life Domains: a Seedset Expansion Approach

Whole-person functional limitations in the areas of mobility, self-care and domestic life affect a majority of individuals with disabilities. Detecting, recording and monitoring such limitations would benefit those individuals, as well as research on whole-person functioning and general public health. Dictionaries of terms related to whole-person function would enable automated identification and extraction of relevant information. However, no such terminologies currently exist, due in part to a lack of standardized coding and their availability mainly in free text clinical notes. In this paper, we introduce terminologies of whole-person function in the domains of mobility, self-care and domestic life, built and evaluated using a small set of manually annotated clinical notes, which provided a seedset that was expanded using a mix of lexical and deep learning approaches.
Compressed Sensing Network based on Wavelet Transform

The traditional compressed sensing algorithm realizes the optimization of image reconstruction through multiple iterative calculation from limited measurements, which cost high computational complexity and long reconstruction time. As the development of deep learning, it is proposed to combine the technology with compressed sensing(CS) which shows great advantages in accurate and fast CS reconstruction. In this paper, we propose a novel algorithm synthesize the advantages of the two technology as well as add another sparse prior technique based sym8 wavelet, which dubbed WCS-Net, is focus on two parts: sampling network based on sparse representation and deep reconstruction elastic network. Experimental results show the WCS-Net has the advanced performance at measurement rates 0.01, 0.04, 0.1, and 0.25, respectively, while maintaining the same running speed as existing image compression methods based on deep learning.
CORPORATE MEMBER TURNOVER, CODE CHANGE, AND INNOVATION IN THE MULTI-UNIT FIRM

are the and and Motorola Solutions Heritage Communications & Archives at Motorola Solutions, Inc., for her insights and cooperation in data collection. The the Carnegie School of Organizational Learning Conference, Carnegie Conference in Honor of Consortium for Strategy Research, Strategy Science Conference, Consortium for Competitiveness and Cooperation, Academy of Management Annual Meeting, 2017 Strategic Management Society Annual Conference, and 2017 MOC-TIM conference, and seminar participants at INSEAD, CEIBS, College, and University of California, for their insightful comments and guidance. The authors also Lee for his work on data cleaning and coding. This study the Center for Organizational Research at the University The contributed are listed in alphabetical order. Abstract This study examines how recombinant innovation is affected by member turnover and organizational learning within a corporate hierarchy. Most prior work has overlooked the role of organizational structure in organizational learning, focusing instead on the knowledge provided by individual new hires or on the disruption caused by individual departures. We address this gap by applying March’s (1991) mutual learning model to a corporate hierarchy. In doing so, we theorize how the contributions of corporate staff to socializing new employees and to learning from the organizational code may differ from those of the organization’s subunit members. Empirically, we examine the learning effects of aggregate corporate and subunit arrivals and departures on novel recombinant innovation by subunits. Using 24 years of Motorola company directories, we construct membership turnover measures for corporate and subunit employees and exploit patent data to capture recombinant innovation. Our results suggest that, whereas the influx of new ideas through arrivals may be critical, breaking the pattern of inertial behavior through departures is more important for recombinant innovation. Corporate departures matter most for recombinant innovation, a result that reflects not only corporate staff’s slower individual learning from the organizational code but also its ability to update that code more quickly. In a post hoc analysis, we also find different effects for technical and non-technical staff. Our study has strong implications for theories of organizational learning and strategic human capital and for the literature on organization design and innovation. minimum number per year; our findings are robust to these alternative cut-offs. This sampling approach yielded 195 subunit–year observations.
Video Smoke Detection with Domain Knowledge and Transfer Learning Based on Deep Convolutional Neural Networks

Video smoke detection (VSD) is a prospective and effective choice for fire detection in spacious buildings and forests. In order to improve the detection rate and reduce the false positive rate of the VSD system, domain knowledge of smoke was used to segment suspected smoke regions in a video frame first. Then we designed a deep neural network to extract the features of smoke regions to do recognition. With relative source domain and target domain, source task and target task, transfer learning of Alex-net, Inception V3 and ResNet pre-trained were used to distinguish smoke regions in this work. The F-beta value can be as high as 0.99. Experiments show that domain knowledge is important for smoke detection in the preliminary stage. Meanwhile, instance-and parameter-transfer learning can be potentially effective solutions for VSD in the future.
An efficient algorithm for reasoning over OWL EL ontologies with nominal schemas

Nominal schemas have been proposed as an extension to Description Logics (DL), the knowledge representation paradigm underlying the Web Ontology Language (OWL). They provide for a very tight integration of DL and rules. Nominal schemas can be understood as syntactic sugar on top of OWL. However, this naive perspective leads to inefficient reasoning procedures. In order to develop an efficient reasoning procedure for the language ELV ++ , which results from extending the OWL profile language OWL EL with nominal schemas, we propose a transformation from ELV ++ ontologies into Datalog-like rule programs that can be used for satisfiability checking and assertion retrieval. The use of this transformation enables the use of powerful Datalog engines to solve reasoning tasks over ELV ++ ontologies. We implement and then evaluate our approach on several real-world, data-intensive ontologies, and find that it can outperform state-of-the-art reasoners such as Konclude and ELK. As a lesser side result we also provide a self-contained description of a rule-based algorithm for EL ++ which does not require a normal form transformation.
Predicting Stock Performance Using 10-K Filings A Natural Language Processing Approach Employing Convolutional Neural Networks

This paper aims to predict company-specific performance based on the textual elements of 10-K filings. Due to their limited information processing capacity, investors need time to incorporate the information contained within the textual content of the 10-K filings into the market. This delay generates an opportunity for the investors to earn abnormal returns using automated text analysis. Using word embeddings to represent the text as input to a convolutional neural network (CNN), we analyze the text of over 29,000 10-K filings from 2010 to 2017. We find that company-specific stock performance is predictable. Furthermore, we control the results for known risk factors using the Fama-French 5 factor model finding that the investors are able to generate significant risk-adjusted returns based on the classifications of the CNN. Based on the findings, we propose several implications. Firstly, we confirm that the textual elements of the 10-K filings contain information which the investors currently do not fully utilize. Secondly, we contribute to the validity of using deep learning models when predicting company-specific performance. Lastly, we provide a practical tool for the investors, the regulatory entities, and the respective company to analyze the textual elements of the 10-K filings.
Prenatal Screening; Cell- Free Fetal DNA Testing

NOTICE: This policy contains information which is clinical in nature. The policy is not medical advice. The information in this policy is used by Wellmark to make determinations whether medical treatment is covered under the terms of a Wellmark member's health benefit plan. Physicians and other health care providers are responsible for medical advice and treatment. If you have specific health care needs, you should consult an appropriate health care professional. If you would like to request an accessible version of this document, please contact customer service at 800-524-9242.
A Comprehensive Survey on Bird Species Identification Models

: Fundamental classes of ecosystem comprise of provisioning, regulating, supporting, and cultural services. Of all the living organisms, ‘birds’ adhere to all the earlier mentioned classes thereby motivating the current work in this paper. Humans coexist with birds and generally identify them via either their physical appearance or their vocal note. Bird species identification (BSI) has attracted global attention lately owing to the rise of technological prowess in aiding their identification. Although birds are identified by their physical appearance and vocal notes; often these two features lead to ambiguity due to image acquisition in a diverse range of postures, intensity, and varied notes of the vocal data. Recent models trained on Deep Neural Networks (DNN) have exhibited better performance in minimizing the said ambiguity. In-depth analysis of proposals in BSI mandates focused study of such prior art, majorly, to identify the inaccuracies generated through ambiguity. The present work provides the reader a detailed survey of preceding art addressing both the dimensions of BSI, namely physical appearance and vocal note based. The analysis stresses mostly on the DNN based models focused on Image Isolation, Background Reduction for physical appearance identification, and all those proposals which evaluate the vocal note datasets. The work in this paper targets those researchers who intend to pursue further study in BSI and design more efficient models and serves as a benchmark for future exploration in BSI using physical appearances and vocal notes. This work provides adequate information for amateur researchers to explore the research on bird species identification models.
Inferring Pore Connectivity from Sorption Hysteresis in Multiscale Porous Media

Vapor adsorption experiments are widely used to assess pore size distributions, but the large hysteresis sometimes observed between sorption and desorption isotherms remains difficult to interpret. Such hysteresis is influenced pore network connectivity, which has previously been modeled by percolation on infinite lattices. Our hypothesis is that percolation occurs instead through finite networks of micropores connecting accessible macropores, always exposed to the outside environment. We derive a general formula for sorption/desorption isotherms that introduces a simple measure of hierarchical pore connectivity -- the fraction $f$ of always exposed pores. The model thus accounts for"small world"connections in finite-size percolation, while also incorporating other hysteresis mechanisms, in single-pore filling, liquid insertion into the solid matrix, and cavitation. Our formula is able to fit and interpret both primary and scanning sorption/desorption isotherms for a variety of adsorbates (noble gases, water, and organics) and porous materials (cement pastes, dental enamels, porous glasses, carbon black and nanotubes), including cases with broad pore-size distributions and large hysteresis. It allows quantification of the prevalence of percolating macropores in the material, even though these pores are never filled during the sorption experiments. A distinct bump in sorption isotherms is explained as a lowering of the barrier to nucleation of the vapor phase with a universal temperature scaling.
Donors’ Views on Charities and the Roles of Accounting Information in Donation Decision

This study examines how donors view charitable organisations and the role of accounting information in donation decisions. We conduct 20 interviews with small donors, using a vignette technique centred on information disclosed by a hypothetical charity, Disaster Savers Australia. We find that small donors do not act like investors who actively seek out the most efficient charities using accounting information, but rather steer themselves towards charities they know and believe they can trust. Although the role of accounting information at the point of donation is limited, by viewing donations as promises to donors involving a series of decisions yet to be performed, we find it can assume a more substantive role in the broader donation process. In particular, financial reports can help to stabilize donors’ trust in charities and produce a feeling of comfort, irrespective of the content of such reports. Yet, in times of crisis or controversy, the content of accounting disclosures assumes greater importance in helping donors to assess whether spending is consistent with the earmarking of donations for specific charitable purposes. Finally, extending beyond traditional forms of accounting information, we find that pictures and stories potentially play a more enduring role, helping small donors to visualize the impact of their donation on beneficiaries and reassuring them that their contributions have been used as intended. * corresponding author (matthew.hall2@monash.edu). This paper is based on Wen Shi He’s honours thesis completed at Monash University. First draft – please do not cite or distribute.
An Improvement of the Myklestad Method for Flexural-Vibration Problems

The paper presents a method of obtaining the next approximation for the natural frequency by means of a simple auxiliary calculation based on the results of a Myklestad table. The basis of the method is the change of natural frequency caused by a small change in either the mass or the elasticity of one component of a flexural system with a finite number of degrees of freedom. This improvement results in a rapid convergence of successive approximations. Various end conditions are considered. An illustrative example is given.
Evaluating the accuracy of self-collected swabs for the diagnosis of monkeypox

We evaluated the accuracy of patient-collected skin lesions, pharyngeal, and rectal swabs amongst 50 individuals enrolled in a study of monkeypox viral dynamics. We found that the performance of self-collected samples was similar to that of physician-collected samples, suggesting that self-sampling is a reliable strategy for diagnosing monkeypox.
Deleterious heteroplasmic mitochondrial mutations increase risk of overall and cancer-specific mortality

Mitochondria are involved in energetic, biosynthetic, and homeostatic processes in eukaryotic cells. Mitochondria carry their own circular genome and disruption of the quantity or quality of mitochondrial genome is associated with various aging - related diseases1 - 3. Unlike the nuclear genome, mitochondrial DNA (mtDNA) can be present at 1,000s to 10,000s copies in somatic cells and variants may exist in a state of heteroplasmy, where only a fraction of the DNA molecules harbor a particular variant. We used MitoHPC, a bioinformatics pipeline, to accurately quantify mtDNA heteroplasmy from whole genome sequencing data in 194,871 participants in the UK Biobank. We found that the presence of heteroplasmy is associated with an increased risk of all - cause mortality (adjusted hazard ratio [aHR] 1.50 - fold; 95% confidence interval [CI] 1.14, 1.98, when comparing participants with 4 or more heteroplasmies to those without any heteroplasmy). In addition, we functionally characterized mtDNA single nucleotide variants (SNVs) using a novel constraint - based score, Mitochondrial local constraint (MLC) score sum (MSS), which demonstrated that SNVs at highly constrained sites were strongly associated with all - cause mortality (aHR for a 1 - unit increase in MSS 1.28; 95% CI 1.20, 1.37) and cancer - related mortality (aHR 1.36; 95% CI 1.24,1.49), particularly lung and breast cancers, lymphoma, and leukemia. MSS was also associated with prevalence and incidence of lung cancer, lymphoma, and leukemia. Moreover, among individuals with prevalent leukemia, high MSS was strongly associated with leukemia mortality (adjusted HR 4.03; 95% CI 1.34, 12.11). These results indicate that mitochondria may have a functional role in certain cancers and mitochondrial heteroplasmic SNVs have the potential to serve as a prognostic markers for cancer incidence and outcome, especially for leukemia.
The impact of cognitive ability on multitalker speech perception in neurodivergent individuals

The ability to selectively attend to one talker in the presence of competing talkers is crucial to communication. Here we investigate whether cognitive deficits in the absences of hearing loss can impair speech perception. We tested typical hearing, neurodivergent adolescents/adults with autism spectrum disorder, fetal alcohol spectrum disorder, and an age- and sex-matched neurotypical group. We found a strong correlation between IQ and speech perception, with individuals with lower IQ scores having worse speech thresholds. These results demonstrate that deficits in cognitive ability, despite intact peripheral encoding, can impair listening under complex conditions. These findings have important implications for conceptual models of speech perception and for audiological services to improve communication in real-world environments for neurodivergent individuals.
Structural parameters for the M31 dwarf spheroidals

(Abridged) The projected structures and integrated properties of the Andromeda I, II, III, V, VI, VII and Cetus dwarf spheroidal galaxies are analysed based upon resolved counts of red giant branch stars. For each object, we have derived isopleth maps, surface brightness profiles, absolute magnitudes, central surface brightnesses, and a large number of other morphological parameters. Our analysis probes to larger radius and fainter surface brightnesses than most previous studies and as a result we find that the galaxies are generally larger and brighter than has previously been recognised. In particular, the luminosity of Andromeda V is found to be consistent with the higher metallicity value which has been derived for it. Andromeda I shows strong evidence of tidal disruption and S-shaped tidal tails are clearly visible. On the other hand, Cetus does not show any evidence of tidal truncation, let alone disruption. Andromeda II shows compelling evidence of a large excess of stars at small radius and suggests that this galaxy consists of a secondary core component. Comparing the M31 dwarf spheroidal population with the Galactic population, we find that the scale radii of the M31 population are larger than those for the Galactic population by at least a factor of two, for all absolute magnitudes. We also find that the two populations are offset from one another in the central surface brightness - luminosity relation. Finally, we find that the M31 dwarf spheroidals show the same correlation with distance-from-host as shown by the Galactic population, such that dwarf spheroidals with a higher central surface brightness are found further from their host. This again suggests that environment plays a significant role in dwarf galaxy evolution.
A conditional Bayesian approach for testing independence in two-way contingency tables

Bayesian methods for exact small-sample analysis with categorical data in contingency tables are considered. Point null hypotheses versus two-sided hypothesis are tested concerning log odds ratios in these tables with fixed row margins. The conditional distribution of the sufficient statistics for interesting parameters conditional on sufficient statistics of other nuisance parameters in the model is obtained and used to eliminate the effect of nuisance parameters. This distribution is Fisher's multivariate noncentral hypergeometric distribution. Three Bayesian approaches, hierarchical Bayes, empirical Bayes, and noninformative Bayes are considered and compared by simulation studies.
Design of Interval Type-2 T-S Fuzzy Logic Control Systems

In this paper, an interval type-2 T-S fuzzy logic control systems (IT2T-S FLCS) is designed. The proposed IT2T-S FLCS is a combination of IT2 fuzzy logic system (FLS) and T-S FLCS, and also inherits the benefits of these two methods. Furthermore, Krasovskii’s method is utilized to testify the sufficient condition for the asymptotic stability of IT2T-S FLCS, which requires the calculation of the Jacobian matrix. Finally, the simulation results show that the IT2T-S FLCS achieves the best tracking performance in comparison with the type-1 (T1) T-S FLCS and the proposed method can handle unpredicted internal disturbance and data uncertainties well.