Miracle-dz/UniFAR-FTUs huggingface dataset:
Revisiting the role of intermittent heat transport towards Reynolds stress anisotropy in convective turbulence

Abstract Thermal plumes are the energy-containing eddy motions that carry heat and momentum in a convective boundary layer. The detailed understanding of their structure is of fundamental interest for a range of applications, from wall-bounded engineering flows to quantifying surface–atmosphere flux exchanges. We address the aspect of Reynolds stress anisotropy associated with the intermittent nature of heat transport in thermal plumes by performing an invariant analysis of the Reynolds stress tensor in an unstable atmospheric surface layer flow, using a field-experimental dataset. Given the intermittent and asymmetric nature of the turbulent heat flux, we formulate this problem in an event-based framework. In this approach, we provide structural descriptions of warm-updraft and cold-downdraft events and investigate the degree of isotropy of the Reynolds stress tensor within these events of different sizes. We discover that only a subset of these events are associated with the least anisotropic turbulence in highly convective conditions. Additionally, intermittent large-heat-flux events are found to contribute substantially to turbulence anisotropy under unstable stratification. Moreover, we find that the sizes related to the maximum value of the degree of isotropy do not correspond to the peak positions of the heat-flux distributions. This is because the vertical velocity fluctuations pertaining to the sizes associated with the maximum heat flux transport a significant amount of streamwise momentum. A preliminary investigation shows that the sizes of the least anisotropic events probably scale with a mixed length scale ($z^{0.5}\lambda ^{0.5}$, where $z$ is the measurement height and $\lambda$ is the large-eddy length scale).
Large Scale Open-Set Deep Logo Detection

We present an open-set logo detection (OSLD) system, which can detect (localize and recognize) any number of unseen logo classes without re-training; it only requires a small set of canonical logo images for each logo class. We achieve this using a two-stage approach: (1) Generic logo detection to detect candidate logo regions in an image. (2) Logo matching for matching the detected logo regions to a set of canonical logo images to recognize them. We also introduce a 'simple deep metric learning' (SDML) framework that outperformed more complicated ensemble and attention models and boosted the logo matching accuracy. Furthermore, we constructed a new open-set logo detection dataset with thousands of logo classes, and will release it for research purposes. We demonstrate the effectiveness of OSLD on our dataset and on the standard Flickr-32 logo dataset, outperforming the state-of-the-art open-set and closed-set logo detection methods by a large margin.
Parallel Single-Pass Text Clustering Algorithm Algorithm Framework and Process

To improve the Internet public opinion monitoring for colleges, this paper designs an efficient Internet public opinion system for college students. The system uses distributed web crawler to collect the structuring information from the news websites, social networking sites, BBS and blogs, which are widely visited by college students. To speed up the text clustering process, the Single-Pass algorithm is parallelized with Spark. The parallel Single-Pass algorithm is used to find hot topics from collected texts. The algorithm improved the clustering efficiency significantly through distributed processing. Simulation results reveal that the proposed system can achieve timely and accurately public opinion monitoring. Introduction Internet public opinion refers to different views about current social problem on Internet, and is a form of social public opinion. In recent years, there has been frequent occurrence of government confidence crisis because of the lack of timely adequate response to Internet public opinion. As a group with high usage rate of Internet, college students are the main producers and transmitters of Internet public opinion. In order to prevent the rapid spread and deterioration of bad Internet public opinion, the department concerned must collect the Internet public opinion information as quickly as possible and keep track of their developments to supervise them effectively. So the Internet public opinion system came into being, which is an effective way to solve the problems above via information collection and topic detection. In order to improve the real-time performance of information collection and adapt to the current rapid growth of data, instead of traditional stand-alone web crawler, distributed web crawler is used for efficient and rapid information collection. On the other hand, text clustering algorithm can aggregate a large amount of text collected into a few meaningful clusters and detect hot topics. But traditional serial text clustering algorithms are inefficient when dealing with massive or high-dimensional data. A number of techniques have been developed to adapt text clustering algorithms to work with large datasets: examples are parallel programing frameworks such as MapReduce [1] and cluster computing system such as Spark [2,3]. To summarize, this paper uses distributed technology to optimize and enhance the Internet public opinion system faced with big data. The distributed web crawler is based on an in-memory data structure store named Redis [4]. And parallel text clustering algorithm is based on the Single-Pass clustering algorithm to generate clusters and on Spark framework to make parallel in-memory clustering tasks. Distributed Web Crawler Web crawler is a computer program used to crawl the source code of web pages and its basic workflow is as follows: 1) Set the initial webpage’s URL seeds to waiting queue.
Effects of a Medical Plant Mixture and a Probiotic

In livestock production systems, antibiotics are commonly fed to animals to prevent disease and metabolic disorders, as well as improve feed efficiency. However in recent years, public concern over routine use of antibiotics in livestock nutrition has increased due to the emergence of antibiotic resistant bacteria that may represent a risk to human health. Consequently, considerable effort has been devoted towards developing alternatives to antibiotics. Plant extracts offer a unique opportunity in this regard (Wallace, 2004), as many plants produce secondary metabolites, such as saponins and tannins, which have antimicrobial properties. These compounds have been shown to modulate ruminal fermentation to improve nutrient utilization in ruminants (Wang et al. 2000; Hristov et al. 1999). Similarly, the well documented antimicrobial activity of essential oils (EO), and their active components, has prompted a number of scientists to examine the potential of these secondary metabolites to manipulate rumen microbial fermentation to improve production efficiency in ruminants. Contrary to their name, EO are not true oils (i.e., lipids) and are commonly derived from the components responsible for fragrance, or Quinta essentia, of plants. The antimicrobial properties of EO have been demonstrated against a wide range of microorganisms, including bacteria, protozoa and fungi (Chao et al. 2000). The aim of this study was to investigate the effects of a medical plant mixture and a probiotic on performance, antioxidant activity and weaning age of suckling Holstein calves. For this experiment, 30 newborn calves (0-10 days of age) with an average birth weight of 42 ± 8 kg were used in a factorial design (3×2) with 6 treatments and 5 replicates. Treatments were: 1) basal diet including a calves starter feed and whole milk, 2) control diet + 2 g probiotic 3) control diet + 1.5% of medical plant, 4) control diet + 1.5% medical plant + 2 g probiotic, 5) control diet + 3% medical plant and 6) control diet + 3% medical plant + 2 g probiotic. The calves were offered experimental pelleted feeds ad libitum and after one month were supplied with imported hay. Results showed that the treatments had no significant effect on dry matter intake during the experiment. However, addition of the 1.5% medical plant increased dry matter intake (P<0.05). However, the 1.5% level of medical plant reduced calves weaning age (P<0.05). Calves receiving control diet + 1.5% of medical plant mixed and 1.5% medical plant mixed + 2 g probiotic showed a higher plasma antioxidant activity (P<0.05). This study suggested that 1.5% of medical plant in calves starter feed will improve performance and the immune system and will also reduce the weaning age of calves.
Deep Discriminative Fine-Tuning for Cancer Type Classification

Determining the primary site of origin for metastatic tumors is one of the open problems in cancer care because the efficacy of treatment often depends on the cancer tissue of origin. Classification methods that can leverage tumor genomic data and predict the site of origin are therefore of great value. Because tumor DNA point mutation data is very sparse, only limited accuracy (64.5% for 12 tumor classes) was previously demonstrated by methods that rely on point mutations as features (1). Tumor classification accuracy can be greatly improved (to over 90% for 33 classes) by relying on gene expression data (2). However, this additional data is often not readily available in clinical setting, because point mutations are better profiled and targeted by clinical mutational profiling. Here we sought to develop an accurate deep transfer learning and fine-tuning method for tumor sub-type classification, where predicted class is indicative of the primary site of origin. Our method significantly outperforms the state-of-the-art for tumor classification using DNA point mutations, reducing the error by more than 30% at the same time discriminating over many more classes on The Cancer Genome Atlas (TCGA) dataset. Using our method, we achieve state-of-the-art tumor type classification accuracy of 78.3% for 29 tumor classes relying on DNA point mutations in the tumor only.
Electronic Health Information Literacy: An Investigation of the Electronic Health Information Knowledge and Skills of Health Education Majors

Electronic Health Information Literacy: An Investigation of the Electronic Health Information Knowledge and Skills of Health Education Majors. (May 2011) Bruce Walter Hanik, B.S., California State University, Northridge; M.S., Texas A&M University Co-Chairs of Committee: Dr. James Eddy Dr. Buzz Pruitt Health educators are expected to serve as a resource for health knowledge and to be e-health literate, thereby enabling health educators to perform that function. However, the e-health literacy level of health education undergraduate students is rarely explored. A systematic literature review was conducted in order to investigate the e-health literacy levels of undergraduate students. The Research Readiness Self-Assessment-health (RRSA-h) was used to measure the ability of health education majors to find and evaluate electronic health information and a Q-study was conducted to investigate student characteristics that distinguish between those with high, middle, and low levels of e-health literacy. A convenience sample of 77 health education majors completed the RRSA-h. A multivariate analysis of variance (MANOVA) revealed that e-health literacy levels differed among classification level [F(4,140) = 2.597, p = . 039]. Thirteen health
A Spark ML driven preprocessing approach for deep learning based scholarly data applications

Big data has found applications in multiple domains. One of the largest sources of textual big data is scientific documents and papers. Big scholarly data have been used in numerous ways to create innovative applications such as collaborator discovery, expert finding and research management systems. With the advent of advanced machine and deep learning techniques, the accuracy and novelty of such applications have risen manifold. However, the biggest challenge in the development of deep learning models for scholarly applications in cloud based environment is the underutilization of resources because of the excessive time taken by textual preprocessing. This paper presents a preprocessing pipeline that makes use of Spark for data ingestion and Spark ML for pipelining preprocessing tasks. The evaluation of the proposed work is done using a case study, which uses LSTM based text summarization for generating title or summary from abstract of any research. The ingestion, preprocessing and cumulative time for the proposed approach (P3SAPP) is much lower than the conventional approach (CA), which manifests in reduction of costs as well.
The reproducibility and responsiveness of a Patient Specific Approach: A new instrument in TMD-treatment evaluation

In low-back pain patients, a ‘Patient Specific Approach’ (PSA) is introduced to evaluate treatment effect. In this approach, the patient is asked to choose, from the activities that are impaired, the activity which is most important in daily life. Aims: To evaluate the choices of activities on the PSA in a sample of TMD patients and to determine the clinimetric properties of the VAS scores of the PSA, in terms of reproducibility and responsiveness were determined. Methods: At treatment start, TMD patients reported the PSA-activity which is impaired due to their TMD complaints. The amount of hindrance during this activity was rated on a Visual Analogue Scale (VAS). During two follow-up measurements, patients rated the VAS and appraised their overall complaints in terms of ‘much worsened’, ‘slightly worsened’, ‘remained stable’, ‘slightly improved’, or ‘much improved’. Results: Of the 132 patients who fulfilled baseline measurements, 13% reported an activity that is not included in existing TMD-disability questionnaires. The reproducibility of the VAS scores of the 78 patients who reported that their complaints had ‘remained stable’ at second measurement, was good (ICC=0.73). The responsiveness of the PSA was high (AUC=0 .91), and the cutoff score for important improvement, where sensitivity (0.85) and specificity (0.84) were as much as possible the same was 58%. Conclusion: The PSA for TMD patients is a new and easy-to0use tool in treatment evaluation. Moreover, the VAS score of the PSA has good reproducibility and responsiveness.
A Sketch-Based System for Human-Guided Constrained Object Manipulation

In this paper, we present an easy to use sketch-based interface to extract geometries and generate affordance files from 3D point clouds for robot-object interaction tasks. Using our system, even novice users can perform robot task planning by employing such sketch tools. Our focus in this paper is employing human-in-the-loop approach to assist in the generation of more accurate affordance templates and guidance of robot through the task execution process. Since we do not employ any unsupervised learning to generate affordance templates, our system performs much faster and is more versatile for template generation. Our system is based on the extraction of geometries for generalized cylindrical and cuboid shapes, after extracting the geometries, affordances are generated for objects by applying simple sketches. We evaluated our technique by asking users to define affordances by employing sketches on the 3D scenes of a door handle and a drawer handle and used the resulting extracted affordance template files to perform the tasks of turning a door handle and opening a drawer by the robot.
VOICe: A Sound Event Detection Dataset For Generalizable Domain Adaptation

The performance of sound event detection methods can significantly degrade when they are used in unseen conditions (e.g. recording devices, ambient noise). Domain adaptation is a promising way to tackle this problem. In this paper, we present VOICe, the first dataset for the development and evaluation of domain adaptation methods for sound event detection. VOICe consists of mixtures with three different sound events ("baby crying", "glass breaking", and "gunshot"), which are over-imposed over three different categories of acoustic scenes: vehicle, outdoors, and indoors. Moreover, the mixtures are also offered without any background noise. VOICe is freely available online (this https URL). In addition, using an adversarial-based training method, we evaluate the performance of a domain adaptation method on VOICe.
Stochastic Magnetoelectric Neuron for Temporal Information Encoding

Emulating various facets of computing principles of the brain can potentially lead to the development of neuro-computers that are able to exhibit brain-like cognitive capabilities. In this letter, we propose a magnetoelectronic neuron that utilizes noise as a computing resource and is able to encode information over time through the independent control of external voltage signals. We extensively characterize the device operation and demonstrate its suitability for neuromorphic computing platforms performing temporal information encoding.
Multitask Learning of Height and Semantics From Aerial Images

Aerial or satellite imagery is a great source for land surface analysis, which might yield land-use maps or elevation models. In this letter, we present a neural network framework for learning semantics and local height together. We show how this joint multitask learning benefits to each task on the large data set of the 2018 Data Fusion Contest. Moreover, our framework also yields an uncertainty map that allows assessing the prediction of the model. Code is available at https://github.com/marcelampc/mtl_aerial_images
Deterministic partial binary circulant compressed sensing matrices

Compressed sensing (CS) is a signal acquisition paradigm to simultaneously acquire and reduce dimension of signals that admit sparse representation. This is achieved by collecting linear, non-adaptive measurements of a signal, which can be formalized as multiplying the signal with a "measurement matrix". Most of matrices used in CS are random matrices as they satisfy the restricted isometry property (RIP) in an optimal regime of number of measurements with high probability. However, these matrices have their own caveats and for this reason, deterministic measurement matrices have been proposed. While there is a wide classes of deterministic matrices in the literature, we propose a novel class of deterministic matrices using the Legendre symbol. This construction has a simple structure, it enjoys being a binary matrix, and having a partial circulant structure which provides a fast matrix-vector multiplication and a fast reconstruction algorithm. We will derive a bound on the sparsity level of signals that can be measured (and be reconstructed) with this class of matrices. We perform quantization using these matrices, and we verify the performance of these matrices (and compare with other existing constructions) numerically.
Deep and Dense Sarcasm Detection

Recent work in automated sarcasm detection has placed a heavy focus on context and meta-data. Whilst certain utterances indeed require background knowledge and commonsense reasoning, previous works have only explored shallow models for capturing the lexical, syntactic and semantic cues present within a text. In this paper, we propose a deep 56 layer network, implemented with dense connectivity to model the isolated utterance and extract richer features therein. We compare our approach against recent state-of-the-art architectures which make considerable use of extrinsic information, and demonstrate competitive results whilst using only the local features of the text. Further, we provide an analysis of the dependency of prior convolution outputs in generating the final feature maps. Finally a case study is presented, supporting that our approach accurately classifies additional uses of clear sarcasm, which a standard CNN misclassifies.
Efficient Ridge Solutions for the Incremental Broad Learning System on Added Inputs by Updating the Inverse or the Inverse Cholesky Factor of the Hermitian matrix in the Ridge Inverse

This brief proposes two BLS algorithms to improve the existing BLS for new added inputs in [7]. The proposed BLS algorithms avoid computing the ridge inverse, by computing the ridge solution (i.e., the output weights) from the inverse or the inverse Cholesky factor of the Hermitian matrix in the ridge inverse. The proposed BLS algorithm 1 updates the inverse of the Hermitian matrix by the matrix inversion lemma [12]. To update the upper-triangular inverse Cholesky factor of the Hermitian matrix, the proposed BLS algorithm 2 multiplies the inverse Cholesky factor with an upper-triangular intermediate matrix, which is computed by a Cholesky factorization or an inverse Cholesky factorization. Assume that the newly added input matrix corresponding to the added inputs is p * k, where p and k are the number of added training samples and the total node number, respectively. When p > k, the inverse of a sum of matrices [11] is utilized to compute the intermediate variables by a smaller matrix inverse in the proposed algorithm 1, or by a smaller inverse Cholesky factorization in the proposed algorithm 2. Usually the Hermitian matrix in the ridge inverse is smaller than the ridge inverse. Thus the proposed algorithms 1 and 2 require less flops (floating-point operations) than the existing BLS algorithm, which is verified by the theoretical flops calculation. In numerical experiments, the speedups for the case of p > k in each additional training time of the proposed BLS algorithms 1 and 2 over the existing algorithm are 1.95 - 5.43 and 2.29 - 6.34, respectively, and the speedups for the case of p < k are 8.83 - 10.21 and 2.28 - 2.58, respectively.
Ambipolar perovskite light electrochemical cell for transparent display devices

Perovskite light-emitting diodes (PeLEDs) have recently attracted great research luminescence at room temperature in interest for their narrow emissions and solution processability. Remarkable progress has been achieved PeLEDs in recent years. Here we present the new configuration of ambipolar transparent perovskite light emitting device. The combination of voltage induced p-i-n formation and ionically doped carbon electrodes and allows electroluminescence in forward and reverse bias. Here we present easy-to-do transparent ambipolar light emitting subpixel based on inorganic perovskite and single wall carbon nanotubes. For this experiment PeLEDs were assembled using a glass substrate with ITO stripes as bottom electrode; spin-coated CsPbBr3/I3:PEO composite as emissive layer; single wall carbon nanotubes deposited by a simple press transfer process at room temperature. We demonstrate a concept of stacked multicolor tandem pixel. Stack of transparent light emitting units might allow fine color tuning in parallel tandem connection without segregation compared to mixed halide perovskites. This configuration conforms pixel downsizing and make to fabrication of emissive multijunction pixels in a stack.