gebhart/patrepeval huggingface dataset:
Identifying computer-generated text using statistical analysis (2017)

Computer-based automatically generated text is used in various applications (e.g., text summarization, machine translation) and has come to play an important role in daily life. However, computer-generated text may produce confusing information due to translation errors and inappropriate wording caused by faulty language processing, which could be a critical issue in presidential elections and product advertisements. Previous methods for detecting computer-generated text typically estimate text fluency, but this may not be useful in the near future due to the development of neural-network-based natural language generation that produces wording close to human-crafted wording. A different approach to detecting computergenerated text is thus needed. We hypothesize that human-crafted wording is more consistent than that of a computer. For instance, Zipf's law states that the most frequent word in human-written text has approximately twice the frequency of the second most frequent word, nearly three times that of the third most frequent word, and so on. We found that this is not true in the case of computer-generated text. We hence propose a method to identify computer-generated text on the basis of statistics. First, the word distribution frequencies are compared with the corresponding Zipfian distributions to extract the frequency features. Next, complex phrase features are extracted because human-generated text contains more complex phrases than computer-generated text. Finally, the higher consistency of the human-generated text is quantified at both the sentence level using phrasal verbs and at the paragraph level using coreference resolution relationships, which are integrated into consistency features. The combination of the frequencies, the complex phrases, and the consistency features was evaluated for 100 English books written originally in English and 100 English books translated from Finnish. The results show that our method achieves better performance (accuracy = 98.0%; equal error rate = 2.9%) compared with the most suitable method for books using parsing tree feature extraction. Evaluation using two other languages (French and Dutch) showed similar results. The proposed method thus works consistently in various languages.
SiC MOSFET with built-in SBD for reduction of reverse recovery charge and switching loss in 10-kV applications (2017)

A silicon carbide (SiC) metal-oxide-semiconductor field-effect transistor (MOSFET) for 10-kV application is proposed in this paper, which features a built-in Schottky barrier diode (SBD). Therefore, the body diode is free from activation during the third quadrant conduction state, which is beneficial for reducing the switching loss and suppressing bipolar degradation. Numerical simulations with Sentaurus TCAD are carried out to investigate the characteristics of the proposed structure in comparison to the conventional MOSFET and SBD pair. It is found that the proposed structure achieves lower reverse recovery charge and switching loss owing to three factors, i.e., faster switching speed, smaller capacitive charge, and body diode deactivation, and therefore is a superior choice for 10-kV applications.
<i>Underwater Acoustic System Analysis</i> by William S. Burdic (1984)

First Page
Prescriptive Analytics Using Synthetic Information (2014)

In this chapter, the authors describe the use of synthetic information for doing prescriptive and predictive analytics. They discuss in detail how synthetic information is created by combining data from multiple sources and then describe its role in an ongoing disaster resilience study where they simulate the aftermath of a hypothetical nuclear detonation in Washington DC.
Three Key Subregions Contribute to the Function of the Downstream RNA Polymerase II Core Promoter (2010)

The RNA polymerase II core promoter is a diverse and complex regulatory element. To gain a better understanding of the core promoter, we examined the motif 10 element (MTE), which is located downstream of the transcription start site and acts in conjunction with the initiator (Inr). We found that the MTE promotes the binding of purified TFIID to the core promoter and that the TAF6 and TAF9 subunits of TFIID appear to be in close proximity to the MTE. To identify the specific nucleotides that contribute to MTE activity, we performed a detailed mutational analysis and determined a functional MTE consensus sequence. These studies identified favored as well as disfavored nucleotides and demonstrated the previously unrecognized importance of nucleotides in the subregion of nucleotides 27 to 29 (+27 to + 29 relative to A(+1) in the Inr consensus) for MTE function. Further analysis led to the identification of three downstream subregions (nucleotides 18 to 22, 27 to 29, and 30 to 33) that contribute to core promoter activity. The three binary combinations of these subregions lead to the MTE (nucleotides 18 to 22 and 27 to 29), a downstream core promoter element (nucleotides 27 to 29 and 30 to 33), and a novel "bridge" core promoter motif (nucleotides 18 to 22 and 30 to 33). These studies have thus revealed a tripartite organization of key subregions in the downstream core promoter.
Augmented reality browsers (2013)

How lessons learned from the evolution of the Web and Web browsers can influence the development of AR browsers.
Detection of nitroxyl (HNO) by a prefluorescent probe (2011)

Recent research has shown that nitroxyl (HNO) has important and unique biological activity, especially as a potential alternative to current treatments of cardiac failure. HNO is a reactive molecule that spontaneously dimerizes and subsequently dehydrates to form nitrous oxide (N 2 O), making its detection in solution or biologically relevant preparations difficult. The prefluorescent probe 4‐((9‐acridinecarbonyl)amino)‐2,2,6,6‐tetramethylpiperidin‐1‐oxyl (TEMPO‐9‐AC) has been used to detect HNO in aqueous solution and to differentiate it from nitric oxide (NO). TEMPO‐9‐AC reacts with HNO via a net hydrogen abstraction to produce the highly fluorescent TEMPO‐9‐AC‐H and NO. The utility of TEMPO‐9‐AC as a probe for HNO has been shown using the common HNO donors Angeli's salt and Piloty's acid (PA) along with a recently reported HNO donor, 2‐bromo‐PA. The use of TEMPO‐9‐AC is complicated by intermolecular fluorescence quenching and competitive HNO trapping by the NO produced, but nonetheless, it can be used to study HNO reactivity in aqueous solution. Copyright © 2011 John Wiley & Sons, Ltd.
Heimdall (2017)

Many of the everyday decisions a user makes rely on the suggestions of online recommendation systems. These systems amass implicit (e.g.,location, purchase history, browsing history) and explicit (e.g.,reviews, ratings) feedback from multiple users, produce a general consensus, and provide suggestions based on that consensus. However, due to privacy concerns, users are uncomfortable with implicit data collection, thus requiring recommendation systems to be overly dependent on explicit feedback. Unfortunately, users do not frequently provide explicit feedback. This hampers the ability of recommendation systems to provide high-quality suggestions. We introduce Heimdall, the first privacy-respecting implicit preference collection framework that enables recommendation systems to extract user preferences from their activities in a privacy respecting manner. The key insight is to enable recommendation systems to run a collector on a user's device and precisely control the information a collector transmits to the recommendation system back-end. Heimdall introduces immutable blobs as a mechanism to guarantee this property. We implemented Heimdall on the Android platform and wrote three example collectors to enhance recommendation systems with implicit feedback. Our performance results suggest that the overhead of immutable blobs is minimal, and a user study of 166 participants indicates that privacy concerns are significantly less when collectors record only specific information--a property that Heimdall enables.
An autonomous solar powered wireless monitoring and surveillance system (2018)

In recent years, there has been a drastic increase in the number of monitoring and surveillance camera systems installed worldwide. This is due to the ever-increasing crime rates. Remote surveillance system have become popular, and enable people to monitor their properties in real time, via the internet. Video surveillance has experienced a number of technology shifts. The latest is the shift from analog CCTV surveillance to fully digital, network-based video surveillance systems, which are meant to be ON, 24-hours a day. It is also noted that commercial home security systems are still unaffordable to a vast majority of the middle and lower class families. However, the emergence of cheap microcontrollers, like the Arduino, has enabled the implementation of low-cost security systems, incorporating the majority of features present in commercial systems. Also, even though a security camera system takes up only a small portion of the total household energy consumption, a small step to green up the home security system will not only diminish energy spending but also reduce carbon footprint in the long run. In this paper, we present a highly scalable, autonomous, solar powered, wireless monitoring system that also provides live video broadcasting. It is based on a low cost microcontroller, a camera module, and an online internet of things (IoT) platform. A motion detection system was also implemented and the microcontroller sends email notification whenever motion is detected. The whole system is powered by a photovoltaic system. Tests show that the system is fully functional and behaves as expected. The system can be used in residential buildings, commercial buildings, and also in remote, mobile offices, whereby there is no supply of electricity from the utilities.
Prediction of Users' Response Time in Q&A Communities (2015)

Social media and online Question and Answer (Q&A) communities in particular have become a successful solution for finding answers on diverse topics. However, not all questions are answered by these communities. Also, many questions are not answered quickly enough. In this paper, we propose a framework for predicting users' response time. The framework uses a diverse set of features including information on users, the content they generate while communicating, question tags, spatial and temporal features. Then these features are used as input for training predictive models by various machine learning algorithms. As a case study, three diverse Q&A communities from Stack Exchange are selected to test the framework. We demonstrate that Deep Belief Networks outperform Logistic Regression (LR), k-nearest neighbors (k-NN), and Decision Trees (DT) in the accuracy of the prediction across the three diverse Q&A communities.
Ether lipid generating enzyme AGPS alters the balance of structural and signaling lipids to fuel cancer pathogenicity (2013)

Aberrant lipid metabolism is an established hallmark of cancer cells. In particular, ether lipid levels have been shown to be elevated in tumors, but their specific function in cancer remains elusive. We show here that the metabolic enzyme alkylglyceronephosphate synthase (AGPS), a critical step in the synthesis of ether lipids, is up-regulated across multiple types of aggressive human cancer cells and primary tumors. We demonstrate that ablation of AGPS in cancer cells results in reduced cell survival, cancer aggressiveness, and tumor growth through altering the balance of ether lipid, fatty acid, eicosanoid, and fatty acid-derived glycerophospholipid metabolism, resulting in an overall reduction in the levels of several oncogenic signaling lipids. Taken together, our results reveal that AGPS, in addition to maintaining ether lipids, also controls cellular utilization of fatty acids, favoring the generation of signaling lipids necessary for promoting the aggressive features of cancer.
Nonlinear Optical Processes Using Electromagnetically Induced Transparency (1990)

It is well known by those practicing the techniques of nonlinear optics, that the power which may be generated in a frequency summing process, or the gain which may be obtained in a parametric process are determined by the interplay of the nonlinear and linear susceptibilities. In general, as an atomic transition to the ground state is approached, the nonlinear susceptibility is resonantly enhanced, but at the same time the media exhibits a rapidly increasing refractive index and becomes opaque.
Observations on Aluminum Water Pump Cavitation Tests (1986)

Two water pump cavitation tests are currently used by automotive coolant suppliers that wish to sell coolants to automobile manufacturers in the United States. One test is ASTM Test for Cavitation Erosion-Corrosion Characteristics of Aluminum Pumps with Engine Coolants (D 2809). The other is Ford Laboratory Test for Cavitation Erosion-Corrosion Characteristics of Engine Coolants on Aluminum Coolant Pumps (BL 3-1). Details of the test methods are given in the respective ASTM and Ford publications. Phosphate is confirmed to have a beneficial effect on cavitation. Silicate is shown to be beneficial when used with borate or benzoate, but it can be detrimental to the performance of phosphate. Materials of construction in the test apparatus, copper primarily, can affect the results obtained in the longer term Ford test. Coolants not particularly subject to cavitation appear to fail the test if copper from the test rig piping deposits inside the water pump. Cavitation is not observed with these coolants in the absence of copper. Coolants truly subject to cavitation will show it regardless of the construction materials in the test rig. Finally, it is observed that coolants that will not pass the ASTM pump test at the specified 1:5 concentration will often do so at a 1:2 concentration.
Solid Oxide Membrane Process for Solar Grade Silicon Production Directly from Silicon Dioxide (2011)

This chapter contains sections titled: Introduction Flux System and Membrane Stability Preliminary Electrolysis Experiments Conclusion Acknowledgements
Msham - A Multi-Hit Sample and Hold Multiplexer (1981)

The MSHAM is a single-width CAMAC module intended to be used for dE/dx or Z-position measurements, with a density of 16 analog channels. It is designed to record up to four hits/event per channel but the design can be easily adapted to eight hits. The charge collection time interval allowed per hit is externally controlled in the range of 50-500 ns, according to the requirements of the experiment. Besides the electrical performance of the MSHAM, i.e., linearity, noise, crosstalk, etc., the goal was to design multi-function circuits and high density packaging in order to achieve low cost per channel.
Efficient journey planning and congestion prediction through deep learning (2017)

The advancements of technology continuously rising over the years has seen many applications that are useful in providing users with sufficient information to make better journey plans on their own. However, commuters still find themselves going through congested routes every day to get to their destinations. This paper attempts to delineate the possibilities of improving urban mobility through big data processing and deep-learning models. Essentially, through a predictive model to predict congestion and its duration, this paper aims to develop and validate a functional journey planning mobile application that can predict traffic conditions, allowing road users to make better informed decisions to their travel plans. This paper proposes a Multi-Layered Perceptron (MLP) deep learning model for congestion prediction and supplements a Linear Regression (LR) model to predict its duration. The proposed MLP-LR model performed reasonably well with an accuracy of 63% in predicting an occurrence of congestion. Some critical discussions on further research opportunities stemming from this study is also presented.
Molecular Phylogeny of<i>Stryphnodendron</i>(Mimosoideae, Leguminosae) and Generic Delimitations in the<i>Piptadenia</i>Group (2015)

Premise of research. Despite recent progress on the systematics of the Leguminosae, the relationships between some genera, as well as their circumscriptions, remain uncertain. Here we investigate the relationships between Stryphnodendron and closely related genera in the Piptadenia group, a clade of problematic taxonomy for which a robust phylogenetic hypothesis is still lacking.Methodology. Taxonomic sampling included 23 species of Stryphnodendron (77% of the genus) and representatives of genera of the Piptadenia group. DNA (matK/trnK, trnD-trnT, trnL-trnF, and ITS) sequences constituting a data set with 6798 bp and 94 terminals were used in parsimony and Bayesian analyses. A set of 17 morphological characters were optimized on the estimated phylogeny to evaluate clade synapomorphies and character evolution.Pivotal results. Our increased molecular and taxonomic sampling improved the resolution of a previously largely unresolved clade that included Microlobius, Parapiptadenia, Pityrocarpa, Pseudopiptadenia, and Stryphnodendron. Major lineages within this clade are an early-diverging clade containing the sister genera Parapiptadenia and Pseudopiptadenia; the genus Pityrocarpa, with Pseudopiptadenia brenanii nested within it; and a Stryphnodendron clade including the monospecific genus Microlobius. Samples of Stryphnodendron grouped into three strongly supported clades: a group of seven species bearing large leaflets and relatively few pairs of pinnae, sister to Microlobius foetidus; a clade containing samples of Stryphnodendron duckeanum; and a large and poorly resolved clade containing the remaining species of Stryphnodendron, including species with small leaflets and higher number of pinnae and highly specialized dwarf subshrubs adapted to savanna fire regimes. Lack of resolution within this clade suggests rapid and recent diversification in both savannas and rain forests in the Neotropics.Conclusions. Our findings highlight the importance of densely sampled phylogenies to rigorously test the monophyly of genera. Key morphological characters are proposed to assist generic delimitations in the Piptadenia group. New taxonomic rearrangements are needed in order to accommodate the results presented here.
Predicting Ion Mobility Collision Cross-Sections Using a Deep Neural Network: DeepCCS (2019)

Untargeted metabolomic measurements using mass spectrometry are a powerful tool for uncovering new small molecules with environmental and biological importance. The small molecule identification step, however, still remains an enormous challenge due to fragmentation difficulties or unspecific fragment ion information. Current methods to address this challenge are often dependent on databases or require the use of nuclear magnetic resonance (NMR), which have their own difficulties. The use of the gas-phase collision cross section (CCS) values obtained from ion mobility spectrometry (IMS) measurements were recently demonstrated to reduce the number of false positive metabolite identifications. While promising, the amount of empirical CCS information currently available is limited, thus predictive CCS methods need to be developed. In this article, we expand upon current experimental IMS capabilities by predicting the CCS values using a deep learning algorithm. We successfully developed and trained a prediction model for CCS values requiring only information about a compound's SMILES notation and ion type. The use of data from five different laboratories using different instruments allowed the algorithm to be trained and tested on more than 2400 molecules. The resulting CCS predictions were found to achieve a coefficient of determination of 0.97 and median relative error of 2.7% for a wide range of molecules. Furthermore, the method requires only a small amount of processing power to predict CCS values. Considering the performance, time, and resources necessary, as well as its applicability to a variety of molecules, this model was able to outperform all currently available CCS prediction algorithms.
Chemotherapy Combines Effectively with Anti–PD-L1 Treatment and Can Augment Antitumor Responses (2018)

Immunotherapy with checkpoint inhibitors has proved to be highly effective, with durable responses in a subset of patients. Given their encouraging clinical activity, checkpoint inhibitors are increasingly being tested in clinical trials in combination with chemotherapy. In many instances, there is little understanding of how chemotherapy might influence the quality of the immune response generated by checkpoint inhibitors. In this study, we evaluated the impact of chemotherapy alone or in combination with anti-PD-L1 in a responsive syngeneic tumor model. Although multiple classes of chemotherapy treatment reduced immune cell numbers and activity in peripheral tissues, chemotherapy did not antagonize but in many cases augmented the antitumor activity mediated by anti-PD-L1. This dichotomy between the detrimental effects in peripheral tissues and enhanced antitumor activity was largely explained by the reduced dependence on incoming cells for antitumor efficacy in already established tumors. The effects of the various chemotherapies were also agent specific, and synergy with anti-PD-L1 was achieved by different mechanisms that ultimately helped establish a new threshold for response. These results rationalize the combination of chemotherapy with immunotherapy and suggest that, despite the negative systemic effects of chemotherapy, effective combinations can be obtained through distinct mechanisms acting within the tumor.
The regulatory roles of small leucine‐rich proteoglycans in extracellular matrix assembly (2013)

Small leucine-rich proteoglycans (SLRPs) are involved in a variety of biological and pathological processes. This review focuses on their regulatory roles in matrix assembly. SLRPs have protein cores and hypervariable glycosylation with multivalent binding abilities. During development, differential interactions of SLRPs with other molecules result in tissue-specific spatial and temporal distributions. The changing expression patterns play a critical role in the regulation of tissue-specific matrix assembly and therefore tissue function. SLRPs play significant structural roles within extracellular matrices. In addition, they play regulatory roles in collagen fibril growth, fibril organization and extracellular matrix assembly. Moreover, they are involved in mediating cell-matrix interactions. Abnormal SLRP expression and/or structures result in dysfunctional extracellular matrices and pathophysiology. Altered expression of SLRPs has been found in many disease models, and structural deficiency also causes altered matrix assembly. SLRPs regulate assembly of the extracellular matrix, which defines the microenvironment, modulating both the extracellular matrix and cellular functions, with an impact on tissue function.