gebhart/patrepeval huggingface dataset:
Metronidazole resistance: A predictor of failure of <i>Helicobacter pylori</i> eradication by triple therapy (1996)

Triple therapy (bismuth and two antibiotics) will eradicate Helicobacter pylori infection in 70-90% of subjects. Treatment failure has been attributed to patient compliance and antimicrobial drug resistance. The aim of this study was to examine factors influencing the eradication of H. pylori following triple therapy. Thirty seven subjects with H. pylori cultured from antral biopsies were treated with colloidal bismuth subcitrate (120 mg qid for 2 weeks), metronidazole (400 mg tid for 1 week) and amoxycillin (500 mg tid for 1 week). Pretreatment isolates of H. pylori were tested for metronidazole susceptibility by agar dilution according to the National Committee for Clinical Laboratory Standards guidelines. Factors including age, sex, clinical diagnosis and metronidazole resistance were evaluated in relation to H. pylori. The overall metronidazole resistance was 32%. Metronidazole resistant strains were more frequent in females, with a resistance rate of 54%. Helicobacter pylori eradication occurred in 68% of patients with a metronidazole susceptible stain and only 17% of patients with a metronidazole resistant strain (P < 0.03). Helicobacter pylori eradication is dependent upon susceptibility to metronidazole. This data would support the role for routine metronidazole susceptibility testing using appropriate standardized methods when triple therapy is to be considered.
Memory Design for Testability and Fault Tolerance (2009)

This chapter contains sections titled: General Design for Testability Techniques RAM Built-In Self-Test (BIST) Embedded Memory DFT and BIST Techniques Advanced BIST and Built-In Self-Repair Architectures DFT and BIST for ROMs Memory Error-Detection and Correction Techniques Memory Fault-Tolerance Designs This chapter contains sections titled: References
An Adoption Study of Antisocial Personality (1974)

To test the hypothesis that heredity contributes to the development of antisocial personality a group of offspring born to female offenders and given up for adoption in infancy was examined. Forty-six probands and an equal number of control adoptees over 18 years old were followed up and interviewed. A significantly higher rate of antisocial personality was found among the probands than among the controls. The nonantisocial probands proved not to be more deviant than the controls. The antisocial probands experienced certain unfavorable conditions in infancy that may be related to the development of antisocial personality, the most notable being the length of time spent in temporary care prior to final placement. Although the control group was equally exposed to the same conditions, they did not develop a high rate of the disorder. The findings point to the importance of interactions between genetic and environmental factors in the development of antisocial personality.
Link Adaptation in MIMO Systems by Using Machine Learning (2018)

Optimizing the performance of link adaption in practice has proven challenging. The core of this problem is how to deal with the channel state information (CSI), modulation and coding scheme (MCS).Furtherly, it is exploring the relationship between CSI and Channel Quality Indicator (CQI). In order to get CQI, prior research struggled to build a single valued signal-to-noise ratio (SNR) by CSI. However, the results of these methods are not good enough. We consider using the machine learning method to explore the relationship between CSI and CQI. It is not a simple classification problem, because the premise of link adaptation is to satisfy the reliability of the system, and the target is maximizing the spectral efficiency. In this paper, present a novel link adaption scheme in the MIMO systems by using an improved kNN algorithm, in the system setting of maximizing the spectral effectivenss. And the improved kNN algorithm is named as E <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cost</sub> kNN. The main emphasis is placed on the problem of how to build a highly individualized cost function for it. With the aid of using E <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cost</sub> kNN, the link adaption of MIMO systems can get better performance. The simulation result reflects the proposed novel method significantly outperforms the existing kNN algorithm in the link adaption technology.
A gatekeeper-based NAT traversal method for media transport in H.323 network system (2005)

H.323 has application to a variety of network media, including local area networks, enterprise networks, Internet and wireless networks. But NAT (network address translation) traversal problem is baffling H.323 communication due to complex network situations and flaws of protocol. The existing traversal methods are not suitable for all network situations. They often require upgrading or adding other deceives and protocols to support call procedure. In this paper, A NAT traversal method based on gatekeeper (GK) for media transport is proposed. This scheme only needs a gatekeeper server, which is able to route call. This resolving method is also very easy to implement without changing network situations. And it is very available for communications with many NAT deceives in applications. The mechanism may be used as the short-term solution for the NAT traversal problem in H.323 system.
Iterative encoding of low-density parity-check codes (2004)

Motivated by the potential to reuse the decoder architecture, and thus reduce circuit space, we explore the use of iterative encoding techniques which are based upon the graphical representation of the code. We design codes by identifying associated encoder convergence constraints and also eliminating some well known undesirable properties for sum-product decoding such as 4-cycles. In particular we show how the Jacobi method for iterative matrix inversion can be viewed as message passing and employed as the core of an iterative encoder. Example constructions of both regular and irregular LDPC codes that are encodable using this method are investigated.
Spatio-Temporal AutoEncoder for Video Anomaly Detection (2017)

Anomalous events detection in real-world video scenes is a challenging problem due to the complexity of "anomaly" as well as the cluttered backgrounds, objects and motions in the scenes. Most existing methods use hand-crafted features in local spatial regions to identify anomalies. In this paper, we propose a novel model called Spatio-Temporal AutoEncoder (ST AutoEncoder or STAE), which utilizes deep neural networks to learn video representation automatically and extracts features from both spatial and temporal dimensions by performing 3-dimensional convolutions. In addition to the reconstruction loss used in existing typical autoencoders, we introduce a weight-decreasing prediction loss for generating future frames, which enhances the motion feature learning in videos. Since most anomaly detection datasets are restricted to appearance anomalies or unnatural motion anomalies, we collected a new challenging dataset comprising a set of real-world traffic surveillance videos. Several experiments are performed on both the public benchmarks and our traffic dataset, which show that our proposed method remarkably outperforms the state-of-the-art approaches.
Optical Communication Through Low-Visibility Weather (1981)

By exploiting the scattered light, it is possible to improve atmospheric optical communication through low-visibility weather. This paper summarizes the multiple-forward-scatter propagation model for a line-of-sight optical link operating in low-visibility weather. Using this model, digital communication performances for diffraction-limited and wide field-of-view receivers are compared. It is shown that background-light suppression is the key to extended link operability in the daytime. Recent work on space-distributed phase compensation techniques for achieving this suppression is described.
Signalling pathways associated with <scp>IL</scp>‐6 production and epithelial–mesenchymal transition induction in prostate epithelial cells stimulated with <i>Trichomonas vaginalis</i> (2016)

Trichomonas vaginalis (Tv) has been found in patient tissue of benign prostatic hyperplasia (BPH), and suggested to cause chronic prostatitis. IL-6 is known as one of the important factors of chronic inflammation in prostate cancer. Patients with chronic prostatitis/chronic pelvic pain syndrome (CP/CPPS) had higher levels of IL-6 in seminal plasma. Furthermore, inflammatory conditions induced by pathogen infections have been shown to promote epithelial-mesenchymal transition (EMT). Here, we investigated the signals involved in IL-6 production by human prostate epithelial cells (PECs) stimulated with Tv and examined whether Tv induces EMT in PECs. We found that PECs stimulated with Tv increased the production of IL-6, as well as the expression of TLR2, TLR4, MAPKs (p38, JNK, ERK), NF-κB and JAK2/STAT3, and levels of ROS. Inhibition of TLR2 or TLR4 reduced IL-6 production as well as expression of these other factors, and agents inhibiting ROS, MAPKs, NF-κB and JAK reduced IL-6 production. However, when PECs were stimulated with Tv, transcripts of mesenchymal cell markers increased, and epithelial cell markers decreased. In addition, the induction of EMT was suppressed by inhibitors of JAK or NF-κB. These findings are the first evidence that Tv infection of prostate epithelial cells may induce EMT.
Substantial rate enhancements of the esterification reaction of phthalic anhydride with methanol at high pressure and using supercritical CO2 as a co-solvent in a glass microreactor (2007)

The esterification reaction of phthalic anhydride with methanol was performed at different temperatures in a continuous flow glass microreactor at pressures up to 110 bar and using supercritical CO(2) as a co-solvent. The design is such that supercritical CO(2) can be generated inside the microreactor. Substantial rate enhancements were obtained, viz. a 53-fold increase was obtained at 110 bar and 60 degrees C. Supercritical CO(2) as a co-solvent gave rise to a 5400-fold increase (both with respect to batch experiments at 1 bar at the same temperature).
Sidelobe reduction techniques for range-resolution radar (2003)

This paper mainly discusses the technique used to reduce sidelobes for polyphase pulse compression codes. A brief explanation is given on pulse compression and range resolution. Binary, ternary, quinquenary, multilevel and Barker codes are also discussed. The details of polyphase pulse compression code that comprises Frank code, Extended Frank code, P3 and P4 codes are described. The basic theory on autocorrelation and discrimination is explained briefly. Furthermore, sidelobe reduction techniques for polyphase pulse compression codes are discussed in this paper. The technique of least square amplitude and phase weighting is focussed on P4, binary and ternary codes.. Finally, comparison of mean square error is made between different choices like all sidelobes reduction, some sidelobes reduction etc. to conclude which type of choice; the reduction technique is achieving less error thus providing a good reduction in autocorrelation with maintaining the same mainlobe level.
Degradation of Recalcitrant Surfactants in Wastewater by Ozonation and Advanced Oxidation Processes: A Review (2004)

ABSTRACT Surfactants are used in varieties of industrial cleansing processes as well as in consumer products. Spent surfactants normally enter domestic or industrial wastewater and are treated biologically. However, some of them are resistant to biodegradation and are released into the environment. Thus, the toxicity and environmental persistence of these surfactants are emerging concerns. Based on extensive review of the literature, ozonation and advanced oxidation using various combinations of ozone, hydrogen peroxide, ultraviolet light irradiation, and iron salts were found effective in degrading recalcitrant surfactants, including linear alkylbenzene sulfonates, alkylphenol ethoxylates, and quaternary ammonium surfactants. Biodegradability of these surfactants was improved after the treatment to some extent in the aqueous solution as well as in real wastewaters. Keywords: OzoneAdvanced Oxidation ProcessesAlkylphenol EthoxylatesFenton ProcessHydrogen PeroxideLinear Alkylbenzene SulfonatesOzonationQuaternary AmmoniumSurfactantWastewater Treatment ACKNOWLEDGMENT This work was funded by the Natural Sciences and Engineering Research Council of Canada and Alberta Ingenuity Fund.
Automatic selection of tuning parameters for feature extraction sequences (1994)

Computer vision algorithms are composed of different sub-algorithms often applied in sequence. Previous work on performance characterization illustrated how random perturbation models can be setup at various stages of an algorithm sequence for the input and output data. In this paper we address the issue of how one could utilize these random perturbation models in order to automate the selection of free parameters used in an algorithm sequence. We consider an operation sequence that involves edge finding, linking, corner finding and matching. Appropriate prior distributions for the parameters that describe the graytone/geometric characteristics of the image features are specified and validated by using an annotation process. The annotation process involves the manual specification (outlining) of the geometry and spatial extent of the image features. Statistics are gathered for parameters describing features of interest and non-interest (clutter features). The appropriate prior distributions are used to derive the theoretical expressions for feature detector performance over a given image population. These performance measures are then optimized to determine the tuning parameters for the feature detector(s).< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Plan-and-Write: Towards Better Automatic Storytelling (2019)

Automatic storytelling is challenging since it requires generating long, coherent natural language to describes a sensible sequence of events. Despite considerable efforts on automatic story generation in the past, prior work either is restricted in plot planning, or can only generate stories in a narrow domain. In this paper, we explore open-domain story generation that writes stories given a title (topic) as input. We propose a plan-and-write hierarchical generation framework that first plans a storyline, and then generates a story based on the storyline. We compare two planning strategies. The dynamic schema interweaves story planning and its surface realization in text, while the static schema plans out the entire storyline before generating stories. Experiments show that with explicit storyline planning, the generated stories are more diverse, coherent, and on topic than those generated without creating a full plan, according to both automatic and human evaluations.
A Pattern for an NFV Virtual Machine Environment (2019)

Network Function Virtualization (NFV) is a recent technology with a high velocity of innovation. It leverages virtualization technology to offer network functions such as firewalls, load balancers, and switches, as software services rather than being hardware as it is in traditional network infrastructures. A key component of virtualization technology is the hypervisor, which is a collection of software modules that provide virtualization of hardware resources and thus enable several virtual machines (VMs) to be run on a single physical server. The hypervisor resides within a Virtual Machine Environment (VME), which is an environment where VMs can be created to run applications (network functions), functionally equivalent to the original machine (computer) environment. We present here a pattern for a virtual machine environment of NFV, which describes the architecture where VMs are created and managed for the purposes of NFV; the created VMs can be used to create virtualized network functions (VNFs).
The fourier transform of poisson multinomial distributions and its algorithmic applications (2016)

An (n, k)-Poisson Multinomial Distribution (PMD) is a random variable of the form X = ∑i=1n Xi, where the Xi’s are independent random vectors supported on the set of standard basis vectors in k. In this paper, we obtain a refined structural understanding of PMDs by analyzing their Fourier transform. As our core structural result, we prove that the Fourier transform of PMDs is approximately sparse, i.e., its L1-norm is small outside a small set. By building on this result, we obtain the following applications:
Cerebral mechanisms of experimental hyperalgesia in fibromyalgia (2011)

The present study examined the hyperresponsiveness of the central nervous system in patients with fibromyalgia syndrome (FMS) related to mechanical hyperalgesia. The goals were to differentiate between increased pain ratings and hyperalgesia related either to peripheral or to central sensitization and to correlate with cerebral activation pattern. Seventeen patients and 17 healthy controls were examined, placing an experimental incision in the right volar forearm and causing tonic pain. Experimental pain, primary and secondary hyperalgesia were assessed during the time course of the experimental pain, and the changes in hyperalgesia were correlated to brain activation (functional magnetic resonance imaging). Patients with FMS experienced the experimental pain during the time course as more painful than healthy controls (F(score) = 3.93, p(score) = 0.008). While they did not present a different course of primary hyperalgesia (F(score) = 1.01, p(score) = 0.40), they did show greater secondary hyperalgesia (F(score) = 5.45, p(score) = 0.004). In patients with FMS, the cerebral pattern corresponding to secondary hyperalgesia was altered. The activity in the dorsolateral prefrontal cortex was inversely correlated with secondary hyperalgesia in healthy controls (R = -0.34 p = 0.005); in patients, this correlation was disrupted (R = 0.19 p = 0.12). These findings point to an alteration of pain transmission at the central level in FMS (e.g., loss of inhibition) and might be related to changes in cerebral-midbrain-spinal mechanisms of pain inhibition.
<i>In vitro</i> secretion of transforming growth factor alpha (TGF-α): A comparison of the A431 cell line with three human oesophageal squamous cell carcinoma lines (1992)

Transforming growth factor alpha (TGF-alpha) is a single chain polypeptide which exists in a variety of forms differing in molecular weight. These forms are variously present in normal and neoplastic cells. Of particular interest are TGF-alpha's well-known mitogenic properties. The transition from a normal to a neoplastic cellular state results from signalling defects that may depend upon, inter alia, abnormal levels of expression and secretion of TGF-alpha. It is known that the secretion of TGF-alpha may be enhanced appreciably by agents such as phorbol 12-myristate 13-acetate (PMA), serum factors and epidermal growth factor (EGF). Here, we compare the efficacy of these three agents in the elevation of TGF-alpha secretion in the well studied A431 cell line with their previously undocumented efficacy in certain interesting, but little known, human oesophageal squamous cell carcinoma (SCC) lines.
Fog Computing: Survey of Trends, Architectures, Requirements, and Research Directions (2018)

Emerging technologies such as the Internet of Things (IoT) require latency-aware computation for real-time application processing. In IoT environments, connected things generate a huge amount of data, which are generally referred to as big data. Data generated from IoT devices are generally processed in a cloud infrastructure because of the on-demand services and scalability features of the cloud computing paradigm. However, processing IoT application requests on the cloud exclusively is not an efficient solution for some IoT applications, especially time-sensitive ones. To address this issue, Fog computing, which resides in between cloud and IoT devices, was proposed. In general, in the Fog computing environment, IoT devices are connected to Fog devices. These Fog devices are located in close proximity to users and are responsible for intermediate computation and storage. One of the key challenges in running IoT applications in a Fog computing environment are resource allocation and task scheduling. Fog computing research is still in its infancy, and taxonomy-based investigation into the requirements of Fog infrastructure, platform, and applications mapped to current research is still required. This survey will help the industry and research community synthesize and identify the requirements for Fog computing. This paper starts with an overview of Fog computing in which the definition of Fog computing, research trends, and the technical differences between Fog and cloud are reviewed. Then, we investigate numerous proposed Fog computing architectures and describe the components of these architectures in detail. From this, the role of each component will be defined, which will help in the deployment of Fog computing. Next, a taxonomy of Fog computing is proposed by considering the requirements of the Fog computing paradigm. We also discuss existing research works and gaps in resource allocation and scheduling, fault tolerance, simulation tools, and Fog-based microservices. Finally, by addressing the limitations of current research works, we present some open issues, which will determine the future research direction for the Fog computing paradigm.
A Recursive Method for Structural Learning of Directed Acyclic Graphs (2008)

In this paper, we propose a recursive method for structural learning of directed acyclic graphs (DAGs), in which a problem of structural learning for a large DAG is first decomposed into two problems of structural learning for two small vertex subsets, each of which is then decomposed recursively into two problems of smaller subsets until none subset can be decomposed further. In our approach, search for separators of a pair of variables in a large DAG is localized to small subsets, and thus the approach can improve the efficiency of searches and the power of statistical tests for structural learning. We show how the recent advances in the learning of undirected graphical models can be employed to facilitate the decomposition. Simulations are given to demonstrate the performance of the proposed method.
PC board design and fabrication using Schematics, PADS-PERFORM, and a laser printer (2002)

The software packages Schematics from MicroSim Corp. and PADS-PERFORM from PADS Software Inc. have been interfaced to give students an integrated environment where they can create schematics for documentation, simulate circuits, and design printed circuit boards. The software packages are available in evaluation versions. The circuits are limited to approximately 25 components but this is not a limitation for undergraduate projects. A package of parts has been developed at Northern Arizona University that contains part symbols and package outlines for parts in a "standard" lab kit. PADS-PERFORM can output artwork on the laser printer. A method for using the laser printer toner for resist when etching the boards is discussed.