Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of different finishing/polishing protocols and systems for monolithic zirconia on surface topography, phase transformation, and biofilm formation

PURPOSE The purpose of this study was to evaluate the effects of various protocols and systems for finishing and polishing monolithic zirconia on surface topography, phase transformation, and bacterial adhesion. MATERIALS AND METHODS Three hundred monolithic zirconia specimens were fabricated and then treated with three finishing and polishing systems (Jota [JO], Meisinger [ME], and Edenta [ED]) using four surface treatment protocols: coarse finishing alone (C); coarse finishing and medium polishing (CM); coarse finishing and fine polishing (CF); and coarse finishing, medium polishing, and fine polishing (CMF). Surface roughness, crystal phase transformation, and bacterial adhesion were evaluated using atomic force microscopy, X-ray diffraction, and streptococcal biofilm formation assay, respectively. One-way and two-way analysis of variance with Tukey post hoc tests were used to analyze the results (α=.05). RESULTS In this study, the surface treatment protocols and systems had significant effects on the resulting roughness. The CMF protocol produced the lowest roughness values, followed by CM and CF. Use of the JO system produced the lowest roughness values and the smallest biofilm mass, while the ME system produced the smallest partial transformation ratio. The ED group exhibited the highest roughness values, biofilm mass, and partial transformation ratio. CONCLUSION Stepwise surface treatment of monolithic zirconia, combined with careful polishing system selection, is essential to obtaining optimal microstructural and biological surface results.
Nonparametric Nonlinear Co-Trending Analysis , With an Application to Interest and Inflation in the U . S . Herman

Given the assumption that the components of a vector time series are stationary about nonlinear deterministic time trends, nonlinear co-trending is the phenomenon that one or more linear combinations of the time series are stationary about a linear trend or a constant, hence the series have common nonlinear deterministic time trends. In this paper we develop nonparametric tests for nonlinear cotrending. These tests are based on generalized eigenvalues, where the two matrices involved are constructed nonparametrically on the basis of partial sums. We apply this approach to the federal funds rate and the CPI inflation rate in the U.S., using monthly data. It appears that these series are nonlinear co-trended, where the nonlinear trend in the inflation rate is positively related to the nonlinear trend in the interest rate. Moreover, the price puzzle seems to a large extent due to this common nonlinear trend. Furthermore, the common nonlinear trend seems to be related to the oil price shocks induced by the OPEC cartel in the early and late seventies.
Rolling element bearing diagnostic based on EEMD and SVM

Considering rolling bearing incipient fault feature is not obvious, with bearing vibration signals, a bearing-condition-monitoring method based on the ensemble empirical mode decomposition(EEMD) and Support Vector Machine(SVM), has been proposed in this paper. EEMD is used for adaptive decomposition of bearing vibration signals into several intrinsic mode functions (IMFs). The IMF component energy characteristics and dimensionless factor of time domain characteristics were extracted as the original features. After that, Back Propagation (BP) neural network and Genetic Algorithm (GA) were employed for feature selection. Then, dimension-reduced feature based on principal component analysis (PCA) is to establish the SVM model and monitor bearing fault state. The experiment shows that with this method diagnosis of rolling element bearing faults could be realized effectively.
Numerical Study of Liquid Metal MHD Duct Flow under Hydrodynamic &quotSlip" Condition

A numerical study for steady MHD flow of liquid metal through a square duct with slip walls has been carried out. An intense external magnetic field is acting normal to two walls of the square duct which are considered as slip walls. The numerical solutions for velocity and induced magnetic field have been obtained by using a 5 point stencil central difference scheme. Solutions for velocity and induced field for different values of Hartmann number and with consideration of a fixed value of slip length parameter are presented graphically.
A Framework for Inter-Subject Prediction of Functional Connectivity From Structural Networks

Functional connections between brain regions are supported by structural connectivity. Both functional and structural connectivity are estimated from in vivo magnetic resonance imaging and offer complementary information on brain organization and function. However, imaging only provides noisy measures, and we lack a good neuroscientific understanding of the links between structure and function. Therefore, inter-subject joint modeling of structural and functional connectivity, the key to multimodal biomarkers, is an open challenge. We present a probabilistic framework to learn across subjects a mapping from structural to functional brain connectivity. Expanding on our previous work [1], our approach is based on a predictive framework with multiple sparse linear regression. We rely on the randomized LASSO to identify relevant anatomo-functional links with some confidence interval. In addition, we describe resting-state functional magnetic resonance imaging in the setting of Gaussian graphical models, on the one hand imposing conditional independences from structural connectivity and on the other hand parameterizing the problem in terms of multivariate autoregressive models. We introduce an intrinsic measure of prediction error for functional connectivity that is independent of the parameterization chosen and provides the means for robust model selection. We demonstrate our methodology with regions within the default mode and the salience network as well as, atlas-based cortical parcellation.
Selections from current literature. Complementary therapies for nausea and vomiting in early pregnancy.

complaints encountered in early pregnancy. Seventy per cent of women report nausea and 30–50% experience vomiting. 1 Most cases are mild and resolve by the twentieth week of gestation. At the other end of the spectrum is hyperemesis gravidarum, characterized by persistent nausea and vomiting that results in dehydration, ketosis, electrolyte imbalances and weight loss. Morning sickness is a misnamed descriptive term describing nausea and vomiting of pregnancy. In 1993, Gadsby et al. published a comprehensive review of the timing, duration and onset of nausea and vomiting in pregnancy and demonstrated that, although the symptoms were more prevalent between 6 a.m. and noon, many pregnant patients reported nausea and vomiting throughout the day. 2 Studies of nausea and vomiting in pregnancy are often made more challenging because of the subjective nature of the symptom of nausea versus the objective sign of vomiting. The pathophysiology of nausea and vomiting in early pregnancy and hyperemesis gravidarum is poorly understood. Various hormonal, biochemical, mechanical and psychological factors have been implicated. The aetiology of nausea and vomiting in pregnancy and hyperemesis gravidarum probably encompasses several factors. While the diagnosis may seem straightforward, it is important for primary care physicians never to overlook the other obstetrical and non-obstetrical causes in the differential diagnosis of nausea and vomiting. The treatment of nausea and vomiting in pregnancy has traditionally been supportive, with dietary modification for mild cases. Such dietary advice consists of eating small portions of foods at frequent intervals, ingesting dry toast or crackers upon arising, and eating bland low-fat foods. Pharmacological treatments, including anti-histamines, antinauseants, antiemetics and promotility agents have been employed in more symptomatic patients. Concerns about the potential teratogenic effects of medications often limit their use. 3 A number of trials have found various antihistamines to be safe during pregnancy, although there have been no major epidemiological studies to look for teratogenic effects. Neither the efficacy nor the safety of phenothiazines or metoclopramide have been firmly established despite continued usage. 4 For those women who are not getting complete relief of symptoms with dietary modification and for those women concerned about the safety of medications, many are turning to complementary and alternative therapies to treat nausea and vomiting in early pregnancy. The landmark study of Eisenberg et al. 5 demonstrated the widespread interest and growing use of alternative therapies. Physicians and patients alike may turn to complementary therapies to gain therapeutic benefits with …
Unsupervised training methods for discriminative language modeling

Discriminative language modeling (DLM) aims to choose the most accurate word sequence by reranking the alternatives output by the automatic speech recognizer (ASR). The conventional (supervised) way of training a DLM requires a large amount of acoustic recordings together with their manual reference transcriptions. These transcriptions are used to determine the target ranks of the ASR outputs, but may be hard to obtain. Previous studies make use of the existing transcribed data to build a confusion model which boosts the training set by generating artificial data: a process known as semi-supervised training. In this study we concentrate on the unsupervised setting where no manual transcriptions are available at all. We propose three ways to determine a sequence that could serve as the missing reference text and two approaches which use this information to (i) determine the ranks of the ASR outputs in order to train the discriminative model directly, and (ii) build a confusion model in order to generate artificial training examples. We compare our techniques with the supervised and the semi-supervised setups. Using the reranking variant of the WER-sensitive perceptron algorithm, we obtain word error rate improvements up to half of those of the supervised case.
COSPIM: a program optimization system for tightly-coupled heterogeneous environments

Processor-in-memory is a new class of computer architecture designed for reducing the performance gap between the processor and the memory. This architecture provides a tightly-coupled heterogeneous environment by integrating different processors in a system. An efficient parallelization and optimization mechanism is necessary for this system to transform the existed applications to achieve better performance. In this paper, we propose a comprehensive framework, COSPIM, based on the statement viewpoint in our early SAGE system. It integrates program decomposition, ETC (expected time to compute) evaluation and scheduling mechanisms together. We describe how COSPIM splits statements and produces schedule to execute on the host processor and the coprocessor simultaneously. The experimental results of this approach are also discussed.
Investigation of layout effect on ESD performance of SCR-nLDMOS devices

A robust ESD device for 5V circuit protection was designed and fabricated in a 0.5-μm 5V/18V CDMOS process. It is composed of paralleled SCR and nLDMOS. The ESD performance of multi-fingered SCR-nLDMOS devices with strip and segment SCR layout style is investigated by employing transmission line pulse (TLP) measurement. The SCR-nLDMOS with strip SCR is superior to SCR-nLDMOS applying segment SCR for its relatively stronger ESD current handling capability of 1.42mA/μm2, which is also much higher than that of traditional gate-grounded 5V NMOS (1.04mA/μm2). The SCR-nLDMOS with segment SCR provides us an option to elevate Vh from 2.9V up to 5.7V.
Online Robust Principal Component Analysis With Change Point Detection

Robust principal component analysis (PCA) is a key technique for dynamical high-dimensional data analysis, including background subtraction for surveillance video. Typically, robust PCA requires all observations to be stored in memory before processing. The batch manner makes robust PCA inefficient for big data. In this paper, we develop an efficient online robust PCA method, namely, online moving window robust principal component analysis (OMWRPCA). Unlike the existing algorithms, OMWRPCA can successfully track not only slowly changing subspaces but also abruptly changing subspaces. Embedding hypothesis testing into the algorithm enables OMWRPCA to detect change points of the underlying subspaces. Extensive numerical experiments, including real-time background subtraction, demonstrate the superior performance of OMWRPCA compared with other state-of-the-art approaches.
Object based Scene Representations using Fisher Scores of Local Subspace Projections

Several works have shown that deep CNNs can be easily transferred across datasets, e.g. the transfer from object recognition on ImageNet to object detection on Pascal VOC. Less clear, however, is the ability of CNNs to transfer knowledge across tasks. A common example of such transfer is the problem of scene classification, that should leverage localized object detections to recognize holistic visual concepts. While this problems is currently addressed with Fisher vector representations, these are now shown ineffective for the high-dimensional and highly non-linear features extracted by modern CNNs. It is argued that this is mostly due to the reliance on a model, the Gaussian mixture of diagonal covariances, which has a very limited ability to capture the second order statistics of CNN features. This problem is addressed by the adoption of a better model, the mixture of factor analyzers (MFA), which approximates the non-linear data manifold by a collection of local sub-spaces. The Fisher score with respect to the MFA (MFA-FS) is derived and proposed as an image representation for holistic image classifiers. Extensive experiments show that the MFA-FS has state of the art performance for object-to-scene transfer and this transfer actually outperforms the training of a scene CNN from a large scene dataset. The two representations are also shown to be complementary, in the sense that their combination outperforms each of the representations by itself. When combined, they produce a state-of-the-art scene classifier.
Implementing an OpenFlow switch on the NetFPGA platform

We describe the implementation of an OpenFlow Switch on the NetFPGA platform. OpenFlow is a way to deploy experimental or new protocols in networks that carry production traffic. An OpenFlow network consists of simple flow-based switches in the datapath, with a remote controller to manage several switches. In practice, OpenFlow is most often added as a feature to an existing Ethernet switch, IPv4 router or wireless access point. An OpenFlow-enabled device has an internal flow-table and a standardized interface to add and remove flow entries remotely. Our implementation of OpenFlow on the NetFPGA is one of several reference implementations we have implemented on different platforms. Our simple OpenFlow implementation is capable of running at line-rate and handling all the traffic that is going through the Stanford Electrical Engineering and Computer Science building. We compare our implementation's complexity to a basic IPv4 router implementation and a basic Ethernet learning switch implementation. We describe the OpenFlow deployment into the Stanford campus and the Internet2 backbone.
FPGA-accelerated key search for cold-boot attacks against AES

Cold-boot attacks exploit the fact that DRAM contents are not immediately lost when a PC is powered off. Instead the contents decay rather slowly, in particular if the DRAM chips are cooled to low temperatures. This effect opens an attack vector on cryptographic applications that keep decrypted keys in DRAM. An attacker with access to the target computer can reboot it or remove the RAM modules and quickly copy the RAM contents to non-volatile memory. By exploiting the known cryptographic structure of the cipher and layout of the key data in memory, in our application an AES key schedule with redundancy, the resulting memory image can be searched for sections that could correspond to decayed cryptographic keys; then, the attacker can attempt to reconstruct the original key. However, the runtime of these algorithms grows rapidly with increasing memory image size, error rate and complexity of the bit error model, which limits the practicability of the approach. In this work, we study how the algorithm for key search can be accelerated with custom computing machines. We present an FPGA-based architecture on a Maxeler dataflow computing system that outperforms a software implementation up to 205x, which significantly improves the practicability of cold-attacks against AES.
HSF1 transcriptional activity mediates alcohol induction of Vamp2 expression and GABA release

Many central synapses are highly sensitive to alcohol, and it is now accepted that short-term alterations in synaptic function may lead to longer-term changes in circuit function. The regulation of postsynaptic receptors by alcohol has been well studied, but the mechanisms underlying the effects of alcohol on the presynaptic terminal are relatively unexplored. To identify a pathway by which alcohol regulates neurotransmitter release, we recently investigated the mechanism by which ethanol induces Vamp2, but not Vamp1, in mouse primary cortical cultures. These two genes encode isoforms of synaptobrevin, a vesicular soluble N-ethylmaleimide-sensitive factor attachment protein receptor (SNARE) protein required for synaptic vesicle fusion. We found that alcohol activates the transcription factor heat shock factor 1 (HSF1) to induce Vamp2 expression, while Vamp1 mRNA levels remain unaffected. As the Vamp2 gene encodes a SNARE protein, we then investigated whether ethanol exposure and HSF1 transcriptional activity alter neurotransmitter release using electrophysiology. We found that alcohol increased the frequency of γ-aminobutyric acid (GABA)-mediated miniature IPSCs via HSF1, but had no effect on mEPSCs. Overall, these data indicate that alcohol induces HSF1 transcriptional activity to trigger a specific coordinated adaptation in GABAergic presynaptic terminals. This mechanism could explain some of the changes in synaptic function that occur soon after alcohol exposure, and may underlie some of the more enduring effects of chronic alcohol intake on local circuit function.
Design of fuzzy Logic controller for Online Speed regulation of DC motor using PWM Technique based on Laboratory Virtual Instrument Engineering Workbench

This study details online speed regulation of DC motor using PWM technique based on LabVIEW. A fuzzy logic controller is designed to change the pulse width of switching signal applied to the converter and thereby the voltage fed to the armature of the separately excited DC motor to regulate the speed. The proposed system is connected to the server computer using a Data Acquisition Card (DAQ) and the server computer is configured to access remote client computer using Web publishing tool in LabVIEW. Now the speed of the DC motor can be controlled by a remote client computer with Internet connection. Laboratory Virtual Instrument Engineering Workbench (LabVIEW) is a graphical programming environment for creating custom applications that interact with real-world data or signals in fields such as science and engineering. The proposed online control system has been implemented to control the speeds of two different motors and the experimental results are plotted. The conventional controllers need design objectives such as steady state and transient characteristics of the closed loop system to be specified. But the designed fuzzy logic controller overcomes the problems with uncertainties in the plant parameters. The proposed online control technique saves time and reduces the manpower.
Automated spectrophotometric analysis of mitochondrial respiratory chain complex enzyme activities in cultured skin fibroblasts.

BACKGROUND Mitochondrial respiratory chain complex (RCC) disorders may occur as commonly as 1 in 8500 individuals. Because of the great variability of phenotypic presentations, measurement of individual RCC enzyme activities is a crucial diagnostic process. Current assay methods are time-consuming and labor-intensive and thus constitute a major impediment to clinical practice. A method with a faster turnaround time would therefore be beneficial. METHOD We developed an automated spectrophotometric method for measuring the respiratory chain enzyme activities of complex I, complex II + III, and complex IV with the Hitachi 912, an automated spectrophotometer. Mitochondrial citrate synthase was also determined for normalization of the RCC activities. RESULTS A blinded method comparison with samples from an external testing center yielded a 91% concordance of interpretations. Mean intraassay imprecision (as CV; n = 20) in a single batch analysis of each RCC was 5.9%. Interassay imprecision, evaluated on 2 samples harvested and analyzed 3 times each, gave mean CVs of 10%-18%. CONCLUSIONS With this automated method, a panel of RCC enzyme activities can be determined in <2 h. In addition, an immunoblot assay using monoclonal antibodies against specific subunits of RCC enzyme complexes can be informative in cases of borderline enzyme activity. Our results suggest that in vitro diagnosis of RCC enzyme deficiencies in skin fibroblasts is an effective alternative to invasive muscle biopsy.
Spontaneous Speech Understanding for Robust Multi-Modal Human-Robot Communication

This paper presents a speech understanding component for enabling robust situated human-robot communication. The aim is to gain semantic interpretations of utterances that serve as a basis for multi-modal dialog management also in cases where the recognized word-stream is not grammatically correct. For the understanding process, we designed semantic processable units, which are adapted to the domain of situated communication. Our framework supports the specific characteristics of spontaneous speech used in combination with gestures in a real world scenario. It also provides information about the dialog acts. Finally, we present a processing mechanism using these concept structures to generate the most likely semantic interpretation of the utterances and to evaluate the interpretation with respect to semantic coherence.
The Differential Impact of Knowledge Depth and Knowledge Breadth on Creativity over Individual Careers

While usually argued to be fostering creativity, the effect of knowledge depth and breadth on creativity is actually mixed. We take a dynamic approach to the knowledge-creativity relationship and argue that the effect of knowledge depth and knowledge breadth is likely to be contingent on career age. We propose that individuals' knowledge structures become increasingly rigid as career age grows and that because of this, knowledge depth and breadth have different effects on creativity at different points of the career. More specifically, we hypothesize that knowledge depth is more beneficial for creativity in earlier stages of one's career, when creators need to increase the complexity of knowledge structures, while knowledge breadth is more beneficial in later stages, when flexibility is most needed. We test and find support for our hypotheses in a longitudinal study set in the context of the Hollywood animation industry, a setting characterized by the presence of a variety of creators involved in knowledge-intensive activities. Theoretical and practical implications of the results are discussed.