Miracle-dz/UniFAR-FTUs huggingface dataset:
Soy sterol esters and b -sitostanol ester as inhibitors of cholesterol absorption in human small bowel 1–3

Background: Plant sterols are natural dietary components with serum cholesterol–lowering properties. The lowering of serum cholesterol by plant sterols is believed to be the result of an inhibition of cholesterol absorption in the small bowel, although increased bile acid excretion has also been suggested. The difference in effect of saturated and unsaturated plant sterols on cholesterol absorption needs to be elucidated further. Objective: The primary aim of this study was to measure small-bowel cholesterol absorption and sterol excretion in addition to hepatic cholesterol synthesis after intake of soy sterol esters and b -sitostanol ester corresponding to 1.5 g plant sterols/d. Design: Seven ileostomy subjects were studied during a control period and 2 intervention periods when either soy sterol esters or b -sitostanol ester was added to a basal diet. Ileostomy bags were collected every other hour and frozen immediately for analysis of nutrients and sterols. Results: Cholesterol absorption was 56% (43–65%) in the control period and decreased to 38% (32–46%) in the soy sterol ester period ( P = 0.00) and to 39% (30–48%) in the b -sitostanol ester period ( P = 0.00). Conclusion: Esterified soy sterols and b -sitostanol inhibited cholesterol absorption equally, despite the different structures of the plant sterols. Am J Clin Nutr 2000;71:908–13.
The use of simulation models to estimate frequency and location of Japanese Edo period wrecks along the canadian pacific coast

Des modeles de simulation sont employes pour estimer la frequence d'epaves japonaises de la periode d'Edo (AD 1603-1867) le long de la cote pacifique canadienne et pour suggerer quels secteurs offrent de plus grandes possibilites interessantes pour la recuperation des materiaux archeologiques. Pendant la periode d'Edo, des navires de conception etrangere et les jonques capables de naviguer la mer ouverte sans risque etaient detruits. Les conceptions de navire ont ete modifiees de facon que les bateaux osant sur la mer ouverte soient handicapes. Ceci a ete fait pour empecher le contact avec le monde exterieur. Les documents historiques indiquent un certain nombre de navires japonais derivant sur les cotes canadiennes et adjacentes, dans beaucoup de cas avec des survivants. Ces documents sont employes pour placer des parametres comme, le point de debut pour les voyages, si une tentative etait faite d'orienter le navire, la duree de voyage et la periode de l'annee. La simulation emploie les donnees dans le U. S. Navy Marine Chmatic Atlas of the World.
Low-Rank Factorization of Determinantal Point Processes

Determinantal point processes (DPPs) have garnered attention as an elegant probabilistic model of set diversity. They are useful for a number of subset selection tasks, including product recommendation. DPPs are parametrized by a positive semi-definite kernel matrix. In this work we present a new method for learning the DPP kernel from observed data using a low-rank factorization of this kernel. We show that this low-rank factorization enables a learning algorithm that is nearly an order of magnitude faster than previous approaches, while also providing for a method for computing product recommendation predictions that is far faster (up to 20x faster or more for large item catalogs) than previous techniques that involve a full-rank DPP kernel. Furthermore, we show that our method provides equivalent or sometimes better test log-likelihood than prior full-rank DPP approaches.
Intelligent Semantic Web Image Search Engine

Search engine services are popular means of information retrieval. Search engines perform a number of tasks to retrieve required information from web based on their respective architecture. This paper proposes a novel approach for web crawling and image retrieval based on the image re-ranking strategy by considering both textual and visual features of the image. A pool of images will be retrieved when the user gives a query keyword, then user has to select a query image which is similar to the target image from the initial search results. User’s search intention can be identified using this one click user feedback. The query image is then fed to a SVM classifier that identifies the reference classes by analyzing the features of the image. Then the images are re-ranked using rank SVM algorithm by considering the query and click data. This image searching strategy has improved accuracy and efficiency.
Metric for Automatic Machine Translation Evaluation based on Universal Sentence Representations

Sentence representations can capture a wide range of information that cannot be captured by local features based on character or word N-grams. This paper examines the usefulness of universal sentence representations for evaluating the quality of machine translation. Al-though it is difficult to train sentence representations using small-scale translation datasets with manual evaluation, sentence representations trained from large-scale data in other tasks can improve the automatic evaluation of machine translation. Experimental results of the WMT-2016 dataset show that the proposed method achieves state-of-the-art performance with sentence representation features only.
Performance evaluation of detecting circulating tumor cells and tumor cells in bronchoalveolar lavage fluid in diagnosis of peripheral lung cancer.

Background To evaluate the diagnostic performances of detecting circulating tumor cells (CTCs) and tumor cells in bronchoalveolar lavage fluid (BALF) for peripheral lung cancer. Methods A total of 247 patients with lung cancer and 70 cases with benign lung disease were recruited in this study. Peripheral blood and BALF samples were collected, in which the tumor cells were enriched by negative immunomagnetic selection and detected by fluorescence in situ hybridization (FISH) of chromosome enumeration probe 8 (CEP8). The levels of tumor-associated markers (e.g., CEA, CA125, and NSE) in peripheral blood plasma were measured by using electrochemiluminescence. Results The numbers of CTCs detected in peripheral blood were significantly higher in patients with lung cancer than those with benign lung disease (5.78±0.57 vs. 1.13±0.39, Z=-8.64, P<0.01). Similarly, tumor cells count in BALF of malignancy were higher than that of benign lesions (6.76±0.89 vs. 0.89±0.23, Z=-6.254, P<0.01). However, as for patients with lung cancer and benign lung disease, the numbers of tumor cells in peripheral blood were comparable with those in BALF (both P>0.05). Detecting CTCs and tumor cells in BALF had similar areas under curves (AUC =0.871 and 0.963, respectively; P>0.05) in discriminating benign lesions from lung cancer (sensitivity 83.8% and 92.6%, specificity 86.5% and 99.9%, respectively), both of which were larger than those of NSE, CEA, and CA125 (AUC =0.564, 0.512 and 0.554, respectively; all P<0.05). The diagnostic performances of discriminating benign lesions and lung cancer in BALF and peripheral blood were both in concordance with that of histopathology (kappa values 0.662 and 0.569, respectively; both P<0.001). Conclusions Detecting tumor cells in peripheral blood and BALF may sensitive to identify benign and malignant peripheral lung lesions and be of value for early diagnosis of lung cancer.
Beyond eating intentions: The role of working memory capacity in moderating the effects of restrained eating and implicit food activiation on eating behavior

Alexander, Leigh E. (Ph.D., Psychology and Neuroscience) Beyond Eating Intentions: The Role of Working Memory Capacity in Moderating the Effects of of Restrained Eating and Implicit Food Activation on Eating Behavior Dissertation directed by Professor Akira Miyake Abstract: Restrained eating refers to individuals who consistently attempt to limit their intake of calories to reduce body weight (Herman & Polivy,1980). Restrained eaters have been noted for failures at converting dieting intentions into eating behavior (Stroebe, 2008). The primary goal of these studies was to determine what differentially contributes to eating behavior in women with highly restrained eating styles when: a) eating intentions alone do not predict intake, and b) someone is exposed to temptation. Given the self-regulatory nature of eating behavior, individual differences in working memory capacity (WMC) was expected to moderate the effect of restrained eating on food intake, over and above the impact of intentions. WMC was also expected to moderate the effect of implicit food activation, which has been shown to influence food choices. It was found that WMC moderated the effects of restrained eating and implicit food activation on self-report intake of unhealthy food. Unrestrained eaters ate less unhealthy food as WMC increased, and this effect was even stronger when implicit food activation was lower. WMC also moderated the effect of restrained eating and implicit food activation on intake of M&Ms. When WMC was lower, restrained eaters ate more M&Ms as implicit food activation increased. There was no effect of restrained eating or implicit food activation when WMC was higher. These results show the importance of considering the balance of control processes (i.e., WMC), explicit and implicit attitudes in determining food intake. The practical implications for regulating dieting and eating behavior are discussed.
Childhood Trauma, DNA Methylation of Stress-Related Genes, and Depression: Findings From Two Monozygotic Twin Studies

Objective DNA methylation has been associated with both early life stress and depression. This study examined the combined association of DNA methylation at multiple CpG probes in five stress-related genes with depressive symptoms and tested whether these genes methylation mediated the association between childhood trauma and depression in two monozygotic (MZ) twin studies. Methods The current analysis comprised 119 MZ twin pairs (84 male pairs [mean = 55 years] and 35 female pairs [mean = 36 years]). Peripheral blood DNA methylation of five stress-related genes (BDNF, NR3C1, SLC6A4, MAOA, and MAOB) was quantified by bisulfite pyrosequencing or 450K BeadChip. We applied generalized Poisson linear-mixed models to examine the association between each single CpG methylation and depressive symptoms. The joint associations of multiple CpGs in a single gene or all five stress-related genes as a pathway were tested by weighted truncated product method. Mediation analysis was conducted to test the potential mediating effect of stress gene methylation on the relationship between childhood trauma and depressive symptoms. Results Multiple CpG probes showed nominal individual associations, but very few survived multiple testing. Gene-based or gene-set approach, however, revealed significant joint associations of DNA methylation in all five stress-related genes with depressive symptoms in both studies. Moreover, two CpG probes in the BDNF and NR3C1 mediated approximately 20% of the association between childhood trauma and depressive symptoms. Conclusions DNA methylation at multiple CpG sites are jointly associated with depressive symptoms and partly mediates the association between childhood trauma and depression. Our results highlight the importance of testing the combined effects of multiple CpG loci on complex traits and may unravel a molecular mechanism through which adverse early life experiences are biologically embedded.
Monetary Policy in a Zero Lower Bound Environment

LAURA E. JACKSON: MONETARY POLICY IN A ZERO LOWER BOUND ENVIRONMENT. (Under the direction of Neville Francis.) In the wake of the Great Recession, the Federal Reserve lowered the federal funds rate (FFR) target to zero and resorted to unconventional monetary policy. With the nominal FFR constrained by the zero lower bound (ZLB), empirical monetary models cannot be estimated as usual. First, in joint work with Neville Francis and Michael Owyang, we consider whether standard models of monetary policy can be preserved without breaks. We consider whether alternative policy instruments can be considered substitutes for the FFR over the ZLB period. Furthermore, we construct a shadow rate via the methods proposed in Krippner (2012) and Wu and Xia (2014) to proxy for the stance of policy. We ask whether the shadow rate is a suffi cient representation of the policy instrument or if the financial crisis requires other modifications. We find that, if using a dataset that spans the pre-ZLB and ZLB environments, the shadow rate acts as a fairly good proxy for monetary policy by producing impulse responses similar to what we’d expect based on the non-ZLB benchmark. However, the linear model exhibits a structural break at the onset of the ZLB and the shadow rate may be insuffi cient for examining the ZLB period in isolation. Second, I describe the joint dynamics of bond yields, monetary policy and macroeconomic variables within a no-arbitrage affi ne term structure framework while explicitly modeling the ZLB using the shadow rate. I include data on the unemployment gap and inflation to build a more comprehensive stance of policy, incorporating the influences of unconventional instruments introduced to combat the Great Recession. I find that shadow rate models incorporating macroeconomic factors suggest a more negative shadow rate and a longer expected duration of the ZLB episode than models including only financial data. Also, including the macro data
C L ] 1 6 A pr 2 01 8 Adversarial Ranking for Language Generation

Generative adversarial networks (GANs) have great successes on synthesizing data. However, the existing GANs restrict the discriminator to be a binary classifier, and thus limit their learning capacity for tasks that need to synthesize output with rich structures such as natural language descriptions. In this paper, we propose a novel generative adversarial network, RankGAN, for generating highquality language descriptions. Rather than training the discriminator to learn and assign absolute binary predicate for individual data sample, the proposed RankGAN is able to analyze and rank a collection of human-written and machinewritten sentences by giving a reference group. By viewing a set of data samples collectively and evaluating their quality through relative ranking scores, the discriminator is able to make better assessment which in turn helps to learn a better generator. The proposed RankGAN is optimized through the policy gradient technique. Experimental results on multiple public datasets clearly demonstrate the effectiveness of the proposed approach.
A novel 3D interactive method of tracking handheld actual rigid objects

In this paper, we propose a 3D interactive method which can track handheld rigid object without markers and recognize human pose simultaneously. Based on depth data of Kinect v2.0, an improved bilateral filtering utilized to solve the failure of recognition caused by occlusion, then the target point cloud can be obtained after segment and tracked by Interactive Closet Point (ICP) algorithm. Through this method, users can operate the actual rigid object with both hands, change posture to implement multiple interactions with 3D scenes. Thus, the method breaks the limitations of a fixed interactive medium such as joystick and is suitable to promote to general application.
ERMMA: Expected Risk Minimization for Matrix Approximation-based Recommender Systems

Matrix approximation (MA) is one of the most popular techniques in today's recommender systems. In most MA-based recommender systems, the problem of risk minimization should be defined, and how to achieve minimum expected risk in model learning is one of the most critical problems to recommendation accuracy. This paper addresses the expected risk minimization problem, in which expected risk can be bounded by the sum of optimization error and generalization error. Based on the uniform stability theory, we propose an expected risk minimized matrix approximation method (ERMMA), which is designed to achieve better tradeoff between optimization error and generalization error in order to reduce the expected risk of the learned MA models. Theoretical analysis shows that ERMMA can achieve lower expected risk bound than existing MA methods. Experimental results on the MovieLens and Netflix datasets demonstrate that ERMMA outperforms six state-of-the-art MA-based recommendation methods in both rating prediction problem and item ranking problem.
Sensorless Control of Dual-Active-Bridge Converter with Reduced-Order Proportional-Integral Observer

When controlling a Dual-Active-Bridge (DAB) DC/DC converter, the high frequency terminal current is usually measured for use in the current feedback controller. In order to measure that current, a wide bandwidth sensor accompanied with high-speed amplifiers are required. Furthermore, a high Analog-to-Digital sampling rate is also necessary for sampling and processing the measured data. To avoid those expensive requirements, this paper proposes an alternative control method for the DAB converter. In the proposed method, the terminal current is estimated by a reduced-order proportional integral observer. A technique is also proposed to reduce the phase drift effect when the voltages at two terminals are not matched. Afterwards, a combined current feedforward—voltage feedback control system is developed to enhance the system dynamics and to regulate the output voltage. This control system needs only the information of the terminal voltages and no current sensor is required. Experimental results show that the observer can estimate the terminal current very quickly with the accuracy of more than 98 % . In addition, the output voltage is well regulated with a fluctuation of less than ± 2 . 6 % and a settling time of less than 6.5 ms in the presence of a 30 % load change.
SemStyle: Learning to Generate Stylised Image Captions Using Unaligned Text

Linguistic style is an essential part of written communication, with the power to affect both clarity and attractiveness. With recent advances in vision and language, we can start to tackle the problem of generating image captions that are both visually grounded and appropriately styled. Existing approaches either require styled training captions aligned to images or generate captions with low relevance. We develop a model that learns to generate visually relevant styled captions from a large corpus of styled text without aligned images. The core idea of this model, called SemStyle, is to separate semantics and style. One key component is a novel and concise semantic term representation generated using natural language processing techniques and frame semantics. In addition, we develop a unified language model that decodes sentences with diverse word choices and syntax for different styles. Evaluations, both automatic and manual, show captions from SemStyle preserve image semantics, are descriptive, and are style shifted. More broadly, this work provides possibilities to learn richer image descriptions from the plethora of linguistic data available on the web.
The-308 G / A Polymorphism of the Tumor Necrosis Factor-alpha Gene Is Associated with the Risk of Upper Aerodigestive Tract Cancer : A Meta-analysis

245 Received September 11, 2012; revised and accepted February 28, 2013. Published online April 17, 2013; doi: 10.1620/tjem.229. 245. *These authors have equally contributed to this work. Correspondence: Ga Liao and Hongxia Dan, State Key Laboratory of Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, P.R. China. e-mail: liaoga@gmail.com (Ga Liao), hxdan@foxmail.com (Hongxia Dan). The -308G/A Polymorphism of the Tumor Necrosis Factor-alpha Gene Is Associated with the Risk of Upper Aerodigestive Tract Cancer: A Meta-analysis
Multiphase water flow inside carbon nanotubes q

We present nonequilibrium molecular dynamics simulations of the flow of liquid–vapour water mixtures and mixtures of water and nitrogen inside carbon nanotubes. A new adaptive forcing scheme is proposed to impose a mean flow through the system. The flow of liquid water is characterised by a distinct layering of the water molecules in the vicinity of the boundary and a slip length that is found to increase with the radius of the carbon nanotube. Increasing the temperature and pressure of the system furthermore results in a decrease in the slip length. For the flow of mixtures of nitrogen and water we find that the slip length is reduced as compared to the slip for the pure water. The shorter slip length is attributed to the fact that nitrogen forms droplets at the carbon surface, thus partially shielding the bulk flow from the hydrophobic carbon surface. 2004 Elsevier Ltd. All rights reserved.
Compressive Sensing Image Reconstruction Using Super-Resolution Convolutional Neural Network

Compressed sensing (CS) can recover a signal that is sparse in certain representation and sample at the rate far below the Nyquist rate. But limited to the accuracy of atomic matching of traditional reconstruction algorithm, CS is difficult to reconstruct the initial signal with high resolution. Meanwhile, scholar found that trained neural network have a strong ability in settling such inverse problems. Thus, we propose a Super-Resolution Convolutional Neural Network (SRCNN) that consists of three convolutional layers. Every layer has a fixed number of kernels and has their own specific function. The process is implemented using classical compressed sensing algorithm to process the input image, afterwards, the output images are coded via SRCNN. We achieve higher resolution image by using the SRCNN algorithm proposed. The simulation results show that the proposed method helps improve PSNR value and promote visual effect.
Representing Large Virtual Worlds

The ubiquity of large virtual worlds and their growing complexity in computer graphics require efficient representations. This means that we need smart solutions for the underlying storage of these complex environments, but also for their visualization. How the virtual world is best stored and how it is subsequently shown to the user in an optimal way, depends on the goal of the application. In this respect, we identify the following three visual representations, which form orthogonal directions, but are not mutually exclusive. Realistic representations aim for physical correctness, while illustrative display techniques, on the other hand, facilitate user tasks, often relating to improved understanding. Finally, artistic approaches enable a high level of expressiveness for aesthetic applications. Each of these directions offers a wide array of possibilities. In this dissertation, our goal is to provide solutions for strategically selected challenges for all three visual directions, as well as for the underlying representation of the virtual world.
Automatic Update of Ontology Concept Hierarchy with New Entity Insertion and New Concept Generation Based on Semantic Measurement

Ontology, as a representation of shared conceptualization for variety of specific domains, is the core of the semantic web. Concept hierarchy is one of the most popular backbones of ontology which organizes the concepts according to hyponymy relationships, and stores massive entities as the instances of the concepts. An open concept hierarchy, e.g., Wikipedia, always needs to be constantly updated by adding new entities and concepts. In this paper, we propose an automatic solution for ontology update by inserting new entities and generating new concepts for concept hierarchy. The method only requires very limited information of new entity, i.e., the attributes of each entity. The solution is based on a hybrid strategy synthesizing the benefits from the structure of the concept tree and the content of the attributes. The content of the attributes is used to measure the similarity between an entity and a concept. The structure of the concept tree is used to determine which concepts need to be measured. During similarity measurement, the solution also synthesizes the statistical and rule-based factors. The effectiveness of the proposed method is verified by the experiments extending the Chinese and English Wikipedia concept hierarchy with new entities and concepts.