Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhancement in the Catalytic Activity of Pd/USY in the Heck Reaction Induced by H2 Bubbling

Pd was loaded on ultra stable Y (USY) zeolites prepared by steaming NH4-Y zeolite under different conditions. Heck reactions were carried out over the prepared Pd/USY. We found that H2 bubbling was effective in improving not only the catalytic activity of Pd/USY, but also that of other supported Pd catalysts and Pd(OAc)2. Moreover, the catalytic activity of Pd/USY could be optimized by choosing appropriate steaming conditions for the preparation of the USY zeolites; Pd loaded on USY prepared at 873 K with 100% H2O gave the highest activity (TOF = 61,000 h−1), which was higher than that of Pd loaded on other kinds of supports. The prepared Pd/USY catalysts were applicable to the Heck reactions using various kinds of substrates including bromo- and chloro-substituted aromatic and heteroaromatic compounds. Characterization of the acid properties of the USY zeolites revealed that the strong acid site (OHstrong) generated as a result of steaming had a profound effect on the catalytic activity of Pd.
Semi supervised keyword based bengali document categorization

Document Categorization is an area of important research over the last couple of decades. The basic task in document categorization is classifying a given document in some predefined classes. Bengali is among the top ten most spoken languages in the world and is spoken by more than 200 million people, but the candid truth is, it still lacks significant research efforts in the area of Bengali Document Categorization. In the first phase of this paper a model has been designed that extracts keywords from a Bengali document. We crawled over 35000 news documents form popular Bengali newspapers and journals. Those documents have been stemmed and less significant words are removed using stemmer and Parts-of-Speech(POS) tagger. Statistical approach is used to extract keywords form the documents. Then probabilistic distribution and semi supervised learning with Naïve Bayes algorithm is used to approximate the category of a given Bengali document. Result and statistical data show the effectiveness of this model.
Capital Subsidies and the Infrastructure Crisis : Evidence from the Local Mass-Transit Industry

The condition of the public capital stockperceived by many to be dilapidated and inadequate-has received considerable attention in political, media, and academic circles in recent years. Pat Choate and Susan Walter's America in Ruins gave striking examples of crumbling infrastructure and suggested that enormous increases in infrastructure investment were needed just to maintain the existing levels of services. The media and political attention given this work was highlighted by tragedies such as the 1983 collapse of the Interstate 95 bridge in Connecticut. More systematic studies by the Urban Institute and the Congressional Budget Ofice (1983) catalogued the existing state of public infrastructure and projected the need for new public investment. ' More recently, the National Council of Public Works Improvement (1988) completed a series of studies examining the state of the nation's public infrastructure, entitled Fragile FoundaBrian A. Cromwell is an economist at the Federal Reserve Bank of Cleveland. The author would like to thank Paul Bauer, Michael Bell, John Davis, Randall Eberts, Erica Groshen, James Poterba, and William Wheaton for useful suggestions and discussion. William Lyons and Dottie Nicholas of the Transportation System Center provided invaluable assistance with the data. Financial support from the National Graduate Fellowship Program and the MIT Center for Transportation Studies is gratefully acknowledged.
Identifying Information-Rich Subspace Trends in High-Dimensional Data

Identifying information-rich subsets in high-dimensional spaces and representing them as order revealing patterns (or trends) is an important and challenging research problem in many science and engineering applications. The information quotient of large-scale high-dimensional datasets is significantly reduced by the curse of dimensionality which makes the traditional clustering and association analysis methods unsuitable. Most interesting patterns cannot be revealed using global methods which consider the entire data and feature spaces during their analysis. Identifying some interesting patterns in large scale high-dimensional data is usually accomplished using popular techniques such as dimensionality reduction, feature selection and subspace clustering. Though these methods are successfully able to identify the groupings in the feature subsets and localized neighborhood data subspaces, none of these methods extract the latent patterns that are present in local information-rich subsets of the data. In this paper, we seek an information-revealing representation of the data subsets and features that may contain local patterns. We formalize the problem of identifying ‘subspace trends’ in high-dimensional datasets focusing on information-rich subsets and develop a new algorithm to extract such subspace trends. We demonstrate our results on both synthetic and real-world datasets and show the superiority of the proposed methodology over traditional clustering and dimensionality reduction techniques.
A Bayesian Kernel Logistic Discriminant Model: An Improvement to the Kernel Fisher's Discriminant

The Kernel Fisher's Discriminant (KFD) has proven to be competitive to several state-of-the-art classifiers. However, it is assuming equal covariance structure for all transformed classes, which is not true in many applications. In this paper, we propose a novel Bayesian Kernel Logistic Discriminant model (BKLD) which goes one step further by representing each transformed class by its own covariance matrix. This can perform better than the KFD. An extensive comparison of the BKLD to the KFD and to other state-of-the-art non-linear classifiers is performed.
A shape reconstruction method for diffuse optical tomography using a transport model and level sets

A two-step shape reconstruction method for diffuse optical tomography (DOT) is presented which uses adjoint fields and level sets. The propagation of near-infrared photons in tissue is modeled by the time-dependent linear transport equation, of which the absorption parameter has to be reconstructed from boundary measurements. In the shape reconstruction approach, it is assumed that the inhomogeneous background absorption parameter and the values inside the obstacles are (approximately) known, but that the number, sizes, shapes, and locations of these obstacles have to be reconstructed from the data. An additional difficulty arises due to the presence of so-called clear regions in the medium. The first step of the reconstruction scheme is a transport-backtransport (TBT) method which provides us with a low-contrast approximation to the sought objects. The second step uses this result as an initial guess for solving the shape reconstruction problem. A key point in this second step is the fusion of the 'level set technique' for representing the shapes of the reconstructed obstacles, and an 'adjoint-field technique' for solving the nonlinear inverse problem. Numerical experiments are presented which show that this novel method is able to recover one or more objects very fast and with good accuracy.
Linking structure and function in glaucoma

A unique online resource, offering personalised education to meet individual needs and interests. This article will give an overview of the latest imaging technology available to assess the optic disc. This will then be compared with the information provided by visual field assessment including standard automated perimetry (SAP), short-wavelength automated perimetry (SWAP) and frequency doubling technology (FDT). The different mechanisms for each type of perimetry will be discussed. About the author Numerous instruments to capture images of the fundus have been developed in recent years, enabling the practitioner to undertake objective assessment of the optic disc. This article will give an overview of the latest imaging technology available, comparing this with information obtained by visual field assessment. ' To understand different approaches when undertaking visual field assessment (Group 3.1.5) To recognise when to refer patients with suspected glaucoma based upon clinical findings (Group 6.1.8) To understand the needs of patients with advanced disease when examining visual fields (Group 7.1.7) To understand the instruments involved in visual field analysis (Group 3.1.5) To understand the implications of glaucoma manifestations (Group 8.
Desirability of Compatibility in the Absence of Network Externalities

The author compares the incentives firms have to produce individual components compatible with components of other manufacturers instead of "systems" composed of components that are incompatible with components of competing manufacturers. He shows that, even in the absence of positive consumption externalities (" network" externalities), prices and profits will be higher in the regime of compatibility. Equilibrium total surplus could be higher in either regime. Both regimes overprovide variety compared to the surplus-maximizing solution. Copyright 1989 by American Economic Association.
EFFICACY OF MODIFIED CONSTRAINT-INDUCED MOVEMENT THERAPY FOR LOWER EXTREMITY IN PATIENTS WITH STROKE: STRENGTH AND QUALITY OF LIFE OUTCOMES

Purpose: This study was aimed to examine the efficacy of modified constraint-induced movement therapy (mCIMT), which was applied to lower extremity, on strength and quality of life (QoL) in patients with stroke. Methods: Thirty patients with stroke were randomly divided into two groups. Both groups received neuro-developmental therapy (NDT) for four weeks as baseline treatment. After four weeks, when the study group received mCIMT, the control group continued NDT for two weeks as the experimental treatment. The strength was evaluated using Motricity Index, and QoL was evaluated using Stroke Specific Quality of Life Scale (SS-QoL), and Stroke Impact Scale (SIS) for three times (baseline, post 4 weeks, and post 6 weeks). Results: The strength and QoL improved in both groups for all treatment periods (p<0.01). The paretic lower limb strength developed more in the mCIMT group for the total treatment period (p=0.029). The total score of the SS-QoL and mobility, self-care, thinking, mood, family, and social roles subdomain scores of the SS-QoL were more pronounced in the mCIMT group after the mCIMT period (p<0.05). The amount of perceived recovery domain of SIS was greater in the mCIMT group after mCIMT and total treatment periods according to the control group (p<0.001). The total changes in strength and QOL were strongly correlated with the improvement in the mCIMT period (r=0.709, p<0.01 and r=0.769, p<0.01) than in the baseline period (r=0.660, p<0.01 and r=0.505, p<0.01). Conclusion: The study showed that mCIMT could be used as an effective treatment method for patients with stroke to improve paretic lower extremity strength and health-related QoL.
Collaborative Recommendation with Auxiliary Data: A Transfer Learning View

Intelligent recommendation technology has been playing an increasingly important role in various industry applications such as e-commerce product promotion and Internet advertisement display. Besides users' feedbacks (e.g., numerical ratings) on items as usually exploited by some typical recommendation algorithms, there are often some additional data such as users' social circles and other behaviors. Such auxiliary data are usually related to users' preferences on items behind the numerical ratings. Collaborative recommendation with auxiliary data (CRAD) aims to leverage such additional information so as to improve the personalization services, which have received much attention from both researchers and practitioners. Transfer learning (TL) is proposed to extract and transfer knowledge from some auxiliary data in order to assist the learning task on some target data. In this paper, we consider the CRAD problem from a transfer learning view, especially on how to achieve knowledge transfer from some auxiliary data. First, we give a formal definition of transfer learning for CRAD (TL-CRAD). Second, we extend the existing categorization of TL techniques (i.e., adaptive, collective and integrative knowledge transfer algorithm styles) with three knowledge transfer strategies (i.e., prediction rule, regularization and constraint). Third, we propose a novel generic knowledge transfer framework for TL-CRAD. Fourth, we describe some representative works of each specific knowledge transfer strategy of each algorithm style in detail, which are expected to inspire further works. Finally, we conclude the paper with some summary discussions and several future directions.
Objective evaluation system for noise reduction performance of hearing aids

Hearing aid is a device, which can acquire, process and feedback acoustic signal in real-time, it is portable and has low power consumption. Its many technologies will influence the effect of hearing, such as manufacturing technology, adjusting procedure. The key technology that influences the effect of hearing is the noise reduction technology. The performance of noise reduction seriously affects the intelligibility of speech, even the physical and mental health of the people who have diminished or defective hearing. So studying on the evaluation for noise reduction performance of hearing aids has a very important significance. However, there is not a clear system to evaluate the noise reduction of hearing aids in the domestic. In this paper, a complete set of objective evaluation scheme is put forward. At first, acoustic signal will be acquired though the experiment system designed in our work which can simulate real working conditions, then signal-to-noise ratio (SNR) and segmental signal-to-noise ratio (segSNR) of signal will be calculated after aligning the output signal and input signal to evaluate the noise reduction performance of hearing aids. The simulation results show that the evaluation method proposed here can evaluate hearing aids automatically and conveniently.
Molecules Altering the Intracellular Thiol Content Modulate NF-kB and STAT-1/IRF-1 Signalling Pathways and IL-12 p40 and IL-27 p28 Production in Murine Macrophages

Background The aim of this study was to investigate the molecular mechanisms involved in the production of Th1 cytokines, namely IL-12 and IL-27, when the intra-macrophage redox state was altered by different chemical entities such as GSH-C4, which is reduced glutathione carrying an aliphatic chain, or I-152, a pro-drug of N-acetyl-cysteine (NAC) and beta-mercaptoethylamine. We had already demonstrated that GSH-C4 and I-152 could shift the immune response towards Th1 in Ovalbumin-immunized mice as well as enhance Th1 response in HIV-1 Tat-immunized mice. Methodology/Principal Findings By a new high performance liquid chromatography method, we found that 20 mM GSH-C4 provided a number of thiol species in the form of GSH, while 20 mM I-152 decreased GSH and increased the thiols in the form of NAC and I-152. Under these experimental conditions, GSH-C4 and I-152 enhanced and suppressed respectively the mRNA expression levels of IL-12 p40 induced by LPS/IFN-γ as assessed by Real-Time PCR. The protein production of IL-12 p40 was increased by GSH-C4 and decreased by I-152 as determined by Enzyme-linked immunosorbent assay. Western immunoblot and electrophoretic mobility shift assays revealed that Nuclear Factor -kB (NF-kB) activation was inhibited by I-152 and prolonged by GSH-C4. Twenty mM I-152 stimulated IL-27 p28 gene expression and sustained Signal Transducer and Activator of Transcription (STAT)-mediated interferon regulator factor 1 (IRF-1) de novo synthesis. By contrast, 20 mM GSH-C4 did not exert any effect on IL-27 p28 gene expression. Conclusions and Significance an increase in the intra-macrophage redox state by GSH-C4 and I-152 enhances Th1 cytokine production although the chemical structure and the intra-cellular metabolism influence differently signalling pathways involved in IL-27 or IL-12 production. GSH-C4 and I-152 may be used as Th1 immunomodulators in some pathologies and in ageing where GSH depletion may contribute to the Th1/Th2 imbalance, and in new immunization strategies.
Bytecode Generation for XQuery Compiler

This paper represents a compiler for XQuery that obtains a more efficient performance by translating its programs to Java bytecode. It gives an overview of the system design with multi-phase translation structure, and describes the translation algorithm in detail. According to the result of performance experiments, running programs in Java bytecodes generated by the compiler have better performance than the interpretation of their source programs.
CAUSES AND EFFECTS OF RESOURCE REDEPLOYMENT IN DOMESTIC AND CROSS-BORDER ACQUISITIONS

Research in the strategy and international business literatures shows that firms often undertake acquisitions to exchange firm-specific resources. The core arguments arise from theories of market failures, where market failures can stem from potential opportunism or from coordination difficulties. We show that the post-acquisition performance of an acquisition varies with the extent of post-acquisition redeployment, which in turn depends on two factors, including the degree of asymmetry in the resource profiles of the firms and the extent of context-specificity of these resources. We reinterpret previous results that suggest differences between domestic and cross-border acquisitions by showing a similar relationship between resource redeployment and performance, as well as a similar relationship between resource redeployment and its antecedents. We discuss differences in research emphases that have traditionally marked research concerning acquisitions in international business literature, which has emphasized cross-border acquisitions, and the strategy literature, which has focused on domestic acquisitions, and then identify opportunities for conceptual cross-fertilization between the literatures. The data draw from a detailed multinational survey with 251 acquisitions during the 1988-1992 period. The results generally support our predictions for domestic and cross-border acquisitions.
Digital Economics

Digital technology is the representation of information in bits. This technology has reduced the cost of storage, computation, and transmission of data. Research on digital economics examines whether and how digital technology changes economic activity. In this review, we emphasize the reduction in five distinct economic costs associated with digital economic activity: search costs, replication costs, transportation costs, tracking costs, and verification costs. (JEL D24, D83, L86, O33, R41)
On the Achievement of a Highly Dependable and Fault-Tolerant Air Traffic Control System

Can we trust the new AAS to deliver highly dependable air traffic control services until the year 2000? Yes, systematic application of fault tolerance offers a solid promise of success. T he Advanced Automation System, or AAS, will provide automation T services to both en-route and terminal air traffic controllers throughout the United States. Although controllers are able to maintain separation between aircraft during periods of interruption of the automatic services, the transition to backup modes of operation is potentially hazardous. The increased controller workload resulting from interruption of the services provided to controllers limits the traffic handling capability of the Air Traffic Control System, which can result in major delays during periods of heavy traffic. As the level of automation services provided to controllers increases, interruption of computer services to the controllers will become even more critical. Accordingly, extremely high reliability and availability of the services provided by the AAS will be required on a continuous basis, 24 hours a day, seven days a week.
One-shot Recognition Using Unsupervised Attribute-Learning

It has been shown that incorporation of human-specified high-level description of the target objects, e.g. labeled prior-knowledge data, can increase the performance of one-shot recognition. In this paper, we introduce latent components as a high level representation of the original objects and propose a cascade model for one-shot image recognition based on latent components learned by Hierarchical Dirichlet Process (HDP). In the proposed approach, instead of solving an optimization problem in the training stage, the latent high-level components are learned efficiently in a unsupervised way from unlabeled prior-knowledge data. Motivated by the facts that HDP is an infinite mixture model proposed in the literature for document modeling that can infer the unknown mixture components and the number of components from the data, and that bag-of-feature model is a standard representation in document retrieval and computer vision areas, we adopt HDP model to infer the mixture components (like latent topics in documents) for target images from unlabeled image visual word vocabulary, and we then train a classifier to associate the components with class labels. The superior performances of the proposed one-shot recognition method are illustrated by testing the Caltech category dataset and the " Animals with Attributes" dataset.
Hierarchical Latent Factors for Preference Data Extended Abstract

In this work we propose a probabilistic hierarchical generative approach for users’ preference data, which is designed to overcome the limitation of current methodologies in Recommender Systems and thus to meet both prediction and recommendation accuracy. The Bayesian Hierarchical User Community Model (BH-UCM) focuses both on modeling the popularity of items and the distribution over item ratings. An extensive evaluation over two popular benchmark datasets shows that the combined modeling of item popularity and rating provides a powerful framework both for rating prediction and for the generation of accurate recommendation lists.
Screening of commercial cyclic peptide as inhibitor NS5 methyltransferase of Dengue virus through Molecular Docking and Molecular Dynamics Simulation

Dengue has become a major global health threat, especially in tropical and subtropical regions. The development of antiviral agent targeting viral replication is really needed at this time. NS5 methyltransferase presents as a novel antiviral target. This enzyme plays an important role in the methylation of 5'-cap mRNA. Inhibition of the NS5 methyltransferase could inhibit dengue virus replication. In this research, two sites of NS5 methyltransferase (S-Adenosyl methionine/SAM binding site and RNA-cap site) were used as targets for inhibition. As much as 300 commercial cyclic peptides were screened to these target sites by means of molecular docking. Analysis of ligand-enzyme binding free energy and pharmacological prediction revealed two best ligands, namely [Tyr123] Prepro Endothelin (110-130), amide, human and Urotensin II, human. According to molecular dynamic simulation, both ligands maintain a stable complex conformation between enzyme and ligand at temperature 310 K and 312 K. Hence, Urotensin II, human is more reactive at 312 K than at 310 K. However, both ligands can be used as potential inhibitor candidates against NS5 methyltransferase of dengue virus with Urotensin II, human exposes more promising activity at 312 K.