Miracle-dz/UniFAR-FTUs huggingface dataset:
Hidden Aspect Rating Discovery from Text Reviews of E-Commerce Web Sites

We investigate hidden aspect mining problem that aims at automatically discovering aspect information from a collection of review texts in an unsupervised manner. The goal is to predict the user's ratings on each aspect. It does not require users to specify predefined seed terms for each aspect. We propose a generative model to tackle the hidden aspect mining problem. Our proposed model can detect the aspects of a particular domain. When predicting reviewer's ratings on each aspect, our model employs l1-regularizer to control the sparsity on the aspect and obtains more reliable predicted aspect ratings. Existing works on predicting aspect ratings fail to explore the aspect sparsity problem in the review texts leading to unreliable prediction. Experimental results on the two real-world product review corpora demonstrate that our model outperforms existing state-of-the-art models in the aspect rating prediction task.
Velocity estimation using recursive ultrasound imaging and spatially encoded signals

Previously we have presented a recursive beamforming algorithm for synthetic transmit aperture focusing. At every emission a beamformed low-resolution image is added to an existing high-resolution one, and the low-resolution image from the previous emission with the current active element is subtracted yielding a new frame at every pulse emission. In this paper the method is extended to blood velocity estimation, where a new color flow mapping (CFM) image is created after every pulse emission. The underlying assumption is that the velocity is constant between two pulse emissions and the current estimates can therefore be used for compensation of the motion artifacts in the data acquired in the next emission. Two different transmit strategies are investigated in this paper: (a) using a single defocused active aperture in transmit, and (b) emitting with all active transmit sub-apertures at the same time using orthogonal spatial encoding signals. The method was applied on data recorded by an experimental system. The estimates of the blood velocity for both methods had a bias less than 3% and a standard deviation around 2% making them a feasible approach for blood velocity estimations.
Serial Review Advances in Chemical Tools for Exploring Oxidative Stress Chemical Tools for the Study of Hydrogen Sulfide (h 2 S) and Sulfane Sulfur and Their Applications to Biological Studies

2016 This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Hydrogen sulfide (H 2 S) functions in many physiological processes, including relaxation of vascular smooth muscles, mediation of neurotransmission, inhibition of insulin signaling, and regulation of inflammation. On the other hand, sulfane sulfur, which is a sulfur atom with six valence electrons but no charge, has the unique ability to bind reversibly to other sulfur atoms to form hydropersulfides (RSSH) and polysulfides ((SSS n S). H 2 S and sulfane sulfur always coexist, and recent work suggests that sulfane sulfur species may be the actual signaling molecules in at least some biological phenomena. For example, one of the mechanisms of activity regulation of proteins by H 2 S is the S sulfhydration of cysteine residues (protein CysSSH). In this review, we summarize recent progress on chemical tools for the study of H 2 S and sulfane sulfur, covering fluorescence probes utilizing various design strategies, H 2 S caged compounds, inhibitors of physiological H 2 Sproducing enzymes (cystathionine γlyase, cystathionine βsynthase and 33mercaptopyruvate sulfurtranss ferase), and labeling reagents. Fluorescence probes offer particular advantages as chemical tools to study physiological functions of biomolecules, including ease of use and realltime, nondestructive visualization of biological processes in live cells and tissues. Introduction H ydrogen sulfide (H 2 S), a toxic gas smelling of rotten eggs, plays key roles in many physiological processes, including relaxation of vascular smooth muscles, (1,2) mediation of neuro-transmission, (3,4) inhibition of insulin signaling, (5) and regulation of inflammation. (6,7) The most commonly used detection methods for H 2 S are the methylene blue method, (8) the electrode method (9) and the monobromobimane method. (10,11) However, these methods require destructive sampling, e.g., homogenization of biological samples. On the other hand, fluorescence detection has been widely used in biological studies to study the physiological roles of H 2 S, because this technology is easy to use, and enables real-time, nondestructive detection in living cells and tissues. Many selective fluorescence probes for H 2 S have been reported. (12–14) On the other hand, sulfane sulfur species consist of sulfur atom(s) with six valence electrons but no charge bound to other sulfur atom(s), as in hydropersulfides (R-S-SH) and polysulfides (-S-S n-S-). They are attracting increasing interest, because it is reported …
Examine the Sanctions' Efficiency on Iran's Non-Oil Trade (Gravity Model)

The United States first imposed economic sanctions against Iran in 1979, and they have been more or less in effect in various ways until now. During this period while Iran has claimed that embargoes have been in vain, Washington has insisted on using this foreign policy tool to force Tehran to change its attitude. This paper examines the direct impact of economic sanctions on Iran's non-oil trade for the purpose of demonstrating whether or not these sanctions have been effective. A modified "gravity model" of bilateral trade flows is utilized which adjusted specific variables to more precisely portray Iran's unique economic situation. The panel analysis incorporates available data, covering the period from 1977 to 2006, drawn from sampling of 42 trading partners of Iran. In order to test the effect of sanctions, this study uses both the extensive and moderate sanctions as two dummy variables influencing the slope coefficient. The result shows that the extensive and moderate embargoes have had significant impacts on Iran's non-oil trade so far; however, this is not the case for the limited blockades. In conclusion despite being statistically significant, real evidence indicates that the trade sanctions have had little meaningful impact on the direction of the Iranian government's foreign policy. Therefore, politically, it could be concluded that sanctions have been in vain; but, economically, this article categorizes the multilateral boycotts, holding all other factors constant, as an ultimately successful initiative.
The Protein Ontology: a structured representation of protein forms and complexes

The Protein Ontology (PRO) provides a formal, logically-based classification of specific protein classes including structured representations of protein isoforms, variants and modified forms. Initially focused on proteins found in human, mouse and Escherichia coli, PRO now includes representations of protein complexes. The PRO Consortium works in concert with the developers of other biomedical ontologies and protein knowledge bases to provide the ability to formally organize and integrate representations of precise protein forms so as to enhance accessibility to results of protein research. PRO (http://pir.georgetown.edu/pro) is part of the Open Biomedical Ontology Foundry.
Prolonged cold ischemia time obviates the benefits of 0 HLA mismatches in renal transplantation.

HYPOTHESIS Recipients of 0 HLA mismatch kidneys with prolonged cold ischemia times of longer than 36 hours do not have superior outcomes compared with recipients of kidneys with 1 or more mismatches. DESIGN Retrospective review. SETTING Transplanation centers. PATIENTS AND METHODS A total of 63,688 recipients who underwent transplantation between January 1, 1990, and July 31, 1998. MAIN OUTCOME MEASURES Delayed graft function, serum creatinine level, and patient and renal graft survival. RESULTS Recipients of 0 HLA mismatch kidneys with fewer than 36 hours of cold ischemia time had better 5-year graft survival (75%) when compared with recipients with 1 or more mismatches (67%) (P<.001). However, recipients of 0 HLA mismatch kidneys with longer than 36 hours of cold ischemia time did not have any graft survival advantage (71% in 0 HLA mismatch kidneys vs 72% in 1 or more mismatches, P =.24). CONCLUSIONS Cold ischemia times of longer than 36 hours obviate the benefits of better graft survival conferred by better matching.
PhenoM: a database of morphological phenotypes caused by mutation of essential genes in Saccharomyces cerevisiae

About one-fifth of the genes in the budding yeast are essential for haploid viability and cannot be functionally assessed using standard genetic approaches such as gene deletion. To facilitate genetic analysis of essential genes, we and others have assembled collections of yeast strains expressing temperature-sensitive (ts) alleles of essential genes. To explore the phenotypes caused by essential gene mutation we used a panel of genetically engineered fluorescent markers to explore the morphology of cells in the ts strain collection using high-throughput microscopy. Here, we describe the design and implementation of an online database, PhenoM (Phenomics of yeast Mutants), for storing, retrieving, visualizing and data mining the quantitative single-cell measurements extracted from micrographs of the ts mutant cells. PhenoM allows users to rapidly search and retrieve raw images and their quantified morphological data for genes of interest. The database also provides several data-mining tools, including a PhenoBlast module for phenotypic comparison between mutant strains and a Gene Ontology module for functional enrichment analysis of gene sets showing similar morphological alterations. The current PhenoM version 1.0 contains 78 194 morphological images and 1 909 914 cells covering six subcellular compartments or structures for 775 ts alleles spanning 491 essential genes. PhenoM is freely available at http://phenom.ccbr. utoronto.ca/.
GsCML27, a Gene Encoding a Calcium-Binding Ef-Hand Protein from Glycine soja, Plays Differential Roles in Plant Responses to Bicarbonate, Salt and Osmotic Stresses

Calcium, as the most widely accepted messenger, plays an important role in plant stress responses through calcium-dependent signaling pathways. The calmodulin-like family genes (CMLs) encode Ca2+ sensors and function in signaling transduction in response to environmental stimuli. However, until now, the function of plant CML proteins, especially soybean CMLs, is largely unknown. Here, we isolated a Glycine soja CML protein GsCML27, with four conserved EF-hands domains, and identified it as a calcium-binding protein through far-UV CD spectroscopy. We further found that expression of GsCML27 was induced by bicarbonate, salt and osmotic stresses. Interestingly, ectopic expression of GsCML27 in Arabidopsis enhanced plant tolerance to bicarbonate stress, but decreased the salt and osmotic tolerance during the seed germination and early growth stages. Furthermore, we found that ectopic expression of GsCML27 decreases salt tolerance through modifying both the cellular ionic (Na+, K+) content and the osmotic stress regulation. GsCML27 ectopic expression also decreased the expression levels of osmotic stress-responsive genes. Moreover, we also showed that GsCML27 localized in the whole cell, including cytoplasm, plasma membrane and nucleus in Arabidopsis protoplasts and onion epidermal cells, and displayed high expression in roots and embryos. Together, these data present evidence that GsCML27 as a Ca2+-binding EF-hand protein plays a role in plant responses to bicarbonate, salt and osmotic stresses.
Improving Early-Grade Literacy in East Africa: Experimental Evidence from Kenya and Uganda *

Primary school enrollments have increased rapidly in sub‐Saharan Africa, spurring concerns about low levels of learning. We analyze field experiments in Kenya and Uganda that assessed whether the Reading to Learn intervention, implemented by the Aga Khan Foundation in both countries, improved early‐grade literacy as measured by common assessments. We find that Ugandan literacy (in Lango) increased by 0.2 standard deviations. We find a smaller effect (0.08) on a Swahili literacy test in Kenya. We find no evidence that differential effects are explained by baseline differences across countries in student test scores, classroom attributes, or implementation fidelity. A plausible explanation that cannot be directly tested is differential effective exposure to the literacy treatment in the tested languages. Students in Kenya were tested in Swahili, which is not necessarily the main language of instruction in primary schools, despite official policy.
Response Surface Modeling and Optimization of Accelerated Solvent Extraction of Four Lignans from Fructus Schisandrae

A new method based on accelerated solvent extraction (ASE) combined with response surface methodology (RSM) modeling and optimization has been developed for the extraction of four lignans in Fructus Schisandrae (the fruits of Schisandra chinensis Baill). The RSM method, based on a three level and three variable Box-Behnken design (BBD), was employed to obtain the optimal combination of extraction condition. In brief, the lignans schizandrin, schisandrol B, deoxyschizandrin and schisandrin B were optimally extracted with 87% ethanol as extraction solvent, extraction temperature of 160 °C, static extraction time of 10 min, extraction pressure of 1,500 psi, flush volume of 60% and one extraction cycle. The 3D response surface plot and the contour plot derived from the mathematical models were applied to determine the optimal conditions. Under the above conditions, the experimental value of four lignans was 14.72 mg/g, which is in close agreement with the value predicted by the model.
Shape Driven Kernel Adaptation in CNN for Robust Facial Trait Recognition

Shaoxin Li1,3, Junliang Xing2,3, Zhiheng Niu3, Shiguang Shan1, Shuicheng Yan3 1Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing, 100190, China 2National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China. 3Department of Electrical and Computer Engineering, National University of Singapore, Singapore
Duration and intensity as perceptual cues for naïve listeners' prominence and boundary perception

I investigate the acoustic correlates of prosodic prominence and boundary, as they are perceived by naive listeners, in spontaneous speech from American English (Buckeye corpus). Prosodic prominence and phrasing serve different functions in speech communication: prosodic phrase boundaries demarcate speech chunks that typically cohere semantically, while prominences encode focus and possibly also rhythmic structure. The acoustic correlates of prominence and phrase boundary are examined through measures of vowel duration and overall intensity of stressed vowels, to see how those measures correlate, individually or in combination, with naive listeners’ perception of prominence and boundary. The results show that most stressed vowels are lengthened in preboundary words (i.e., those final in the prosodic phrase). Prosodic prominence is also cued by increased duration, but in combination with higher overall intensity for some vowels. These acoustic differences associated with perceived prominence and boundary suggest different mechanisms underlying their production. This claim finds support from consideration of the different functions that prominence and boundary play in encoding information structure, and in speech production planning.
Kernel-Based Hybrid Random Fields for Nonparametric Density Estimation

Hybrid random fields are a recently proposed graphical model for pseudo-likelihood estimation in discrete domains. In this paper, we develop a continuous version of the model for nonparametric density estimation. To this aim, Nadaraya-Watson kernel estimators are used to model the local conditional densities within hybrid random fields. First, we introduce a heuristic algorithm for tuning the kernel bandwidhts in the conditional density estimators. Second, we propose a novel method for initializing the structure learning algorithm originally employed for hybrid random fields, which was meant instead for discrete variables. In order to test the accuracy of the proposed technique, we use a number of synthetic pattern classification benchmarks, generated from random distributions featuring nonlinear correlations between the variables. As compared to state-of-the-art nonparametric and semiparametric learning techniques for probabilistic graphical models, kernel-based hybrid random fields regularly outperform each considered alternative in terms of recognition accuracy, while preserving the scalability properties (with respect to the number of variables) that originally motivated their introduction.
On using SPEEDES as a platform for a parallel swarm simulation

Unmanned aerial vehicle (UAV) research is an increasingly important pillar of national security and military interest. A high fidelity discrete event simulation is prerequisite to any systems implementation. The synchronous parallel environment for emulation and discrete event simulation (SPEEDES) is a versatile and powerful tool that can be used for realization of this objective. A suite of five expert measures the efficiency a parallel UAV swarming SPEEDES application. Results indicate that the conservative time management produces more than twice the speed up as optimistic time management.
Polynomial shape from shading

We examine the shape from shading problem without boundary conditions as a polynomial system. This view allows, in generic cases, a complete solution for ideal polyhedral objects. For the general case we propose a semidefinite programming relaxation procedure, and an exact line search iterative procedure with a new smoothness term that favors folds at edges. We use this numerical technique to inspect shading ambiguities.
On the Automatic Detection of Loop Invariants

Loop invariants play a pivotal role in software verification. Since not all programs and the loops therein are annotated with their invariants, systems for automatically extracting loop invariants have been developed. In this paper, we will discuss the problem of f inding loop invariants and the various methods that have been used for this problem. We will also propose potential avenues for future research in this area.
Build your own 3D scanner: 3D photography for beginners

Over the last decade, digital photography has entered the mainstream with inexpensive, miniaturized cameras for consumer use. Digital projection is poised to make a similar breakthrough, with a variety of vendors offering small, low-cost projectors. As a result, active imaging is a topic of renewed interest in the computer graphics community. In particular, low-cost homemade 3D scanners are now within reach of students and hobbyists with a modest budget. This course provides a beginner with the necessary mathematics, software, and practical details to leverage projector-camera systems in their own 3D scanning projects. An example-driven approach is used throughout; each new concept is illustrated using a practical scanner implemented with off-the-shelf parts. The course concludes by detailing how these new approaches are used in rapid prototyping, entertainment, cultural heritage, and web-based applications.
Controlling complex networks: How much energy is needed?

The outstanding problem of controlling complex networks is relevant to many areas of science and engineering, and has the potential to generate technological breakthroughs as well. We address the physically important issue of the energy required for achieving control by deriving and validating scaling laws for the lower and upper energy bounds. These bounds represent a reasonable estimate of the energy cost associated with control, and provide a step forward from the current research on controllability toward ultimate control of complex networked dynamical systems.
SCIAMACHY TOA REFLECTANCE CORRECTION EFFECTS ON AEROSOL OPTICAL DEPTH RETRIEVAL

Aerosol retrieval from SCIAMACHY data processing at ISAC-CNR has been carried-out to retrieve Aerosol Optical Depth (AOD) at 500 nm wavelength together with a parameter (class) which defines a set of chemico-physical properties pertaining to suspended particles. SCIAMACHY Level 1B data from nadir measurements both over ocean and land are used as input for this processor. SCIAMACHY, onboard ENVISAT, is a remote sensing spectrometer measuring sunlight scattered, transmitted and reflected by the Earth's surface-atmosphere system. Its wavelength range spans from about 240 nm to about 2440 nm, with a spectral resolution varying from 0.24 nm in the UV to 1.48 nm in the NIR. In this aerosol retrieval algorithm, working wavelengths are selected in the UV-VIS-NIR range characterized by negligible gases absorption, i.e. lower than 1%. At these wavelengths, measured TOA reflectance are corrected taking into account that SCIAMACHY underestimates the reflectance in the UV and VIS range by 10 to 25% and by 10 to 20%, respectively. Once reflectances, presenting values of cloud coverage fraction lower than 0.05, are properly corrected, they are fitted with modelled reflectances. The couple formed by AOD and class that minimizes the merit function represent the retrieved aerosols parameters for the spectrum being analysed. The effect on the aerosol retrieval of the applied reflectance correction factors will be critically discussed and quantified. Then, SCIAMACHY data relative to 2003 and 2004 and in spatio-temporal coincidence with AERONET measurements have been considered.
Priorities for growth in OECD economies

This chapter discusses the main results of work carried out at the OECD to benchmark economic performance and policy in its member countries, in order to make policy recommendations that may improve economic performance. It examines differences in policy recommendations that have been made for countries at various levels of development, characterises the main challenges each group of countries faces, and considers the main distinctions between these countries and five emerging non-members. Product market competition and human capital reforms are found to be especially important priorities in lower-income countries, which face substantial gaps in productivity to the frontier countries.
Aspects of Uncertainty Handling for Knowledge Discovery in Databases

In this paper we discuss the role of uncertainty in Knowledge Discovery in Databases (KDD) and discuss the applicability of Evidence Theory towards achieving the goal of handling the uncertainty successfully, incorporating it into the discovery process. We claim that Evidence Theory is more suitable for representing and handling uncertainty within KDD than the Bayesian Model and present a case for the same. We discuss , EDM, our framework for KDD based on Evidence Theory. EDM consists of representation methods for data and knowledge and operators on the data and knowledge that together form the discovery process. Of the different types of operators within EDM, in this paper we limit our discussion to combination operators. We introduce a combination operator called the Proportional Belief Transfer operator and discuss its properties. In particular, we show how it differs from the Dempster-Shafer Orthogonal Sum.