Miracle-dz/UniFAR-FTUs huggingface dataset:
Precoding for Multiuser Orthogonal Space-Time Block-Coded OFDM Downlink over Spatially-Correlated Channels

This paper considers precoding for closed-loop multiuser orthogonal space-time block-coded (OSTBC) orthogonal frequency-division multiplexing (OFDM) downlink over multiple-input multiple-output (MIMO) channels. The authors present a general transmit-antenna-correlated, frequency-selective fading MIMO channel model with imperfect channel estimates and feedback delay, and derive the conditional means of the channel response. Exploiting the channel statistics, the authors develop new linear precoding and non-linear Tomlinson-Harashima precoding (THP) to maximize the signal-to-noise power ratio (SNR). By considering the conditional mean as the equivalent channel matrix, our precoding takes into account the estimation errors and channel time variations over feedback delay. The authors confirm the intuition that when channel state information at the transmitter (CSIT) becomes accurate, the full-CSIT precoder outperforms the covariance-feedback precoder. The proposed precoder has a bit error rate (BER) gain over unprecoded systems. As well, non-linear THP is shown to outperform linear precoding.
Comparative organizational analysis: An introduction

Comparative organizational analysis once dominated American organizational sociology, grounded in rich case studies about organizational processes and outcomes. The Columbia school's approach to organizational research was exemplary in this regard. Following the publication of Robert K. Merton's (1940) essay, “Bureaucratic Structure and Personality,” he attracted a group of talented doctoral students to his formal organizations seminar (Crothers, 1990), the core of whom would go on to write dissertations, books, and articles forming the substance of American organizational sociology in the decades to come. Among those students were Philip Selznick, Alvin Gouldner, Peter Blau, Seymour Martin Lipset, Rose Coser, and James Coleman. While their work varied greatly in substantive content, their studies shared a theoretical interest in explaining intra-organizational dynamics and the unexpected outcomes of bureaucratic administration. Organizations, they demonstrated, developed “lives of their own,” quite outside the intents of their founders (Haveman, 2009; refer, especially, Selznick, 1957). Organizations, in other words, were adaptive to the needs of their constituents, but adaptations did not always produce the intended results. One of the unintended consequences of organizational development was increasing variety in the kinds of organizations that emerged to meet particular societal goals or ends. Thus, an inherent focus of this early comparative research was the explanation of variety in organizational types, policies, and outcomes and an emphasis on the ways in which organizations diverged from ideal types.
Addressing Implicit Bias to Improve Cross-cultural Care

Health disparities cluster around race, ethnicity, education, neighborhoods, and income. Systems of exclusion that correlate with social determinants compound the disproportionate burden of poor health experienced by people of color. By 2056, ~50% of the population is expected to fall into categories currently labeled “under-represented minorities” (URMs), primarily African Americans, Latinxs, and American Indians. Although URMs comprise 30% of the general population, only 9% of medical doctors [Association of American Medical Colleges (AAMC)] are URMs. We can lessen the negative effects of implicit bias and minimize inequities and unequal treatment by medical providers with training in cross-disciplinary diversity knowledge and communication skills.
Evolutionary Limitation and Opportunities for Developing tRNA Synthetase Inhibitors with 5-Binding-Mode Classification

Aminoacyl-tRNA synthetases (aaRSs) are enzymes that catalyze the transfer of amino acids to their cognate tRNAs as building blocks for translation. Each of the aaRS families plays a pivotal role in protein biosynthesis and is indispensable for cell growth and survival. In addition, aaRSs in higher species have evolved important non-translational functions. These translational and non-translational functions of aaRS are attractive for developing antibacterial, antifungal, and antiparasitic agents and for treating other human diseases. The interplay between amino acids, tRNA, ATP, EF-Tu and non-canonical binding partners, had shaped each family with distinct pattern of key sites for regulation, with characters varying among species across the path of evolution. These sporadic variations in the aaRSs offer great opportunity to target these essential enzymes for therapy. Up to this day, growing numbers of aaRS inhibitors have been discovered and developed. Here, we summarize the latest developments and structural studies of aaRS inhibitors, and classify them with distinct binding modes into five categories.
Hippocampal shape analysis: surface-based representation and classification

Surface-based representation and classification techniques are studied for hippocampal shape analysis. The goal is twofold: (1) develop a new framework of salient feature extraction and accurate classification for 3D shape data; (2) detect hippocampal abnormalities in schizophrenia using this technique. A fine-scale spherical harmonic expansion is employed to describe a closed 3D surface object. The expansion can then easily be transformed to extract only shape information (i.e., excluding translation, rotation, and scaling) and create a shape descriptor comparable across different individuals. This representation captures shape features and is flexible enough to do shape modeling, identify statistical group differences, and generate similar synthetic shapes. Principal component analysis is used to extract a small number of independent features from high dimensional shape descriptors, and Fisher's linear discriminant is applied for pattern classification. This framework is shown to be able to perform well in distinguishing clear group differences as well as small and noisy group differences using simulated shape data. In addition, the application of this technique to real data indicates that group shape differences exist in hippocampi between healthy controls and schizophrenic patients.
Detecting Morphological Filtering of Binary Images

Morphological operators are widely used in binary image processing for several purposes, such as removing noise, detecting contours or particular structures, and regularizing shapes. In particular, morphological filters are largely adopted in scanned documents to correct the artifacts caused by acquisition and binarization, as well as other processing. In this paper, we propose a novel approach for forensics detection of morphological filtering on binary images. The proposed technique exploits some mathematical properties of the two basic morphologic operators, erosion and dilation, to define an algorithm able not only to detect the application of the filter, but also to estimate the shape of the relevant structuring element. Experimental tests demonstrate that the technique is effective and robust to the most common operations performed on binary image documents.
CLUSTERING-BASED FEATURE LEARNING ON VARIABLE STARS

The success of automatic classification of variable stars depends strongly on the lightcurve representation. Usually, lightcurves are represented as a vector of many descriptors designed by astronomers called features. These descriptors are expensive in terms of computing, require substantial research effort to develop, and do not guarantee a good classification. Today, lightcurve representation is not entirely automatic; algorithms must be designed and manually tuned up for every survey. The amounts of data that will be generated in the future mean astronomers must develop scalable and automated analysis pipelines. In this work we present a feature learning algorithm designed for variable objects. Our method works by extracting a large number of lightcurve subsequences from a given set, which are then clustered to find common local patterns in the time series. Representatives of these common patterns are then used to transform lightcurves of a labeled set into a new representation that can be used to train a classifier. The proposed algorithm learns the features from both labeled and unlabeled lightcurves, overcoming the bias using only labeled data. We test our method on data sets from the Massive Compact Halo Object survey and the Optical Gravitational Lensing Experiment; the results show that our classification performance is as good as and in some cases better than the performance achieved using traditional statistical features, while the computational cost is significantly lower. With these promising results, we believe that our method constitutes a significant step toward the automation of the lightcurve classification pipeline.
Imperfect Knowledge, Asset Price Swings and Structural Slumps: A Cointegrated VAR Analysis of Their Interdependence

This paper is an empirically based discussion of interactions between speculative behavior in the currency markets and aggregate fluctuations in the real economy. It builds on the recent theory of Imperfect Knowledge Economics in Frydman and Goldberg (2007) and combines this with the Structural Slumps theory in Phelps (1994). The paper argues that this is likely to imcrease our understanding of the long recurrent spells of high unemployment that continue to mar our economies.
Using social media mining technology to assist in price prediction of stock market

Price prediction in stock market is considered to be one of the most difficult tasks, because of the price dynamic. Previous study found that stock price volatility in a short term is closely related to the market sentiment; especially for small-cap stocks. This paper used the social media mining technology to quantitative evaluation market segment, and in combination with other factors to predict the stock price trend in short term. Experiment results show that by using social media mining combined with other information, the stock prices prediction model can forecast more accurate.
Baggage Transportation and Navigation by a Wheeled Inverted Pendulum Mobile Robot

Our goal is to configure an automatic baggage-transportation system by an inverted pendulum robot and realize a navigation function in a real environment. The system consists of two cooperative subsystems: a balancing-and-traveling control subsystem and a navigation subsystem. Position errors of the inverted pendulum robot are often caused by a drift error in the gyro sensor and a change in the center of gravity by a loaded baggage when applying the linear state feedback control method for balancing and traveling. We have reduced the position errors for navigation by resetting the balance position occasionally while traveling with simple methods without an external observer or alternative sensors. In this paper, we state the method and show the experimental results of navigation in a real environment by the implemented robot system.
Cu,Zn Superoxide Dismutase Genes in Tribolium castaneum: Evolution, Molecular Characterisation, and Gene Expression during Immune Priming

The production of reactive oxygen species (ROS) is a normal consequence of the aerobic cell metabolism. Despite their high and potentially detrimental reactivity with various biomolecules, the endogenous production of ROS is a vital part of physiological, immunological, and molecular processes that contribute to fitness. The role of ROS in host–parasite interactions is frequently defined by their contribution to innate immunity as effectors, promoting parasite death during infections. In vertebrates, ROS and antioxidant system enzymes, such as superoxide dismutase (SOD) are also involved in acquired immune memory, where they are responsible for T-cell signalling, activation, proliferation, and viability. Based on recent findings, ROS are now also assumed to play a role in immune priming, i.e., a form of memory in invertebrates. In this study, the potential involvement of Cu,Zn SODs in immunity of the red flour beetle Tribolium castaneum is described for the first time, applying an approach that combines an in silico gene characterisation with an in vivo immune priming experiment using the Gram-positive entomopathogen Bacillus thuringiensis. We identified an unusually high number of three different transcripts for extracellular SOD and found that priming leads to a fine-tuned modulation of SOD expression, highlighting the potential of physiological co-adaptations for immune phenotypes.
Online data migration for autonomic provisioning of databases in dynamic content web servers

This paper introduces an efficient data migration technique for provisioning new databases to workloads in dynamic content servers. Although many solutions for database replication exist, an efficient method for joining new replicas to a running system has not been implemented. We propose and implement a data migration algorithm that allows quick addition of replicas with minimal disruption of transaction processing. Furthermore, we show that our approach can also be used for fault management and re-integrating failed databases. We investigate our algorithm for transparent provisioning in the database tier using the TPC-W e-commerce benchmark and the RUBIS online auction benchmark. We demonstrate that our technique provides quality of service under different load and failure scenarios.
Using Local Binary Pattern Operators for Colour Constant Image Indexing

The Local Binary Pattern (LBP) operator computes a local texture measure that is invariant to monotonic transformations of the image grey-scale. As a result of this property, calculating the LBP value in each channel of a colour image results in a triplet of values that are invariant to changes in illumination colour. Previous research has shown that histograms of grey-scale LBP values, and histograms of LBP values calculated in the R, G and B channels independently, form useful feature vectors for image retrieval. In this paper we generate 3D histograms of LBP values for colour images. Our experiments demonstrate that image retrieval performance, on a database of objects viewed under different illuminants, is greatly improved when using the 3D histogram compared to when the histogram is calculated for each colour channel independently. Furthermore, we find that this method can give better performance than using colour-histogram based features.
The retinal hypercircuit: a repeating synaptic interactive motif underlying visual function

Abstract The vertebrate retina generates a stack of about a dozen different movies that represent the visual world as dynamic neural images. The stack is embodied as separate strata that span the inner plexiform layer (IPL). At each stratum, ganglion cell dendrites reach up to read out inhibitory interactions between three different amacrine cell classes that shape bipolar‐to‐ganglion cell transmission. The nexus of these five cell classes represents a functional module, a retinal ‘hypercircuit’, that is repeated across the surface of each of the dozen strata that span the depth of the IPL. Individual differences in the characteristics of each cell class at each stratum lead to the unique processing characteristics of each neural image throughout the stack. This review shows how the interactions between different morphological and physiological cell classes generate many of the known retinal visual functions including motion detection, directional selectivity, local edge detection, looming detection, object motion and saccadic suppression.
Elevated Urine Heparanase Levels Are Associated with Proteinuria and Decreased Renal Allograft Function

Heparanase is an endo-β-glucuronidase that cleaves heparan sulfate side chains, leading to structural modifications that loosen the extracellular matrix barrier and associated with tumor metastasis, inflammation and angiogenesis. In addition, the highly sulfated heparan sulfate proteoglycans are important constituents of the glomerular basement membrane and its permselective properties. Recent studies suggest a role for heparanase in several experimental and human glomerular diseases associated with proteinuria such as diabetes, minimal change disease, and membranous nephropathy. Here, we quantified blood and urine heparanase levels in renal transplant recipients and patients with chronic kidney disease (CKD), and assessed whether alterations in heparanase levels correlate with proteinuria and renal function. We report that in transplanted patients, urinary heparanase was markedly elevated, inversely associated with estimated glomerular filtration rate (eGFR), suggesting a relationship between heparanase and graft function. In CKD patients, urinary heparanase was markedly elevated and associated with proteinuria, but not with eGFR. In addition, urinary heparanase correlated significantly with plasma heparanase in transplanted patients. Such a systemic spread of heparanase may lead to damage of cells and tissues alongside the kidney.The newly described association between heparanase, proteinuria and decreased renal function is expected to pave the way for new therapeutic options aimed at attenuating chronic renal allograft nephropathy, leading to improved graft survival and patient outcome.
The gendering of entrepreneurship: theoretical and empirical insights

Purpose – This extended Editorial outlines the genesis and theoretical interests of the Gender and Enterprise Network from which this special issue of the International Journal of Entrepreneurial Behaviour & Research emerged. In the call for papers, researchers were asked to employ existing gender theories to explore entrepreneurship. The theories and empirical insights presented in the five papers are summarised and compared. Key directions for future work are outlined. Design/methodology/approach – Articles in the special issue include cross-national studies, multi-level analyses drawing on qualitative and quantitative methods, longitudinal analysis and feminist research. The Editorial explores methodological challenges, including how to encourage cross-national collaboration, research the circumstances in which entrepreneurship is gender liberating and embed gender theory in research on male entrepreneurship. Findings – A comparison is made of the findings from the papers in this special issue, to draw.
Link between heart disease, cholesterol, and Alzheimer's disease: A review

Increased prevalence of Alzheimer's disease‐like β‐amyloid deposits in the neuropil and within neurons occurs in the brains of non‐demented individuals with heart disease. Heart disease is a prevalent finding in Alzheimer's disease, and may be a forerunner to the dementing disorder. In the cholesterol‐fed rabbit model of human coronary heart disease there is production and accumulation of β‐amyloid in the brain. This accumulation of β‐amyloid can be reversed by removing cholesterol from the rabbits' diet. In culture cells, a cholesterol challenge has been shown to increase production of β‐amyloid, and dramatic reductions of cholesterol produced by HMG Co‐A reductase inhibitors decrease production of β‐amyloid. Increased β‐amyloid production is also produced by dietary cholesterol in a number of transgenic mouse models of Alzheimer's disease. Administration of HMG Co‐A reductase inhibitors may block β‐amyloid production caused by dietary cholesterol in rabbits. Clinical trials testing the benefit of HMG Co‐A reductase inhibitors in the treatment of Alzheimer's disease are underway. Microsc. Res. Tech. 50:287–290, 2000. © 2000 Wiley‐Liss, Inc.
Predictive Factors for Decline in Activities of Daily Living in Alzheimer's Disease Dementia with More than 2 Follow-up

Background: Impairment in activities of daily living (ADL) is a major problem in Alzheimer's disease (AD), and is related to increased caregiver burden.The present study evaluated whether there are any compo- nents of initial dementia evaluation that could predict ADL decline in years follow-up. Methods: The 32 subjects underwent more than two consecutive neuropsychological evaluation and maintained anti-de- mentia medication from the Ewha Dementia Cohort. The first clinical, neuropsychological test results, medial temporal atrophy rating and white matter ratings were correlated with the final ADL scores. The subjects were further divided into ADL-preserved and declined groups for the comparison depending on final ADL scores. Results: The annual decline of the Korean Mini-mental status examination (K-MMSE) score was 1.5 ± 1.2 and of the Seoul-instrumental ADL score was 6.1 ± 4.6. The Factors correlated with the ADL at baseline were the clinical dementia rating, K-MMSE, memory function score and the total neuropsychological test score, left medial temporal lobe atrophy rating, and the neuropsychiatric total score. Only the neuropsychological component including total test, frontal and visuospatial function scores were statistically different between the two groups in the baseline evaluation. Conclusions: The result of our preliminary study emphasize the other study results that the initial cognitive and dementia status are the strong predictive factors not only for the initial ADL dysfunction but also for the ADL decline in years followed-up dementia cohort.
Snack patterns are associated with biomarkers of glucose metabolism in US men

Abstract Few studies have made distinctions between dietary intake from meals and snacks in relating them to biomarkers. We aimed to examine if snack patterns are associated with biomarkers of glucose metabolism, specifically hemoglobin A1c and HOMA-IR in US adults. Using 24-h dietary recall data from National Health and Nutrition Examination Survey (NHANES) in 2007–2008, we derived snack patterns using factor analyses. Multivariate logistic regressions were performed to estimate adjusted odds ratios (AOR) for biomarkers of glucose metabolism by quintiles of snack pattern scores. Men in the highest quintile of dairy and sugary snack pattern had higher risk of having hemoglobin A1c ≥ 6.5% (AOR: 2.06; 95% CI: 1.20–3.51) and HOMA-IR > 3.0 (AOR: 1.73; 95% CI: 1.01–2.95) than did those in the lowest quintile. No significant association was found in women between snack patterns and biomarkers of glucose metabolism. Dairy and sugary snack patterns of US men had the greatest association with poor control of glucose metabolism.
Siglec-G Regulates B1 Cell Survival and Selection

Siglec-G is a negative regulator of BCR-mediated signaling in B1a cells. This population of B cells is highly increased in Siglec-G–deficient mice, but the mechanism of this expansion is not known so far. In this study, we demonstrate that Siglecg−/− B1a cells show a lower level of spontaneous apoptosis and a prolonged life span. Mechanistically, the lower apoptosis could result from higher expression levels of the transcription factor NFATc1 in Siglec-G–deficient B1a cells. Interestingly, Siglecg−/− B1a cells display an altered BCR repertoire compared with wild-type B1a cells. As the BCR repertoire and the VDJ composition of Igs of Siglecg−/− B1a cells resembles more the Abs produced by adult bone marrow-derived B cells rather than canonical fetal liver-derived B1a cells, this suggest that the selection into the B1a cell population is altered in Siglec-G–deficient mice.
LEARNING BY DESIGN COGNITIVE AND EMOTIONAL FACTORS INFLUENCING INFORMAL LEARNING EXPERIENCES IN INTERACTIVE ENVIRONMENTS

This paper offers an overview of a study synthesizing current literature in educational and design psychology, information design, human-computer interaction, and museum studies to identify cognitive and emotional factors that influence learning. The purpose is to produce a set of cognitive and emotional factors that museum educators, exhibit designers, information designers, and interaction designers should consider when designing informal learning experiences in interactive environments. Nine identified factor groups include affect, cognition, context, engagement