Miracle-dz/UniFAR-FTUs huggingface dataset:
Image recognition and classification based on REM with LBP feature

The computer vision research aims to enable computer to recognize images as easily as human. Human can segregate target from its surrounding environment, which is associated with human memory mechanism. However, it is not quite clear about how the visual images are stored and retrieved in the human brain. This paper attempts to introduce the REM (Retrieving Effective from Memory) model into image learning and recognition and study how a computer can learn and recognize visual images as human do. First, the feature vector of the visual image is extracted by the local binary pattern (LBP) method. Then the probe image is matched in parallel to the studied images. Finally the Bayesian decision is used to calculate the likelihood ratio between the probe image feature vector and that of each studied image. If this ratio is greater than value 1, the probe image is thought to have been studied and match with the studied image with the maximal likelihood. Experimental results show that the REM model can gain good recognition performance not only in the classification of the same object with small rotation angels but also in the classification of the same image category, and the false alarm rate is far lower than other recognition method.
The PANDA Framework for Comparing 1 Arbitrarily Complex Patterns

8 Data Mining techniques are commonly used to extract condensed artifacts, like as9 sociation rules, decision trees, etc., from huge volumes of data. The comparison 10 of such compact and rich in semantics patterns is a fundamental issue, which can 11 be exploited, among others, to synthetically measure dissimilarities in evolving or 12 different datasets, as well as to compare the output produced by Data Mining al13 gorithms on a same dataset. In this paper we present the Panda framework for 14 the assessment of dissimilarity between both simple and complex patterns, defined 15 upon raw data and other patterns, respectively. With the aim of demonstrating the 16 flexibility of Panda to adapt to specific needs/requirements and its applicability 17 to a wide range of applications, we illustrate how it can be applied to a variety of 18 pattern types, including sets of itemsets, clusterings, and decision trees. 19
Surface properties of Entamoeba: increased rates of human erythrocyte phagocytosis in pathogenic strains

The assertion that ingestion of human erythrocytes is restricted to invasive strains of Entamoeba histolytica has not been evaluated previously by comparative studies. In this report we describe the in vitro ingestion of human erythrocytes by pathogenic and nonpathogenic Entamoeba. Microscopic evaluation of erythrophagocytosis by eight different Entamoeba grown in culture revealed that strains of E. histolytica isolated from cases of human dysentery show a much higher rate of erythrocyte ingestion than nonpathogenic strains. However, all strains are able to phagocytize erythrocytes. The extremely high rate of phagocytic activity shown by pathogenic E. histolytica could be one of the properties related to the pathogenicity of this parasitic protozoan.
Facile one-step coating approach to magnetic submicron particles with poly(ethylene glycol) coats and abundant accessible carboxyl groups

Purpose Magnetic submicron particles (MSPs) are pivotal biomaterials for magnetic separations in bioanalyses, but their preparation remains a technical challenge. In this report, a facile one-step coating approach to MSPs suitable for magnetic separations was investigated. Methods Polyethylene glycol) (PEG) was derived into PEG-bis-(maleic monoester) and maleic monoester-PEG-succinic monoester as the monomers. Magnetofluids were prepared via chemical co-precipitation and dispersion with the monomers. MSPs were prepared via one-step coating of magnetofluids in a water-in-oil microemulsion system of aerosol-OT and heptane by radical co-polymerization of such monomers. Results The resulting MSPs contained abundant carboxyl groups, exhibited negligible nonspecific adsorption of common substances and excellent suspension stability, appeared as irregular particles by electronic microscopy, and had submicron sizes of broad distribution by laser scattering. Saturation magnetizations and average particle sizes were affected mainly by the quantities of monomers used for coating magnetofluids, and steric hindrance around carboxyl groups was alleviated by the use of longer monomers of one polymerizable bond for coating. After optimizations, MSPs bearing saturation magnetizations over 46 emu/g, average sizes of 0.32 μm, and titrated carboxyl groups of about 0.21 mmol/g were obtained. After the activation of carboxyl groups on MSPs into N-hydroxysuccinimide ester, biotin was immobilized on MSPs and the resulting biotin-functionalized MSPs isolated the conjugate of streptavidin and alkaline phosphatase at about 2.1 mg/g MSPs; streptavidin was immobilized at about 10 mg/g MSPs and retained 81% ± 18% (n = 5) of the specific activity of the free form. Conclusion The facile approach effectively prepares MSPs for magnetic separations.
A high power CMOS differential T/R switch using multi-section impedance transformation technique

A high-power single-pole-double-throw (SPDT) antenna switch using a differential architecture and a multi-section impedance transformation technique is demonstrated in a standard 0.18-µm CMOS process. The differential architecture prevents unwanted channel formation of OFF-state Rx switch transistors by relieving the voltage swing over the Rx switch devices. In addition to this architecture, impedance transformation technique helps to reduce the voltage swing even more, contributing to significant enhancement of power handling capability. A loss of the whole design block including switch and matching networks has been analyzed, considering the integration issue of the front-end circuitries. The measured performance of the differential switch shows input 1-dB compression point (P1dB) of 33.8 dBm with insertion losses of 0.5 dB and 1.1 dB for Tx and Rx modes at 1.9 GHz, respectively.
Evaluating the therapeutic potential of a dimeric HER 3-binding affibody construct in comparison with a monoclonal antibody , seribantumab MANUSCRIPT

The use of monoclonal antibodies or their derivatives to improve survival of patients with disseminated cancer is today established. Several mechanisms of action are utilized, including delivery of cytotoxic payloads (e.g. toxins, drugs or radionuclides) and recruitment of the immune system. An important type of mechanism of action is based on prevention of mitogenic signaling through critical transmembrane receptors. Currently, a major group of cancer-associated antibody targets belong to the tyrosine kinase receptor family I (the human epidermal growth factor receptor (HER) family). Clinically successful antibodies, such as cetuximab, panitumumab, trastuzumab, ado-trastuzumab emtansine and pertuzumab target EGFR or HER2, i.e. members of the HER family. Relatively recently, the importance of another member of this family, HER3, as a cancer-associated target has also been recognized [1,2]. Although the tyrosine kinase of HER3 is not fully functional, heterodimers of HER3 with EGFR and especially HER2 provide extremely potent mitogenic signaling [3]. Furthermore, there are accumulated evidences that HER3 expression and its signaling is associated with resistance to anti-cancer therapy [4].
An Integrative Risk Evaluation Model for Market Risk and Credit Risk

Not only financial institutions but all firms have their own portfolio consisting of various financial assets. These portfolios are exposed to many kinds of risks, such as market risk, credit risk, liquidity risk, operational risk, and so on. Basel Accord in 1998 prompted us to recognize the importance of quantitatively evaluating these financial risks, and risk valuation models have been developed, first for market risk and then credit risk. However, in these models, each risk is evaluated separately, not integratively. Recently, Kijima and Muromachi [17] proposed a general framework for the integrative evaluation of market risk (interest rate risk) and credit risk. In this paper, we extend Kijima and Muromachi [17], and propose a framework to evaluate in a integrative way market risk (not only interest rate risk but also stock price variation risk and foreign exchange risk) and credit risk. At the center of the framework are stochastic differential equations (SDEs) which describe the dynamics of interest rate and default probability. We (i) run simulations with in the MonteCarlo method and generate scenarios based on these basic equations, (ii) calculate asset values for a pre-specified future date (risk horizon) using no-arbitrage theory, and (iii) obtain the future value of the portfolio by summing up all asset values. The main features of this framework are: (i) we can take the correlation between the interest rate and the default probability into account, (ii) the theoretical values of bonds derived in this setting are consistent with those observed in the real market, (iii) we can incorporate the term structure of the default probability into the model. Moreover, we can obtain the distribution of the portfolio or each asset value in the future, and so we can calculate any risk measure (e.g. standard deviation, VaR, T-VaR etc.). Also, the expected return can be computed, so the risk-return analysis can be made in a consistent way. This framework enables us to construct many types of models by changing the basic SDEs. We briefly present results for a Gaussian model, which is relatively easy to calculate.
Application of Visual Data Mining in Higher-Education Evaluation System

In this paper, we design a Visual Data Mining (VDM) model by combing visualization technology and data mining algorithm and apply it in the higher-education evaluation system. It provides a visual environment for users to participate each step of evaluation. Besides, some hidden information also be mined in a visual way by the mining function of the system and that would be helpful to instruct the further work.
Many faces of bankers' identity: how (not) to study dishonesty

In a recent Nature article, Cohn et al. (2014, henceforth CFM) boldly claim that their results “suggest that the prevailing business culture in the banking industry weakens and undermines the honesty norm” (p. 1). The main empirical finding of the CFM paper is that a group of bank employees that answered a series of questions about their work subsequently reported higher proportion of wins in a coin flipping task (58.2%) than a control group of bank employees (51.6%) that answered questions of a similar form but focused on their leisure activities instead of banking. Because one can on average correctly guess the outcome of only half of the flips, a higher rate of success reported by a group indicates that some of its members cheated. From this result the authors infer that asking bankers about their work makes their professional identity more salient, which leads them to cheat more. That in turn is supposed to suggest that “prevailing business culture in the banking industry favors dishonest behavior” (CFM, p. 3). We do not want to question the reality of their empirical findings. However, in line with previous critiques of priming research (Stafford, 2014), we want to point out certain limitations of the inferences which can be drawn from the empirical results: First, the observed dishonest behavior after the banking identity prime does not necessarily mean that there are social norms encouraging dishonesty in the banking industry. Instead of norms, a negative stereotype of bankers' dishonesty might be responsible for the observed effect: other stereotypes were shown to influence behavior—for example priming of the “hooligan” stereotype decreases performance in a trivia quiz (Dijksterhuis and van Knippenberg, 1998). Second, it is not even necessary that the observed difference in cheating had been caused by the priming of the professional identity. Either the other group could have been primed to cheat less, or other related concepts could have been primed in the experimental condition together with the banking identity and caused more cheating. Our goal is to highlight the fact that the study of cultural norms on behavior using indirect methods often precludes us from making strong conclusions, because alternative explanations are plentiful and hard to rule out. We offer suggestions how future studies can build on the CFM design and more reliably answer the question whether the social norm in banking really supports dishonesty.
Quantitative Proteomics Analysis of Altered Protein Expression in the Placental Villous Tissue of Early Pregnancy Loss Using Isobaric Tandem Mass Tags

Many pregnant women suffer miscarriages during early gestation, but the description of these early pregnancy losses (EPL) can be somewhat confusing because of the complexities of early development. Thus, the identification of proteins with different expression profiles related to early pregnancy loss is essential for understanding the comprehensive pathophysiological mechanism. In this study, we report a gel-free tandem mass tags- (TMT-) labeling based proteomic analysis of five placental villous tissues from patients with early pregnancy loss and five from normal pregnant women. The application of this method resulted in the identification of 3423 proteins and 19647 peptides among the patient group and the matched normal control group. Qualitative and quantitative proteomic analysis revealed 51 proteins to be differentially abundant between the two groups (≥1.2-fold, Student's t-test, P < 0.05). To obtain an overview of the biological functions of the proteins whose expression levels altered significantly in EPL group, gene ontology analysis was performed. We also investigated the twelve proteins with a difference over 1.5-fold using pathways analysis. Our results demonstrate that the gel-free TMT-based proteomic approach allows the quantification of differences in protein expression levels, which is useful for obtaining molecular insights into early pregnancy loss.
References for Haplotype Imputation in the Big Data Era

Imputation is a powerful in silico approach to fill in those missing values in the big datasets. This process requires a reference panel, which is a collection of big data from which the missing information can be extracted and imputed. Haplotype imputation requires ethnicity-matched references; a mismatched reference panel will significantly reduce the quality of imputation. However, currently existing big datasets cover only a small number of ethnicities, there is a lack of ethnicity-matched references for many ethnic populations in the world, which has hampered the data imputation of haplotypes and its downstream applications. To solve this issue, several approaches have been proposed and explored, including the mixed reference panel, the internal reference panel and genotype-converted reference panel. This review article provides the information and comparison between these approaches. Increasing evidence showed that not just one or two genetic elements dictate the gene activity and functions; instead, cis-interactions of multiple elements dictate gene activity. Cis-interactions require the interacting elements to be on the same chromosome molecule, therefore, haplotype analysis is essential for the investigation of cis-interactions among multiple genetic variants at different loci, and appears to be especially important for studying the common diseases. It will be valuable in a wide spectrum of applications from academic research, to clinical diagnosis, prevention, treatment, and pharmaceutical industry.
Nanostructured Colloidal Particles by Confined Self-Assembly of Block Copolymers in Evaporative Droplets

Block copolymers (BCPs) can create various morphology by self-assembly in bulk or film. Recently, using BCPs in confined geometries such as thin film (one-dimension), cylindrical template (two-dimension), or emulsion droplet (three-dimension), nanostructured BCP particles have been prepared, in which unique nanostructures of the BCP are formed via solvent annealing process and can be controlled depending on molecular weight ratio and interaction parameter of the BCPs, and droplet size. Moreover, by tuning interfacial property of the BCP particles, anisotropic particles with unique nanostructures have been prepared. Furthermore, for practical application such as drug delivery system, sensor, self-healing, metamaterial, and optoelectronic device, functional nanoparticles can be incorporated inside BCP particles. In this article, we summarize recent progress on the production of structured BCP particles and composite particles with metallic nanoparticles.
Automated image registration: II. Intersubject validation of linear and nonlinear models.

PURPOSE Our goal was to validate linear and nonlinear intersubject image registration using an automated method (AIR 3.0) based on voxel intensity. METHOD PET and MRI data from 22 normal subjects were registered to corresponding averaged PET or MRI brain atlases using several specific linear and nonlinear spatial transformation models with an automated algorithm. Validation was based on anatomically defined landmarks. RESULTS Automated registration produced results that were superior to a manual nine parameter variant of the Talairach registration method. Increasing the degrees of freedom in the spatial transformation model improved the accuracy of automated intersubject registration. CONCLUSION Linear or nonlinear automated intersubject registration based on voxel intensities is computationally practical and produces more accurate alignment of homologous landmarks than manual nine parameter Talairach registration. Nonlinear models provide better registration than linear models but are slower.
A Study for Important Criteria of Feature Selection in Text Categorization

A major difficulty of text categorization is the high dimensionality of the feature space. Feature selection is an important step in text categorization to reduce the feature space. Empirical studies of text categorization show that good text categorization performance is related to some feature selection criteria, and when a criterion is not satisfied, it often indicates non-optimality of the method. According to our analysis, there are some reasons for good performance of feature selection in text categorization tasks: favoring common terms, using category information and using term frequency information), and so on. Automatic feature selection methods such as document frequency thresholding (DF), information gain (IG), mutual information (MI), and so on are commonly applied in text categorization, but none of them satisfies all the criteria above. In this paper, we present some Important criteria of FS in TC. Experimental results indicate that the empirical performance of a FS function is tightly related to how well it satisfies these criteria
Research on Structural Holes and Closeness of Multi-Granularity Software Networks

Abstract—Object-Oriented software structures represent multi-level characteristics. In this paper, two parameters -- structural holes and closeness used in complex networks are introduced to study topological characteristics of software networks from multi-granularity perspective. By this method, software networks are observed and analyzed in three levels: package granularity, class granularity and method granularity. Then correlations between these two parameters and node degrees are investigated and analyzed by case studies, respectively. The results show that this method is useful for measuring the extent of dependence and centralization in software networks, and help us deeply understand different scales of software structural characteristics.
Signal processing for AUV based interferometric synthetic aperture sonar

This paper presents signal processing techniques particularly suited for interferometric Synthetic Aperture Sonar (SAS) systems onboard Autonomous Underwater Vehicles (AUV) (or other platforms carrying high grade navigation systems). The signal processing is applied to data collected in a controlled rail experiment at Elba Island, Italy, using a wideband interferometric SAS and an Inertial Navigation System (INS). We evaluate different strategies in fusing sonar micronavigation by the Displaced Phase Center Antenna (DPCA) technique with Aided INS (AINS). We obtained highest navigation accuracy using DPCA as aiding sensor into the AINS, then using raw DPCA surge and sway in combination with the AINS attitude and position. Coarse cross correlation based bathymetry and full resolution interferometry (based on the interferogram) is tested on the full swath and objects. Coarse bathymetry is more reliable than the interferogram technique. Phase wraparounds are avoided by estimating the coarse bathymetry first, then using the full resolution phase estimates as correction. Although much work remains, this technique does show a clear potential in improving object classification ability.
Running Head: Stranger Danger 1 Stranger Danger Awareness in Williams syndrome

The developmental disorder Williams syndrome (WS) is characterised by a distinctive cognitive profile and an intriguing social phenotype. Individuals with the disorder are often highly social engaging in interactions with familiar and unfamiliar people and once in those interactions they often show subtle abnormalities of social behaviour. Atypically increased approach to unfamiliar people is widely reported in the existing literature for both children and adults. Parents frequently report interactions with unfamiliar people as a major concern. In this study we aimed to evaluate ‘Stranger Danger’ awareness in individuals with WS. When linked to other components of the WS phenotype (e.g. reduced intellectual ability, increased social approach) an awareness of stranger danger is particularly important. Using a video vignette task we show that young people with WS have difficulties making judgements about whether or not to trust and engage in conversation with unfamiliar people. Qualitative data showed that individuals with WS often suggested that they would engage in an interaction with an unfamiliar person. The findings have substantial implications for the safety of young people with the disorder and emphasise the timely need for further intervention regarding this behaviour.
Service Quality Attributes SME's Desire in Their Choice and Retention of a Bank : An Australian Perspective

This paper identifies service quality attributes that small-to-medium-sizedenterprises (SME’s) in Australia desire in their choice and retention of a bank. A qualitative research method was adopted for the purposes of the paper. In-depth interviews were undertaken with a convenience sample of twenty-three practising accountants. The results of the study show that SME’s are desirous of a range of service quality attributes from their banking partners, these attributes can be categorised under five broad themes, namely, employee capabilities, credit accommodation, price competitiveness, accessibility, and internet banking. Further, the findings of the study suggest that banks operating in Australia stand to benefit from the development and management of SME customer relationships. The study offers marketing implications for banks that are providing, or are planning to provide services to the SME customer segment in Australia.
A qualitative study of practitioners' experience of working with bilingual interpreters in providing individual psychotherapy to clients with limited spoken English

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