Miracle-dz/UniFAR-FTUs huggingface dataset:
1 Our Approach : Spike-and-Slab Lasso Biclustering

Biclustering methods aim to group samples using only subsets of their associated features. In this way, biclustering methods differ from traditional clustering methods, which utilize the entire set of features to group samples. Motivating applications for biclustering include genomics data, where the goal is to cluster patients or samples by their gene expression profiles; and recommender systems, which seek to group customers based on their product preferences. Biclusters of interest often manifest as rank1 submatrices of the data matrix. This submatrix detection problem can be viewed as a factor analysis problem in which both the factors and loadings are sparse. In this paper, we propose a new biclustering method called Spike-and-Slab Lasso Biclustering (SSLB) which utilizes the Spike-and-Slab Lasso of Ročková and George (2018) to find such a sparse factorization of the data matrix. SSLB also incorporates an Indian Buffet Process prior to automatically choose the number of biclusters. Many biclustering methods make assumptions about the size of the latent biclusters; either assuming that the biclusters are all of the same size, or that the biclusters are very large or very small. In contrast, SSLB can adapt to find biclusters which have a continuum of sizes. SSLB is implemented via a fast EM algorithm with a variational step. In a variety of simulation settings, SSLB outperforms other biclustering methods. We apply SSLB to both a microarray dataset and a single-cell RNA-sequencing dataset and highlight that SSLB can recover biologically meaningful structures in the data. The SSLB software is available as an R/C++ package at https://github.com/gemoran/SSLB.
Towards surface anomaly detection with deep learning

In this paper we investigate whether shallower CNN architectures, which exhibit less parameters that need to be learned, can be used in the domain of automated visualinspection of surface anomalies, while retaining a high classification accuracy. We examine whether learning can be done merely on positive examples as this further reduces the overall computational complexity. We evaluate a shallow CNN architecture on a dataset consisting of different textured surfaces with variously-shaped weaklylabeled anomalies. We find that high classification accuracy can be achieved with shallow CNN models.
Collective Action and Common Agricultural Policy Lobbying : Evidence of EuroGroup In uence , 1986-2003

This paper attempts to explain Common Agricultural Policy (CAP) subsidies to farmers by the inuence of farmer interest-groups with an EU-wide membership (so called Euro-groups). The analysis is based on panel-data for fteen commodities over the period 1986-2003. Because the CAP is set as an overall EU policy, e¤ective lobbying presents a collective action problem to the farmers in the EU as a whole. Indicators of lobbying, which are based on this perception, are found to explain part of the variation in agricultural support, suggesting that farmer Euro-groups inuence agricultural policy within the EU. Key words: Agricultural policy, political economy, lobbying. JEL Classi cation: Q18, H43. An earlier version of this paper was presented at the First World Meeting of the Public Choice Society 2007 in Amsterdam. The author wishes to thank participants at this presentation, Thomas Aronsson, Kurt Brännäs, David Granlund, Tomas Sjögren and Magnus Wikström for helpful comments and suggestions. A research grant from FORMAS is also gratefully acknowledged.
Taraması Branchial Cleft Anomalies : Our Clinical Experience and Literature Review

GİRİŞ ve AMAÇ: Brankial yarık anomalileri embriyolojik gelişimin nadir anomalilerindendir. Fetal gelişim sırasında brankial aparatın gelişiminin tamamlanmamasına bağlı olarak gelişir ve kist, sinüs traktı, fistül veya kıkırdak kalıntısı olarak gözlenebilir. Bu çalışmanın amacı, kliniğimizde opere edilen brankial yarık anomalilerin klinik özelliklerini ve cerrahi tedavi sonuçlarını gözden geçirmektir.
FROM ZO TO CO WITH DIFFRACTIONS : COMPLEX DATA EXAMPLES

The main goal of conventional seismic processing is to enhance and image reflections. Since reflected waves are not suitable for high-resolution structural imaging, diffractions are a key feature in seismic data. Diffracted waves behave physically different than reflected waves and carry important information about small-scale structures. In order to combine the stability of zero-offset processing with the improved illumination of common-offset processing, we introduced a straightforward decomposition principle for diffractions, which establishes a direct connection between zero-offset and commonoffset wavefield attributes based on the decoupling of diffraction raypaths. The new method allows the direct prediction of common-offset diffraction attributes from the zero-offset section. Application to complex synthetic and marine field data reveals its ability to reliably image diffractions in the common-offset domain using only results from zero-offset processing as input. Particularly diffracted energy from sparsely illuminated areas is enhanced by the new method. The promising results in terms of both image and attributes reveal a high potential for improved pre-stack diffraction separation and diffraction-stereotomography.
Managing expectations at the ZLB through Forward Guidance

We investigate the stabilization power of forward-guidance on macroeconomic dynamics in so-called liquidity traps. The research proceeds within the baseline New Keynesian model in which non-linearities give rise to multiple equilibria and deflationary recession paths. We relax the assumption of homogeneous and rational expectations, and introduce heterogeneous expectations using a social learning algorithm. The resulting framework displays local stability around two steady states – the target and the stagnation state – and fits better the persistence at the Zero Lower Bound (ZLB) than the rational expectation benchmark. We find that if the central bank signals the rational expectation solution, the stability of the economy is significantly improved. Nonetheless, our model displays a strong expectation feedback loop: when agents’ expectations perform better than the rational expectation solution, these expectations move further away from the optimal stable solution signaled by the Central Bank. In this model, instability is driven by a expectation miscoordination at the ZLB rather than strong exogenous shocks.
Tail Coskewness and the Momentum Effect in Equity Returns

We form portfolios based on size and momentum over long horizons and explain their average returns using a new measure of coskewness that puts more we ight on tail events. Our new measure of coskewness, tail coskewness, allows more appropriat ely for the extreme observations associated with the momentum effect. We therefore can better chara terize the risk associated with momentum strategies, namely that winner portfolios may suffe r an extreme left-tail event or that loser portfoli os experience an extreme right-tail event. We call th is risk associated with momentum strategies tail coskewness risk. Up to 65.8% of momentum returns ar e explained by our tail coskewness measure. Tail coskewness has greater explanatory power over siz and momentum portfolios compared to the coskewness measure of Harvey and Siddique (2000) an d asymmetric beta. Tail coskewness also help explain the size effect in average returns.
Pragmatic Transfer in Pakistani English Speakers Apology Responses : Impact of Social Power

The current study investigates the impact of social power on the performance and perception of Pakistani English Speakers’ use of apology responses. Two instruments, a discourse completion test (DCT, translated version for Pakistani Urdu speakers) and a scale response questionnaire (SRQ, both in Urdu and English), are used for data collection. The findings illustrate that Pakistani English speakers (PakE) pragmatic choices are clearly influenced by their perceptions of various sociocultural, socio-religious and contextual variables. The PakE and Pakistani Urdu speakers (PakU) are found being influenced by the social power of their interlocutors. The participants of both PakE and PakU groups used Acceptance strategies (Adrefiza & Jones, 2013) when they responded to the apologies of higher status speakers, and preferred to acknowledge the apologies of equal and lower level interlocutors. They are also found using more Rejection strategies than British English speakers (BritE) while responding to lower level interlocutors, indicating that Pakistani society abides by non-egalitarian status. In contrast, British English speakers preferred to use Acceptance and Evasion strategies more often while interacting with the people of higher, equal and lower level interlocutors. The findings further highlight the influence of socio-religious aspects on the PakE and PakU groups, as they more often used positive politeness (Holmes, 1995), in contrast, British English speakers prefer to use both (Holmes 1995; Adrefiza & Jones, 2013) positive and negative politeness.
Estimating Behavioral Learning Equilibria in DSGE Models ∗

We estimate DSGE models under a simple misspecification learning equilibrium that arises from expectational frictions. The representative agent does not know the true underlying mechanism of the economy, but acts as an econometrician and uses a simple univariate sample autocorrelation learning rule to form his expectations about the future state of the economy. Over time, she learns the best univariate rule and his expectations become self-fulfilling, giving rise to Behavioral Learning Equilibria (BLE). Under fairly general conditions, we show that DSGE models can be estimated under a BLE using standard Bayesian estimation methods without using projection facilities. We apply our estimation approach to two models that are commonly used in the DSGE literature: the baseline 3-equation NKPC à la Woodford (2003), and the workhorse Smets-Wouters (2007) model. Our results show that the empirical fit and forecasting performance of both models under BLE improve compared to the Rational Expectations models, while we observe important differences in our parameter estimates and the propagation mechanism of the model. JEL Classification: E37; E47; C63; D83; C11.
Chapter 2 Microbes , Metabolites and Health

High throughput DNA sequencing has revolutionised the analysis of complex communities of microorganisms (microbiota). In humans, this paradigm shift, allowing analysis of the membership of microbial communities without the need to culture individual organisms, has spawned an avalanche of research and a number of important collaborative programs characterising not only the catalogue of species comprising the microbiota associated with different ecological niches in the body in states of both health and disease (Group et al. 2009) but also their genetic potential (Qin et al. 2010). Nowhere in the human body are the microbes more numerous or the microbiota more complex than in the colon of the gastrointestinal tract (Savage 1977). With its trillions of bacteria, comprising over one thousand individual microbial species present in varying abundances, the microbiota of the human gut and the genes they carry (collectively the microbiome) provides the host with a vast metabolic potential over and above that afforded by the 20,000–25,000 protein coding genes of the human genome. The composition of the human colonic microbiota is remarkably diverse between individuals with each person’s microbiota being unique (Qin et al. 2010; Yatsunenko et al. 2012). Despite this diversity at the taxonomic level, when analysed for functional potential by whole genome analysis at the gene level, conservation of bacterial genes between individuals was high suggesting that
05 UGT 1 A 1 POLYMORPHISMS AS A PHARMACOGENETIC MARKERS FOR PREDICTION OF IRINOTECAN-INDUCED TOXICITIES IN METASTATIC COLORECTAL CANCER

1Division of Radiotherapy and Medical Oncology, University Hospital for Tumors, Sestre milosrdnice University Hospital Center, Zagreb, Croatia; 2Department of Paediatrics, Division for Medical Genetics and Inherited Metabolic Diseases, University Hospital Centre Zagreb, Zagreb, Croatia; 3Department of Oncological Pathology, ‘Ljudevit Jurak’ University Department of Pathology, Sestre milosrdnice University Hospital Center, Zagreb, Croatia; 4Department of Surgical Oncology, University Hospital for Tumors, Sestre milosrdnice University Hospital Center, Zagreb, Croatia; 5Division of Diagnostic and Interventional Radiology, Department of Oncological Radiology, University Hospital for Tumors, Sestre milosrdnice University Hospital Center, Zagreb, Croatia; 6Genetic Counselling Unit, University Hospital for Tumors, Sestre milosrdnice University Hospital Center, Zagreb, Croatia
A performance measurement system for change initiatives An action research study from design to evaluation

Purpose – The purpose of this paper is to develop, implement, test and further enhance a framework for measuring organizational change initiatives. Design/methodology/approach – The conceptual part of the framework is based on the structured analysis of existing literature. The framework was further developed during an action research (AR) study where the authors developed, implemented, evaluated and improved the measurement system for organizational change initiatives. Findings – The academic literature is rich in conceptual articles providing required characteristics of a “good” measurement system and frameworks for how organizations should measure performance. However, academia provides less empirical evidence of how these performance measurement systems can be implemented, evaluated and improved. In this paper, the authors present a study where the developed measurement system has been implemented, evaluated and improved. The results in terms of how the actual framework worked as well as the response from the case organizations are equally positive. Research limitations/implications – The framework has been implemented in two different, major change initiatives in one case organization. While the results are truly encouraging, the framework needs to be further tested and refined in more organizations. Practical implications – There is a gap between academic perception and practical reality regarding how organizations should measure performance in general as well as measuring organizational change initiatives. The presented, and empirically tested, framework measures both the results of the change initiative (effectiveness) the actual change process (efficiency) as well as the perception of the change initiative and process from different key stakeholders. Originality/value – This is the first developed, implemented and further improved measurement system for organizational change which measures both the efficiency and effectiveness of the change initiative (process).
Problem Solving and Resilience Self-Efficacy as Factors of Subjective Well-Being in Greek Individuals with and without Physical Disabilities.

The way that individuals perceive their well-being may vary depending on their self-efficacy especially towards a difficult life situation, such as the physical disability. The aim of this study is to examine the relationships among problem solving self-efficacy, resilience self-efficacy and subjective well-being and the differentiation among these factors between people with and without physical disabilities. A self-report questionnaire was administered to 150 individuals with physical disabilities and 150 individuals without disabilities in Greece. Results showed positive correlations among problem solving self-efficacy, resilience self-efficacy and subjective well-being, while statistically significant differences were found in the level of the above factors between participants with and without disability. Furthermore, the need for an escort and the satisfaction with transportation autonomy could predict much variance in self-efficacy and subjective well-being for individuals with physical disabilities. The implications of findings for the psychosocial adjustment of people with physical disabilities are discussed.
PERFORMANCE MANAGEMENT AND ORGANIZATIONAL RESILIENCE : A STUDY OF COMMERCIAL BANKS IN PORT HARCOURT

This study examines the relationship between performance management and organizational resilience in selected commercial banks in Port Harcourt. The study adopted a cross sectional research survey. Target population comprises of selected commercial banks in Port Harcourt. The accessible population for this study is 10 selected commercial banks. 424 workers’ were surveyed from the 10 selected commercial banks. A simple random sampling technique was adopted. The sample size for the study is 206 using Taro Yamane formula. A total number of 132 copies of questionnaire were found useful for data analysis. Spearman Rank Order Correlation Coefficient was used as a statistical tool to test the hypotheses with the aid of SPSS 20.0. The study found that performance management enhances organizational resilience. The study concluded that effective performance management has the capacity to enhance organizational resilience of commercial banks in Port Harcourt. The study recommended that bank managers should ensure that performance planning is in place to enable the organization prepare for unforeseen circumstances.
Cornea Relations Among Corneal Curvature , Thickness , and Volume in Keratoconus as Evaluated by Anterior Segment – Optical Coherence Tomography

Citation: Morishige N, Magome K, Ueno A, Matsui T-A, Nishida T. Relations among corneal curvature, thickness, and volume in keratoconus as evaluated by anterior segment–optical coherence tomography. Invest Ophthalmol Vis Sci. 2019;60:3794–3802. https://doi.org/10.1167/iovs.19-27619 PURPOSE. To provide insight into the mechanism underlying corneal deformation in keratoconus, we examined the relations among corneal curvature, thickness, and volume as well as the association of corneal scar formation with these parameters.
Assessment of the Fetal Middle Cerebral Artery in Postdate Pregnancy using Doppler Ultrasound

Original Research Article This was prospective study carried out in -Khartoum state –Sudan, Khartoum Bahri Teaching hospital in the period from august 2016 to December 2018. The problem of study was the post term pregnancy constitutes a challenge to clinicians; knowing who to induce, who will respond to induction and who will require a caesarean section (CS) and the risk of stillbirth increases beyond 41 weeks, additional fatal risks of postdate pregnancies include macrosomia, caesarean deliveries and shoulder dystocia, as well as meconium aspiration syndrome and oligohydramnios, on the other hand the Doppler ultrasound can be used as an excellent tools to aid in management. The main aim of the study was to assess MCA in postdate pregnancies by spectral Doppler ultrasound. The study was done in 64 pregnant women with normal singleton pregnancy with mean age 27 years, with gestational age ranged from 40 weeks 1day to 46 week, no maternal medical condition that may affect pregnancy outcome such as diabetic and hypertension. The data was collected by data collection sheet designed especially for this study and including all variables; then analyzed by statistical package for social sciences (SPSS). The study found that most of postdate were nulliparous 73.4%, 67.2% had adverse outcome. 40.6% delivered by CS, 6.2% had oligohydramnios, 10.9% had macrosomia, 1.6 % prenatal mortality. The study concluded that no significant difference in Doppler indices of MCA in postdate with adverse outcome and normal outcome (PI, RI,S\D ratio and CU ratio) (p >0.05) but are slightly increased in case with adverse outcome than in normal outcome.
DeepInfer: Diffusion Network Inference through Representation Learning

The diffusion of a contagion (e.g. news, meme, virus) is a common event in online and offline social networks. Unfortunately, such networks, known as diffusion networks , are often unknown: that is, one can observe when subjects are infected by a given contagion (e.g., when a piece of information arrives, when a product is adopted, when a virus is caught), but does not know through which connection the infection has been transmitted. The goal of this study is to infer such networks based on nodes infection contexts associated to diffusion events (cascades). Previous studies mostly relied on delay patterns between infection events of nodes to infer edges. It has been argued, however, that delay-agnostic approaches are also efficient for such inference. Motivated by this finding, we present a novel delay-agnostic algorithm that is largely inspired by representation learning of words in documents and nodes in networks. Moreover, unlike some delay-agnostic methods, we only consider infection context of nodes in a restricted window. After empirically observing a similarity between the distribution of words in documents and nodes in cascades, we employ the Skip-Gram model to learn a representation of nodes from cascades. The learned representation is then used to compute the probability that an edge exists between a pair of nodes. Through extensive experiments we validate the effectiveness of our algorithm, showing that it is able to recover up to 95% of the hidden network in realistic datasets. We have also compared our algorithm to the state-of-the-art algorithm InfoPath, and achieved a large improvement on the quality of results.
Understanding bid-price generation for road freight transport by analysing online market data

This paper aims to improve the understanding of road freight transport price generation by analysing quantitative and qualitative data already available. The online freight marketplace in Australia is used as a case study and a mixed-methodology adopted to obtain insights from qualitative information collected around bid-price generation, to identify patterns for freight transport and predict prices for palletised road transport. The results suggest three clusters of consignments, each with slightly varying predictors of the bidding price. Although traditionally transport operators based their prices on distance alone, many other factors (customer type, flexibility, loading facilities) could be easily incorporated in the bidding model. The paper shows how mixed-method research on publicly available data can contribute to more competitive decision making in daily operations. To the best of the authors’ knowledge this type of research has not been undertaken before when analysing freight transport.
Classification of C 2 C e-Commerce Product Images using Deep Learning Algorithm

C2C (consumer-to-consumer) is a business model where two individuals transact or conduct business with each other using a platform. A consumer act as a seller put their product in a platform later will be displayed to another consumer act as a buyer. This condition encourages platform to maintain high quality product information especially image that is provided by the seller. Product images need to be relevant to the product itself. It can be controlled automatically using image classification. In this paper, we carried out a research to find out the best deep learning model in image classification for ecommerce products. A dataset of 12,500 product images is collected from various web sources to be used in training and testing process. Five models are selected and fine-tuned using a uniform hyperparameter set-up. Those hyperparameters are found by using a manual process by trying a lot of hyperparameters. The testing result from every model is presented and evaluated. The result shows that NASNetLarge yield the best performance among all evaluated models with 84% testing accuracy. Keywords—Image classification; e-commerce; product images; deep learning; hyperparameter tuning