Miracle-dz/UniFAR-FTUs huggingface dataset:
Geographical variability of bacterial communities of cryoconite holes of Andean glaciers

Cryoconite holes, ponds full of melting water with a sediment on the bottom, are hotspot of biodiversity of glacier surface. They host a metabolically active bacterial community that is involved in different dynamics concerning glacier ecosystems. Indeed, they are responsible of organic matter production and with other microorganisms establish a real microecosystem. Cryoconite holes have been described in different areas of the world (e.g., Arctic, Antarctic, Alps, Himalaya), and with this study we will provide the first description of bacterial communities of cryoconite holes of the Andes in South America. We collected samples on three high elevation glaciers of the Andes (Iver, Iver East and Morado glaciers) and two Patagonian glaciers located at sea level (Exploradores glacier and Perito Moreno). Results show that the most abundant orders are Burkholderiales, Cytophagales, Sphingobacteriales, Actinomycetales, Pseudomonadales, Rhodospiarillales, Rhizobiales, Sphingomonadales and Bacteroidales, which have been reported on glaciers of other areas of the world, Bacterial communities change from one glacier to another and both water pH and O2 concentration affect bacterial communities composition.
a diagram of split short exact sequences

We study the relationship between the cohomology of the function ﬁeld of a curve over a complete discretely valued ﬁeld and that of the function ring of curves resulting over its residue ﬁeld. The results are applied to prove the existence of noncrossed product division algebras and indecomposable division algebras of unequal period and index over the function ﬁeld of any p -adic curve, generalizing the results and methods of a previous work of the authors and McKinnie.
Selecting and weighting users in collaborative ﬁl-tering based POI recommendation

: Location-based recommender systems (LBRSs) provide a technological solution for helping users to cope with the vast amount of information coming from geo-localization services. Most online social networks capture the geographic location of users and their points-of-interests (POIs). Location-based social networks (LBSNs), like Foursquare, leverage technologies such as GPS, Web 2.0 and smartphones allow users to share their locations (check-ins), search for POIs, look for discounts, comment about speciﬁc places, connect with friends and ﬁnd the ones who are near a speciﬁc location. LBRSs play an important role in social networks nowadays as they generate suggestions based on techniques such as collaborative ﬁltering (CF). In this traditional recommendation approach, prediction about a user preferences are based on the opinions of like-minded people. Users that can provide valuable information for prediction need to be ﬁrst selected from the complete network and, then, their opinions weighted according to their expected contribution. In this paper, we propose and analyze a number of strategies for selecting neighbors within the CF framework leveraging on information contained in the users’ social network, common visits, visiting area and POIs categories as inﬂuential factors. Experimental evaluation with data from Foursquare social network shed some light on the impact of different mechanisms on user weighting for prediction.
MIT Open Access Articles Providing protection in multi-hop wireless networks

—We consider the problem of providing protection against failures in wireless networks subject to interference constraints. Typically, protection in wired networks is provided through the provisioning of backup paths. This approach has not been previously considered in the wireless setting due to the prohibitive cost of backup capacity. However, we show that in the presence of interference, protection can often be provided with no loss in throughput. This is due to the fact that after a failure, links that previously interfered with the failed link can be activated, thus leading to a “recapturing” of some of the lost capacity. We provide both an ILP formulation for the optimal solution, as well as algorithms that perform close to optimal. More importantly, we show that providing protection in a wireless network uses as much as 72% less protection resources as compared to similar protection schemes designed for wired networks, and that in many cases, no additional resources for protection are needed.
Decomposition of the Google PageRank and Optimal Linking Strategy

We provide the analysis of the Google PageRank from the perspective of the Markov Chain Theory. First we study the Google PageRank for a Web that can be decomposed into several connected components which do not have any links to each other. We show that in order to determine the Google PageRank for a completely decomposable Web, it is sufficient to compute a subPageRank for each of the connected components separately. Then, we study incentives for the Web users to form connected components. In particular, we show that there exists an optimal linking strategy that benefits a user with links inside its Web community and on the other hand inappropriate links penalize the Web users and their Web communities.
The Marginal Propensity to Spend on Adult

We examine how much of an extra dollar of parental lifetime resources will ultimately be passed on to adult children in the form of intervivos transfers and bequests. We infer bequests from the stock of wealth late in life. We use mortality rates and age specific estimates of the response of transfers and wealth to permanent income to compute the expected present discounted values of these responses to permanent income. Our estimates imply parents pass on between 2 and 3 cents out of an extra dollar of expected lifetime resources in bequests and about 2 cents in transfers. The estimates increase with parental income and are smaller for nonwhites. They imply that about 15 percent of the effect of parental income on lifetime resources of adult children is through transfers and bequests and about 85 percent is through the intergenerational correlation in earnings, although these estimates are sensitive to assumptions about the intergenerational earnings correlation, taxes, and the number of children. We compare our estimates to the implications of alternative computable benchmark models of savings behavior in order to assess the likely importance of intended bequests for the wealth/income relationship. * This is a heavily revised version of a paper entitled " The Effect of Parental Income on Wealth and Expected Bequests " , September 1999. We are grateful to participants in seminar presentations at the EM-TMR conference on savings and pensions at Università Ca' Foscari di helpful comments. We owe a special debt to Mariacristina De Nardi for valuable discussions and for providing us with simulations from her model.
Varieties of flexibilisation? The working lives of information and communications technology professionals in the UK and Germany

One feature of ‘flexibilisation’ concerns the growth of more individualised employment arrangements and career trajectories less connected to employing organisations. Informed by the Varieties of Capitalism approach, which emphasises the embeddedness of employment practices within discrete types of capitalist market economy, and based on rich qualitative data from interviews with 32 self-employed and directly employed ICT professionals in the United Kingdom and Germany, we investigate comparative variation in their experience of flexibilisation. The research findings not only indicate some commonality, particularly in respect of perceptions of independence
Towards Generalized Dominance Structures for Multi-Objective Optimization

Various dominance structures are proposed in the multi-objective optimization literature. However, a systematic procedure to understand their effect in determining the resulting efﬁcient set (Pareto-optimal front) is lacking. In this paper, we analyze and lay out properties of generalized dominance structures which will result in a non-empty efﬁcient set. We introduce a concept of anti-dominance structure, derived from the chosen dominance structure, to explain how the resulting non-dominated or efﬁcient set can be identiﬁed easily compared to using the dominance structure directly. The concept allows a uniﬁed explanation of optimal solutions for both single and multi-objective optimization problems. The anti-dominance structure is applied to analyze respective efﬁcient solutions for most popularly-used static and spatially-changing dominance structures. The theoretical and deductive results of this study can be utilized to create more meaningful dominance structures for practical problems, understand and identify resulting efﬁcient solutions, and help develop better test problems and algorithms for multi-objective optimization.
DATA GOVERNANCE STRATEGIES FOR DATA PLATFORMS – A DATA GOVERNANCE STRATEGIES FOR DATA PLATFORMS – A MULTIPLE CASE STUDY IN NURSING CARE MULTIPLE CASE STUDY IN NURSING CARE

It is long established that data from platforms can be useful for deriving patterned insights into people’s behavior and conduct. Data platforms are important in fields with limited data availability and strict regulatory and hierarchical structures, such as healthcare and nursing analytics. Hence, we carefully examine three forerunner initiatives in establishing data platforms in the context of nursing care along normative, organizational, and technical dimensions of governance. The cases were selected due to their high level of comparability and to demonstrate three different types of data governance strategies understood as actions to reconcile conflicting interests regarding data and dealing with prevalent data protection law – ranging from strictly processual approaches to the creation of synthetic data. These findings highlight the importance of considering data governance strategies concisely when building data platforms and suggest considerable variety in the configuration of data governance arrangements.
(2018). Instance-level Object Recognition Using Deep Temporal Coherence. In Advances in Visual Computing: 13th International Symposium, ISVC 2018,

In this paper we design and evaluate methods for exploiting temporal coherence present in video data for the task of instance object recognition. First, we evaluate the performance and generalisation capabilities of a Convolutional Neural Network for learning individual objects from multiple viewpoints coming from a video sequence. Then, we exploit the assumption that on video data the same object remains present over a number of consecutive frames. A-priori knowing such number of consecutive frames is a diﬃcult task however, specially for mobile agents interacting with objects in front of them. Thus, we evaluate the use of temporal ﬁlters such as Cumulative Moving Average and a machine learning approach using Recurrent Neural Networks for this task. We also show that by exploiting temporal coherence , models trained with a few data points perform comparably to when the whole dataset is available.
Short-term exposures to PM 10 and PM 2.5 and lung cancer hospitalization risk in Shenzhen, China: A Double Negative Controls Study

Few studies assess the short-term exposure to particulate matter (PM), including inhalable particles with an aerodynamic diameter of 10 F or less (PM 10 ) and �ne PM with an aerodynamic diameter of 2.5 e or less (PM 2.5 ), on lung cancer hospitalization risk, especially in developing countries. This study examines whether short-term exposures to PM 10 and PM 2.5 affect lung cancer hospitalization risk in Shenzhen, China. We evaluated short-term PM-lung cancer hospitalization associations using cancer hospital-based data during 2018-2019 in Shenzhen, China. Daily data on air pollutants and weather conditions were collected from publicly available online resources. We estimated the corresponding associations based on a double negative control design after adjusting for unmeasured confounding. Two-pollutant models were �tted to examine the robustness of associations. On average, per 10 w increase in weekly mean PM 10 concentration was associated with an 0.25 (95% con�dence interval [CI] 0.04-0.45) higher lung cancer hospitalization risk. The corresponding increase in lung cancer hospitalization risk for the same increase in PM 2.5 was 0.36 (95% CI 0.13-0.61). These detrimental effects remained after adjusting for sulfur dioxide and carbon monoxide and became stronger among males and those aged <75 years. No delayed effects were observed. Signi�cant unmeasured confounding effects were also observed.
Journal of Hospitality Financial Management Journal of Hospitality Financial Management

This study presents an empirical insight into the relationship between return on equity (ROE), financial leverage and size of firms in the restaurant industry for the period 1998 to 2003 using OLS regressions. Research results suggest that at least during the test period firm size had a more dominant effect on ROE of restaurant firms than debt use, larger firms earning significantly higher equity returns. Results also suggest that regardless of having lower financial leverage, smaller restaurant firms were significantly more risky than larger firms. As such, the dominance of size effect in the ROE-financial leverage relationship within the restaurant industry is better understood.
UWM Digital Commons UWM Digital Commons

Many recent studies focus on developing mechanisms to explain the black-box behaviors of neural networks (NNs). However, little work has been done to extract the potential hidden semantics (mathematical representation) of a neural network. A succinct and explicit mathematical representation of a NN model could improve the understanding and interpretation of its behaviors. To address this need, we propose a novel symbolic regression method for neural works (called SRNet) to discover the mathematical expressions of a NN. SRNet creates a Cartesian genetic programming (NNCGP) to represent the hidden semantics of a single layer in a NN. It then leverages a multi-chromosome NNCGP to represent hidden semantics of all layers of the NN. The method uses a (1+ 𝜆 ) evolutionary strategy (called MNNCGP-ES) to extract the final mathematical expressions of all layers in the NN. Experiments on 12 symbolic regression benchmarks and 5 classification benchmarks show that SRNet not only can reveal the complex relationships between each layer of a NN but also can extract the mathematical representation of the whole NN. Compared with LIME and MAPLE, SRNet has higher interpolation accuracy and trends to approximate the real model on the practical dataset 1 .
Drug Delivery and Pharmaceutical Technology Activation of Human Monocytes by Colloidal Aluminum Salts

The transcriptomeandproteomeof the monocytes were investigated by using quantitative polymerase chain reaction and mass spectrometry. Human monocytic THP-1 cells were used to investigate the effect of the particles on cellular maturation, differentiation, activation, and cytokine secretion, as measured by ﬂ ow cytometry and enzyme-linked immunosorbent assay. Each particle type resulted in a speci ﬁ c gene expression pro ﬁ le. IL-1ß and IL-6 secretion wassigni ﬁ cantlyupregulatedbyboehmiteandalum. Of the7surface markersinvestigated,onlyCD80was signi ﬁ cantly upregulated by alum and none by gibbsite or boehmite. Gibbsite hardly activated the monocytes. Boehmite activated human primary monocytes equally to alum, but induced a much milder stress-related response. Therefore, boehmite was identi ﬁ ed as a promising adjuvant candidate. © 2019 The Authors. Published by Elsevier Inc. on behalf of the American Pharmacists Association ® . This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/ 4.0/).
Credit Card Fraud Detection Prediction: Machine Learning Algorithm

Someone commits payment fraud when they obtain the payment information of another person and use it for unauthorized transactions or purchases. Owing tothe ease and convenience ofe-commerce, digital purchasingisbecoming increasingly popular today and because of the convenience of online shopping, many individuals prefer to shop online. This has resulted in a substantial rise in credit card fraud. Detecting and preventing payment fraud is difﬁcult because the standard rules-based anti-fraud systems deployed by banks cannot manage the high volume of online transactions. This creates unique difﬁculties for banks and a substantial increase in losses. Therefore, it is crucial to effectively identify and eliminate fraud. In our research, we use machine learning methods to construct models that can detect and analyze fraudulent payments. We primarily employ the Generalized Linear, Decision Tree, Gradient Boosting, and Naive Bayes Models, and determine that the Generalized Linear Model is the most effective.
Enabling Realms with the Arm Confidential Compute Architecture

The Armv9 architecture will have a new secure execution environment, called Realms. Realms includes minimal hardware changes, relying on firmware for most of its features, so that existing applications can run unchanged yet be secured. In this article, we explain how Realms works, how we used Coq to prove the correctness of the firmware, run benchmarks to compare performance using Realms, and contrast Realms with existing secure enclaves.
The Qualitative Report The

Abstract The subjectivity of qualitative researchers can be a contribution to qualitative research which at the same time requires commitment to on-going critical reflexivity regarding one’s positionality. More specifically, we address how to navigate the possibility that researcher subjectivity can culminate in role-confusion when the researcher is highly familiar with the research setting or research participants, when positioned as an “insider. ” We do this by adopting a critical paradigm approach that investigates the efficacy of “unlearning” as a strategy for challenging one’s assumptions as a researcher, particularly those assumptions that challenge the co-construction of knowledge that extends from research presuppositions. Drawing upon theoretical and methodological literature, we argue that intersubjective reflection is crucial to the process of unlearning. By critically reflecting on subjectivity, it becomes possible to deconstruct our research approach and its underlying assumptions, as well as our research findings. In turn, this creates space to unpack our role in how these approaches, assumptions, and findings are formulated, as well as space to challenge and reformulate these based on dialogue with participants. Through critical reflexivity addressing subjectivity and positionality in the context of research relations, researchers are challenged to consider how their insider knowledge, based on their individual experiences and personal meanings, can impinge on the research process.
Natural variation in the expression of a transmembrane protein influences cell growth in Arabidopsis thaliana petals by altering auxin availability

Background The same species of plant can exhibit highly diverse sizes and shapes of organs that are genetically determined. Characterising genetic variation underlying this morphological diversity is an important objective in evolutionary studies and it also helps identify the functions of genes influencing plant growth and development. Extensive screens of mutagenised Arabidopsis populations have identified multiple genes and mechanisms affecting organ size and shape, but relatively few studies have exploited the rich diversity of natural populations to identify genes involved in growth control. Results We screened a relatively well characterised collection of Arabidopsis thaliana ecotypes for variation in petal size. Association analyses identified sequence and gene expression variation on chromosome 4 that made a substantial contribution to differences in petal area. Variation in the expression of a previously uncharacterised gene At4g16850 (named as KSK ) had a substantial role on variation in organ size by influencing cell size. Over-expression of KSK led to larger petals with larger cells and promoted the formation of stamenoid features. The expression of auxin-responsive genes known to limit cell growth was reduced in response to KSK over-expression. ANT expression was also reduced in KSK over-expression lines, consistent with altered floral identities. Auxin availability was reduced in KSK over-expressing cells, consistent with changes in auxin-responsive gene expression. KSK may therefore influence auxin availability during petal development. Conclusions Understanding how genetic variation influences plant growth is important for both evolutionary and mechanistic studies. We used natural populations of Arabidopsis thaliana to identify sequence variation in a promoter region of Arabidopsis ecotypes that mediated differences in the expression of a previously undescribed membrane protein. This variation contributed to altered auxin availability and cell size during petal growth.
Mimir: Finding Cost-efﬁcient Storage Conﬁgurations in the Public Cloud

Public cloud providers offer a diverse collection of block storage options with different costs and performance SLAs. As a consequence, it is difﬁcult to select the right allocations for storage backends when moving data-heavy applications to the cloud. Mimir is a tool for automatically ﬁnding a cost-efﬁcient virtual storage cluster (VSC) conﬁguration for a customer’s storage workload and performance requirements. Importantly, since no single allocation type is best for all workloads, Mimir considers all allocation types and even heterogeneous mixes of them. In our experiments, compared to state-of-the-art approaches that consider only one allocation type, Mimir ﬁnds VSC conﬁgurations that reduce cost by up to 81% for substantial storage workloads. Acknowledgements: We thank the members and companies of the PDL Consortium—Alibaba, Amazon, Datrium, Facebook, Google, Hewlett Packard Enterprise, Hitachi, IBM Research, Intel, Micron, Microsoft Research, NetApp, Oracle, Salesforce, Samsung, Seagate, and Two Sigma. Hojin Park is supported in part by Korea Foundation for Advanced Studies.