Miracle-dz/UniFAR-FTUs huggingface dataset:
Linoleic acid and breast cancer risk: a meta-analysis

Abstract Objective Prior studies on linoleic acid, the predominant n-6 fatty acid, and breast cancer risk have generated inconsistent results. We performed a meta-analysis to summarize the evidence regarding the relationship of dietary and serum linoleic acid with breast cancer risk. Design Pertinent studies were identified by a search of PubMed and EMBASE. The fixed- or random-effect pooled measure was selected based on between-study heterogeneity. Results Eight prospective cohort studies and four prospective nested case–control studies, involving 10 410 breast cancer events from 358 955 adult females across different countries, were included in present study. Compared with the lowest level of linoleic acid, the pooled relative risk (RR; 95 % CI) of breast cancer was 0·98 (0·93, 1·04) for the highest level of linoleic acid. The pooled RR (95 % CI) for dietary and serum linoleic acid were 0·99 (0·92, 1·06) and 0·98 (0·88, 1·08), respectively. The RR (95 % CI) of breast cancer was 0·97 (0·91, 1·04), 0·95 (0·85, 1·07), 0·96 (0·86, 1·07), 0·98 (0·87, 1·10) and 0·99 (0·85, 1·14) for linoleic acid intake of 5, 10, 15, 20 and 25 g/d, respectively. The risk of breast cancer decreased by 1 % (RR=0·99; 95 % CI 0·93, 1·05) for every 10 g/d increment in linoleic acid intake. Conclusions This meta-analysis indicated that both dietary linoleic acid intake and serum linoleic acid level were associated with decreased risk of breast cancer, although none of the associations were statistically significant. Further investigations are warranted.
Incremental Tag-Aware User Profile Building to Augment Item Recommendations

Folksonomic system allows users to use tags to describe items. These tags do not just exist in the form of textual description, and they actually bear more meaning underneath, such as user preference. In this paper, we first show the distribution of preferences and semantic categories across a folksonomic system, and then develop a hybrid design to cope with the cold-start problem. Specifically, we speculate that the semantic categories formed in users‟ perspective and in items‟ perspective are different. They represent different preferences and meaning and are believed to be crucial in recommender algorithm design. Through a dimensionality reduction technique, the Latent Dirichlet Allocation, we demonstrate our speculation is correct. In this regards, we design a hybrid strategy for a movie recommender system. Our system consists of two core modules, namely tag recommendation and profilebased item recommendation. The former module leverages user-tags and item-tags to generate recommended tags, whereas the later one enables the use of the mixture of tags and keywords during the recommendation process. It is worth noting that such design requires no prior information of users and hence the cold-start problem prevented.
Evolutionary and ecological causes of species richness patterns in North American angiosperm trees

Climate and evolutionary factors (e.g. diversification, time-for-speciation, niche conservatism) are both thought to be major drivers of species richness in regional assemblages. However, few studies have simultaneously investigated the relative effects of climate and evolutionary factors on species richness across a broad geographical extent. Here, we assess their relative effects on species richness of angiosperm trees across North America. Species richness of angiosperm trees in 1175 regional assemblages were related to climate and phylogenetic structure using a structural equation modeling (SEM) approach. Climate was quantified based on the mean temperature of the coldest month and mean annual precipitation. Evolutionary factors (time-for-speciation vs diversification) were inferred from phylogeny-based measures of mean root distance, phylogenetic species variability, and net relatedness index. We found that at the continental scale, species richness is correlated with temperature and precipitation with approximately similar strength. In the SEM with net relatedness index and phylogenetic species variability and with all the 1175 quadrats, the total direct effect size of phylogenetic structure on species richness is greater than the total direct effect size of climate on species richness by a factor of 3.7. The specific patterns of phylogenetic structure (i.e. greater phylogenetic distances in more species rich regions) are consistent with the idea that time and niche conservatism drive richness patterns in North American angiosperm trees. We conclude that angiosperm tree species richness in regional assemblages in North America is more strongly related to patterns of phylogenetic relatedness than to climatic variation. The results of the present study support the idea that climatic and evolutionary explanations for richness patterns are not in conflict, and that evolutionary processes explain both the relationship between climate and richness and substantial variation in richness that is independent of climate.
Some HCI Priorities for GDPR-Compliant Machine Learning

In this short paper, we consider the roles of HCI in enabling the better governance of consequential machine learning systems using the rights and obligations laid out in the recent 2016 EU General Data Protection Regulation (GDPR)---a law which involves heavy interaction with people and systems. Focussing on those areas that relate to algorithmic systems in society, we propose roles for HCI in legal contexts in relation to fairness, bias and discrimination; data protection by design; data protection impact assessments; transparency and explanations; the mitigation and understanding of automation bias; and the communication of envisaged consequences of processing.
Autonomous driving based on accurate localization using multilayer LiDAR and dead reckoning

In this study, we developed an autonomous driving system for mountainous public roads. Three-dimensional normal distribution transform (NDT) scan matching is employed for localization and a model predictive controller is utilized for vehicle motion control. In order to increase the robustness of the localization method, the estimated poses are computed by an extended Kalman filter using dead reckoning and NDT information. The uncertainty of the pose estimated by NDT is determined by using the Hessian matrix computed in the optimization process for scan matching. We conducted experiments in a public road environment over 20 times and all of the tests were successful. The experimental results confirmed that the autonomous driving system can operate reliably in mountainous public roads. In addition, the evaluation results obtained for the localization method showed that accurate and robust localization can be achieved in mountainous rural environments.
Time-to-lane-change prediction with deep learning

Predicting driver behavior in general, and the problem of predicting an impending lane change in particular have been studied in the community under different aspects and setups. Mostly the problem is approached as a classification problem in which each class represents a possible action in the next few seconds. In this work, we re-define the task as a regression problem, in which the time until the ego-vehicle touches the other lane is predicted. In particular, we use Long-Short-Term-Memory (LSTM) networks to learn to predict the time to lane change. Even though a regression-based formulation of the problem captures more information and should thus be harder to learn, we can show that it provides slightly better results than a comparable classification-based approach. Moreover, the additional precision that we get about the exact time of the impending the lane change could be used to further increase a user's acceptance of advanced driver assistance systems. We also show that it is possible to individualize the network by fine-tuning it to a particular driver's behavior. The fine-tuning results in an improvement of F1-Score while observing about 20 minutes of driving data from that driver.
Field procedures in the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS)

The Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS) is a multi‐component epidemiological and neurobiological study of unprecedented size and complexity designed to generate actionable evidence‐based recommendations to reduce US Army suicides and increase basic knowledge about determinants of suicidality by carrying out coordinated component studies. A number of major logistical challenges were faced in implementing these studies. The current report presents an overview of the approaches taken to meet these challenges, with a special focus on the field procedures used to implement the component studies. As detailed in the paper, these challenges were addressed at the onset of the initiative by establishing an Executive Committee, a Data Coordination Center (the Survey Research Center [SRC] at the University of Michigan), and study‐specific design and analysis teams that worked with staff on instrumentation and field procedures. SRC staff, in turn, worked with the Office of the Deputy Under Secretary of the Army (ODUSA) and local Army Points of Contact (POCs) to address logistical issues and facilitate data collection. These structures, coupled with careful fieldworker training, supervision, and piloting, contributed to the major Army STARRS data collection efforts having higher response rates than previous large‐scale studies of comparable military samples. Copyright © 2013 John Wiley & Sons, Ltd.
Interleukin-6 and IgA-rheumatoid factor are crucial for baseline erosiveness, and anti-citrullinated peptide antibodies for radiographic progression in early rheumatoid arthritis treated according to a treat-to-target strategy

Objectives: To define baseline clinical and immunological characteristics [anti-citrullinated peptide antibodies (ACPAs), immunoglobulin M (IgM)- and IgA-rheumatoid factor (RF), and interleukin-6 (IL-6) levels] involved in determining baseline erosiveness, outcome, and radiographic progression among seropositive and seronegative early rheumatoid arthritis (ERA) patients. Method: The 408 ERA patients enrolled in the study were monitored every 3 months according to the treat-to-target strategy. At baseline and after 12 months, hand and foot radiographs were evaluated using the Sharp/van der Heijde erosion score. Results: At diagnosis, seronegative patients were older and had higher Disease Activity Scores (DASs) than seropositive patients. A higher risk of erosiveness at baseline was conferred by IgA-RF positivity and IL-6 plasma levels ≥7.6 pg/mL, particularly when simultaneously present. In multivariate analysis, disease duration and IL-6 plasma levels ≥7.6 pg/mL arose as independent variables associated with presence of erosions at onset. Radiographic progression at 1 year follow-up, which occurred in 11.1% of ERA patients, was predicted by ACPA positivity, together with higher age at diagnosis. Despite similar percentages of good European League Against Rheumatism response, DAS and Boolean remission being observed over time among seropositive and seronegative patients and between erosive and non-erosive subjects, ERA patients who were erosive at onset, IgA-RF seropositive, and simultaneously having high baseline IL-6 plasma levels (≥7.6 pg/mL) were treated to a greater extent with tumour necrosis factor blockers after 12 months. Conclusion: IgA-RF positivity and IL-6 plasma levels are crucial for baseline erosiveness, while ACPA positivity represents the strongest risk factor for developing radiographic progression in ERA.
Superhydrophobic and transparent surfaces on cotton fabrics coated with silica nanoparticles for hierarchical roughness

Superhydrophobic and transparent surfaces on cotton fabrics have been developed using silica nanomaterials. Initially, trichlorododecylsilane was treated on the silica nanoparticles to lower the surface energy of the fabric. By simply spraying alcohol suspensions containing hydrophobized silica nanoparticles, extremely water repellent coatings were formed on the textile fabrics. The effect of three types of alcohol solvent on the hydrophobicity of the coated cotton fabrics was examined by measuring the surface wettability. The treated cotton textiles in methanol exhibited contact angles higher than 160°, contact angle hysteresis lower than 10°, and good water repellency. It proved to be essential to form hierarchical morphology in achieving superhydrophobicity.
Next generation solar cells using flexible transparent electrodes based on silver nanowires and grapheme

Organic photovoltaic (OPV) devices have been developed extensively and optimised due to the use of nanomaterials in their construction. More recently, the demand for such devices to be flexible and mechanically robust has been a major area of research. Presently, Indium Tin Oxide (ITO) is the material that is used almost exclusively for transparent electrode. However, it has several drawbacks such as brittleness, high refractive index and high processing temperature. Furthermore, the price of ITO has been highly volatile due to scarcity of indium resources and the increased consumption of the material. Therefore, cheap, flexible and solution-processed transparent conductors are required for emerging optoelectronic devices with flexible construction which can be promising for wearable or environmentally adaptable devices purposes such as flexible solar cells and displays. Therefore, over the past decade an alternative material has been sought intensively, particularly in the need for producing large area flexible transparent electrodes. Many materials have been investigated but most investigations have focused on carbon nanotube (CNT), graphene flakes and metallic nanowires. Silver nanowires (Ag NWs) networks have been proven to show a high electrical conductivity with high optical transmittance. This special characteristic is desirable in transparent conductive electrodes in optoelectronic applications such as solar cells, light emitting diodes, and touch screen. On the other hand, Polymeric substrates that act as a non-brittle scaffold as well as protective packaging of the OPV are an essential element for such an “All-plastic” device. However, for such applications where the coating should be relatively hard a bottleneck to fabricating large area homogeneous films is associated with the formation of cracks as a result of local mismatches in mechanical properties during film formation. In this work, the fabrication and characterization of flexible transparent electrodes of Ag NWs on flexible substrates by spray deposition technique have been described. Furthermore, a way to enhance the electrical and mechanical properties of the Ag NWs transparent electrodes by incorporating a low density ensemble of graphene on top of the metal electrode networks using the Langmuir-Schafer has been achieved. Interestingly, the electrical conductivity in these hybrid electrodes is stable over relatively large strains during mechanical agitation indicating that such electrodes may have important application in future applications. Finally, producing crack-free monolayer latex over large area has been fabricated and characterised. Therefore, the polymer latex thin film has promising applications as purposes of hard coatings.
Scrutable Feature Sets for Stance Classification

This paper describes and evaluates a novel feature set for stance classification of argumentative texts; i.e. deciding whether a post by a user is for or against the issue being debated. We model the debate both as attitude bearing features, including a set of automatically acquired ‘topic terms’ associated with a Distributional Lexical Model (DLM) that captures the writer’s attitude towards the topic term, and as dependency features that represent the points being made in the debate. The stance of the text towards the issue being debated is then learnt in a supervised framework as a function of these features. The main advantage of our feature set is that it is scrutable: The reasons for a classification can be explained to a human user in natural language. We also report that our method outperforms previous approaches to stance classification as well as a range of baselines based on sentiment analysis and topic-sentiment analysis.
Undirected Rigid Formations Are Problematic

In a recent paper, a systematic method was proposed for devising gradient control laws for asymptotically stabilizing a large class of rigid, undirected formations in two-dimensional space assuming all agents are described by kinematic point models. The aim of this paper is to explain what happens to such formations if neighboring agents have slightly different understandings of what the desired distance between them is supposed to be. What one would expect would be a gradual distortion of the formation from its target shape as discrepancies in desired distances increase. While this is observed for the gradient laws in question, something else quite unexpected happens at the same time. It is shown for any rigidity-based, undirected formation of this type which is comprised of three or more agents, that if some neighboring agents have slightly different understandings of what the desired distances between them are supposed to be, then almost for certain, the trajectory of the resulting distorted but rigid formation will converge exponentially fast to a closed circular orbit in two-dimensional space which is traversed periodically at a constant angular speed.
Reducing the intrusive driving behaviour in lane departure avoidance system using machine learning approach

This paper proposes two components to reduce the intrusive behaviour in Lane Departure Avoidance System (LDAS) during safety intervention. The components are; i) human mimic Driver Model (DM) for lane recovery and keeping assessment, and ii) Predictive Model (PM) that has the ability to predict unintentional lane departure. The lane departure prediction is used to minimise the lane keeping steering angle (μk) output from the Lane Keeping Driver Model (LKDM) and the lane recovery steering angle (μr) from the Lane Recovery Driver Model (LRDM). With this design, a small corrective input (steering angle) that guides the vehicle to compensate disturbance (unintentional lane departure) shall reduce the intrusive behaviour during safety intervention of the LDAS. Both model, LKDM and LRDM, are combined to form a single DM. Subsequently, both of the DM and PM are merged to form the LDAS. Two methods were used to formulate the LDAS; i) Neural Network (NN, benchmark approach) and ii) Nonlinear Autoregressive with Exogenous Inputs (NARX, proposed solution). Both LDAS models are evaluated against three road curvatures (ρ = 800m, 3000m, 10000m) and injected with a simulated unintentional lane departure disturbance. The response of each LDAS model is analyzed against the intrusive driving assessment data. The result shows that with a sufficient prediction of time to lane crossing (tLC) provided by the PM, the DM is able to provide a small lane recovery and lane keeping steering angle thus ensuring the LDAS to operate within the non-intrusive driving behaviour. This study is important in providing a more human-behavior like LDAS performance.
How to set listing criteria for small and medium-sized enterprises in Hungary?

Since the onset of the crisis, several studies have demonstrated that the positive effect of bank lending on economic growth is limited. After a certain level, financial deepening has no positive effect on the real economy, and moreover the financial sector may actually have a negative effect on growth. Therefore, providing more diversified financing opportunities for the corporate sector is of key importance, especially for small and medium-sized enterprises (SMEs). The Budapest Stock Exchange (BSE) is taking a step in this direction now, by establishing a new stock exchange strategy for the next five years, i.e. it intends to actively open towards the SME segment as well. The objective of the study is to create a scoring system that may assist in finding those companies ? primarily in the SME sector ? for which it may be worth considering going public. We develop and test a quantitative scoring and ranking method on a database containing Hungarian corporate balance sheet and profit and loss account data. We present that there are companies in the SME sector for which it may be worth considering going public; moreover, the group of the top companies based on the ranking had better performance in the past than the companies currently appearing in the T and standard categories of the BSE.
All-digital SoC thermal sensor using on-chip high order temperature curvature correction

Accurate, compact thermal sensors are desirable in many applications, including on-chip temperature monitoring for processors with dynamic throttling and reliability management. Modern thermal sensors are limited in either area, robustness, or accuracy. This work sidesteps strong linearity requirements for reference and PTAT elements in the sensor by performing a higher-order fitting of more relaxed PTAT and CTAT elements using an embedded calculation compute engine. A compact 24 × 10μm sensing element (40nm CMOS) achieves inaccuracy of <;1°C across 30 chips with 2-point calibration and a resolution of 0.02°C.
Invariant Object Recognition and Pose Estimation with Slow Feature Analysis

Primates are very good at recognizing objects independent of viewing angle or retinal position, and they outperform existing computer vision systems by far. But invariant object recognition is only one prerequisite for successful interaction with the environment. An animal also needs to assess an object's position and relative rotational angle. We propose here a model that is able to extract object identity, position, and rotation angles. We demonstrate the model behavior on complex three-dimensional objects under translation and rotation in depth on a homogeneous background. A similar model has previously been shown to extract hippocampal spatial codes from quasi-natural videos. The framework for mathematical analysis of this earlier application carries over to the scenario of invariant object recognition. Thus, the simulation results can be explained analytically even for the complex high-dimensional data we employed.