Miracle-dz/UniFAR-FTUs huggingface dataset:
Architectural Style Classification Based on Feature Extraction Module

Standard classification tasks have already achieved good results in computer vision. However, the task of Architectural style classification yet faces many challenges, since the rich inter-class relationships between different styles may disturb the classification accuracy. To better classify buildings, we propose a feature extraction module based on image preprocessed with Deformable Part-based Models (DPM). Specifically, we first use DPM to remove elements that are not related to classification, and capture representative elements of buildings, and then these elements are sent to our feature extraction module. In our feature extraction module, we adopt our improved ensemble projection method to maximize the inter-class distance and minimize the intra-class distance to find the common features in the same style and differences among different styles. Finally, the performances of several classifiers are tested and the best one of SVM classifier is selected to output the ultimate accuracy. Experimental results show that our approach achieves promising performance and is superior to previous methods.
Attacking Network Isolation in Software-Defined Networks: New Attacks and Countermeasures

With the development of virtualization technology and fast expansion of network-scale, SDN has been employed in various cases from campus networks to cloud data center networks. However, SDN networks are also facing some new security issues, relative to the traditional networks. In this work, we demonstrate a novel network isolation attack in SDN networks, called Network Harvesting, that lets an attacker can access to the user's network privileges without the awareness of victim and OpenFlow SDN architecture, which significantly increases persistence. We then present a defense, SpoofDefender, that prevents network isolation attacks or other spoofing attacks by leveraging SDN's data and control plane separation, global network view, and programmatic control of the network, while building upon IEEE 802.1x and encryption. In addition, we also implement SpoofDefender on ONOS 1.10. 4 and Mininet with a real network, and extensive simulation results demonstrate that our proposed SpoofDefender is highly effective in terms of computation and communication costs.
Saliency Detection by Deep Network with Boundary Refinement and Global Context

A novel end-to-end fully convolutional neural network for saliency detection is proposed in this paper, aiming at refining the boundary and covering the global context (GBR-Net). Previous CNN based methods for saliency detection are universally accompanied with blurring edge and ambiguous salient object. To tackle this problem, we propose to embed the boundary enhancement block (BEB) into the network to refine edge. It keeps the details by the mutual-coupling con-volutionallayers. Besides, we employ a pooling pyramid that utilizes the multi-level feature informations to search global context, and it also contributes as an auxiliary supervision. The final saliency map is obtained by fusing the edge refinement with global context extraction. Experiments on four benchmark datasets prove that the proposed saliency detection model gains an edge over the state-of-the-art approaches.
Blood Biochemical Profile in Fertile and Repeat Breeder Ongole Cross Breed Cows.

BACKGROUND AND OBJECTIVE Repeat breeding is a major problem in beef cows. The objective of the present study was to determine the blood biochemical levels in repeat breeder and fertile Ongole cross breed cows. MATERIALS AND METHODS This study compared the blood biochemical profiles from 30 repeat breeders and 30 fertile Ongole cross breed cows. Plasma glucose, total cholesterol, total protein and urea nitrogen concentrations were examined in repeat breeder cows and compared with those of fertile cows. Blood samples were collected from the caudal vein into anticoagulant-coated tubes. Cells were removed from plasma by centrifugation and blood glucose, total cholesterol and urea nitrogen concentrations were measured using commercially available kits; a non-commercial kit was used to determine total protein content. RESULTS Significantly different blood profiles between repeat breeder and fertile cows were reported. Specifically, repeat breeder cows had lower levels of glucose, total cholesterol and total protein, but higher levels of urea nitrogen than fertile cows. CONCLUSION Finally, a low level of total protein, total cholesterol and glucose, accompanied by a high level of urea nitrogen, may cause reproductive problems in repeat breeders. Additionally, present findings indicated that biochemical blood profiling could be used as a diagnostic tool for repeat breeding.
Posterior Prototyping: Bridging the Gap between Bayesian Record Linkage and Regression

Record linkage (entity resolution or de-deduplication) is the process of merging noisy databases to remove duplicate entities. While record linkage removes duplicate entities from the data, many researchers are interested in performing inference, prediction or post-linkage analysis on the linked data, which we call the downstream task. Depending on the downstream task, one may wish to find the most representative record before performing the post-linkage analysis. Motivated by the downstream task, we propose first performing record linkage using a Bayesian model and then choosing representative records through prototyping. Given the information about the representative records, we then explore two downstream tasks - linear regression and binary classification via logistic regression. In addition, we explore how error propagation occurs in both of these settings. We provide thorough empirical studies for our proposed methodology, and conclude with a discussion of practical insights into our work.
Documentation of alcohol, cigarette and cannabis use by psychiatric and general practice trainees

Alcohol misuse and the consumption of cigarettes and cannabis are associated with well-established impairments in physical and psychological health. Excessive consumption of these substances in Ireland now poses a major public health problem. Per capita consumption of alcohol in Ireland increased by 41% between 1989 and 1999, the highest such increase among EU countries, leaving Ireland with the second highest alcohol consumption levels of all EU countries. Tobacco smoke remains the leading preventable cause of death and disability in Ireland. While statistics on cannabis use are less clear, a recent UN study found Ireland to have the highest levels of cannabis use in the 23 countries of Western Europe.
Novel 3-D Irregular-Shaped Geometry-Based Channel Modeling for Semi-Ellipsoid Vehicle-to-Vehicle Scattering Environments

This letter presents a 3-D irregular-shaped geometry-based stochastic model for vehicle-to-vehicle (V2V) radio propagation environments, in which an asymmetric directional multiple input and multiple output technique is employed at the mobile transmitter (MT). The time-varying geometric path lengths are introduced in the proposed channel model to capture the non-stationarity, which is caused by the motion of the MT and mobile receiver. More importantly, the impacts of the model parameters and non-stationarity, i.e., relative moving directions and relative moving time, on the channel statistical properties are investigated. The results show that the proposed 3-D model fits the prior results very well, which demonstrate that the model is practical to provide guidelines for the system design and performance analysis of V2V communication systems.
MPTV: Matching Pursuit-Based Total Variation Minimization for Image Deconvolution

Total variation (TV) regularization has proven effective for a range of computer vision tasks through its preferential weighting of sharp image edges. Existing TV-based methods, however, often suffer from the over-smoothing issue and solution bias caused by the homogeneous penalization. In this paper, we consider addressing these issues by applying inhomogeneous regularization on different image components. We formulate the inhomogeneous TV minimization problem as a convex quadratic constrained linear programming problem. Relying on this new model, we propose a matching pursuit-based total variation minimization method (MPTV), specifically for image deconvolution. The proposed MPTV method is essentially a cutting-plane method that iteratively activates a subset of nonzero image gradients and then solves a subproblem focusing on those activated gradients only. Compared with existing methods, the MPTV is less sensitive to the choice of the trade-off parameter between data fitting and regularization. Moreover, the inhomogeneity of MPTV alleviates the over-smoothing and ringing artifacts and improves the robustness to errors in blur kernel. Extensive experiments on different tasks demonstrate the superiority of the proposed method over the current state of the art.
An Online Learning Approach to Network Application Optimization with Guarantee

Network application optimization is essential for improving the performance of the application as well as its user experience. The network application parameters are crucial in making proper decisions for network application optimizations. However, many works are impractical by assuming a priori knowledge of the parameters which are usually unknown and need to be estimated. There have been studies that consider optimizing network application in an online learning context using multi-armed bandit models. However, existing frameworks are problematic as they only consider to find the optimal decisions to minimize the regret, but neglect the constraints (or guarantee) requirements which may be excessively violated. In this paper, we propose a novel online learning framework for network application optimizations with guarantee. To the best of our knowledge, we are the first to formulate the stochastic constrained multi-armed bandit model with time-varying “multi-level rewards” by taking both “regret” and “violation” into consideration. We are also the first to design a constrained bandit policy, Learning with Minimum Guarantee (LMG), with provable sub-linear regret and violation bounds. We illustrate how our framework can be applied to several emerging network application optimizations, namely, (1) opportunistic multichannel selection, (2) data-guaranteed crowdsensing, and (3) stability-guaranteed crowdsourced transcoding. To show the effectiveness of LMG in optimizing these applications with different minimum requirements, we also conduct extensive simulations by comparing LMG with existing state-of-the-art policies.
The status of wound care units in Spain

often been implemented in isolation. Among these responses are the specific training and specialisation of healthcare practitioners (Cowman e al, 2012; Sibbald et al, 2012), the development of certification and accreditation systems (Sibbald et al, 2012; Masturzo et al, 2013), the implementation of standardisation tools for clinical practice such as clinical practice guidelines (Hernández MartínezEsparza and Verdú Soriano, 2012; Sibbald et al, 2012), the use of telehealth or telemedicine (Moore et al, 2015; Sood et al, 2016), and the adoption of new organisational strategies (van Acker, 2012; Moore et al, 2014). In the latter case, the adoption of interdisciplinary or transdisciplinary approaches that allow the effective treatment of these lesions by a team is particularly relevant (Moore et al, 2014; Scarborough, 2017). In this context, in recent years the wound clinic or wound care unit has emerged, with the aim of integrating responses to poor healing. They do this by designing specific care structures for patients with chronic wounds. Wound care units usually have heterogeneous and uneven characteristics, receiving different denominations (i.e. different names, such as wound clinics, wound units, wound commission, etc), depending on where they are located geographically. These structures generally have one feature in common — they are built around an organisational system based on a department, service or structure that integrates professionals from different disciplines (Gottrup, 2004a; Kim The term ‘chronic wounds’ encompasses a large number of injuries, such as pressure ulcers, lower extremity ulcers and diabetic foot ulcers, among others (Vowden, 2011). Although these types of injury have their own, very differentiated, characteristics derived from a diverse pathophysiology and aetiology, we can define chronic wounds as those wounds that have failed to progress through the normal phases of healing within expected timeframes (Sibbald et al, 2011; Vowden, 2011). It is necessary to know the particularities associated with the pathophysiology and aetiology of each type of chronic wound in order to achieve adequate healing within an acceptable period of time, and have the ability to identify, address and understand factors that may slow or prevent full healing (Sibbald et al, 2011; Vowden, 2011). Chronic wounds are a serious public health problem, as they are associated with high morbidity and mortality, and are affecting increasing numbers of people across the world (Sen et al, 2009). They put an enormous economic burden on health services, due to direct and indirect costs (Posnett et al, 2009; Guest et al, 2017). In addition, they cause a significant loss of well-being and impair quality of life for sufferers, which has a considerable social impact (González-Consuegra et al, 2011; International Consensus, 2012). The theoretical and practical answers to the complex and growing problem of chronic The status of wound care units in Spain
Multi-Grained Deep Feature Learning for Pedestrian Detection

In this paper, we address the challenging problem of detecting pedestrians who are heavily occluded or far from camera. Unlike most existing pedestrian detection methods which only use coarse-resolution feature maps with fixed receptive field, our approach exploits multi-grained deep features to make the detector more robust to visible parts of occluded pedestrians and small-size targets. Specifically, we jointly train a scale-aware network and a human parsing network in a semi-supervised manner with only bounding box annotation. We carefully design the scale-aware network to predict pedestrians of particular scales using most appropriate feature maps, by matching their receptive field with the target sizes. The human parsing network generates a fine-grained attentional map which helps guide the detector to focus on the visible parts of occluded pedestrians and small-size instances. Both networks are computed in parallel and form an unified single stage pedestrian detector, which assures a great trade-off between accuracy and speed. Experiments on two challenging benchmarks, Caltech and KITTI, demonstrate the effectiveness of our proposed approach, which in addition, executes 2× faster than competitive methods.
Impact of CNT Diameter Distribution on CNT filled Via Scaling

Via scaling at a given CNT synthesis process is evaluated in terms of resistance variation among vias. As the underlying source of resistance variation, the relation between CNT diameter distribution and the catalyst thickness is characterized. CNT resistance model is used to predict via resistance from CNT structures in a via. Catalyst thickness scaling scheme considering CNT diameter variation is proposed.
Sexistential Crisis: An Intersectional Analysis of Gender Expression and Sexual Orientation in Masculine Overcompensation

ABSTRACT Masculine overcompensation—a phenomenon where men react to masculinity threats by endorsing hypermasculine ideals—has been demonstrated among straight men but has yet to be examined among gay men. The current study therefore proposed to examine whether gay men overcompensate similarly to their straight counterparts by providing participants with randomized feedback that threatened their masculinity. Overcompensation was measured in 867 online respondents by administering a series of questionnaires regarding views of pornography, rape, sex roles, and political orientation. Although our hypothesis was not confirmed, results revealed the intersectionality of both sexual orientation and self-reported gender expression regarding the formation of different views and beliefs. Specifically, masculinity was differentially related to homophobic attitudes, more callous views toward victims of sexual assault, and various components of attitudes toward pornography in gay and straight men. Masculine gay males held stereotypically masculine views less strongly than their masculine straight counterparts, providing evidence that gay males adopt a different type of masculinity than straight males—something of a “masculinity lite. ” Such findings point to the converging influence of sexual orientation and gender expression as contributors relevant to the attitudes of gay and straight men. This information adds to a growing body of literature on differences between gay and straight men and can be used to inform theory, education, and clinical practice, particularly in settings where men grapple with the implications of their masculinity.
3D-Xpath: high-density managed DRAM architecture with cost-effective alternative paths for memory transactions

The advance of DRAM manufacturing technology slows down, whereas the density and performance needs of DRAM continue to increase. This desire has motivated the industry to explore emerging Non-Volatile Memory (e.g., 3D XPoint) and the high-density DRAM (e.g., Managed DRAM Solution). Since such memory technologies increase the density at the cost of longer latency, lower bandwidth, or both, it is essential to use them with fast memory (e.g., conventional DRAM) to which hot pages are transferred at runtime. Nonetheless, we observe that page transfers to fast memory often block memory channels from servicing memory requests from applications for a long period. This in turn significantly increases the high-percentile response time of latency-sensitive applications. In this paper, we propose a high-density managed DRAM architecture, dubbed 3D-XPath for applications demanding both low latency and high capacity for memory. 3D-XPath DRAM stacks conventional DRAM dies with high-density DRAM dies explored in this paper and connects these DRAM dies with 3D-XPath. Especially, 3D-XPath allows unused memory channels to service memory requests from applications when primary channels supposed to handle the memory requests are blocked by page transfers at given moments, considerably increasing the high-percentile response time. This can also improve the throughput of applications frequently copying memory blocks between kernel and user memory spaces. Our evaluation shows that 3D-XPath DRAM decreases high-percentile response time of latency-sensitive applications by ~30% while improving the throughput of an I/O-intensive applications by ~39%, compared with DRAM without 3D-XPath.
Comparative population genomics reveals key barriers to dispersal in Southern Ocean penguins

The mechanisms that determine patterns of species dispersal are important factors in the production and maintenance of biodiversity. Understanding these mechanisms helps to forecast the responses of species to environmental change. Here, we used a comparative framework and genomewide data obtained through RAD‐Seq to compare the patterns of connectivity among breeding colonies for five penguin species with shared ancestry, overlapping distributions and differing ecological niches, allowing an examination of the intrinsic and extrinsic barriers governing dispersal patterns. Our findings show that at‐sea range and oceanography underlie patterns of dispersal in these penguins. The pelagic niche of emperor (Aptenodytes forsteri), king (A. patagonicus), Adélie (Pygoscelis adeliae) and chinstrap (P. antarctica) penguins facilitates gene flow over thousands of kilometres. In contrast, the coastal niche of gentoo penguins (P. papua) limits dispersal, resulting in population divergences. Oceanographic fronts also act as dispersal barriers to some extent. We recommend that forecasts of extinction risk incorporate dispersal and that management units are defined by at‐sea range and oceanography in species lacking genetic data.
Duration and Intensity of Kindergarten Attendance and Secondary School Track Choice

This paper investigates the relationship between kindergarten attendance and secondary school track choice in West-Germany. Our analysis is based on a panel of 12 to 14-year olds with information from age two on, drawn from the German SocioEconomic Panel (GSOEP) 1984? 2005. We estimate binary probit models to assess the impact of the duration (in years) and the intensity (half-day or full-day) of kindergarten attendance. Our results indicate that kindergarten non-attendance is associated with a significantly lower probability to attend the highest secondary school track (? Gymnasium? ). Further, full-day attendance is associated with a decreasing probability of attending the highest secondary school track for every duration of preschool child care. Thus, intensity seems to matter more than duration.
Housing Tenure, Energy Consumption and the Split-Incentive Issue in Australia

Abstract In recent years, there has been growing global recognition of the need to reduce carbon emissions in response to climate change concerns. It is generally acknowledged that the energy efficiency of existing homes can be improved, but there are significant barriers to its uptake. In particular, improving the energy efficiency of private rental housing presents unique policy challenges due to a split-incentive problem. This has prompted some governments to introduce programmes that encourage landlords to improve the energy efficiency of their properties. While landlords are responsible for the purchase of many energy-consuming household appliances, tenants are responsible for energy bills. Since the landlord does not reap the immediate benefits of investment in energy-efficient equipment, the incentives motivating such investment are weaker than for homeowners. This paper aims to quantify the magnitude of the split-incentive problem in the Australian private rental housing market by invoking a modelling approach where energy expenditure is estimated as a function of housing tenure, dwelling type, location, climate and other socio-demographic variables. We find no evidence in support of the split-incentive hypothesis in Australia. The paper concludes that differences in housing policy arrangements could be critical to the presence and importance of split incentives.
Free and Low Cost Datasets for International Mountain Cartography

Analytical relief shading rendered from digital elevation models (DEM) has become a common method to illustrate topography. Effective cartographic displays are achieved when DEMs are combined with landcover information derived from satellite imagery. Until recently, however, finding appropriate DEMs and satellite imagery for mountain cartography in remote international areas was a resource intensive proposition. In the last two years, three new sources of remote sensing imagery have become available free or almost free of costs to mountain cartographers: multispectral imagery and DEMs from the Advanced Spaceborne Thermal Emission and Reflection (ASTER) instrument on board the Terra platform; the Shuttle Radar Topography Mission’s (SRTM) C-Band InSAR 3 arc second (~90m) DEMs; and, Earthsat Landsat Geocover mosaics. In addition some service providers have begun offering custom DEMs at various resolutions extracted from Russian topographic maps at reasonable prices. Unfortunately for our purposes some of these datasets are prone to higher failure rates (i.e. data gaps) in high mountain areas with complex terrain, high incidences of cloud cover and/or glaciated landcover. We discuss the relative merits, limitations and sources of errors associated with these new data sources. We will examine and evaluate proposed and implemented mitigation measures and using actual case studies will demonstrate our methods for fusing these new datasets in order to minimize their limitations.