Miracle-dz/UniFAR-FTUs huggingface dataset:
Continuous Local Parameterization of Polygons

Texture mapping has now become a standard technique for adding visual details to 3d models. Nevertheless, intuitive mapping tools have made little progress in the past decade, compared to other computer graphics areas: deﬁning a good quality parameterization is still a tedious process, requiring experienced and skilled users. In this paper, we speciﬁcally address the problem of ﬁnding automatically a parameterization of a reasonably small portion of the surface. This method is simple and efﬁcient, which makes it suitable for interactive applications. It is also continuous in terms of the user input, so that it is stable during animation and interaction. Finally, it only requires the user to deﬁne a small set of intuitive parameters, mostly position, size and orientation of the area to be parameterized. Thus, it can be used by inexperienced users and easily connected to existing applications. We show how our method can improve 3D-paint systems and decal placement on surfaces. The same approach can also be used to adapt 2D image processing tools to geometry ﬁltering.
A Framework to Assess the Impact of New Animal Management Technologies on Welfare: A Case Study of Virtual Fencing

To be ethically acceptable, new husbandry technologies and livestock management systems must maintain or improve animal welfare. To achieve this goal, the design and implementation of new technologies need to harness and complement the learning abilities of animals. Here, from literature on the cognitive activation theory of stress (CATS), we develop a framework to assess welfare outcomes in terms of the animal's affective state and its learned ability to predict and control engagement with the environment, including, for example, new technologies. In CATS, animals' perception of their situation occurs through cognitive evaluation of predictability and controllability (P/C) that influence learning and stress responses. Stress responses result when animals are not able to predict or control both positive and negative events. A case study of virtual fencing involving avoidance learning is described. Successful learning occurs when the animal perceives cues to be predictable (audio warning always precedes a shock) and controllable (operant response to the audio cue prevents receiving the shock) and an acceptable welfare outcome ensues. However, if animals are unable to learn the association between the audio and shock cues, the situation retains low P/C leading to states of helplessness or hopelessness, with serious implications for animal welfare. We propose a framework for determining welfare outcomes and highlight examples of how animals' cognitive evaluation of their environment and their ability to learn relates to stress responses. New technologies or systems should ensure that predictability and controllability are not at low levels and that operant tasks align with learning abilities to provide optimal animal welfare outcomes.
On the Compression of Messages in the Multi-Party Setting

We consider the following communication task in the multi-party setting, which involves joint random variables <inline-formula> <tex-math notation="LaTeX">$XYZMN$ </tex-math></inline-formula> with the property that <inline-formula> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> is independent of <inline-formula> <tex-math notation="LaTeX">$YZN$ </tex-math></inline-formula> conditioned on <inline-formula> <tex-math notation="LaTeX">$X$ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> is independent of <inline-formula> <tex-math notation="LaTeX">$XZM$ </tex-math></inline-formula> conditioned on <inline-formula> <tex-math notation="LaTeX">$Y$ </tex-math></inline-formula>. Three parties Alice, Bob and Charlie, respectively, observe samples <inline-formula> <tex-math notation="LaTeX">$x,y$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$z$ </tex-math></inline-formula> from <inline-formula> <tex-math notation="LaTeX">$XYZ$ </tex-math></inline-formula>. Alice and Bob communicate messages to Charlie with the goal that Charlie can output a sample <inline-formula> <tex-math notation="LaTeX">${ \left ({m,n }\right) }$ </tex-math></inline-formula> such that the distribution of <inline-formula> <tex-math notation="LaTeX">${ \left ({x,y,z,m,n }\right) }$ </tex-math></inline-formula> is close to <inline-formula> <tex-math notation="LaTeX">$XYZMN$ </tex-math></inline-formula>. This task reflects the <italic>simultaneous message passing</italic> communication complexity. Furthermore, it is a generalization of some well studied problems in information theory, such as distributed source coding, source coding with a helper and one sender and one receiver message compression. It is also closely related to the lossy distributed source coding task. Our main result is an achievable communication region for this task in the one-shot setting, through which we obtain a nearly optimal characterization using auxiliary random variables of bounded size. We employ our achievability result to provide a nearly optimal one-shot communication region for the task of lossy distributed source coding, in terms of auxiliary random variables of bounded size. Finally, we show that interactions are necessary to achieve the optimal expected communication cost.
Association Between BRCA Status and Triple-Negative Breast Cancer: A Meta-Analysis

Triple-negative breast cancer (TNBC) is a subtype of aggressive breast cancer and characterized by a lack of the expression of estrogen receptor, progesterone receptor and human epidermal growth factor receptor 2. BRCA genes are tumor-suppressor genes that are involved in DNA damage repair and mutations of BRCA genes may increase the risk of developing breast cancer and/or ovarian cancer due to defective DNA repair mechanisms. However, the relationship between BRCA status and TNBC needs to be further investigated and validated. The aim of this meta-analysis was to evaluate the association between BRCA status and TNBC. We systematically searched the electronic databases of MEDLINE (PubMed), Embase, and Cochrane Library to identify relevant publications from April, 1959 to November, 2017. The data from the studies were examined by a meta-analysis using STATA software to calculate the odds ratio (OR) with 95% confidence interval (CI) by fixed-effect and random-effect models. We identified 16 qualified studies from 527 publications with 46,870 breast cancer patients including 868 BRCA1 mutations (BRCA1Mut) carriers, 739 BRCA2 mutations (BRCA2Mut) carriers, and 45,263 non-carriers. The results showed that breast cancer patients with BRCA1Mut carriers were more likely to have TNBC than those of BRCA2Mut carriers (OR: 3.292; 95% CI: 2.773–3.909) or non-carriers (OR: 8.889; 95% CI: 6.925–11.410). Furthermore, high expression of nuclear grade and large tumor burden (>2 cm) were significantly more common in breast cancer patients with BRCA1Mut carriers than those of BRCA2Mut carriers (OR: 2.663; 95% CI: 1.731–4.097; P = 0.211) or non-carriers (OR: 1.577; 95% CI: 1.067–2.331; P = 0.157). The data suggest that breast cancer patients with BRCA1Mut are more likely to have TNBC, high nuclear grade, and larger tumor burden.
Security Namespace: Making Linux Security Frameworks Available to Containers

Lightweight virtualization (i.e., containers) offers a virtual host environment for applications without the need for a separate kernel, enabling better resource utilization and improved efficiency. However, the shared kernel also prevents containers from taking advantage of security features that are available to traditional VMs and hosts. Containers cannot apply local policies to govern integrity measurement, code execution, mandatory access control, etc. to prevent application-specific security problems. Changes have been proposed to make kernel security mechanisms available to containers, but such changes are often adhoc and expose the challenges of trusting containers to make security decisions without compromising host system or other containers. In this paper, we propose security namespaces, a kernel abstraction that enables containers to have an autonomous control over their security. The security namespace relaxes the global and mandatory assumption of kernel security frameworks, thus enabling containers to independently define security policies and apply them to a limited scope of processes. To preserve security, we propose a routing mechanism that can dynamically dispatch an operation to a set of containers whose security might be affected by the operation, therefore ensuring the security decision made by one container cannot compromise the host or other containers. We demonstrate security namespace by developing namespaces for integrity measurement and mandatory access control in the Linux kernel for use by Docker containers. Results show that security namespaces can effectively mitigate security problems within containers (e.g., malicious code execution) with less than 0.7% additional latency to system call and almost identical application throughput. As a result, security namespaces enable containers to obtain autonomous control over their security without compromising the security of other containers or the host system.
Analysis of Efficient Processes for Optimization in a Distributed Database

Distributed databases (DDB) help store and process data, which grows due to the current demand for voice, video and data; that require efficient processes for their optimization. The objective is to analyze the efficient processes available for the optimization of a distributed database, to determine the impact on data processing. We used the deductive method for the analysis of the information of the reviewed articles and the exploratory research; to provide a reference of the current situation in the optimization of a database. It turned out a table with the summaries of the optimization problems and the proposed solutions for the processes; in this table a score of zero to five was made based on the information provided by the researchers of the reference and table 1; considering the higher qualification that allows to improve the efficient processes for the optimization in a distributed database. It was concluded that each researcher aims to present an alternative to improve response times through proposals such as: Algorithms, models or optimization methods; this information allows an evaluation to determine which proposals could be more efficient in a distributed database.
Valuing the Benefits of Rock Climbing and the Welfare Gains from Decreasing Injury Risk

Rock climbing is an increasingly popular recreation activity worldwide, nevertheless there are only few studies that estimate the demand for it. In this article the Kuhn–Tucker approach is used to estimate the demand for rock climbing in Sicily, the recreation value of different climbing sites, and the welfare impact of reducing injury risk. In modeling the demand for climbing we account for the heterogeneity of preferences. We find that decreasing the level of injury risk generates remarkable welfare gains to climbers. This is the first study we know of that provides a welfare measure of risk reduction in rock climbing and one of a very few studies in environmental economics that uses a revealed preference approach to estimate a welfare measure associated with a reduction in risk.
A Fast and Robust Matching Framework for Multimodal Remote Sensing Image Registration

While image registration has been studied in remote sensing community for decades, registering multimodal data [e.g., optical, light detection and ranging (LiDAR), synthetic aperture radar (SAR), and map] remains a challenging problem because of significant nonlinear intensity differences between such data. To address this problem, we present a novel fast and robust matching framework integrating local descriptors for multimodal registration. In the proposed framework, a local descriptor (such as Histogram of Oriented Gradient (HOG), Local Self-Similarity or Speeded-Up Robust Feature) is first extracted at each pixel to form a pixel-wise feature representation of an image. Then we define a similarity measure based on the feature representation in frequency domain using the Fast Fourier Transform (FFT) technique, followed by a template matching scheme to detect control points between images. We also propose a novel pixel-wise feature representation using orientated gradients of images, which is named channel features of orientated gradients (CFOG). This novel feature is an extension of the pixel-wise HOG descriptor, and outperforms that both in matching performance and computational efficiency. The major advantages of the proposed framework include (1) structural similarity representation using the pixel-wise feature description and (2) high computational efficiency due to the use of FFT. Moreover, we design an automatic registration system for very large-size multimodal images based on the proposed framework. Experimental results obtained on many different types of multimodal images show the superior matching performance of the proposed framework with respect to the state-of-the-art methods and the effectiveness of the designed system, which show very good potential large-size image registration in real applications.
Estimating Positions and Radiances of a Small Number of Light Sources for Real-Time Image-Based Lighting

Image-based lighting (IBL) of virtual objects has become a popular approach to blending virtual and real scenes. In IBL an omni-directional image of a scene is used as the illumination environment for rendering virtual objects. Typically, this rendering is based on global illumination techniques which are far from capable of real-time performance. In this paper we describe how to estimate the positions and radiances of a small number of point light sources, e.g., on the order of 5 to 10, which will produce virtual object appearances which are consistent with those obtained using IBL. The estimated light source parameters can be used directly in OpenGL rendering for real-time performance. We demonstrate the approach on natural scenes. Categories
2-D Exact Analytical Method for Steady-State Heat Transfer Prediction in Rotating Electrical Machines

Actually, the heat transfer prediction and temperature distribution in electrical machines are realized by using the finite-element method (Fem) and/or thermal equivalent circuit. The analytical resolution of partial differential equations representing the temperature distribution in electrical machines does not exist. In this paper, a 2-D exact analytical calculation of steady-state temperature distribution in rotating electrical machines by solving the heat equation in homogenous and nonhomogenous regions by using Fourier’s series and the separation of variables method is presented. It is based on the new subdomain technique where the solution depends on both directions ( $r$ , $\theta $ ) and able to model different materials of the machine with different thermal conductivities. The heat sources are volumique power losses due to hysteresis, eddy-current, and Joule losses in all the regions of machine. A simplified method is used to determine the power losses in permanent-magnet motors. The main studied problem is conductive with conductive interface conditions, although convective heat transfer in the air gap is also investigated. The boundary conditions considered for solving the conduction problem is a convective heat transfer between the machine and external air and at the rotor internal air. The semi-analytical results are in very good agreement with those obtained by Fem, considering both amplitude and waveform.
Compact Orthomode Transducer with Broadband Beamforming Capability

A compact and broadband orthomode transducer (OMT) with two dual linearly polarized ports is reported here for the first time. The component is based on a combination of two ridged Boifot junctions through a series of power dividers that act as an embedded first level of the beamforming network. The design technique of the new OMT is discussed in the paper through an example that operates at Ku-band (10.7 GHz to 14.5 GHz) suitable for Satellite-on-the-Move (SOTM) user terminals. Measurement results of an additive manufactured (AM) monolithic prototype are presented, showing a good agreement with the simulation results.
Effect of SNR normalization on the estimation of muscle synergies from EMG datasets

Muscle Synergies Analysis (MSA) has gained significant attention in the last two decades as a method to derive motor control strategies and biomarkers of motor impairment and recovery. MSA is usually performed using the non-negative matrix factorization algorithm (NMF) and critically depends on the selection of the order of the model used in the analysis. The most common methodologies for the selection of the model order utilize arbitrary thresholds on the quality of reconstruction. However, the quality of the EMG data analyzed critically affects the model order selection. Here we present a simulative study on the effect of the SNR of the EMG in the selection of the model order for MSA using an objective criterion based on the Akaike Information Criterion (AIC). We show that low data quality leads to an overestimation of the muscle synergies. We then present a methodology for normalizing the SNR of the EMG before MSA. This methodology is based on the detection of the muscle activation intervals followed by a modification of the variance of the interburst noise. By applying this technique to a dataset collected during isometric reaching movements, we show that SNR normalization leads to a decrease in overall AIC values and in the optimal model order for synergies extraction.
A Recurrent CNN for Automatic Detection and Classification of Coronary Artery Plaque and Stenosis in Coronary CT Angiography

Various types of atherosclerotic plaque and varying grades of stenosis could lead to different management of patients with a coronary artery disease. Therefore, it is crucial to detect and classify the type of coronary artery plaque, as well as to detect and determine the degree of coronary artery stenosis. This paper includes retrospectively collected clinically obtained coronary CT angiography (CCTA) scans of 163 patients. In these, the centerlines of the coronary arteries were extracted and used to reconstruct multi-planar reformatted (MPR) images for the coronary arteries. To define the reference standard, the presence and the type of plaque in the coronary arteries (no plaque, non-calcified, mixed, calcified), as well as the presence and the anatomical significance of coronary stenosis (no stenosis, non-significant, i.e., <50% luminal narrowing, and significant, i.e., ≥50% luminal narrowing) were manually annotated in the MPR images by identifying the start- and end-points of the segment of the artery affected by the plaque. To perform an automatic analysis, a multi-task recurrent convolutional neural network is applied on coronary artery MPR images. First, a 3D convolutional neural network is utilized to extract features along the coronary artery. Subsequently, the extracted features are aggregated by a recurrent neural network that performs two simultaneous multi-class classification tasks. In the first task, the network detects and characterizes the type of the coronary artery plaque. In the second task, the network detects and determines the anatomical significance of the coronary artery stenosis. The network was trained and tested using the CCTA images of 98 and 65 patients, respectively. For detection and characterization of coronary plaque, the method was achieved an accuracy of 0.77. For detection of stenosis and determination of its anatomical significance, the method was achieved an accuracy of 0.80. The results demonstrate that automatic detection and classification of coronary artery plaque and stenosis are feasible. This may enable automated triage of patients to those without coronary plaque and those with coronary plaque and stenosis in need for further cardiovascular workup.
Insulator Materials for Interface Passivation of Cu(In,Ga)Se2 Thin Films

In this work, metal–insulator–semiconductor structures were fabricated in order to study different types of insulators, namely, aluminum oxide (Al<sub>2</sub>O<sub>3</sub>), silicon nitride, and silicon oxide (SiO<italic> <sub>x</sub></italic>) to be used as passivation layers in Cu(In,Ga)Se<sub>2</sub> (CIGS) thin-film solar cells. The investigated stacks consisted of SLG/Mo/CIGS/insulator/Al. Raman scattering and photoluminescence measurements were done to verify the insulator deposition influence on the CIGS surface. In order to study the electrical properties of the CIGS–insulator interface, capacitance versus conductance and voltage (<italic>C–G–V</italic>) measurements were done to estimate the number and polarity of fixed insulator charges (<italic>Q<sub>f</sub></italic> ). The density of interface defects (<italic>D</italic><sub>it</sub>) was estimated from capacitance versus conductance and frequency (<italic>C–G–f</italic>) measurements. This study evidences that the deposition of the insulators at high temperatures (300 °C) and the use of a sputtering technique cause surface modification on the CIGS surface. We found that, by varying the SiO<italic><sub>x</sub></italic> deposition parameters, it is possible to have opposite charges inside the insulator, which would allow its use in different device architectures. The material with lower <italic>D</italic><sub>it</sub> values was Al<sub>2</sub>O<sub>3</sub> when deposited by sputtering.
5 Process synthesis and intensification of hybrid separations

: Hybrid ﬂowsheets are deﬁned, in the context of process intensiﬁcation, as alternatives suitable for replacing energy-intensive separation methods through the combination of more than one unit operation. Distillation is one of the ﬁrst options considered for achieving a required separation; therefore, this chapter examines dis-tillation-based hybrid alternatives. Due to the extent of the topic, this analysis is limited to the separation of bioalcohols utilizing pervaporation and liquid–liquid extraction as assisted distillation methods. For each case, different hybrid ﬂowsheets are reported and commented on. The corresponding distillation-based processes are considered for comparison. Synthesis of the possible hybrid ﬂowsheets appears to be important, especially when multicomponent mixtures are considered. This aspect is discussed for the combination of liquid–liquid extraction and distillation as applied to the separation of biobutanol from its fermentation broth. The synthesis of alternative hybrid ﬂowsheets is reported, showing that one conﬁguration can realize a 43% reduction in the total annual cost. Bioalcohol production by fermentation perfectly represents the case where distillation alone is penalized by the thermodynamics of the mixture, but its applicability can be extended using valid alternative hybrid ﬂowsheets.