Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of chosen numerical methods for the application in high order interior ballistics simulations

Considering constant development of the interior ballistics, along with new gun and ammunition designs, the necessity of in-depth analysis of the shot event is continuously increasing. Numerical simulations of interior ballistics problems are useful for optimising new designs or explaining complex issues, regarding performance instabilities and catastrophic failures. With the rise of the computing power, there is a significant urge to drive the numerical errors towards machine zero. This goal demands using methods of high order of accuracy in both space and time. Current methods allow to achieve an arbitrary order of numerical accuracy, thus allowing to shift the focus towards sophistication of the mathematical model of the studied phenomenon. Therefore, in this work, some numerical schemes, in context of finite volume method, are reviewed and studied using well established test problems. The results of the presented analysis are meant to become the basis for future development of a high order numerical scheme for simulation of interior ballistics problems.
Deep Learning-Based Segmentation and Uncertainty Assessment for Automated Analysis of Myocardial Perfusion MRI Datasets Using Patch-Level Training and Advanced Data Augmentation

In this work, we develop a patch-level training approach and a task-driven intensity-based augmentation method for deep-learning-based segmentation of motion-corrected perfusion cardiac magnetic resonance imaging (MRI) datasets. Further, the proposed method generates an image-based uncertainty map thanks to a novel spatial sliding-window approach used during patch-level training, hence allowing for uncertainty quantification. Using the quantified uncertainty, we detect the out-of-distribution test data instances so that the end-user can be alerted that the test data is not suitable for the trained network. This feature has the potential to enable a more reliable integration of the proposed deep learning-based framework into clinical practice. We test our approach on external MRI data acquired using a different acquisition protocol to demonstrate the robustness of our performance to variations in pulse-sequence parameters. The presented results further demonstrate that our deep-learning image segmentation approach trained with the proposed data-augmentation technique incorporating spatiotemporal (2D+time) patches is superior to the state-of-the-art 2D approach in terms of generalization performance.
Linear equation systems for structural analysis: imagining resolutions

This work is based on a study into new ways of resolving the equilibrium equation systems for manual analyses of certain structures commonly found in building. It suggests finding solutions based on images that reproduce the operations of current methods, which may inspire the design of others that qualitatively reflect those of other more effective procedures. To date three methods (Gauss, Cholesky & Crout) have been imagined: (i) by “visualising” their operations through the mechanical behaviour of models during the equilibrium phase. These visualisations may help suggest other physical responses that can balance models more quickly and identify with new, more direct numerical methods; (ii) by “geometrising” operations by means of lines sketched freehand. This geometrisation may reveal hidden links between the parts of the calculation of current methods that enable more direct but equally precise new methods to be created. The paper shows four images to reinforce these viewpoints. Two visualise the methods of Gauss-Jordan and Cramer, confirming that the abstract procedures that resolve the systems may be linked to specific mechanical behaviours. The other two geometrise the resolutions by Gauss and Gauss-Jordan when the stiffness matrices are asymmetric. Their systems could emerge from the analysis of cracked models or from obtaining the equivalent actions in the P-Δ method, in line with a procedure drawn up previously. The paper ends by geometrising the resolution of a system at different scales and comparing the outcomes with those of numerical methods. The results (i) confirm that geometrising scalar and vectorial magnitudes for numerical analysis procedures reduces application times if they are calculated freehand; and (ii) point to possible lines of research for developing further graphic methods that can analyse other types of structure directly and accurately.
Using group delay function to assess glottal flows estimated by inverse filtering

The estimation of the excitation of voiced speech, or the glottal flow, is usually carried out using inverse filtering, where vocal tract resonances are cancelled from the speech signal. The parameters of inverse filtering are typically adjusted using subjective evaluations of estimated flow signals. Proposed is a method to assess objectively the quality of inverse filtered glottal flows. The method computes the group delay function over a single cycle of the estimated flow. Incorrect settings of the inverse result in the distortion of the group delay function. This distortion is easily detectable and can be used in a straightforward manner to quantify how accurately the zeros of the inverse filter match the poles of the vocal tract.
“Are You One of Us?” Points of Social Exclusion amongst Youths of Diverse Religious Groups in Indonesia

Deriving from basis of the social identity theory and its development, the research aimed to explore the points of exclusion and how individuals and groups perceived themselves as experiencing victimhood of social injustice. The rise of intolerance in Indonesia was alarming and threatened the diversity and inclusivity of the nation. Throughout several political milestones such as gubernatorial and presidential elections, identity had been used as one of the most efficient ways to segregate and discriminate against people belonging to different groups. Applying a qualitative approach, data were mined from two focus group discussions of university student respondents with various religious and ethnic backgrounds representing the majority and minority groups in Indonesia. Groups sessions were strictly differentiated between majority and minority representatives to minimize the risk of potential conflict. The findings suggest that both groups’ initial perceptions towards members of outgroups are heavily influenced by transferred stereotypes and prejudices from the older generations. While the majority group struggles to counter the prejudices and perceived victimhood through direct exposure, the minority group, on the other hand, takes language into account as a subtle gesture of exclusion.
Tuning the electronic and magnetic properties of defect blue phosphorene by the adsorption of nonmetal atoms

Based on the first principles of density functional theory, the adsorption of nonmetallic atoms on the surface of defective blue phosphorene was investigated. The results show that the most stable sites of different nonmetallic atoms on the defect blue phosphorene are different. The nonmetal (B, C, N, O) atoms were adsorbed on SV and SW defects blue phosphorene respectively. It was observed that B, N adsorbed SV defect blue phosphorene systems exhibited semiconducting behavior, whereas O adsorbed SV defect blue phosphorene system exhibited metallic behavior, and C adsorbed SV defect blue phosphorene system exhibited magnetic semiconducting behavior. For SW defect blue phosphorene, the results show that B, N, adsorbed SW defect blue phosphorene showed magnetic semiconductor behavior, while C, O adsorbed SW defect blue phosphorene showed semiconductor behavior.
Transitive Inverse Consistent Rigid Longitudinal Registration of Diffusion Weighted Magnetic Resonance Imaging: A Case Study in Athletes With Repetitive Non-Concussive Head Injuries

Significant longitudinal changes in metrics derived from diffusion weighted magnetic resonance (MR) images of the brain have been observed in athletes subject to repetitive non-concussive head injuries (RHIs). Accurate alignment of longitudinal scans of a subject is an important step in detecting and quantifying these changes. Currently, tools such as DSI Studio [1], FreeSurfer [2], and FSL [3] perform pairwise rigid registration of all scans in a longitudinal sequence to the first time-point scan (or to another reference scan or template). While the rigid transformations obtained using this strategy can be computed in a manner that enforces inverse consistency, for the case of three or more scans, the transformations are not transitive. This can lead to discrepancy in the rigid transformations that can be measured in physical units. Using a diffusion MRI dataset collected and analyzed as part of a larger study in [4], [5], [6], we illustrate this discrepancy, and we show how it can lead to uncertainty in local/regional estimates of diffusion metrics including fractional anistropy (FA), mean diffusivity (MD), and quantitatve anisotropy (QA). Additionally, we propose a method to perform transitive longitudinal rigid registration of a sequence of scans in a manner that guarantees that the discrepancy in the transformations will be eliminated.Clinical relevance— This paper establishes that standard processing pipelines for performing longitudinal analysis of diffusion MR images of the brain exhibit registration discrepancies that can be eliminated.
Application of correlated component analysis to dynamic PET time-activity curves denoising

Positron emission tomography (PET) is a physiological, non-invasive imaging technique, which forms an essential part of nuclear medicine. The data obtained in a PET scan represent the concentration of an administered radiotracer in tissues over time. Quantitative analysis of PET data makes possible the assessments of in-vivo physiological processes. The Logan graphical analysis (LGA) is one of the methods that are used for quantitative analysis of PET data. LGA transforms PET data into a simple linear relationship. The slope of the LGA linear relationship is a physiological quantity denoting receptor availability. This quantity is termed distribution volume ratio (DVR). LGA-based estimates of the DVR are negatively affected by the noise in PET data —leading to the DVR being underestimated. A number of approaches proposed to address this issue have been observed to reduce the bias at the cost precision. An alternative regression method, least-squares cubic (LSC), was recently applied to estimate the DVR in order to reduce the bias. LSC was observed to reduce the bias in the LGA-based estimates. However, slight increases were also observed in the variance of the LSC-based estimates. This calls for methods to act against the variance in the LSC-based estimates. In this study, an alternative method is applied for tTAC denoising. This method is referred to as correlated component analysis (CorrCA). CorrCA transform the data by searching for dimensions of maximum correlation. This technique is closely related to other well-known methods such as principal component analysis and independent component analysis. In this study, the data were denoised by CorrCA (to act against the variance in the estimate) and the DVR was estimated by LSC, which provides for minimal bias. The resulting method LSC-CorrCA, gave less-biased estimated with increased precision. This was observed for both simulation results as well as for clinical data, both for 11C Pittsburgh compound B. Simulation data revealed reduced variances in LCS-CorrCA-based estimates, and the clinical data showed improved contrast between gray and white matter regions.Clinical Relevance—Improved DVR estimates would ease the interpretation of medical images, which will in turn positively influence the clinical processes, from diagnosis to treatment and follow-ups.
How Unbalanced are Bicycle Dynamics? Demand-Supply Shortage Detection with Spatiotemporal Tensor Factorization in station-less bike Systems

Station-less bike sharing systems are a fast growing smart transportation trend today, with more and more cities around the world implementing them. They are emerging as a novel tool that can help reduce the burden on public transport system as well as reduce traﬃc congestions in cities. They are also an aﬀordable and green way to get from point A to point B for daily commuters. Station-less bike sharing systems are not constrained to bike stations like traditional bike sharing systems. By using just an app on the phone, users can rent and pick up a bike from any location most closest to them and then return it back at any arbitrary location at the end of the trip. Station-less bike sharing systems have also been instrumental in addressing and solving the commuters last mile problem. However, along with all the advantages, the station-less bike sharing systems also pose new sets of challenges to be addressed and solved. Some of the primary challenges are identifying regions within the city suﬀer-ing from demand shortage of bikes, supply shortage of bikes and regions with parking problems. In this paper we propose a multidimensional tensor model to address these problems. We use Mobike dataset for the city of Beijing to evaluate our model, and the experimental results show the superior performance of the proposed model.
Accelerated image reconstruction with separable Hankel regularization in parallel MRI

Magnetic resonance imaging has been widely adopted in clinical diagnose, however, it suffers from relatively long data acquisition time. Sparse sampling with reconstruction can speed up the data acquisition duration. As the state-of-the-art magnetic resonance imaging methods, the structured low rank reconstruction approaches embrace the advantage of holding low reconstruction errors and permit flexible undersampling patterns. However, this type of method demands intensive computations and high memory consumptions, thereby resulting in a lengthy reconstruction time. In this work, we proposed a separable Hankel low rank reconstruction method to explore the low rankness of each row and each column. Furthermore, we utilized the self-consistence and conjugate symmetry property of k-space data. The experimental results demonstrated that the proposed method outperforms the state-of-the-art approaches in terms of lower reconstruction errors and better detail preservation. Besides, the proposed method requires much less computation and memory consumption.Clinical Relevance— Parallel imaging, image reconstruction, Hankel low-rank
Design of Embedded Wireless Sensor and its Soft Encapsulation for Embedded Monitoring of Helicopter Planetary Gear Set

Planetary gear set, as an important part of helicopter, is with the characteristics of multi-point and time-varying position engagement. For the revolution of planetary gears round sun gear, directions of vibration and pulse created by tooth damage change continuously. If an accelerometer fixed on the surface of gearbox, the angle between the directions of pulse force and accelerometer sensitivity will change continuously, which will causes that the components of pulse force on the sensitivity direction vary with time and the features of damage are very difficult to extract from the signals. Aiming at this problem, a type of embedded wireless sensor node was designed firstly, which can be fixed on the carrier of planetary gear, and acquires the damage-related vibration signals in a fixed direction of pulse force. Then, to avoid the corrosion of electronic components by the lubrication oil in gearbox, the protect restrictions of the sensor node was investigated and a kind of soft encapsulation method is applied. Finally, real vibration signal is measured and transmitted by the designed and/or encapsulated sensor node. The experiments show that the sensor can measure vibration effectively.
Innovative moments on online Cognitive Behavioural Therapy for alcohol dependence

Online Cognitive Behavioural Therapy (CBT) is a promising intervention for reducing alcohol consumption among the population. However, due to the lack of studies in the area, the present study aims to evaluate the effects of the therapeutic process of online CBT for alcohol addicts in a Brazilian sample. Thirty-six recordings of six male participants diagnosed with alcohol dependence, who sought treatment to cease or reduce the substance’s consumption, were analysed using the Innovative Moments Coding System (IMCS). The IMCS is a method that analyses changes occurred during the therapeutic process. The results suggested: (1) an increase in the number of Innovative Moments (IMs) from the beginning to the end of the sessions in all analysed cases, and (2) a correlation between a decrease in the doses of alcohol consumption at the end of the sessions and the increase of IMs. The present study successfully applies for the first time the IMCS in alcohol dependence and proved to be an adequate method to evaluate the online therapy process for this sample. However, it is necessary to conduct further research to confirm the IMCS’s effectiveness for alcohol dependence.
Design of a Hyper-Spectral Imaging System for Gross Pathology of Pigmented Skin Lesions

Pigmented skin lesions (PSL) are prevalent in Asian populations and their gross pathology remains a manual, tedious task. Hyper-spectral imaging (HSI) is a non-invasive non-ionizing acquisition technique, allowing malignant tissue to be identified by its spectral signature. We set up a hyper-spectral imaging (HSI) system targeting cancer margin detection of PSL. Because classification among PSL is achieved via comparison of spectral signatures, appropriate calibration is necessary to ensure sufficient data quality. We propose a strategy for system building, calibration and pre-processing, under the requirements of fast acquisition and wide field of view. Preliminary results show that the HSI-based system is able to effectively resolve reflectance signatures of ex-vivo tissue.Clinical Relevance—The imaging system proposed in this study can recover reflectance spectra from PSL during gross pathology, providing a wide imaging area.
An Inkjet Printed CO2 Gas Sensor

In this paper a low cost CO2 sensor realized on a plastic substrate is presented. The sensor is realized by the deposition of a double layer of PEDOT/PSS (CLEVIOSTM PHCV4, by H.C.Starck) and Graphene on InterDigiTed electrodes printed onto a PET (PolyEthylene Terephthalate) substrate. The InterDigiTed electrodes have been realized by inkjet printing a conductive pattern of a silver nano-particle solution (Metalon® JS-B15P by Novacentrix), through a commercial EPSON® inkjet printer. The sensing principle of the sensor exploits the change in electrical conductivity of graphene due to gas molecules adsorbtion. A device responsivity, at 30°C, of 45μOhm/ppm and a sensitivity of 100 ppm have been obtained respectively. Compared to solutions proposed in the literature, the inkjet printed device here presented has advantages related to its low cost, flexibility and low demanding fabrication. The device flexibility is crucial for application requiring the device shaping to irregular surfaces, such as dresses or food packaging. © 2015 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the organizing committee of EUROSENSORS 2015.
Forward Model and Deep Learning Based Iterative Deconvolution for Robust Dynamic CT Perfusion

Perfusion maps obtained from low-dose computed tomography (CT) data suffer from poor signal to noise ratio. To enhance the quality of the perfusion maps, several works rely on denoising the low-dose CT (LD-CT) images followed by conventional regularized deconvolution. Recent works employ deep neural networks (DNN) for learning a direct mapping between the noisy and the clean perfusion maps ignoring the convolution-based forward model. DNN-based methods are not robust to practical variations in the data that are seen in real-world applications such as stroke. In this work, we propose an iterative framework that combines the perfusion forward model with a DNN-based regularizer to obtain perfusion maps directly from the LD-CT dynamic data. To improve the robustness of the DNN, we leverage the anatomical information from the contrast-enhanced LD-CT images to learn the mapping between low-dose and standard-dose perfusion maps. Through empirical experiments, we show that our model is robust both qualitatively and quantitatively to practical perturbations in the data.
Islamic Legal Reasoning for the Justification of Tax Evasion: The Case of Indonesia*

Religiosity is often perceived as a trait that can effectively suppress attitudes and behavior that lead to people evading taxes. However, this study finds that Islamic religiosity has a complicated relationship with tax evasion. This study employs a mixed-method, which involves collecting, analyzing, and integrating quantitative and qualitative research, and this integration provides a better understanding of the research problem. Qualitative analyses of the sermons of prominent Islamic clerics speaking on taxes on YouTube channels revealed different views. The textualists/conservative clerics viewed taxes are prohibited based on the fact of Islamic history and tax prohibition stated in a hadith. Furthermore, tax compliance is determined solely by the threat of punishment from the government. On the other hand, the moderate preachers seem to be more flexible in response to tax issues. They involve the framework of contextual and analogical-historical practice. Quantitative analyses from the survey found that Islamic religiosity is not directly correlated with tax evasion. However, the sentiment of Islamic law adoption by the state has significantly mediated the correlation between them. In addition, respondents who affiliate with moderate Islamic organizations tend to have less justification of tax evasion than those who affiliate with conservative organizations or movements.
Using Biologically-inspired Image Features to Model Retinal Response: Evidence from Biological Datasets

Retinal models are needed to simulate the translation of visual percepts to Retinal Ganglion Cells (RGCs) neural spike trains, through which visual information is transmitted to the brain. Restoring vision through neural prostheses motivates the development of accurate retinal models. We integrate biologically-inspired image features to RGC models. We trained Linear-Nonlinear models using response data from biological retinae. We show that augmenting raw image input with retina-inspired image features leads to performance improvements: in a smaller (30sec. of retina recordings) set integration of features leads to improved models in approximately $\frac{2}{3}$ of the modeled RGCS; in a larger (4min. recording) we show that utilizing Spike Triggered Average analysis to localize RGCs in input images and extract features in a cell-based manner leads to improved models in all (except two) of the modeled RGCs.
AN OCTA-BAND MONOPOLE ANTENNA FOR LTE/WLAN MOBILE PHONES

-In this letter, a compact multi resonant octa-band antenna for 5.7 in mobile phone with the size of 80mm×6mm×5.8mm is proposed and studied. The proposed antenna consists of coupled line, ground branch and monopole branch. The 0.25-, 0.5and 0.75wavelength modes are used in order to cover the lower band (704-960 MHz) and the higher band(1710-2690 MHz). The attractive merits of the proposed antenna are that the height of the non-ground portion is only 6mm and no other lumped elements are used. A prototype of the proposed antenna is simulated and measured. Its simulated -6db bandwidths are 341.5MHz(0.673-1.014 GHz) and 1.292GHz(1.6074-2.9 GHz) at lower and higher bands respectively, which covers LTE700, GSM850, GSM900, GSM1800, GSM1900, UMTS, LTE2300 and LTE2500 bands. Within the two frequency bands, good radiation performances are obtained.