Miracle-dz/UniFAR-FTUs huggingface dataset:
Orbital dynamics in realistic galaxy models: NGC 3726, NGC 3877 and NGC 4010

En el presente trabajo, utilizando una generalización del potencial de Miyamoto-Nagai, se ajustan las curvas de rotación observadas de tres galaxias espirales a las velocidades circulares analiticas. Los datos observacionales se tomaron de un conjunto de imágenes de línea de 21 centímetros (o línea HI) obtenidos con el Westerbork Synthesis Radio Telescope (WSRT), para tres galaxias particulares en el grupo de la Ursa Major: NGC 3726, NGC 3877 y NGC 4010. Seguidamente, se analiza la dinámica del sistema en términos del método de secciones de Poincaré, encontrando que para valores grandes del momento angular de la partícula de prueba o valores bajos su energía total, la din´amica es principalmente regular, mientras que en los casos opuestos, la dinámica es principalmente ca´otica. Nuestro modelo abre la posibilidad de encontrar órbitas caóticas acotadas para estrellas presentes en esas galaxias partículares. © 2019. Acad. Colomb. Cienc. Fis. Nat.
VARENN: Graphical representation of spatiotemporal data and application to climate studies

Analyzing and utilizing spatiotemporal big data are essential for studies concerning climate change. However, such data are not fully integrated into climate models owing to limitations in statistical frameworks. Herein, we employ VARENN (visually augmented representation of environment for neural networks) to efficiently summarize monthly observations of climate data for 1901-2016 into 2-dimensional graphical images. Using red, green, and blue channels of color images, three different variables are simultaneously represented in a single image. For global datasets, models were trained via convolutional neural networks. These models successfully classified rises and falls in temperature and precipitation. Moreover, similarities between the input and target variables were observed to have a significant effect on model accuracy. The input variables had both seasonal and interannual variations, whose importance was quantified for model efficacy. VARENN is thus an effective method to summarize spatiotemporal data objectively and accurately.
Towards Unsupervised Grammatical Error Correction using Statistical Machine Translation with Synthetic Comparable Corpus

We introduce unsupervised techniques based on phrase-based statistical machine translation for grammatical error correction (GEC) trained on a pseudo learner corpus created by Google Translation. We verified our GEC system through experiments on various GEC dataset, includi ng a low resource track of the shared task at Building Educational Applications 2019 (BEA 2019). As a result, we achieved an F_0. 5 score of 28.31 points with the test data of the low resource track.
ZKlaims: Privacy-preserving Attribute-based Credentials using Non-interactive Zero-knowledge Techniques

In this paper we present ZKlaims: a system that allows users to present attribute-based credentials in a privacy-preserving way. We achieve a zero-knowledge property on the basis of Succinct Non-interactive Arguments of Knowledge (SNARKs). ZKlaims allow users to prove statements on credentials issued by trusted third parties. The credential contents are never revealed to the verifier as part of the proving process. Further, ZKlaims can be presented non-interactively, mitigating the need for interactive proofs between the user and the verifier. This allows ZKlaims to be exchanged via fully decentralized services and storages such as traditional peer-to-peer networks based on distributed hash tables (DHTs) or even blockchains. To show this, we include a performance evaluation of ZKlaims and show how it can be integrated in decentralized identity provider services.
On the Equivalence Between Abstract Dialectical Frameworks and Logic Programs

Abstract Abstract Dialectical Frameworks (ADFs) are argumentation frameworks where each node is associated with an acceptance condition. This allows us to model different types of dependencies as supports and attacks. Previous studies provided a translation from Normal Logic Programs (NLPs) to ADFs and proved the stable models semantics for a normal logic program has an equivalent semantics to that of the corresponding ADF. However, these studies failed in identifying a semantics for ADFs equivalent to a three-valued semantics (as partial stable models and well-founded models) for NLPs. In this work, we focus on a fragment of ADFs, called Attacking Dialectical Frameworks (ADF+s), and provide a translation from NLPs to ADF+s robust enough to guarantee the equivalence between partial stable models, well-founded models, regular models, stable models semantics for NLPs and respectively complete models, grounded models, preferred models, stable models for ADFs. In addition, we define a new semantics for ADF+s, called L-stable, and show it is equivalent to the L-stable semantics for NLPs.
Towards Courteous Behavior and Trajectory Planning for Automated Driving

Efficient behavior and trajectory planning is one of the major challenges for automated driving. Especially intersection scenarios are very demanding due to their complexity arising from the variety of maneuver possibilities and other traffic participants. A key challenge is to generate behaviors which optimize the comfort and progress of the ego vehicle but at the same time are not too aggressive towards other traffic participants. In order to maintain real time capability for courteous behavior and trajectory planning, an efficient formulation of the optimal control problem and corresponding solving algorithms are required. Consequently, a novel planning framework is presented which considers comfort and progress as well as the courtesy of actions in a graph-based behavior planning module. Utilizing the low level trajectory generation, the behavior result can be further optimized for driving comfort while satisfying constraints over the whole planning horizon. According experiments show the practicability and real time capability of the framework.
Voltage Security Analysis of VSC-HVDC Transmission Lines

Due to generation retirements and growth of renewable energy sources integration, there will be widespread changes in the real and reactive power flow in nowadays power systems. These changes also bring about challenges on system operation and stability. High voltage direct current (HVDC) technology seems to be a promising solution to these new challenges. A study on the impact of the replacement of 500KV AC transmission lines by VSC-HVDC transmission line on system voltage security is conducted. The analysis is based on reactive load margin method and implemented on one of the Dominion Energy planning models. Different control schemes of HVDC are considered. Using k-means clustering method, three representative zones within the network are selected. The results corresponding to them are demonstrated and discussed. It is shown that HVDC lines with P-V control remarkably improve system voltage security, while those with P-PF control scheme have negative effects.
An Overview of Enhanced Massive MIMO With Array Signal Processing Techniques

In the past ten years, there have been tremendous research progresses on massive MIMO systems, most of which stand from the communications viewpoint. A new trend to investigate massive MIMO, especially for the sparse scenario like millimeter wave (mmWave) transmission, is to re-build the transceiver design from array signal processing viewpoint that could deeply exploit the half-wavelength array and provide enhanced performances in many aspects. For example, the high dimensional channel could be decomposed into small amount of physical parameters, e.g., angle of arrival (AoA), angle of departure (AoD), multi-path delay, Doppler shift, etc. As a consequence, transceiver techniques like synchronization, channel estimation, beamforming, precoding, multi-user access, etc., can be re-shaped with these physical parameters, as opposed to those designed directly with channel state information (CSI). Interestingly, parameters like AoA/AoD and multi-path delay are frequency insensitive and thus can be used to guide the downlink transmission from uplink training even for FDD systems. Moreover, some phenomena of massive MIMO that were vaguely revealed previously can be better explained now with array signal processing, e.g., the beam squint effect. In all, the target of this paper is to present an overview of recent progress on merging array signal processing into massive MIMO communications as well as its promising future directions.
Fast radio burst dispersion measures and rotation measures and the origin of intergalactic magnetic fields

We investigate the possibility of measuring intergalactic magnetic fields using the dispersion measures and rotation measures of fast radio bursts. With Bayesian methods, we produce probability density functions for values of these measures. We distinguish between contributions from the intergalactic medium, the host galaxy, and the local environment of the progenitor. To this end, we use constrained, magnetohydrodynamic simulations of the local Universe to compute lines-of-sight integrals from the position of the Milky Way. In particular, we differentiate between predominantly astrophysical and primordial origins of magnetic fields in the intergalactic medium. We test different possible types of host galaxies and probe different distribution functions of fast radio burst progenitor locations inside the host galaxy. Under the assumption that fast radio bursts are produced by magnetars, we use analytic predictions to account for the contribution of the local environment. We find that less than 100 fast radio bursts from magnetars in stellar-wind environments hosted by starburst dwarf galaxies at redshift z ≳ 0.5 suffice to discriminate between predominantly primordial and astrophysical origins of intergalactic magnetic fields. However, this requires the contribution of the Milky Way to be removed with a precision of ≈1 rad m−2. We show the potential existence of a subset of fast radio bursts whose rotation measures carry information on the strength of the intergalactic magnetic field and its origins.
Position Focused Attention Network for Image-Text Matching

Image-text matching tasks have recently attracted a lot of attention in the computer vision field. The key point of this cross-domain problem is how to accurately measure the similarity between the visual and the textual contents, which demands a fine understanding of both modalities. In this paper, we propose a novel position focused attention network (PFAN) to investigate the relation between the visual and the textual views. In this work, we integrate the object position clue to enhance the visual-text joint-embedding learning. We first split the images into blocks, by which we infer the relative position of region in the image. Then, an attention mechanism is proposed to model the relations between the image region and blocks and generate the valuable position feature, which will be further utilized to enhance the region expression and model a more reliable relationship between the visual image and the textual sentence. Experiments on the popular datasets Flickr30K and MS-COCO show the effectiveness of the proposed method. Besides the public datasets, we also conduct experiments on our collected practical news dataset (Tencent-News) to validate the practical application value of proposed method. As far as we know, this is the first attempt to test the performance on the practical application. Our method can achieve the state-of-art performance on all of these three datasets.
Spatially Informed Independent Vector Analysis

We present a Maximum A Posteriori (MAP) derivation of the Independent Vector Analysis (IVA) algorithm, a blind source separation algorithm, by incorporating a prior over the demixing matrices, relying on a free-field model. In this way, the outer permutation ambiguity of IVA is avoided. The resulting MAP optimization problem is solved by deriving majorize-minimize update rules to achieve convergence speed comparable to the well-known auxiliary function IVA algorithm. The performance of the proposed algorithm is investigated and compared to a benchmark algorithm using real measurements.
An ordinal measure of interrater absolute agreement

A measure of interrater absolute agreement for ordinal scales is proposed capitalizing on the dispersion index for ordinal variables proposed by Giuseppe Leti. The procedure allows to avoid the problem of restriction of variance that sometimes affect traditional measures of interrater agreement in different fields of application. An unbiased estimator of the proposed measure is introduced and its sampling properties are investigated. In order to construct confidence intervals for interrater absolute agreement both asymptotic results and bootstrapping methods are used and their performance is evaluated. Simulated data are employed to demonstrate the accuracy and practical utility of the new procedure for assessing agreement. Finally, an application to a real case is provided.
Performance Analysis of C-V2X Mode 4 Communication Introducing an Open-Source C-V2X Simulator

Autonomous vehicles, on the ground and in the air, are the next big evolution in human mobility. While autonomous driving in highway scenarios is already possible using only the vehicles sensors, the complex scenarios of big cities with all its different traffic participants is still a vision. Cellular Vehicle-to-Everything (C-V2X) communication is a necessary enabler of this vision and and an emerging field of interest in today's research. However, to the best of our knowledge open source simulators essential for open research do not exist yet. In this work we present our open source C-V2X mode 4 simulator based on the discrete-event network simulator ns-3. To analyze the performance of C-V2X mode 4 using our simulator, we created a worst case scenario and the 3GPP reference Manhattan grid scenario using the microscopic traffic simulator SUMO. We also added the WINNER+ B1 channel model to ns-3, as this is also used by 3GPP. Our results show, that C-V2X is scalable to 250 vehicles within a worst case scenario on a playground of 100 m x 100 m, with respect to the LTE rel. 14 V2X requirements. For the more realistic Manhattan grid scenario, the performance is better, as to be expected. We also analyzed the Packet Inter-Reception time with an outcome of max. 100 ms for more than 99 % of all transmissions. In addition, we investigated the impact of the Resource Reservation Period and the Resource Reselection Probability on the system's Packet Reception Ratio.
The use of crash courses to promote active engagement in large class sizes

Active engagement of students in large classes is imperative to ensure deep learning. This paper discusses the use of 'crash courses' as a teaching strategy for the Level I engineering course 'Engineering Mechanics: Statics' to facilitate student understanding. They have been developed as an integral part of the educational methodology of the author to promote active engagement and develop problem solving accuracy and efficiency. Examples of student and peer assessment are presented to indicate the impact of this teaching strategy on student understanding.
Pseudo-evolution of galaxies in Λ CDM cosmology

Our knowledge about galaxy evolution comes from transforming observed galaxy properties at different redshifts to comoving physical scales. This transformation depends on using a cosmological model. Here, the effects of unintentional mixing of two different cosmological models on the size evolution of galaxies is studied. As a gedanken experiment, a galaxy of fixed proper size and luminosity is moved across different redshifts. The apparent size of this galaxy is then interpreted with a cosmological model presumed by the observer, which is different compared to the cosmology exhibited by the Universe. In such a case, a spurious size evolution of the galaxy is observed. A galaxy behaving according to the Rh = ct and Neumann’s cosmology, when interpreted with the Λ cold dark matter (ΛCDM) cosmological model, shows an increase in size by a factor of 1.1 and 1.3 from $z$ = 7.5 to ≈ 0, respectively. The apparent size of a galaxy in a static Euclidean cosmology, when interpreted in the ΛCDM model, shows a factor of 23.8 increase in size between $z$ = 7.5 to ≈ 0. This is in close agreement with the observational data with a size increase of a factor of 6.8 between $z$ = 3.2 to ≈ 0. Furthermore, using the apparent size data, it is shown that the difference between the derived proper sizes in Rh = ct, Neumann’s and ΛCDM cosmological models are minimal.
Turn Prediction for Special Intersections and Its Case Study

The effect of growing population brings heavy traffic which in turn leads to increased number of traffic accidents. In particular, the majority of traffic accidents happen at special intersections in situations such as heavy traffic, poor intersection design, etc. In this paper, we propose a turn prediction system to predict which road a vehicle will take at special intersection, e.g., T-junction, Y-junction, or junction where more than 4 roads meet. The proposed system uses the radar installed at the intersection to collect vehicle dynamics. The collected data is processed to calculate deflection angles of vehicles corresponding to the road. The smoothing technique is adopted to filter the noise of calculated deflection angles. The ensemble methods are utilized to construct the model to predict future deflection angles of vehicles corresponding to the road. According to the predicted deflection angle, we can predict which road a vehicle will take at a special intersection and alert other vehicles when necessary. To assess the performance of the model prediction, a real-world experiment is carried out, which utilizes radar to collect the dataset at Kaixuan 4th Rd. and Zhenxing Rd., Qianzhen Dist. , Kaohsiung City, Taiwan. The experiment results show that the accuracy of the Random Forest algorithm is the highest among all datasets.
Towards Computer-aided Analysis of Readability and Comprehensibility of Patient Information in the Context of Clinical Research Projects

Clinical trials aim to examine the safety and efficiency of novel procedures such as certain medication, specific treatments, or medical devices. They constitute a compulsory requirement for the marketing authorization of these trial objects. As such, they are a vital part of a modern and innovative health care system. However, clinical trials (CT) also involve risks for the participant's health.
Constructing Skeleton for Parallel Applications with Machine Learning Methods

Performance prediction has always been important in the domain of parallel computing. For programs which are executed on workstation clusters and super computing systems, precise prediction of execution time can help task scheduling and resource management. A practical and effective type of prediction method is the skeleton-based method. It extracts an executable code snippet, called skeleton, from the traces of program executions, and uses the skeleton to replay the behaviors and predict the performance of the original program. However, traditional skeleton-based methods require fixed inputs to construct reliable skeletons. This requirement limits the application scope of skeleton-based methods. In this paper, we present a novel method to construct skeleton for parallel programs. Our method combines code instrument and machine learning techniques, which enable skeletons to dynamically respond varying inputs and make corresponding performance prediction. In our evaluations on three benchmarks, MCB, LULESH and STREAM, the proposed method can achieve 27%, 7% and 9% average prediction error rate, respectively.
On the Quality of Wall Time Estimates for Resource Allocation Prediction

Today's HPC systems experience steadily increasing problems with the storage I/O bottleneck. At the same time, new storage technologies are emerging in the compute nodes of HPC systems. There are many ideas and approaches how compute-node local storage can be made usable for HPC systems. One consideration is to copy job data to the compute-node local disks in advance. To accomplish this, the allocated nodes must be known in advance. In this paper, we look at the node allocation behavior of a HPC batch scheduling system. Our goal is to determine whether it is possible to stage data in advance, based on scheduler predictions. We show that wall time estimates must be excellent to reliably predict node allocations. In reality, the required accuracy enabling advance data staging is hard to achieve. Therefore, the behavior of (standard) batch scheduler have to be modified in order to enable efficient advance data staging.
Pyne: A programming framework for parallel simulation development

This paper proposes Pyne, a parallel programming framework for developing parallel simulations. Pyne is designed such that parallel applications are developed with sequential programming. Pyne provides a programming environment in which programmers select data structures, define the sequence of operations for the data, and implement some sequential functions to describe the detailed calculation rules for the operations. Pyne automatically creates parallel data structures and performs parallel executions. This paper discusses the programming interface and runtime optimization algorithms that enable the parallel programming model. We evaluated Pyne by implementing a fluid dynamics simulation with both Pyne and a state-of-the-art parallel library. The results show that Pyne succeeds at reducing the code size by 70%, while maintaining performance and scalability.
Translating AArch64 Floating-Point Instruction Set to the x86-64 Platform

Binary translation translates binary programs from one instruction set to another. It is widely used in virtual machines and emulators. We extend mc2llvm, which is an LLVM-based retargetable 32-bit binary translator developed in our lab in the past several years, to support 64-bit ARM instruction set. In this paper, we report the translation of AArch64 floating-point instructions in our mc2llvm. For floating-point instructions, due to the lack of floating-point support in LLVM [13, 14], we add support for the flush-to-zero mode, not-a-number processing, floating-point exceptions, and various rounding modes. On average, mc2llvm-translated binary can achieve 47% and 24.5% of the performance of natively compiled x86-64 binary on statically translated EEMBC benchmark and dynamically translated SPEC CINT2006 benchmarks, respectively. Compared to QEMU-translated binary, mc2llvm-translated binary runs 2.92x, 1.21x and 1.41x faster on statically translated EEMBC benchmark, dynamically translated SPEC CINT2006, and CFP2006 benchmarks, respectively. (Note that the benchmarks contain both floating-point instructions and other instructions, such as load and store instructions.)