Miracle-dz/UniFAR-FTUs huggingface dataset:
Curved optimal delaunay triangulation

Meshes with curvilinear elements hold the appealing promise of enhanced geometric flexibility and higher-order numerical accuracy compared to their commonly-used straight-edge counterparts. However, the generation of curved meshes remains a computationally expensive endeavor with current meshing approaches: high-order parametric elements are notoriously difficult to conform to a given boundary geometry, and enforcing a smooth and non-degenerate Jacobian everywhere brings additional numerical difficulties to the meshing of complex domains. In this paper, we propose an extension of Optimal Delaunay Triangulations (ODT) to curved and graded isotropic meshes. By exploiting a continuum mechanics interpretation of ODT instead of the usual approximation theoretical foundations, we formulate a very robust geometry and topology optimization of Bézier meshes based on a new simple functional promoting isotropic and uniform Jacobians throughout the domain. We demonstrate that our resulting curved meshes can adapt to complex domains with high precision even for a small count of elements thanks to the added flexibility afforded by more control points and higher order basis functions.
A Hypothetical Bottleneck in the Plant Microbiome

The plant microbiome may be bottlenecked at the level of endophytes of individual seeds. Strong defense of developing seeds is predicted by optimal defense theory, and we have experimentally demonstrated exclusionary interactions among endophytic microbes infecting individual seeds of Centaurea stoebe. Having found a single, PDA-culturable microbe per seed or none in an exploratory study with Centaurea stoebe, we completed a more extensive survey of an additional 98 plant species representing 39 families. We again found that individual, surface-sterilized seeds of all species hosted only one PDA-culturable bacterial or fungal endophyte per seed, or none. PDA-unculturables were not determined but we expect them to also be bottlenecked in individual seeds, as they too should be governed by exclusionary interactions. If the bottleneck were confirmed with high-throughput sequencing of individual seeds then it would make sense to further investigate the Primary Symbiont Hypothesis (PSH). This includes the prediction that primary symbionts (i.e., the winners of the exclusionary battles among seed endophytes) have strong effects on seedlings depending on symbiont identity.
Workplace Racial Composition Explains High Perceived Discrimination of High Socioeconomic Status African American Men

Background: Sociological and epidemiological literature have both shown that socioeconomic status (SES) protects populations and individuals against health problems. Recent research, however, has shown that African Americans gain less from their SES and African Americans of high SES, particularly males, may be vulnerable to perceived discrimination, as explained by the Minorities’ Diminished Returns theory. One potential mechanism for this phenomenon is that high SES African Americans have a higher tendency to work in predominantly White workplaces, which increases their perceived discrimination. It is, however, unknown if the links between SES, working in predominantly White work groups and perceived discrimination differ for male and female African Americans. Aim: To test the associations between SES, workplace racial composition and perceived discrimination in a nationally representative sample of male and female African American adults. Methods. This study included a total number of 1775 employed African American adults who were either male (n = 676) or female (n = 1099), all enrolled from the National Survey of American Life (NSAL). The study measured gender, age, SES (educational attainment and household income), workplace racial composition and perceived discrimination. Structural Equation Modeling (SEM) was applied in the overall sample and also by gender. Results: In the pooled sample that included both genders, high education and household income were associated with working in a predominantly White work group, which was in turn associated with more perceived discrimination. We did not find gender differences in the associations between SES, workplace racial composition and perceived discrimination. Conclusion: Although racial composition of workplace may be a mechanism by which high SES increases discriminatory experiences for African Americans, males and females may not differ in this regard. Policies are needed to reduce discrimination in racially diverse workplaces. This is particularly the case for African Americans who work in predominantly White work environments.
Early Adaptations to a Two-Week Uphill Run Sprint Interval Training and Cycle Sprint Interval Training

This study sought to compare early physiological and performance adaptations between a two-week cycle sprint interval training (SIT) and uphill run sprint training (UST) programs. Seventeen recreationally active adult males (age = 28 ± 5 years; body mass (BM) = 78 ± 9 kg) were assigned to either a control (n = 5), SIT (n = 6), or UST (n = 6) group. A discrete group of participants (n = 6, age = 33 ± 6 years, and body mass = 80 ± 9 kg) completed both training protocols to determine acute physiological responses. Intervention groups completed either a run or cycle peak oxygen uptake (VO2peak) test (intervention type dependent) prior to and following two weeks of training. Training comprised of three sessions per week of 4 × 30-s “all-out” sprints with a four-minute active recovery between bouts on a cycle ergometer against 7.5% of body mass in the SIT group and on a 10% slope in the UST group. The VO2peak values remained unchanged in both training groups, but time-to-exhaustion (TTE) was significantly increased only in the UST group (pre—495 ± 40 s, post—551 ± 15 s; p = 0.014) and not in the SIT group (pre—613 ± 130 s, post—634 ± 118 s, p = 0.07). Ventilatory threshold (VT) was significantly increased in both training groups (SIT group: pre—1.94 ± 0.45 L·min−1, post—2.23 ± 0.42 L·min−1; p < 0.005, UST group: pre—2.04 ± 0.40 L·min−1, post—2.33 ± 0.34 L·min−1, p < 0.005). These results indicate that UST may be an effective alternative to SIT in healthy individuals.
Image super-resolution via adaptive sparse representation and self-learning

This study proposes a novel super-resolution regularisation model based on adaptive sparse representation and self-learning frameworks. The fidelity term in the model ensures that the reconstructed image is consistent with the observation image. The adaptive sparsity regularisation term constrains the reconstructed image with an adaptive sparse representation, which successfully harmonises the sparse representation and the collaborative representation adaptively via producing suitable coefficients. To construct a more effective dictionary, the high-frequency features from the underlying image patches are extracted, and the dictionary learning and sparse representation are integrated. To this end, the alternating minimisation algorithm is used to divide this model into three subproblems, and the alternating direction method of multipliers and iterative back-projection method are used to solve the subproblems. To illustrate the effectiveness of the proposed method, additional experiments are conducted on some generic images. Compared with some state-of-the-art algorithms, the experimental results demonstrate that the proposed method achieves better results in terms of both visual quality and noise immunity.
Evaluation of interval regression analysis for uncertain resistance spot welding quality data

ABSTRACT This article presents an interval regression analysis method to model uncertain resistance spot welding (RSW) quality data. The goal of this approach is to support making decisions using the RSW quality data (e.g. determining welding conditions on a combination of materials). The RSW data include various welding conditions where each welding condition represents material characteristics and welding process parameters associated with uncertain welding quality. Our experiments reveal that interval regression analysis is capable of mapping welding conditions to their uncertain welding quality. Due to its high uncertainty in the RSW quality data, the interval regression models are unable to provide high fitness measure. However, these models provide advantages, including the capability of providing valuable insights to analyse the uncertainty and support making decisions.
Effect of Word Sense Disambiguation on Neural Machine Translation: A Case Study in Korean

With the advent of robust deep learning, neural machine translation (NMT) has achieved great progress and recently become the dominant paradigm in machine translation (MT). However, it is still confronted with the challenge of word ambiguities that force NMT to choose among several translation candidates that represent different senses of an input word. This research presents a case study using Korean word sense disambiguation (WSD) to improve NMT performance. First, we constructed a Korean lexical semantic network (LSN) as a large-scale lexical semantic knowledge base. Then, based on the Korean LSN, we built a Korean WSD preprocessor that can annotate the correct sense of Korean words in the training corpus. Finally, we conducted a series of translation experiments using Korean-English, Korean-French, Korean-Spanish, and Korean-Japanese language pairs. The experimental results show that our Korean WSD system can significantly improve the translation quality of NMT in terms of the BLEU, TER, and DLRATIO metrics. On average, it improved the precision by 2.94 BLEU points and improved translation error prevention by 4.04 TER points and 4.51 DLRATIO points for all the language pairs.
Exploration of runtime distributed mapping techniques for emerging large scale MPSoCs

MPSoCs with hundreds of cores are already available in the market. According to the ITRS roadmap, such systems will integrate thousands of cores by the end of the decade. The definition of where each task will execute in the system is a major issue in the MPSoC design. In the literature, this issue is defined as task mapping. The growth in the number of cores increases the complexity of the task mapping. The main concerns in task mapping in large systems include: (i) scalability; (ii) dynamic workload; and (iii) reliability. It is necessary to distribute the mapping decision across the system to ensure scalability. The workload of emerging large MPSoCs may be dynamic, i.e., new applications may start at any moment, leading to different mapping scenarios. Therefore, it is necessary to execute the mapping process at runtime to support a dynamic workload. Reliability is tightly connected to the system workload distribution. Load imbalance may generate hotspots zones and consequently thermal implications, which may result in unreliable system operation. In large scale MPSoCs, reliability issues get worse since the growing number of cores on the same die increases power densities and, consequently, the system temperature. The literature presents different task mapping techniques to improve system reliability. However, such approaches use a centralized mapping approach, which are not scalable. To address these three challenges, the main goal of this Thesis is to propose and evaluate distributed mapping heuristics, executed at runtime, ensuring scalability and a fair workload distribution. Distributing the workload and the traffic inside the NoC increases the system reliability in long-term, due to the minimization of hotspot regions. To enable the design space exploration of large MPSoCs the first contribution of the Thesis lies in a multi-level modeling framework, which supports different models and debugging capabilities that enrich and facilitate the design of MPSoCs. The simulation of lower level models (e.g. RTL) generates performance parameters used to calibrate abstract models (e.g. untimed models). The abstract models pave the way to explore mapping heuristics in large systems. Most mapping techniques focus on optimizing communication volume in the NoC, which may compromise reliability due to overload processors. On the other hand, a heuristic optimizing only the workload distribution may overload NoC links, compromising its reliability. The second significant contribution of the Thesis is the proposition of dynamic and distributed mapping heuristics, making a tradeoff between communication volume (NoC links) and workload distribution (CPU usage). Results related to execution time, communication volume, energy consumption, power traces and temperature distribution in large MPSoCs (144 processors) confirm the tradeoff hypothesis. Trading off workload and communication volume improves system reliably through the reduction of hotspots regions, without compromising system performance.
Occluded Joints Recovery in 3D Human Pose Estimation based on Distance Matrix

Albeit the recent progress in single image 3D human pose estimation due to the convolutional neural network, it is still challenging to handle real scenarios such as highly occluded scenes. In this paper, we propose to address the problem of single image 3D human pose estimation with occluded measurements by exploiting the Euclidean distance matrix (EDM). Specifically, we present two approaches based on EDM, which could effectively handle occluded joints in 2D images. The first approach is based on 2D-to-2D distance matrix regression achieved by a simple CNN architecture. The second approach is based on sparse coding along with a learned over-complete dictionary. Experiments on the Human3.6M dataset show the excellent performance of these two approaches in recovering occluded observations and demonstrate the improvements in accuracy for 3D human pose estimation with occluded joints. 1
Transfer Learning versus Multiagent Learning regarding Distributed Decision-Making in Highway Traffic

Transportation and traffic are currently undergoing a rapid increase in terms of both scale and complexity. At the same time, an increasing share of traffic participants are being transformed into agents driven or supported by artificial intelligence resulting in mixed-intelligence traffic. This work explores the implications of distributed decision-making in mixed-intelligence traffic. The investigations are carried out on the basis of an online-simulated highway scenario, namely the MIT \emph{DeepTraffic} simulation. In the first step traffic agents are trained by means of a deep reinforcement learning approach, being deployed inside an elitist evolutionary algorithm for hyperparameter search. The resulting architectures and training parameters are then utilized in order to either train a single autonomous traffic agent and transfer the learned weights onto a multi-agent scenario or else to conduct multi-agent learning directly. Both learning strategies are evaluated on different ratios of mixed-intelligence traffic. The strategies are assessed according to the average speed of all agents driven by artificial intelligence. Traffic patterns that provoke a reduction in traffic flow are analyzed with respect to the different strategies.
Strategic Alignment Between IT Flexibility and Dynamic Capabilities: An Empirical Investigation

Dynamic capabilities theory (DCT) emerged as a leading framework in the process of value creation for firms. Its core notion complements the premise of the resource-based view of the firm and is considered an important theoretical and management framework in modern information systems research. However, despite DCT's significant contributions, its strength and core focus are essentially in its use for historical firm performance explanation. Furthermore, valuable contributions have been made by several researchers to extend the DCT to fit the constantly changing IT environments and other imperative drivers for competitive performance. However, no DCT extension has been developed which allows firms to integrally assess their current state of maturity to derive imperative steps for further performance enhancements. In light of empirical advancement, this article aims to develop a strategic alignment model for IT flexibility and dynamic capabilities and empirically validates proposed hypotheses using correlation and regression analyses on a large data sample of 322 international firms. The authors conjecture that the combined synergetic effect of the underlying dimensions of a firm's IT flexibility architecture and dynamic capabilities enables organizations to cope with changing environmental conditions and drive competitive firm performance. Findings of this study suggest that there is a significant positive relationship between firms' degree of strategic alignment—defined as the degree of balance between all dimensions—and competitive firm performance. Strategic alignment can, therefore, be seen as an important condition that significantly influences a firm's competitive advantage in constantly changing environments. The proposed framework helps firms assess and improve their maturity and alignment of IT flexibility and dynamic capabilities. This article concludes with a discussion, suggestions for future research and managerial implications are also discussed.
A Flexible, Efficient and Accurate Framework for Community Question Answering Pipelines

Although deep neural networks have been proving to be excellent tools to deliver state-of-the-art results, when data is scarce and the tackled tasks involve complex semantic inference, deep linguistic processing and traditional structure-based approaches, such as tree kernel methods, are an alternative solution. Community Question Answering is a research area that benefits from deep linguistic analysis to improve the experience of the community of forum users. In this paper, we present a UIMA framework to distribute the computation of cQA tasks over computer clusters such that traditional systems can scale to large datasets and deliver fast processing.
Evaluation of the Impact of Data Uncertainty on the Prediction of Physiological Patient Deterioration

In this paper, we investigate the impact of data uncertainty on the prediction of a vital sign of abnormalities. We analyze the evolution of mean arterial blood pressure (MAP) and heart rate (HR) vital signs time series over a given forecasting window, whose values are classified according to a model of normality that models patient’s physiological deterioration. To this end, we developed a generic prediction framework that enables the deployment of different methods for data preparation, training, prediction, and classification so as to perform a set of experiments. To address our purpose, we selected a general regression neural network model (to predict vital sign trends) and a support vector machine (to recognize abnormalities) as the appropriate methods for a baseline analysis. However, other algorithm choices could be tailored in the defined prediction framework. We extract from the multiparameter intelligent monitoring in intensive care II waveform database a set of 302, 940 vital sign samples (organized as 459 records) to define training and testing data sets. From our experiments, we obtained evidence demonstrating the incidence of null values in the MAP/HR input time series on forecasting tasks. This was performed under different conditions in terms of null values in the observation window, and varying other factors like labeling methods and forecast window sizes. In our experiments, we concentrate on analyzing the extent of prediction performance under such conditions and identify that a tolerable level of null values is around 25% where it is possible to still derive forecast windows with an accuracy target $\geq 0.9$ for both MAP and HR vital signs. The presented evaluation contributes to acknowledge the importance of uncertainty conditions on the prediction performance of vital signs and paves the way for future extensions involving other prediction approaches.
Effects of cannabis on eyewitness memory: A field study

Summary Eyewitnesses to crimes are regularly under the influence of drugs, such as cannabis. Yet there is very little research on how the use of cannabis affects eyewitness memory. In the present study, we assessed the effects of cannabis on eyewitness recall and lineup identification performance in a field setting. One hundred twenty visitors of coffee shops in Amsterdam viewed a videotaped criminal event, were interviewed about the event, and viewed a target‐present or target‐absent lineup. Witnesses under the influence of cannabis remembered significantly fewer correct details about the witnessed event than did sober witnesses, with no difference in incorrect recall. Cannabis use was not significantly associated with lineup identification performance, but intoxicated witnesses were significantly better at judging whether their lineup identification was accurate. Theoretical and practical implications of the findings are discussed.
Prototyping a functional language using higher-order logic programming: a functional pearl on learning the ways of λProlog/Makam

We demonstrate how the framework of higher-order logic programming, as exemplified in the λProlog language design, is a prime vehicle for rapid prototyping of implementations for programming languages with sophisticated type systems. We present the literate development of a type checker for a language with a number of complicated features, culminating in a standard ML-style core with algebraic datatypes and type generalization, extended with staging constructs that are generic over a separately defined language of terms. We add each new feature in sequence, with little to no changes to existing code. Scaling the higher-order logic programming approach to this setting required us to develop approaches to challenges like complex variable binding patterns in object languages and performing generic structural traversals of code, making use of novel constructions in the setting of λProlog, such as GADTs and generic programming. For our development, we make use of Makam, a new implementation of λProlog, which we introduce in tutorial style as part of our (quasi-)literate development.
Efficient Energy-Compensated VPLs using Photon Splatting

Efficient light transport simulation is important for many applications in computer graphics. Many-light methods based on virtual point lights (VPLs) especially work well for scenes consist mainly of diffuse materials. Rendered images with VPLs, however, are known to suffer from energy loss due to clamping of the VPL contributions. Removing this clamping, unfortunately, introduces significant variance in images due to the singularities of VPLs. We propose an efficient and practical algorithm which compensates energy loss of VPLs using yet another efficient rendering method -- photon splatting. Recent unified path sampling frameworks show that VPLs are in fact photons. Based on this unified formulation, we propose to reuse VPLs as photons to compensate missing energy of clamped VPLs. This combination is very natural: the singularities of VPLs happen if a shading point is too close to VPLs, but such VPLs can also be seen as neighboring photons that are indeed useful for photon density estimation. We formulate this energy compensation process using multiple importance sampling to combine rendering with VPLs and photon splatting optimally. Our algorithm can be efficiently implemented on GPUs since rendering with VPLs and photon splatting are both suitable for GPUs. The results demonstrate that our method produces more accurate images in the same rendering time than the existing methods. For common use cases of rendering mostly diffuse scenes, our work leads to an efficient, accurate, and practical rendering algorithm based on VPLs.