Miracle-dz/UniFAR-FTUs huggingface dataset:
State-Continuity Approximation of Markov Decision Processes via Finite Element Analysis for Autonomous System Planning

Motion planning under uncertainty for an autonomous system can be formulated as a Markov Decision Process. In this paper, we propose a solution to this decision theoretic planning problem using a continuous approximation of the underlying discrete value function and leveraging finite element methods. This approach allows us to obtain an accurate and continuous form of value function even with a small number of states from a very low resolution of state space. We achieve this by taking advantage of the second order Taylor expansion to approximate the value function, where the value function is modeled as a boundary-conditioned partial differential equation which can be naturally solved using a finite element method. We have validated our approach via extensive simulations, and the evaluations reveal that our solution provides continuous value functions, leading to better path results in terms of path smoothness, travel distance and time costs, even with a smaller state space.
Cumulative culture spontaneously emerges in artificial navigators who are social and memory-guided

Cumulative cultural evolution occurs when adaptive innovations are passed down to consecutive generations through social learning. This process has shaped human technological innovation, but also occurs in non-human species. While it is traditionally argued that cumulative culture relies on high-fidelity social transmission and advanced cognitive skills, here I show that a much simpler system suffices. Cumulative culture spontaneously emerged in artificial agents who navigate with a minimal cognitive architecture of goal-direction, social proximity, and route memory. Within each generation, naive individuals benefitted from being paired with experienced navigators because they could follow previously established routes. Crucially, experienced navigators also benefitted from the presence of naive individuals through regression to the goal. As experienced agents followed their memorised path, their naive counterparts (unhindered by route memory) were more likely to err towards than away from the goal, and thus biased the pair in that direction. This improved route efficiency within each generation. In control experiments, cumulative culture was attenuated when agents' social proximity or route memory were lesioned, whereas eliminating goal-direction only reduced efficiency. These results demonstrate that cumulative cultural evolution occurs even in the absence of sophisticated communication or thought. One interpretation of this finding is that current definitions are too loose, and should be narrowed. An alternative conclusion is that rudimentary cumulative culture is an emergent property of systems that seek social proximity and have an imprecise memory capacity, providing a flexible complement to traditional evolutionary mechanisms.
Long-run forecasting in multi- and polynomially cointegrated systems.

In this paper long-run forecasting in multi- and polynomially cointegrated models is investigated. It is shown that the result of Christoersen and Diebold (1998) derived for I(1) cointegrated models generalizes to multi- and polynomially cointegrated systems. That is, in terms of the trace mean squared forecast error criterion, imposing the multi- and polynomially cointegrating restrictions does not lead to improved long-run horizon forecast accuracy when compared to forecasts generated from the univariate representations of the system variables. However, when one employs a loss function derived from the triangular representations of the (polynomially-) cointegrating systems, gains in forecastability are achieved for system forecasts as opposed to the univariate forecasts. The paper highlights the importance of carefully selecting loss functions in forecast evaluation of cointegrated systems.
Black Holes, White Dwarfs and Neutron Stars: The Physics of Compact Objects

Stuart L Shapiro and Saul A Teukolsky 1983 Chichester: John Wiley xvii + 645 pp price £37.95 (£20.45 paperback) ISBN 0 471 87317 9 Hdbk, 0 471 87316 0 Pbk No physics course is really complete without some astronomical content. The inclusion of basic astrophysics serves to deepen students' appreciation of the character and scope of physical laws.
Vector Field Histogram* with look-ahead tree extension dependent on time variable environment

The article deals with a novel approach to reactive navigation. A proposed reactive navigation is based on the Vector Field Histogram (VFH) method, which is easily modifiable. The biggest advantage of the proposed method is that it allows the robot to avoid static as well as moving obstacles in an unknown environment in a more effective way and without the need of switching any algorithm or the robot’s behaviour. Moreover, the proposed extension allows the avoidance of several moving obstacles in real time. The article contains three proposed improvements of the original VFH* method: representation of moving objects, look-ahead tree improvements and approach to criterion function. The method is verified by several experiments in various scenarios, which include few static and moving obstacles.
Overlapping Variable Clustering with Statistical Guarantees

Variable clustering is one of the most important unsupervised learning methods, ubiquitous in most research areas. In the statistics and computer science literature, most of the clustering methods lead to non-overlapping partitions of the variables. However, in many applications, some variables may belong to multiple groups, yielding clusters with overlap. It is still largely unknown how to perform overlapping variable clustering with statistical guarantees. To bridge this gap, we propose a novel Latent model-based OVErlapping clustering method (LOVE) to recover overlapping sub-groups of a potentially very large group of variables. In our model-based formulation, a cluster is given by variables associated with the same latent factor, and can be determined from an allocation matrix A that indexes our proposed latent model. We assume that some of the observed variables are pure, in that they are associated with only one latent factor, whereas the remaining majority has multiple allocations. We prove that the corresponding allocation matrix A, and the induced overlapping clusters, are identifiable, up to label switching. We estimate the clusters with LOVE, our newly developed algorithm, which consists in two steps. The first step estimates the set of pure variables, and the number of clusters. In the second step we estimate the allocation matrix A and determine the overlapping clusters. Under minimal signal strength conditions, our algorithm recovers the population level clusters consistently. Our theoretical results are fully supported by our empirical studies, which include extensive simulation studies that compare LOVE with other existing methods, and the analysis of a RNA-seq dataset.
Software Engineering for Intelligent and Autonomous Systems: Report from the GI Dagstuhl Seminar 18343

Software systems are increasingly used in application domains characterised by uncertain environments, evolving requirements and unexpected failures; sudden system malfunctioning raises serious issues of security, safety, loss of comfort or revenue. During operation, these systems will likely need to deal with several unpredictable situations including variations in system performance, sudden changes in system workload and component failures. These situations can cause deviation from the desired system behaviour and require dynamic adaptation of the system behaviour, parameters or architecture. Through using closed-loop control, typically realized with software, intelligent and autonomous software systems can dynamically adapt themselves, without any or with limited human involvement, by identifying abnormal situations, analysing alternative adaptation options, and finally, self-adapting to a suitable new configuration. This report summarises the research carried out during SEfIAS GI Dagstuhl seminar which provided a forum for strengthening interaction and collaboration for early-career researchers and practitioners from the research communities of SEAMS, ICAC/ICCAC, SASO, Self-Aware Computing and AAMAS.
Robust Image Segmentation Quality Assessment

Deep learning based image segmentation methods have achieved great success, even having human-level accuracy in some applications. However, due to the black box nature of deep learning, the best method may fail in some situations. Thus predicting segmentation quality without ground truth would be very crucial especially in clinical practice. Recently, people proposed to train neural networks to estimate the quality score by regression. Although it can achieve promising prediction accuracy, the network suffers robustness problem, e.g. it is vulnerable to adversarial attacks. In this paper, we propose to alleviate this problem by utilizing the difference between the input image and the reconstructed image, which is conditioned on the segmentation to be assessed, to lower the chance to overfit to the undesired image features from the original input image, and thus to increase the robustness. Results on ACDC17 dataset demonstrated our method is promising.
Three consecutive cytosolic glycolysis enzymes modulate autophagic flux

Abstract Autophagy serves as an important recycling route for the growth and survival of eukaryotic organisms in nutrient-deficient conditions. Since starvation induces massive changes in the metabolic flux that are coordinated by key metabolic enzymes, specific processing steps of autophagy may be linked with metabolic flux-monitoring enzymes. We attempted to identify carbon metabolic genes that modulate autophagy using VIGS screening of 45 glycolysis- and Calvin–Benson cycle-related genes in Arabidopsis (Arabidopsis thaliana). Here, we report that three consecutive triose-phosphate-processing enzymes involved in cytosolic glycolysis, triose-phosphate-isomerase (TPI), glyceraldehyde-3-phosphate dehydrogenase (GAPC), and phosphoglycerate kinase (PGK), designated TGP, negatively regulate autophagy. Depletion of TGP enzymes causes spontaneous autophagy induction and increases AUTOPHAGY-RELATED 1 (ATG1) kinase activity. TGP enzymes interact with ATG101, a regulatory component of the ATG1 kinase complex. Spontaneous autophagy induction and abnormal growth under insufficient sugar in TGP mutants are suppressed by crossing with the atg101 mutant. Considering that triose-phosphates are photosynthates transported to the cytosol from active chloroplasts, the TGP enzymes would be strategically positioned to monitor the flow of photosynthetic sugars and modulate autophagy accordingly. Collectively, these results suggest that TGP enzymes negatively control autophagy acting upstream of the ATG1 complex, which is critical for seedling development.
A quantitative polymerase chain reaction assay for the detection of equine (horse and donkey)-originated meat in processed bovine meat products

Meat is one of the most important basic foodstuffs in human nutrition. Nowadays, adulteration and authenticity are common problems for meat products. Identification of meat species is important in terms of consumer protection and prevention of adulteration. There are different methods to determine adulteration of meat and meat products. These methods are histological controls, serological tests, and quantitative polymerase chain reaction. In this study, species identification and quantification analysis of meat and meat products were done by using horse-, donkey-, and bovine-specific primers with quantitative polymerase chain reaction method. Triple meat mixtures containing horse and donkey meat ranging from 0.1 to 50% levels were prepared within a bovine mixture for using species identification and quantification analysis. The method specificity was confirmed by melting curve analysis. In conclusion, quantitative polymerase chain reaction is an easy, rapid, and reliable method for meat species identification, and with this study an applicable method was developed for the detection and quantification of equine-originated meat in bovine meat products.
Web Semantics: Science, Services and Agents on the World Wide Web

Modern Web search engines still have many limitations: search terms are not disambiguated, search terms in one query cannot be in different languages, the retrieved media items have to be in the same language as the search terms and search results are not integrated across a live stream of different media channels, including TV, online news and social media. The system described in this paper enables all of this by combining a media stream processing architecture with cross-lingual and cross-modal semantic annotation, search and recommendation. All those components were developed in the xLiMe project. © 2017 Elsevier B.V. All rights reserved.
Web Semantics: Science, Services and Agents on the World Wide Web

Ontologies are becoming a key component of numerous applications and research fields. But knowledge captured within ontologies is not static. Some ontology updates potentially have a wide ranging impact; others only affect very localised parts of the ontology and their applications. Investigating the impact of the evolution gives us insight into the editing behaviour but also signals ontology engineers and users how the ontology evolution is affecting other applications. However, such research is in its infancy. Hence, we need to investigate the evolution itself and its impact on the simplest of applications: the materialisation. In this work, we define impact measures that capture the effect of changes on the materialisation. In the future, the impact measures introduced in this work can be used to investigate how aware the ontology editors are about consequences of changes. By introducing five different measures, which focus either on the change in the materialisation with respect to the size or on the number of changes applied, we are able to quantify the consequences of ontology changes. To see these measures in action, we investigate the evolution and its impact on materialisation for nine open biomedical ontologies, most of which adhere to the EL ++ description logic. Our results show that these ontologies evolve at varying paces but no statistically significant difference between the ontologies with respect to their evolution could be identified. We identify three types of ontologies based on the types of complex changes which are applied to them throughout their evolution. The impact on the materialisation is the same for the investigated ontologies, bringing us to the conclusion that the effect of changes on the materialisation can be generalised to other similar ontologies. Further, we found that the materialised concept inclusion axioms experience most of the impact induced by changes to the class inheritance of the ontology and other changes only marginally touch the materialisation.
A UML/MARTE Model Analysis Method for Uncovering Scenarios Leading to Starvation and Deadlocks in Concurrent Systems

Concurrency problems such as starvation and deadlocks should be identified early in the design process. As larger, more complex concurrent systems are being developed, this is made increasingly difficult. We propose here a general approach based on the analysis of specialized design models expressed in the Unified Modeling Language (UML) that uses a specifically designed genetic algorithm to detect concurrency problems. Though the current paper addresses deadlocks and starvation, we will show how the approach can be easily tailored to other concurrency issues. Our main motivations are 1) to devise solutions that are applicable in the context of the UML design of concurrent systems without requiring additional modeling and 2) to use a search technique to achieve scalable automation in terms of concurrency problem detection. To achieve the first objective, we show how all relevant concurrency information is extracted from systems' UML models that comply with the UML Modeling and Analysis of Real-Time and Embedded Systems (MARTE) profile. For the second objective, a tailored genetic algorithm is used to search for execution sequences exhibiting deadlock or starvation problems. Scalability in terms of problem detection is achieved by showing that the detection rates of our approach are, in general, high and are not strongly affected by large increases in the size of complex search spaces.
TMAV: Temporal Motionless Analysis of Video using CNN in MPSoC

Analyzing video for traffic categorization is an important pillar of Intelligent Transport Systems. However, it is difficult to analyze and predict traffic based on image frames because the representation of each frame may vary significantly within a short time period. This also would inaccurately represent the traffic over a longer period of time such as the case of video. We propose a novel bio-inspired methodology that integrates analysis of the previous image frames of the video to represent the analysis of the current image frame, the same way a human being analyzes the current situation based on past experience. In our proposed methodology, called IRON-MAN (Integrated Rational prediction and Motionless ANalysis), we utilize Bayesian update on top of the individual image frame analysis in the videos and this has resulted in highly accurate prediction of Temporal Motionless Analysis of the Videos (TMAV) for most of the chosen test cases. The proposed approach could be used for TMAV using Convolutional Neural Network (CNN) for applications where the number of objects in an image is the deciding factor for prediction and results also show that our proposed approach outperforms the state-of-the-art for the chosen test case. We also introduce a new metric named, Energy Consumption per Training Image (ECTI). Since, different CNN based models have different training capability and computing resource utilization, some of the models are more suitable for embedded device implementation than the others, and ECTI metric is useful to assess the suitability of using a CNN model in multi-processor systems-on-chips (MPSoCs) with a focus on energy consumption and reliability in terms of lifespan of the embedded device using these MPSoCs.
Neural Networks Models for Entity Discovery and Linking

This paper describes the USTC_NELSLIP systems submitted to the Trilingual Entity Detection and Linking (EDL) track in 2016 TAC Knowledge Base Population (KBP) contests. We have built two systems for entity discovery and mention detection (MD): one uses the conditional RNNLM and the other one uses the attention-based encoder-decoder framework. The entity linking (EL) system consists of two modules: a rule based candidate generation and a neural networks probability ranking model. Moreover, some simple string matching rules are used for NIL clustering. At the end, our best system has achieved an F1 score of 0.624 in the end-to-end typed mention ceaf plus metric.
Acta Botanica Brasilica

Cleistocactus baumannii is the only ornithophilous cactus species in the Brazilian Chaco. In addition, this species of Cactaceae invests heavily in flowering in the ecoregion. Such characteristics motivated us to evaluate the temporal availability of flowers in the context of its floral visitors. The reproductive system of C. baumannii, the number of individuals in flowering, flower abundance and the frequency and richness of floral visitors were evaluated and quantified. Nectar robbery was a frequent phenomenon in the studied population; therefore, we compared the pollen load deposited on the stigma of damaged (robbery) and undamaged flowers. In the Brazilian Chaco, C. baumannii is self-incompatible and has a continuous flowering pattern, providing floral resources throughout the year for nine species of floral visitors. One hummingbird species acted as a potential pollinator, and we consider Xylocopa splendidula to be a nectar robber. We found stigma of flowers damaged by nectar robbers to have lower pollen loads than those of undamaged flowers. This study highlights the importance of studying reproductive traits in different populations to understand changes in the reproductive success of plant species at different scales and possible causes, such as availability of floral visitors, incidence of robbers and flowering patterns.
Survival and growth of tree seedlings as a function of seed size in a gallery forest under restoration

Seed size is commonly related to higher rates of emergence and survival, and biomass of seedlings when introduced by direct seeding. However, few studies have evaluated whether this relationship persists when species are reintroduced as seedlings or if this eff ect persists after seed reserves decline. Th is study evaluated the eff ect of seed size (mass) on the probability of survival and growth of seedlings of nine native tree species introduced into a pasture area, which was originally a gallery forest in the state of São Paulo in Southeastern Brazil. Th e experiment occurred over the course of 24 months, in which was divided into three separate time intervals: 14 to 61 days, 61 days to 12 months, and 12 to 24 months. Seedling survival in the fi eld was high for all three time-intervals. Seed mass positively infl uenced the probability of seedling survival throughout all three time-intervals after planting, but the intensity of this eff ect decreased with time. Species with smaller seeds exhibited higher relative growth rates (height), but only until the end of the fi rst year. Our results suggest that seed mass is a functional trait that can predict the probability of the establishment of individual trees during forest restoration.
Cometary coma dust size distribution from in situ IR spectra

Dust is the most abundant component in cometary comae. Here, we investigate the dust size distribution in 67P/Churyumov-Gerasimenko (67P/CG) using data from the Rosetta spacecraft that was in close proximity to the comet from 2014 August to 2016 September. The Visual, Infrared and Thermal Imaging Spectrometer (VIRTIS-M), spectral range of 0.25–5 μ m, and the Grain Impact Analyser and Dust Accumulator (GIADA), both part of the Rosetta payload, together provide a powerful means to characterize the dust coma properties. On March 28, Rosetta performed a ﬂyby close to the nucleus that allowed GIADA to detect a large amount of dust particles used to constraint the differential size distribution power-law index of − 2.2 ± 0.3. In April 2015, VIRTIS-M observed the spectral radiance in the wavelength range of 1–5 μ m. A simple radiative transfer model has been applied to simulate the VIRTIS-M radiances, thus allowing to infer the dust properties. We assumed an optically thin dust coma and spherical amorphous carbon particles in the size range between 0.1 to 1000 μ m. We obtained the infrared data best ﬁt with a differential dust size distribution power-law index of − 3 . 1 + 3 − 0 . 1 . This index matches the one determined using GIADA March 2015 data indicating that, before perihelion, the inner coma radiance is dominated by particles larger than 10 μ m; and the dust coma did not change its properties during most of the 67P/CG inbound orbit.