Miracle-dz/UniFAR-FTUs huggingface dataset:
Online Gaussian Process State-Space Models: Learning and Planning for Partially Observable Dynamical Systems

Gaussian process state-space model (GPSSM) is a probabilistic dynamical system that represents unknown transition and/or measurement models as the Gaussian process (GP). The majority of approaches to learning GPSSM are focused on handling given time series data. However, in most dynamical systems, data required for model learning arrives sequentially and accumulates over time. Storing all the data requires large ammounts of memory, and using it for model learning can be computationally infeasible. To overcome this challenge, this paper develops an online inference method for learning the GPSSM (onlineGPSSM) that fuses stochastic variational inference (VI) and online VI. The proposed method can mitigate the computation time issue without catastrophic forgetting and supports adaptation to changes in a system and/or a real environments. Furthermore, we propose a GPSSM-based reinforcement learning (RL) framework for partially observable dynamical systems by combining onlineGPSSM with Bayesian filtering and trajectory optimization algorithms. Numerical examples are presented to demonstrate the applicability of the proposed method.
Looking for Reasons behind Success in Dealing with Requirements Change

During development, requirements of software systems are subject to change. Unfortunately, managing changing requirements can take a lot of time and effort. Yet some companies show a better management of changes in requirements than others. Why? What is it that makes some projects deal with changing requirements better than others? We pursue the long term goal of understanding the mechanisms used to successfully deal with change in requirements. In this paper we gather knowledge about the state-of-the-art and the state-of-practice. We studied eight software development projects in four different companies --large and small, inclined toward structured and toward agile principles of development--, interviewing their project managers and analyzing their answers. Our findings include a list of practical (rather than theoretical) factors affecting the ability to cope with small changes in requirements. Results suggest a central role of size as a factor determining the flexibility showed either by the organization or by the software development team. We report the research method used and validate our results via expert interviews, who could relate to our findings.
Closing the Loop in Webpage Understanding

The two most important tasks in information extraction from the Web are webpage structure understanding and natural language sentences processing. However, little work has been done toward an integrated statistical model for understanding webpage structures and processing natural language sentences within the HTML elements. Our recent work on webpage understanding introduces a joint model of Hierarchical Conditional Random Fields (HCRFs) and extended Semi-Markov Conditional Random Fields (Semi-CRFs) to leverage the page structure understanding results in free text segmentation and labeling. In this top-down integration model, the decision of the HCRF model could guide the decision making of the Semi-CRF model. However, the drawback of the top-down integration strategy is also apparent, i.e., the decision of the Semi-CRF model could not be used by the HCRF model to guide its decision making. This paper proposed a novel framework called WebNLP, which enables bidirectional integration of page structure understanding and text understanding in an iterative manner. We have applied the proposed framework to local business entity extraction and Chinese person and organization name extraction. Experiments show that the WebNLP framework achieved significantly better performance than existing methods.
Clustering and Sequential Pattern Mining of Online Collaborative Learning Data

Group work is widespread in education. The growing use of online tools supporting group work generates huge amounts of data. We aim to exploit this data to support mirroring: presenting useful high-level views of information about the group, together with desired patterns characterizing the behavior of strong groups. The goal is to enable the groups and their facilitators to see relevant aspects of the group's operation and provide feedback if these are more likely to be associated with positive or negative outcomes and indicate where the problems are. We explore how useful mirror information can be extracted via a theory-driven approach and a range of clustering and sequential pattern mining. The context is a senior software development project where students use the collaboration tool TRAC. We extract patterns distinguishing the better from the weaker groups and get insights in the success factors. The results point to the importance of leadership and group interaction, and give promising indications if they are occurring. Patterns indicating good individual practices were also identified. We found that some key measures can be mined from early data. The results are promising for advising groups at the start and early identification of effective and poor practices, in time for remediation.
Bayesian deconvolution: an R vinaigrette

Nonparametric maximum likelihood estimation of general mixture models pioneered by the work of Kiefer and Wolfowitz (1956) has been recently reformulated as an exponential family regression spline problem in Efron (2016). Both approaches yield a low dimensional estimate of the mixing distribution, g-modeling in the terminology of Efron. Some casual empiricism suggests that the Efron approach is preferable when the mixing distribution has a smooth density, while Kiefer-Wolfowitz is preferable for discrete mixing settings. In the classical Gaussian deconvolution problem both maximum likelihood methods appear to be preferable to (Fourier) kernel methods. Kernel smoothing of the Kiefer-Wolfowitz estimator appears to be competitive with the Efron procedure for smooth alternatives.
Learning Fast Matching Models from Weak Annotations

This paper proposes a novel training scheme for fast matching models in Search Ads, which is motivated by the real challenges in model training. The first challenge stems from the pursuit of high throughput, which prohibits the deployment of inseparable architectures, and hence greatly limits the model accuracy. The second problem arises from the heavy dependency on human provided labels, which are expensive and time-consuming to collect, yet how to leverage unlabeled search log data is rarely studied. The proposed training framework targets on mitigating both issues, by treating the stronger but undeployable models as annotators, and learning a deployable model from both human provided relevance labels and weakly annotated search log data. Specifically, we first construct multiple auxiliary tasks from the enumerated relevance labels, and train the annotators by jointly learning from those related tasks. The annotation models are then used to assign scores to both labeled and unlabeled training samples. The deployable model is firstly learnt on the scored unlabeled data, and then fine-tuned on scored labeled data, by leveraging both labels and scores via minimizing the proposed label-aware weighted loss. According to our experiments, compared with the baseline that directly learns from relevance labels, training by the proposed framework outperforms it by a large margin, and improves data efficiency substantially by dispensing with 80% labeled samples. The proposed framework allows us to improve the fast matching model by learning from stronger annotators while keeping its architecture unchanged. Meanwhile, our training framework offers a principled manner to leverage search log data in the training phase, which could effectively alleviate our dependency on human provided labels.
A high‐order element based adaptive mesh refinement strategy for three‐dimensional unstructured grid

Adaptive mesh refinement (AMR) shows attractive properties in automatically refining the flow region of interest, and with AMR, better prediction can be obtained with much less labor work and cost compared to manually remeshing or the global mesh refinement. Cartesian AMR is well established; however, AMR on hybrid unstructured mesh, which is heavily used in the high‐Reynolds number flow simulation, is less matured and existing methods may result in degraded mesh quality, which mostly happens in the boundary layer or near the sharp geometric features. User intervention or additional constraints, such as freezing all boundary layer elements or refining the whole boundary layer, are required to assist the refinement process. In this work, a novel AMR strategy is developed to handle existing difficulties. In the new method, high‐order unstructured elements are first generated based on the baseline mesh; then the refinement is conducted in the parametric space; at last, the mesh suitable for the solver is output. Generating refined elements in the parametric space with high‐order elements is the key of this method and this helps to guarantee both the accuracy and robustness. With the current method, 3‐dimensional hybrid unstructured mesh of huge size and complex geometry can be automatically refined, without user intervention nor additional constraints. With test cases including the 2‐dimensional airfoil and 3‐dimensional full aircraft, the current AMR method proves to be accurate, simple, and robust.
Solar and stellar coronae and winds

Abstract Solar-like stars influence their environments through their coronal emis- sion and winds. These processes are linked through the physics of the stellar magnetic field, whose strength and geometry has now been explored for a large number of stars through spectropolarimetric observations. We have now detected trends with mass and rotation rate in the distribution of magnetic energies in different geometries and on also different length scales. This has implications both for the dynamo processes that generate the fields and also for the dynamics and evolution of the coronae and winds. Modelling of the surface driving processes on stars of various masses and rotation rates has revealed tantalising clues about the dynamics of stellar coronae and their ejecta. These new observations have also prompted a resurgence in the modelling of stellar winds, which is now uncovering the range of different interplanetary conditions that exoplanets might experience as they evolve.
2 5 A ug 2 01 6 An Incremental Parser for Abstract Meaning Representation

We describe an incremental parser for mapping input text into Abstract Meaning Representation (AMR) graphs. Our parser is based on a transition system for dependency parsing, and allows to incrementally parse the sentence into an AMR graph, if all other components that are needed by the parser (such as the partof-speech tagger and the dependency parser) are incremental as well. We show that our parser has an advantage over state-of-the-art AMR parsers on several metrics.
Material characterisation with methods of nonlinear optics

In this review, we present nonlinear optical methods, based on the second and third order nonlinear polarization, especially in the context of material characterization tasks outside the area of life sciences—for which these techniques are mostly designed. An overview of application studies reported to date is given, together with a discussion on the advantages and limits of the individual methods. Furthermore, new ways of experimentally combining different optical concepts are introduced, and their potential for characterisation and inspection tasks is evaluated in the context of various case studies, including the investigation of semiconductor surfaces, metals and related corrosion products, as well as of organic materials.
Quantile Lower Bounds to Reliability Based on Splits

Author(s): Bentler, Peter; Hunt, Tyler D | Abstract: Extending the theory of lower bounds to reliability based on splits given by Guttman (1945), this paper introduces quantile lower bound coefficients λ4(Q ) that refer to cumulative proportions of potential “split-half” coefficients that are below a particular point Q in the distribution of split-halves based on different partitions of variables into two sets. Interesting quantile values are Q=. 05,. 50,. 95,1.00 with λ4(.05)≤λ4(.50)≤λ4(.95)≤λ4(1.0) . Only λ4(1.0), Guttman’s maximal λ4, has previously been considered to be interesting, but in small samples it substantially overestimates population reliability ρ. The three coefficients λ4(.05), λ4(.50), and λ4(.95) provide new lower bounds to reliability. The smallest, λ4(.05), provides the most protection against capitalizing on chance associations and thus overestimation, λ4(.50) approximates coefficient α when α is an appropriate estimator of population reliability but improves on α otherwise, while λ4(.95) tends to overestimate reliability but also exhibits less bias than previous estimators. Computational theory, algorithm, and publicly available code based in R are provided to compute these coefficients. Simulation studies evaluate the performance of these coefficients and compare them to coefficient alpha and the greatest lower bound under several population reliability structures.
Li astrophysical S factors from the no-core shell model with continuum

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Li astrophysical S factors from the no-core shell model with continuum J. Dohet-Eraly, P. Navrátil, S. Quaglioni, W. Horiuchi, G. Hupin, F. Raimondi
Real-time Indoor Scene Reconstruction with RGBD and Inertia Input

Camera motion estimation is a key technique for 3D scene reconstruction and Simultaneous localization and mapping (SLAM). To make it be feasibly achieved, previous works usually assume slow camera motions, which limits its usage in many real cases. We propose an end-to-end 3D reconstruction system which combines color, depth and inertial measurements to achieve robust reconstruction with fast sensor motions. Our framework extends Kalman filter to fuse the three kinds of information and involve an iterative method to jointly optimize feature correspondences, camera poses and scene geometry. We also propose a novel geometry-aware patch deformation technique to adapt the feature appearance in image domain, leading to a more accurate feature matching under fast camera motions. Experiments show that our patch deformation method improves the accuracy of feature tracking, and our 3D reconstruction outperforms the state-of-the-art solutions under fast camera motions.
Acta Botanica Brasilica

When foraging on a specific range of fruits or seeds, animals can represent a natural selection force, affecting plant population realized fecundity. Most studies have focused on characteristics of dispersed seeds, but the higher predation of seeds of specific size or shape is also an important force that influences the populations’ realized fecundity and can help to understand patterns of plants genetic variability. In the Brazilian Pantanal, we investigated the predation of Acuri (Attalea phalerata) palm fruits by macaws, whose seeds go intact through tapirs digestive tract and then are deposited on latrines, where they are often consumed by seed predators. We found 251 fruits, containing between 1 and 5 seeds per fruit; 170 were intact and 81 had been partially damaged by macaws, mostly those containing 3 or 4 seeds per fruit and with thin endocarps. We argue that the higher consumption of fruits with these traits reflects a trade-off in the energy spent by macaws to open the fruits and the energy provided by them. Higher predation on 3-4 seeded fruits favors the selection of 1-2 seeded fruits, revealing a selection force opposed to the one exerted by bruchids and small rodents, which favors multi-seeded palm fruits.
Generalized Morita equivalence for linearly topologized rings

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Volumetric and MultiView CNNs for Object Classification on 3 D Data Supplementary Material

Training for Our Volumetric CNNs To produce occupancy grids from meshes, the faces of a mesh are subdivided until the length of the longest edge is within a single voxel; then all voxels that intersect with a face are marked as occupied. For 3D resolution 10,30 and 60 we generate voxelizations with central regions 10, 24, 54 and padding 0, 3, 3 respectively. This voxelization is followed by a hole filling step that fills the holes inside the models as occupied voxels. To augment our training data with azimuth and elevation rotations, we generate 60 voxelizations for each model, with azimuth uniformly sampled from [0, 360] and elevation uniformly sampled from [−45, 45] (both in degrees). We use a Nesterov solver with learning rate 0.005 and weight decay 0.0005 for training. It takes around 6 hours to train on a K40 using Caffe [2] for the subvolume supervision CNN and 20 hours for the anisotropic probing CNN. For multi-orientation versions of them, SubvolumeSup splits at the last conv layer and AniProbing splits at the second last conv layer. Volumetric CNNs trained on single orientation inputs are then used to initialize their multiorientation version for fine tuning. During testing time, 20 orientations of a CAD model occupancy grid (equally distributed azimuth and uniformly sampled elevation from [−45, 45]) are input to MO-VCNN to make a class prediction.
Efficient Planning under Uncertainty with Macro-actions

A wide variety of artificial intelligence systems require th e ability to perform planning under uncertainty in a partially observable domain. Such plannin g is particularly challenging when good control performance requires considering situations far i nto the future. In this paper, we propose the use of macro-actions in forward search planning to handl e such problems. By using macroactions consisting of fixed-length open-loop policies, we r estrict the policy class considered during planning in return for significant computational gains that allow us to perform much longer-horizon forward search. In a certain subset of domains, we also show t at it is possible to analytically compute the distribution over posterior beliefs that results f rom a single macro-action; this distribution captures any observation sequence that could occur during t he macro-action, and allows significant additional computational savings. We present a formal anal ysis of our approach, and demonstrate performance on two simulation experiments: a scientific exp loration domain and a target tracking and monitoring domain. We also demonstrate our algorithm wi th a real robotic helicopter in a target monitoring experiment. This suggests that our approach h s practical potential for planning in real-world, long-horizon, partially observable domains.
O ct 2 01 6 Knowledge-Based Biomedical Word Sense Disambiguation with Neural Concept Embeddings and Distant Supervision A uthors

Objective: Biomedical word sense disambiguation (WSD) is an important intermediate task in many natural language processing applications such as named entity recogni tion, syntactic parsing, and relation extraction. In this paper, we propose methods that improve over current state-o f-the-art in unsupervised and knowledge-based WSD for biomedicine. Materials and Methods: We employ knowledge based approaches that also exploit rece nt advances in neural word/concept embeddings to improve over the state-of-the-art in biomedical WSD using the MSH WSD dataset [ 1] as the test set. Our methods involve distant supervision – we do not use any hand-labeled examples for WSD to build our prediction models; however, we employ an existing named entity recognition and concept mapping program, MetaMap, to obtain our concept vectors. Results: Over the MSH WSD dataset, our linear time (in terms of numbers of senses and words in the test instance) method achieves an accuracy of 92.24% which is a 3% improveme nt over the best known results [ 2] obtained via unsupervised means. A more expensive approach that we devel op d relies on a nearest neighbor framework and achieves accuracy of 94.34%. Discussion: Using neural word and concept vector representations and ex isting knowledge based approaches, we produce a new state-of-art performance on the MSH WSD datase t without using training data. Employing dense vector representations learned from unlabeled free text ha s been shown to benefit many language processing tasks recently and our e fforts show that biomedical WSD is no exception to this trend. Conclusion: For a complex and rapidly evolving domain such as biomedicin e, building labeled datasets for larger sets of ambiguous terms may be impractical. Here, we show tha t dist nt supervision that leverages recent advances in representation learning can rival supervised approache s in biomedical WSD.
Repositório ISCTE-IUL

The rapid growth of social media in the last decades led e-commerce into a new era of value co-creation between the seller and the consumer. Since there is no contact with the product, people have to rely on the description of the seller, knowing that sometimes it may be biased and not entirely true. Therefore, review systems emerged to provide more trustworthy sources of information, since customer opinions may be less biased. However, the need to control the consumers’ opinion increased once sellers realized the importance of reviews and their direct impact on sales. One of the methods often used was to offer customers a specific product in exchange for an honest review. Yet, these incentivized reviews bias results and skew the overall rating of the products. The current study uses a data mining approach to predict whether or not a new review published was incentivized based on several review features such as the overall rating, the helpfulness rate, and the review length, among others. Additionally, the model was enriched with sentiment score features of the reviews computed through the VADER algorithm. The results provide an in-depth understanding of the phenomenon by identifying the most relevant features which enable to differentiate an incentivized from a non-incentivized review, thus providing users and companies with a simple set of rules to identify reviews that are biased without any disclaimer. Such rules include the length of a review, its helpfulness rate, and the overall sentiment polarity score.