Miracle-dz/UniFAR-FTUs huggingface dataset:
Joint Inpainting of Depth and Reflectance with Visibility Estimation I

This paper presents a novel strategy to generate, from 3-D lidar measures, dense depth and reflectance images coherent with given color images. It also estimates for each pixel of the input images a visibility attribute. 3-D lidar measures carry multiple information, e.g. relative distances to the sensor (from which we can compute depths) and reflectances. When projecting a lidar point cloud onto a reference image plane, we generally obtain sparse images, due to undersampling. Moreover, lidar and image sensor positions typically differ during acquisition; therefore points belonging to objects that are hidden from the image view point might appear in the lidar images. The proposed algorithm estimates the complete depth and reflectance images, while concurrently excluding those hidden points. It consists in solving a joint (depth and reflectance) variational image inpainting problem, with an extra variable to concurrently estimate handling the selection of visible points. As regularizers, two coupled total variation terms are included to match, two by two, the depth, reflectance, and color image gradients. We compare our algorithm with other image-guided depth upsampling methods, and show that, when dealing with real data, it produces better inpainted images, by solving the visibility issue. This study has been carried out with financial support from the French State, managed by the French National Research Agency (ANR) in the frame of the Investments for the future Programme IdEx Bordeaux (ANR-10-IDEX-03-02). J.-F. Aujol also acknowledges the support of the Institut Universitaire de France. Preprint submitted to ISPRS J PHOTOGRAMM January 6, 2017
All-memristive neuromorphic computing with level-tuned neurons

In the new era of cognitive computing, systems will be able to learn and interact with the environment in ways that will drastically enhance the capabilities of current processors, especially in extracting knowledge from vast amount of data obtained from many sources. Braininspired neuromorphic computing systems increasingly attract research interest as an alternative to the classical von Neumann processor architecture, mainly because of the coexistence of memory and processing units. In these systems, the basic components are neurons interconnected by synapses. The neurons, based on their nonlinear dynamics, generate spikes that provide the main communication mechanism. The computational tasks are distributed across the neural network, where synapses implement both the memory and the computational units, by means of learning mechanisms such as spike-timing-dependent plasticity. In this work, we present an all-memristive neuromorphic architecture comprising neurons and synapses realized by using the physical properties and state dynamics of phase-change memristors. The architecture employs a novel concept of interconnecting the neurons in the same layer, resulting in level-tuned neuronal characteristics that preferentially process input information. We demonstrate the proposed architecture in the tasks of unsupervised learning and detection of multiple temporal correlations in parallel input streams. The efficiency of the neuromorphic architecture along with the homogenous neuro-synaptic dynamics implemented with nanoscale phase-change memristors represent a significant step towards the development of ultrahighdensity neuromorphic co-processors.
From the American manufacturing belt to spatial clustering in transnational networks: the evolution of industrial geography as reflected in Geografiska Annaler

ABSTRACT This article features the Swedish geographer Sten De Geer’s contribution to the concept and delimitation of the American manufacturing belt, as published in Geografiska Annaler in 1927, and the reception of his article among US geographers. The marked attention paid to this article contributed to the positioning of Geografiska Annaler as one of the leading European journals of geography in the North American academic debate. Later articles published in Geografiska Annaler during the ensuing nine decades illustrate how the field of industrial geography developed in Sweden and internationally. It is demonstrated that today’s economic geographers have made deliberate attempts to more closely cooperate across disciplinary boundaries, particularly towards the field of international business, in order to better understand and explain the spatial concentration and distribution of economic activities in the same spirit as Sten De Geer’s seminal work.
The Mesolithic levels of Grotta del Santuario della Madonna at Praia a Mare (Cosenza, Italy): new excavations, chronological data and techno-typological features of the lithic assemblages

1 Sezione di Bioarcheologia, Museo Nazionale Preistorico Etnografico “L. Pigorini”, Museo delle Civiltà, Piazza G. Marconi 14, 00144 Roma, Italy 2 Dipartimento di Storia, Archeologia, Geografia, Arte e Spettacolo, Università degli Studi di Firenze, Via S. Egidio 21, 50122 Firenze, Italy 3 CeDaD (Centro di Datazione e Diagnostica), Dipartimento di Ingegneria e Innovazione, Università del Salento, Via per Monteroni 7, 73100 Lecce, Italy 4 Soprintendenza Archeologica, Belle arti e Paesaggio della Liguria, Via Balbi 10, 16126 Genova, Italy
Anisotropy in subduction zones: Insights from new source side S wave splitting measurements from India

This study presents 106 splitting and 40 null measurements of source side anisotropy in subduction zones, utilizing direct S waves registered at two stations sited on the Indian continent, which show null shear wave splitting measurements for SKS phases. Our results suggest that trench‐parallel anisotropy is dominant beneath the Philippines, Mariana, Izu‐Bonin, and edge of the Java slab, while plate motion‐parallel anisotropy is observed beneath the Solomon, Aegean, Japan, and Java slabs. Results from Kuril and Aleutian regions reveal trench‐oblique anisotropy. We chose to interpret these observations primarily in terms of mantle flow beneath a subduction zone. While the two‐dimensional (2‐D) slab entrained flow model offers a simple explanation for trench‐normal fast polarization azimuths (FPA), the trench‐parallel FPA can be reconciled by extension due to slab rollback. The model that invokes age of the subducting lithosphere can explain anisotropy in the subslab, derived from rays recorded at the updip stations. However, when downdip stations are used, contributions from the slab and supraslab need to be considered. In Japan, anisotropy in the subslab mantle shallower than 300 km might be associated with trench‐parallel mantle flow resulting in the alignment of FPA in the same direction. Anisotropy in the deeper part, above the transition zone, is probably associated with 2‐D flow resulting in trench‐normal FPA. Anisotropy in the Mariana Trench might be associated with trench‐parallel mantle flow in the supraslab region, with similar deformation in the upper mantle and the transition zone.
Acquisition and Rendering of Transparent and Refractive Objects

This paper introduces a new image-based approach to capturing and modeling highly specular, transparent, or translucent objects. We have built a system for automatically acquiring high quality graphical models of objects that are extremely difficult to scan with traditional 3D scanners. The system consists of turntables, a set of cameras and lights, and monitors to project colored backdrops. We use multi-background matting techniques to acquire alpha and environment mattes of the object from multiple viewpoints. Using the alpha mattes we reconstruct an approximate 3D shape of the object. We use the environment mattes to compute a high-resolution surface reflectance field. We also acquire a low-resolution surface reflectance field using the overhead array of lights. Both surface reflectance fields are used to relight the objects and to place them into arbitrary environments. Our system is the first to acquire and render transparent and translucent 3D objects, such as a glass of beer, from arbitrary viewpoints under novel illumination.
The Effect of Author Set Size and Data Size in Authorship Attribution

s10579-009-9111-2], for instance in social networks, will likely involve large sets of candidate authors and only limited data per author. In this article, we present the results of a systematic study of two important parameters in supervised machine learning that significantly affect performance in computational authorship attribution: (1) the number of candidate authors (i.e. the number of classes to be learned), and (2) the amount of training data available per candidate author (i.e. the size of the training data). We also investigate the robust-ness of different types of lexical and linguistic features to the effects of author set size and data size. The approach we take is an operationalization of the standard text categorization model, using memory-based learning for discriminating between the candidate authors. We performed authorship attribution experiments on a set of three benchmark corpora in which the influence of topic could be controlled. The short text fragments of e-mail length present the approach with a true challenge. Results show that, as expected, authorship attribution accuracy deteriorates as the number of candidate authors increases and size of training data decreases, although the machine learning approach continues performing significantly above chance. Some feature types (most notably character n-grams) are robust to changes in author set size and data size, but no robust individual features emerge.
Local Broadcast in the Physical Interference Model Technical Report

In this work we analyze the complexity of local broadcasting in the physical interference model. We present two distributed randomized algorithms: one that assumes that each node knows how many nodes there are in its geographical proximity, and another, which makes no assumptions about topology knowledge. We show that, if the transmission probability of each node meets certain characteristics, the analysis can be decoupled from the global nature of the physical interference model, and each node performs a successful local broadcast in time proportional to the number of neighbors in its physical proximity. We also provide worst-case optimality guarantees for both algorithms and demonstrate their behavior in average scenarios through simulations.
Assessing the impact of ozone pollution on food security using a combined experimental and flux modelling approach

Maximising global food production is a priority for the international community. Ground-level ozone (O3) – a greenhouse gas and air pollutant – reduces yield in many important crops, and is a likely contributing factor to the global yield gap. This body of work applies experimental and modelling approaches to investigate how O3 reduces yield in soybean and wheat, and how these responses can be represented in models.
Pattern-coupled sparse Bayesian learning for recovery of block-sparse signals

In this paper, we develop a new sparse Bayesian learning method for recovery of block-sparse signals with unknown cluster patterns. A pattern-coupled hierarchical Gaussian prior model is introduced to characterize the statistical dependencies among coefficients, where a set of hyperparameters are employed to control the sparsity of signal coefficients. Unlike the conventional sparse Bayesian learning framework in which each individual hyperparameter is associated independently with each coefficient, in this paper, the prior for each coefficient not only involves its own hyperparameter, but also the hyperparameters of its immediate neighbors. In doing this way, the sparsity patterns of neighboring coefficients are related to each other and the hierarchical model has the potential to encourage structured-sparse solutions. The hyperparameters, along with the sparse signal, are learned by maximizing their posterior probability via an expectation-maximization (EM) algorithm.
AI-Human Collaboration Via EEG

As AI becomes ever more competent and integrated into our lives, the issue of AI-human goal misalignment looms larger. This is partially because there is often a rift between what humans explicitly command and what they actually mean. Most contemporary AI systems cannot bridge this gap. In this study we attempted to reconcile the goals of human and machine by using EEG signals from a human to help a simulated agent complete a task.
Detecting and disrupting criminal networks

Objectives: This study aims to gain insight into what constitutes the drug community in the Russian Federation; in- formation that is absent in official governmental data but is vital for developing effective and much needed intervention strategies to counter the on-going ‘drug epidemic’. Methods Members of the on-line drug community are identified from a crawled set of almost 100,000 users from the social network ‘LiveJournal’ by context sensitive text mining of the users’ blogs using a dictionary of known drug- related official and ‘slang’ terminology. The interests that are more (or less) common within this sub-community are determined using Fisher’s exact tests and Hochberg and Benjamini’s false discovery rate control procedure. A ‘psychological portrait’ of the ‘average’ Russian drug user is created by clustering these indicative interests. Methods: A naive Bayesian classifier is presented for assessing one’s susceptibility to the ‘drug virus’. Results A total of 268 significant interests separating between users that most actively spread information on narcotics and the rest of the network and a set of themes summarizing these interests. Three sub-networks of users which can be uniquely classified as being either ‘infectious’, ‘susceptible’ or ‘immune’ to the ‘drug virus’.
International Journal of Computer Mathematics Editors’ Report

A new Chebyshev ﬁnite difference method for solving class of optimal control problem is proposed. The algorithm is based on Chebyshev approximations of the derivatives arising in system dynamics. In the performance index, we use Chebyshev approximations for integration. The numerical examples illustrate the robustness, accuracy and efﬁciency of the proposed technique.
Strain Partitioning and Present‐Day Fault Kinematics in NW Tibet From Envisat SAR Interferometry

An 8 year archive of Envisat synthetic aperture radar (SAR) data over a 300 × 500 km2 wide area in northwestern Tibet is analyzed to construct a line‐of‐sight map of the current surface velocity field. The resulting velocity map reveals (1) a velocity gradient across the Altyn Tagh fault, (2) a sharp velocity change along a structure following the base of the alluvial fans in southern Tarim, and (3) a broad velocity gradient, following the Jinsha suture. The interferometric synthetic aperture radar velocity field is combined with published GPS data to constrain the geometry and slip rates of a fault model consisting of a vertical fault plane under the Altyn Tagh fault and a shallow flat décollement ending in a steeper ramp on the Tarim side. The solutions converge toward 0.7 mm/yr of pure thrusting on the décollement‐ramp system and 10.5 mm/yr of left‐lateral strike‐slip movement on the Altyn Tagh fault, below a 17 km locking depth. A simple elastic dislocation model across the Jinsha suture shows that data are consistent with 4–8 mm/yr of left‐lateral shear across this structure. Interferometric synthetic aperture radar processing steps include implementing a stepwise unwrapping method starting with high‐quality interferograms to assist in unwrapping noisier interferograms, iteratively estimating long‐wavelength spatial ramps, and referencing all interferograms to bedrock pixels surrounding sedimentary basins. A specific focus on atmospheric delay estimation using the ERA‐Interim model decreases the uncertainty on the velocity across the Tibet border by a factor of 2.
Forests and biodiversity

All forest types from tropical to boreal, offer a huge diversity of habitats for plants, animals and micro-organisms. However, these biologically rich systems are increasingly threatened, largely as a result of human activity. We encourage countries to investigate their local and national forests, discover why they are so important biologically and investigate ways of helping to protect them. We also encourage countries to learn about the ecological web, knowledge and understanding of ecological interplay between single species, within different ecosystems and within bigger areas with many specific structures leading to a better understanding of nature’s processes.
BMC Medical Informatics and Decision Making

Background: As described in the Institute of Medicine's Crossing the Quality Chasm report, the quality of health care in the U.S. today leaves much to be desired. Discussion: One major opportunity for improving quality relates to increasing the use of information technology, or IT. Health care organizations currently invest less in IT than in any other information-intensive industry, and not surprisingly current systems are relatively primitive, compared with industries such as banking or aviation. Nonetheless, a number of organizations have demonstrated that quality can be substantially improved in a variety of ways if IT use is increased in ways that improve care. Specifically, computerization of processes that are error-prone and computerized decision support may substantially improve both efficiency and quality, as well as dramatically facilitate quality measurement. This report discusses the current levels of IT and quality in health care, how quality improvement and management are currently done, the evidence that more IT might be helpful, a vision of the future, and the barriers to getting there. Summary: This report suggests that there are five key policy domains that need to be addressed: standards, incentives, security and confidentiality, professional involvement, and research, with financial incentives representing the single most important lever.
Neutrosophic Sets and Systems Neutrosophic Sets and Systems

Many past studies largely described the concept of neutrosophic sets, neutrosophic multisets, rough sets, and rough neutrosophic sets in many areas. However, no paper has discussed about rough neutrosophic multisets. In this paper, we present some definition of rough neutrosophic multisets such as complement, union and intersection. We also have examined some desired properties of rough neutrosophic multisets based on these definitions. We use the hybrid structure of rough set and neutrosophic multisets since these theories are powerful tool for managing uncertainty, indeterminate, incomplete and imprecise information.
Motion planning under uncertainty using iterative local optimization in belief space

We present a new approach to motion planning under sensing and motion uncertainty by computing a locally optimal solution to a continuous partially observable Markov decision process (POMDP). Our approach represents beliefs (the distributions of the robot’s state estimate) by Gaussian distributions and is applicable to robot systems with non-linear dynamics and observation models. The method follows the general POMDP solution framework in which we approximate the belief dynamics using an extended Kalman filter and represent the value function by a quadratic function that is valid in the vicinity of a nominal trajectory through belief space. Using a belief space variant of iterative LQG (iLQG), our approach iterates with second-order convergence towards a linear control policy over the belief space that is locally optimal with respect to a user-defined cost function. Unlike previous work, our approach does not assume maximum-likelihood observations, does not assume fixed estimator or control gains, takes into account obstacles in the environment, and does not require discretization of the state and action spaces. The running time of the algorithm is polynomial (O[n6]) in the dimension n of the state space. We demonstrate the potential of our approach in simulation for holonomic and non-holonomic robots maneuvering through environments with obstacles with noisy and partial sensing and with non-linear dynamics and observation models.
BioASQ: A Challenge on Large-Scale Biomedical Semantic Indexing and Question Answering

This article provides an overview of BioASQ, a new competition on biomedical semantic indexing and question answering (QA). BioASQ aims to push towards systems that will allow biomedical workers to express their information needs in natural language and that will return concise and user-understandable answers by combining information from multiple sources of different kinds, including biomedical articles, databases, and ontologies. BioASQ encourages participants to adopt semantic indexing as a means to combine multiple information sources and to facilitate the matching of questions to answers. It also adopts a broad semantic indexing and QA architecture that subsumes current relevant approaches, even though no current system instantiates all of its components. Hence, the architecture can also be seen as our view of how relevant work from fields such as information retrieval, hierarchical classification, question answering, ontologies, and linked data can be combined, extended, and applied to biomedical question answering. BioASQ will develop publicly available benchmarks and it will adopt and possibly refine existing evaluation measures. The evaluation infrastructure of the competition will remain publicly available beyond the end of BioASQ.
Bringing Energy Aware Routing Closer to Reality with SDN Hybrid Networks

Energy aware routing aims at reducing the energy consumption of ISP networks. The idea is to adapt routing to the traffic load in order to turn off some hardware. However, it implies to make dynamic changes to routing configurations which is almost impossible with legacy protocols. The Software Defined Network (SDN) paradigm bears the promise of allowing a dynamic optimization with its centralized controller. In this work, we propose SENAtoR, an algorithm to enable energy aware routing in a scenario of progressive migration from legacy to SDN hardware. Since in real life, turning off network equipments is a delicate task as it can lead to packet losses, SENAtoR provides also several features to safely enable energy saving services: tunneling for fast rerouting, smooth node disabling and detection of both traffic spikes and link failures. We validate our solution by extensive simulations and by experimentation. We show that SENAtoR can be progressively deployed in a network using the SDN paradigm. It allows to reduce the energy consumption of ISP networks by 5 to 35% depending on the penetration of SDN hardware, while diminishing the packet loss rate compared to legacy protocols.
Why is a raven like a writing desk?: lessons in interface consistency and analogical reasoning from two cognitive architectures

Users who have worked with just one or two pieces of application software on a computer system are often faced with the need to use a new piece of software on the same system. Consistency between program interfaces is intended to make the new program easier to learn in this situation, but how “consistency” should be defined is not always clear. We present a model of analogical reasoning that describes how users rely on interface consistency to induce correct actions in a new situation. Versions of the model are implemented in ACT-R and Soar. The model yields a clearer and more principled understanding of design guidelines that recommend interface consistency.