Miracle-dz/UniFAR-FTUs huggingface dataset:
Lunar trapped xenon

Evidence is presented for the presence in Apollo 16 light matrix breccias of trapped xenon which is isotopically similar to terrestrial xenon. This trapped component is predominantly released above 900 C, together with cosmic ray produced spallation gases. The relative abundances of Ar, Kr, and Xe are distinct from both solar and terrestrial patterns. Terrestrial-type xenon has so far been observed in both Apollo 14 and 16 breccias.
Armed E-Bunny: a selective dynamic compiler for embedded Java virtual machine targeting ARM processors

This paper presents a new selective dynamic compilation technique targeting ARM 16/32-bit embedded system processors. This compiler is built inside the J2ME/CLDC (Java 2 Micro Edition for Connected Limited Device Configuration) platform [8]. The primary objective of our work is to come up with an efficient, lightweight and low-footprint accelerated Java virtual machine ready to be executed on embedded machines. This is achieved by implementing a selective ARM dynamic compiler called Armed E-Bunny into Sun's Kilobyte Virtual Machine (KVM) [9]. In this paper, we present the motivations, the requirements, the architecture, the design, the implementation and debugging issues of Armed E-Bunny. The modified KVM is ported on an Embedded-Linux PDA and is tested using standard J2ME benchmarks. The experimental results on its performance demonstrate that a speedup of 360% over the last version of Sun's KVM is accomplished with a footprint overhead that does not exceed 119KB.
Evaluation of Corona and Ikonos high resolution satellite imagery for archaeological prospection in western Syria

Satellite surveys in Syria have made use of imagery recorded some 30 years apart. By comparing the earlier pictures (Corona) with the later (Ikonos), sites captured on the former can be accurately located by the latter. The comparison also reveals the stark implications for archaeology as large parts of west Asian landscape change from a state of ‘benign neglect’ to active redevelopment. Based on their experience in the Homs survey, the authors have important advice to offer in the design and costing of surveys using satellite imagery.
Grasping for the Seabed: Developing a New Underwater Robot Arm for Shallow-Water Intervention

A new underwater robot arm was developed through intensive cooperation between different academic institutions and an industrial company. The manipulator, which was initially designed to be teleoperated, was adapted for our autonomy needs. Its dimensions and weight were reduced, and its kinematic model was developed so that autonomous control can be performed with it. We compare several commercially available underwater manipulators and describe the development of the new one, from its initial configuration to its mechanical adaptation, modeling, control, and final assembly on an autonomous underwater vehicle (AUV). The feasibility and reliability of this arm is demonstrated in water tank conditions, where various innovative autonomous object-recovery operations are successfully performed, both in stand-alone operation and integrated in an AUV prototype.
Ejes de crecimiento espacial y nuevos territorios de desarrollo en España: algunas reflexiones

RESUMEN Este articulo pretende valorar diversas aportaciones hechas desde la Geografia, pero tambien desde otras ciencias, respecto a las llamadas «regiones emergentes» en Espana. Despues de unas reflexiones iniciales, el trabajo estudia su distribucion espacial, expone los distintos tipos de figuras y estructuras en que se manifiestan, sintetiza los fundamentos y factores sobre los que se configuran estas regiones y termina planteando interrogantes que pueden orientar futuras profundizaciones sobre estos nuevos territorios de desarrollo. RESUME A xes de la croissance spatiale et nouveaux territoires du developpement dans l'Espagne: quelques reflexions. - Cet article pretend l'evaluation de diverses contributions faites par la Geographie, mais aussi par d'autres sciences, sur les nommees «regions emergentes» dans l'Espagne. Apres quelques reflexions initiales, l'article etude leur distribution spatiale, expose les differentes formes et structures qu'elles adoptent, synthetise les fondements et les facteurs qui les conforment et termine en posant divers questions qui peuvent orienter prochains approfondissements sur ces nouveaux territoires du developpement. ABSTRACT Spatial growth shafts and new development territories in Spain: some reflections. - This article aims to evaluate some contributions made from the Geography, but also from other sciences, about the spaces known as «emerging regions» in Spain. After some initial reflections, the work studies their spatial distribution, exposes the different types of figures and structures in which they are expresed, synthesizes the bases and factors which configure them and ends outlining some questions that can guide further deepenings on these new development territories. Palabras clave / Mots cle / Key words Regiones emergentes, ejes de desarrollo, ejes de crecimiento, dinamismos espaciales, desarrollo local. Regions emergentes, axes du developpement, axes du croissance, dynamiques spatiales, developpement local. Emerging regions, development shafts, growth shafts, spatial dynamisms, local development.
Discussion on the role of the Red River shear zone, Yunnan and Vietnam, in the continental extrusion of SE Asia Journal, Vol. 163, 2006, 1025–1036

P. H. Leloup, P. Tapponnier & R. Lacassin write: In his recent paper, Searle (2006) acknowledges that the 1000 km long Ailao Shan–Red River shear zone is a large Miocene left-lateral shear zone, but speculates that left-lateral slip started after 21 Ma and claims that the total finite offset remains unknown. From this he concludes that continental extrusion was only a relatively minor tectonic factor during the India–Asia collision, as long argued by other workers (e.g. England & Houseman 1986; Cobbold & Davy 1988; Dewey et al . 1989; Houseman & England 1993). We summarize below the field and geochronological evidence that makes us maintain a viewpoint in better accordance with facts. Timing of left-lateral shear along the Ailao Shan–Red River shear zone . The Ailao Shan–Red River shear zone is composed mostly of high-grade metamorphic rocks and deformed granitoids with ubiquitous evidence for left-lateral shear parallel to the belt (e.g. Tapponnier et al . 1986, 1990; Leloup et al . 1993, 1995, 2001). The crystallization of the granitoids has been dated between 22 and 35 Ma (Fig. 1d–f), for example by Scharer et al . (1990, 1994) and Zhang & Scharer (1999), leading those workers to propose that left-lateral shear started at least c . 35 Ma ago. In contrast, Searle (2006) claims that all the deformed granitoids found within the shear zone predate left-lateral shear and that their crystallization ages should thus be interpreted to provide an upper limit for the onset of deformation, rather than a lower limit, thus suggesting a maximum age of 21 Ma for this deformation. A clear understanding of the P – T history and in situ deformation history of the intrusions, as well as of their relationships with surrounding paragneisses, is fundamental for interpreting correctly the geochronological …
Uniformly Accurate Discontinuous Galerkin Fast Sweeping Methods for Eikonal Equations

In [F. Li, C. -W. Shu, Y. -T. Zhang, H. Zhao, J. Comput. Phys., 227 (2008) pp. 8191-8208], we developed a fast sweeping method based on a hybrid local solver which is a combination of a discontinuous Galerkin (DG) finite element solver and a first order finite difference solver for Eikonal equations. The method has second order accuracy in the $L^1$ norm and a very fast convergence speed, but only first order accuracy in the $L^\infty$ norm for the general cases. This is an obstacle to the design of higher order DG fast sweeping methods. In this paper, we overcome this problem by developing uniformly accurate DG fast sweeping methods for solving Eikonal equations. We design novel causality indicators which guide the information flow directions for the DG local solver. The values of these indicators are initially provided by the first order finite difference fast sweeping method, and they are updated during iterations along with the solution. We observe both a uniform second order accuracy in the $L^\infty$ norm (in smooth regions) and the fast convergence speed (linear computational complexity) in the numerical examples.
Floyd-Warshall Reinforcement Learning Learning from Past Experiences to Reach New Goals

Consider mutli-goal tasks that involve static environments and dynamic goals. Examples of such tasks, such as goal-directed navigation and pick-and-place in robotics, abound. Two types of Reinforcement Learning (RL) algorithms are used for such tasks: model-free or model-based. Each of these approaches has limitations. Model-free RL struggles to transfer learned information when the goal location changes, but achieves high asymptotic accuracy in single goal tasks. Model-based RL can transfer learned information to new goal locations by retaining the explicitly learned state-dynamics, but is limited by the fact that small errors in modelling these dynamics accumulate over long-term planning. In this work, we improve upon the limitations of model-free RL in multi-goal domains. We do this by adapting the Floyd-Warshall algorithm for RL and call the adaptation Floyd-Warshall RL (FWRL). The proposed algorithm learns a goal-conditioned action-value function by constraining the value of the optimal path between any two states to be greater than or equal to the value of paths via intermediary states. Experimentally, we show that FWRL is more sample-efficient and learns higher reward strategies in multi-goal tasks as compared to Q-learning, model-based RL and other relevant baselines in a tabular domain.
Generalized Tsallis Entropy Reinforcement Learning and Its Application to Soft Mobile Robots

—In this paper, we present a new class of entropy-regularized Markov decision processes (MDPs), which will be referred to as Tsallis MDPs. that inherently generalize well-known maximum entropy reinforcement learning (RL) by introducing an additional real-valued parameter called an entropic index. Our theoretical result enables us to derive and analyze different types of optimal policies with interesting properties relate to the stochasticity of the optimal policy by controlling the entropic index. To handle complex and model-free problems, such as learning a controller for a soft mobile robot, we propose a Tsallis actor-critic (TAC) method. We ﬁrst observe that different RL problems have different desirable entropic indices where using proper entropic index results in superior performance compared to the state-of-the-art actor-critic methods. To mitigate the exhaustive search of the entropic index, we propose a quick-and-dirty curriculum method of gradually increasing the entropic index which will be referred to as TAC with Curricula (TAC 2 ). TAC 2 shows comparable performance to TAC with the optimal entropic index. Finally, We apply TAC 2 to learn a controller of a soft mobile robot where TAC 2 outperforms existing actor-critic methods in
Cloud detection and analysis on the Tibetan Plateau using Meteosat and CloudSat

Previous studies of clouds over the Tibetan Plateau (TiP) were subject to limitations. Surface observations are scarce, and satellite retrievals are not well adapted to the peculiar conditions of the TiP. For the most comprehensive existing cloud data set, provided by the International Satellite Cloud Climatology Project (ISCCP), issues were reported for the TiP. It also lacks sufficient spatiotemporal resolution for this topographically complex region. With the Indian Ocean Data Coverage service, European Organisation for the Exploitation of Meteorological Satellites provides a Meteosat data set between 1998 and 2008. The resolution of around 6 km at the study area is sufficient even for complex terrain. Based on this data set and on products of the active sensor onboard CloudSat, we develop a novel gross‐cloud retrieval for the TiP using logistic regression models. The approach maintains the original Meteosat resolution. Validation against independent CloudSat data reveals good performance. The approach also outperforms the ISCCP pixel level (DX) data set. The resulting data set is the first for the TiP that provides cloud information with sufficient resolution for both day and night. Patterns of cloud frequencies during winter, premonsoon, and monsoon seasons are analyzed. Strong diurnal forcing is found for the plateau. Peaks of cloud frequencies above the slopes occur during afternoon, while they are delayed in the valleys, where high cloud frequencies persist throughout the nights. Above the lower parts of the southern foothills of the Himalayas cloud frequencies were for the first time found to increase until the early morning. Katabatic flows are suspected to be responsible for this pattern by initiating the formation of mesoscale convective systems.
Few-shot Learning for Domain-specfic Fine-grained Image Classfication

Learning to recognize novel visual categories from a few examples is a challenging task for machines in real-world applications. In contrast, humans have the ability to discriminate even similar objects with little supervision. This paper attempts to address the few-shot fine-grained recognition problem. We propose a feature fusion model to explore the largest discriminative features by focusing on key regions. The model utilizes focus-area location to discover the perceptually similar regions among objects. High-order integration is employed to capture the interaction information among intra-parts. We also design a Center Neighbor Loss to form robust embedding space distribution for generating discriminative features. Furthermore, we build a typical fine-grained and few-shot learning dataset miniPPlankton from the real-world application in the area of marine ecological environment. Extensive experiments are carried out to validate the performance of our model. First, the model is evaluated with two challenging experiments based on the miniDogsNet and Caltech-UCSD public datasets. The results demonstrate that our model achieves competitive performance compared with state-of-the-art models. Then, we implement our model for the real-world phytoplankton recognition task. The experimental results show the superiority of the proposed model compared with others on the miniPPlankton dataset.
Hard Label Black-box Adversarial Attacks in Low Query Budget Regimes

We focus on the problem of black-box adversarial attacks, where the aim is to generate adversarial examples for deep learning models solely based on information limited to output labels (hard label) to a queried data input. We use Bayesian optimization (BO) to specifically cater to scenarios involving low query budgets to develop efficient adversarial attacks. Issues with BO's performance in high dimensions are avoided by searching for adversarial examples in structured low-dimensional subspace. Our proposed approach achieves better performance to state of the art black-box adversarial attacks that require orders of magnitude more queries than ours.
Identification of Influential Sea Surface Temperature Locations and Predicting Streamflow for Six Months Using Bayesian Machine Learning Regression

Sea surface temperature (SST) has significant influence in the hydrological cycle and affects the discharge in the stream. SST is an atmospheric circulation indicator which provides the predictive information about the hydrologic variability in the region around the world. Use of right location of SST for a given location of stream gage can capture the effect of oceanic-atmospheric interaction, improving the predictive ability of the model. This study aims on identifying the best locations of SST at the selected stream gage in the state of Utah that spatially covers the state from south to north, and use them for next six-month streamflow volume predictions. The data-driven model derived from the statistical learning theory was used in this study. Using an appropriate location of SST together with local climatic conditions and state of basin, an accurate and reliable streamflow was predicted for next six months. Influence of Pacific Ocean SST was observed to be stronger than that of Atlantic Ocean SST in the state of Utah. The SST of North Pacific developed the best model in most of the selected stream gages. Each model was ensured to be robust by the bootstrap analysis. The long-term streamflow prediction is important for water resource planning and management in the river basin scale and is a key step for successful water resource management in arid regions.
High-resolution spatial normalization for microarrays containing embedded technical replicates

MOTIVATION Microarray data are susceptible to a wide-range of artifacts, many of which occur on physical scales comparable to the spatial dimensions of the array. These artifacts introduce biases that are spatially correlated. The ability of current methodologies to detect and correct such biases is limited. RESULTS We introduce a new approach for analyzing spatial artifacts, termed 'conditional residual analysis for microarrays' (CRAM). CRAM requires a microarray design that contains technical replicates of representative features and a limited number of negative controls, but is free of the assumptions that constrain existing analytical procedures. The key idea is to extract residuals from sets of matched replicates to generate residual images. The residual images reveal spatial artifacts with single-feature resolution. Surprisingly, spatial artifacts were found to coexist independently as additive and multiplicative errors. Efficient procedures for bias estimation were devised to correct the spatial artifacts on both intensity scales. In a survey of 484 published single-channel datasets, variance fell 4- to 12-fold in 5% of the datasets after bias correction. Thus, inclusion of technical replicates in a microarray design affords benefits far beyond what one might expect with a conventional 'n = 5' averaging, and should be considered when designing any microarray for which randomization is feasible. AVAILABILITY CRAM is implemented as version 2 of the hoptag software package for R, which is included in the Supplementary information.
Inverted barometer contributions to recent sea level changes along the northeast coast of North America

Regional sea level (SL) changes reflect dynamic and isostatic ocean effects. Recent works have interpreted accelerated and extreme SL changes along the northeast coast of North America primarily in terms of dynamic changes; however, dedicated study of isostatic changes related to surface atmospheric pressure loading—the inverted barometer (IB) effect—has been lacking. This investigation uses five different atmospheric pressure products to analyze the influence of the IB effect on annual mean SL from tide gauge records. The IB effect explains ∼25% of interannual SL variance and accounts for ∼50% of the magnitude of a recent extreme event of SL rise along Atlantic Canada and New England. Estimated IB effects also amount to ∼10–30% of recent multidecadal SL accelerations over the Mid‐Atlantic Bight and Southern New England. These findings reiterate the need for careful estimation and removal of isostatic effects for studies of dynamic SL.
Synthesis of Brain Tumor MR Images for Learning Data Augmentation

Medical image analysis using deep neural networks has been actively studied. Deep neural networks are trained by learning data. For accurate training of deep neural networks, the learning data should be sufficient, of good quality, and should have a generalized property. However, in medical images, it is difficult to acquire sufficient patient data because of the difficulty of patient recruitment, the burden of annotation of lesions by experts, and the invasion of patients' privacy. In comparison, the medical images of healthy volunteers can be easily acquired. Using healthy brain images, the proposed method synthesizes multi-contrast magnetic resonance images of brain tumors. Because tumors have complex features, the proposed method simplifies them into concentric circles that are easily controllable. Then it converts the concentric circles into various realistic shapes of tumors through deep neural networks. Because numerous healthy brain images are easily available, our method can synthesize a huge number of the brain tumor images with various concentric circles. We performed qualitative and quantitative analysis to assess the usefulness of augmented data from the proposed method. Intuitive and interesting experimental results are available online at this https URL
TTR-Based Reward for Reinforcement Learning with Implicit Model Priors

Model-free reinforcement learning (RL) is a powerful approach for learning control policies directly from highdimensional state and observation. However, it tends to be data-inefficient, which is especially costly in robotic learning tasks. On the other hand, optimal control does not require data if the system model is known, but cannot scale to models with high-dimensional states and observations. To exploit benefits of both model-free RL and optimal control, we propose time-to-reach-based (TTR-based) reward shaping, an optimal control-inspired technique to alleviate data inefficiency while retaining advantages of model-free RL. This is achieved by summarizing key system model information using a TTR function to greatly speed up the RL process, as shown in our simulation results. The TTR function is defined as the minimum time required to move from any state to the goal under assumed system dynamics constraints. Since the TTR function is computationally intractable for systems with high-dimensional states, we compute it for approximate, lower-dimensional system models that still captures key dynamic behaviors. Our approach can be flexibly and easily incorporated into any model-free RL algorithm without altering the original algorithm structure, and is compatible with any other techniques that may facilitate the RL process. We evaluate our approach on two representative robotic learning tasks and three well-known model-free RL algorithms, and show significant improvements in data efficiency and performance.
Robust Reserve-Crew Scheduling for Airlines

We study the practical setting in which regular-and reserve-crew schedules are dynamically maintained up to the day of executing the schedule. At each day preceding the execution of the schedule, disruptions occur due to sudden unavailability of personnel, making the planned regular and reserve-crew schedules infeasible for its execution day. This paper studies the fundamental question how to repair the schedules’ infeasibility in the days preceding the execution, taking into account labor regulations. We propose a robust repair strategy that maintains ﬂexibility in order to cope with additional future disruptions. The ﬂexibility in reserve-crew usage is explicitly considered through evaluating the expected shortfall of the reserve-crew schedule based on a Markov chain formulation. The core of our approach relies on iteratively solving a set-covering formulation, which we call the Robust Crew Recovery Problem, which encapsulates this ﬂexibility notion for reserve crew usage. A tailored branch-and-price algorithm is developed for solving the Robust Crew Recovery Problem to optimality. The corresponding pricing problem is eﬃciently solved by a newly developed pulse algorithm. Based on actual data from a medium-sized hub-and-spoke airline, we show that embracing our approach leads to fewer ﬂight cancellations and fewer last-minute alterations, compared to repairing disrupted schedules without considering our robust measure.