Miracle-dz/UniFAR-FTUs huggingface dataset:
HiQuake : The Human-Induced Earthquake Database.

ABSTRACT HiQuake —The Human‐Induced Earthquake Database is the most complete database of anthropogenic projects proposed, on scientific grounds, to have induced earthquake sequences. It is freely available to download from the website given in Data and Resources. At the time this article was written, HiQuake contained ∼730 anthropogenic projects proposed to have induced earthquakes, as well as associated project‐related and seismic data. The most commonly reported anthropogenic activities proposed to have induced earthquakes are mining and water reservoir impoundment. In recent years, the number of earthquake sequences proposed to have been induced by fluid‐injection activities has grown. The most commonly reported maximum observed magnitude in an induced earthquake sequence is 3≤ M MAX HiQuake proposed to have been induced had a magnitude of M w 7.9 and occurred in China. Such large earthquakes release mostly stress of natural tectonic origin, but are conceivably triggered by small anthropogenic stress changes. The data in HiQuake are of variable quality because they are drawn from publications that span almost a century. We estimate underreporting to be ∼30% for M ∼4 events, ∼60% for M ∼3 events, and ∼90% for M ∼2 events. The degree of certitude that the given earthquake sequences were anthropogenically induced is variable. HiQuake includes all earthquake sequences proposed on scientific grounds to have been human induced without regard to the strength of the case made. HiQuake is offered freely as a resource to interested parties, and judging the reliability of any particular case is the responsibility of the database user. HiQuake will be routinely updated to correct errors, update existing entries, and add new entries. It has the potential to help improve our understanding of induced earthquakes and to manage their impact on society.
The Trend of Changes in Surface Wind in the Indian Ocean, in the Period from 1981 to 2015, Using Reanalysis Data, NCEP/NCAR

Knowing the pattern of surface winds on the seas and oceans and how it changes over time is of great importance. In this research, the monthly surface wind fields on the Indian Ocean have been studied and analyzed for a 35-year period (1981-2015), using NCEP/NCAR data reanalysis. The results show that transition from cold to warm pattern happens in May and that the summer monsoon pattern begins in June and continues until August. The wind speed pattern tends to the winter monsoon from November on. The maximum average wind speed in June is 13 m/s and its minimum is 2 m/s in October. Direction of prevailing winds is the southwest in the summer. The highest wind speed happens in the latitude of 10 - 15 degrees. Analysis of the wind distribution shows that the wind speed of 2 - 5 m/s happens in about 60% of the cases. There is probability of blowing 0.5 - 4 m/s wind for all months; but this probability is higher in the autumn (October and November) than that in the summer (July and August). Probability of the monthly over 5 m/s winds shows a definitely opposite distribution; that is, wind speed in July and August is higher than that in October. A long-term survey on the speed of surface water wind and sea surface temperature shows an opposite changing trend in wind speed and sea surface temperature during a 55-year statistical period. Wind speed reduced, while the sea surface temperature was increasing. The wind speed gradient in the upper levels of atmosphere graph has been increasing; this phenomenon confirms the effects of global warming and ocean warming on the monsoon system patterns in the Indian Ocean. Keywords North
A Distributed Monitoring Strategy for Detecting Version Number Attacks in RPL-Based Networks

The Internet of Things is characterized by the large-scale deployment of low power and lossy networks (LLN), interconnecting pervasive objects. The routing protocol for LLN (RPL) protocol has been standardized by IETF to enable a lightweight and robust routing in these constrained networks. A versioning mechanism is incorporated into RPL in order to maintain an optimized topology. However, an attacker can exploit this mechanism to significantly damage the network and reduce its lifetime. After analyzing and comparing existing work, we propose in this paper a monitoring strategy with dedicated algorithms for detecting such attacks and identifying the involved malicious nodes. The performance of this solution is evaluated through extensive experiments, and its scalability is quantified with the support of a monitoring node placement optimization method.
Modeling the productivity of forest fire suppression operations using production functions: a methodological approach

Society, forest landscapes and fires constitute a triangle of interaction with important requirements and consequences. These interdependencies offer a field for the application and development of research with a clear focus on social sciences, in which the fire prevetion, risk analysis and socio-economic conditions are shaped as basic pillars. Environmental dryness, climate and the consequences of fire exclusion, aggravated by urban growth in forest areas, with absence of preventive norms and under a scenario of high forest fires risk creates an environment susceptible to large forest fires that can affect the well-being of citizens. In this sense, the authorities and goverment are commiting large budgetary investments for the prevention and suppression of wildfires. The knowledge advances of the dynamics of fire behavior have allowed to dimension the expansive capacity of fire and, as a function of it, to identify a priori the potential impacts. In this sense, the integration of this knowledge together with social and economic aspects, have allowed us to advance in the studies of costs and benefits, spatial-temporal determination of risks in probabilistic terms and others such as the evaluation of the fire severity, the operational strategy, the reduction of uncertainty and the help in the decision making process. The present work explores the development of a methodology to incorporate fire suppression experience, detailed historical data on forest fire management from Mediterranean ecosystems, and empirical models to analyze efficiency and production. The resulting models of wildfire suppression production provides a basis for generating a conceptual framwork to study suppression operations. The development of decision support tools based on econometric models of fire suppression production functions requires the selection of variables that define and determine the problem. The present work includes the main methodological considerations for the definition and development of an econometric model based on the study of efficiency and productivity through the development of production functions that can predict the operational suppression yields in different scenarios of forest fires (meters per hour of fire spreading containment and suppression). The resulting model may represent an opportunity to aid in the development of suppression operations plans, helping the fire managers determine the production rates of the resources dispatched to fire suppression. Results
CLAss-Specific Subspace Kernel Representations and Adaptive Margin Slack Minimization for Large Scale Classification

In kernel-based classification models, given limited computational power and storage capacity, operations over the full kernel matrix becomes prohibitive. In this paper, we propose a new supervised learning framework using kernel models for sequential data processing. The framework is based on two components that both aim at enhancing the classification capability with a subset selection scheme. The first part is a subspace projection technique in the reproducing kernel Hilbert space using a CLAss-specific Subspace Kernel representation for kernel approximation. In the second part, we propose a novel structural risk minimization algorithm called the adaptive margin slack minimization to iteratively improve the classification accuracy by an adaptive data selection. We motivate each part separately, and then integrate them into learning frameworks for large scale data. We propose two such frameworks: the memory efficient sequential processing for sequential data processing and the parallelized sequential processing for distributed computing with sequential data acquisition. We test our methods on several benchmark data sets and compared with the state-of-the-art techniques to verify the validity of the proposed techniques.
Increasing SINR of microwave Applebaum adaptive arrays using analog 2D-IIR space-time network-resonant beam filters

Application of two-dimensional (2-D) network resonant analog beam filters towards increasing the spatial selectivity of microwave Applebaum adaptive arrays is explored. By employing a continuous-time 2-D planar-resonant beam filter as a pre-filtering stage to an existing Applebaum adaptive array, the zero-manifold only array transfer function is modified to include complex 2-D pole manifolds at guaranteed practical-BIBO stability. Increased spatial selectivity of the proposed architecture is observed via a reduction in the side lobe level on average. Furthermore, introduction of the 2-D network-resonant beam filter increases the usable temporal bandwidth of the original adaptive filter and therefore reduces the directional beam pointing errors present in the original Applebaum system due to operational frequency mismatches. Proposed system exhibits average signal to interference and noise ratio (SINR) improvement of 7.4 dB for beam directions from 0° to 90° where relative improvement increases towards the end fire direction of the antenna array.
APOBEC Enzymes: Mutagenic Fuel for Cancer Evolution and Heterogeneity.

UNLABELLED Deep sequencing technologies are revealing the complexities of cancer evolution, casting light on mutational processes fueling tumor adaptation, immune escape, and treatment resistance. Understanding mechanisms driving cancer diversity is a critical step toward developing strategies to attenuate tumor evolution and adaptation. One emerging mechanism fueling tumor diversity and subclonal evolution is genomic DNA cytosine deamination catalyzed by APOBEC3B and at least one other APOBEC family member. Deregulation of APOBEC3 enzymes causes a general mutator phenotype that manifests as diverse and heterogeneous tumor subclones. Here, we summarize knowledge of the APOBEC DNA deaminase family in cancer, and their role as driving forces for intratumor heterogeneity and a therapeutic target to limit tumor adaptation. SIGNIFICANCE APOBEC mutational signatures may be enriched in tumor subclones, suggesting APOBEC cytosine deaminases fuel subclonal expansions and intratumor heterogeneity. APOBEC family members might represent a new class of drug target aimed at limiting tumor evolution, adaptation, and drug resistance.
Formation of dimethyl sulfide and methanethiol in anoxic freshwater sediments

Concentrations of volatile organic sulfur compounds (VOSC) were measured in water and sediment columns of ditches in a minerotrophic peatland in The Netherlands. VOSC, with methanethiol (4 to 40 nM) as the major compound, appeared to be mainly of sediment origin. Both VOSC and hydrogen sulfide concentrations decreased dramatically towards the water surface. High methanethiol and high dimethyl sulfide concentrations in the sediment and just above the sediment surface coincided with high concentrations of hydrogen sulfide (correlation factors, r = 0.91 and r = 0.81, respectively). Production and degradation of VOSC were studied in 32 sediment slurries collected from various freshwater systems in The Netherlands. Maximal endogenous methanethiol production rates of the sediments tested (up to 1.44 (mu)mol per liter of sediment slurry (middot) day(sup-1)) were determined after inhibition of methanogenic and sulfate-reducing populations in order to stop VOSC degradation. These experiments showed that the production and degradation of VOSC in sediments are well balanced. Statistical analysis revealed multiple relationships of methanethiol production rates with the combination of methane production rates (indicative of total anaerobic mineralization) and hydrogen sulfide concentrations (r = 0.90) or with the combination of methane production rates and the sulfate/iron ratios in the sediment (r = 0.82). These findings and the observed stimulation of methanethiol formation in sediment slurry incubations in which the hydrogen sulfide concentrations were artificially increased provided strong evidence that the anaerobic methylation of hydrogen sulfide is the main mechanism for VOSC formation in most freshwater systems. Methoxylated aromatic compounds are likely a major source of methyl groups for this methylation of hydrogen sulfide, since they are important degradation products of the abundant biopolymer lignin. Increased sulfate concentrations in several freshwater ecosystems caused by the inflow of water from the river Rhine into these systems result in higher hydrogen sulfide concentrations. As a consequence, higher fluxes of VOSC towards the atmosphere are conceivable.
Evolution of the climatic tolerance and postglacial range changes of the most primitive orchids (Apostasioideae) within Sundaland, Wallacea and Sahul

The location of possible glacial refugia of six Apostasioideae representatives is estimated based on ecological niche modeling analysis. The distribution of their suitable niches during the last glacial maximum (LGM) is compared with their current potential and documented geographical ranges. The climatic factors limiting the studied species occurrences are evaluated and the niche overlap between the studied orchids is assessed and discussed. The predicted niche occupancy profiles and reconstruction of ancestral climatic tolerances suggest high level of phylogenetic niche conservatism within Apostasioideae.
Interference-aware relay selection scheme in cooperative relay networks

Relay selection scheme with multiple source-destination pairs and multiple potential relays in cooperative relay networks considering co-channel interference (CCI) is proposed in this paper. In cooperative relay networks, which relay nodes are selected has great impact on the system performance. It is an optimization problem for selecting suitable relay nodes. The exhaustive search can solve this problem but the complexity will increase factorially with the network size, i.e., the number of source-destination pairs and the number of relays in the network. This paper proposes a novel quantum particle swarm optimization (QPSO) based relay selection scheme which can maximize the system throughput of the cooperative relay networks but has less computational complexity. Simulation results show that QPSO based relay selection scheme has the ability to search global optimal solution compared with other relay selection schemes in the literature.
Scientific summary of ocean acidification in Washington State marine waters

Mention of a commercial company or product does not constitute an endorsement by NOAA/OAR. Use of information from this publication concerning proprietary products or the tests of such products for publicity or advertising purposes is not authorized. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Oceanic and Atmospheric Administration.ington Center for Environmental Visualization for their assistance with figures and graphics.
Pseudo-recurrent Connectionist Networks: An Approach to the 'Sensitivity-Stability' Dilemma

In order to solve the 'sensitivity-stability' problem-and its immediate correlate, the problem of sequential learning-it is crucial to develop connectionist architectures that are simultaneously sensitive to, but not excessively disrupted by, new input. French (1992) suggested that to alleviate a particularly severe form of this disruption, catastrophic forgetting, it was necessary for networks to separate dynamically their internal representations during learning. McClelland et al. (1995) went even further. They suggested that nature's way of implementing this obligatory separation was the evolution of two separate areas of the brain, the hippocampus and the neocortex. In keeping with this idea of radical separation, a 'pseudo-recurrent' memory model is presented here that partitions a connectionist network into two functionally distinct, but continually interacting areas. One area serves as a final-storage area for representations; the other is an early-processing area where new representations are firs.
Interactive physical robot guidance through advanced 3D dynamic simulation-based robot control — A new eRobotics approach

Manual robot guidance is an intuitive and flexible approach to teach robots. It is particularly useful for manufacturers because of the low programming efforts. However, this method often requires compliance control that is generally not available in conventional position-controlled industrial robots. Addressing this issue from the perspective of simulation-driven engineering, we introduce in this contribution a novel approach for interactive physical robot guidance based upon simulated adaptable joint admittance control. The developed simulation-based controller is driven in real-time with real external joint torques estimated during interaction with a physical robot. Since the simulator closely replicates the dynamic behavior of the real robot, it enriches and enhances the robot guidance by providing unique and reliable information on the robot that is useful to the operator. The simulated compliant joint trajectories are fed back into the real robot controller to enable full-body guidance. By opening new and practical perspectives in assisted physical guidance of position-controlled robots, this approach hightlights the effectiveness of control-by-3D-simulation [1] as pursued by eRobotics [2] to address challenging issues in robotics and automation. Simulation and experimental case studies conducted on a physical 7 DoF KUKA LWR 4+ robot manipulator are provided to illustrate the performance of the approach.
An online approach for intersection navigation of autonomous vehicle

Navigation through an intersection is a fundamental task that will enable an autonomous car to operate in a real traffic environment. Previous studies about intersection navigation generally assume vehicle to vehicle communication ability for all of the vehicles. Since this is unattainable in the near future, we focus on the scenario that vehicles on the road cannot communicate with each other. A new model is presented for this kind of intersection navigation as a Partially Observable Markov Decision Process problem. The proposed model can handle multiple numbers of cars in a dynamic environment. To validate the feasibility of the model, experiments are carried out with an autonomous golf cart in the university campus.
Adjusting Sense Representations for Word Sense Disambiguation and Automatic Pun Interpretation

Word sense disambiguation (WSD)—the task of determining which meaning a word carries in a particular context—is a core research problem in computational linguistics. Though it has long been recognized that supervised (machine learning–based) approaches to WSD can yield impressive results, they require an amount of manually annotated training data that is often too expensive or impractical to obtain. This is a particular problem for under-resourced languages and domains, and is also a hurdle in well-resourced languages when processing the sort of lexical-semantic anomalies employed for deliberate effect in humour and wordplay. In contrast to supervised systems are knowledge-based techniques, which rely only on pre-existing lexical-semantic resources (LSRs). These techniques are of more general applicability but tend to suffer from lower performance due to the informational gap between the target word's context and the sense descriptions provided by the LSR. This dissertation is concerned with extending the efficacy and applicability of knowledge-based word sense disambiguation. First, we investigate two approaches for bridging the information gap and thereby improving the performance of knowledge-based WSD. In the first approach we supplement the word's context and the LSR's sense descriptions with entries from a distributional thesaurus. The second approach enriches an LSR's sense information by aligning it to other, complementary LSRs. Our next main contribution is to adapt techniques from word sense disambiguation to a novel task: the interpretation of puns. Traditional NLP applications, including WSD, usually treat the source text as carrying a single meaning, and therefore cannot cope with the intentionally ambiguous constructions found in humour and wordplay. We describe how algorithms and evaluation methodologies from traditional word sense disambiguation can be adapted for the "disambiguation" of puns, or rather for the identification of their double meanings. Finally, we cover the design and construction of technological and linguistic resources aimed at supporting the research and application of word sense disambiguation. Development and comparison of WSD systems has long been hampered by a lack of standardized data formats, language resources, software components, and workflows. To address this issue, we designed and implemented a modular, extensible framework for WSD. It implements, encapsulates, and aggregates reusable, interoperable components using UIMA, an industry-standard information processing architecture. We have also produced two large sense-annotated data sets for under-resourced languages or domains: one of these targets German-language text, and the other English-language puns.
Droplet routing and wash droplet scheduling algorithm to remove cross-contamination in digital microfluidic biochip

Cross-contamination is incurred during droplet routing in digital microfluidic biochip (DMFB) because of intersection and overlap in the routing paths. Those intersected or overlapped regions are termed as contamination sites. In this paper, we propose a scheme for concurrent routing of multiple droplets with avoidance or minimization in cross-contamination, followed by a wash droplet scheduling operation for residue removal in the contamination sites. The proposed method is based on a hierarchical partitioning method that generates an unbalanced tree structure containing routing nets in the different nodes on the tree. The proposed partitioning technique is guided by the bounding boxes of the routing nets that help in avoiding intersection in routing paths in different partitioned zone/area. During droplet routing, our aim is to optimize resource utilizations, and minimize cross-contamination in a partition. Any cross-contamination site is washed with proper scheduling of wash droplets in the intermediate routing stages, and a shortest path based wash droplet operation is adopted to minimize routing overhead, or total routing completion time. Different observations are made by applying our algorithm on real benchmarks, and experimental results show improvement in many cases.