Miracle-dz/UniFAR-FTUs huggingface dataset:
Dryadlinq: a System for General-purpose Distributed Data- Parallel Computing Using a High-level Language. Osdi, 2008. History and Successors

Of the many developments in data management over the past decade, MapReduce and subsequent large-scale data processing systems have been among the most disruptive and the most controversial. Cheap commodity storage and rising data volumes led many Internet service vendors to discard conventional database systems and data warehouses and build custom, home-grown engines instead. Google's string of publications on their large-scale systems, including Google File System [10], MapReduce, Chubby [6], and BigTable [7], are perhaps the most famous and influential in the market. In almost all cases, these new, homegrown systems implemented a small subset of the features found in conventional databases, including high-level languages, query optimizers, and efficient execution strategies. However , these systems and the resulting open source Hadoop ecosystem proved highly popular with many developers. This led to considerable investment, marketing, research interest , and development on these platforms, which, today are in flux, but, as an ecosystem, have come to resemble traditional data warehouses—with some important modifications. We reflect on these trends here. Our first reading is the original Google MapReduce paper from 2004. MapReduce was a library built for simplifying parallel, distributed computation over distributed data at Google's scale—particularly, the batch rebuild of web search indexes from crawled pages. It is unlikely that, at the time, a traditional data warehouse could have handled this workload. However, compared to a conventional data warehouse, MapReduce provides a very low-level interface (two-stage dataflow) that is closely tied to a fault-tolerant execution strategy (intermediate materialization between two-stage dataflow). Equally importantly, MapReduce was designed as a library for parallel programming rather than an end-to-end data warehousing solution; for example, MapRe-duce delegates storage to Google File System. At the time, members of the database community decried the architecture as simplistic, inefficient, and of limited use [8]. While the original MapReduce paper was released in 2003, there was relatively little additional activity external to Google until 2006, when Yahoo! open-sourced the Hadoop MapReduce implementation. Subsequently, there was an explosion of interest: within a year, a range of projects including Dryad (Microsoft) [15], Hive (Facebook) [26], Pig (Ya-hoo) [22] were all under development. These systems, which we will call post-MapReduce systems, gained considerable traction with developers—who were largely concentrated in Silicon Valley—as well as serious VC investment. A multitude of research spanning the systems, databases, and networking communities investigated issues including scheduling , straggler mitigation, fault tolerance, UDF query optimization , and alternative …
Spatio-temporal Weather Forecasting and Attention Mechanism on Convolutional LSTMs

—Numerical weather forecasting on high-resolution physical models consume hours of computations on supercomputers. Application of deep learning and machine learning methods in forecasting revealed new solutions in this area. In this paper, we forecast high-resolution numeric weather data using both input weather data and observations by providing a novel deep learning architecture. We formulate the problem as spatio-temporal prediction. Our model is composed of Convolutional Long-short Term Memory , and Convolutional Neural Network units with encoder-decoder structure. We enhance the short-long term performance and interpretability with an attention and a context matcher mechanism. We perform experiments on high-scale, real-life, benchmark numerical weather dataset, ERA5 hourly data on pressure levels , and forecast the temperature. The results show signiﬁcant improvements in capturing both spatial and temporal correlations with attention matrices focusing on different parts of the input series. Our model obtains the best validation and the best test score among the baseline models, including ConvLSTM forecasting network and U-Net. We provide qualitative and quantitative results and show that our model forecasts 10 time steps with 3 hour frequency with an average of 2 degrees error. Our code and the data are publicly available.
Maximum likelihood approach to the detection of changes between multitemporal SAR images

The authors introduce maximum likelihood techniques for optimised discrimination between agricultural and wooded regions, based on a multitemporal sequence of ERS images. The inherent resolution of the system is inadequate to make such a classification on an individual image. However, the different temporal change patterns of the two classes can be exploited. One approach uses joint annealed segmentation of the image sequence, providing optimised exploitation of the speckle model in determining the common set of region boundaries in the underlying radar cross-section. This is followed by maximum likelihood change detection using a normalised log temporal texture measure. This is shown to be superior to constructing the normalised log measure directly, including speckle fluctuations, followed by a single annealed segmentation process. Finally, it is demonstrated how simple filtering of this normalised log measure can provide reasonable classification with greatly reduced computation load.
Energy-Efficient Baseband Function Deployments for Service-Oriented Open RAN

—Recently open radio access network (Open RAN), which splits baseband functions into multiple process units at different locations has received considerable attentions from both industries and academia with the potential to enable a fully disaggregated RAN with more ﬂexibility in delivering energy-saving and latency-sensitive applications. However, the signiﬁcant increases of resource usage dynamics in both geographical and time and network complexity may lead to unnecessary high energy consumption in RANs without an efﬁcient RAN func- tion management policy. Many studies have proposed baseband function management solutions, however, the activation cost and data network resources of edge computing capacities have not been evaluated in much detail, as far as the authors know. In this paper, with the objective of minimizing energy consumption, meanwhile, satisfying the requests over the network under the constraints of latency and resource capacity, we propose a completed mixed integer linear programming (MILP) formula- tion, a multi-agent deep reinforcement learning-based algorithm and a heuristic (DCUH), to take user plane functions (UPFs) on the multi-access edge computing servers (MECs) and the activation consumption of MECs into consideration. Moreover, we prototype an OpenDaylight, OpenStack and Open Source Management and Orchestration-based Open RAN testbed to verify the feasibility of the proposed solutions. Results show the importance of hibernating the MEC after a certain time of network vacancy. DRL-based algorithm and DCUH can approach a similar performance as the benchmark of MILP and save more than 40% energy consumption compared to the ﬁrst-ﬁt algorithm. This study offers an important insight into the design of baseband deployment policies that greatly enhance user experience with better service and save
The two cultures: mashing up web 2.0 and the semantic web

A common perception is that there are two competing visions for the future evolution of the Web: the Semantic Web and Web 2.0. A closer look, though, reveals that the core technologies and concerns of these two approaches are complementary and that each field can and must draw from the other's strengths. We believe that future web applications will retain the Web 2.0 focus on community and usability, while drawing on Semantic Web infrastructure to facilitate mashup-like information sharing. However, there are several open issues that must be addressed before such applications can become commonplace. In this paper, we outline a semantic weblogs scenario that illustrates the potential for combining Web 2.0 and Semantic Web technologies, while highlighting the unresolved issues that impede its realization. Nevertheless, we believe that the scenario can be realized in the short-term. We point to recent progress made in resolving each of the issues as well as future research directions for each of the communities.
CO2 and CH4 exchanges between land ecosystems and the atmosphere in northern high latitudes over the 21st century

Terrestrial ecosystems of the northern high latitudes (above 50°N) exchange large amounts of CO2 and CH4 with the atmosphere each year. Here we use a process‐based model to estimate the budget of CO2 and CH4 of the region for current climate conditions and for future scenarios by considering effects of permafrost dynamics, CO2 fertilization of photosynthesis and fire. We find that currently the region is a net source of carbon to the atmosphere at 276 Tg C yr‐1. We project that throughout the 21st century, the region will most likely continue as a net source of carbon and the source will increase by up to 473 Tg C yr−1 by the end of the century compared to the current emissions. However our coupled carbon and climate model simulations show that these emissions will exert relatively small radiative forcing on global climate system compared to large amounts of anthropogenic emissions.
C# and the .NET Framework: Ready for Real Time?

Microsoft's integrated development environment, Visual Studio.NET, includes a new programming language C# (pronounced "C sharp"), which targets the . NET Framework. Both the . NET Framework and C# are fairly well-documented technologies, but the platform's appropriateness for real-time systems has not received much attention. Microsoft doesn't specifically claim that C# and NET are intended for real-time systems, but many of the platform's general purpose features including type unsafe features, thread synchronization, and overflow-sensitive arithmetic apply to real-time systems. This article will further explore C# and the . NET Framework's suitability for real-time systems.
Telemedicine in Dentistry, Lessons to be Learned: A Case Study

Most of the telemedicine projects do not pass the pilot stage. A comprehensive evaluation of telemedicinepilot projects is needed in order to improve their success rate and to create a knowledge base for futureprojects. In this paper we address this issue through the study case on a pilot teledentistryproject with 5 different implementation sites. A literature review of teledentistry and telemedicine in general, pointstowards a rather one sided approach whenevaluating these projects. The organizational aspect does not seem to be a priority inthese evaluationswhile we consider its crucial impact on project’s continuity and adoption. To support this assumption we usea healthcare information systems implemen-tation perspective which allowed us to assess the adoption barriersfor the teledentistry pilot. We make assumptions on how it could be used for telemedicine pilots and for telemedicine projects in general. We have collected data using qualitative methods such as semi-structured interviews and non-participantobservation of teledentistryactivities and meetings related to the project. Afterdrawingthe dental teleconsultation process and observing that most of it turns around elec-tronic medical records(EMR)wefocusedto identifyall potential adoption barriers and link-ing them with the list of EMR adoption barriersby Pare et al.(2014). We have identified 23 adoption barriers for teledentristry: 4 in 5/5 implementation sites,1 in 4/5 sites, 6 in 3/5 sites, 2 in 2/5 sitesand 10 in 1/5sites. Out of the 23teledentistry adoption barriers, 17 have been linked with EMR adoption barriers. By adding the remaining 6 items to the EMR adoption barriers list and adapting the items to the telemedicine context we pro-posea telemedicine pilot project adoption barriers list.
Irrigation impact on annual water balance of the oases in Tarim Basin, Northwest China

By taking the sum of annual precipitation and lateral water input (in which irrigation water withdrawal is the main component) for water availability, the Budyko hypothesis and Fu's formula derived from it was extended to the study of oases in the Tarim Basin, Northwest China. For both long‐term (multi‐year) and annual values on water balances in the 26 oases subregions, the extended Fu's formula was confirmed. Regional patterns on water balance on the 26 oases subregions were related to change in land‐use types due to increased area for irrigation. Moreover, an empirical formula for the parameter was established to reflect the influences of change in land use on water balance. The extended Budyko framework was employed to evaluate the impact of irrigation variability on annual water balance. According to the multi‐year mean timescale, variabilities in actual evapotranspiration in the oases were mainly controlled by variability in irrigation water withdrawal rather than potential evapotranspiration. The influences of variability on potential evapotranspiration became increasingly apparent together with increases in irrigation water withdrawal. Copyright © 2010 John Wiley & Sons, Ltd.
Zero-Shot Action Recognition by Word-Vector Embedding

The number of categories for action recognition is growing rapidly and it has become increasingly hard to label sufficient training data for learning conventional models for all categories. Instead of collecting ever more data and labelling them exhaustively for all categories, an attractive alternative approach is "zeroshot learning" (ZSL). To that end, in this study we construct a mapping between visual features and a semantic descriptor of each action category, allowing new categories to be recognised in the absence of any visual training data. Existing ZSL studies focus primarily on still images, and attribute-based semantic representations. In this work, we explore word-vectors as the shared semantic space to embed videos and category labels for ZSL action recognition. This is a more challenging problem than existing ZSL of still images and/or attributes, because the mapping between the semantic space and video space-time features of actions is more complex and harder to learn for the purpose of generalising over any cross-category domain shift. To solve this generalisation problem in ZSL action recognition, we investigate a series of synergistic improvements to the standard ZSL pipeline. First, we enhance significantly the semantic space mapping by proposing manifold-regularised regression and data augmentation strategies. Second, we evaluate two existing post processing strategies (transductive self-training and hubness correction), and show that they are complementary. We evaluate extensively our model on a wide range of human action datasets including HMDB51, UCF101, OlympicSports, CCV and TRECVID MED 13. The results demonstrate that our approach achieves the state-of-the-art zero-shot action recognition performance with a simple and efficient pipeline, and without supervised annotation of attributes.
Magnetostratigraphy of the Plio‐Pleistocene carbonate section of the Great Australian Bight

ODP Leg 182 drilled two north‐south transects in the western Great Australian Bight close to longitude 128°E. These transects penetrated a remarkably thick section of uppermost Pliocene and Pleistocene carbonate sediments, which are separated from Miocene sections by a major hiatus. In the eastern transect, at sites 1129, 1131 and 1127, the Brunhes‐Matuyama boundary (onset of C1n) was found at 343 m, between 280 and 300 m, and at 343 m below the sea floor, respectively. In the western transect, at sites 1132 and 1130, it was found between 170 and 181 m, and at 200 m, respectively. Within the Brunhes chron, inclination and intensity fluctuations and correlations between susceptibility and standard δ18O records were used to give age‐depth relations. These age relations from the palaeomagnetics are broadly consistent with the biostratigraphy and the δ18O results from Leg 182, but do not provide an entirely independent dataset. Rock magnetism stratigraphy at site 1131 revealed the principal bryozoan buildup to be at the time of the last glacial lowstand, as suggested by previous workers. At all sites the sedimentation rate increases from the basal unconformity up into the thick Upper Pleistocene section. In the western transect there appears to be a partial record of most of the Late Pliocene and Early Pleistocene chron boundaries (C1r1n to C2An3n) in a condensed section, but in the eastern transect only the Jaramillo (C1r1n) is observed.
Overview of the INEX 2013 Social Book Search Track

The goal of the INEX 2013 Social Book Search Track is to evaluate approaches for supporting users in reading, searching, and nav- igating collections of books based on book metadata, the full texts of digitised books or associated user-generated content. The investigation is focused around three tasks: 1) the Social Book Search (SBS) task in- vestigates the complex nature of relevance in book search and the role of traditional and user-generated book metadata in retrieval, 2) the Prove It (PI) task evaluates focused retrieval approaches for searching pages in books that can conrm or refute a given factual claim, 3) the Struc- ture Extraction (SE) task evaluates automatic techniques for deriving book structure from OCR text and layout information. Both the SBS and SE tasks have a growing number of active participants, while the PI task is only tackled by a small number of core groups. In the SBS task, we extended last year's investigation into the nature of book sug- gestions from the LibraryThing forums and how they compare to book relevance judgements. We found further support that such suggestions are a valuable alternative to traditional test collections that are based on top-k pooling and editorial relevance judgements. The PI task added a further relevance criterion that pages should not only conrm or refute a given factual claim, but should also come from an authoritative source that is of the appropriate genre. The relevance assessments have not yet commenced at the time of writing. The SE task has reached a record number of active participants and has, for the rst time, witnessed an improvement in the state of the art.
The Generalized Extreme Value (GEV) Distribution, Implied Tail Index and Option Pricing

Crisis events such as the 1987 stock market crash, the Asian Crisis and the bursting of the Dot-Com bubble have radically changed the view that extreme events in financial markets have negligible probability. This paper argues that the use of the Generalized Extreme Value (GEV) distribution to model the Risk Neutral Density (RND) function provides a flexible framework that captures the negative skewness and excess kurtosis of returns, and also delivers the market implied tail index of asset returns. We obtain an original analytical closed form solution for the Harrison and Pliska (1981) no arbitrage equilibrium price for the European option in the case of GEV asset returns. The GEV based option prices successfully remove the well known pricing bias of the Black-Scholes model. We explain how the implied tail index is efficacious at identifying the fat tailed behaviour of losses and hence the left skewness of the price RND functions, particularly around crisis events.
Single versus Multiple Tree Genetic Programming for Dynamic Decision Making

This paper considers genetic programming (GP) for dynamic decision making. Standard genetic programming only uses a single decision tree for decision making. In contrast, this paper proposes a general multiple tree framework for dynamic decision problems, where evaluation is contingent on the previous output of the program. The working hypothesis is that “recurrent” multiple trees are superior compared to conventional single trees for dynamic decision problems. To test this hypothesis, a single and a dual tree representation is considered. Both representations return Boolean values, but for the dual trees, evaluation is contingent on their previous output. Specifically, if the previous output was , the first tree is evaluated, otherwise it is the second. The single and dual trees are applied within two different domains. The first domain consists of a coevolutionary predator-prey type environment where the single and dual trees are treated as different species. The objective of a predator is to capture the phenotypic behavior of a prey. Naturally, the objective of the prey is to evade the predator. It is found that the dual trees have greater expressive capabilities, since they can capture the dynamics of the single trees when acting as predators, while evading when acting as prey. The second domain is closer related to finance. The single and dual trees are used to evolve successful trading strategies on artificial financial time series. Two different processes are constructed that exhibit some features also found in real financial data, i.e., mean-reversionand momentum effects. It is found that the single trees are unable to capture the dynamics of the mean-reverting process, but the dual trees succeed. For the trending series, both representation are capableof capturing the underlying dynamics, but the single trees have better out-of-sample performance compared to the dual trees. This is found to be a manifestation of Ockham’s razor. JEL classifications: C0, C45, C53, C63
Reservoir property prediction using abductive networks

Statistical methods, such as linear regression and neural networks, are commonly used to predict reservoir properties from seismic attributes. However, a huge number of attributes can be extracted from seismic data and an efficient method for selecting an attribute subset with the highest correlation to the property being predicted is essential. Most statistical methods, however, lack an optimized approach for this attribute selection. We propose to predict reservoir properties from seismic attributes using abductive networks, which use iterated polynomial regression to derive high-degree polynomial predictors. The abductive networks simultaneously select the most relevant attributes and construct an optimal nonlinear predictor. We applied the approach to predict porosity from seismic data of an area within the 'Uthmaniyah portion of the Ghawar oil field, Saudi Arabia. The data consisted of normal seismic amplitude, acoustic impedance, 16 other seismic attributes, and porosity logs from seven wells located in the study area. Out of 27 attributes, the abductive network selected only the best two to six attributes and produced a more accurate and robust porosity prediction than using the more common neural-network predictors. In addition, the proposed method requires no effort in choosing the attribute subset or tweaking their parameters.