Miracle-dz/UniFAR-FTUs huggingface dataset:
Ensuring Reliable Datasets for Environmental Models and Forecasts

At the dawn of the 21 st century, environmental scientists are collecting more data more rapidly than at any time in the past. Nowhere is this change more evident than in the advent of sensor networks able to collect and process (in real time) simultaneous measurements over broad areas and at high sampling rates. At the same time there has been great progress in the development of standards, methods, and tools for data analysis and synthesis, including a new standard for descriptive metadata for ecological datasets (Ecological Metadata Language) and new workflow tools that help scientists to assemble datasets and to diagram, record, and execute analyses. However these developments (important as they are) are not yet sufficient to guarantee the reliability of datasets created by a scientific process – the complex activity that scientists carry out in order to create a dataset. We define a dataset to be reliable when the scientific process used to create it is (1) reproducible and (2) analyzable for potential defects. To address this problem we propose the use of an analytic web, a formal representation of a scientific process that consists of three coordinated graphs (a data-flow graph, a dataset-derivation graph, and a process-derivation graph) originally developed for use in software engineering. An analytic web meets the two key requirements for ensuring dataset reliability: (1) a complete audit trail of all artifacts (e.g., datasets, code, models) used or created in the execution of the scientific process that created the dataset, and (2) detailed process metadata that precisely describe all sub-processes of the scientific process. Construction of such metadata requires the semantic features of a high-level process definition language. In this paper we illustrate the use of an analytic web to represent the scientific process of constructing estimates of ecosystem water flux from data gathered by a complex, real-time multi-sensor network. We use Little-JIL, a high-level process definition language, to precisely and accurately capture the analytical processes involved. We believe that incorporation of this approach into existing tools and evolving metadata specifications (such as EML) will yield significant benefits to science. These benefits include: complete and accurate representations of scientific processes; support for rigorous evaluation of such processes for logical and statistical errors and for propagation of measurement error; and assurance of dataset reliability for developing sound models and forecasts of environmental change.
The Neural Basis of Successful Word Reading in Aphasia

Abstract Understanding the neural basis of recovery from stroke is a major research goal. Many functional neuroimaging studies have identified changes in brain activity in people with aphasia, but it is unclear whether these changes truly support successful performance or merely reflect increased task difficulty. We addressed this problem by examining differences in brain activity associated with correct and incorrect responses on an overt reading task. On the basis of previous proposals that semantic retrieval can assist pronunciation of written words, we hypothesized that recruitment of semantic areas would be greater on successful trials. Participants were 21 patients with left-hemisphere stroke with phonologic retrieval deficits. They read words aloud during an event-related fMRI paradigm. BOLD signals obtained during correct and incorrect trials were contrasted to highlight brain activity specific to successful trials. Successful word reading was associated with higher BOLD signal in the left angular gyrus. In contrast, BOLD signal in bilateral posterior inferior frontal cortex, SMA, and anterior cingulate cortex was greater on incorrect trials. These data show for the first time the brain regions where neural activity is correlated specifically with successful performance in people with aphasia. The angular gyrus is a key node in the semantic network, consistent with the hypothesis that additional recruitment of the semantic system contributes to successful word production when phonologic retrieval is impaired. Higher activity in other brain regions during incorrect trials likely reflects secondary engagement of attention, working memory, and error monitoring processes when phonologic retrieval is unsuccessful.
ELECTROMAGNETIC PROSPECTING ON ARCHAEOLOGICAL SITES USING A SOIL CONDUCTIVITY METER

In addition to their use as metal detectors, electromagnetic prospecting instruments can, in principle, be used to locate buried archaeological features such as pits, ditches, kilns and walls. Location is achieved as a result of either the electrical conductivity or magnetic susceptibility contrast between the archaeological feature and the surrounding soil. In the former case the possibility of electromagnetic prospecting is extremely attractive since this type of instrument would avoid the tedious business of probe insertion necessary with the conventional resistivity method. Initial attempts to use an electromagnetic detector for archaeological prospecting were unsuccessful (Tite 1961) but recently a new instrument, referred to as the soii conductivity meter or SCM (Howell 1966 and 1968) has been successfully employed to detect buried pits and ditches. The SCM consists of a vertical transmitter coil which is fed with a continuous sinusoidal current at a frequency of 4 kc/s and a receiver coil which is mounted, at a distance of I m, on the axis and perpendicular to the transmitter coil, thus avoiding any direct energy transfer. Although designed to detect differences in soil conductivity, it has been shown that in its present form the SCM normally responds to the magnetic properties of the soil (Tite and Mullins 1969). The purpose of the research described in this paper was to establish the conditions under which electromagnetic instruments, and in particular the SCM, respond to the conductivity rather than the magnetic susceptibility of the soil and to estimate the depth at which typical archaeological features could be located. The majority of the results were obtained using a scaled model of the SCM.
It's no secret: measuring the security and reliability of authentication via 'secret' questions

All four of the most popular webmail providers ‐ AOL, Google, Microsoft, and Yahoo! ‐ rely on personal questions as the secondary authentication secrets used to reset account passwords. The security of these questions has received limited formal scrutiny, almost all of which predates webmail. We ran a user study to measure the reliability and security of the questions used by all four webmail providers. We asked participants to answer these questions and then asked their acquaintances to guess their answers. Acquaintances with whom participants reported being unwilling to share their webmail passwords were able to guess 17% of their answers. Participants forgot 20% of their own answers within six months. What’s more, 13% of answers could be guessed within five attempts by guessing the most popular answers of other participants, though this weakness is partially attributable to the geographic homogeneity of our participant pool.
Common and unique elements of the ABA-regulated transcriptome of Arabidopsis guard cells Supplementary Materials

To check whether those significant 5-mer motifs enriched in our gene sets are guard cellor leafspecific, we calculated the enrichment P-values of these motifs in published ABA-regulated gene sets using all genes in the whole genome as the background, and then calculated the enrichment of these motifs in our gene sets using published ABA-regulated gene sets as the background. The merged previously published ABA-regulated gene sets were from Hoth et al. (2002), Li et al. (2006), Xin et al. (2005), Matsui et al. (2008), Huang et al. (2007), Zeller et al. (2009), Sánchez et al. (2004), Nemhauser et al. (2006), Seki et al. (2002), and Okamoto et al. (2010). The results are shown in Table S1 and Table S2 respectively. Guard cell-specific or leaf-specific motifs are those that are significant in Table S2 but not significant in Table S1.
Error Rates for Kernel Classification under Source and Capacity Conditions

In this manuscript, we consider the problem of kernel classiﬁcation. Works on kernel regression have shown that the rate of decay of the prediction error with the number of samples for a large class of data-sets is well characterized by two quantities: the capacity and source of the data-set. In this work, we compute the decay rates for the misclassiﬁcation (prediction) error under the Gaussian design, for data-sets satisfying source and capacity assumptions. We derive the rates as a function of the source and capacity coeﬃcients for two standard kernel classiﬁcation settings, namely margin-maximizing Support Vector Machines (SVM) and ridge classiﬁcation, and contrast the two methods. As a consequence, we ﬁnd that the known worst-case rates are loose for this class of data-sets. Finally, we show that the rates presented in this work are also observed on real data-sets.
Measurement, Modeling, and Analysis of a Peer-to-peer File-sharing Workload

Peer-to-peer (P2P) file sharing accounts for an astonishing volume of current Internet traffic. This paper probes deeply into modern P2P file sharing systems and the forces that drive them. By doing so, we seek to increase our understanding of P2P file sharing workloads and their implications for future multimedia workloads. Our research uses a three-tiered approach. First, we analyze a 200-day trace of over 20 terabytes of Kazaa P2P traffic collected at the University of Washington. Second, we develop a model of multimedia workloads that lets us isolate, vary, and explore the impact of key system parameters. Our model, which we parameterize with statistics from our trace, lets us confirm various hypotheses about file-sharing behavior observed in the trace. Third, we explore the potential impact of locality-awareness in Kazaa. Our results reveal dramatic differences between P2P file sharing and Web traffic. For example, we show how the immutability of Kazaa's multimedia objects leads clients to fetch objects at most once; in contrast, a WorldWide Web client may fetch a popular page (e.g., CNN or Google) thousands of times. Moreover, we demonstrate that: (1) this " fetch-at-most-once " behavior causes the Kazaa popularity distribution to deviate substantially from Zipf curves we see for the Web, and (2) this deviation has significant implications for the performance of multimedia file-sharing systems. Unlike the Web, whose workload is driven by document change, we demonstrate that clients' fetch-at-most-once behavior, the creation of new objects, and the addition of new clients to the system are the primary forces that drive multimedia workloads such as Kazaa. We also show that there is substantial untapped locality in the Kazaa workload. Finally, we quantify the potential bandwidth savings that locality-aware P2P file-sharing architectures would achieve. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee.
An Overview on Artificial Intelligence Techniques for Diagnosis of Schizophrenia Based on Magnetic Resonance Imaging Modalities: Methods, Challenges, and Future Works

Schizophrenia (SZ) is a mental disorder that typically emerges in late adoles-cence or early adulthood. It reduces the life expectancy of patients by 15 years. Abnormal behavior, perception of emotions, social relationships, and reality perception are among its most signiﬁcant symptoms. Past studies have revealed the temporal and anterior lobes of hippocampus regions of brain get aﬀected by SZ. Also, increased volume of cerebrospinal ﬂuid (CSF) and decreased volume of white and gray matter can be observed due to this disease. The magnetic resonance imaging (MRI) is the popular neuroimaging technique used to explore structural/functional brain abnormalities in SZ disorder owing to its high spatial resolution. Various artiﬁcial intelligence (AI) techniques have been employed with advanced image/signal processing methods to obtain accurate diagnosis of SZ. This paper presents a comprehensive overview of studies conducted on automated diagnosis of SZ using MRI modalities. Main ﬁndings, various challenges, and future works in developing the automated SZ detection are described in this paper.
Adversarial Open Domain Adaption for Sketch-to-Photo Synthesis

In this paper, we explore the open-domain sketch-to-photo translation, which aims to synthesize a realistic photo from a freehand sketch with its class label, even if the sketches of that class are missing in the training data. It is challenging due to the lack of training supervision and the large geometry distortion between the freehand sketch and photo domains. To synthesize the absent freehand sketches from photos, we propose a framework that jointly learns sketch-to-photo and photo-to-sketch generation. However, the generator trained from fake sketches might lead to un-satisfying results when dealing with sketches of missing classes, due to the domain gap between synthesized sketches and real ones. To alleviate this issue, we further propose a simple yet effective open-domain sampling and optimization strategy to “fool” the generator into treating fake sketches as real ones. Our method takes advantage of the learned sketch-to-photo and photo-to-sketch mapping of in-domain data and generalizes them to the open-domain classes. We validate our method on the Scribble and SketchyCOCO datasets. Compared with the recent competing methods, our approach shows impressive results in synthesizing realistic color, texture, and maintaining the geometric composition for various categories of open-domain sketches.
Understanding the Impacts of the Indian Ocean on ENSO Variability in a Coupled GCM

Abstract This study investigates the impacts of the Indian Ocean on El Nino–Southern Oscillation (ENSO) variability through numerical simulations with a coupled atmosphere–ocean general circulation model, composite analyses with the coupled model output, and simple atmosphere model experiments with specified sea surface temperature (SST) forcing. It is found that, when the Indian Ocean is decoupled from the atmosphere, the ENSO variability in the coupled model is significantly reduced. Conditional SST distributions indicate that the warm (cold) ENSO state is stronger and occurs more frequently when the Indian Ocean SST in summer is relatively cold (warm), whereas it is weaker and occurs less frequently when the Indian Ocean is relatively warm (cold). The impacts of the Indian Ocean are suggested by a comparison of SST composites under warm, normal, and cold Indian Ocean SST conditions in the developing stage of ENSO. It is demonstrated that the Indian Ocean affects the ENSO variability through modulating .
Wavelet Environment matting

In this paper we present a novel approach for capturing the environment matte of a scene. We impose no restrictions on material properties of the objects in the captured scene and exploit scene characteristics (e.g. material properties and self-shadowing) to minimize recording time and to bound the error. Using a CRT monitor, wavelet patterns are emitted onto the scene in order of importance to efficiently construct the environment matte. This order of importance is obtained by means of a feedback loop that takes advantage of the knowledge learned from previously recorded photographs. Once the recording process is finished, new backdrops can be efficiently placed behind the scene.
Mining Electronic Health Records (EHRs)

The continuously increasing cost of the US healthcare system has received significant attention. Central to the ideas aimed at curbing this trend is the use of technology in the form of the mandate to implement electronic health records (EHRs). EHRs consist of patient information such as demographics, medications, laboratory test results, diagnosis codes, and procedures. Mining EHRs could lead to improvement in patient health management as EHRs contain detailed information related to disease prognosis for large patient populations. In this article, we provide a structured and comprehensive overview of data mining techniques for modeling EHRs. We first provide a detailed understanding of the major application areas to which EHR mining has been applied and then discuss the nature of EHR data and its accompanying challenges. Next, we describe major approaches used for EHR mining, the metrics associated with EHRs, and the various study designs. With this foundation, we then provide a systematic and methodological organization of existing data mining techniques used to model EHRs and discuss ideas for future research.
Earthquakes as self-organized critical phenomena

A cellular automation model with threshold elements, previously proposed by BAK et al. (1988), which generates 1/f noise and self-organizes into a critical state, is studied as a model for earthquake occurrence. The model explains some of fractal features of earthquakes such as the power law distribution of the size of the earthquake. The model is modified so that every earthquake may trigger aftershocks. The modified trigger model explains other fractal features of earthquakes, such as the power law decay of aftershock frequency and the power law decay of aftershock frequency and the power law correlation of the hypocenter distribution.
Dynamic-Model-Based Seasonal Prediction of Meteorological Drought over the Contiguous United States

AbstractA simple method was developed to forecast 3- and 6-month standardized precipitation indices (SPIs) for the prediction of meteorological drought over the contiguous United States based on precipitation seasonal forecasts from the NCEP Climate Forecast System (CFS). Before predicting SPI, the precipitation (P) forecasts from the coarse-resolution CFS global model were bias corrected and downscaled to a regional grid of 50 km. The downscaled CFS P forecasts, out to 9 months, were appended to the P analyses to form an extended P dataset. The SPIs were calculated from this new time series. Five downscaling methods were tested: 1) bilinear interpolation; 2) a bias correction and spatial downscaling (BCSD) method based on the probability distribution functions; 3) a conditional probability estimation approach using the mean P ensemble forecasts developed by J. Schaake, 4) a Bayesian approach that bias corrects and downscales P using all ensemble forecast members, as developed by the Princeton University .
On the usability of the ERA‐40 reanalysis in the estimation of past surface UV radiation over Europe

[1] Erythemal daily UV doses have been calculated using input data from the ERA-40 reanalysis for years 1958–2002. The quality of input parameters for UV calculations has been validated with available ground based total ozone data and estimates of Cloud Modification Factor (CMF), and the results have been compared with existing UV data. Owing to limited availability of validation data especially during 1960s and 1970s the analysis focused on sites located in central and northern Europe. The current work is the first step in deriving reliable long-term UV time series from the ERA-40 reanalysis. Total ozone from the ERA-40 reanalysis is affected by significant biases, especially before satellite ozone measurements were available for assimilation. Estimations of the effect of clouds on surface UV were made using global radiation (300–3000 nm) budgets at the surface because available ERA-40 cloud data do not allow good estimates of surface daily UV doses. There are some problems with ERA-40 solar radiation budgets which cause systematic biases in calculated daily UV doses. Comparison of calculated daily erythemal UV doses against ground-based UV data indicate that ERA-40 UV doses are typically overestimated by 6–18% in central and northern Europe and underestimated by 9–17% at Davos, Switzerland. Root-mean-square errors of the calculated daily UV doses are usually in the range of 30–40%. Trends of UV doses were calculated for the concurrent period of the TOMS satellite UV data (1979–2002). The trends of zonally averaged ERA-40 and TOMS UV agree well and are mostly of the same sign and magnitude.
3D characterization of sandstone by means of X-ray computed tomography

Due to technological development, stateof-the-art high-resolution X-ray computed tomography (CT) systems can be developed, enabling the internal visualization of geomaterials in three dimensions. However, in order to obtain structural information one also needs proper three-dimensional (3D) analysis software. In this paper, the potential for petrographic purposes of high-resolution X-ray CT in combination with the 3D analysis software Morpho+ is explored for a Belgian sandstone sample. The advantage of the CT technique is the fact that it is an ideal tool to characterize the internal structure of a rock in three dimensions in a nondestructive way while a limitation of this CT technique is that only small samples can be combined with a high spatial resolution and therefore often many samples will need to be scanned in order to obtain representative volumes. The relationship between sample size and obtained spatial resolution are discussed as well as the infl uence on the spatial resolution exerted by some important technical aspects like the used X-ray source and detector. This paper focuses in detail on the structures that can be determined by means of micro- and high-resolution X-ray CT in combination with 3D algorithms.
A Technique for Touch Force Sensing using a Waterproof Device's Built-in Barometer

We present BaroTouch, a technique that leverages a waterproof mobile device's built-in barometer to measure the touch force. When an airtight waterproof device is touched, the distorted surface changes the air pressure inside that device and thus changes the built-in barometer value. Although this change varies under different airtightness conditions, our technique can measure the touch force independent of the airtightness conditions. To investigate BaroTouch's characteristics, we conducted three experiments. First, since the change in the barometer value varies under different airtightness conditions, we evaluated BaroTouch's characteristics under two levels of airtightness conditions. Second, we investigated the relationship between the sensor value and the touch positions or forces and found that the touch screen increased approximately in a linear manner. Last, in a controlled user study with 10 participants, the participants could exert three levels of touch force with an accuracy of over 92.2% accuracy using BaroTouch.
Optical Switching Networks With Minimum Number of Limited-Range Wavelength Converters

We study the problem of determining the minimum number of limited-range wavelength converters needed to construct strictly, wide-sense, and rearrangeably nonblocking optical cross-connects for both unicast and multicast traffic patterns. We give the exact formula to compute this number for rearrangeably and wide-sense nonblocking cross-connects under both the unicast and multicast cases. We also give optimal cross-connect constructions with respect to the number of limited-range wavelength converters.