Miracle-dz/UniFAR-FTUs huggingface dataset:
CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies

The Conference on Computational Natural Language Learning (CoNLL) features a shared task, in which participants train and test their learning systems on the same data sets. In 2017, the task was devoted to learning dependency parsers for a large number of languages, in a real-world setting without any gold-standard annotation on input. All test sets followed a unified annotation scheme, namely that of Universal Dependencies. In this paper, we define the task and evaluation methodology, describe how the data sets were prepared, report and analyze the main results, and provide a brief categorization of the different approaches of the participating systems.
Cultural Diversity, Geographical Isolation, and the Origin of the Wealth of Nations

This research argues that variations in the interplay between cultural assimilation and cultural diffusion have played a significant role in giving rise to differential patterns of economic development across the globe. Societies that were geographically less vulnerable to cultural diffusion benefited from enhanced assimilation, lower cultural diversity, and more intense accumulation of society-specific human capital. Thus, they operated more efficiently with respect to their production-possibility frontiers and flourished in the technological paradigm that characterized the agricultural stage of development. The lack of cultural diffusion and its manifestation in cultural rigidity, however, diminished the ability of these societies to adapt to a new technological paradigm, which delayed their industrialization and, hence, their take-off to a state of sustained economic growth. The theory thus contributes to the understanding of the advent of divergence and overtaking in the process of development. Consistently with the theory, the empirical analysis establishes that (i) geographical isolation prevalent in pre-industrial times (i.e., prior to the advent of airborne transportation technology) has had a persistent negative impact on the extent of contemporary cultural diversity; (ii) pre-industrial geographical isolation had a positive impact on economic development in the agricultural stage but has had a negative impact on income per capita in the course of industrialization; and (iii) cultural diversity, as determined exogenously by pre-industrial geographical isolation, has had a positive impact on economic development in the process of industrialization.
Iterated partitioned block frequency-domain adaptive filtering for acoustic echo cancellation

For high quality acoustic echo cancellation long echoes have to be suppressed. classical LMS-based adaptive filters are not attractive as they are suboptimal from a computational point of view. Multirate adaptive filters such as the partitioned block frequency-domain adaptive filter (PBFDAF) are good alternatives and are widely used in commercial echo cancellers nowadays. In this paper the PBFDRAP is analyzed, which combines frequency-domain adaptive filtering with so-called "row action projection. " Fast versions of the algorithm are derived and it is shown that the PBFDRAP outperforms the PBFDAF in a realistic echo cancellation setup.
Active Learning with Weak Supervision for Cost-Effective Panicle Detection in Cereal Crops

Panicle density of cereal crops such as wheat and sorghum is one of the main components for plant breeders and agronomists in understanding the yield of their crops. To phenotype the panicle density effectively, researchers agree there is a significant need for computer vision-based object detection techniques. Especially in recent times, research in deep learning-based object detection shows promising results in various agricultural studies. However, training such systems usually requires a lot of bounding-box labeled data. Since crops vary by both environmental and genetic conditions, acquisition of huge amount of labeled image datasets for each crop is expensive and time-consuming. Thus, to catalyze the widespread usage of automatic object detection for crop phenotyping, a cost-effective method to develop such automated systems is essential. We propose a weak supervision based active learning approach for panicle detection in cereal crops. In our approach, the model constantly interacts with a human annotator by iteratively querying the labels for only the most informative images, as opposed to all images in a dataset. Our query method is specifically designed for cereal crops which usually tend to have panicles with low variance in appearance. Our method reduces labeling costs by intelligently leveraging low-cost weak labels (object centers) for picking the most informative images for which strong labels (bounding boxes) are required. We show promising results on two publicly available cereal crop datasets - Sorghum and Wheat. On Sorghum, 6 variants of our proposed method outperform the best baseline method with more than 55% savings in labeling time. Similarly, on Wheat, 3 variants of our proposed methods outperform the best baseline method with more than 50% of savings in labeling time.
Investigating the locality of neural network training dynamics

—In the recent past a certain property of neural training trajectories in weight-space had been isolated, that of “local elasticity” ( S rel ) - which attempts to quantify the propagation of inﬂuence of a sampled data point on the prediction at another data point. In this work, we embark on a comprehensive study of local elasticity. Firstly, speciﬁc to the classiﬁcation setting, we suggest a new deﬁnition of the original idea of S rel . Via experiments on state-of-the-art neural networks training on SVHN, CIFAR-10 and CIFAR-100 we demonstrate how our new S rel detects the property of the weight updates preferring to make changes in predictions within the same class as of the sampled data. Next, we demonstrate via examples of neural regression that the original S rel reveals a 2 − phase behavior: that their training proceeds via an initial elastic phase when S rel changes rapidly and an eventual inelastic phase when S rel remains large. Lastly, we give multiple examples of learning via gradient ﬂows for which one can get a closed-form expression of the original S rel function. By studying the plots of these derived formulas we give theoretical demonstrations of some of the experimentally detected properties of S rel in the regression setting.
Efficiently Querying RDF(S) Ontologies with Answer Set Programming

Ontologies are pervading many areas of knowledge representation and management. To date, most research efforts have been spent on the development of sufficiently expressive languages for the representation and querying of ontologies; however, querying efficiency has received attention only recently, especially for ontologies referring to large amounts of data. In fact, it is still uncertain how reasoning tasks will scale when applied on massive amounts of data. This work is a first step toward this setting: it first shows that Resource Description Framework(Schema) [RDF(S)] ontologies can be expressed, without loss of semantics, into Answer Set Programming (ASP). Then, based on a previous result showing that the SPARQL query language (a candidate W3C recommendation for RDF(S) ontologies) can be mapped to a rule-based language, it shows that efficient querying of big ontologies can be accomplished with a database oriented extension of the well known ASP system DLV, which we recently developed. Results reported in the article show that our proposed framework is promising for the improvement of both scalability and expressiveness of available RDF(S) storage and query systems.
Clustering threshold gradient descent regularization: with applications to microarray studies

MOTIVATION An important goal of microarray studies is to discover genes that are associated with clinical outcomes, such as disease status and patient survival. While a typical experiment surveys gene expressions on a global scale, there may be only a small number of genes that have significant influence on a clinical outcome. Moreover, expression data have cluster structures and the genes within a cluster have correlated expressions and coordinated functions, but the effects of individual genes in the same cluster may be different. Accordingly, we seek to build statistical models with the following properties. First, the model is sparse in the sense that only a subset of the parameter vector is non-zero. Second, the cluster structures of gene expressions are properly accounted for. RESULTS For gene expression data without pathway information, we divide genes into clusters using commonly used methods, such as K-means or hierarchical approaches. The optimal number of clusters is determined using the Gap statistic. We propose a clustering threshold gradient descent regularization (CTGDR) method, for simultaneous cluster selection and within cluster gene selection. We apply this method to binary classification and censored survival analysis. Compared to the standard TGDR and other regularization methods, the CTGDR takes into account the cluster structure and carries out feature selection at both the cluster level and within-cluster gene level. We demonstrate the CTGDR on two studies of cancer classification and two studies correlating survival of lymphoma patients with microarray expressions. AVAILABILITY R code is available upon request. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Accelerated Motion Correction for MRI Using Score-Based Generative Models

Magnetic Resonance Imaging (MRI) is a powerful medical imaging modality, but unfortunately suffers from long scan times which, aside from increasing operational costs, can lead to image artifacts due to patient motion. Motion during the acquisition leads to inconsistencies in measured data that manifest as blurring and ghosting if unaccounted for in the image reconstruction process. Various deep learning based reconstruction techniques have been proposed which decrease scan time by reducing the number of measurements needed for a high fidelity reconstructed image. Additionally, deep learning has been used to correct motion using end-to-end techniques. This, however, increases susceptibility to distribution shifts at test time (sampling pattern, motion level). In this work we propose a framework for jointly reconstructing highly sub-sampled MRI data while estimating patient motion using score-based generative models. Our method does not make specific assumptions on the sampling trajectory or motion pattern at training time and thus can be flexibly applied to various types of measurement models and patient motion. We demonstrate our framework on retrospectively accelerated 2D brain MRI corrupted by rigid motion.
Research Article: Who's Regulating Who? Analyzing Fracking Policy in Colorado, Wyoming, and Louisiana

Significant controversy surrounds the use of hydraulic fracturing (fracking) in oil and gas extraction in the United States. To address some of these concerns, states are leading the charge by regulating the process. Yet, variations exist in state regulatory approaches, and it is still unclear what drives these differences. Thus, this research applies policy subsystem theory to examine original interview data to determine what may cause these variations in regulations through an examination of Colorado, Wyoming, and Louisiana. This research finds that policy subsystem theory is useful in explaining this variation. In particular, the position of agency staff and privileged stakeholder groups, often industry related, help to explain why Colorado has a much stronger regulatory program than Louisiana. In comparison, Wyoming's stringent regulations represent the anomaly in this case, but the results of this research suggest that this dominant energy subsystem will likely not be setting any pro-environment trends going forward.
Simultaneous long offset (SLO) towed streamer seismic acquisition

An innovative 3D towed streamer project in offshore Gabon used a dual-vessel continuous long offset streamer configuration to acquire 0-12 km offsets with ten dual- sensor streamers. Streamer control for the 6 km streamers was robust and avoided operational complications or logistical penalties. Simultaneous shooting maximized inline shot density for long record lengths, thus capturing unaliased deep target reflections from rugose base-salt and sub-salt horizons. Survey design benefited from prior 2D survey experiences with a variety of broadband source and streamer technologies, and the use of 2D streamers as long as 12 km. 3D illumination modelling further suggested that offsets as long as 16 km could be expected to yield useful base-salt and sub-salt reflections. Wavefield separation processing yielded full receiver- side deghosting onboard, followed by an inversion-based separation of simultaneous shots onshore. The ultra-long 12 km offsets combined with strong amplitudes of deghosted low frequencies have yielded encouraging sub- salt and pre-salt imaging.
Transition-Based Left-Corner Parsing for Identifying PTB-Style Nonlocal Dependencies

This paper proposes a left-corner parser which can identify nonlocal dependencies. Our parser integrates nonlocal dependency identification into a transition-based system. We use a structured perceptron which enables our parser to utilize global features captured by nonlocal dependencies. An experimental result demonstrates that our parser achieves a good balance between constituent parsing and nonlocal dependency identification.
To Flow and Not to Freeze: Applying Flow Experience to Mobile Learning

A key design goal of mobile learning is that its built-in experiences are enjoyable and proactive, empowering the learner with the knowledge and ability to self-manage. This implies that the benefits and critical success factors of mobile learning activities should not simply be based only on learning performance, but also on learners' experiences so that we can assess the added qualities of these activities over and above rote knowledge acquisition. To empirically demonstrate this premise, we report on an assessment of flow experience in three different learning spaces, where learners explore a built environment as part of a simulated security guard training program. Our results seem to show that the true cost-benefit ratio of mobile learning may not be evident in measures of learning performance or rote knowledge acquisition alone, but that mobile learning activities could provide a better learning experience by providing the conditions for optimal flow experience. Further, in the spatial navigation tasks given in our experimental context, it can be seen that simple learning performance measures can be highly dependent on the learner's spatial capability, but this was not the case for flow experience measures.
Wave-Front Diagrams in Seismic Interpretation

Subsurface structure can be derived from seismic data by graphical methods based upon wave-front diagrams. The method of construction of these diagrams is shown, and the nature and origin of the important underlayer wave is explained. Coincident-time curves and secondary shotpoints are defined and some applications are illustrated. The diagrams are used to explain some of the simpler seismic rules, and the original determination of simple underlayer structure is illustrated. The paper is only an introduction to wave-front methods; the principles developed can be applied by experienced seismologists to more complex situations.
Hsdpa-Style Adaptive Spread-Spectrum Transceivers and Smart Antenna-Aided Networking

This light-hearted overview is based on an amalgam of the Wiley/IEEE Press monographs, considering the joint benefits of adaptive physical and adaptive network-layer performance enhancement techniques. More specifically, conventional systems would drop a call in progress, if the communications quality falls below the target quality of service and it cannot be improved by handing over to another physical channel. By contrast, the adaptive quadrature amplitude modulation (AQAM) aided transceivers of the near future are expected to simply 'instantaneously drop the throughput, rather than dropping the call' by reconfiguring themselves in a more robust mode of operation. It is demonstrated that the proposed beam-forming and adaptive transmission techniques may double the expected teletraffic capacity of the system, whilst maintaining the same AVERAGE performance as their conventional fixed-mode counterparts. Further capacity gains may be attained by employing genetic algorithm (GA) aided network-layer schedulers
Atmospheric response to NOy source due to energetic electron precipitation

We have introduced additional NOy sources caused by energetic electron precipitation (EEP) during 1987 into a Chemistry‐Climate model. Comparison of two model runs with and without EEP reveals increase of reactive nitrogen by about 2 ppbv in the middle stratosphere over the tropical and middle latitudes. In the upper stratosphere over the polar winter regions the simulated NOy enhancement reaches 10 ppbv. Decreases of the ozone mixing ratio in the stratosphere by up to 5% over midlatitudes and up to 30% over southern high‐latitudes are calculated. A ∼0.5 K cooling in the middle stratosphere over the tropics and up to 2 K over southern high‐latitudes is calculated with detectable changes in the surface air temperatures. These results confirm that the magnitude of the atmospheric response to EEP events can potentially exceed the effects from solar UV fluxes. These mechanisms work in phase outside polar latitudes, but can compensate each other within polar latitudes.
WebUAV-3M: A Benchmark Unveiling the Power of Million-Scale Deep UAV Tracking

In this work, we contribute a new million-scale Unmanned Aerial Vehicle (UAV) tracking benchmark, called WebUAV-3M. Firstly, we collect 4,485 videos with more than 3M frames from the Internet. Then, an efﬁcient and scalable Semi-Automatic Target Annotation (SATA) pipeline is devised to label the tremendous WebUAV-3M in every frame. To the best of our knowledge, the densely bounding box annotated WebUAV-3M is by far the largest public UAV tracking benchmark. We expect to pave the way for the follow-up study in the UAV tracking by establishing a million-scale annotated benchmark covering a wide range of target categories. Moreover, considering the close connections among visual appearance, natural language and audio, we enrich WebUAV-3M by providing natural language speciﬁcation and audio description, encouraging the exploration of natural language features and audio cues for UAV tracking. Equipped with this benchmark, we delve into million-scale deep UAV tracking problems, aiming to provide the community with a dedicated large-scale benchmark for training deep UAV trackers and evaluating UAV tracking approaches. Extensive experiments on WebUAV-3M demonstrate that there is still a big room for robust deep UAV tracking improvements. The dataset, toolkits and base-line results will be available at https://github.com/ 983632847
A theoretical model of the optical properties of sea ice in the visible and near infrared

A radiative transfer model is presented that makes it possible to calculate the interaction between sea ice and solar radiation from the near ultraviolet to 2750 nm as a function of the physical characteristics of the ice. The bubble density and brine distribution are related to the ice salinity, temperature, bulk density, initial growth rate, and thermal history. Spectral albedos and corresponding asymptotic extinction coefficients are presented for a representative range of initial formation conditions and subsequent temperature variations. A comparison is made with observations for conditions where the ice can be approximated by a single homogeneous layer, and theoretical and observational results are found to be in agreement. Of particular significance is that the wavelength dependence for both albedos and extinction coefficients appears to be accurate. For more general application, however, a multilayer model is needed
A Staged Model for the Software Life Cycle

Software engineers have traditionally considered any work after initial delivery as simply software maintenance. Some researchers have divided this work into various tasks, including making changes to functionality (perfective), changing the environment (adaptive), correcting errors (corrective), and making improvements to avoid future problems (preventive). However, many have considered maintenance basically uniform over time. Because software development has changed considerably since its early days, the authors believe this approach no longer suffices. They describe a new view of the software life cycle in which maintenance is actually a series of distinct stages, each with different activities, tools, and business consequences. While the industry still considers postdelivery work as simply software maintenance, the authors claim that the process actually falls into stages. They think both business and engineering can benefit from understanding these stages and their transitions.
Striped Smith-Waterman speeds database searches six times over other SIMD implementations

MOTIVATION The only algorithm guaranteed to find the optimal local alignment is the Smith-Waterman. It is also one of the slowest due to the number of computations required for the search. To speed up the algorithm, Single-Instruction Multiple-Data (SIMD) instructions have been used to parallelize the algorithm at the instruction level. RESULTS A faster implementation of the Smith-Waterman algorithm is presented. This algorithm achieved 2-8 times performance improvement over other SIMD based Smith-Waterman implementations. On a 2.0 GHz Xeon Core 2 Duo processor, speeds of >3.0 billion cell updates/s were achieved. AVAILABILITY http://farrar.michael. googlepages.com/Smith-waterman
Who Will You "@"?

In Twitter-like social networking services, people can use the "@" symbol to mention other users in tweets and send them a message or link to their profiles. In recent years, social media services are rapidly growing with thousands of millions of users participating in them every day. When the "@" symbol is entered, there should be an automatic suggestion function which recommends a small list of candidates in order to help users to easily identify and input usernames. In this paper, we present our work on building a recommendation system for the mention function in microblogging services. The recommendation strategy we used takes into consideration not only content of the microblog but also histories of candidate users. To better handle these textual information, we propose a novel method that extends the translation-based model. Experimental results on the dataset we collected from a real world microblogging service demonstrate that the proposed method outperforms state-of-the-art approaches.
Changing Frequency of Heavy Rainfall over the Central United States

AbstractRecords of daily rainfall accumulations from 447 rain gauge stations over the central United States (Minnesota, Wisconsin, Michigan, Iowa, Illinois, Indiana, Missouri, Kentucky, Tennessee, Arkansas, Louisiana, Alabama, and Mississippi) are used to assess past changes in the frequency of heavy rainfall. Each station has a record of at least 50 yr, and the data cover most of the twentieth century and the first decade of the twenty-first century. Analyses are performed using a peaks-over-threshold approach, and, for each station, the 95th percentile is used as the threshold. Because of the count nature of the data and to account for both abrupt and slowly varying changes in the heavy rainfall distribution, a segmented regression is used to detect changepoints at unknown points in time. The presence of trends is assessed by means of a Poisson regression model to examine whether the rate of occurrence parameter is a linear function of time (by means of a logarithmic link function). The results point to.