Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Characterizing Markov Equivalence Classes for Directed Acyclic Graphs with Latent Variables

It is well known that there may be many causal explanations that are consistent with a given set of data. Recent work has been done to represent the common aspects of these explanations into one representation. In this paper, we address what is less well known: how do the relationships common to every causal explanation among the observed variables of some DAG process change in the presence of latent variables? Ancestral graphs provide a class of graphs that can encode conditional independence relations that arise in DAG models with latent and selection variables. In this paper we present a set of orientation rules that construct the Markov equivalence class representative for ancestral graphs, given a member of the equivalence class. These rules are sound and complete. We also show that when the equivalence class includes a DAG, the equivalence class representative is the essential graph for the said DAG
The Stratospheric Sudden Warming Event in February 2018 and its Prediction by a Climate System Model

A major stratospheric sudden warming (SSW) event was observed in February 2018 after a 4‐year absence since the winter of 2013/2014. Based on the reanalysis data, the polar night jet changed from a very strong state to a moderate state during 12–19 January, and the moderate westerlies directly reversed to easterlies during 5–15 February. The intensified East Asian trough, Alaskan blocking, and East U.S. trough amplified the extratropical climatological wave 2, which propagated upward into the stratosphere, leading to a vortex‐splitting SSW event. Predictions of the February 2018 SSW event are explored in hindcasts initialized 0–4 weeks in advance by the Beijing Climate Center Climate System Model. Less than 20% of the 28 ensemble members predict the reversal of [U]60°N, 10hPa in hindcasts initialized 3 or 4 weeks in advance if a 5‐day error is allowed, while this ratio increases to 43% in hindcasts initialized 1 week in advance. Based on the climatological occurrence of SSW events in the forecast system, the maximum deterministic predictable limit of this event is 1–2 weeks in this forecast system. The eddy heat flux and its domination by wave 2 can only be predicted within the predictable time limit. A comparison between hindcast members initialized 2 weeks in advance suggests that the extratropical troughs and blockings are responsible for the upward propagation of waves from the troposphere to the stratosphere. The predictable limit of the stratospheric circulation pattern for the February 2018 SSW, 1–2 weeks, also generalizes to other vortex split SSW events such as the January 2009 and February 1999 cases.
Dating the late Proterozoic stratigraphic record.

The Tonian and Cryogenian periods (ca. 1000-635.5 Ma) witnessed important biological and climatic events, including diversification of eukaryotes, the rise of algae as primary producers, the origin of Metazoa, and a pair of Snowball Earth glaciations. The Tonian and Cryogenian will also be the next periods in the geological time scale to be formally defined. Time-calibrating this interval is essential for properly ordering and interpreting these events and establishing and testing hypotheses for paleoenvironmental change. Here, we briefly review the methods by which the Proterozoic time scale is dated and provide an up-to-date compilation of age constraints on key fossil first and last appearances, geological events, and horizons during the Tonian and Cryogenian periods. We also develop a new age model for a ca. 819-740 Ma composite section in Svalbard, which is unusually complete and contains a rich Tonian fossil archive. This model provides useful preliminary age estimates for the Tonian succession in Svalbard and distinct carbon isotope anomalies that can be globally correlated and used as an indirect dating tool.
Two-Level, Many-Paths Generation

Large-scale natural language generation requires the integration of vast amounts of knowledge: lexical, grammatical, and conceptual. A robust generator must be able to operate well even when pieces of knowledge are missing. It must also be robust against incomplete or inaccurate inputs. To attack these problems, we have built a hybrid generator, in which gaps in symbolic knowledge are filled by statistical methods. We describe algorithms and show experimental results. We also discuss how the hybrid generation model can be used to simplify current generators and enhance their portability, even when perfect knowledge is in principle obtainable.
A proposed information systems framework for effective delivery of user research findings

Integration of user research findings into product design process is a well-received issue. There is a tremendous research on how to conduct user research and methodologies for generating user data. However delivering that information effectively is as important as the quality of data generated through user research, since if it is not delivered effectively the impact that is demanded by the product developers would not be achieved. Therefore the requirements for the deliverables should be conceptualized before designing them, in order to have an effective integration. Thus the aim of this paper is to propose an information systems framework for effective delivery of user research findings to the product development team members. In this paper, a preliminary effort to construct this framework is made by retrieving the framework's constructs from the literature.
Soils of a medieval burial mound as a paleoenvironmental archive (Leningrad region, Northwest Russia)

Investigations of the composition of the buried soils formed on the surface of the kame hill (the northwestern part of the East European Plain, Leningrad region) to which the medieval Izhora burial mound was tied showed that the original soil cover was represented by combinations of automorphic Entic (and/or Albic) Podzols (Lamellic, Arenic). These soils are also characteristic of modern biogeocenoses, which indicates a practically changeless trend of pedogenesis in the average time interval. It was determined that during the construction of the mound and leveling of its surface the soil cover was scalped to a significant degree. The diagenetic changes in soils during the time of their burial (~800 years) were determined. Based on the data of phytolith and spore-pollen analyses it was shown that the original coniferous forest was cut down, partly burnt. Then the site with ash was plowed for cereal crops. After a short time, when the fertility of the soil had decreased, the site was abandoned and began to overgrow with forest (from small-leaved species to coniferous). At this stage, the forest was rebuilt for burial mounds.
Involvement of Sib Proteins in the Regulation of Cellular Adhesion in Dictyostelium discoideum

ABSTRACT Molecular mechanisms ensuring cellular adhesion have been studied in detail in Dictyostelium amoebae, but little is known about the regulation of cellular adhesion in these cells. Here, we show that cellular adhesion is regulated in Dictyostelium, notably by the concentration of a cellular secreted factor accumulating in the medium. This constitutes a quorum-sensing mechanism allowing coordinated regulation of cellular adhesion in a Dictyostelium population. In order to understand the mechanism underlying this regulation, we analyzed the expression of recently identified Dictyostelium adhesion molecules (Sib proteins) that present features also found in mammalian integrins. sibA and sibC are both expressed in vegetative Dictyostelium cells, but the expression of sibC is repressed strongly in conditions where cellular adhesion decreases. Analysis of sibA and sibC mutant cells further suggests that variations in the expression levels of sibC account largely for changes in cellular adhesion in response to environmental cues.
Interval Insensitive Loss for Ordinal Classification

We address a problem of learning ordinal classifier from partially annotated examples. We introduce an interval-insensitive loss function to measure discrepancy between predictions of an ordinal classifier and a partial annotation provided in the form of intervals of admissible labels. The proposed interval-insensitive loss is an instance of loss functions previously used for learning of different classification models from partially annotated examples. We propose several convex surrogates of the interval-insensitive loss which can be efficiently optimized by existing solvers. Experiments on standard benchmarks and a real-life application show that ordinal classifiers learned from partially annotated examples can achieve accuracy close to the accuracy of classifiers learned from completely annotated examples.
Prioritization of the vector factors controlling Emiliania huxleyi blooms in subarctic and arctic seas: A multidimensional statistical approach

Abstract. Producing very extensive blooms in the world's oceans in both hemispheres, a coccolithophore E. huxleyi is capable of affecting both the marine ecology and carbon fluxes at the atmosphere-ocean interface. At the same time, it is subject to the impact of multiple co-acting environmental forcings, which determine the spatio-temporal dynamics of E. huxleyi blooming phenomenon. To reveal the individual importance of each forcing factor (FF) that is known to significantly control the extent and intensity of E. huxleyi blooms and can be retrieved from remote sensing data, we used long-term spatial time series (1998–2016) of sea surface temperature and salinity, incident photosynthetically active radiation, and Ekman layer depth relevant to the marine environments located in the North Atlantic, Arctic and North Pacific oceans, namely the North, Norwegian, Greenland, Labrador, Barents and Bering seas. The FFs retrieved were subjected to statistical analyses. The descriptive statistical approach has shown that E. huxleyi phytoplankton were highly adaptive to the environmental conditions and capable of arising and developing within wide FFs ranges, which proved to be expressly sea-specific. It was also found that there were FFs optimal ranges (also sea-specific), within which the blooms were particularly extensive. The application of the Random Forest Classifier (RFC) approach to each target sea allowed to reliably rank the FFs considered in terms of their role in the spatio-temporal dynamics of E. huxleyi blooms. With the only exception of the Bering Sea, allegedly due to temporally established untypical hydrological conditions, the prediction ability of RFC modeling characterized in terms of precision, recall, and f1-score generally was in excess of 70 %, thus indicating the adequacy of the developed models for FFs prioritization with regard to E. huxleyi blooms.
CUTOFF THRESHOLD OF VARIABLE IMPORTANCE IN PROJECTION FOR VARIABLE SELECTION

At present, variable selection turns to prominence since it obviously alleviate a trouble of measuring multiple variables per sample. The partial least squares regression (PLS-R) and the score of Variable Importance in Projection (VIP) are combined together for variable selection. The value of VIP score which is greater than 1 is the typical rule for selecting relevant variables. Due to a constant cutoff threshold is not sometimes suitable for every data structure, a new cutoff threshold for VIP in classification task has been proposed and then compared to the classical one thru the interesting situation simulation. There were 180 situations generated based on four parameters: Percentage of the number of relevant variables, Magnitude of mean difference of relevant variables between two groups, Degree of correlation between relevant variables, and the sample size. The result of this study presents that the new cutoff threshold can improve in identifying relevant variables more than the previous threshold as seeing of good value of the average balanced accuracy in most of situations.
An Integrated Inspection of the Somatic Mutations in a Lung Squamous Cell Carcinoma Using Next-Generation Sequencing

Squamous cell carcinoma (SCC) of the lung kills over 350,000 people annually worldwide, and is the main lung cancer histotype with no targeted treatments. High-coverage whole-genome sequencing of the other main subtypes, small-cell and adenocarcinoma, gave insights into carcinogenic mechanisms and disease etiology. The genomic complexity within the lung SCC subtype, as revealed by The Cancer Genome Atlas, means this subtype is likely to benefit from a more integrated approach in which the transcriptional consequences of somatic mutations are simultaneously inspected. Here we present such an approach: the integrated analysis of deep sequencing data from both the whole genome and whole transcriptome (coding and non-coding) of LUDLU-1, a SCC lung cell line. Our results show that LUDLU-1 lacks the mutational signature that has been previously associated with tobacco exposure in other lung cancer subtypes, and suggests that DNA-repair efficiency is adversely affected; LUDLU-1 contains somatic mutations in TP53 and BRCA2, allelic imbalance in the expression of two cancer-associated BRCA1 germline polymorphisms and reduced transcription of a potentially endogenous PARP2 inhibitor. Functional assays were performed and compared with a control lung cancer cell line. LUDLU-1 did not exhibit radiosensitisation or an increase in sensitivity to PARP inhibitors. However, LUDLU-1 did exhibit small but significant differences with respect to cisplatin sensitivity. Our research shows how integrated analyses of high-throughput data can generate hypotheses to be tested in the lab.
Analysis of Utility-Based Data Dissemination Approaches in VANETs

By disseminating data through Vehicular Ad-hoc Networks (VANETs), vehicles are able to share relevant sensor data to acquire information about accidents, traffic, and even pollution. Data relevance is measured by a utility function which considers the contextual information that vehicles currently have about their environment. To be effective, data dissemination protocols must cope with intermittent connectivity due to the high speeds of vehicles. Problems arise when not all data can be exchanged due to the limited time available. In this paper, we explore and compare two fundamentally distinct approaches to tackling this problem. The first aims to maximize the system efficiency. In contrast, the second trades efficiency by a fair data distribution over vehicles by means of Nash Bargaining as used in game theory. By means of an extensive simulation campaign, an approach relying on fairness is shown to outperform efficiency in terms of delivery ratio, Jain's fairness index, sum of utility gains, number of hops and number of files downloaded.
Isolated-utterance speech recognition using hidden Markov models with bounded state durations

Hidden Markov models (HMMs) with bounded state durations (HMM/BSD) are proposed to explicitly model the state durations of HMMs and more accurately consider the temporal structures existing in speech signals in a simple, direct, but effective way. A series of experiments have been conducted for speaker dependent applications using 408 highly confusing first-tone Mandarin syllables as the example vocabulary. It was found that in the discrete case the recognition rate of HMM/BSD (78.5%) is 9.0%, 6.3%, and 1.9% higher than the conventional HMMs and HMMs with Poisson and gamma distribution state durations, respectively. In the continuous case (partitioned Gaussian mixture modeling), the recognition rates of HMM/BSD (88.3% with 1 mixture, 88.8% with 3 mixtures, and 89.4% with 5 mixtures) are 6.3%, 5.0%, and 5.5% higher than those of the conventional HMMs, and 5.9% (with 1 mixture), 3.9% (with 3 mixtures) and 3.1% (with 1 mixture), 1.8% (with 3 mixtures) higher than HMMs with Poisson and gamma distributed state durations, respectively. >
Enhanced mortality from perfluorocarbon administration in a rat model of kerosene aspiration.

BACKGROUND Aspiration of low-viscosity hydrocarbons may lead to fulminant pneumonitis and acute respiratory distress syndrome. Animal and human studies suggest that partial liquid ventilation with perfluorocarbon improves gas exchange and pulmonary function in acute respiratory failure. The objective of this investigation was to determine the effect of intratracheal perfluorocarbon administration and a brief period of partial liquid ventilation on survival in a rat model of severe hydrocarbon aspiration. METHODS Two randomized, non-blinded, controlled experiments were performed. First, male Wistar rats (n = 12) were given 0.3 mL/kg kerosene via direct intratracheal instillation and after 5 minutes were randomized to partial liquid ventilation or standard gas ventilation (control) groups. Partial liquid ventilation rats (n = 6) received 20 mL/kg of pre-oxygenated FC-77 intratracheally and positive-pressure gas ventilation (FiO2 = 1.0), and control rats (n = 6) received positive-pressure gas ventilation alone. Animals were observed for survival and 7-day mortality was compared using the Fisher Exact test. The study was then repeated in 12 additional animals using a 15-minute interval between kerosene instillation and treatment (PLV vs control). RESULTS Mortality was significantly greater in the partial liquid ventilation group (5 of 6; 83%) as compared to the control group (1 of 6; 17% [p = 0.039]). Results were identical in the repeat study. All animals that died succumbed from acute respiratory failure within 24 hours of kerosene instillation. CONCLUSION In this rat model of severe kerosene aspiration, intratracheal perfluorocarbon administration and a brief period of partial liquid ventilation proved detrimental and significantly increased mortality.
Coupled regional climate and hydrology modelling at the catchment scale

template This first page is for our information purposes only. Do not delete it! Begin your abstract on the next page. Maximum is 2 pages in the given layout including figures and tables. Here, please indicate below: This abstract is intended for • Oral presentation X • Poster presentation (type an x after your choice) This abstract fits best into the topic (see the conference website for more information): 1. Regional Climate and Earth System Models X 2. Very-high-resolution RCMs 3. Challenges for RCM Evaluation and Application
Nitric Oxide Has Differential Effects on Currents in Different Subsets of Manduca sexta Antennal Lobe Neurons

Nitric oxide has been shown to regulate many biological systems including olfaction. In the moth olfactory system nitric oxide is produced in the antennal lobe in response to odor stimulation and has complex effects on the activity of both projection neurons and local interneurons. To examine the cell autonomous effects of nitric oxide on these cells, we used patch-clamp recording in conjunction with pharmacological manipulation of nitric oxide to test the hypothesis that nitric oxide differentially regulates the channel properties of these different antennal lobe neuron subsets. We found that nitric oxide caused increasing inward currents in a subset of projection neurons while the effects on local neurons were variable but consistent within identifiable morphological subtypes.
Physical activity and brain plasticity in late adulthood: a conceptual review

A growing body of evidence from neuroscience, epidemiology, and kinesiology suggests that physical activity is effective as both a prevention and treatment for cognitive problems throughout the lifespan. Given the expected increase in the proportion of older adults in most countries over the next 40 years, physical activity could be a low-cost and relatively accessible method for maintaining cognitive function throughout later life. Despite the emerging recognition of physical activity as a powerful method to enhance brain health, there is continued confusion from both the public and scientific communities about what the extant research has discovered about the potential for physical activity to improve neurocognitive health and which questions remain unanswered. In this review, we outline four overarching themes that provide a conceptual structure for understanding the questions that have been asked and have been addressed, as well as those that have yet to be answered. These themes are descriptive, mechanistic, applied, and moderating questions. We conclude from our review that descriptive questions have been the first and most thoroughly studied, but we have much yet to learn about the underlying mechanisms, application, and moderating factors that explain how and to what extent physical activity improves brain health.
Fast and accurate quasi-three-dimensional capacitance determination of multilayer VLSI interconnects

A new fast and accurate capacitance determination methodology for intricate multilayer VLSI interconnects is presented. Since a multilayer interconnect structure is too complicated to be directly tractable, it is simplified by investigating charge distributions within the system. The quasi-three-dimensional (3-D) capacitances of the structure are then determined by combining a set of solid-ground-based two-dimensional (2-D) capacitances and shielding effects that can be independently calculated from the simplified structure. The shielding effects due to the neighboring lines of a line can be analytically determined from the given layout dimensions. The solid-ground-based 2-D capacitances can also be quickly computed from the simplified structure. Thus, the proposed capacitance determination methodology is much more cost-efficient than conventional 3-D-based methods. It is shown that the calculated quasi-3-D capacitances have excellent agreement with 3-D field-solver-based results within 5% error.
Predicting Offensive Play Types in the National Football League

—In this paper, we apply tools from machine learning to the burgeoning ﬁeld of football analytics and predict whether a team will run or pass the ball on a given play. After training four different classiﬁcation algorithms on data from the 2012-2014 NFL seasons, we developed an ensemble method that combines the predictions of our two best-performing individual models and achieved a test accuracy of 75.9%, improving upon previously published results. We also explored general trends in offensive predictability and found that teams are most predictable on late downs and in the fourth quarter. Finally, we conclude with an error analysis and assess whether our models could provide value to an NFL coaching staff.
Semantic Role Labeling System Using Maximum Entropy Classifier

A maximum entropy classifier is used in our semantic role labeling system, which takes syntactic constituents as the labeling units. The maximum entropy classifier is trained to identify and classify the predicates' semantic arguments together. Only the constituents with the largest probability among embedding ones are kept. After predicting all arguments which have matching constituents in full parsing trees, a simple rule-based post-processing is applied to correct the arguments which have no matching constituents in these trees. Some useful features and their combinations are evaluated.