Miracle-dz/UniFAR-FTUs huggingface dataset:
SemEval-2010 Task 1: Coreference Resolution in Multiple Languages

This paper presents the SemEval-2010 task on Coreference Resolution in Multiple Languages. The goal was to evaluate and compare automatic coreference resolution systems for six different languages (Catalan, Dutch, English, German, Italian, and Spanish) in four evaluation settings and using four different metrics. Such a rich scenario had the potential to provide insight into key issues concerning coreference resolution: (i) the portability of systems across languages, (ii) the relevance of different levels of linguistic information, and (iii) the behavior of scoring metrics.
The Prevalence of Psychological Distress in Employees and Associated Occupational Risk Factors

Objective: There is limited occupational health industry data pertaining to 1) the prevalence of psychological distress in various employee subtypes and 2) risk factors for employee psychological distress. Method: The employees of 58 large public and private sector employers were invited to complete the Kessler 6 (K6) as part of the Health and Performance at Work Questionnaire. A K6 score of ≥13 was chosen to indicate high psychological distress. Results: Data on 60,556 full-time employees indicate that 4.5% of employees have high psychological distress of which only 22% were in current treatment. Occupational risk factors identified include long working hours, sales staff and non-traditional gender roles. Conclusion: High psychological distress is pervasive across all employee subtypes and remains largely untreated. Risk factors identified will guide the targeting of mental health promotion, prevention and screening programs.
Post-supervised Fuzzy c-Means Classifier with Hard Clustering

A fuzzy c-means classifier (FCMC) based on a generalized fuzzy c-means clustering with iteratively reweighted least square technique (IRLS-FCM) has been proposed. In this paper, we derive a generalized hard c-means (HCM-g) clustering algorithm by defuzzifying IRLS-FCM. Many hard clustering results are obtained from local minima of the HCM-g objective function. Although HCM-g is not a fuzzy clustering algorithm, it is applied to a fuzzy classifier and the best values of the parameters such as the fuzzifiers were chosen by using golden section search method. Whereas the goal of FCMC is to minimize classification error rate on unseen new test data, the proposed classifier aims at minimizing resubstitution error rate by using only a small number of clusters. The proposed classifier with two clusters for each class achieves low resubstitution error rate on several benchmark data sets
Reuse, Don't Recycle: Transforming Lock-Free Algorithms That Throw Away Descriptors

In many lock-free algorithms, threads help one another, and each operation creates a descriptor that describes how other threads should help it. Allocating and reclaiming descriptors introduces significant space and time overhead. We introduce the first descriptor abstract data type (ADT), which captures the usage of descriptors by lock-free algorithms. We then develop a weak descriptor ADT which has weaker semantics, but can be implemented significantly more efficiently. We show how a large class of lock-free algorithms can be transformed to use weak descriptors, and demonstrate our technique by transforming several algorithms, including the leading k-compare-and-swap (k-CAS) algorithm. The original k-CAS algorithm allocates at least k+1 new descriptors per k-CAS. In contrast, our implementation allocates two descriptors per process, and each process simply reuses its two descriptors. Experiments on a variety of workloads show significant performance improvements over implementations that reclaim descriptors, and reductions of up to three orders of magnitude in peak memory usage.
Intimin type influences the site of human intestinal mucosal colonisation by enterohaemorrhagic Escherichia coli O157:H7

Background: Enterohaemorrhagic (EHEC) and enteropathogenic (EPEC) Escherichia coli epithelial cell adhesion is characterised by intimate attachment, and attaching and effacing (A/E) lesion formation. This event is mediated in part by intimin binding to another bacterial protein, Tir (translocated intimin receptor), which is exported by the bacteria and integrated into the host cell plasma membrane. Importantly, EPEC (O127:H6) and EHEC (O157:H7) express antigenically distinct intimin types known as intimin α and γ, respectively. EHEC (O157:H7) colonises human intestinal explants although adhesion is restricted to the follicle associated epithelium of Peyer's patches. This phenotype is also observed with EPEC O127:H6 engineered to express EHEC intimin γ. Aims: To investigate the influence of intimin on colonisation of human intestine by E coli O157:H7, and intimin types on tissue tropism in humans. Methods: Human intestinal in vitro organ culture with wild type and mutant strains of O157:H7 were employed. Results: Introducing a deletion mutation in the eae gene encoding intimin γ in EHEC (O157:H7) caused the strain (ICC170) to fail to colonise human intestinal explants. However, colonisation of Peyer's patches and A/E lesion formation were restored with intimin γ expression from a plasmid (ICC170 (pICC55)). In contrast, complementing the mutation with intimin α resulted in a strain (ICC170 (pCVD438)) capable of colonising and producing A/E lesions on both Peyer's patch and other small intestinal explants. Conclusion: Intimin is necessary for human intestinal mucosal colonisation by E coli O157:H7. Intimin type influences the site of colonisation in a Tir type independent mechanism; intimin γ appears to restrict colonisation to human follicle associated epithelium.
Equal susceptibility and size-selective mobility in aeolian saltation

Natural wind-eroded soils generally contain a mixture of particle sizes. However, models for aeolian saltation are typically derived for sediment bed surfaces containing only a single particle size. To treat natural mixed beds, models for saltation and associated dust aerosol emission have typically simplified aeolian transport either as a series of non-interacting single particle size beds or as a bed containing only the median or mean particle size. Here, we test these common assumptions underpinning aeolian transport models using measurements of size-resolved saltation fluxes at three natural field sites. We find that a wide range of sand size classes experience "equal susceptibility" to saltation at a single common threshold wind shear stress, contrary to the "selective susceptibility" expected for treatment of a mixed bed as multiple single particle size beds. Furthermore, we observe strong size-selectivity in the mobility of different particle sizes, which is not adequately accounted for in current models. At all field sites, mobility is enhanced for particles that are 0.4-0.8 times the median bed particle diameter, while mobility declines rapidly with increasing particle size above this range. We further observe that the most mobile particles also experience the largest saltation heights, which helps to explain variations in size-selective mobility. These observations refute the common simplification of saltation as a series of non-interacting single particle sizes. Sand transport and dust emission models that use this incorrect assumption can be both simplified and improved by instead using a single particle size representative of the mixed bed.
Combination Therapy With Glucagon-Like Peptide-1 and Gastrin Restores Normoglycemia in Diabetic NOD Mice

OBJECTIVE—Glucagon-like peptide-1 (GLP-1) and gastrin promote pancreatic β-cell function, survival, and growth. Here, we investigated whether GLP-1 and gastrin can restore the β-cell mass and reverse hyperglycemia in NOD mice with autoimmune diabetes. RESEARCH DESIGN AND METHODS—Acutely diabetic NOD mice were treated with GLP-1 and gastrin, separately or together, twice daily for 3 weeks. Blood glucose was measured weekly and for a further 5 weeks after treatments, after which pancreatic insulin content and β-cell mass, proliferation, neogenesis, and apoptosis were measured. Insulin autoantibodies were measured, and adoptive transfer of diabetes and syngeneic islet transplant studies were done to evaluate the effects of GLP-1 and gastrin treatment on autoimmunity. RESULTS—Combination therapy with GLP-1 and gastrin, but not with GLP-1 or gastrin alone, restored normoglycemia in diabetic NOD mice. The GLP-1 and gastrin combination increased pancreatic insulin content, β-cell mass, and insulin-positive cells in pancreatic ducts, and β-cell apoptosis was decreased. Insulin autoantibodies were reduced in GLP-1–and gastrin-treated NOD mice, and splenocytes from these mice delayed adoptive transfer of diabetes in NOD-scid mice. Syngeneic islet grafts in GLP-1–and gastrin-treated NOD mice were infiltrated by leukocytes with a shift in cytokine expression from interferon-γ to transforming growth factor-β1, and β-cells were protected from apoptosis. CONCLUSIONS—Combination therapy with GLP-1 and gastrin restores normoglycemia in diabetic NOD mice by increasing the pancreatic β-cell mass and downregulating the autoimmune response.
A Joint Model of Rhetorical Discourse Structure and Summarization

In Rhetorical Structure Theory, discourse units participate in asymmetric relationships, with one element acting as the nucleus and the other as the satellite . In the resulting tree-like nuclearity structure, the importance of each discourse unit can be measured by the number of relations in which it acts as the nucleus or as the satellite. Existing approaches to automatically parsing such structures suffer from two problems: they employ local inference techniques that do not capture document-level structural regularities, and they rely on annotated training data, which is expensive to obtain at the discourse level. We investigate the SampleRank structure learning algo-rithm as a potential solution to both problems. SampleRank allows us to incorporate arbitrary document-level features in a global stochastic inference algorithm. Furthermore, it enables the training of a joint model of discourse structure and summarization, which can be learned from document-level summaries alone, without discourse-level supervision. We obtain mixed results in the fully supervised case, and negative results for the joint model of discourse structure and summarization.
Symbolic Processing in Neural Networks

In this paper we show that programming languages can be translated into recurrent (analog, rational weighted) neural nets. Implementation of programming languages in neural nets turns to be not only theoretical exciting, but has also some practical implications in the recent efforts to merge symbolic and sub symbolic computation. To be of some use, it should be carried in a context of bounded resources. Herein, we show how to use resource bounds to speed up computations over neural nets, through suitable data type coding like in the usual programming languages. We introduce data types and show how to code and keep them inside the information flow of neural nets. Data types and control structures are part of a suitable programming language called NETDEF. Each NETDEF program has a specific neural net that computes it. These nets have a strong modular structure and a synchronization mechanism allowing sequential or parallel execution of subnets, despite the massive parallel feature of neural nets. Each instruction denotes an independent neural net. There are constructors for assignment, conditional and loop instructions. Besides the language core, many other features are possible using the same method.
Conditional Planning under Partial Observability as Heuristic-Symbolic Search in Belief Space

Planning under partial observability in nondeterministic domains is a very significant and challenging problem, which requires dealing with uncertainty together with and-or search. In this paper, we propose a new algorithm for tackling this problem, able to generate conditional plans that are guaranteed to achieve the goal despite of the uncertainty in the initial condition and the uncertain effects of actions. The proposed algorithm combines heuristic search in the and-or space of beliefs with symbolic BDD-based techniques, and is fully amenable to the use of selection functions. The experimental evaluation shows that heuristic-symbolic search may behave much better than state-of-the-art search algorithms, based on a depth-first search (DFS) style, on several domains.
Fast Probabilistic Modeling for Combinatorial Optimization

Probabilistic models have recently been utilized for the optimization of large combinatorial search problems. However, complex probabilistic models that attempt to capture inter-parameter dependencies can have prohibitive computational costs. The algorithm presented in this paper, termed COMIT, provides a method for using probabilistic models in conjunction with fast search techniques. We show how COMIT can be used with two very different fast search algorithms: hillclimbing and Population-based incremental learning (PBIL). The resulting algorithms maintain many of the benefits of probabilistic modeling, with far less computational expense. Extensive empirical results are provided; COMIT has been successfully applied to jobshop scheduling, traveling salesman, and knapsack problems. This paper also presents a review of probabilistic modeling for combinatorial optimization.
Sequential training of semi-supervised classification based on sparse Gaussian process regression

Gaussian process regression (GPR) is a very important Bayesian approach in machine learning applications. It has been extensively used in semi-supervised learning tasks. In this paper, we propose a sequential training method for solving semi-supervised binary classification problem. It assigns targets to test inputs sequentially making use of sparse Gaussian process regression models. The proposed approach deals with only one part of the whole data set at a time. Firstly, the IVM produces a sparse approximation to a Gaussian process (GP) by combining assumed density filtering (ADF) with a heuristic for choosing points based on minimizing posterior entropy, and then a sparse GPR classifier is learnt on part of the whole data set. Secondly, the representative points selected in the first step including part of remainder examples are used to train another sparse GPR classifier. Repeat the two steps sequentially until all unlabeled examples are deal with. The proposed approach is simple and easy to implement. The hyperparameters are estimated easily by maximizing the marginal likelihood without resorting to expensive cross-validation technique. The evaluations of the proposed method on several real world data sets reveal promising results.
A data-driven reflectance model

We present a generative model for isotropic bidirectional reflectance distribution functions (BRDFs) based on acquired reflectance data. Instead of using analytical reflectance models, we represent each BRDF as a dense set of measurements. This allows us to interpolate and extrapolate in the space of acquired BRDFs to create new BRDFs. We treat each acquired BRDF as a single high-dimensional vector taken from a space of all possible BRDFs. We apply both linear (subspace) and non-linear (manifold) dimensionality reduction tools in an effort to discover a lower-dimensional representation that characterizes our measurements. We let users define perceptually meaningful parametrization directions to navigate in the reduced-dimension BRDF space. On the low-dimensional manifold, movement along these directions produces novel but valid BRDFs.
Study of Mean Wind Variations and Gravity Wave Forcing Via a Meteor Radar Chain and Comparison with HWM‐07 Results

We present an analysis of long‐term observation of the mean winds in the mesosphere and lower thermosphere region. The neutral wind data are collected over the period of January 2009 to November 2017 from a chain of meteor radars along the 120°E meridian in the Northern Hemisphere which consists of four stations at Mohe (MH; 53.5°N, 122.3°E), Beijing (BJ; 40.3°N, 116.2°E), Wuhan (WH; 30.5°N, 114.6°E), and Sanya (SY; 18.3°N, 109.6°E). The annual oscillation dominates the neutral wind pattern at midlatitudes while both the annual oscillation and semiannual oscillations are important at low latitudes. The mean zonal wind pattern in midlatitudes exhibits two eastward jets. One is in the period from April to October above 85 km and the other is from November to March below 90 km. A reversal of the mean zonal winds from eastward to westward is observed in midlatitudes around the spring equinox. Gravity wave forcing during this season shows a rapid change from westward to eastward, which is consistent with the reversals. A three‐cell southward wind pattern is observed over WH and SY. A strong vertical wind shear with northward flow below ~90 km and southward flow above is observed over SY from September to April. Composite‐year comparisons between the observations and the Horizontal Wind Model‐07 show large discrepancies during winter time. HWM‐07 predictions agree with the meteor radar observations better in the zonal component than in the meridional component. The model predictions at low latitude are not as accurate as the middle latitudes.
Fingerprint image compression standard based on wave atoms decomposition and self organizing feature map

Multiresolution analysis tools have been successfully applied to fingerprint image compression for more than a decade. Fingerprint image compression and reconstruction standards used by the US Federal Bureau of Investigation (FBI) are based upon the popular 9/7 discrete wavelet transform (DWT). A novel fingerprint image compression technique based on recently proposed multiresolution analysis tool i.e. wave atoms decomposition is proposed. Wave atoms decomposition has been specifically designed for enhanced representation of oscillatory patterns and to convey temporal and spatial information more precisely. Our proposed compression scheme is based upon vector quantization of decomposed wave atoms representation of fingerprint images using self organizing feature map (SOM). The quantized information is later encoded using arithmetic entropy scheme. The proposed compression scheme outperforms the FBI fingerprint image compression standard, the wavelet scalar quantization (WSQ). Data mining, law enforcement, border security, and forensic applications can potentially benefit from our proposed compression technique.
Structurally oriented coherent noise filtering

All seismic data, whether 2D, 3D, post-stack or pre-stack, contains noise. Typically, this noise is comprised of both coherent and random components. Coherent noise presents itself as regular patterns in the seismic data. It may appear to be random or coherent depending on the orientation of the slice on which it is being observed. For example, coherent noise associated with acquisition may appear random on vertical slices through the volume, with its coherent nature becoming apparent on horizontal slices through the volume. When seismic data is processed, an effort is made to reduce the coherent noise in the seismic data by applying a variety of signal processing techniques. However, when the seismic volume is delivered to the client, it often includes remnant noise that processing was unable to remove without having a deleterious effect on the amplitudes and bandwidth of the seismic data. Since the processed seismic volume typically contains coherent noise, managing that noise within an interpretation system is critical, as it has a significant negative impact on semi-automatic and automatic interpretation workflows and techniques including autotracking of horizons, imaging of faults and fractures, automatic extraction of faults, and interpretation of stratigraphy and geomorphology. An obvious example is the effect of noise on edge attributes (i.e., ‘coherence’ class attributes). Any noise in the seismic data, whether random or coherent, will appear as edges in the seismic attribute volume and may obscure the geologic features of interest in the edge attribute volume (faults, fractures, stratigraphy, and geomorphology). Finally, many interpreters today must use older 3D seismic volumes, merged seismic volumes of a variety of acquisition designs and processing workflows. Even if the pre-stack data is still available, their company may not be in a position to spend the money required to re-process the data. Yet they still need and expect to be able to use modern advanced interpretation technology on the data. In response to this need a post-stack structurally oriented coherent noise filtering process has been developed and is described, with an application to 3D seismic surveys from the North Sea. The results are evaluated by comparing seismic volumes, edge attribute volumes, and amplitude and phase spectra pre- and post-filtering.
Changes in cardiac index during labour analgesia: A double-blind randomised controlled trial of epidural versus combined spinal epidural analgesia - A preliminary study

Background and Aims: Combined spinal-epidural (CSE) analgesia for labour and delivery is occasionally associated with foetal bradycardia. Decreases in cardiac index (CI) and/or uterine hypertonia are implicated as possible aetiological factors. No study has evaluated CI changes following combined spinal analgesia for labour and delivery. This prospective, double-blind, randomised controlled trial evaluates haemodynamic trends during CSE and epidural analgesia for labour. Methods: Twenty-six parturients at term requesting labour analgesia were randomised to receive either epidural (E) or CSE analgesia. The Electrical Cardiometry Monitor ICON® was used to continuously determine maternal CI non-invasively, heart rate (HR) and stroke volume at baseline and up to 60 min after initiation of either intrathecal or epidural analgesia. In addition, maternal systolic blood pressure (SBP) and diastolic blood pressure (DBP) were recorded. Results: Both SBP and DBP had a similar, significant decrease following initiation of either epidural or CSE analgesia. However, parturients in the CSE group (n = 10) demonstrated a significant decrease in HR and CI compared to the baseline measurements. On the other hand, the parturients in the E (n = 13) group showed no decreases in either maternal HR or CI. Foetal heart changes were observed in four patients following CSE and one patient following an epidural. Conclusion: Labour analgesia with CSE is associated with a significant decrease in HR and CI when compared to labour analgesia with epidural analgesia. Further studies are necessary to determine whether a decrease in CI diminishes placental blood flow.
Asymptotic weight and stopping set distributions for detailedly represented irregular LDPC code ensembles

This work presents an ensemble of irregular low-density parity check (LDPC) codes based on an ensemble of bipartite graphs, which formulated the average weight distribution. An analysis of the error performance of LDPC codes over a binary erasure channel together with an iterative decoding algorithm based on belief propagation has been clarified that a notion of stopping sets and their distribution with the asymptotic expression are derived. An irregular LDPC code ensemble that exhibits better performance in the sense of threshold is obtained by density evolution and explicitly formulates weight and stopping set distributions are defined.