Miracle-dz/UniFAR-FTUs huggingface dataset:
Can Automatic Post-Editing Make MT More Meaningful

Automatic post-editors (APEs) enable the re-use of black box machine translation (MT) systems for a variety of tasks where different aspects of translation are important. In this paper, we describe APEs that target adequacy errors, a critical problem for tasks such as cross-lingual question-answering, and compare different approaches for post-editing: a rule-based system and a feedback approach that uses a computer in the loop to suggest improvements to the MT system. We test the APEs on two different MT systems and across two different genres. Human evaluation shows that the APEs significantly improve adequacy, regardless of approach, MT system or genre: 30-56% of the post-edited sentences have improved adequacy compared to the original MT.
Category-Related Brain Activation to Represent Semantic Organization

Background: Categorization is one of the basic cognitive skills in human beings. In fact, it can organize knowledge and arrange human behavior. Objectives: The present study deals with the organization of conceptual knowledge by investigating and studying the ways in which different semantic domains are categorized. Materials and Methods: In the present study, event-related potentials (ERPs) have been recorded from 12 healthy right-handed male university students, who performed a semantic judgment task using 100 animal and tool words. Results: The interesting results of this study show a significant difference between both conditions in all time-windows, with more negativity for the tool category. Also, more negativity for both conditions is noticed when moving from anterior to posterior regions. Conclusions: These results are in accordance with findings of previous stud ies in this field and may be explained on the basis of the feature-based account of semantic knowledge organization.
Analysis of big data for data-intensive applications

In the recent decade big data has attracted attention from decision and policy makers in enterprises and governments, market analysts, and data scientists. The growth of information in the current decade has exceeded the Moore's law, and the vast amount of data is increasing the pain towards managing and analyzing. However, this high amount of data has a great potential and extremely useful information is hidden in it. Data-intensive scientific discovery helps to identify big data problems. The big data problems are found in various areas and sectors such as economic activities to provide effective public administration, national security, and scientific research. Several progressions in various fields were made possible because of big data and there is no doubt that the future challenges in business enhancements will converge to explore big data. Few difficulties that arise in big data are data visualisation, data storage, data analysis, and data capture. The aim of our paper is to give a clear idea about big data and its data-intensive applications. We have also covered several schemes to handle quantum computing, bio-inspired computing, cloud computing, and granular computing.
Climate change has affected the breeding date of tree swallows throughout North America

Increasing evidence suggests that climate change has affected the breeding and distribution of wildlife. If such changes are due to global warming, then we should expect to see large‐scale effects. To explore for such effects on avian reproduction, we examined 3450 nest records of tree swallows from across North America. The egg‐laying date in tree swallows advanced by up to nine days during 1959 to 1991. This advance in phenology was associated with increasing surface air temperatures at the time of breeding. Our analysis controlled for several potentially confounding variables such as latitude, longitude, breeding density and elevation.We conclude that tree swallows across North America are breeding earlier and that the most likely cause is a long‐term increase in spring temperature
Effect of Superior Cervical Ganglionectomy on 24‐h Variations in Hormone Secretion from the Anterior Hypophysis and in Hypothalamic Monoamine Turnover During the Preclinical Phase of Freund's Adjuvant Arthritis in Rats

The effect of superior cervical ganglionectomy (SCGx) on 24‐h rhythms of circulating adrenocorticotropic hormone (ACTH), growth hormone (GH), prolactin and luteinizing hormone (LH) and of hypothalamic noradrenaline content and dopamine and serotonin turnover, was assessed in rats 3 days after administering Freund's complete adjuvant. In sham‐operated rats, Freund's adjuvant injection increased serum ACTH without affecting its diurnal rhythmicity. SCGx, performed 10 days earlier, suppressed 24‐h rhythmicity and augmented mean values of circulating ACTH. A depressive effect of immunization on GH release was found in both sham‐operated and SCGx rats. GH concentrations did not exhibit diurnal rhythmicity and decreased after immunization. Time‐of‐day‐related changes in serum prolactin were significant for all examined groups, except for SCGx‐immunized rats. Freund's adjuvant administration augmented prolactin secretion. Daily changes in serum LH concentration and a decrease after immunization were found in both sham‐operated and SCGx rats. SCGx: (i) counteracted inhibition of daily variations of noradrenaline content in medial hypothalamus of Freund's adjuvant‐injected rats; (ii) decreased anterior hypothalamic dopamine turnover and augmented it in the medial hypothalamus; (iii) lowered amplitude of serotonin turnover rhythm in medial hypothalamus. The data indicate that several early changes in levels and 24‐h rhythms of circulating ACTH and prolactin, and in hypothalamic noradrenaline content and dopamine and serotonin turnover, were modified by a previous SCGx in Freund's adjuvant‐injected rats.
Annotator: an AI approach to engineering drawing annotation

Annotator is a prototype to investigate the application of AI techniques to the annotation of engineering drawings. In particular, Annotator addresses drawings of piping systems such as those for chemical plants or waste treatment facilities. The isometric representation of the piping system is selected because it is the most numerous type of drawing in plant design. Knowledge contained in hierarchies represents the CAD model of the piping system, features of the model and features of the drawing. Production rules about relationships among model items and features make annotation decisions, creating instances in a hierarchy of drawing annotations. Issues in geometry such as parallelism and perpendicularity and the translation of the 3-D model world into the 2-D drawing representation are addressed. Algorithms determine geometric intersections; rules specify whether intersections between given object classes are considered acceptable. Although Annotator prototype is separate from the CAD/CAM environment, the Annotator prototype illustrates the potential for fully automating the annotation process through representation of the knowledge about a piping model and its dimensioning requirements within a rule-based, object-oriented hierarchy.
SchemeUnit and SchemeQL: Two Little Languages

We present two little languages implemented in Scheme: SchemeUnit, a language for writing unit tests, and SchemeQL, a language for manipulating relational databases. We discuss their design and implementation and show how the features of functional languages in general, and Scheme in particular, contribute to the ease of use and implementation of our languages.
Parameter optimization of micro ECDM process of borosilicate glass

Received: April 25 2018 Accepted: June 3 2018 Electro Chemical Discharge Machining Process (ECDM) is a prominent hybrid machining process used in distinctness machining of hard and brittle non-conducting materials. ECDM has been exploited to fabricate micro-holes in nonconductive brittle materials. An experiment has been conducted on ECDM for micro drilling operation on borosilicate glass material according to the Taguchi Orthogonal Array (L9) technique in order to ascertain the effect of certain variables in the same manner with material removal rate (MRR) & tool wear rate (TWR). In superintending the machining performance, signal to noise ratio (S/N ratio) was calculated to find out the involvement in the main machining variables, such as applied voltage, electrolyte concentration and inter electrode gap. During the experiments, the nonconducting and highly brittle borosilicate glass material is used as a work-piece and NaOH as electrolyte solution. In addition to that, Graphite and Tungsten were used as cathode and anode respectively. In this research of Taguchi method was utilized to optimize the process variables which affect MRR and TWR. From main effect plot through Minitab software, it turns out that the inter-electrode gap has the supreme affect on MRR and TWR.
Instruction completion through instance-based learning and semantic analogical reasoning

As autonomous, mobile robots are increasingly entering our everyday lives and the tasks they are to perform are getting continuously more complex and versatile, instructing robots by means of natural-language commands becomes more and more important. Such instructions, stated by humans and originally intended for human use, are typically formulated very vaguely and lack critical information about how to perform particular actions. Probabilistic relational models have shown promise in filling in missing information pieces that have been omitted in such instructions. However, the enormous size of these models and the computational expense in learning and reasoning often impedes their practical applicability to real-world domains. In this work, we propose a novel instance-based learning approach towards building up knowledge bases for instruction completion, which combines probabilistic methods with semantic analogical reasoning. Probabilistic reasoning is employed to build up a knowledge base of natural-language instruction sheets, while instruction completion can be achieved through fast database queries. We showcase the scalabilty of our approach by building up a KB of more than 100,000 instruction steps that have been mined from the wikihow.com web site and which are publicly accessible from within the Prac [21] natural-language interpreter.
Sleep Spindles in Humans: Insights from Intracranial EEG and Unit Recordings

Sleep spindles are an electroencephalographic (EEG) hallmark of non-rapid eye movement (NREM) sleep and are believed to mediate many sleep-related functions, from memory consolidation to cortical development. Spindles differ in location, frequency, and association with slow waves, but whether this heterogeneity may reflect different physiological processes and potentially serve different functional roles remains unclear. Here we used a unique opportunity to record intracranial depth EEG and single-unit activity in multiple brain regions of neurosurgical patients to better characterize spindle activity in human sleep. We find that spindles occur across multiple neocortical regions, and less frequently also in the parahippocampal gyrus and hippocampus. Most spindles are spatially restricted to specific brain regions. In addition, spindle frequency is topographically organized with a sharp transition around the supplementary motor area between fast (13–15 Hz) centroparietal spindles often occurring with slow-wave up-states, and slow (9–12 Hz) frontal spindles occurring 200 ms later on average. Spindle variability across regions may reflect the underlying thalamocortical projections. We also find that during individual spindles, frequency decreases within and between regions. In addition, deeper NREM sleep is associated with a reduction in spindle occurrence and spindle frequency. Frequency changes between regions, during individual spindles, and across sleep may reflect the same phenomenon, the underlying level of thalamocortical hyperpolarization. Finally, during spindles neuronal firing rates are not consistently modulated, although some neurons exhibit phase-locked discharges. Overall, anatomical considerations can account well for regional spindle characteristics, while variable hyperpolarization levels can explain differences in spindle frequency.
The study of range-scaling transformation of nanoparticle compounds on thin films of gold-centered monolayer protected nanoparticles by molecular modeling

A quantitative structure–retention relation (QSRR) study was conducted on the range-scaling transformation (Xa) of the nanoparticle compounds which obtained by comprehensive two-dimensional gas chromatography (GC×GC) stationary phases consisting of thin films of the gold-centered monolayer protected nanoparticles (MPNs) system. The genetic algorithm was used as descriptor selection and model development method. Modeling of the relationship between the selected molecular descriptors and the retention time was achieved by linear (partial least square; PLS) and nonlinear (Levenberg-Marquardt artificial neural network; L-M ANN) methods. Linear and nonlinear methods resulted in an accurate prediction whereas more accurate results were obtained by L-M ANN model.
Regularization and variable selection for data with interdependent structures

Variable selection methods are powerful tools in analysis of high dimensional massive data. Specifically, the methods have been applied in gene expression microarray data analysis. It is well known that for genes sharing a common biological pathway or a similar function, the correlations among them can be very high. However, most of the available variable selection methods cannot deal with complicated interdependence among data. We propose two new algorithms, namely gLars and gRidge, to select groups of highly correlated variables together in regression models. The new approaches intent to conduct grouping and selecting at the same time. Simulations and a real example show that our proposed methods often outperform the existing variable selection methods, including LARS and elastic net, in terms of both prediction error and preserving sparsity of representation.
Accelerated wound healing in leukocyte‐specific, protein 1‐deficient mouse is associated with increased infiltration of leukocytes and fibrocytes

Wound healing is a complex process involving the integrated actions of numerous cell types, soluble mediators, and ECM. Recently, a newly identified cell type, the fibrocyte, has been reported to contribute to wound healing and fibrotic conditions such as hypertrophic scarring. We previously established leukocyte‐specific protein 1 (LSP1) as a marker for fibrocytes. LSP1 is an F‐actin binding protein and substrate of p38 mitogen‐activated protein kinase and protein kinase C, and has been reported to be important in leukocyte chemotaxis. We examine the biological roles of LSP1 in skin wound healing using Lsp1−/− null mice. These animals showed accelerated healing of full‐thickness skin wounds, with increased re‐epithelialization rates, collagen synthesis, and angiogenesis. Healing wounds in Lsp1−/− mice had higher densities of neutrophiles, macrophages, and fibrocytes. Along with increased leukocyte infiltration, levels of macrophage‐derived chemokine expression, TGF‐β1, and VEGF were all up‐regulated. These results demonstrate that the absence of LSP1 promotes healing of skin wounds. The primary mechanism seems to be an increase in leukocyte infiltration, leading to locally elevated synthesis and release of chemokines and growth factors. Further analysis of Lsp1−/− mice may suggest ways to improve wound healing and/or treat fibrotic conditions of skin and other tissue.
Automatic spot detection in large-scale fluorescence microscopy image dataset

In this thesis, we consider the problem of detecting spots that encapsulates biological experiments printed on array plates. Our primary goal is to automatically detect and annotate all individual experiment spots in fluorescence microscopy images for further quantitative analysis. The proposed approach can be summarized as follows. Initially, image data is decimated, enhanced and montaged into a single composite image. Then, a k-NN classifier is used to segment the spots and sub-arrays are detected with a k-means clustering approach. Each sub-array is analyzed individually and a new gridding algorithm yields the automatic segmentation of the array image into spot quadrants. In contrast to other methods, the gridding is based on intensity profiles. Finally, the spots are detected within each quadrant by thresholding and by computing centroids of connecting components. An extensive evaluation on several data sets with ground-truth has been carried out. The results indicate that our method efficiently detects the spots in array images and outperforms the currently used method, which relies on pre-saved annotation coordinate data.
Role of the NADPH Oxidases in the Subfornical Organ in Angiotensin II–Induced Hypertension

Reactive oxygen species and the NADPH oxidases contribute to hypertension via mechanisms that remain undefined. Reactive oxygen species produced in the central nervous system have been proposed to promote sympathetic outflow, inflammation, and hypertension, but the contribution of the NADPH oxidases to these processes in chronic hypertension is uncertain. We therefore sought to identify how NADPH oxidases in the subfornical organ (SFO) of the brain regulate blood pressure and vascular inflammation during sustained hypertension. We produced mice with loxP sites flanking the coding region of the NADPH oxidase docking subunit p22phox. SFO-targeted injections of an adenovirus encoding cre-recombinase markedly diminished p22phox, Nox2, and Nox4 mRNA in the SFO, as compared with a control adenovirus encoding red-fluorescent protein injection. Increased superoxide production in the SFO by chronic angiotensin II infusion (490 ng/kg min–1 ×2 weeks) was blunted in adenovirus encoding cre-recombinase–treated mice, as detected by dihydroethidium fluorescence. Deletion of p22phox in the SFO eliminated the hypertensive response observed at 2 weeks of angiotensin II infusion compared with control adenovirus encoding red-fluorescent protein-treated mice (mean arterial pressures=97±15 versus 154±6 mm Hg, respectively; P=0.0001). Angiotensin II infusion also promoted marked vascular inflammation, as characterized by accumulation of activated T-cells and other leukocytes, and this was prevented by deletion of the SFO p22phox. These experiments definitively identify the NADPH oxidases in the SFO as a critical determinant of the blood pressure and vascular inflammatory responses to chronic angiotensin II, and further support a role of reactive oxygen species in central nervous system signaling in hypertension.
Development of the Feminine Gender Role Stress Scale

The cognitive appraisal of threats and challenges to stereotypical feminine gender role coping behavior was defined as feminine gender role stress (FGRS). This article describes the development of a self-report measure of FGRS. Situations perceived as more stressful for women than for men were categorized by factor analysis, yielding the following constellation of maladaptive stress responses particularly salient for women: (a) fear of unemotional relationships, (b) fear of being unattractive, (c) fear of victimization, (d) fear of behaving assertively, and (e) fear of not being nurturant. Women demonstrated significantly higher FGRS appraisal scores than men, and scores among women showed good 2-week test-retest reliability. The tendency to appraise situations on the FGRS scale as stressful was associated with the tendency to consider daily hassles stressful but was not related to self-perceived femininity in women. It was predicted that women who tend to exhibit the FGRS appraisal style would have more difficulty in coping with stressors related to depression. Supporting this hypothesis, women with higher FGRS scores reported greater depression than those with lower scores. The assessment of FGRS appraisal and coping style in women provides useful information for devising treatment strategies to improve women's health through promotion of adaptive coping.
Elastic Instabilities in Polymer-Solution Flow Through Porous Media

Crude oil has been an important source of energy for several decades now. Upon discovery of a crude oil reservoir until its abandonment, typically the oil recovery process can be classified in three stages. The primary and the secondary stage involve utilizing and maintaining reservoir pressure to recover oil respectively. The tertiary, or the Enhanced Oil Recovery (EOR) stage relates to employing various chemical or thermal methods to recover oil. Polymer flooding is the most promising enhanced oil recovery method that has shown potential to increase the oil recovery. Considering all the proven oil reserves from which mankind has recovered oil until now and average the oil recovery, it is less than 40%. EOR technologies have the potential to tap into the ∼ 60% crude oil that is still present in the oil reservoirs. One of the biggest technical challenges in perfecting a polymer EOR process is predicting the polymer injectivity. The viscoelastic behaviour of polymer solutions coupled with the complex pore-scale flow geometry of geological porous media gives rise to non-linear resistance to flow. Consequently, during injection of polymer solutions in an oil reservoir, the pressure-drop can increase dramatically beyond certain flow rates. This behaviour is known as apparent shear-thickening. In this thesis, we focus on understanding the apparent shear-thickening behaviour of polymer solutions as they flow through porous media. We identify the mechanism causing the non-linear resistance to flow of polymer solutions through porous media. Our experiments reveal the mechanisms at the pore-scale and also at the molecular-scale. \par
Mutual Localization and Positioning of Vehicles Sharing GNSS Pseudoranges: Sequential Bayesian Approach and Experiments

In many cooperative Intelligent Transportation Systems (ITS) applications, absolute positioning and relative localization are key issues. When vehicles share GNSS positions, there are often non negligible common-mode errors due mainly to GNSS signal-in-space. Cooperative observation techniques allow estimating common biases on the measured pseudodistances to correct these errors and to increase absolute positioning and relative localization accuracy. After having studied some structural properties of the problem in its general form, a low computational cooperative tightly-coupled approach is proposed using sequential Kalman filtering and convex data fusion. As a case study, we consider two vehicles which cooperate and exchange information in such a way that each vehicle can track the partner's position and improves its absolute position by merging common biases estimates. Experimental results are presented to illustrate the performance of the proposed approach in comparison with a classic standalone method.
Feedback-Directed Optimizations with Estimated Edge Profiles from Hardware Event Sampling

Traditional feedback-directedoptimization (FDO) uses st atic instrumentation to collect profiles. This method has shown good application performance gains, but is not commonly used in practice due to the high runtime overhead of profile collection, the tedious dual-compile usage model, and difficulties in generating representative training data se ts. In this paper, we show that edge frequency estimates can be successfully constructed with heuristics using profile dat a collected by sampling of hardware events, incurring low run time overhead (e.g., less then 2%), and requiring no instrumentation, yet achieving competetive performance gains. Our initial results show a 3-4% performance gain on the SPEC C benchmarks.