Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparing the Quality of Focused Crawlers and of the Translation Resources Obtained from them

Comparable corpora have been used as an alternative for parallel corpora as resources for computational tasks that involve domain-specific natural language processing. One way to gather documents related to a specific topic of interest is to traverse a portion of the web graph in a targeted way, using focused crawling algorithms. In this paper, we compare several focused crawling algorithms using them to collect comparable corpora on a specific domain. Then, we compare the evaluation of the focused crawling algorithms to the performance of linguistic processes executed after training with the corresponding generated corpora. Also, we propose a novel approach for focused crawling, exploiting the expressive power of multiword expressions.
Egalitarianism and the Returns to Education during the Great Transformation of American Education

Secondary school education greatly expanded in the United States from 1910 to 1940, setting its schooling attainment apart from that of all other countries. Barely 10 percent of youth were high school graduates in 1910, but by the mid 1930s the median youth had a high school diploma. In some regions, by the 1930s enrollment and graduation rates rose to levels that were as high as they would be two decades later. The issue addressed here concerns the economic impact of the large increase in the supply of educated labor. Evidence is presented concerning the sharp decline in the wage premium to ordinary white‐collar workers. With the expansion of the high school, large numbers of Americans competed for positions in the coveted white‐collar sector. Although the return to a year of high school remained considerable on the eve of World War II, egalitarianism had evened the playing field for a substantial segment of Americans.
Supplementary motor area activation in patients with frontal lobe tumors and arteriovenous malformations.

BACKGROUND AND PURPOSE Some patients who undergo surgical resection of portions of the supplementary motor area (SMA) have severe postoperative motor and language deficits, whereas others have no deficits. We tested the hypothesis that in some patients with lesions affecting the SMA, the contralateral SMA exhibits some of the activation normally associated with the ipsilateral SMA. METHODS Functional MR imaging studies in seven healthy volunteers and 19 patients with frontal lobe tumors or arteriovenous malformations were reviewed retrospectively. The hemisphere in which the SMA activation predominated was tabulated for right and left motor tasks. The relative hemispheric dominance in the SMA for the right and left motor tasks was compared in the healthy and patient groups and with the location of the lesion in the patient group. RESULTS None of the control subjects performing a right hand motor task activated predominantly the right SMA. Fifty percent of the patients with lesions overlapping the left SMA performing the right motor task activated predominantly the right SMA. Fifty-seven percent of control subjects performing the left hand motor task activated the left SMA predominantly. One hundred percent of patients with lesions overlapping the right frontal SMA performing the left motor task activated the left SMA predominantly. Differences between patients and controls were statistically significant. CONCLUSION A lesion that contacts or overlaps the SMA is associated with an increased functional MR imaging response within the contralateral SMA.
Candidate Constrained CRFs for Loss-Aware Structured Prediction

When evaluating computer vision systems, we are often concerned with performance on a task-specific evaluation measure such as the Intersection-Over-Union score used in the PASCAL VOC image segmentation challenge. Ideally, our systems would be tuned specifically to these evaluation measures. However, despite much work on loss-aware structured prediction, top performing systems do not use these techniques. In this work, we seek to address this problem, incorporating loss-aware prediction in a manner that is amenable to the approaches taken by top performing systems. Our main idea is to simultaneously leverage two systems: a highly tuned pipeline system as is found on top of leaderboards, and a traditional CRF. We show how to combine high quality candidate solutions from the pipeline with the probabilistic approach of the CRF that is amenable to loss-aware prediction. The result is that we can use loss-aware prediction methodology to improve performance of the highly tuned pipeline system.
Characterization of two factors enhancing choline acetyltransferase activity in cultures of purified rat motoneurons

Motoneurons from E14 rat embryos have been retrogradely labeled with the carbocyanine derivative dil and purified 12-fold by centrifugation on a density gradient made of Nycodenz, as assessed by the increase in CAT activity per cell and in the percentage of dil-labeled cells. A 20- to 36-fold purification was achieved by the microdissection of the labeled lumbar motor columns followed by density-gradient centrifugation. Motoneurons were then purified to near homogeneity by fluorescence-activated cell sorting. In certain experiments, motoneurons were purified by a novel cell-sorting procedure that only uses light-scatter parameters and does not necessitate retrograde labeling. In cultures of motoneurons enriched by centrifugation, muscle- conditioned media (CM) had only a marginal effect on the overall neuronal survival but increased up to 10-fold the number of labeled motoneurons surviving after 7 d in culture. CAT activity per culture was quantitatively stimulated to the same extent, suggesting that the increase in CAT activity caused by CM mostly reflected better survival of motoneurons. In cultures of motoneurons purified by cell sorting, CM increased both survival and CAT expression per motoneuron. We have partially purified 2 factors from CM that stimulate CAT activity per motoneuron 2- to 3-fold without affecting their survival. One of these factors is probably identical to the factor involved in the cholinergic/noradrenergic choice of sympathetic neurons (Fukada, 1985).
Fibonacci Heaps Revisited

The Fibonacci heap is a classic data structure that supports deletions in logarithmic amortized time and all other heap operations in O(1) amortized time. We explore the design space of this data structure. We propose a version with the following improvements over the original: (i) Each heap is represented by a single heap-ordered tree, instead of a set of trees. (ii) Each decrease-key operation does only one cut and a cascade of rank changes, instead of doing a cascade of cuts. (iii) The outcomes of all comparisons done by the algorithm are explicitly represented in the data structure, so none are wasted. We also give an example to show that without cascading cuts or rank changes, both the original data structure and the new version fail to have the desired efficiency, solving an open problem of Fredman. Finally, we illustrate the richness of the design space by proposing several alternative ways to do cascading rank changes, including a randomized strategy related to one previously proposed by Karger. We leave the analysis of these alternatives as intriguing open problems.
Sparse representation of terrains for procedural modeling

In this paper, we present a simple and efficient method to represent terrains as elevation functions built from linear combinations of landform features (atoms). These features can be extracted either from real world data‐sets or procedural primitives, or from any combination of multiple terrain models. Our approach consists in representing the elevation function as a sparse combination of primitives, a concept which we call Sparse Construction Tree, which blends the different landform features stored in a dictionary. The sparse representation allows us to represent complex terrains using combinations of atoms from a small dictionary, yielding a powerful and compact terrain representation and synthesis tool. Moreover, we present a method for automatically learning the dictionary and generating the Sparse Construction Tree model. We demonstrate the efficiency of our method in several applications: inverse procedural modeling of terrains, terrain amplification and synthesis from a coarse sketch.
Design of Near-Field Synthesis Arrays Through Global Optimization

We provide a study of the relation between the near- and far-fields in antenna systems by working with a concrete design problem. The task of finding an antenna array capable of synthesizing a desired near-field distribution is tackled within the general framework of near-field theories recently proposed in literature. We use a genetic algorithm to search for a set of small antennas by working only with far-field data. It is shown, as predicted theoretically, that such information in the far zone are sufficient for reconstructing the entire near-field in the exterior region. We provide a set of examples demonstrating the procedure and validating the proposals. The methodology can be applied to arbitrary antennas and any desired near-field pattern, and we hope it will help automating design methods in the emerging research area of near-field array synthesis.
Line-edge roughness characterized by polymer aggregates in photoresists

We investigate the origin of the line-edge roughness (LER) of line patterns of chemically amplified photo resist for the purpose of reducing size fluctuations of patterns in present and future deep-UV lithography. An atomic force microscope analysis of the pattern sidewall reveals that there are tow kinds of roughness in the LER: short-range roughness with an average period of about 50 nm and long- range roughness with an average period of about 500 nm. The short-range roughness can be identified as polymer aggregates, which are essentially formed by the base polymer in the resist film. This is because the average period of the surface roughness due to polymer aggregates observed in the base polymer films is about the same as that of the short-range roughness. In addition, it is confirmed that aggregate extraction development occurs in the photoresist. On the other hand, the long-range roughness is generated not by the base polymer only but also by the exposure process because its average period increases with the exposure dose. The origin of the long-range roughness is also discussed.
Nonvolatile CBRAM-Crossbar-Based 3-D-Integrated Hybrid Memory for Data Retention

This paper explores the design of 3-D-integrated hybrid memory by conductive-bridge random-access-memory (CBRAM). Considering internal states, height, and radius of the conductive bridge of one CBRAM device, an accurate CBRAM device model is developed for CBRAM-crossbar-based nonvolatile memory design with efficient estimation of area, access time, and power. Based on this design platform, one 3-D-integrated hybrid memory is designed by stacking one tier of CBRAMcrossbar with tiers of static random access memory (SRAM) and dynamic random access memory (DRAM), where the tier of CBRAM-crossbar is deployed for data retention during power gating of SRAM/DRAM tiers. One corresponding block-level data retention is developed to only write back dirty data from SRAM/DRAM to CBRAM-crossbar. When compared with phase-change random-access-memory-based system-level data retention, our design achieves 11× faster data-migration speed and 10× less data-migration power. When compared with ferroelectric random-access-memory-based bit-level data retention, our design also achieves 17× smaller area and 56× smaller power under the same data-migration speed.
Expression of Pla2g2a prevents carcinogenesis in Muc2‐deficient mice

Goblet cell depletion and down‐regulation of MUC2 expression are observed in a significant percentage of human non‐mucinous colorectal adenocarcinomas. Direct evidence for the role of MUC2 in gastrointestinal tumor formation was demonstrated by a knockout of Muc2 in mice that resulted in the development of adenocarcinomas in the small and large intestine. The secretory phospholipase Pla2g2a is a protein that confers resistance to ApcMin/+‐induced intestinal tumorigenesis. Like Muc2, in the large intestine Pla2g2a is exclusively expressed by the goblet cells and Pla2g2a's tumor resistance is also strongest in the large intestine. Possible genetic interactions between Muc2 and Pla2g2a were examined by creating C57BL/6‐Muc2−/–Pla2g2a transgenic mice. Expression of a Pla2g2a transgene reduced tumorigenesis in the large intestine by 90% in male Muc2−/– mice and by nearly 100% in female Muc2−/– mice. Expression of Pla2g2a also inhibited tumor progression. Microarray gene expression studies revealed Pla2g2a target genes that modulate intestinal energy metabolism, differentiation, inflammation, immune responses and proliferation. Overall, results of the present study demonstrate an Apc‐independent role for Pla2g2a in tumor resistance and indicate that Pla2g2a plays an important role, along with Muc2, in protection of the intestinal mucosa. (Cancer Sci 2008; 99: 2113–2119)
Impact of Speciation Heuristic on Crossover and Search in NEAT

Crossover is an important genetic operator that can combine beneficial genes together. Unfortunately, neuro-evolution (NE) has not experienced the benefits of crossover, despite significant efforts that enabled crossover for neural networks. Orthogonally, speciation has become an important feature in NE for diversity maintenance; however, speciation research has focused on what measure is driving speciation versus how the measure determines species. This research posits that an appropriate speciation heuristic can enable effective crossover in NE by determining potential mating partners. This paper investigates these concepts and presents empirical evidence that demonstrates; (1) the impact of the speciation heuristic, (2) crossover's negative effect, and (3) a speciation heuristic that enables effective crossover in NE.
Towards Automatic Construction of Reusable Prediction Models for Component-Based Performance Engineering

Performance predictions for software architectures can reveal performance bottlenecks and quantitatively support design decisions for different architectural alternatives. As software architects aim at reusing existing software components, their performance properties should be included into performance predictions without the need for manual modelling. However, most prediction approaches do not include automated support for modelling implemented components. Therefore, we propose a new reverse engineering approach, which generates Palladio performance models from Java code. In this paper, we focus on the static analysis of Java code, which we have implemented as an Eclipse plugin called Java2PCM. We evaluated our approach on a larger component-based software architecture, and show that a similar prediction accuracy can be achieved with generated models compared to completely manually specified ones.
Associative Memories to Accelerate Approximate Nearest Neighbor Search

Nearest neighbor search is a very active field in machine learning. It appears in many application cases, including classification and object retrieval. In its naive implementation, the complexity of the search is linear in the product of the dimension and the cardinality of the collection of vectors into which the search is performed. Recently, many works have focused on reducing the dimension of vectors using quantization techniques or hashing, while providing an approximate result. In this paper, we focus instead on tackling the cardinality of the collection of vectors. Namely, we introduce a technique that partitions the collection of vectors and stores each part in its own associative memory. When a query vector is given to the system, associative memories are polled to identify which one contains the closest match. Then, an exhaustive search is conducted only on the part of vectors stored in the selected associative memory. We study the effectiveness of the system when messages to store are generated from i.i. d. uniform ±1 random variables or 0–1 sparse i.i. d. random variables. We also conduct experiments on both synthetic data and real data and show that it is possible to achieve interesting trade-offs between complexity and accuracy.
TRECVID 2011 - An Overview of the Goals, Tasks, Data,Evaluation Mechanisms, and Metrics

The TREC Video Retrieval Evaluation (TRECVID) 2011 was a TREC-style video analysis and retrieval evaluation, the goal of which remains to promote progress in content-based exploitation of digital video via open, metrics-based evaluation. Over the last ten years this effort has yielded a better understanding of how systems can effectively accomplish such processing and how one can reliably benchmark their performance. TRECVID is funded by the National Institute of Standards and Technology (NIST) and other US government agencies. Many organizations and individuals worldwide contribute significant time and effort
Classification of Subtype of Acute Ischemic Stroke: Definitions for Use in a Multicenter Clinical Trial

Background and Purpose The etiology of ischemic stroke affects prognosis, outcome, and management. Trials of therapies for patients with acute stroke should include measurements of responses as influenced by subtype of ischemic stroke. A system for categorization of subtypes of ischemic stroke mainly based on etiology has been developed for the Trial of Org 10172 in Acute Stroke Treatment (TOAST). Methods A classification of subtypes was prepared using clinical features and the results of ancillary diagnostic studies. “Possible” and “probable” diagnoses can be made based on the physician's certainty of diagnosis. The usefulness and interrater agreement of the classification were tested by two neurologists who had not participated in the writing of the criteria. The neurologists independently used the TOAST classification system in their bedside evaluation of 20 patients, first based only on clinical features and then after reviewing the results of diagnostic tests. Results The TOAST classification denotes five subtypes of ischemic stroke: 1) large-artery atherosclerosis, 2) cardioembolism, 3) small-vessel occlusion, 4) stroke of other determined etiology, and 5) stroke of undetermined etiology. Using this rating system, interphysician agreement was very high. The two physicians disagreed in only one patient. They were both able to reach a specific etiologic diagnosis in 11 patients, whereas the cause of stroke was not determined in nine. Conclusions The TOAST stroke subtype classification system is easy to use and has good interobserver agreement. This system should allow investigators to report responses to treatment among important subgroups of patients with ischemic stroke. Clinical trials testing treatments for acute ischemic stroke should include similar methods to diagnose subtypes of stroke.
Cautious Collective Classification

Many collective classification (CC) algorithms have been shown to increase accuracy when instances are interrelated. However, CC algorithms must be carefully applied because their use of estimated labels can in some cases decrease accuracy. In this article, we show that managing this label uncertainty through cautious algorithmic behavior is essential to achieving maximal, robust performance. First, we describe cautious inference and explain how four well-known families of CC algorithms can be parameterized to use varying degrees of such caution. Second, we introduce cautious learning and show how it can be used to improve the performance of almost any CC algorithm, with or without cautious inference. We then evaluate cautious inference and learning for the four collective inference families, with three local classifiers and a range of both synthetic and real-world data. We find that cautious learning and cautious inference typically outperform less cautious approaches. In addition, we identify the data characteristics that predict more substantial performance differences. Our results reveal that the degree of caution used usually has a larger impact on performance than the choice of the underlying inference algorithm. Together, these results identify the most appropriate CC algorithms to use for particular task characteristics and explain multiple conflicting findings from prior CC research.
Krylov Sequences as a Tool for Analysing Iterated Regression Algorithms

Abstract. We use Krylov sequences to analyse a class of regression methods based on successive identification of latent factors. Some results already proved for partial least squares regression (PLSR) are shown to hold for other methods also. We prove that the well‐known peculiar pattern of alternating shrinkage and inflation of the principal components is not unique for PLSR. We also show that for any method in the class under study, the coefficient of determination is always at least as high as for principal components regression with the same number of factors.
Guiding model search using segmentation

In this paper we show how segmentation as preprocessing paradigm can be used to improve the efficiency and accuracy of model search in an image. We operationalize this idea using an over-segmentation of an image into superpixels. The problem domain we explore is human body pose estimation from still images. The superpixels prove useful in two ways. First, we restrict the joint positions in our human body model to lie at centers of superpixels, which reduces the size of the model search space. In addition, accurate support masks for computing features on half-limbs of the body model are obtained by using agglomerations of superpixels as half limb segments. We present results on a challenging dataset of people in sports news images
Experimental study of oscillation mark depth in continuous casting of steel

Mould oscillation is needed to reduce friction and thus prevent sticking and breakout of the liquid metal during casting. However, this oscillation is known to cause surface defects in the solidified steel slabs, so called oscillation marks. In this paper, the depth and the depth variation of these oscillation marks were studied using a two-level full factorial experiment (2 4 ) with four additional centre point runs. Four factors were studied: stroke length of the mould, oscillation frequency, motion pattern (strip factor) and casting speed. The stroke length affected the depth of the marks the most, where larger strokes created deeper marks. The interaction between the oscillation frequency and the strip factor of the mould also affected the oscillation mark depth. The oscillation mark depth variation was also increased by increased stroke lengths and at higher oscillation frequencies. The largest effect on the oscillation depth variation was found for the interaction between the stroke length and the oscillation frequency.
Dissection of Ammonium Uptake Systems in Corynebacterium glutamicum: Mechanism of Action and Energetics of AmtA and AmtB

ABSTRACT Corynebacterium glutamicum has two different Amt-type proteins. While AmtB has a low substrate affinity and is not saturable up to 3 mM methylammonium, AmtA has a high substrate affinity and mediates saturable, membrane potential-dependent transport, resulting in a high steady-state accumulation of methylammonium, even in the absence of metabolic trapping.