Miracle-dz/UniFAR-FTUs huggingface dataset:
Notice of RetractionParallel block multigrid preconditioner for 3D Navier-Stokes equations on unstructured grids

In this paper, we explore a parallel block multigrid preconditioner based on factorization of the coefficient matrix generated in three-dimensional unstructured grids system. This preconditioner is robust with respect to the magnitude of the mesh size and the coefficient complexity, apply smoothed aggregation algebraic multigrid which using block lower-upper symmetric Gauss-Seidel(BLU-SGS) as smoother to accelerate convergent rate. We focus on this method to three-dimensional, unstructured, large-scale, parallel and NS equations' steady-state simulations. The numerical results which conducted on NACA0012 airfoil indicate that nearly optimal convergence rates and computational complexity for parallel solutions, this method also provide high speedup and efficiency for a series of processors.
Assessment and Curriculum Reform

This volume of New Directions for Higher Education is about linking assessments of students' learning with the patterns of their coursework. The contributors illustrate how and why assessment and curriculcum reform need to be combined, and they describe an innovative way in which to use student transcripts and college catalogues to analyze the curriculum in orcer to associate the coursework of students with the learning that they gain. The contributors also show how such assessment data can be used to examine questions about the curriculum, faculty development, and student advising. This is the 80th issue of the quarterly journal New Directions for Higher Education. For more information on the series, please see the Journals and Periodicals page.
Replica identification using genetic programming

Identifying and handling replicas are important to guarantee the quality of the information made available by modern data storage services. There has been a large investment from companies and governments in the development of effective methods for removing replicas from large databases. Typically, this investment has produced significant results, since cleaned replica-free databases not only allow the retrieval of higher-quality information but also lead to a more concise data representation and to potential savings in computational time and resources to process and maintaining this data. In this paper, we propose a GP-based approach to automatic replica identification that combines evidence based on the data content in order to find a similarity function that is able to identify whether two entries in a repository are replicas or not. As shown by our experiments, our approach outperforms an SVM-based method used as baseline by at least 6.5%. Moreover, the suggested functions are computationally less demanding since they use fewer evidence. In addition, our approach is capable to automatically adapt to any given replica identification boundary.
Verbal Semantics Drives Early Anticipatory Eye Movements during the Comprehension of Verb-Initial Sentences

Studies on anticipatory processes during sentence comprehension often focus on the prediction of postverbal direct objects. In subject-initial languages (the target of most studies so far), however, the position in the sentence, the syntactic function, and the semantic role of arguments are often conflated. For example, in the sentence “The frog will eat the fly” the syntactic object (“fly”) is at the same time also the last word and the patient argument of the verb. It is therefore not apparent which kind of information listeners orient to for predictive processing during sentence comprehension. A visual world eye tracking study on the verb-initial language Tagalog (Austronesian) tested what kind of information listeners use to anticipate upcoming postverbal linguistic input. The grammatical structure of Tagalog allows to test whether listeners' anticipatory gaze behavior is guided by predictions of the linear order of words, by syntactic functions (e.g., subject/object), or by semantic roles (agent/patient). Participants heard sentences of the type “Eat frog fly” or “Eat fly frog” (both meaning “The frog will eat the fly”) while looking at displays containing an agent referent (“frog”), a patient referent (“fly”) and a distractor. The verb carried morphological marking that allowed the order and syntactic function of agent and patient to be inferred. After having heard the verb, listeners fixated on the agent irrespective of its syntactic function or position in the sentence. While hearing the first-mentioned argument, listeners fixated on the corresponding referent in the display accordingly and then initiated saccades to the last-mentioned referent before it was encountered. The results indicate that listeners used verbal semantics to identify referents and their semantic roles early; information about word order or syntactic functions did not influence anticipatory gaze behavior directly after the verb was heard. In this verb-initial language, event semantics takes early precedence during the comprehension of sentences, while arguments are anticipated temporally more local to when they are encountered. The current experiment thus helps to better understand anticipation during language processing by employing linguistic structures not available in previously studied subject-initial languages.
Latent Dirichlet Allocation Model for Raga Identification of Carnatic Music

Problem statement: In this study the Raga of South Indian Carnatic music is determined by constructing a model. Raga is a pre-determined arrangement of notes, which is characterized by an Arohana and Avarohana, which is the ascending and descending arrangement of notes and Raga lakshana. Approach: In this study a Latent Dirichlet Allocation (LDA) model is constructed to identify the Raga of South Indian Carnatic music. LDA is an unsupervised statistical approach which is being used for document classification to determine the underlying topics in a given document. The construction of LDA is based on the assumption that the notes of a given music piece can be mapped to the words in a topic and the topics in a document can be mapped to the Raga. The identification of notes is very difficult due to the narrow range of frequency and the characteristics of Carnatic music. This inclined us in moving to a probabilistic approach for the identification of Raga. In this study we identify the notes of a given signal and using these notes and Raga lakshana, a probabilistic model in terms of LDA's parameters ∝ and θ are computed and constructed for every Raga by initially assuming a value which is constant for every Raga. This value of ∝ is cultured after determining θ for a given Raga. The θ of a given Raga is computed using the characteristic phrases which is a sequence of notes and is unique for a given Raga. During the Raga identification phase, the value of ∝ and θ are computed and is matched with the constructed LDA model to identify the given Raga. Results: Using this model, the Raga identification of Parent Ragas had a lower error rate than that of Child Raga. For parent Raga an average identification rate of 75% was achieved. Conclusion/Recommendations: The accuracy of the algorithm can be improved by using more features of Raga lakshana. After identifying the Raga, it can be used as features to be used by a Music Information Retrieval system.
ShARe/CLEF eHealth Evaluation Lab 2013, Task 3: Information Retrieval to Address Patients' Questions when Reading Clinical Reports

This paper presents the results of task 3 of the ShARe/CLEF eHealth Evaluation Lab 2013. This evaluation lab focuses on improving access to medical information on the web. The task objective was to investigate the eect of using additional information such as the discharge summaries and external resources such as medical ontologies on the IR eectiveness. The participants were allowed to submit up to seven runs, one mandatory run using no additional information or external resources, and three each using or not using discharge summaries.
Feature Co-occurrence Maps: Appearance-based localisation throughout the day

In this paper we present a new method, Feature Co-occurrence Maps, for appearance-based localisation over the course of a day. We show that by quantising local features in both feature and image space, discriminative statistics can be learned on the co-occurrences of features at different times of the day. This allows for matching at any time, without requiring individual images to be stored representing each time of day, and matching is performed efficiently by simultaneously matching to the entire database. We further show how matching along image sequences can be incorporated into the system and adapt existing methods by allowing for non-zero acceleration. Results on a 20km outdoor dataset show improved performance in precision-recall over state of the art.
Fitting a 3D Particle System Model to a Non-dense Data Set in Medical Applications

In the scope of a radiotherapy application, we have previously de- veloped a methodology to reconstruct the 3D shape of deformable organs from their CT scan sections using particle systems. In this paper, we propose a new technique to track changes in the object's shape from some partial information provided by an ultrasound sensor. The aim of this method is to quickly obtain the shape alteration while preserving organ's volume which can be a strong re- quirement in medical applications. Our approach is based on the deformation of the initial model by applying physical forces to its surface in order to flt the new data set. The amplitude of this force is calculated to preserve the cohesion of the organ's model. The advantages of our method include:
Chinese sentiment classification using a neural network tool — Word2vec

Sentiment classification is the main and popular task in the field of sentiment analysis. Most of the existing researches focus on how to extract the effective features, such as lexical features and syntactic features, while limited work has been done on the extraction of semantic features, which can make more contributions to sentiment classification. This paper presents a method for sentiment classification based on word2vec. Word2vec is a tool, which establishes the neural network models to learn the vector representations of words in the high dimensional vector space. So it can extract the deep semantic relationships between words. In this paper, firstly, we cluster the similar features together using word2vec. And then we use word2vec again to learn the word representations as candidate feature vectors. After feature selection, the SVMperf package is adopted to train and classify the comment texts. To conduct the experiments, we collect a large number of Chinese comments on clothing products as data set. The experimental results show that the accuracy of sentiment classification is over 90 percent, which proves the effectiveness of proposed method for Chinese sentiment classification.
Generalizing the localization formula in equivariant cohomology

We give two generalizations of the Atiyah-Bott-Berline-Vergne localization theorem for the equivariant cohomology of a torus action: 1) replacing the torus action by a compact connected Lie group action, 2) replacing the manifold having a torus action by an equivariant map. This provides a systematic method for calculating the Gysin homomorphism in ordinary cohomology of an equivariant map. As an example, we recover a formula of Akyildiz-Carrell for the Gysin homomorphism of flag manifolds.
Modelling the transmission of airborne infections in enclosed spaces

The Wells–Riley equation for modelling airborne infection in indoor environments is incorporated into an SEIR epidemic model with a short incubation period to simulate the transmission dynamics of airborne infectious diseases in ventilated rooms. The model enables the effect of environmental factors such as the ventilation rate and the room occupancy to be examined, and allows the long-term impact of infection control measures to be assessed. A theoretical parametric study is carried out to demonstrate how changes to both the physical environment and infection control procedures may potentially limit the spread of short-incubation-period airborne infections in indoor environments such as hospitals.
The Contribution of Mathematical Modeling to Understanding Dynamic Aspects of Rumen Metabolism

All mechanistic rumen models cover the main drivers of variation in rumen function, which are feed intake, the differences between feedstuffs and feeds in their intrinsic rumen degradation characteristics, and fractional outflow rate of fluid and particulate matter. Dynamic modeling approaches are best suited to the prediction of more nuanced responses in rumen metabolism, and represent the dynamics of the interactions between substrates and micro-organisms and inter-microbial interactions. The concepts of dynamics are discussed for the case of rumen starch digestion as influenced by starch intake rate and frequency of feed intake, and for the case of fermentation of fiber in the large intestine. Adding representations of new functional classes of micro-organisms (i.e., with new characteristics from the perspective of whole rumen function) in rumen models only delivers new insights if complemented by the dynamics of their interactions with other functional classes. Rumen fermentation conditions have to be represented due to their profound impact on the dynamics of substrate degradation and microbial metabolism. Although the importance of rumen pH is generally acknowledged, more emphasis is needed on predicting its variation as well as variation in the processes that underlie rumen fluid dynamics. The rumen wall has an important role in adapting to rapid changes in the rumen environment, clearing of volatile fatty acids (VFA), and maintaining rumen pH within limits. Dynamics of rumen wall epithelia and their role in VFA absorption needs to be better represented in models that aim to predict rumen responses across nutritional or physiological states. For a detailed prediction of rumen N balance there is merit in a dynamic modeling approach compared to the static approaches adopted in current protein evaluation systems. Improvement is needed on previous attempts to predict rumen VFA profiles, and this should be pursued by introducing factors that relate more to microbial metabolism. For rumen model construction, data on rumen microbiomes are preferably coupled with knowledge consolidated in rumen models instead of relying on correlations with rather general aspects of treatment or animal. This helps to prevent the disregard of basic principles and underlying mechanisms of whole rumen function.
Integrating Active Methods and Flow Meters - an implementation using NeTraMet

purpose of this paper is to present an implementation of a method that combines active and passive methods to measure and estimate the main performance parameters in IP networks. More specifically the method integrates performance and flow measurements. The main features of the method and an implementation using the flow meter NeTraMet are described. Test results for packet delays and delay variations, losses and throughput are presented. Applications to monitoring of traffic from server-based networks and an adaptive monitoring system are also considered. I. INTRODUCTION There are a number of good reasons for operators of IP networks to measure and verify the actual level of performance in their networks. In addition to traditional areas such as daily operations, usage information for billing purposes, customer reports, planning and dimensioning, the ambition to offer services with different quality assurances to customers makes measurement even more important. We present an implementation of a method that combines the traditional ingredients in passive and active methods into a synthesis with new characteristics and possibilities. This combined approach is realised as an enhancement of the network traffic flow meter NeTraMet. The paper is organised as follows. After a short background in section II, the monitoring method and the NeTraMet implementation are briefly presented in sections III and IV. Test results are evaluated in section V. Applications to virtual private networks and server networks, and NeTraMet as component in an adaptive monitoring system are considered in section VI. Tables and figures are assembled in two appendices.
Exploration of Magnetic RAM Based Memory Hierarchy for Multicore Architecture

Today's memory systems mainly integrate SRAM, DRAM and FLASH technologies. SRAM and DRAM are generally used for cache and working memory, while FLASH memory is used for non-volatile storage at low speed. But all are facing to manufacturing constraints in the most advanced node, which compromises further evolution. Besides, with the increasing size of the memory system, a significant portion of the total system power is spent into memories. Magnetic RAM (MRAM) technology is a very attractive alternative offering simultaneously reasonable performance and power consumption efficiency, high density and non-volatility. While MRAM is always under severe investigation to improve manufacturing process, the state of the art shows that this memory technology can be accessed in less than 5ns with a read/write dynamic energy not so far to SRAM dynamic energy. Besides, non-volatility of MRAM can be used for optimizing leakage current thanks to instant on/off policies. This paper demonstrates how current characteristics of MRAM can be used into memory hierarchy of multiprocessor chips (CMPs). The goal is to highlight the interest to use MRAM for cache memory in order to keep overall application performance saving static power.
A New Specification to Gene Signals Sensors by Neural Self Organizing Feature Map (SOFM)

Two different paradigms by the goals in gene-finding research have been recognized: 1) to offer computational aid in the annotation of the large volume of genomic data and 2) to provide a computational model helpful in elucidating the mechanisms involved in transcription, splicing, polyadynalation and other important processes on the pathway from genome to proteome . New findings in gene regulation appear to focus a new interest in the latter paradigm approaches. Therefore, a single weight matrix for the genomic patterns consensus scoring can be substituted by SOFM clusters matrices. This should result in the detection improvement of gene functional sites or signals, and therefore a gain evaluation across the known Burset's and Guigo's collection of the genes of 570 vertebrates is provided by a percentile measure on an exemplary site detection statistics. Such an improvement is important in both the "extrinsic" and "intrinsic" approaches. In the "signal" case of the latter, a demand is addressed for a neural approach which translates into likelihood scoring. This includes, but is not limited to, applications with HMM (hidden Markov model) derived gene finders. The approach is also scalable into a clusters-based solution to genes recognition with the capability of integrating DNA-string-contained knowledge in a novel way
Advances in Analogy-Based Learning: False Friends and Exceptional Items in Pronunciation By Paradigm

When looked at from a multilingual perspective, grapheme-to-phoneme conversion is a challenging task, fraught with most of the classical NLP "vexed questions": bottleneck problem of data acquisition, pervasiveness of exceptions, diiculty to state range and order of rule application, proper treatment of context-sensitive phenomena and long-distance dependencies , and so on. The hand-crafting of transcription rules by a human expert is onerous and time-consuming, and yet, for some European languages, still stops short of a level of correctness and accuracy acceptable for practical applications. We illustrate here a self-learning multilingual system for analogy-based pronunciation which was tested on Ital-ian, English and French, and whose performances are assessed against the output of both statistically and rule-based transcribers. The general point is made that analogy-based self-learning techniques are no longer just psycholinguistically-plausible models, but competitive tools, combining the advantages of using language-independent, self-learning, tractable algorithms , with the welcome bonus of being more reliable for applications than traditional text-to-speech systems. In Computational Linguistics, generalization by analogy is deened as the inferential process by which an unfamiliar linguistic object O (the target object) is seen as an analogue of more familiar objects (the base objects), so that whatever piece of knowledge is acquired about the latter can be used to deal with the former too (Owen 1990). For the present purposes this means that a written unknown word can be read aloud on the basis of its analogy to known words whose spelling and pronunciation are already familiar to the reader. In g. 1 overleaf, the pronunciation of the (target) Ital-ian name 'bellini' is inferred from the pronunciation of the known 'bellomo' and 'martini', based on their orthographic overlap (shared letters) with the input string. The approach is conceptually close to both instance-based (Dietterich, Hild, & Bakiri 1990; Lu-cassen & Mercer 1984) and case-based (Lehnert 1987; Stannll 1987) learning, although in analogy-based pronunciation more emphasis is laid on the cognitive plausibility of base retrieval and the role of analogiz-ing factors, rather than on automatic data clustering and extraction of statistically-grounded grapheme-to-phoneme regularities. Several proposals have been put forward in psy-cholinguistic circles as to what counts as an analogi-cally relevant factor, that is as a clue to a true analogy between words (Glushko 1979). Here, we will not go into this issue: for the sake of simplicity, orthographic similarity is the only relevant analogizing factor. Moreover , it is assumed that knowing how a …
Incremental Dataset Definition for Large Scale Musicological Research

Conducting experiments on large scale musical datasets often requires the definition of a dataset as a first step in the analysis process. This is a classification task, but metadata providing the relevant information is not always available or reliable and manual annotation can be prohibitively expensive. In this study we aim to automate the annotation process using a machine learning approach for classification. We evaluate the effectiveness and the trade-off between accuracy and required number of annotated samples. We present an interactive incremental method based on active learning with uncertainty sampling. The music is represented by features extracted from audio and textual metadata and we evaluate logistic regression, support vector machines and Bayesian classification. Labelled training examples can be iteratively produced with a web-based interface, selecting the samples with lowest classification confidence in each iteration. We apply our method to address the problem of instrumentation identification, a particular case of dataset definition, which is a critical first step in a variety of experiments and potentially also plays a significant role in the curation of digital audio collections. We have used the CHARM dataset to evaluate the effectiveness of our method and focused on a particular case of instrumentation recognition, namely on the detection of piano solo pieces. We found that uncertainty sampling led to quick improvement of the classification, which converged after ca. 100 samples to values above 98%. In our test the textual metadata yield better results than our audio features and results depend on the learning methods. The results show that effective training of a classifier is possible with our method which greatly reduces the effort of labelling where a residual error rate is acceptable.