Miracle-dz/UniFAR-FTUs huggingface dataset:
Experiments in topic indexing of broadcast news using neural networks

The paper deals with the problem of extracting topic information from news show stories by statistical methods. It is shown that the traditional topic-dependent n-gram language modeling approach can be decomposed in order to apply neural networks for topic indexing. Two specific problems in training of these networks are addressed: a very sparse data distribution in the stories and the superposition of different topics in a story. The first problem is tackled by an integrated smoothing approach in the backpropagation method; an expansion of the neural network structure can be used to cope with topic mixtures in stories. Due to the efficient parameter sharing the application of neural networks results in a small improvement in topic indexing performance on a small corpus of broadcast news compared to the traditional topic-dependent n-gram method.
Comparison of sleep-wake classification using electroencephalogram and wrist-worn multi-modal sensor data

This paper presents the comparison of sleep-wake classification using electroencephalogram (EEG) and multi-modal data from a wrist wearable sensor. We collected physiological data while participants were in bed: EEG, skin conductance (SC), skin temperature (ST), and acceleration (ACC) data, from 15 college students, computed the features and compared the intra-/inter-subject classification results. As results, EEG features showed 83% while features from a wrist wearable sensor showed 74% and the combination of ACC and ST played more important roles in sleep/wake classification.
Parallel high resolution real-time Visual Hull on GPU

In this paper we present an efficient high resolution Image Based Visual Hull (IBVH) algorithm that entirely runs in real-time on a single consumer graphics card. The target application is a real-time 3D video conferencing system. One major contribution of this paper is a novel caching strategy for the reduction of line segment intersection tests. In contrast to existing approaches, it additionally allows us to pre-select a close estimation of the set of relevant pixels in the desired view. Based on this, we obtain a significant computational speedup, especially for high resolutions. Further, we propose an efficient way to use IBVH for the generation of voxel equivalent 3D models. We compare our techniques in terms of resolution and runtime with state of the art real-time multi GPU voxel based approaches. Our experiments show that we achieve a speed-up by a factor of five and more for high resolutions.
L'entreprise flexible : réalité ou obsession ?

Cet article remet en cause la validite conceptuelle et operationnelle des notions de «flexibilite » et d' «entreprise flexible » presentees depuis les annees 1980, comme le modele de la solution a la recession. En premier lieu, les differents traits associes de facon souvent confuse a ces notions sont confrontes aux donnees empiriques pour montrer que le modele ne resiste pas a une analyse detaillee de revolution des marches de remploi, tenant compte des differences sectorielles et ponderant changements et continuites. Ensuite, la mise en evidence du melange de description, de prescription et de prediction, aboutit a la contestation de leur validite methodologique. Enfin, l'auteur aborde le contexte institutionnel et politique du succes de «l'entreprise flexible ».
Benchmarking and Analysis of NoSQL Technologies

The social web generates terabytes of unstructured, user generated data, spread across thousands of commodity servers. The changed face of web based applications forced to invent new approaches to data management. The NoSQL databases were created as a mean to offer high performance (both in terms of speed and size) and high availability. They share the goals of massive scaling "on demand" (elasticity). Hence NoSQL is best suited solution to the big data needs posed by the evolving web application. With the advent of newer NoSQL technologies each day, developers face a serious issue in selecting the best suited datastore for their application as each of them place very different demands. The NoSQL databases haven't been tested for their claims. In this paper we discuss the benchmarking results of the 4 most popular NoSQL technologies namely- Cassandra, HBase, MongoDB and CouchDB. Also we analyze the results based on the performance of each datastore on various parameters. We are using the YCSB (Yahoo! Cloud Serving Benchmark) tool to compare Cassandra vs. HBase (key-value stores), and MongoDB vs. CouchDB (document-oriented databases). We are benchmarking the NoSQL datastore on tiers of Performance, Scalability and Availability.
Energy Efficiency Improvements through Micro Sites in Cellular Mobile Radio Networks

Efforts to increase the energy efficiency of infor- mation and communication systems in general and cellular mobile radio networks in particular has recently gained mo- mentum. Besides positive environmental effects, lowering the energy consumption of mobile radio systems appears beneficial from an economical perspective. In this regard, the deployment of small, low power base stations, alongside conventional sites is often believed to greatly lower the energy consumption of cellular mobile radio networks. In this paper we investigate that matter in more detail from a deployment perspective. We evaluate potential improvements of the area power consumption achievable with network layouts featuring varying numbers of micro sites in addition to conventional macro sites for given system performance targets under full load conditions.
Constant generation for the financial domain using grammatical evolution

This study reports the work to date on the analysis of different methodologies for constant creation with the aim of applying the most advantageous method to the dynamic real world problem of a live trading system. The methodologies explored here are Digit Concatenation and Grammatical Ephemeral Random Constants with clear advantages identified for a digit concatenation approach in combination with the ability to form new constants through their recombination within expressions.
Altered placental DNA methylation patterns associated with maternal smoking: current perspectives

The developmental origins of health and disease hypothesis states that adverse early life exposures can have lasting, detrimental effects on lifelong health. Exposure to maternal cigarette smoking during pregnancy is associated with morbidity and mortality in offspring, including increased risks for miscarriage, stillbirth, low birth weight, preterm birth, asthma, obesity, altered neurobehavior, and other conditions. Maternal cigarette smoking during pregnancy interferes with placental growth and functioning, and it has been proposed that this may occur through the disruption of normal and necessary placental epigenetic patterns. Epigenome-wide association studies have identified a number of differentially methylated placental genes that are associated with maternal smoking during pregnancy, including RUNX3, PURA, GTF2H2, GCA, GPR135, and HKR1. The placental methylation status of RUNX3 and NR3C1 has also been linked to adverse infant outcomes, including preterm birth and low birth weight, respectively. Candidate gene analyses have also found maternal smoking-associated placental methylation differences in the NR3C1, CYP1A1, HTR2A, and HSD11B2 genes, as well as in the repetitive elements LINE-1 and AluYb8. The differential methylation patterns of several genes have been confirmed to also exhibit altered gene expression patterns, including CYP1A1, CYP19A1, NR3C1, and HTR2A. Placental methylation patterns associated with maternal smoking during pregnancy may be largely gene-specific and tissue-specific and, to a lesser degree, involve global changes. It is important for future research to investigate the mechanistic roles that these differentially methylated genes may play in mediating the association between maternal smoking during pregnancy and disease in later life, as well as to elucidate the potential influence of emerging tobacco product use during pregnancy, including the use of electronic cigarettes, on placental epigenetics.
Multi Domain Semantic Information Retrieval Based on Topic Model

Over the last decades, there have been remarkable shifts in the area of Information Retrieval (IR) as huge amount of information is increasingly accumulated on the Web. The gigantic information explosion increases the need for discovering new tools that retrieve meaningful knowledge from various complex information sources. Thus, techniques primarily used to search and extract important information from numerous database sources have been a key challenge in current IR systems. Topic modeling is one of the most recent techniquesthat discover hidden thematic structures from large data collections without human supervision. Several topic models have been proposed in various fields of study and have been utilized extensively for many applications. Latent Dirichlet Allocation (LDA) is the most well-known topic model that generates topics from large corpus of resources, such as text, images, and audio.It has been widely used in many areas in information retrieval and data mining, providing efficient way of identifying latent topics among document collections. However, LDA has a drawback that topic cohesion within a concept is attenuated when estimating infrequently occurring words. Moreover, LDAseems not to consider the meaning of words, but rather to infer hidden topics based on a statisticalapproach. However, LDA can cause either reduction in the quality of topic words or increase in loose relations between topics. In order to solve the previous problems, we propose a domain specific topic model that combines domain concepts with LDA. Two domain specific algorithms are suggested for solving the difficulties associated with LDA. The main strength of our proposed model comes from the fact that it narrows semantic concepts from broad domain knowledge to a specific one which solves the unknown domain problem. Our proposed model is extensively tested on various applications, query expansion, classification, and summarization, to demonstrate the effectiveness of the model. Experimental results show that the proposed model significantly increasesthe performance of applications. INDEX WORDS: Information retrieval, Semantics, Domain concepts, Topic model, Query expansion, Text classification, Text summarization MULTI DOMAIN SEMANTIC INFORMATION RETRIEVAL BASED ON TOPIC MODEL
Heterogeneous Uptake of Nanoparticles in Mouse Models of Pediatric High-Risk Neuroblastoma

Liposomal chemotherapeutics are exemplified by DOXIL® are commonly used in adult cancers. While these agents exhibit improved safety profile compared to their free drug counterparts, their treatment response rates have been ~ 20%, often attributed to the heterogeneous intratumoral uptake and distribution of liposomal nanoparticles. Non-invasive and quantitative monitoring of the uptake and distribution of liposomal nanoparticles in solid tumors could allow for patient stratification and personalized cancer nanomedicine. In this study, the variability of liposomal nanoparticle intratumoral distribution and uptake in orthotopic models of pediatric neuroblastoma was investigated using a liposomal nanoprobe visualized by high-resolution computed tomography (CT). Two human neuroblastoma cell lines (NGP: a MYCN-amplified line, and SH-SY5Y a MYCN non-amplified line) were implanted in the renal capsule of nude mice to establish the model. Intratumoral nanoparticle uptake was measured at tumor ages 1, 2, 3 and 4 weeks post implantation. The locations of uptake within the tumor were mapped in the 3-dimensional reconstructed images. Total uptake was measured by integration of the x-ray absorption signal over the intratumoral uptake locations. Both tumor models showed significant variation in nanoparticle uptake as the tumors aged. Observation of the uptake patterns suggested that the nanoparticle uptake was dominated by vascular leak at the surface/periphery of the tumor, and localized, heterogeneous vascular leak in the interior of the tumor. Slow growing SH-SY5Y tumors demonstrated uptake that correlated directly with the tumor volume. Faster growing NGP tumor uptake did not correlate with any tumor geometric parameters, including tumor volume, tumor surface area, and R30 and R50, measures of uptake localized to the interior of the tumor. However, uptake for both SH-SY5Y and NGP tumors correlated almost perfectly with the leak volume, as measured by CT. These results suggest that the uptake of nanoparticles is heterogeneous and not governed by tumor geometry. An imaging nanoprobe remains the best measure of nanoparticle uptake in these tumor models.
An Experimental Survey of Investment Decisions for Retirement Savings

We implement a choice experiment to investigate the extent to which retirement savers follow standard mean-variance analysis when choosing from a menu of investment options. We conduct this survey once during a period of …nancial tranquility (March 2007) and once during …nancial crisis (October 2008). We model choices using the scale-adjusted version of a latent class choice (…nite mixture) model. We identify income and age as important determinants of the preference classes, and underlying risk tolerance and age as key determi- nants of variability (scale). Estimated marginal eects of variations in net expected returns and risk on choice of investment option show classes populated by young and low income individuals as more likely to respond consistently with standard mean-variance analysis. However classes populated by older and higher income individuals react positively to both higher returns (lower fees) and increasing risk in returns where risk is presented as a widen- ing range of possible investment outcomes. Crisis-period results con…rm these conclusions, although we see some moderating of overall risk exposures compared with tranquil-period results.
What deliberately degrading search quality tells us about discount functions

Deliberate degradation of search results is a common tool in user experiments. We degrade high-quality search results by inserting non-relevant documents at different ranks. The effect of these manipulations, on a number of commonly-used metrics, is counter-intuitive: the discount functions implicit in P@k, MRR, NDCG, and others do not account for the true relationship between rank and value to the user. We propose an alternative, based on visibility data.
To JIT or not to JIT: The Effect of Code Pitching on the Performance of .NET Framework

The . NET Compact Framework is designed to be a high-performance virtual machine for mobile and embedded devices that operate on Windows CE (version 4.1 and later). It achieves fast execution time by compiling methods dynamically instead of using interpretation. Once compiled, these methods are stored in a portion of the heap called code-cache and can be reused quickly to satisfy future method calls. While code-cache provides a high-level of reusability, it can also use a large amount of memory. As a result, the Compact Framework provides a “code pitching” mechanism that can be used to discard the previously compiled methods as needed. In this paper, we study the eﬀect of code pitching on the overall performance and memory utilization of . NET applications. We conduct our experiments using Microsoft’s Shared-Source Common Language Infrastructure (SSCLI). We proﬁle the access behavior of the compiled methods. We also experiment with various code-cache conﬁgurations to perform pitching. We ﬁnd that programs can operate eﬃciently with a small code-cache without incurring substantial recompilation and execution overheads.
Dual Language Education: A Promising 50–50 Model

Abstract Dual language education programs have become extremely popular. Although these programs share common characteristics, they vary in several respects. Programs use different languages and include students with varying characteristics. For instance, many of these programs include students with fluent English proficiency and those with limited English proficiency; students identified with learning disabilities and those who are gifted; and students who are economically advantaged and those who are disadvantaged. Two basic dual language program models are the 90–10 and 50–50 models. This article describes a unique 50–50 model that divides language of instruction by content area as well as by time. The model has been successfully implemented in regions with high concentrations of Latino students. It does not require a 50–50 balance of native English speakers and native Spanish speakers. In addition to describing the model, the authors report results of standardized tests, administered in English, that indicate that students in schools following this model are achieving high levels of academic proficiency in reading and mathematics.
A Two-Stage Spectrum Sensing Technique in Cognitive Radio Systems Based on Combining Energy Detection and One-Order Cyclostationary Feature Detection

In cognitive radio systems, the main requirement of cognitive radio architectures is the ability to detect the presence of the primary user with fast speed and precise accuracy. To achieve that, we suggest a possible two-stage spectrum sensing approach. More specifically, a fast spectrum sensing algorithm based on the energy detection is introduced focusing on the coarse detection. A complementary fine spectrum sensing algorithm adopts one- order cyclostationary properties of primary user's signals in time domain. This feature detection technique in time domain realizes simple and low computational complexity compared to spectral feature detection methods. Also, it drastically reduces hardware burdens and power consumption as opposed to two-order feature detection. The sensing performance of the proposed method is studied and the analytical performance results are given. The results indicate that better performance can be achieved in proposed two-stage sensing detection compared to the conventional energy detector.
Locally Adaptive Bayesian Multivariate Time Series

In modeling multivariate time series, it is important to allow time-varying smoothness in the mean and covariance process. In particular, there may be certain time intervals exhibiting rapid changes and others in which changes are slow. If such locally adaptive smoothness is not accounted for, one can obtain misleading inferences and predictions, with over-smoothing across erratic time intervals and under-smoothing across times exhibiting slow variation. This can lead to miscalibration of predictive intervals, which can be substantially too narrow or wide depending on the time. We propose a continuous multivariate stochastic process for time series having locally varying smoothness in both the mean and covariance matrix. This process is constructed utilizing latent dictionary functions in time, which are given nested Gaussian process priors and linearly related to the observed data through a sparse mapping. Using a differential equation representation, we bypass usual computational bottlenecks in obtaining MCMC and online algorithms for approximate Bayesian inference. The performance is assessed in simulations and illustrated in a financial application.
Computer virus-antivirus coevolution

A S RECENTLY AS SIX YEARS AGO, COMPUTER viruses were considered an urban myth by many. At the time, only a handful of PC viruses had been written and infection was relatively uncommon. Today the situation is very different. As of November 1996, virus writers have programmed more than 10,000 DOS-based computer viruses. In addition to the sheer increase in the number of viruses, the virus writers have also become more clever. Their newer creations are significantly more complex and difficult to detect and remove. These “improvements” can be at least partially attributed to the efforts of antivirus producers. As antivirus products improve and detect the “latest and greatest” viruses, the virus authors invent new and more devious ways to hide their progeny. This coevolution has led to the creation of the most complex class of virus to date: the polymorphic computer virus. The polymorphic virus avoids detection by mutating itself each time it infects a new program; each mutated infection is capable of performing the same tasks as its parent, yet it may look entirely different. These cunning viruses simply cannot be detected costeffectively using traditional antivirus scanning algorithms. Fortunately, the antivirus producers have responded, as they have in the past, with an equally creative solution to the polymorphic virus threat. Many antivirus programs are now starting to employ a technique known as generic decryption to detect even the most complex polymorphic viruses quickly and cost effectively. A computer virus is a self-replicating computer pro-
Mapping texts into graphs: An improved text similarity algorithm

An improved graph-based text similarity (GBTS) algorithm which based on original GBTS is illustrated in this paper. A text is mapped into a graph which consists of terms as its nodes and term sequences as its undirected edges. The Maximum Common Subgraph (MCS) of two graphs is useful for analyzing their similarity. What's more, the similarity of two texts is divided into two parts: nodes similarity and edges similarity. Each part is calculated respectively and text similarity is the sum of two parts. At last, the improved algorithm is compared with the original one.
A Particle Swarm Optimization-Neural Network Prediction Model for Typhoon Intensity Based on Isometric Mapping Algorithm

In terms of the Particle Swarm Optimization-Neural Network (PSO-NN), a new prediction model has been developed using the stepwise regression method combined with the feature extraction technique of Isometric Mapping (ISOMAP) algorithm to treat the Climatology and Persistence (CLIPER) predictors. The model is validated with forecasts of ten years of typhoon intensity formed and numbered in the Western Pacific Ocean over May-October, 2001-2010. Using identical sample cases, predictions of the PSO-NN model based on ISOMAP algorithm are compared with the CLIPER model widely used in China and overseas, and it has been proven experimentally that the former is more accurate.
Clinical usefulness of microsatellite instability for the prediction of gastric adenoma or adenocarcinoma in patients with chronic gastritis

To assess a role of microsatellite instability (MSI) in the development of gastric adenocarcinoma or adenoma from chronic gastritis, we analysed mutations of five microsatellite loci in gastritis, adenoma and adenocarcinoma retrospectively. Gastric mucosa was biopsied from the same area in each patient at different periods and examined for MSI. Only one of 55 patients with chronic gastritis revealed MSI-H phenotype and the other 54 patients showed microsatellite stable (MSS) phenotypes. In six of 17 patients with gastric adenoma or well-differentiated adenocarcinoma, MSI-positive phenotypes were demonstrated. Interestingly, all of six patients showing MSI, including three high-level MSI (MSI-H) cases and three low-level (MSH-L) cases, had already revealed MSI at the stage of chronic gastritis. In two of three MSI-H cases, the identical MSI patterns had been observed at the stage of gastritis 1.5–7 years before the final diagnosis of adenocarcinoma. The adjacent gastritis mucosa within 10 mm from the carcinoma demonstrated MSI as well. MSI was not found in any of 35 patients with Helicobacter pylori infection, but found in one of 30 patients without infection. Moreover, two of three cases of gastric adenoma or well-differentiated adenocarcinoma with MSI-H at the stage of chronic gastritis showed no evidence of Helicobacter pylori infection throughout the observation periods. These results indicate that MSI in biopsy specimens at the stage of chronic gastritis may predict the risk of the progression to adenoma and well-differentiated adenocarcinoma, and that Helicobacter pylori infection itself may not induce MSI directly in the gastric mucosa. © 2000 Cancer Research Campaign