Miracle-dz/UniFAR-FTUs huggingface dataset:
Quality-based technique for routing and playout buffering in IP Telephony over Mobile ad Hoc Network

Luigi Atzori MCLab, Department of Electronic Engineering University of Cagliari Cagliari 09123 Italy l.atzori@diee.unica. it In the last few years, Voice over IP (VoIP) providers have been a strongly growth compared with traditional voice service operators. The main motivations are the low-cost of the packetbased technologies and the reliability of the current (wired) IP network. Nowadays a new network for VoIP applications have been placed: Mobile Ad hoc NETwork (MANET). We strongly believe that this technologies will have success in the next future. However this particular network includes different limitations one of these is the highly variability of the network topology and channel behavior. This is an obstacle to the service quality due to route losses and significant delay variations. In this our study, we want to present a new strategy to improve Quality of Service (QoS) in MANETs. Our strategy is divided in two tasks: selecting of transmission paths and adjusting the playout delay. In the first task we select the route that at the moment is characterized by low network delay and jitter through the use of QOLSR routing algorithm, while in the second task we use an adaptive intertalkspurt strategy founded on a objective quality-based approach as ITU-T E-Model. We also present preliminary results of the proposed strategy, which encourage further investigations.
Iterative Hybrid Method for Electromagnetic Scattering from a 3-D Object Above a 2-D Random Dielectric Rough Surface

An iterative hybrid method combining the Kirchhofi approximation (KA) and the multilevel fast multipole algorithm (MLFMA) is studied for electromagnetic scattering from a three- dimensional (3-D) object above a two-dimensional (2-D) random dielectric rough surface. In order to reduce the computational costs, some treatments have been studied. Firstly, the fast far-fleld approximation (FAFFA) is utilized to speed up the electromagnetic coupling interaction process between the rough surface and the object. Secondly, based on the scattering mechanism of the rough surface, a truncation rule on moderate rough surface for bi-static scattering is proposed under the plane wave illumination, which can further speed up the iteration. Compared with the conventional methods, the hybrid method with the above treatments is very e-cient to analyze the scattering of a 3-D object above random rough surfaces. Simulation results validate the efiectiveness and accuracy of the iterative hybrid method.
Reader preferences and behavior on Wikipedia

Wikipedia is a collaboratively-edited online encyclopaedia that relies on thousands of editors to both contribute articles and maintain their quality. Over the last years, research has extensively investigated this group of users while another group of Wikipedia users, the readers, their preferences and their behavior have not been much studied. This paper makes this group and its %their activities visible and valuable to Wikipedia's editor community. We carried out a study on two datasets covering a 13-months period to obtain insights on users preferences and reading behavior in Wikipedia. We show that the most read articles do not necessarily correspond to those frequently edited, suggesting some degree of non-alignment between user reading preferences and author editing preferences. We also identified that popular and often edited articles are read according to four main patterns, and that how an article is read may change over time. We illustrate how this information can provide valuable insights to Wikipedia's editor community.
Scale Economies in Indian Commercial Banking Sector: Evidence from DEA and Translog Estimates

Two alternative estimation models, i.e., a translog cost function and data envelopment analysis DEA based on a cost model are compared and contrasted in revealing scale economies in the Indian commercial banking sector. The empirical results indicate that while the translog cost model exhibits increasing returns to scale for all the ownership groups, the DEA model reveals economies of scale only for foreign banks, diseconomies of scale for nationalized banks, and both economies and diseconomies of scale for private banks. The divergence of the results obtained from these two estimation models should concern model builders. From an empirical perspective the definition of scale economies through a constant input mix is very restrictive. The DEA cost model is much more flexible in this respect: It neither requires the restrictive assumptions that the unit factor prices are always available with certainty, nor that these prices are exogenous to the firms. However, the very volatile nature of the banking industry might question the validity of the empirical estimates in this deterministic setting. Therefore, further research is required to examine the bank performance behavior using both SFA and chance constrained DEA for the comparison in a stochastic setting.
25th Anniversary Article: Recent Advances in n‐Type and Ambipolar Organic Field‐Effect Transistors

The advantages of organic field‐effect transistors, such as low cost, mechanical flexibility and large‐area fabrication, make them potentially useful for electronic applications such as flexible switching backplanes for video displays, radio frequency identifications and so on. A large amount of molecules were designed and synthesized for electron transporting (n‐type) and ambipolar organic semiconductors with improved performance and stability. In this review, we focus on the advances in performance and molecular design of n‐type and ambipolar semiconductors reported in the past few years.
Bmc Medical Research Methodology Open Access Data Enhancement for Co-morbidity Measurement among Patients Referred for Sleep Diagnostic Testing: an Observational Study

Background: Observational outcome studies of patients with obstructive sleep apnea (OSA) require adjustment for co-morbidity to produce valid results. The aim of this study was to evaluate whether the combination of administrative data and self-reported data provided a more complete estimate of co-morbidity among patients referred for sleep diagnostic testing.
ANC2Go: A Web Application for Customized Corpus Creation

We describe a web application called ANC2Go that enables the user to select data from the Open American National Corpus (OANC) and the Manually Annotated Sub-corpus (MASC) together with some or all of the annotations available. The user also may select from among a variety of options for output format, or may receive the selected portions of the corpus and annotations in their original GrAF XML standoff format. The request is processed by merging the annotations selected and rendering them in the desired output format, then bundling the results and making it available for download. Thus, users can create a customized corpus with data and annotations of their choosing, delivered in the format that is most convenient for their use. ANC2Go will be released as a web service in the near future. Both the OANC and MASC are freely available for any use from the American National Corpus website and may be accessed through the ANC2Go application, or they may downloaded in their entirety.
MING: mining informative entity relationship subgraphs

Many modern applications are faced with the task of knowledge discovery in entity-relationship graphs, such as domain-specific knowledge bases or social networks. Mining an "informative" subgraph that can explain the relations between k(>= 2) given entities of interest is a frequent knowledge discovery scenario on such graphs. We present MING, a principled method for extracting an informative subgraph for given query nodes. MING builds on a new notion of informativeness of nodes. This is used in a random-walk-with-restarts process to compute the informativeness of entire subgraphs.
A noise-robust front-end for multi-stream HMM-based speech recognition systems

This paper present new robust front-end processing whose main aim is the improvement of Distributed Speech Recognition (DSR) systems performance when perturbed by severe environmental noise. The improvement is performed by using a combination of post proceeded Mel-cepstral coefficients (MFCCs) and line spectral frequencies features (LSFs) projected in linear discriminate analysis (LDA) space. The experiments carried out on the Aurora 2.0 database show that, even with fewer parameters, the proposed front-end provides comparable recognition results to the standard ETSI WI008 advanced frontend, nowadays used as codec in 3rd generation mobile communication systems, while achieving higher accuracy when the signal-to-noise ratio (SNR) is very low. Keywords—distributed speech recognition; linear discriminate analysis; multi-stream hidden Markov models; Mel-cepstral coefficients; line spectral frequencies.
Parallel hierarchical clustering on shared memory platforms

Hierarchical clustering has many advantages over traditional clustering algorithms like k-means, but it suffers from higher computational costs and a less obvious parallel structure. Thus, in order to scale this technique up to larger datasets, we present SHRINK, a novel shared-memory algorithm for single-linkage hierarchical clustering based on merging the solutions from overlapping sub-problems. In our experiments, we find that SHRINK provides a speedup of 18–20 on 36 cores on both real and synthetic datasets of up to 250,000 points. Source code for SHRINK is available for download on our website, http://cucis.ece. northwestern.edu.
Salicylate Suppresses Macrophage Nitric-oxide Synthase-2 and Cyclo-oxygenase-2 Expression by Inhibiting CCAAT/Enhancer-binding Protein-β Binding via a Common Signaling Pathway*

We determined whether salicylate at pharmacological concentrations inhibits nitric-oxide synthase-2 (NOS-2) and cyclo-oxygenase-2 (COX-2) expressions in RAW 264.7 stimulated with lipopolysaccharide (LPS) and interferon-γ (IFN-γ). Cells were treated with sodium salicylate (10−7-10−4 m) or vehicle for 30 min followed by LPS+IFN-γ for up to 24 h. Salicylate suppressed NOS-2 and COX-2 protein levels and promoter activities stimulated by LPS+IFN-γ for 4 h in a concentration-dependent manner but had no effect on NOS-2 expression stimulated by the combined agonists for 24 h. Results from promoter analysis indicate that the binding of CCAAT/enhancer-binding protein β (C/EBPβ) to its cognate site at −150/−142 on the NOS-2 promoter region was essential for NOS-2 expression at 4 h but not at 24 h. Salicylate reduced C/EBPβ binding at 4 h and did not alter its binding at 24 h. NOS-2 and COX-2 protein levels and C/EBPβ binding stimulated by LPS+IFN-γ for 4 h were inhibited by a similar battery of signaling inhibitors, suggesting a common pathway for NOS-2 and COX-2 expression. Kinetic analysis indicates that NOS-2, similar to COX-2 expression, at 4 h was largely due to the action of LPS, which induced C/EBPβ binding, whereas its expression at a longer time point was contributed by IFN-γ. Our findings implicate two distinct pathways for NOS-2 expression induced by LPS+IFN-γ. Salicylate at pharmacological concentrations is capable of suppressing the early phase of NOS-2 and COX-2 expression by blocking C/EBPβ binding.
Crowding in Cellular Environments at an Atomistic Level from Computer Simulations

The effects of crowding in biological environments on biomolecular structure, dynamics, and function remain not well understood. Computer simulations of atomistic models of concentrated peptide and protein systems at different levels of complexity are beginning to provide new insights. Crowding, weak interactions with other macromolecules and metabolites, and altered solvent properties within cellular environments appear to remodel the energy landscape of peptides and proteins in significant ways including the possibility of native state destabilization. Crowding is also seen to affect dynamic properties, both conformational dynamics and diffusional properties of macromolecules. Recent simulations that address these questions are reviewed here and discussed in the context of relevant experiments.
On the minimum distance of generalized spatially coupled LDPC codes

Families of generalized spatially-coupled low-density parity-check (GSC-LDPC) code ensembles can be formed by terminating protograph-based generalized LDPC convolutional (GLDPCC) codes. It has previously been shown that ensembles of GSC-LDPC codes constructed from a protograph have better iterative decoding thresholds than their block code counterparts, and that, for large termination lengths, their thresholds coincide with the maximum a-posteriori (MAP) decoding threshold of the underlying generalized LDPC block code ensemble. Here we show that, in addition to their excellent iterative decoding thresholds, ensembles of GSC-LDPC codes are asymptotically good and have large minimum distance growth rates.
Synergistic antitumor effect of puerarin combined with 5-fluorouracil on gastric carcinoma

Combination chemotherapy is a crucial method in the treatment of gastric cancer. The aim of the present study was to investigate the inhibitory effects of puerarin and 5-fluorouracil (5-FU) on BGC-823 gastric cancer cells in vitro and in vivo. The in vitro growth inhibition of puerarin or 5-FU alone or combined on BGC-823 cells was determined using a cell counting kit 8 (CCK-8) on living cells. Apoptotic morphological features and proteins expression levels were detected by Hoechst 33258 staining, an Annexin V/propidium iodide apoptosis kit and western blot analysis, respectively. Tumor xenografts were established in nude mice and the inhibitory effects and side effects were detected. Results of the CCK-8, Hoechst 33258 staining and flow cytometry revealed that the combined treatment was more effective than the separate treatments. The tumor volume was 90.65% of that of the controls and the mean tumor weight was only 0.125 g at the end of the experiment in the combination group compared with the control group (0.822 g). In addition, it was determined that liver and renal toxicity did not increase in combined treatment. These findings showed that puerarin and 5-FU produced a significant synergic effect on gastric cancer cells, while there was no increase in side effects.
Performance Analysis and Constituent Code Design for Space-Time Turbo Coded Modulation over Fading Channels

Recent advances in space-time coding and array processing have lead to substantial improvements in broadband wireless access systems. A design method for constituent recursive space-time trellis codes (Rec-STTrCs) and parallel concatenated space-time turbo coded modulation (STTuCM) was introduced recently as a new framework for building low complexity, large equivalent constraint-length STTrCs. In this paper, STTuCM is analyzed in terms of the two-dimensional distance spectrum (DS), the truncated union bound (UB) and the iterative decoding convergence. The derived bounds appeared to be tight over fast fading channels but rather optimistic over quasi-static fading channels. Nevertheless, the rank deficient part of the DS has been well enumerated, which turned out to be crucial for the accurate design of the full transmit diversity STTuCM in the second half of the paper. Modified design criteria for space-time (ST) codes over quasi-static and fast fading channels were further introduced to capture the joint effects of error exponents and multiplicities in the code’s DS. A recursive systematic form with a primitive equivalent feed-backward polynomial was assumed for constituent codes (CCs) to assure good convergence of iterative decoding. The DS optimization produced new sets of CCs for STTuCM resulting in a robust ST coding scheme with improved performance over both quasi-static and fast fading channels. Full transmit diversity was achieved with no constrains on the implemented pseudo-random bit-wise odd-even information interleaving.
An Empirically-based System for Processing Definite Descriptions

We present an implemented system for processing definite descriptions in arbitrary domains. The design of the system is based on the results of a corpus analysis previously reported, which highlighted the prevalence of discourse-new descriptions in newspaper corpora. The annotated corpus was used to extensively evaluate the proposed techniques for matching definite descriptions with their antecedents, discourse segmentation, recognizing discourse-new descriptions, and suggesting anchors for bridging descriptions.
Perturbation and pitch normalization as enhancements to speaker recognition

This study proposes an approach to improving speaker recognition through the process of minute vocal tract length perturbation of training files, coupled with pitch normalization for both train and test data. The notion of perturbation as a method for improving the robustness of training data for supervised classification is taken from the field of optical character recognition, where distorting characters within a certain range has shown strong improvements across disparate conditions. This paper demonstrates that acoustic perturbation, in this case analysis, distortion, and resynthesis of vocal tract length for a given speaker, significantly improves speaker recognition when the resulting files are used to augment or replace the training data. A pitch length normalization technique is also discussed, which is combined with perturbation to improve open-set speaker recognition from an EER of 20% to 6.7%.
Above- and Belowground Biomass Allocation in Shrub Biomes across the Northeast Tibetan Plateau

Biomass partitioning has been explored across various biomes. However, the strategies of allocation in plants still remain contentious. This study investigated allocation patterns of above- and belowground biomass at the community level, using biomass survey from the Tibetan Plateau. We explored above- and belowground biomass by conducting three consecutive sampling campaigns across shrub biomes on the northeast Tibetan Plateau during 2011–2013. We then documented the above-ground biomass (AGB), below-ground biomass (BGB) and root: shoot ratio (R/S) and the relationships between R/S and environment factors using data from 201 plots surveyed from 67 sites. We further examined relationships between above-ground and below-ground biomass across various shrub types. Our results indicated that the median values of AGB, BGB, and R/S in Tibetan shrub were 1102.55, 874.91 g m-2, and 0.85, respectively. R/S showed significant trend with mean annual precipitation (MAP), while decreased with mean annual temperature (MAT). Reduced major axis analysis indicated that the slope of the log-log relationship between above- and belowground biomass revealed a significant difference from 1.0 over space, supporting the optimal hypothesis. Interestingly, the slopes of the allometric relationship between log AGB and log BGB differed significantly between alpine and desert shrub. Our findings supported the optimal theory of above- and belowground biomass partitioning in Tibetan shrub, while the isometric hypothesis for alpine shrub at the community level.
Probabilistic Model Based Similarity Measures for Audio Query-by-Example

This paper proposes measures for estimating the similarity of two audio signals, the objective being in query-by-example. Both signals are first represented using a set of features calculated in short intervals, and then probabilistic models are estimated for the feature distributions. Gaussian mixture models and hidden Markov models are tested in this study. The similarity of the signals is measured by the congruence between the feature distributions or by a cross-likelihood ratio test. We calculate the Kullback-Leibler divergence between the distributions by sampling the distributions at the points of the observations vectors. The cross-likelihood ratio test is evaluated using the likelihood of the first signal being generated by the model of the second signal, and vice versa. Simulations were conducted to test the accuracy of the proposed methods on query-by-example of audio. On a database consisting of of speech, music, and environmental sounds the proposed methods enable better retrieval accuracy than the existing methods.
A skeleton-free body surface area estimation from depth images using deep neural networks

Body surface area is an important measure in many clinical trials. It is a critical parameter that is used in estimating radiation and substance doses for human trials. Traditionally, these trials relied on skin-fold tests which are very invasive and uncomfortable to the subjects. In this paper we present a skeleton-free Kinect system to estimate body surface area of human bodies. The proposed system employs the state-of-the-art deep convolutional network to extract meaningful features and estimate the body surface area with a 12 mm2 precision.
Iterative-tuning support vector machine for network traffic classification

Accurate and timely traffic classification is a key to providing Quality of Service (QoS), application-level visibility, and security monitoring for network operations and management. A class of traffic classification techniques have emerged that apply machine learning technology to predict the application class of a traffic flow based on the statistical properties of flow-features. In this paper, we propose a novel iterative-tuning scheme to increase the training speed of the classification algorithm using Support Vector Machine (SVM) learning. Meanwhile we derive the equations to obtain SVM parameters by conducting theoretical analysis of iterative-tuning SVM. Traffic classification is carried out using flow-level information extracted from NetFlow data. Performance evaluation demonstrates that the proposed iterative-tuning SVM exhibits a training speed that is two to ten times faster than eight other previously proposed SVM techniques found in the literature, while maintaining comparable classification accuracy as those eight SVM techniques. In the presence of millions of flows and Terabytes of data in the network, faster training speeds is essential to making SVM techniques a viable option for real-world deployment of traffic classification modules. In addition, network operators and cloud service providers can apply network traffic classification to address a range of issues including semi-real-time security monitoring and traffic engineering.
An Evaluation of Human and Computer-Based Predictions of the 2003 Rugby Union World Cup

The purpose of the current investigation was to compare the accuracy of human and computer-based methods of predicting the 2003 Rugby Union World Cup. The computer-based methods were multiple linear regression, binary logistic regression, artificial neural networks and a simulation package. The computer-based methods used data from the previous 4 Rugby Union World Cups to develop predictive models of international rugby union results based on team strength, distance travelled to the tournament and recovery days between matches. The computer-based methods correctly predicted between 39 and 44.5 of the 48 matches which was more accurate than the 40.66 averaged by 42 individual humans. However, there was a far greater range of accuracies for the human predictors with the most successful human correctly predicting the outcomes of 46 of the 48 matches. Furthermore, an expert focus group successfully predicted 43 of the 48 matches which was better than the average computer-based method. The most successful of the computer-based methods was a simultion model. The accuracy of the artificial neural network predictions increased with the number of nodes within the middle layer. The study provided evidence that computer-based methods are more successful at predicting the outcomes of international rugby union matches than the average human, but are not as successful as human experts.