Miracle-dz/UniFAR-FTUs huggingface dataset:
Online map-matching based on Hidden Markov model for real-time traffic sensing applications

In many Intelligent Transportation System (ITS) applications that crowd-source data from probe vehicles, a crucial step is to accurately map the GPS trajectories to the road network in real time. This process, known as map-matching, often needs to account for noise and sparseness of the data because (1) highly precise GPS traces are rarely available, and (2) dense trajectories are costly for live transmission and storage. We propose an online map-matching algorithm based on the Hidden Markov Model (HMM) that is robust to noise and sparseness. We focused on two improvements over existing HMM-based algorithms: (1) the use of an optimal localizing strategy, the variable sliding window (VSW) method, that guarantees the online solution quality under uncertain future inputs, and (2) the novel combination of spatial, temporal and topological information using machine learning. We evaluated the accuracy of our algorithm using field test data collected on bus routes covering urban and rural areas. Furthermore, we also investigated the relationships between accuracy and output delays in processing live input streams. In our tests on field test data, VSW outperformed the traditional localizing method in terms of both accuracy and output delay. Our results suggest that it is viable for low latency applications such as traffic sensing.
MIDAS: Middlebox discovery and selection for on-path flow processing

The deployment of micro-datacenters for network function virtualization (NFV) by Internet Service Providers creates opportunities for flow processing along the traffic path. On-path processing requires the discovery of the middleboxes that will be traversed by each flow and the assignment of network functions (NFs) to middleboxes, while preserving the order of the NFs as specified in the service chain. NF location dependencies may require flow processing establishment across multiple NF Providers (NFPs). This entails additional challenges for middlebox discovery and selection, stemming from the NFPs' restrictions in information disclosure and interoperability. To address these issues, we present MIDAS, an architecture for the coordination of middlebox discovery and selection across multiple NFPs. MIDAS relies on a centralized middlebox controller in each NFP to provide interoperability among NFPs for flow processing setup. MIDAS establishes on-path processing via middlebox signaling, controller chaining, and Multi-Party Computation (MPC) based middlebox selection. We particularly employ MPC to preserve the confidentiality of middlebox utilization across the NFPs. We study the feasibility of MIDAS using a prototype implementation and further present simulation results to assess the efficiency of our middlebox selection approach.
BPPGD: Budgeted Parallel Primal Gradient Descent Kernel SVM on Spark

Stochastic Gradient Descent (SGD) is the best known method to optimize the primal objective for linear support vector machines (SVM) to dispose large data. However, when equipped with kernel functions, SGD performance is vulnerable that causes unbounded linear growth in model size and update time with data size. This paper describes a budgeted parallel pack gradient descent algorithm (BPPGD) that can improve SVM optimize problem with Gaussian Radial Basis Function (RBF) to large-scale data and run efficiently on Apache Spark with high degree of parallelization. Apache Spark is a fast and general engine for large-scale data processing which has advantage on big data parallel computing and dealing with iterative algorithms. BPPGD algorithm has constant time complexity per update. It uses a new distributed hash table -- IndexedRDD to increase the parallel degree, packing strategy to improve SGD performance with reducing the number of communication and removal budget maintenance method to keep the number of support vectors (SVs). The experiment results show that BPPGD achieves higher accuracy than P-packSVM (Zhu et al., 2009) and BSGD (Zhuang et al., 2012) algorithms on Spark environment, and it takes shorter time.
Statistical parametric speech synthesis using generative adversarial networks under a multi-task learning framework

In this paper, we aim at improving the performance of synthesized speech in statistical parametric speech synthesis (SPSS) based on a generative adversarial network (GAN). In particular, we propose a novel architecture combining the traditional acoustic loss function and the GAN's discriminative loss under a multi-task learning (MTL) framework. The mean squared error (MSE) is usually used to estimate the parameters of deep neural networks, which only considers the numerical difference between the raw audio and the synthesized one. To mitigate this problem, we introduce the GAN as a second task to determine if the input is a natural speech with specific conditions. In this MTL framework, the MSE optimization improves the stability of GAN, and at the same time GAN produces samples with a distribution closer to natural speech. Listening tests show that the multi-task architecture can generate more natural speech that satisfies human perception than the conventional methods.
Termight: Identifying and Translating Technical Terminology

We propose a semi-automatic tool, termight, that helps professional translators and terminologists identify technical terms and their translations. The tool makes use of part-of-speech tagging and word-alignment programs to extract candidate terms and their translations. Although the extraction programs are far from perfect, it isn't too hard for the user to filter out the wheat from the chaff. The extraction algorithms emphasize completeness. Alternative proposals are likely to miss important but infrequent terms/translations. To reduce the burden on the user during the filtering phase, candidates are presented in a convenient order, along with some useful concordance evidence, in an interface that is designed to minimize keystrokes. Termight is currently being used by the translators at AT&T Business Translation Services (formerly AT&T Language Line Services).
Dynamic light scattering in ophthalmology: results of in vitro and in vivo experiments.

OBJECTIVE To calibrate new dynamic light scattering (DLS) devices in defined solutions and post mortem porcine and human eyes. To examine all segments of the eye and to become familiar with the usage of the technique in living subjects. METHODS, DESIGN: Three new DLS devices for the usage in patients were developed. Mono-disperse solutions, poly-disperse solutions, gels, post mortem porcine and human eyes as well as healthy volunteers were studied. The detected signals were inverted into autocorrelation functions. RESULTS We constructed three DLS devices appropriate for in vitro as well as in vivo examinations. In mono disperse solution precise disintegration rates could be calculated. In poly-disperse solutions, in gel and in the vitreous the results did not correlate with movements of individual particles but we could calculate characteristics of the complete scattering system. In vivo measurements demonstrated that DLS can be used in all human eye segments. DISCUSSION DLS is a unique technique. With DLS the molecular composition of eye segments can be studied in living subjects. This can be used to understand the molecular basis of severe eye diseases. The presented data demonstrate that DLS delivers reproducible data from all eye segments. CONCLUSIONS It is possible to study the molecular structures of eye segments in living subjects. The developed devices were proved successfully in vitro as well as in vivo. Limitations are the low specificity of DLS and its sensitivity to background noise. Now clinical studies are necessary to demonstrate potential diagnostic benefits of DLS in specific eye diseases.
Kinect @ Home : Crowdsourced RGB-D data

Algorithms for 3D localisation and mapping and 3D reconstruction are getting increasingly mature. In this paper we argue that it is time to also make the dataset on which they are tested more realistic to reflect the real world in the homes of real people rather then testing on data gathered in the lab or at best in a few places, and almost always by the people that designed the algorithm. We present the first RGB-D dataset from the crowd sourced data collection project Kinect@Home which contains 54 challenging recordings. We also present an evaluation of two different algorithms, the Kinfu algorithm and a key point based method. Our experimental evaluation highlights the different characteristics and error modes of the two methods.
Formation of nanoparticles in flames; measurement by particle mass spectrometry and numerical simulation

The size distributions of nanoparticles in flames are measured using a novel particle mass spectrometer (PMS), which is developed for the size range between 0.3 and 50 nm and for number concentrations between 109 and 1013. Using this instrument the particles are sampled without prior dilution from the flame into a molecular beam. The charged nanoparticles are then deflected by an electric field, to determine the mass according to the time-of-flight principle. The PMS is installed in a low pressure combustion chamber operated at 30 mbar. Measurements are made on primary soot particles and iron oxide particles in a laminar, premixed acetylene/oxygen flame. The soot particles increase in size as a function of the height above the burner and the C/O ratio from 2 up to 10 nm. Iron oxide particles of 3–5 nm are detected as a function of burner height. The soot particles form more rapidly than the iron oxide particles. A model calculation for the formation of silica and iron oxide in hydrogen/oxygen flames is developed, based on previously published reaction mechanisms. On adding a mono-disperse particle coagulation scheme, the time history of the particle number concentration and the particle size is calculated. In agreement with experimental data, the calculations show that iron oxide particles are formed more slowly than silica particles.
Enhancement of Hierarchy Cluster-Tree Routing for Wireless Sensor Network

Many protocols such as clustering are proposed to minimize and balance energy consumption of the network because WSN (wireless sensor network) is energy-limited. In clustering protocols, CHs (cluster head) consume much more energy than its CMs (cluster member) which leads to the faster death of CHs. Many traditional protocols are designed to solve the problem, but they have some drawbacks respectively. In this paper, EHCT (enhancement of hierarchy cluster-tree routing for WSN) is proposed to further balance the energy consumption. The simulation results show that the performance of EHCT has an improvement of 41% over LEACH and 14% over UDACH in the area of 500m*500m, 28% over LEACH and 18% over UDACH in the area of 1000m*1000m.
Optimization of the Delivery of Heterologous Proteins by the Salmonella enterica Serovar Typhimurium Type III Secretion System for Vaccine Development

ABSTRACT Type III protein secretion systems, which are organelles with the capacity to deliver bacterial proteins into host cells, have been adapted to deliver heterologous antigens for vaccine development. A limitation of these antigen delivery systems is that some proteins are not amenable to secretion through this pathway. We show here that proteins from the simian and human immunodeficiency viruses that are not permissive for secretion through a Salmonella enterica serovar Typhimurium type III secretion system can be modified to travel this secretion pathway by introduction of discrete mutations. Proteins optimized for secretion were presented more efficiently via the major histocompatibility complex class I pathway and were able to induce a better immune response.
TceSR two‐component regulatory system of Brucella melitensis 16M is involved in invasion, intracellular survival and regulated cytotoxicity for macrophages

The mechanisms of invasion and intracellular survival of Brucella are still poorly understood. Previous studies showed that the two‐component regulatory systems (TCSs) play an important role in the intracellular survival of Brucella. To investigate if TCSs involve in the virulence and cytotoxicity of Brucella melitensis, we introduced a mutation into one of the TCSs in chromosome II in Br. melitensis 16M strain, and generated 16MΔTceSR, a mutant of Br. melitensis 16M strain. In vitro infection experiments using murine macrophage cell line (RAW 264.7) showed that the survival of 16MΔTceSR mutant in macrophages decreased 0·91‐log compared with that of wild type Br. melitensis 16M strain at 2 h postinfection, replication of 16MΔTceSR mutant in macrophages was 5·65‐log, which was much lower than that wild type strain. Results of lactate dehydrogenase cytotoxicity assays in macrophages demonstrated high dose infection with wide type strain produced high level cytotoxicity to macrophages, but 16MΔTceSR mutant had very low level cytotoxicity, indicating mutation of TCSs impaired the cytotoxicity of Br. melitensis to macrophages. Animal experiments showed that the spleen colonization of 16MΔTceSR was significantly reduced compared with its wild type strains. The lower levels of survival of 16MΔTceSR in various stress conditions suggested that the mutation of the TCSs of Br. melitensis was the causative factor of its reduced resistance to stress conditions. Taken together, our results demonstrated TCS TceSR involves in the intracellular survival, virulence and cytotoxicity of Br. melitensis during its infection.
Learning conformation rules for linked data integration

Over the last years, manifold applications that consume, link and integrate Linked Data have been developed. Yet, the specification of integration processes for Linked Data is rendered increasingly tedious by several factors such as the great number of knowledge bases in the Linked Data Cloud as well as schema mismatches and heterogeneous conventions for property values across knowledge bases. Especially the specification of rules for transforming property values has been carried out mostly manually so far. In this paper, we present CaRLA, an algorithm that allows learning transformation rules for pairs of property values expressed as strings. We present both a batch and an active learning version of CaRLA. The batch version of CaRLA uses a three-step learning approach to retrieve probable transformation rules. The active learning version extends the batch version by requesting highly informative property value pairs from the user so as to improve the learning speed of the system. We evaluate both versions of our approach on four experiments with respect to runtime and accuracy. Our results show that we can improve the precision of the data integration process by up to 12% by discovering transformation rules with human accuracy even when provided with small training datasets. In addition, we can even discover rules that were missed by human experts.
Extraction of Vectorized Graphical Information from Scientific Chart Images

Graphical components information extraction is a crucial step in the chart recognition and understanding process. However, existing methods of information extraction from chart images either are type-dependent or rely on certain assumptions. In this paper, we present a general method to extract vectorized graphical information from scientific chart images. Our algorithm firstly constructs a data structure called directional single-connected chains (DSCC). It then employs ellipse-specific fitting and orthogonal diagonalization to calculate the curvatures of the chains and classify the chains into either straight lines or arcs. Finally we combine all straight lines and all arcs accordingly and use linear regression to compute their attributes. The DSCC has a good property in that it is less susceptible to noise. The experiment results show that our algorithm is efficient, robust and accurate.
Selection of optimal vocal tract regions using real-time magnetic resonance imaging for robust voice activity detection

Real time magnetic resonance imaging (rtMRI) enables direct video capture of the moving vocal tract concurrent with audio signal providing valuable data for speech research. We consider a multimodal approach to voice activity detection (VAD) in the rtMRI recording that uses audio signal as well as MRI image sequence. The degraded quality of the audio recorded in the scanner motivates this multimodal scheme for robust VAD. Optimal regions in the MRI image are selected for performing VAD with a novel algorithm. VAD experiments using rtMRI data of two male and two female subjects show that VAD performance using optimally selected regions from MRI images is comparable to that using only audio signal. The optimal regions turn out to be parts of jaw, velum, glottis and lips. VAD performance using audio signal and MRI image sequence together is found to be significantly better (∼14% absolute improvement in VAD accuracy) than that using the audio only when the audio is contaminated with additive noise at low SNR. Index Terms: voice activity detection, vocal tract imaging, optimal region selection.
Screw Pullout Strength in Particleboards Manufactured with Waste of Eucalyptus grandis Wood Specie and Oat Hulls

Eucalyptus wood species are usually found in rural constructions, civil construction, furniture and utensils, among other applications. The processing of Eucalyptus wood, which follows policies of reforestation in Brazil, generates a lot of waste, as the case of the oat hulls, which constitutes an aggravating to the environment, causing environmental impacts, which motivates the development of applications that make use of these residues, such as the case of particleboards. Among the mechanical properties commonly investigated in the characterization of the particleboards, there is the strength to screw pullout, which evaluates the bond strength of the manufactured materials. This research aimed to produce particleboards, in laboratory scale, using Eucalyptus grandis wood particles (proportions 0, 70, 85 e 100%); oat hulls particles (proportions 0, 14, 30 and 100%), both with controlled size distribution; castor oil based polyurethane resin (10, 12 and 14%, related to solid mass). These panels were evaluated concerning to pullout strength of screws (AP), from face and edge. Particle size distribution: 70 to 75% of these were found in interval ranging between 0.595 to 1.190 mm (16 to 30 mesh). Apparent density of Eucalyptus grandis and oat hulls were 0.64 and 0.29 g/cm 3 , respectively. All panels were classified as high density (greater than 800 kg/m 3 ). Compression ratio of the panels showed range from 1.25 to 3.48, remaining above the level usually recommended in literature (1.3 to 1.6), but similar to other related studies. Values of pullout strength of screws (face) met the requirements of Brazilian Standard ABNT NBR 14810:2006, for three of the twelve experimental conditions evaluated, and of pullout screw (edge), all panels met the cited requirements.
Special Issue on Design and Development: Recombinant Uncertainty in Technological Search

While the course of technological change is widely accepted to be highly uncertain and unpredictable, little work has identified or studied the ultimate sources and causes of that uncertainty. This paper proposes that purely technological uncertainty derives from inventors' search processes with unfamiliar components and component combinations. Experimentation with new components and new combinations leads to less useful inventions on average, but it also implies an increase in the variability that can result in both failure and breakthrough. Negative binomial count and dispersion models with patent citation data demonstrate that new combinations are indeed more variable. In contrast to predictions, however, the reuse of components has a nonmonotonic and eventually positive effect on variability.
No Time to Observe: Adaptive Influence Maximization with Partial Feedback

Although influence maximization problem has been extensively studied over the past ten years, majority of existing work adopt one of the following models: \emph{full-feedback model} or \emph{zero-feedback model}. In the zero-feedback model, we have to commit the seed users all at once in advance, this strategy is also known as non-adaptive policy. In the full-feedback model, we select one seed at a time and wait until the diffusion completes, before selecting the next seed. Full-feedback model has better performance but potentially huge delay, zero-feedback model has zero delay but poorer performance since it does not utilize the observation that may be made during the seeding process. To fill the gap between these two models, we propose \emph{Partial-feedback Model}, which allows us to select a seed at any intermediate stage. We develop a novel $\alpha$-greedy policy that, for the first time, achieves a bounded approximation ratio.
Cell wall construction in Saccharomyces cerevisiae

In this review, we discuss new insights in cell wall architecture and cell wall construction in the ascomycetous yeast Saccharomyces cerevisiae. Transcriptional profiling studies combined with biochemical work have provided ample evidence that the cell wall is a highly adaptable organelle. In particular, the protein population that is anchored to the stress‐bearing polysaccharides of the cell wall, and forms the interface with the outside world, is highly diverse. This diversity is believed to play an important role in adaptation of the cell to environmental conditions, in growth mode and in survival. Cell wall construction is tightly controlled and strictly coordinated with progression of the cell cycle. This is reflected in the usage of specific cell wall proteins during consecutive phases of the cell cycle and in the recent discovery of a cell wall integrity checkpoint. When the cell is challenged with stress conditions that affect the cell wall, a specific transcriptional response is observed that includes the general stress response, the cell wall integrity pathway and the calcineurin pathway. This salvage mechanism includes increased expression of putative cell wall assemblases and some potential cross‐linking cell wall proteins, and crucial changes in cell wall architecture. We discuss some more enzymes involved in cell wall construction and also potential inhibitors of these enzymes. Finally, we use both biochemical and genomic data to infer that the architectural principles used by S. cerevisiae to build its cell wall are also used by many other ascomycetous yeasts and also by some mycelial ascomycetous fungi. Copyright © 2006 John Wiley & Sons, Ltd.
Notice of RetractionMarket improvement, changes of correlated structure and contagion

With the implementation of related reform measures, the efficiency of Chinese securities market having been improved since it reflects information and psychological expectation factors better and better, which will result in some changes to correlated structure between securities markets of China to the other major economies. America exploded sub-prime mortgage crisis in 2007 which made world economy suffer from severe shocks. Combining the improvement of Chinese securities market and its correlated structure to other markets to investigate our sample by related test methods of vector autoregression (VAR) model, we find corresponding evidence for those views above.