Miracle-dz/UniFAR-FTUs huggingface dataset:
Translations as a Semantic Knowledge Source

Theories of meaning are sometimes used to throw light on the phenomenon of translation. We argue that light can fruitfully be thrown in the opposite direction: we can use translations to get a handle on meaning. More specifically, we will motivate and present a method for the automatic extraction of wordnet-type information from translational data, and review some results. The basic insight behind the method is that much information about the semantic relations among the words in a language resides in the way in which the sets of their possible translations into some other language overlap. Therefore, if we take the translational relation between two languages as a theoretical primitive, languages can serve as each other’s “semantic mirrors”.
Eye Tracking for Personal Visual Analytics

In many research fields, eye tracking has become an established method to analyze the distribution of visual attention in various scenarios. In the near future, eye tracking is expected to become ubiquitous, recording massive amounts of data in everyday situations. To make use of this data, new approaches for personal visual analytics will be necessary to make the data accessible, allowing nonexpert users to re-experience interesting events and benefit from self-reflection. This article discusses how eye tracking fits in the context of personal visual analytics, the challenges that arise with its application to everyday situations, and the research perspectives of personal eye tracking. As an example, the authors present a technique for representing areas of interest (AOIs) from multiple videos: the AOI cloud. They apply this technique to examine a user's personal encounters with other people.
High Proper-Motion Stars in the Vicinity of Sagittarius A*: Evidence for a Supermassive Black Hole at the Center of Our Galaxy

Over a 2 year period we have conducted a diffraction-limited imaging study at 2.2 μm of the inner 6'' × 6'' of the central stellar cluster of the Galaxy using the W. M. Keck 10 m telescope. The K-band images obtained in 1995 June, 1996 June, and 1997 May have the highest angular resolution obtained at near-infrared wavelengths from ground or space (θres = 0.″05 = 0.002 pc) and reveal a large population of faint stars. We use an unbiased approach for identifying and selecting stars to be included in this proper-motion study, which results in a sample of 90 stars with brightness ranging from K = 9-17 mag and two-dimensional velocities as large as 1400 ± 100 km s-1. Compared to earlier work (Eckart et al. 1997; Genzel et al. 1997), the source confusion is reduced by a factor of 9, the number of stars with proper-motion measurement in the central 25 arcsec2 of our Galaxy is doubled, and the accuracy of the velocity measurements in the central 1 arcsec2 is improved by a factor of 4. The peaks of both the stellar surface density and the velocity dispersion are consistent with the position of the unusual radio source and black hole candidate Sgr A*, which suggests that Sgr A* is coincident (±0.″1) with the dynamical center of the Galaxy. As a function of distance from Sgr A*, the velocity dispersion displays a falloff well-fitted by Keplerian motion (σv ~ r-0.5±0.1) about a central dark mass of 2.6 ± 0.2 × 106 M☉ confined to a volume of at most 10-6 pc3, which is consistent with earlier results. Although uncertainties in the measurements mathematically allow for the matter to be distributed over this volume as a cluster, no realistic cluster is physically tenable. Thus, independent of the presence of Sgr A*, the large inferred central density of at least 1012 M☉ pc-3, which exceeds the volume-averaged mass densities found at the center of any other galaxy, leads us to the conclusion that our Galaxy harbors a massive central black hole.
A Route Flap Suppression Mechanism Based on Dynamic Timers in OSPF Network

Route flap caused by link flap in OSPF always lead to network instability and unreliability. Most of the prevailing solutions are network-wide and require all routers to participate in the route flap suppressing process, which not only lack flexibility and scalability but also occupy additional computation resources across the network. In this paper, a localized approach is proposed to suppress the routing flap through dynamically tuning the hello interval of OSPF. To support the real-time adjustment of the hello timer, an analytical model is also established to investigate the relationship between link flap and route flap. Simulation results show that our approach can achieve better performance in eliminating routing flap and at the same time consume much less computation resources than the network-wide method.
The social, economic and sexual networks of prostitution

Prostitution is, on one hand, a unique socioeconomic phenomenon, on the other hand, it can tell us much about society as a whole. Selling sex has rarely been accepted socially; it has often been prohibited and suppressed; still it exists in almost all the World’s countries, and has done so for thousands of years [1]. Furthermore, prostitution is female dominated, labor intensive, requires little education, and still it is well paid. This contradicts mainstream theories of labor economics [2]. Moreover, sexbuyers are almost exclusively male, but apart from that, they have few traits in common. They are very uniformly distributed among different age classes, professions and ethnicities [3]. These examples illustrate the special nature of prostitution as a socioeconomic system. One may wonder if prostitution can give us any information at all about society as a whole? Is it an isolated sys-tem with a unique set of rules and mechanisms? Yet more peculiarities are that the social norms around prostitution vary much between different societies. The same is true for the legal statuses that range from legal and regulated to laws against both buying and selling sex. Economists are also interested in the unusual age–wage curve of sex sellers (leveling around 30–40 years to decrease for yet older sellers) [4]. The fact that prostitution has to circumvent the mainstream channels of advertisement creates a more laterally organized economy. Not unlike the new network economy driven by social media. We argue that, even though most research questions about prostitution are rather specific, it can also teach us something more general about network-economic systems emerging today. To reach these conclusions, we review prostitution research with a focus on Internet mediated forms. In particular, we will study many aspects of a dataset derived from an online community serving sex-buyers needs [5,6]. At this community, the users log and review their sex purchases, giving the date and time of the encounter as well as a rating of the experience. We use this data to study online-offline feedback effects over a sex-sellers time of presence in the data as well as spatial factors. We briefly mention (with the above-mentioned dataset as example), the use of this type of dataset for the study of infectious disease spreading [7].
Modeling Lactic Fermentation of Gowé Using Lactobacillus Starter Culture

A global model of the lactic fermentation step of gowé was developed by assembling blocks hosting models for bacterial growth, lactic acid production, and the drop of pH during fermentation. Commercial strains of Lactobacillus brevis and of Lactobacillus plantarum were used; their growth was modeled using Rosso’s primary model and the gamma concept as a secondary model. The optimum values of pH and temperature were 8.3 ± 0.3, 44.6 ± 1.2 °C and 8.3 ± 0.3, 3.2 ± 37.1 °C with μmax values of 1.8 ± 0.2 and 1.4 ± 0.1 for L. brevis and L. plantarum respectively. The minimum inhibitory concentration of undissociated lactic acid was 23.7 mM and 35.6 mM for L. brevis and L. plantarum, respectively. The yield of lactic acid was five times higher for L. plantarum than for L. brevis, with a yield of glucose conversion to lactic acid close to 2.0 for the former and 0.8 for the latter. A model was developed to predict the pH drop during gowé fermentation. The global model was partially validated during manufacturing of gowé. The global model could be a tool to aid in the choice of suitable starters and to determine the conditions for the use of the starter.
Evolving an Immersive Medical Communication Skills Trainer

This paper presents our experiences in evolving the Virtual Objective Structured Clinical Exam (VOSCE) system. This system allows medical students to experience the interaction between a patient and a medical doctor using natural methods of interaction with a high level of immersion. These features enable the system to provide training on medical communication skills. We discuss the experiences of a group of medical and physician assistant students that pilot tested the system. Further, we examine the impact of evolving the system based on their feedback. The VOSCE system's performance in subsequent studies has indicated that end-user feedback improvements have significantly impacted overall performance and efficacy.
Bayesian Maximum Margin Clustering

Most well-known discriminative clustering models, such as spectral clustering (SC) and maximum margin clustering (MMC), are non-Bayesian. Moreover, they merely considered to embed domain-dependent prior knowledge into data-specific kernels, while other forms of prior knowledge were seldom considered in these models. In this paper, we propose a Bayesian maximum margin clustering model (BMMC) based on the low-density separation assumption, which unifies the merits of both Bayesian and discriminative approaches. In addition to stating prior distribution on functions explicitly as traditional Gaussian processes, special prior knowledge can be embedded into BMMC implicitly via the Universum set easily. Furthermore, it is much easier to solve a BMMC than an MMC since the integer variables in the optimization are eliminated. Experimental results show that the BMMC achieves comparable or even better performance than state-of-the-art clustering methods and solving BMMC is more efficiently.
Mesenchymal Stromal Cells Primed with Paclitaxel Provide a New Approach for Cancer Therapy

Background Mesenchymal stromal cells may represent an ideal candidate to deliver anti-cancer drugs. In a previous study, we demonstrated that exposure of mouse bone marrow derived stromal cells to Doxorubicin led them to acquire anti-proliferative potential towards co-cultured haematopoietic stem cells (HSCs). We thus hypothesized whether freshly isolated human bone marrow Mesenchymal stem cells (hMSCs) and mature murine stromal cells (SR4987 line) primed in vitro with anti-cancer drugs and then localized near cancer cells, could inhibit proliferation. Methods and Principal Findings Paclitaxel (PTX) was used to prime culture of hMSCs and SR4987. Incorporation of PTX into hMSCs was studied by using FICT-labelled-PTX and analyzed by FACS and confocal microscopy. Release of PTX in culture medium by PTX primed hMSCs (hMSCsPTX) was investigated by HPLC. Culture of Endothelial cells (ECs) and aorta ring assay were used to test the anti-angiogenic activity of hMSCsPTX and PTX primed SR4987(SR4987PTX), while anti-tumor activity was tested in vitro on the proliferation of different tumor cell lines and in vivo by co-transplanting hMSCsPTX and SR4987PTX with cancer cells in mice. Nevertheless, despite a loss of cells due to chemo-induced apoptosis, both hMSCs and SR4987 were able to rapidly incorporate PTX and could slowly release PTX in the culture medium in a time dependent manner. PTX primed cells acquired a potent anti-tumor and anti-angiogenic activity in vitro that was dose dependent, and demonstrable by using their conditioned medium or by co-culture assay. Finally, hMSCsPTX and SR4987PTX co-injected with human cancer cells (DU145 and U87MG) and mouse melanoma cells (B16) in immunodeficient and in syngenic mice significantly delayed tumor takes and reduced tumor growth. Conclusions These data demonstrate, for the first time, that without any genetic manipulation, mesenchymal stromal cells can uptake and subsequently slowly release PTX. This may lead to potential new tools to increase efficacy of cancer therapy.
Interstatement must aliases for data dependence analysis of heap locations

Data dependences are of critical importance in many software engineering activities. Due to the dynamic allocation mechanism, it is still difficult to extract them precisely for heap locations. This paper firstly proposes two notion of interstatement must aliases and then exploits these aliases in improving data depend-ence analysis for heap locations. We carry out a preliminary ex-periment on some programs, the result of which indicates that the new technique can effectively improve the precision of depend-ence analysis for heap locations with an endurable cost.
Hierarchical network-aware placement of service oriented applications in Clouds

In cloud environments, resources can be requested on-demand when they are needed. A cloud management system is responsible for determining which physical machines are responsible for processing the requests. The problem of determining which servers are used for which services is referred to as the Cloud Application Placement Problem (CAPP), and multiple criteria such as cost and number of migrations must be taken into account. When applications are constructed as a collection of communicating services, such as in Service-Oriented Architectures, it becomes important to take the underlying network properties into account when these placement decisions are made. In this paper, we propose an Integer Linear Programming (ILP) formulation for the CAPP, which optimizes multiple criteria such as cost, latency and number of migrations between subsequent invocations by using multiple optimization criteria. We also present hierarchical algorithms based on particle swarm optimization and genetic algorithms to solve the CAPP. These algorithms are be executed within a management hierarchy, which reduces the amount of information needed for the algorithms to function, increasing scalability of the management system. Finally, we evaluate the hierarchical algorithms by comparing them to an optimal algorithm based on the ILP formulation.
Fusion of Cells by Flipped SNAREs

The SNARE (soluble N-ethylmaleimide–sensitive factor attachment protein receptor) hypothesis suggests that pairs of proteins known as vesicle (v-) SNAREs and target membrane (t-) SNAREs interact specifically to control and mediate intracellular membrane fusion events. Here, cells expressing the interacting domains of v- and t-SNAREs on the cell surface were found to fuse spontaneously, demonstrating that SNAREs are sufficient to fuse biological membranes.
The Relationship between the Blood Pressure Responses to Exercise following Training and Detraining Periods

Background Exercise training lowers blood pressure (BP), while BP increases and returns to pre-training values with detraining. Yet, there is considerable variability in these BP responses. We examined the relationship between the BP responses after 6 months of training followed by 2 weeks of detraining among the same people. Methodology/Principal Findings Subjects (n = 75) (X+SD, 50.2±10.6 yr) were sedentary, obese, and had prehypertension. They completed an aerobic (n = 34); resistance (n = 28); or aerobic + resistance or concurrent (n = 13) exercise training program. We calculated a metabolic syndrome z score (MetSz). Subjects were classified as BP responders (BP decreased) or non-responders (BP increased) to training and detraining. Linear and multivariable regression tested the BP response. Chi Square tested the frequency of responders and non-responders. The systolic BP (SBP, r = −0.474) and diastolic (DBP, r = −0.540) response to training negatively correlated with detraining (p<0.01), independent of modality (p>0.05). Exercise responders reduced SBP 11.5±7.8 (n = 29) and DBP 9.8±6.2 mmHg (n = 31); non-responders increased SBP 7.9. ±10.9 (n = 46) and DBP 4.9±7.1 mmHg (n = 44) (p<0.001). We found 65.5% of SBP training responders were SBP detraining non-responders; while 60.9% of SBP training non-responders were SBP detraining responders (p = 0.034). Similarly, 80.6% of DBP training responders were DBP detraining non-responders; while 59.1% of DBP training non-responders were DBP detraining responders (p<0.001). The SBP detraining response (r = −0.521), resting SBP (r = −0.444), and MetSz (r = 0.288) explained 44.8% of the SBP training response (p<0.001). The DBP detraining response (r = −0.553), resting DBP (r = −0.450), and MetSz (r = 0.463) explained 60.1% of the DBP training response (p<0.001). Conclusions/Significance As expected most subjects that decreased BP after exercise training, increased BP after detraining. An unanticipated finding was most subjects that increased BP after exercise training, decreased BP after detraining. Reasons why the negative effects of exercise training on BP maybe reversed with detraining among some people should be explored further. Trial Registration Information ClinicalTrials.gov 1R01HL57354; 2003–2008; NCT00275145
Preconditioning Highly Indefinite and Nonsymmetric Matrices

Standard preconditioners, like incomplete factorizations, perform well when the coefficient matrix is diagonally dominant, but often fail on general sparse matrices. We experiment with nonsymmetric permutations and scalings aimed at placing large entries on the diagonal in the context of preconditioning for general sparse matrices. The permutations and scalings are those developed by Olschowka and Neumaier [Linear Algebra Appl. , 240 (1996), pp. 131--151] and by Duff and Koster [SIAM J. Matrix Anal. Appl. , 20 (1999), pp. 889--901; Tech. report Ral-Tr-99-030, Rutherford Appleton Laboratory, Chilton, UK, 1999]. We target highly indefinite, nonsymmetric problems that cause difficulties for preconditioned iterative solvers. Our numerical experiments indicate that the reliability and performance of preconditioned iterative solvers are greatly enhanced by such preprocessing.
A Study of Quality Issues for Image Auto-Annotation With the Corel Dataset

The Corel Image set is widely used for image annotation performance evaluation although it has been claimed that Corel images are relatively easy to annotate. The aim of this paper is to demonstrate some of the disadvantages of datasets like the Corel set for effective auto-annotation evaluation. We first compare the performance of several annotation algorithms using the Corel set and find that simple near neighbor propagation techniques perform fairly well. A support vector machine (SVM)-based annotation method achieves even better results, almost as good as the best found in the literature. We then build a new image collection using the Yahoo Image Search engine and query-by-single-word searches to create a more challenging annotated set automatically. Then, using three very different image annotation methods, we demonstrate some of the problems of annotation using the Corel set compared with the Yahoo-based training set. In both cases the training sets are used to create a set of annotations for the Corel test set
Low-level analysis as a basic process for content-based image retrieval (CBIR) system

Abstract This article describes the way in which image is prepared for content-based image retrieval system. Automated image extraction is crucial for CBIR systems. Our system is dedicated to support estate agents. All efforts have been put into extracting elements of houses and bungalows from an image and finding their characteristic features in the unsupervised way. Hence, the paper presents segmentation algorithm based on a pixel colour and a novel method of texture identification which is based on wavelet transformation. After a set of low-level features for all objects is computed, the database is filled with these features.
On the impact of sentence length on recognition accuracy

The goal of this article is to analyse how the length of utterances affects performance of an automatic speech recognizer (ASR). Benchmarks of an ASR system were performed for utterances of various lengths on English and Czech corpora. Then the observed phenomena are tried to be explained theoretically. Eventually, results are summarized and some conclusions drawn.
An Improved Deterministic Implementation Method for Bayesian Mixture Distributions

This paper presents the branching approach, an improved deterministic implementation method (such as variational inference and expectation propagation) for Bayesian learning of mixture distributions. The proposed approach uses a set of artifi-cial conditions defined by latent (hidden) variables of the mixture distribution. This condition set is updated iteratively by branching of a condition selected from the previous set. The approximated Bayesian inference is obtained by combining the conditional inferences under all conditions in the set. The proposed approach is compared with several standard implementation methods by using a mixture of normal distributions as an example.
Dynamic rolling-walk motion by limb mechanism robot ASTERISK

New dynamic rolling-walk motion for multi-legged robot is proposed. The motion is realized by using the isotropic leg arrangement and the dynamic center of mass (COM) control inspired by bipedal robots. In the new dynamic rolling-walk motion, the robot switches between two phases to achieve the motion; three legs supporting phase and two legs supporting phase. By using the Preview Control with table-cart model based on the bipedal robot technique, the robot's COM motion is planned to achieve the dynamic motion. The Resolved Momentum Control is also implemented to manipulate the multi-links robot as a single mass model. We analyzed the required torque for the proposed Dynamic rolling-walk motion and simulated the motion in the dynamics simulator on Open Dynamic Engine (ODE) to confirm the feasibility.
An improved RBM based on Bayesian Regularization

Restricted Boltzmann Machine is a fundamental method in deep learning networks. Training and generalization is an ill-defined problem in that many different networks may achieve the training goal; however each will respond differently to an unknown input. Traditional approaches include stopping the training early and/or restricting the size of the network These approaches ameliorate the problem of over-fitting where the network learns the patterns presented but is unable to generalize. Bayesian regularization addresses these issues by requiring the weights of the network to attain a minimum magnitude. This ensures that non-contributing weights are reduced significantly and the resulting network represents the essence of the inter-relations of the training. Bayesian Regularization simply introduces an additional term to the objective function. This term comprises the sum of the squares of the weights. The optimization process therefore not only achieves the objective of the original cost (i.e. the minimization of an error metric) but it also ensures that this objective is achieved with minimum-magnitude weights. We have introduced Bayesian Regularization in the training of Restricted Boltzmann Machines and have applied this method in experiments of hand-written numbers classification. Our experiments showed that by adding Bayesian regularization in the training of RBMs, we were able to improve the generalization capabilities of the trained network by reducing its recognition errors by more than 1.6%.