Miracle-dz/UniFAR-FTUs huggingface dataset:
Journal of Cardiovascular Magnetic Resonance Right Ventricular Wall Motion Abnormalities Found in Healthy Subjects by Cardiovascular Magnetic Resonance Imaging and Characterized with a New Segmental Model

Aim. To evaluate right ventricular wall motion abnormalities in healthy subjects using a new segmental model for the right ventricle. Methods and Results. 29 healthy subjects (9 female, 20 male, mean age 48.9 ± 15 years) underwent cardiovascular magnetic resonance imaging (CMR; 1.5-Tesla Sonata, Siemens, Erlangen, Germany) for the evaluation of cardiac function and right ventricular wall motion. A steady-state free precession gradient-echo sequence (TrueFISP) was used. Right ventricular wall motion was analyzed, and the site of areas of disordered motion was classified according to the new segmental model. The number of wall motion abnormalities diagnosed was significantly higher in the transverse plane (86.2%) than in the short-axis plane (13.8%) and the horizontal longitudinal plane (41.4%; p = 0.000). Conclusion. Right ventricular wall motion abnormalities are one of the criteria for the diagnosis of arrhythmogenic right ventricular cardiomyopathy. However, our findings indicate that they may also be seen around the insertion of the moderator band in healthy subjects, so that the significance of their presence at this site in patients undergoing diagnostic investigations for this disease should be interpreted with caution.
A new measure to identify the hub in urban traffic networks

In this paper, a new scheme that measures the importance of hub streets is proposed, in which the variety of the intersections is investigated based on the topological community structure. The practicality of the proposed methodology is demonstrated through a case study. Simulations have been carried out and analyzed in detail, illustrating the feasibility of the pro-posed scheme.
Pedestrian detection based on deep convolutional neural network with ensemble inference network

Pedestrian detection is an active research topic for driving assistance systems. To install pedestrian detection in a regular vehicle, however, there is a need to reduce its cost and ensure high accuracy. Although many approaches have been developed, vision-based methods of pedestrian detection are best suited to these requirements. In this paper, we propose the methods based on Convolutional Neural Networks (CNN) that achieves high accuracy in various fields. To achieve such generalization, our CNN-based method introduces Random Dropout and Ensemble Inference Network (EIN) to the training and classification processes, respectively. Random Dropout selects units that have a flexible rate, instead of the fixed rate in conventional Dropout. EIN constructs multiple networks that have different structures in fully connected layers. The proposed methods achieves comparable performance to state-of-the-art methods, even though the structure of the proposed methods are considerably simpler.
Type B plactic relations for r-domino tableaux

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Type B plactic relations for r-domino tableaux Müge Taşkın
Hardness measures and resolution lower bounds

Various "hardness" measures have been studied for resolution, providing theoretical insight into the proof complexity of resolution and its fragments, as well as explanations for the hardness of instances in SAT solving. In this report we aim at a unified view of a number of hardness measures, including different measures of width, space and size of resolution proofs. We also extend these measures to all clause-sets (possibly satisfiable).
Bridging temporal context gaps using time-aware re-contextualization

Understanding a text, which was written some time ago, can be compared to translating a text from another language. Complete interpretation requires a mapping, in this case, a kind of time-travel translation between present context knowledge and context knowledge at time of text creation. In this paper, we study time-aware re-contextualization, the challenging problem of retrieving concise and complementing information in order to bridge this temporal context gap. We propose an approach based on learning to rank techniques using sentence-level context information extracted from Wikipedia. The employed ranking combines relevance, complimentarity and time-awareness. The effectiveness of the approach is evaluated by contextualizing articles from a news archive collection using more than 7,000 manually judged relevance pairs. To this end, we show that our approach is able to retrieve a significant number of relevant context information for a given news article.
Multimodal "Sensory Illusions" for Improving Spatial Awareness in Virtual Environments

Inaccurate judgement of distances in virtual environments (VEs) restricts their usefulness for engineering development, in which engineers must have a good understanding of the spaces they are designing. Multimodal feedback can improve depth perception in VEs, but this has yet to be implemented and tested in engineering applications with systems which provide haptic feedback to the body. The project reported in this paper will develop a multimodal VE to improve engineers' understanding of 3D spaces. It will test the concept of "sensory illusions" where the point of collision in the VE differs to the point of haptic feedback on the body. This will permit the use of fewer vibrotactile devices and therefore the development of a more wearable system. This paper describes related work in multisensory and tactile stimulation which suggests that our perception of a stimulus is not fixed to the point of contact.
A Detailed Investigation into the Effectiveness of Earcons

A detailed experimental evaluation of earcons was carried out to see whether they are an effective means of communicating information in sound. An initial experiment showed that earcons were better than unstructured bursts of sound and that musical timbres were more effective than simple tones. Musicians were shown to be no better than non-musicians when using musical timbres. A second experiment was then carried out which improved upon some of the weaknesses of the pitches and rhythms used in Experiment 1 to give a significant improvement in recognition. From the results some guidelines were drawn up for designers to use when creating earcons. These experiments have formally shown that earcons are an effective method for communicating complex information in sound.
Efficient Content Based Spam Filtering Using Bayesian Method

There are two types of emails one in wanted emails from authorised users and other is unsolicited or unwanted emails, these unsolicited emails are called spams. This is a rapidly growing problem in the domain of emails these days. Emails are used by millions of people and send billions of mails daily all over the world. Over the last 1.5 decade it has become a very big problem. Every day a very huge amount of spam emails are received by the users. Due to this the business in not only this industry but also in every domain loses productivity, and it costs billions of dollars. It also strains the IT infrastructure. These emails become very frustrating for the users. So we need an efficient method to filter these mails and its necessity is increasing day by day. In this paper, we represent a method to do the same based on Naïve Bayes Classifier. It works by evaluating the probability of occurrence of a keyword in spam and in legitimate emails and classify by comparing them.
Traffic sign shape classification based on Support Vector Machines and the FFT of the signature of blobs

In many traffic sign recognition systems, one of the main tasks is the classification of the shape of the blob, which is intended to simplify the recognition process. In this paper, we have developed a new shape classification algorithm based on Support Vector Machines classifiers and the FFT of the signature of the blob. The FFT of the signature yields invariance to object scalings and rotations. Furthermore, the FFT is the vector input to the classifier. This classifier is trained to cope with projection deformations and occlusions. The algorithm has been tested under adverse conditions, such as geometric distortions, i.e. scaling, rotations and projection deformations, and occlusions. The experimental results show good robustness when the system is working with real, outdoor road images.
After the Euro: Shaping Institutions for Governance in the Wake of European Monetary Union

Now that the process of full implementation of European Monetary Union has begun, it is time to shift attention away from the process of introduction to the implications that the common currency will have for a wide range of institutions and policy areas. The wider political and social institutions of the European Union are not well developed there is an institutional deficit which parallels the more widely know democratic deficit. Monetary arrangements of nation states are imbedded in a range of political, cultural, economic and historical factors. Will mechanisms of these kinds eventually develop at the European level? Can national structures adapt to meet the challenge? The contributors to After the Euro tackle these questions and in doing so, take the debate beyond the economic and sovereignty questions which have so far dominated the debate.
Fast and Robust Generation of Semantic Urban Terrain Models from UAV Video Streams

We present an algorithm for extracting Level of Detail 2 (LOD2) building models from video streams captured by Unmaned Aerial Vehicles (UAVs). Typically, such imagery is of limited radiometric quality but the surface is captured with large redundancy. The first contribution of this paper is a novel algorithm exploiting this redundancy for precise depth computation. This is realized by fusing consistent depth estimations across single stereo models and generating a 2.5D elevation map from the resulting point clouds. Disparity maps are derived by a coarse-to-fine Semi-Global-Matching (SGM) method performing well on noisy imagery. The second contribution concerns a challenging step of the context-based urban terrain modeling: Dominant planes extraction for building reconstruction. Because of noisy data and complicated roof structures, both dominant plane parameters and initial values for support sets of planes are obtained by the J-Linkage algorithm. An improved point-to-plane labeling is presented to encourage the assignment of proximate points to the same plane. This is accomplished by non-local, Markov Random Field (MRF) - based optimization and segmentation of color information. The potential and the limitations of the proposed methods are shown using an UAV video sequence of limited radiometric quality.
Combined Inhibition of the Renin-Angiotensin System and Neprilysin Positively Influences Complex Mitochondrial Adaptations in Progressive Experimental Heart Failure

Background Inhibitors of the renin angiotensin system and neprilysin (RAS-/NEP-inhibitors) proved to be extraordinarily beneficial in systolic heart failure. Furthermore, compelling evidence exists that impaired mitochondrial pathways are causatively involved in progressive left ventricular (LV) dysfunction. Consequently, we aimed to assess whether RAS-/NEP-inhibition can attenuate mitochondrial adaptations in experimental heart failure (HF). Methods and Results By progressive right ventricular pacing, distinct HF stages were induced in 15 rabbits, and 6 animals served as controls (CTRL). Six animals with manifest HF (CHF) were treated with the RAS-/NEP-inhibitor omapatrilat. Echocardiographic studies and invasive blood pressure measurements were undertaken during HF progression. Mitochondria were isolated from LV tissue, respectively, and further worked up for proteomic analysis using the SWATH technique. Enzymatic activities of citrate synthase and the electron transfer chain (ETC) complexes I, II, and IV were assessed. Ultrastructural analyses were performed by transmission electron microscopy. During progression to overt HF, intricate expression changes were mainly detected for proteins belonging to the tricarboxylic acid cycle, glucose and fat metabolism, and the ETC complexes, even though ETC complex I, II, or IV enzymatic activities were not significantly influenced. Treatment with a RAS-/NEP-inhibitor then reversed some maladaptive metabolic adaptations, positively influenced the decline of citrate synthase activity, and altered the composition of each respiratory chain complex, even though this was again not accompanied by altered ETC complex enzymatic activities. Finally, ultrastructural evidence pointed to a reduction of autophagolytic and degenerative processes with omapatrilat-treatment. Conclusions This study describes complex adaptations of the mitochondrial proteome in experimental tachycardia-induced heart failure and shows that a combined RAS-/NEP-inhibition can beneficially influence mitochondrial key pathways.
Social Topic Detection for Web Forum

Kinds of topics and discussions come forth in Web forums every day, we can talk about newsletters and daily trivial matters in virtual communities, communicate with each other deeply in thought essentially. Part subjects are hot topics, which attract a lot of users, are widely viewed and massively discussed. Hot topic can be classified as isolated topic and social topic. The characteristics of social topic are multi-thematic, higher relevance between topics also. Isolated topics are lesser in quantity, a spot of relevance in it besides. Social topic' detecting algorithm is mainly based on subject relevance. This paper presents a density-based clustering model of subject words to detect social hot event from quantity and content relevance. Experiments on Tian YaZaTan community of TianYa BBS demonstrate the efficiency of the proposed model, extracting social topics which are better organized for search but also discovering communities of these topics.
Multi-scale/multi-resolution Kronecker compressive imaging

As a universal sampling procedure, compressive sensing (CS) considers that all samples of compressible signal are equally important. However, it is not true in in image/video signal since human visual system is more sensitive to low frequency components. Therefore, CS theory has been extended to hybrid and multi-scale CS to better capture the low-frequency samples. The computational complexity is another challenge in CS which can be solved by multi-resolution sensing matrix. In this paper, we propose a multi-scale/multi-resolution sensing matrix for Kronecker CS (KCS) based on separable wavelet transform and address measurement allocation problem with and without information of to-be-sensed image. The proposed methods not only perform better (3.72dB gain) but also low complexity and compatible with conventional reconstruction methods.
Role of TGFβ in regulation of the tumor microenvironment and drug delivery (Review)

Deregulation of cell signaling homeostasis is a predominant feature of cancer initiation and progression. Transforming growth factor β (TGFβ) is a pleiotropic cytokine, which regulates numerous biological processes of various tissues in an autocrine and paracrine manner. Aberrant activity of TGFβ signaling is well known to play dual roles in cancer, depending on tumor stage and cellular context. The crucial roles of TGFβ in modulating the tumor microenvironment, its contribution to the accumulation of mechanical forces within the solid constituents of a tumor and its effects on the effective delivery of drugs are also becoming increasingly clear. In this review, we discuss the latest advances in the efforts to unravel the effects of TGFβ signaling in various components of the tumor microenvironment and how these influence the generation of forces and the efficacy of drugs. We also report the implications of tumor mechanics in cancer therapy and the potential usage of anti-TGFβ agents to enhance drug delivery and augment existing therapeutic approaches. These findings provide new insights towards the significance of targeting TGFβ pathway to enhance personalized tumor treatment.
Auditory Thalamus and Auditory Cortex Are Equally Modulated by Context during Flexible Categorization of Sounds

In a dynamic world, animals must adapt rapidly to changes in the meaning of environmental cues. Such changes can influence the neural representation of sensory stimuli. Previous studies have shown that associating a stimulus with a reward or punishment can modulate neural activity in the auditory cortex (AC) and its thalamic input, the medial geniculate body (MGB). However, it is not known whether changes in stimulus–action associations alone can also modulate neural responses in these areas. We designed a categorization task for rats in which the boundary that separated low- from high-frequency sounds varied several times within a behavioral session, thus allowing us to manipulate the action associated with some sounds without changing the associated reward. We developed a computational model that accounted for the rats' performance and compared predictions from this model with sound-evoked responses from single neurons in AC and MGB in animals performing this task. We found that the responses of 15% of AC neurons and 16% of MGB neurons were modulated by changes in stimulus–action association and that the magnitude of the modulation was comparable between the two brain areas. Our results suggest that the AC and thalamus play only a limited role in mediating changes in associations between acoustic stimuli and behavioral responses.
IMPLEMENTATION IN DECISION SUPPORT SYSTEM OF ONTOLOGICAL APPROACH TO EXTRACTION OF DATA ABOUT INFORMATION OBJECTS FROM NEWS FLOW

The objective of this article is the study of existing approaches to extraction and structuring of data about information objects from news flow and development of the original approach to solution of that task. The article covers the approach to extraction of data about information objects based on domain otology. The developed model of information objects extracted from text is described, as well as the complex of linguistic resources applied for implementation of extraction from text of data about various types of information objects. This article gives a brief description of processing. The stage of extraction of data about information objects includes three phases: named entity recognition; inter-object relationship extraction; building complex information objects (events). For extraction of information from text, rulebased approach is used.
Comparisons of the Dynamics of Local Field Potential and Multiunit Activity Signals in Macaque Visual Cortex

The local field potential (LFP) and multiunit activity (MUA) are extracellularly recorded signals that describe local neuronal network dynamics. In our experiments, the LFP and MUA, recorded from the same electrode in macaque primary visual cortex V1 in response to drifting grating visual stimuli, were evaluated on coarse timescales (∼1–5 s) and fine timescales (<0.1 s). On coarse timescales, MUA and the LFP both produced sustained visual responses to optimal and non-optimal oriented visual stimuli. The sustainedness of the two signals across the population of recording sites was correlated (correlation coefficient, ∼0.4). At most recording sites, the MUA was at least as sustained as the LFP and significantly more sustained for optimal orientations. In previous literature, the blood oxygen level-dependent (BOLD) signal of functional magnetic resonance imaging studies was found to be more strongly correlated with the LFP than with the MUA as a result of the lack of sustained response in the MUA signal. Because we found that MUA was as sustained as the LFP, MUA may also be correlated with BOLD. On fine timescales, we computed the coherence between the LFP and MUA over the frequency range 10–150 Hz. The LFP and MUA were weakly but significantly coherent (∼0.14) in the gamma band (20–90 Hz). The amount of gamma-band coherence was correlated with the power in the gamma band of the LFP. The data were consistent with the proposal that the LFP and MUA are generated in a noisy, resonant cortical network.
Review and Perspective for Distance Based Trajectory Clustering

In this paper we tackle the issue of clustering trajectories of geolocalized observations. Using clustering technics based on the choice of a distance between the observations, we first provide a comprehensive review of the different distances used in the literature to compare trajectories. Then based on the limitations of these methods, we introduce a new distance : Symmetrized Segment-Path Distance (SSPD). We finally compare this new distance to the others according to their corresponding clustering results obtained using both hierarchical clustering and affinity propagation methods.
Interaction and market structure : essays on heterogeneity in economics

D. Delli Gatti, M. Gallegati, A. Kirman: Introduction. - H. Dawid: Learning to Trade and Mediate. - A. Kirman, N. Vriend: Learning to Be Loyal. A Study of the Marseille Fish Market. - M. Kopel, G. I. Bischi, L. Gardini: On New Phenomena in Dynamic Promotional Competition Models with Homogeneous and Quasi-Homogeneous Firms. - R. Franke: A Reconsideration of Professor Iwai's Schumpeterian Dynamics. - D. Delli Gatti, M. Gallegati, A. Palestrini: Agents' Heterogeneity, Aggregation, and Economic Fluctuations. - C. Chiarella, A. Khomin: The Dynamic Interaction of Rational Fundamentalists and Trend Chasing Chartists in a Monetary Economy. - S. Focardi, M. Marchesi: Self-Organization in Global Stochastic Models of Production and Inventory Dynamics. - A. Bassanini, G. Dosi: Heterogeneous Agents, Complementaries and Diffusion. Do Increasing Returns Imply Convergence to International Technological Monopolies? - S. Thoron: Market Organization: Noncooperative Models of Coalition Formation. - A. Antoci, M. Galeotti, P. Sacco: Evolutionary Selection of Correlation Mechanism for Coordination Games. - P. Lupi: The Propagation of Cooperation in a Spatial Model of Learning with Endogenous Aspirations. - C. Hommes, J. Sonnemans, H. van de Velden: Expectation Formation in a Cobweb Economy: Some One Person Experiments. - P. Mirowski, K. Somefun: Fecund, Cheap and Out of Control: Heterogeneous Economic Agents as Flawed Computers vs. Markets as Evolving Computational Entities.
Eurycoma longifolia: Medicinal Plant in the Prevention and Treatment of Male Osteoporosis due to Androgen Deficiency

Osteoporosis in elderly men is now becoming an alarming health issue due to its relation with a higher mortality rate compared to osteoporosis in women. Androgen deficiency (hypogonadism) is one of the major factors of male osteoporosis and it can be treated with testosterone replacement therapy (TRT). However, one medicinal plant, Eurycoma longifolia Jack (EL), can be used as an alternative treatment to prevent and treat male osteoporosis without causing the side effects associated with TRT. EL exerts proandrogenic effects that enhance testosterone level, as well as stimulate osteoblast proliferation and osteoclast apoptosis. This will maintain bone remodelling activity and reduce bone loss. Phytochemical components of EL may also prevent osteoporosis via its antioxidative property. Hence, EL has the potential as a complementary treatment for male osteoporosis.