Miracle-dz/UniFAR-FTUs huggingface dataset:
A precise converter for resolvers and sinusoidal encoders based on a novel ratiometric technique

Resolvers and other sinusoidal encoders provide electrical signals related to the sine and cosine of the unknown angular position of their rotor shafts. Conventional tangent/cotangent converter used with these transducers for determining the angular position is based on the computation of the ratio between the smallest and largest absolute values of the sine and cosine signals. This produces highly non-linear segments of tangent and cotangent requiring large-size look-up tables for the determination of the unknown angle. In this work, a novel method is presented for implementing this technique without the need for the determination of the absolute values of the sensor signals and of the cotangent. Additionally the precision of the proposed technique is guaranteed even by using a small size look-up table or a simple linear computation. The method is based on computing the ratio of sine and cosine segments of a number of phase-shifted sine and cosine signals such that non-linearity of the resulting tangent is greatly reduced due to the limitation of the angle interval in which it needs to be computed. The paper describes full details of the proposed method and provides a comparison with previously-reported techniques through extensive computer simulation.
Generation of landmark-based navigation instructions from open-source data

We present a system for the real-time generation of car navigation instructions with landmarks. Our system relies exclusively on freely available map data from OpenStreetMap, organizes its output to fit into the available time until the next driving maneuver, and reacts in real time to driving errors. We show that female users spend significantly less time looking away from the road when using our system compared to a baseline system.
Q-RAN: A Constructive Reinforcement Learning Approach for Robot Behavior Learning

This paper presents a learning system that uses Q-learning with a resource allocating network (RAN) for behavior learning in mobile robotics. The RAN is used as a function approximator, and Q-learning is used to learn the control policy in 'off-policy' fashion that enables learning to be bootstrapped by a prior knowledge controller, thus speeding up the reinforcement learning. Our approach is verified on a PeopleBot robot executing a visual servoing based docking behavior in which the robot is required to reach a goal pose. Further experiments show that the RAN network can also be used for supervised learning prior to reinforcement learning in a layered architecture, thus further improving the performance of the docking behavior
Robust High Quality Image Guided Depth Upsampling

Time-of-Flight (ToF) depth sensing camera is able to obtain depth maps at a high frame rate. However, its low resolution and sensitivity to the noise are always a concern. A popular solution is upsampling the obtained noisy low resolution depth map with the guidance of the companion high resolution color image. However, due to the constrains in the existing upsampling models, the high resolution depth map obtained in such way may suffer from either texture copy artifacts or blur of depth discontinuity. In this paper, a novel optimization framework is proposed with the brand new data term and smoothness term. The comprehensive experiments using both synthetic data and real data show that the proposed method well tackles the problem of texture copy artifacts and blur of depth discontinuity. It also demonstrates sufficient robustness to the noise. Moreover, a data driven scheme is proposed to adaptively estimate the parameter in the upsampling optimization framework. The encouraging performance is maintained even in the case of large upsampling e.g. $8\times$ and $16\times$.
Investigation of Sequence-level Knowledge Distillation Methods for CTC Acoustic Models

This paper presents knowledge distillation (KD) methods for training connectionist temporal classification (CTC) acoustic models. In a previous study, we proposed a KD method based on the sequence-level cross-entropy, and showed that the conventional KD method based on the frame-level cross-entropy did not work effectively for CTC acoustic models, whereas the proposed method improved the performance of the models. In this paper, we investigate the implementation of sequence-level KD for CTC models and propose a lattice-based sequence-level KD method. Experiments investigating model compression and the training of a noise-robust model using the Wall Street Journal (WSJ) and CHiME4 datasets demonstrate that the sequence-level KD methods improve the performance of CTC acoustic models on both two tasks, and show that the lattice-based method can compute the sequence-level KD more efficiently than the N-best-based method proposed in our previous work.
Development of an Initial Taxonomy and Classification Scheme for Artificial Space Objects

As space gets more and more populated, a classification scheme based upon scientific taxonomy is needed to properly identify, group, and discriminate space objects. Using artificial space object taxonomy also allows for scientific understanding of the nature of the space object population and the processes, natural or not, that drive changes of an artificial space object class from one to another. In a first step, an ancestral-dynamic hierarchical tree based on a priori knowledge is established, motivated by taxonomy schemes used in biology. In a second step, available orbital element data has been clustered. Therefore, a normalization of a reduced orbital element space has been established to provide a weighting of the input values. The clustering in the five dimensional normalized parameter space is divided in two sub-steps. In a first sub-step, a pre-clustering in a modified cluster-feature tree has been applied, to initially group the objects and reduce the sheer number of single entities, which need to be clustered. In a second sub-step, a Euclidean minimal tree algorithm has been applied, to determine arbitrarily shaped clusters. The clusters also allow determination of a passive hazard value for the single clusters, making use of their closest neighbors in the minimal tree and the radar cross section of the cluster in question.
Automatic facial feature extraction and expression recognition based on neural network

In this paper, an approach to the problem of automatic facial feature extraction from a still frontal posed image and classification and recognition of facial expression and hence emotion and mood of a person is presented. Feed forward back propagation neural network is used as a classifier for classifying the expressions of supplied face into seven basic categories like surprise, neutral, sad, disgust, fear, happy and angry. For face portion segmentation and localization, morphological image processing operations are used. Permanent facial features like eyebrows, eyes, mouth and nose are extracted using SUSAN edge detection operator, facial geometry, edge projection analysis. Experiments are carried out on JAFFE facial expression database and gives better performance in terms of 100% accuracy for training set and 95.26% accuracy for test set.
Virtuoso, a Hybrid RDBMS/Graph Column Store

We discuss applying column store techniques to both graph (RDF) and relational data for mixed workloads ranging from lookup to analytics in the context of the OpenLink Virtuoso DBMS. In so doing, we need to obtain the excellent memory efficiency, locality and bulk read throughput that are the hallmark of column stores while retaining low-latency random reads and updates, under serializable isolation.
Coreference resolution improves extraction of Biological Expression Language statements from texts

We describe a system that automatically extracts biological events from biomedical journal articles, and translates those events into Biological Expression Language (BEL) statements. The system incorporates existing text mining components for coreference resolution, biological event extraction and a previously formally untested strategy for BEL statement generation. Although addressing the BEL track (Track 4) at BioCreative V (2015), we also investigate how incorporating coreference resolution might impact event extraction in the biomedical domain. In this paper, we report that our system achieved the best performance of 20.2 and 35.2 in F-score for the full BEL statement level on both stage 1, and stage 2 using provided gold standard entities, respectively. We also report that our results evaluated on the training dataset show benefit from integrating coreference resolution with event extraction.
SIRT1 Mediates the Effect of GLP-1 Receptor Agonist Exenatide on Ameliorating Hepatic Steatosis

GLP-1 and incretin mimetics, such as exenatide, have been shown to attenuate hepatocyte steatosis in vivo and in vitro, but the specific underlying mechanism is unclear. SIRT1, an NAD+-dependent protein deacetylase, has been considered as a crucial regulator in hepatic lipid homeostasis by accumulated studies. Here, we speculate that SIRT1 might mediate the effect of the GLP-1 receptor agonist exenatide (exendin-4) on ameliorating hepatic steatosis. After 8 weeks of exenatide treatment in male SIRT1+/− mice challenged with a high-fat diet and their wild-type (WT) littermates, we found that lipid deposition and inflammation in the liver, which were improved dramatically in the WT group, diminished in SIRT1+/− mice. In addition, the protein expression of SIRT1 and phosphorylated AMPK was upregulated, whereas lipogenic-related protein, including SREBP-1c and PNPLA3, was downregulated in the WT group after exenatide treatment. However, none of these changes were observed in SIRT1+/− mice. In HepG2 cells, exendin-4–reversed lipid deposition induced by palmitate was hampered when SIRT1 was silenced by SIRT1 RNA interference. Our data demonstrate that SIRT1 mediates the effect of exenatide on ameliorating hepatic steatosis, suggesting the GLP-1 receptor agonist could serve as a potential drug for nonalcoholic fatty liver disease (NAFLD), especially in type 2 diabetes combined with NAFLD, and SIRT1 could be a therapeutic target of NAFLD.
Correlation of Depression Symptoms and Interleukin-6 in Chronic Renal Disease with Regular Hemodialysis Patients

Depression is psychosomatic disorder are common in patients with chronic kidney disease (CKD) undergoing hemodialysis (HD). Correlated with morbidity and mortality associated with chronic inflammation in patients, that characterized by elevated levels of plasma cytokines, especially interleukin (IL)-6. Objective: To investigate the correlation of depressive symptoms with IL-6 levels in patients with CKD who underwent regular HD. Methods: One hundred and 29 CKD patients were enrolled in this cross- sectional study. All eligible patients were screened for IL-6 level. Beck Depression Inventory (BDI) II questioner were then given to determine the presence of depression. Level of IL-6 was then correlated with BDI score using Spearman test. Result: The mean BDI score was 19,32 ± 6,18 indicating the level of mild depression symptoms, and the mean IL-6 level (8,697 ± 6,947 pg/mL) was still within normal range. There was a moderate positive correlation (r = 0.417) between the level of depressive symptoms and IL- 6 levels. Conclusion: There was a moderate positive correlation (r = 0.417) between IL-6 levels and depressive symptoms in patient with HD. However, mean IL-6 levels in depressive patients are still within normal limits indicating that the cause of depression is multifactorial.
Autoimmune gastritis: relationships with anemia and Helicobacter pylori status

Abstract Background: Autoimmune gastritis (AIG) is a gastric pathologic condition affecting the mucosa of the fundus and the body and eventually leading to hypo-achlorhydria. Aims: We report our clinical and pathological experience with AIG. Methods: Data from patients with a diagnosis of AIG seen in the period January 2002–December 2012 were retrieved. Only patients with complete sets of biopsies were analyzed. Results: Data from 138 patients were available for analysis. Pernicious anemia was present in 25% of patients, iron deficiency anemia was found in 29.7% of patients, hypothyroidism in 23% of patients, type 1 diabetes in 7.9% of patients, and vitiligo in 2.8% of patients. Parietal cell antibodies were positive in 65% of patients, and no patient had serology positive for celiac disease. All gastric biopsies showed glandular atrophy associated with enterochromaffin-like (ECL)-cells hyperplasia, features limited to the mucosa of the fundus and body, and focal glandular intestinal metaplasia. Helicobacter pylori was negative in all cases. Conclusions: AIG was strongly associated with anemia; atrophy, intestinal metaplasia and ECL hyperplasia in the gastric fundus and body are hallmarks of this condition.
A Needle in a Haystack? Harnessing Onomatopoeia and User-specific Stylometrics for Authorship Attribution of Micro-messages

The world is facing a new era in which social media communication plays a fundamental role in people’s lives. Along with irrefutable benefits, several collateral drawbacks have risen, one being the wide spread of false information with malicious intents, what is now commonly called "Fake News". The fight against this problem is not easy, especially when taking into account the nature of text messages involved on social media platforms (a sea of small messages and myriad users). In this work, we cope with the challenging problem of authorship attribution of small text messages posted on social media platforms. Differently from what has been done with longer texts, we rely upon data-driven approaches, exploiting recent advances of deep neural networks in the field of pattern recognition. By viewing small texts usually employed in social media as unidimensional signals, we devise modern deep-learning techniques tailored for this kind of data to find the author of these posts with promising results.
Fibrinogen Substrate Recognition by Staphylocoagulase·(Pro)thrombin Complexes*

Thrombin generation and fibrinogen (Fbg) clotting are the ultimate proteolytic reactions in the blood coagulation pathway. Staphylocoagulase (SC), a protein secreted by the human pathogen Staphylococcus aureus, activates prothrombin (ProT) without proteolysis. The SC·(pro)thrombin complex recognizes Fbg as a specific substrate, converting it directly into fibrin. The crystal structure of a fully active SC fragment containing residues 1-325 (SC-(1-325)) bound to human prethrombin 2 showed previously that SC inserts its Ile1-Val2 N terminus into the Ile16 pocket of prethrombin 2, inducing a functional active site in the cognate zymogen conformationally. Exosite I of α-thrombin, the Fbg recognition site, and proexosite I on ProT are blocked by domain 2 of SC-(1-325). In the present studies, active site-labeled fluorescent ProT analogs were used to quantitate Fbg binding to the SC-(1-325)·ProT complex. Fbg binding and cleavage are mediated by expression of a new Fbg-binding exosite on the SC-(1-325)·ProT complex, resulting in formation of an (SC-(1-325)·ProT)2·Fbg pentameric complex with a dissociation constant of 8-34 nm. In both crystal structures, the SC-(1-325)·(pre)thrombin complexes form dimers, with both proteinases/zymogens facing each other over a large U-shaped cleft, through which the Fbg substrate could thread. On this basis, a molecular model of the pentameric (SC-(1-325)·thrombin)2·Fbg encounter complex was generated, which explains the coagulant properties and efficient Fbg conversion. The results provide new insight into the mechanism that mediates high affinity Fbg binding and cleavage as a substrate of SC·(pro)thrombin complexes, a process that is central to the molecular pathology of S. aureus endocarditis.
Potential multi-component structure of the debris disk around HIP 17439 revealed by Herschel/DUNES ⋆

Context. The dust observed in debris disks is produced through collisions of larger bodies left over from the planet/planetesimal formation process. Spatially resolving these disks permits to constrain their architecture and thus that of the underlying planetary/planetesimal system. Aims. Our Herschel open time key program DUNES aims at detecting and characterizing debris disks around nearby, sun-like stars. In addition to the statistical analysis of the data, the detailed study of single objects through spatially resolving the disk and detailed modeling of the data is a main goal of the project. Methods. We obtained the first observations spatially resolving the debris disk around the sun-like star HIP 17439 (HD23484) using the instruments PACS and SPIRE on board the Herschel Space Observatory. Simultaneous multi-wavelength modeling of these data together with ancillary data from the literature is presented. Results. A standard single component disk model fails to reproduce the major axis radial profiles at 70 mu m, 100 mu m, and 160 mu m simultaneously. Moreover, the best-fit parameters derived from such a model suggest a very broad disk extending from few au up to few hundreds of au from the star with a nearly constant surface density which seems physically unlikely. However, the constraints from both the data and our limited theoretical investigation are not strong enough to completely rule out this model. An alternative, more plausible, and better fitting model of the system consists of two rings of dust at approx. 30 au and 90 au, respectively, while the constraints on the parameters of this model are weak due to its complexity and intrinsic degeneracies. Conclusions. The disk is probably composed of at least two components with different spatial locations (but not necessarily detached), while a single, broad disk is possible, but less likely. The two spatially well-separated rings of dust in our best-fit model suggest the presence of at least one high mass planet or several low-mass planets clearing the region between the two rings from planetesimals and dust.
fAPEbook - Animal Social Life Monitoring with Wireless Sensor and Actor Networks

Wild life monitoring requires a sophisticated process of planning, installation, execution, data collection, and data interpretation. The effort and time spent increases tremendously with the area and the number of observed objects as well as with the time frame of the observations. However, the application of wireless sensor nodes enables a scalable sampling method and fine granularity of data difficult to obtain otherwise. By including resource rich actor nodes, the data collection and evaluation are further optimized. In this paper, a wireless sensor and actor network (WSAN) protocol is designed for capturing and monitoring the social interactions of the complex social network of gorillas. The nodes are intended to be attached on the apes forming a mobile network. The local interaction patterns among the nodes are analyzed throughout the network life time. The protocol then determines the social roles of the gorillas based on previous research findings about their social structure, and builds a profile for each ape, which demonstrates the status, kinship and role played within the society. As a result, the proposed protocol generates a social directory of the ape troop under observation, namely fAPEbook. The contribution of this paper is to evaluate the efficiency of the proposed algorithm by its ability to capture the different characteristics in the society with the established mobility models. The results suggest the applicability of the algorithm in field tests with wild life.
Contribution of the spleen, lymph nodes and bone marrow to the antibody response in collagen‐induced arthritis in the rat

The relative contribution of different lymphoid tissues to the anti‐CII antibody response was studied in rats with arthritis produced as a result of immunizing them with collagen type II (CII). Antibody production was measured by maintaining lymphoid cells in short‐term culture in collagen‐coated microculture wells: the antibody they secreted was determined directly by a modified ELISA. Systemic sensitization to CII was established within a week of immunization, and a stronger response in the local draining lymph nodes relative to the spleen was associated with the development of clinical disease. From experiments involving splenectomy and adoptive cell transfer, the spleen was ascribed a suppressive role in controlling both arthritis and total antibody production. The bone marrow was found to be an important site of antibody production and the greater production of antibody by cells from tibial marrow in limbs with arthritis, compared with healthy limbs, argues for a local immune response to degrading joint antigens that may have systemic suppressive or protective properties. It is concluded that local immunity reflects the state of disease and that the antibodies produced by different lymphoid tissues may be made in response to different stimuli, and that the antibodies in turn may have different pathological effects.
Detection of Humoral Antigen and Antibody by Enzyme-Linked Immunosorbent Assay in Horses with Experimentally Induced Ehulichia Equi Infection

An enzyme-linked immunosorbent assay (ELISA) was used to detect antigen in plasma and antibody in serum of 3 horses inoculated with Ehrlichia equi. Clinical signs, including rectal temperature, were correlated with the antigen and antibody detection. ELISA was very efficient in detection of serum antibody. Antigen detection using monoclonal antibodies to E. equi and ELISA should be considered as a diagnostic method.
Anisotropic simplicial meshing using local convex functions

We present a novel method to generate high-quality simplicial meshes with specified anisotropy. Given a surface or volumetric domain equipped with a Riemannian metric that encodes the desired anisotropy, we transform the problem to one of functional approximation. We construct a convex function over each mesh simplex whose Hessian locally matches the Riemannian metric, and iteratively adapt vertex positions and mesh connectivity to minimize the difference between the target convex functions and their piecewise-linear interpolation over the mesh. Our method generalizes optimal Delaunay triangulation and leads to a simple and efficient algorithm. We demonstrate its quality and speed compared to state-of-the-art methods on a variety of domains and metrics.