Miracle-dz/UniFAR-FTUs huggingface dataset:
Alternate ascending/descending directional navigation approach for imaging magnetization transfer asymmetry

A new method for imaging magnetization transfer (MT) asymmetry with no separate saturation pulse is proposed in this article. MT effects were generated from sequential two‐dimensional balanced steady‐state free precession imaging, where interslice MT asymmetry was separated from interslice blood flow and magnetic field inhomogeneity with alternate ascending/descending directional navigation (ALADDIN). Alternate ascending/descending directional navigation provided high‐resolution multislice MT asymmetry images within a reasonable imaging time of ∼3 min. MT asymmetry signals measured with alternate ascending/descending directional navigation were 1–2% of baseline signals (N = 6), in agreement with those from the conventional methods. About 70% of MT asymmetry signals were determined by the first prior slice. The frequency offset ranges in this study were >8 ppm from the water resonance frequency, implying that the MT effects were mostly associated with solid‐like macromolecules. Potential methods to make alternate ascending/descending directional navigation feasible for imaging amide proton transfer (∼3.5 ppm offset from the water resonance frequency) were discussed. Magn Reson Med, 2011. © 2010 Wiley‐Liss, Inc.
Optical-inertial tracking with active markers and changing visibility

This paper presents an optical-inertial tracking algorithm with explicit and assured optical marker identification. Active optical markers are sequentially or simultaneously triggered to achieve a maximum in quantity and quality of measurements available for tracking. Markers that appear in a camera image are identified by individual activation and are locally tracked in the 2D-images after initial identification. The 2D-position measurements of the cameras are combined with low latency measurements of acceleration and angular velocity from an inertial measurement unit. An Extended Kalman Filter is used for an ultra-tightly coupled data fusion, that takes advantage of all marker measurements with verified identity. The accurate, low latency tracking is robust with respect to temporary marker occlusions, as needed in applications where a robot is directly controlled with a tracked device. The tracking algorithms are implemented in real-time and verified with a test bed in a medical robotics context.
Image Classification and Retrieval from User-Supplied Tags

This paper proposes direct learning of image classification from user-supplied tags, without filtering. Each tag is supplied by the user who shared the image online. Enormous numbers of these tags are freely available online, and they give insight about the image categories important to users and to image classification. Our approach is complementary to the conventional approach of manual annotation, which is extremely costly. We analyze of the Flickr 100 Million Image dataset, making several useful observations about the statistics of these tags. We introduce a large-scale robust classification algorithm, in order to handle the inherent noise in these tags, and a calibration procedure to better predict objective annotations. We show that freely available, user-supplied tags can obtain similar or superior results to large databases of costly manual annotations.
Structural and biochemical studies of human lysine methyltransferase Smyd3 reveal the important functional roles of its post-SET and TPR domains and the regulation of its activity by DNA binding

The SET- and MYND-domain containing (Smyd) proteins constitute a special subfamily of the SET-containing lysine methyltransferases. Here we present the structure of full-length human Smyd3 in complex with S-adenosyl-l-homocysteine at 2.8 Å resolution. Smyd3 affords the first example that other region(s) besides the SET domain and its flanking regions participate in the formation of the active site. Structural analysis shows that the previously uncharacterized C-terminal domain of Smyd3 contains a tetratrico-peptide repeat (TPR) domain which together with the SET and post-SET domains forms a deep, narrow substrate binding pocket. Our data demonstrate the important roles of both TPR and post-SET domains in the histone lysine methyltransferase (HKMT) activity of Smyd3, and show that the hydroxyl group of Tyr239 is critical for the enzymatic activity. The characteristic MYND domain is located nearby to the substrate binding pocket and exhibits a largely positively charged surface. Further biochemical assays show that DNA binding of Smyd3 can stimulate its HKMT activity and the process may be mediated via the MYND domain through direct DNA binding.
Automatic image registration based on plain objects detection and recognition in remote sensing tasks

Image registration is central problem to many tasks in digital image processing and therefore it has a vast range of applications. A scheme of automatic image registration based on plain objects detection and recognition in remote sensing tasks is presented in this paper. It relates to the concept of plain object in image and includes three subsystems. A subsystem of plain objects detection in image is based on a new method of plain objects detection in image. It relates to the land use classification process, different image segmentation methods and depends on the particular application. A subsystem of conjugate plain objects determination relate to a new method of plain objects recognition in images by shape based on stochastic geometry. It is invariant with respect to projective distortions. A subsystem of image registration determines an optimal geometric transformation and register given input images based on it. This paper reviews existing methods of image registration and emphasizes parametric, non-parametric and hybrid image registration techniques. Experimental results confirm the efficiency of the proposed system.
Detection of Visual Concepts and Annotation of Images Using Predictive Clustering Trees

In this paper, we present a multiple targets classification system for visual concepts detection and image annotation. Multiple targets classification (MTC) is a variant of classification where an instance may belong to multiple classes at the same time. The system is composed of two parts: feature extraction and classification/annotation. The feature extraction part provides global and local descriptions of the images. These descriptions are then used to learn a classifier and to annotate an image with the corresponding concepts. To this end, we use predictive clustering trees (PCTs), which are capable to classify an instance to multiple classes at once, thus exploit the interactions that may occur among the different visual concepts (classes). Moreover, we constructed ensembles (random forests) of PCTs, to improve the predictive performance. We tested our system on the image database from the visual concept detection and annotation task part of ImageCLEF 2010. The extensive experiments conducted on the benchmark database show that our system has very high predictive performance and can be easily scaled to large number of images and visual concepts.
Secondary Stiefel-Whitney class and diffeomorphisms of rational and ruled symplectic 4-manifolds

We introduce the secondary Stiefel-Whitney class $\tilde w_2$ of homotopically trivial diffeomorphisms and show that a homotopically trivial symplectomorphism of a ruled 4-manifold is isotopic to identity if and only if the class $\tilde w_2$ vanishes. Using this, we give a detailed description of the combinatorial structure of the diffeotopy group of ruled symplectic 4-manifolds $X$, either minimal or blown-up, and its action on the homology and homotopy groups $H_2(X,\zz)$, $\pi_1(X)$, and $\pi_2(X)$.
An Adaptive Performance Management Method for Failure Detection

Failure detection is an essential part to build high dependable distributed real-time embedded systems. However, the performance degradation of production work due to the execution of failure detection cannot be omitted. We present an adaptive performance management method for failure detection based on feedback control theory. This method is autonomous and thus allows the system to self-manage the CPU resources allocated for failure detection, with only the high-level policy input. Therefore, it limits the performance impact due to the execution of failure detection, even in the dynamic environment.
Addiction and Mindfulness; Pornography Addiction and Mindfulness-Based Therapy ACT

Abstract Mindfulness, more specifically acceptance and commitment therapy (ACT) may be the most optimal form of therapy to be used in treating problematic pornography use—use that may qualify as addiction, which falls within the realm of sex addiction. ACT would be beneficial to apply in a clinical setting for 1-on-1 therapy, coaching and sexuality workshops using the ACT model. Though some ambiguity exists on whether pornography addiction falls within the realm of sex addiction, there is sufficient theoretical framework to apply addiction identifiers to problematic pornography use, which further has implications to the benefits of using ACT in treating perceived problematic or addictive pornography use.
Prediction of attractive evaluation objects based on trend rules and topic dictionary

This paper proposes an improvement method for prediction of attractive evaluation objects based on trend rules. The trend rules represent relationships among evaluation objects, key phrases, and numerical changes related to the evaluation objects. The trend rules are inductively acquired from text sequential data and numerical sequential data. The method assigns evaluation objects to the text one by activating topic dictionary. The dictionary stores key phrases representing the numerical change. It can expand the amount of the learning data. It is anticipated that the expansion leads to acquire more valid trend rules. This paper applies the method to a task which predicts attractive stock brands based on both news headlines and stock price sequences. It shows that the method can improve the detection performance of evaluation objects through experiments.
LQG control of steam temperature in power plants

This paper presents an application of LQG control to the regulation of steam temperature in a power plant. The (simulation) results are compared to those obtained with optimised PID controllers, and it is shown that LQG yields much tighter setpoint control especially during transients. It is shown that these improved control performance have significant positive economic repercussions.
Tyrosylprotein Sulfotransferase-1 and Tyrosine Sulfation of Chemokine Receptor 4 Are Induced by Epstein-Barr Virus Encoded Latent Membrane Protein 1 and Associated with the Metastatic Potential of Human Nasopharyngeal Carcinoma

The latent membrane protein 1 (LMP1), which is encoded by the Epstein-Barr virus (EBV), is an important oncogenic protein that is closely related to carcinogenesis and metastasis of nasopharyngeal carcinoma (NPC), a prevalent cancer in China. We previously reported that the expression of the functional chemokine receptor CXCR4 is associated with human NPC metastasis. In this study, we show that LMP1 induces tyrosine sulfation of CXCR4 through tyrosylprotein sulfotransferase-1 (TPST-1), an enzyme that is responsible for catalysis of tyrosine sulfation in vivo, which is likely to contribute to the highly metastatic character of NPC. LMP1 could induce tyrosine sulfation of CXCR4 and its associated cell motility and invasiveness in a NPC cell culture model. In contrast, the expression of TPST-1 small interfering RNA reversed LMP1-induced tyrosine sulfation of CXCR4. LMP1 conveys signals through the epidermal growth factor receptor (EGFR) pathway, and EGFR-targeted siRNA inhibited the induction of TPST-1 by LMP1. We used a ChIP assay to show that EGFR could bind to the TPST-1 promoter in vivo under the control of LMP1. A reporter gene assay indicated that the activity of the TPST-1 promoter could be suppressed by deleting the binding site between EGFR and TPST-1. Finally, in human NPC tissues, the expression of TPST-1 and LMP1 was directly correlated and clinically, the expression of TPST-1 was associated with metastasis. These results suggest the up-regulation of TPST-1 and tyrosine sulfation of CXCR4 by LMP1 might be a potential mechanism contributing to NPC metastasis.
The Spike Response Model

1. The Spike Response Model 1 ABSTRACT A description of neuronal activity on the level of ion channels, as in the Hodgkin-Huxley model, leads to a set of coupled nonlinear diier-ential equations which are diicult to analyze. In this paper, we present a conceptual framework for a reduction of the nonlinear spike dynamics to a threshold process. Spikes occur if the membrane potential u(t) reaches a threshold #. The voltage response to spike input is described by the postsy-naptic potential. Postsynaptic potentials of several input spikes are added linearly until u reaches #. The output pulse itself and the reset/refractory period which follow the pulse are described by a function. Since and can be interpreted as response kernels, the resulting model is called the Spike Response Model (SRM). After a short review of the Hodgkin-Huxley model we show that (i) Hodgkin-Huxley dynamics with time-dependent input can be reproduced to a high degree of accuracy by the SRM; (ii) the simple integrate-and-re neuron is a special case of the Spike Response Model; (iii) compartmental neurons with a passive dendritic tree and a threshold process for spike generation can be treated in SRM-framework; (iv) small nonlinearities lead to interactions between spikes to be described by higher-order kernels.
An Exploration of Formalized Retrieval Heuristics

Empirical studies of information retrieval methods show that good retrieval performance is closely related to the use of various retrieval heuristics, such as TF-IDF weighting. Any effective retrieval formula, no matter how it is originally motivated, also often boils down to an explicit or implicit implementation of these heuristics. One basic research question is thus what are exactly these “necessary” heuristics that seem to cause good retrieval performance. In this paper, we present a form l study of these retrieval heuristics. We formally define a set of basic desirable constraints that any reasonable retrieval function should satisfy, and check these constraints on a variety of representative retrieval functions. We find that none of these retrieval functions satisfies all the constraints unconditionally. Empirical results show that when a constraint is not satisfied, it often indicates non-optimality of the method, and when a constraint is only satisfied for a certain range of parameter values, its performance tends to be poor when the parameter is out of the range. In general, we find that the empirical performance of a retrieval formula is tightly related to how well they satisfy these constraints. Thus the proposed constraints can provide a good explanation of many empirical observations and make it possible to evaluate any existing or new retrieval formula analytically.
From neural activation to symbolic alignment: A network-based approach to the formation of dialogue lexica

We present a lexical network model, called TiTAN, that captures the formation and the structure of natural language dialogue lexica. The model creates a bridge between neural connectionist networks and symbolic architectures: On the one hand, TiTAN is driven by the neural motor of lexical alignment, namely priming. On the other hand, TiTAN accounts for observed symbolic output of interlocutors, namely uttered words. The TiTAN series update is driven by the dialogue inherent dynamics of turns and incorporates a measure of the structural similarity of graphs. This allows to apply and evaluate the model: TiTAN is tested classifying 55 experimental dialogue data according to their alignment status. The trade-off between precision and recall of the classification results in an F-score of 0.92.
Relationship between Resting Pulse Rate and Lipid Metabolic Dysfunctions in Chinese Adults Living in Rural Areas

Background Resting pulse rate has been observed to be associated with cardiovascular diseases. However, its association with lipid metabolic dysfunctions remains unclear, especially resting pulse rate as an indicator for identifying the risk of lipid metabolic dysfunctions. The purpose of this study was to examine the association between resting pulse rate and lipid metabolic dysfunctions, and then evaluate the feasibility of resting pulse rate as an indicator for screening the risk of lipid metabolic dysfunctions. Methods A cross-sectional survey was performed, and 16,926 subjects were included in this study from rural community residents aged 35–78 years. Resting pulse rate and relevant covariates were collected from a standard questionnaire. The fasting blood samples were collected and measured for lipid profile. Predictive performance was analyzed by receiver operating characteristic (ROC) curve. Results A significant correlation was observed between resting pulse rate and TC (r = 0.102, P = 0.001), TG (r = 0.182, P = 0.001), and dyslipidemia (r = 0.037, P = 0.008). In the multivariate models, the adjusted odds ratios for hypercholesterolemia (from 1.07 to 1.15), hypertriglyceridemia (1.11 to 1.16), low HDL hypercholesterolemia (1.03 to 1.06), high LDL hypercholesterolemia (0.92 to 1.14), and dyslipidemia (1.04 to 1.07) were positively increased across quartiles of resting pulse rate (P for trend <0.05). The ROC curve indicated that resting pulse rate had low sensitivity (78.95%, 74.18%, 51.54%, 44.39%, and 54.22%), specificity (55.88%, 59.46%, 57.27%, 65.02%, and 60.56%), and the area under ROC curve (0.70, 0.69, 0.54, 0.56, and 0.58) for identifying the risk of hypercholesterolemia, hypertriglyceridemia, low HDL hypercholesterolemia, high LDL hypercholesterolemia, and dyslipidemia, respectively. Conclusion Fast resting pulse rate was associated with a moderate increased risk of lipid metabolic dysfunctions in rural adults. However, resting pulse rate as an indicator has limited potential for screening the risk of lipid metabolic dysfunctions.
A combined process algebraic, agent and fluid flow approach to emergent crowd behaviour

Emergent phenomena occur due to the pattern of non-linear and distributed local interactions be-tween the elements of a system over time. Surprisingly, agent based crowd models in which the movement of each individual follows a limited set of simple rules often re-produce quite closely the emergent behaviour of crowds that can be observed in reality. An example of such phenomena is the spontaneous self-organisation of drinking parties in the squares of cities in Spain, also known as “El Botell ´ on” [22]. We revisit this case study providing an elegant stochastic process algebraic model in Bio-PEPA amenable to several forms of analyses among which simulation and ﬂuid ﬂow analysis. We show that a ﬂuid ﬂow approximation, i.e. a deterministic reading of the average behaviour of the system, can provide an alternative and efﬁcient way to study the same emergent behaviour as that explored in [22] where simulation was used instead. Besides empirical evidence also an analytical justiﬁcation is provided for the good correspondence found between simulation results and the ﬂuid ﬂow approximation. Scalability features of the ﬂuid ﬂow approach may make it particularly useful when studying models of more complex city topologies with very large populations.
Towards optimized systems requirements specifications (SRS): Multiple regressional statistics as catalyst — Work in progress

The proposed approach was based on predictive analytics technique that would be applied by Joint Application Development (JAD) session's planners to expedite and build an accurate systems requirement specification document. Relevant literature of analytical prediction as well as JAD best practices were synthesized and employed to build a use scenario which guided decisions in this paper. Applying predictive analytics driven by multiple regressional statistics in the requirements gathering process will hasten and augment probabilities of producing a more qualitative systems requirement specification rich with historical planning experience of JAD sessions. This approach is recommended to JAD session planners in order to mitigate the information overload from the early stages of systems development.
Decoding, Calibration and Rectification for Lenselet-Based Plenoptic Cameras

Plenoptic cameras are gaining attention for their unique light gathering and post-capture processing capabilities. We describe a decoding, calibration and rectification procedure for lenselet-based plenoptic cameras appropriate for a range of computer vision applications. We derive a novel physically based 4D intrinsic matrix relating each recorded pixel to its corresponding ray in 3D space. We further propose a radial distortion model and a practical objective function based on ray reprojection. Our 15-parameter camera model is of much lower dimensionality than camera array models, and more closely represents the physics of lenselet-based cameras. Results include calibration of a commercially available camera using three calibration grid sizes over five datasets. Typical RMS ray reprojection errors are 0.0628, 0.105 and 0.363 mm for 3.61, 7.22 and 35.1 mm calibration grids, respectively. Rectification examples include calibration targets and real-world imagery.
What novel scientific and technological questions developmental robotics bring to HRI? — Are we ready for a loop?

New fundamental concepts, experimental methods and techniques have been growing recently about the understanding of developmental social learning in humans and robots. They are at the crossroads of research in developmental robotics, human-robot interaction, and the psychology and neuroscience of human social learning. This newsletter features a stimulating dialog illustrating the high-importance of these concepts and techniques both for building machines capable of learning in interaction with nonengineer humans across an extended time-scale, but also for better understanding human development. Together with an initiation and a synthesis by Katharina Rohlfing and Britta Wrede, the newsletter features contributions from Yukie Nagaï, Andrea Thomaz, Lakshmi Gogate, Adriana Tapus, Stevo Bozinoski, Anthony Morse, Thomas Hannagan, Rachel Wu, and Helge Ritter.