Miracle-dz/UniFAR-FTUs huggingface dataset:
A New Method to Retrieve Topographic Shadow Based on Multi-spectral Operation

Topographic shadow and water body are seriously mixed in the classification of remotely sensed images by traditional methods, such as ISODATA, maximum likelihood and simple index (NDWI) based classification, especially in the roughly terrain areas. There are some theoretical methods for the retrieval and correction of topographic shadow, but they are not operable in practice because of some parameters involved. In this paper, a novel multi-spectral operational method (i.e., (( 4) ( 5)) ( ( 4) ( 3)) d band d band d band d band −× − ) is proposed for the retrieval of topographic shadow from the Landsat images. In this study, the multi-spectral operational method is combined with normalized difference water index (NDWI) for discriminating topographic shadows from water bodies. And then GIS analysis and variable-window focal analysis method, which is similar to per-field classification, are employed to remove noises for post classification smoothing of remotely sensed thematic images. The proposed method is employed for the retrieval of topographic shadows from remotely sensed images in dry-hot areas located in the Southwest of China, where the terrain is quite roughly. The results show that the proposed approach could discriminate topographic shadows from water body successfully.
Recognizing Human Actions by Learning and Matching Shape-Motion Prototype Trees

A shape-motion prototype-based approach is introduced for action recognition. The approach represents an action as a sequence of prototypes for efficient and flexible action matching in long video sequences. During training, an action prototype tree is learned in a joint shape and motion space via hierarchical K-means clustering and each training sequence is represented as a labeled prototype sequence; then a look-up table of prototype-to-prototype distances is generated. During testing, based on a joint probability model of the actor location and action prototype, the actor is tracked while a frame-to-prototype correspondence is established by maximizing the joint probability, which is efficiently performed by searching the learned prototype tree; then actions are recognized using dynamic prototype sequence matching. Distance measures used for sequence matching are rapidly obtained by look-up table indexing, which is an order of magnitude faster than brute-force computation of frame-to-frame distances. Our approach enables robust action matching in challenging situations (such as moving cameras, dynamic backgrounds) and allows automatic alignment of action sequences. Experimental results demonstrate that our approach achieves recognition rates of 92.86 percent on a large gesture data set (with dynamic backgrounds), 100 percent on the Weizmann action data set, 95.77 percent on the KTH action data set, 88 percent on the UCF sports data set, and 87.27 percent on the CMU action data set.
RED: A Reference Dependency Based MT Evaluation Metric

Most of the widely-used automatic evaluation metrics consider only the local fragments of the references and translations, and they ignore the evaluation on the syntax level. Current syntaxbased evaluation metrics try to introduce syntax information but suffer from the poor parsing results of the noisy machine translations. To alleviate this problem, we propose a novel dependency-based evaluation metric which only employs the dependency information of the references. We use two kinds of reference dependency structures: headword chain to capture the long distance dependency information, and fixed and floating structures to capture the local continuous ngram. Experiment results show that our metric achieves higher correlations with human judgments than BLEU, TER and HWCM on WMT 2012 and WMT 2013. By introducing extra linguistic resources and tuning parameters, the new metric gets the state-of-the-art performance which is better than METEOR and SEMPOS on system level, and is comparable with METEOR on sentence level on WMT 2012 and WMT 2013.
Supramolecular Aggregates in Vacuum: Positively Mono-Charged Sodium Alkanesulfonate Clusters

The formation and structural features of positively mono-charged aggregates of sodium bis(2-ethylhexyl) sulfosuccinate (AOT) and sodium methane—(MetS), butane—(ButS) and octane—(OctS) sulfonate molecules in the gas phase have been investigated by electrospray ionization mass spectrometry, energy-resolved mass spectrometry and density functional theory (DFT) calculations. The experimental results show that the center-of-mass collision energy required to dissociate 50% of these mono-charged aggregates scantly depends on the length of the alkyl chain as well as on the aggregation number. This, together with the large predominance of mono-charged species in the mass spectra, was rationalized in terms of an aggregation pattern mainly driven by the counter ions and head groups electrostatic interactions while minor effects were attributed to the steric hindrance caused by the size of the surfactant head group and alkyl chain. DFT calculations show that the most favored structural arrangement of these aggregates is always characterized by an internal polar core constituted by the sodium counter ions and surfactant head groups surrounded by an external layer composed by the surfactant alkyl chains.
Microbranching instability and the dynamic fracture of brittle materials.

We describe experiments on the dynamic fracture of the brittle plastic, PMMA. The results suggest a view of the fracture process that is based on the existence and subsequent evolution of an instability, which causes a single crack to become unstable to frustrated microscopic branching events. We demonstrate that a number of long-standing questions in the dynamic fracture of amorphous, brittle materials may be understood in this picture. Among these are the transition to crack branching, ``roughness'' and the origin of nontrivial fracture surface, oscillations in the velocity of a moving crack, the origin of the large increase in the energy dissipation of a crack with its velocity, and the large discrepancy between the theoretically predicted asymptotic velocity of a crack and its observed maximal value. Also presented are data describing both microbranch distribution and evidence of a new three-dimensional to two-dimensional transition as the ``correlation width'' of a microbranch diverges at high propagation velocities. \textcopyright{} 1996 The American Physical Society.
Architecture Query Language Framework

Software architecture is a key factor in the successof software product line (SPL) engineering. A SPL architecturehas to incorporate the commonalities as well as the variabilitiesof the products in a SPL. Presenting variability in the views andmodels that suite the stakeholders’ concerns is essential for thesuccess of a SPL. However, a literature study has taught us thatcurrent research provides insufficient attention to incorporatevariability in the architecture description. Moreover, experienceswith stakeholders in the field reveal that there is a need forflexible generation of architecture views tailored to the specificneeds of the stakeholders of a SPL.To tackle these challenges we propose the Architecture QueryLanguage Framework. Central in this framework are an architecturemodel for SPL, Dynamic Viewpoints (DV), and an ArchitectureQuery Language (AQL). The architecture model supportsmodeling of domain-specific architectures, covering structural,allocation and variability concerns. The model is supported byan architecture repository that reifies architectural knowledgeof the SPL. A DV defines the data model to support on-demandview derivation from the architecture repository according to thestakeholder needs. DV are supported by an Architecture QueryLanguage and an accompanying engine which allows flexible andfine-grained querying of the model repository. The research willbe evaluated in two case studies with industrial partners andan empirical study in the context of master course on advanced software architecture.
Moving window RtR fault/disturbance tolerant control approach in high-mix semiconductor manufacturing process

In this work, a new state estimation method based on linear model for high-mix semiconductor manufacturing processes is proposed. IMA(1,1) is a general disturbance model adopted in semiconductor manufacturing process, which can reflect the practical process disturbance. Taking IMA(1,1) disturbance into account, our algorithm exerts moving window approach on the linear model of the process and employs minimum norm estimation method to provide better estimates for the states without the IMA(1,1) model parameters. Furthermore, a step fault is considered and our approach can achieve fault-tolerant control. Comparing to the t-EWMA algorithm, this method provide better control performance, which is proved by simulation results.
A Model for Distance Delivery in Personnel Preparation

A model for delivery of a personnel-preparation program in education of visually handicapped people via distance-education formats is described. Critical shortages in personnel in the field of blindness and visual impairment are addressed through the description of the University of South Carolina's innovative distance-education program, delivered via telecommunications instruction, videocassettes, and alternative scheduling. Suggestions for transferring the content of traditional teacher-preparation curricula into instruction via distance-education formats are presented, as are future developments in distance education.
Neural Signatures of User-Centered Security: An fMRI Study of Phishing, and Malware Warnings

The security of computer systems often relies upon decisions and actions of end users. In this paper, we set out to investigate user-centered security by concentrating at the most fundamental component governing user behavior - the human brain. We introduce a novel neuroscience-based study methodology to inform the design of user-centered security systems. Specifically, we report on an fMRI study measuring users' security performance and the underlying neural activity with respect to two critical security tasks: (1) distinguishing between a legitimate and a phishing website, and (2) heeding security (malware) warnings. At a higher level, we identify neural markers that might be controlling users' performance in these tasks, and establish relationships between brain activity and behavioral performance as well as between users' personality traits and security behavior. Our results provide a largely positive perspective towards users' capability and performance vis-a-vis these crucial security tasks. First , we show that users exhibit significant brain activity in key regions associated with decision-making, attention, and problem-solving (phishing and malware warnings) as well as language comprehension and reading (malware warnings), which means that users are actively engaged in these security tasks. Second , we demonstrate that certain individual traits, such as impulsivity measured via an established questionnaire, can have a significant negative effect on brain activation in these tasks. Third , we discover a high degree of correlation in brain activity (in decision-making regions) across phishing detection and malware warnings tasks, which implies that users' behavior in one task may potentially be predicted by their behavior in the other task. Finally , we discuss the broader impacts and implications of our work on the field of user-centered security, including the domain of security education, targeted security training, and security screening.
, MD , FACP , FACE † Case presentations and discussions with

Although every clinician hopes to diagnose type 2 diabetes in the earliest stages, the United Kingdom Prospective Diabetes Study (UKPDS) revealed that 50% of diabetic complications are present upon the diagnosis of diabetes, even with a fasting plasma glucose (FPG) of 6.1 mM/L. This emphasizes the need for diagnosing diabetes in the earliest stages of insulin resistance, before complications such as dyslipidemia and coronary artery disease occur. Diagnosing insulin resistance can be difficult in busy clinical practices. Euglycemic clamp studies, intravenous glucose tolerance tests (IVGTT), the homeostasis model assessment (HOMA), and the quantitative insulin sensitivity check index (QUICKI) were developed to facilitate diagnosis; however, they are impractical for the clinician. Using the insulin-to-glucose ratio has long since proved unreliable. Thus, a simple method to determine insulin resistance is needed. My colleagues and I conducted 2 studies to determine an insulin sensitivity index (ISI). In the first study, the FPG was multiplied by the fasting plasma insulin (FPI), a common denominator in HOMA and its variations, to determine the ISI in 44 patients with impaired glucose tolerance (IGT), in 53 patients with type 2 diabetes mellitus, and in 41 healthy subjects.The validity and reliability of FPG x FPI was tested by comparison with the ISI, as determined by IVGTT in 26 type 2 diabetic patients, before and after treatment with glyburide or metformin. There were 78 pairs of values. The mean ISI was 100 (±7) in subjects with IGT, 217 (±19) in patients with type 2 diabetes, and 34 (±2) in healthy research subjects (IGT versus normals, P<.001; type 2 diabetes versus normals, P<.0005; and type 2 diabetes versus IGT, P<.001). Linear regression analysis between FPG x FPI and ISI as calculated by IVGTT minimal model showed a highly significant correlation (r = -0.53, P<.00005). We concluded that FPG x FPI could be used to reliably diagnose insulin resistance in clinical practice. Patients were treated with metformin or glyburide at baseline. After treatment, the product of FPG x FPI decreased in patients who received metformin, indiPROCEEDINGS
Inhibition of Lymphangiogenesis of Endothelial Progenitor Cells with VEGFR-3 siRNA Delivered with PEI-alginate Nanoparticles

Lymphangiogenesis is implicated in lymphatic metastasis of tumor cells. Recently, growing evidences show that endothelial progenitor cells (EPCs) are involved in lymphangiogenesis. This study has investigated effects of VEGF-C/VEGFR-3 (vascular endothelial growth factor receptor-3) signaling pathway on EPC differentiation and effectiveness of inhibiting lymphatic formation of EPCs with VEGFR-3 siRNA delivered in PEI (polyethylenimine)-alginate nanoparticles. CD34+VEGFR-3+ EPCs were sorted from mononuclear cells of human cord blood. Under induction with VEGF-C, the cells differentiated toward lymphatic endothelial cells. The nanoparticles were formulated with 25 kDa branched PEI and alginate. The size and surface charge of PEI-alginate nanoparticles loading VEGFR-3 siRNA (N/P = 16) are 139.1 nm and 7.56 mV respectively. VEGFR-3 siRNA specifically inhibited expression of VEGFR-3 mRNA in the cells. After treatment with PEI-alginate/siRNA nanocomplexes, EPCs could not differentiate into lymphatic endothelial cells, and proliferation, migration and lymphatic formation of EPC-derived cells were suppressed significantly. These results demonstrate that VEGFR-3 signaling plays an important role in differentiation of CD34+VEGFR-3+ EPCs. VEGFR-3 siRNA delivered with PEI-alginate nanoparticles can effectively inhibit differentiation and lymphangiogenesis of EPCs. Inhibiting VEGFR-3 signaling with siRNA/nanocomplexes would be a potential therapy for suppression of tumor lymphangiogenesis and lymphatic metastasis.
Optimizing Locally Linear Classifiers with Supervised Anchor Point Learning

Kernel SVM suffers from high computational complexity when dealing with large-scale nonlinear datasets. To address this issue, locally linear classifiers have been proposed for approximating nonlinear decision boundaries with locally linear functions using a local coding scheme. The effectiveness of such coding scheme depends heavily on the quality of anchor points chosen to produce the local codes. Existing methods usually involve a phase of unsupervised anchor point learning followed by supervised classifier learning. Thus, the anchor points and classifiers are obtained separately whereas the learned anchor points may not be optimal for the discriminative task. In this paper, we present a novel fully supervised approach for anchor point learning. A single optimization problem is formulated over both anchor point and classifier variables, optimizing the initial anchor points jointly with the classifiers to minimize the classification risk. Experimental results show that our method outperforms other competitive methods which employ unsupervised anchor point learning and achieves performance on par with the kernel SVM albeit with much improved efficiency.
Resting State Changes in Functional Connectivity Correlate With Movement Recovery for BCI and Robot-Assisted Upper-Extremity Training After Stroke

Background. Robot-assisted training may improve motor function in some hemiparetic patients after stroke, but no physiological predictor of rehabilitation progress is reliable. Resting state functional magnetic resonance imaging (RS-fMRI) may serve as a method to assess and predict changes in the motor network. Objective. The authors examined the effects of upper-extremity robot-assisted rehabilitation (MANUS) versus an electroencephalography-based brain computer interface setup with motor imagery (MI EEG-BCI) and compared pretreatment and posttreatment RS-fMRI. Methods. In all, 9 adults with upper-extremity paresis were trained for 4 weeks with a MANUS shoulder-elbow robotic rehabilitation paradigm. In 3 participants, robot-assisted movement began if no voluntary movement was initiated within 2 s. In 6 participants, MI-BCI–based movement was initiated if motor imagery was detected. RS-fMRI and Fugl-Meyer (FM) upper-extremity motor score were assessed before and after training. Results. The individual gain in FM scores over 12 weeks could be predicted from functional connectivity changes (FCCs) based on the pre-post differences in RS-fMRI measurements. Both the FM gain and FCC were numerically higher in the MI-BCI group. Increases in FC of the supplementary motor area, the contralesional and ipsilesional motor cortex, and parts of the visuospatial system with mostly association cortex regions and the cerebellum correlated with individual upper-extremity function improvement. Conclusion. FCC may predict the steepness of individual motor gains. Future training could therefore focus on directly inducing these beneficial increases in FC. Evaluation of the treatment groups suggests that MI is a potential facilitator of such neuroplasticity.
Evaluation of a MCA-based approach to organize data cubes

In the OLAP context, exploration of huge and sparse data cubes is a tedious task that does not always lead to efficient results. We propose to use a Multiple Correspondence Analysis (MCA) in order to enhance data cube representations and make them more suitable for visualization and thus, easier to analyze. We also provide an original quality criterion to measure the relevance of the obtained data representations. Experimental results we led on real data samples have shown the interest and the efficiency of our approach.
Research and application of access control technique in distributed 3D virtual environment

Aiming at the lack of adequate and flexible access control in 3D virtual environment when used in education, according to RBAC model, we present an authority control method which can manage manipulations to all objects, e.g. a variety of assets, avatars, scripts and network communication pipes in 3D virtual environment. The proposed technique overcomes the insufficiency on access control aspect in original platform, and provides more powerful and fexible ability on it. We experiment with the method in OpenSim, a free open source 3D virtual platform, the results show its effectiveness and versatility.
Pathological Impact of Hepatitis B Virus Surface Proteins on the Liver Is Associated with the Host Genetic Background

Background While the immune pathogenesis caused by hepatitis B virus (HBV) infection has been studied extensively, little is known about direct pathogenic effects of HBV surface proteins. Here, we have investigated pathological cellular effects of HBV surface protein expression in the liver of transgenic mice with different genetic background. Methods The impact of HBV surface protein expression on the liver was studied in two mouse strains, BALB/c and C57BL/6. Histology and hydroxyproline assays were performed to investigate liver morphology and fibrosis. Gene expression and signaling were analyzed by microarray, qPCR and Western blotting. Results Expression of HBV surface proteins in the liver of transgenic mice induced activation of protein kinase-like endoplasmic reticulum kinase (PERK) and eukaryotic initiation factor 2α (eIF2α) phosphorylation. Phosphorylation of eIF2α resulted in activation of the ER stress markers glucose regulated protein (GRP) 78 and pro-apoptotic C/EBP homologous protein (CHOP) in transgenic mice on BALB/c genetic background leading to stronger liver injury and fibrosis in comparison with transgenic mice on C57BL/6 background. Hepatic stellate cells represented the main collagen-producing liver cells in HBV transgenic mice. The key regulators of hepatocyte proliferation, transcription factors c-Jun and STAT3 were activated in HBV transgenic mice. Tumour incidence in transgenic mice was strain- and sex-dependent. Conclusions Extent of liver injury, fibrosis, and tumour development induced by hepatic HBV surface protein expression considerably depends on host genetic background.
EEG and Eye Tracking Demonstrate Vigilance Enhancement with Challenge Integration

Maintaining vigilance is possibly the first requirement for surveillance tasks where personnel are faced with monotonous yet intensive monitoring tasks. Decrement in vigilance in such situations could result in dangerous consequences such as accidents, loss of life and system failure. In this paper, we investigate the possibility to enhance vigilance or sustained attention using “challenge integration,” a strategy that integrates a primary task with challenging stimuli. A primary surveillance task (identifying an intruder in a simulated factory environment) and a challenge stimulus (periods of rain obscuring the surveillance scene) were employed to test the changes in vigilance levels. The effect of integrating challenging events (resulting from artificially simulated rain) into the task were compared to the initial monotonous phase. EEG and eye tracking data is collected and analyzed for n = 12 subjects. Frontal midline theta power and frontal theta to parietal alpha power ratio which are used as measures of engagement and attention allocation show an increase due to challenge integration (p < 0.05 in each case). Relative delta band power of EEG also shows statistically significant suppression on the frontoparietal and occipital cortices due to challenge integration (p < 0.05). Saccade amplitude, saccade velocity and blink rate obtained from eye tracking data exhibit statistically significant changes during the challenge phase of the experiment (p < 0.05 in each case). From the correlation analysis between the statistically significant measures of eye tracking and EEG, we infer that saccade amplitude and saccade velocity decrease with vigilance decrement along with frontal midline theta and frontal theta to parietal alpha ratio. Conversely, blink rate and relative delta power increase with vigilance decrement. However, these measures exhibit a reverse trend when challenge stimulus appears in the task suggesting vigilance enhancement. Moreover, the mean reaction time is lower for the challenge integrated phase (RTmean = 3.65 ± 1.4s) compared to initial monotonous phase without challenge (RTmean = 4.6 ± 2.7s). Our work shows that vigilance level, as assessed by response of these vital signs, is enhanced by challenge integration.