Miracle-dz/UniFAR-FTUs huggingface dataset:
An object-based and heterogeneous segment filter convolutional neural network for high-resolution remote sensing image classiﬁcation

ABSTRACT In recent years, object-based segmentation methods and shallow-model classification algorithms have been widely integrated for remote sensing image supervised classification. However, as the image resolution increases, remote sensing images contain increasingly complex characteristics, leading to higher intraclass heterogeneity and interclass homogeneity and thus posing substantial challenges for the application of segmentation methods and shallow-model classification algorithms. As important methods of deep learning technology, convolutional neural networks (CNNs) can hierarchically extract higher-level spatial features from images, providing CNNs with a more powerful recognition ability for target detection and scene classification in high-resolution remote sensing images. However, the input of the traditional CNN is an image patch, the shape of which is scarcely consistent with a given segment. This inconsistency may lead to errors when directly using CNNs in object-based remote sensing classification: jagged errors may appear along the land cover boundaries, and some land cover areas may overexpand or shrink, leading to many obvious classification errors in the resulting image. To address the above problem, this paper proposes an object-based and heterogeneous segment filter convolutional neural network (OHSF-CNN) for high-resolution remote sensing image classiﬁcation. Before the CNN processes an image patch, the OHSF-CNN includes a heterogeneous segment filter (HSF) to process the input image. For the segments in the image patch that are obviously different from the segment to be classified, the HSF can differentiate them and reduce their negative influence on the CNN training and decision-making processes. Experimental results show that the OHSF-CNN not only can take full advantage of the recognition capabilities of deep learning methods but also can effectively avoid the jagged errors along land cover boundaries and the expansion/shrinkage of land cover areas originating from traditional CNN structures. Moreover, compared with the traditional methods, the proposed OHSF-CNN can achieve higher classification accuracy. Furthermore, the OHSF-CNN algorithm can serve as a bridge between deep learning technology and object-based segmentation algorithms thereby enabling the application of object-based segmentation methods to more complex high-resolution remote sensing images.
When Optimal Feedback Control Is Not Enough: Feedforward Strategies Are Required for Optimal Control with Active Sensing

Movement planning is thought to be primarily determined by motor costs such as inaccuracy and effort. Solving for the optimal plan that minimizes these costs typically leads to specifying a time-varying feedback controller which both generates the movement and can optimally correct for errors that arise within a movement. However, the quality of the sensory feedback during a movement can depend substantially on the generated movement. We show that by incorporating such state-dependent sensory feedback, the optimal solution incorporates active sensing and is no longer a pure feedback process but includes a significant feedforward component. To examine whether people take into account such state-dependency in sensory feedback we asked people to make movements in which we controlled the reliability of sensory feedback. We made the visibility of the hand state-dependent, such that the visibility was proportional to the component of hand velocity in a particular direction. Subjects gradually adapted to such a sensory perturbation by making curved hand movements. In particular, they appeared to control the late visibility of the movement matching predictions of the optimal controller with state-dependent sensory noise. Our results show that trajectory planning is not only sensitive to motor costs but takes sensory costs into account and argues for optimal control of movement in which feedforward commands can play a significant role.
Spatial-aware Multimodal Location Estimation for Social Images

Nowadays the locations of social images play an important role in geographic knowledge discovery. However, most social images still lack the location information, driving location estimation for social images to have recently become an active research topic. With the rapid growth of social images, new challenges have been posed: 1) data quality of social images is an issue because they are often associated with noises and error-prone user-generated content, such as junk comments and misspelled words; and 2) data sparsity exists in social images despite the large volume, since most of them are unevenly distributed around the world and their contextual information is often missing or incomplete. In this paper, we propose a spatial-aware multimodal location estimation (SMLE) framework to tackle the above challenges. Specifically, a spatial-aware language model (SLM) is proposed to detect the high quality location-indicative tags from large datasets. We also design a spatial-aware topic model, namely spatial-aware regularized latent semantic indexing (SRLSI), to discover geographic topics and alleviate the data sparseness problem existing in language modeling. Taking multi-modalities of social images into consideration, we employ the learning to rank approach to fuse multiple evidences derived from textual features represented by SLM and SRLSI, and visual features represented by bag-of-visual-words (BoVW). Importantly, an ad hoc method is introduced to construct the training dataset with spatial-aware relevance labels for learning to rank training. Finally, given a query image, its location is estimated as the location of its most relevant image returned from the learning to rank model. The proposed framework is evaluated on a public benchmark provided by MediaEval 2013 Placing Task, which contains more than 8.5 million images crawled from Flickr. Extensive experiments on this dataset demonstrate the superior performance of the proposed methods over the state-of-the-art approaches.
Design of Mixed-Voltage Crystal Oscillator Circuit in Low-Voltage CMOS Technology

In the nanometer-scale CMOS technology, the gate-oxide thickness has been scaled down to support a higher operating speed under a lower power supply (1timesVDD). However, the board-level voltage levels could be still in a higher voltage levels (2timesVDD, or even more) for compatible to some earlier interface specifications in a microelectronics system. The I/O interface circuits have been designed with consideration on the gate-oxide reliability in such mixed-voltage applications. In this work, a new mixed-voltage crystal oscillator circuit realized with low-voltage CMOS devices is proposed without suffering the gate-oxide reliability issue. The proposed mixed-voltage crystal oscillator circuit, which is one of the key I/O cells in a cell library, has been designed and verified in a 90-nm I-V CMOS process to serve 1/1.8-V mixed-voltage interface applications.
Two Regions of the Ryanodine Receptor Involved in Coupling withl-Type Ca2+ Channels*

Ryanodine receptors (RyRs) are present in the endoplasmic reticulum of virtually every cell type and serve critical roles, including excitation-contraction (EC) coupling in muscle cells. In skeletal muscle the primary control of RyR-1 (the predominant skeletal RyR isoform) occurs via an interaction with plasmalemmal dihydropyridine receptors (DHPRs), which function as both voltage sensors for EC coupling and as l-type Ca2+ channels (Rios, E., and Brum, G. (1987) Nature325, 717–720). In addition to “receiving” the EC coupling signal from the DHPR, RyR-1 also “transmits” a retrograde signal that enhances the Ca2+ channel activity of the DHPR (Nakai, J., Dirksen, R. T., Nguyen, H. T., Pessah, I. N., Beam, K. G., and Allen, P. D. (1996) Nature 380, 72–76). A similar kind of retrograde signaling (from RyRs tol-type Ca2+ channels) has also been reported in neurons (Chavis, P., Fagni, L., Lansman, J. B., and Bockaert, J. (1996) Nature 382, 719–722). To investigate the molecular mechanism of reciprocal signaling, we constructed cDNAs encoding chimeras of RyR-1 and RyR-2 (the predominant cardiac RyR isoform) and expressed them in dyspedic myotubes, which lack an endogenous RyR-1. We found that a chimera that contained residues 1,635–2,636 of RyR-1 both mediated skeletal-type EC coupling and enhanced Ca2+channel function, whereas a chimera containing adjacent RyR-1 residues (2,659–3,720) was only able to enhance Ca2+ channel function. These results demonstrate that two distinct regions are involved in the reciprocal interactions of RyR-1 with the skeletal DHPR.
Social networking service (SNS) enhancing the learning environment of youth: As an effective tool*

This research is focused on the quality of education as the fundamental goal of the study, and Nepalese youths are taken into consideration to evaluate their quality of education. In this internet era, the use of social networking services has become an essential activity in our daily lives, especially among the younger generation where its affects can be found in nearly every aspect of their lives. The use of social networking services has been an important activity of our daily routine. Social network services are extensively used by the majority of people nowadays for various purposes [1]. The use of SNS is dramatically increasing among youths. This research has analyzed the effectiveness of SNS on youth education as an important tool to adopt educational resources.As a research framework; this research has analyzed the behavior of youths by using different social networking services (SNS) and has examined its impact on their education as an effective tool for study. The negative aspects of SNS cannot be neglected, but this research focused more on the positive effects of SNS on their daily lives and on the education of youths. This research has been divided into three different phases: the first phase is to focus on analysis, the second phase is to conduct a case study and the third phase is to conduct an evaluation with its different data.
Deriving Effectiveness Measures for Data Quality Rules

The poor quality of data constitutes a major concern worldwide, and an obstacle to data integration and analysis efforts. Detecting errors and inconsistencies using application specic data quality rules play an important role in data quality assessment. These rules have dierent ecacy and cost under dierent circumstances. In our previous work, we have proposed a quantitative framework for measuring and comparing data quality rules in terms of their eectiveness. Eectiveness formulas are built from variables that represent probabilistic assumptions about the occurrence of errors in data values, and our earlier work gave examples of how to derive these formulas in an ad-hoc fashion. This paper lays the foundations of a workbench-approach for systematically deriving eectiveness formulas. The approach involves several steps, including building Bayesian network graphs, adding (symbolic) probabilities to the nodes in the graph, and deriving eectiveness formulas. The graphs are built algorithmically, for a large and useful class of data quality rules. We present this approach and its implementation in Python, and report its evaluation results, which show that the resulting formulas give reasonable estimates of eectiveness scores under various scenarios.
Clinicopathological Evaluation of Neck Masses

FNAC is the most cost-effective invasive pre-operative investigation, whose simplicity and safety justify its use for “selective” surgery in diagnosing different types of masses. Masses located in the head and neck most commonly originates from cervical lymph nodes, thyroid, salivary glands and other neck masses. The primary objective of the study was to assess the spectrum of head and neck masses and to determine the accuracy of FNAC in detection of various lesions in National Institute of Medical Science and Research Hospital. A prospective study was conducted between the period from December, 2013 to May, 2015 and patients willing to be a part of the study were included. The study was time period specific i.e. the patients which came to NIMS OPD during the specific time period of our study, initiated on December 2013 to May 2015. A total 55 patients were included in the study for statistical ease. All patients were clinically examined. Out of 55 cases, FNAC was done in 52 cases and rest 03 cases were thyroglossal cyst (02) & plunging ranula (01) which was diagnosed clinically & sonographically and later confirmed directly with histopath. Histopathological examination was done in all 55 cases.The results of the FNAC were compared with the tissue diagnosis obtained after surgical removal of the mass. Out of 55 patients diagnosed clinically, in 43 cases the histopathological report confirmed the FNAC diagnosis. In 12 cases, the histopathology report was different. Out of the 55 patients, 13 (23.63%) were of lymph node, 26 (47%) were of thyroid, 7 (12.72%) were of salivary gland and 09 (16.36%) were of other neck masses. Our study evaluated 55 patients and found the overall sensitivity of FNAC in the diagnosis of neck masses to be 78.03%, specificity to be 100% and accuracy 89.57%. Sensitivity was highest (85.71%) for thyroid and lowest for other neck swellings (66.66%). Fine needle aspiration cytology offers a simple method of diagnosis of neoplastic and non neoplastic lesions in the neck to confirm clinical diagnosis. It can be performed as an outpatient procedure.It is the most accurate where there is a close cooperation between clinician, cyto-pathologist and radiologist. Ultrasound guided FNAC allows samples to be taken from a more representative site and increase in accuracy. FNAC is an important tool for preoperative clinical diagnosis of non cystic neck masses with history of more than 3 months. Thus, it helps in planning the surgical management of neck masses and can be confirmed with HPE which is a “gold standard” for diagnosis. Hence we conclude that Fine needle aspiration cytology is a safe, simple and rapid method that can be done in diagnosing wide range of neck masses.
Efficient modeling of wave propagation in a vertical transversely isotropic attenuative medium based on fractional Laplacian

To efficiently simulate wave propagation in a vertical transversely isotropic (VTI) attenuative medium, we have developed a viscoelastic VTI wave equation based on fractional Laplacian operators under the assumption of weak attenuation ([Formula: see text]), where the frequency-independent [Formula: see text] model is used to mathematically represent seismic attenuation. These operators that are nonlocal in space can be efficiently computed using the Fourier pseudospectral method. We evaluated the accuracy of numerical solutions in a homogeneous transversely isotropic medium by comparing with theoretical predictions and numerical solutions by an existing viscoelastic-anisotropic wave equation based on fractional time derivatives. To accurately handle heterogeneous [Formula: see text], we select several [Formula: see text] values to compute their corresponding fractional Laplacians in the wavenumber domain and interpolate other fractional Laplacians in space. We determined its feasibility by modeling wave propagation in a 2D heterogeneous attenuative VTI medium. We concluded that the new wave equation is able to improve the efficiency of wave simulation in viscoelastic-VTI media by several orders and still maintain accuracy.
Inflammatory Effects of Menthol vs. Non-menthol Cigarette Smoke Extract on Human Lung Epithelial Cells: A Double-Hit on TRPM8 by Reactive Oxygen Species and Menthol

Clinical studies suggest that smokers with chronic obstructive pulmonary disease who use menthol cigarettes may display more severe lung inflammation than those who smoke non-menthol cigarette. However, the mechanisms for this difference remain unclear. Menthol is a ligand of transient receptor potential melastatin-8 (TRPM8), a Ca2+-permeant channel sensitive to reactive oxygen species (ROS). We previously reported that exposure of human bronchial epithelial cells (HBECs) to non-menthol cigarette smoke extract (Non-M-CSE) triggers a cascade of inflammatory signaling leading to IL-8 induction. In this study, we used this in vitro model to compare the inflammatory effects of menthol cigarette smoke extract (M-CSE) and Non-M-CSE and delineate the mechanisms underlying the differences in their impacts. Compared with Non-M-CSE, M-CSE initially increased a similar level of extracellular ROS, suggesting the equivalent oxidant potency. However, M-CSE subsequently produced more remarkable elevations in intracellular Ca2+, activation of the mitogen-activated protein kinases (MAPKs)/nuclear factor-κB (NF-κB) signaling, and IL-8 induction. The extracellular ROS responses to both CSE types were totally inhibited by N-acetyl-cysteine (NAC; a ROS scavenger). The intracellular Ca2+ responses to both CSE types were also totally prevented by NAC, AMTB (a TRPM8 antagonist), or EGTA (an extracellular Ca2+ chelator). The activation of the MAPK/NF-κB signaling and induction of IL-8 to both CSE types were suppressed to similar levels by NAC, AMTB, or EGTA. These results suggest that, in addition to ROS generated by both CSE types, the menthol in M-CSE may act as another stimulus to further activate TRPM8 and induce the observed responses. We also found that menthol combined with Non-M-CSE induced greater responses of intracellular Ca2+ and IL-8 compared with Non-M-CSE alone. Moreover, we confirmed the essential role of TRPM8 in these responses to Non-M-CSE or M-CSE and the difference in these responses between the both CSE types using HBECs with TRPM8 knockdown and TRPM8 knockout, and using HEK293 cells transfected with hTRPM8. Thus, compared with exposure to Non-M-CSE, exposure to M-CSE induced greater TRPM8-mediated inflammatory responses in HBECs. These augmented effects may be due to a double-hit on lung epithelial TRPM8 by ROS generated from CSE and the menthol in M-CSE.
Cognitive Dynamic Systems: A Technical Review of Cognitive Radar

We start with the history of cognitive radar, where origins of the PAC, Fuster research on cognition and principals of cognition are provided. Fuster describes five cognitive functions: perception, memory, attention, language, and intelligence. We describe the Perception-Action Cyclec as it applies to cognitive radar, and then discuss long-term memory, memory storage, memory retrieval and working memory. A comparison between memory in human cognition and cognitive radar is given as well. Attention is another function described by Fuster, and we have given the comparison of attention in human cognition and cognitive radar. We talk about the four functional blocks from the PAC: Bayesian filter, feedback information, dynamic programming and state-space model for the radar environment. Then, to show that the PAC improves the tracking accuracy of Cognitive Radar over Traditional Active Radar, we have provided simulation results. In the simulation, three nonlinear filters: Cubature Kalman Filter, Unscented Kalman Filter and Extended Kalman Filter are compared. Based on the results, radars implemented with CKF perform better than the radars implemented with UKF or radars implemented with EKF. Further, radar with EKF has the worst accuracy and has the biggest computation load because of derivation and evaluation of Jacobian matrices. We suggest using the concept of risk management to better control parameters and improve performance in cognitive radar. We believe, spectrum sensing can be seen as a potential interest to be used in cognitive radar and we propose a new approach Probabilistic ICA which will presumably reduce noise based on estimation error in cognitive radar. Parallel computing is a concept based on divide and conquers mechanism, and we suggest using the parallel computing approach in cognitive radar by doing complicated calculations or tasks to reduce processing time.
Analysis of partial volume effects in diffusion‐tensor MRI

The diffusion tensor is currently the accepted model of diffusion in biological tissues. The measured diffusion behavior may be more complex when two or more distinct tissues with different diffusion tensors occupy the same voxel. In this study, a partial volume model of MRI signal behavior for two diffusion‐tensor compartments is presented. Simulations using this model demonstrate that the conventional single diffusion tensor model could lead to highly variable and inaccurate measurements of diffusion behavior. The differences between the single and two‐tensor models depend on the orientations, fractions, and exchange between the two diffusion tensor compartments, as well as the diffusion‐tensor encoding technique and diffusion‐weighting that is used in the measurements. The current single compartment model's inaccuracies could cause diffusion‐based characterization of cerebral ischemia and white matter connectivity to be incorrect. A diffusion‐tensor MRI imaging experiment on a normal human brain revealed significant partial volume effects between oblique white matter regions when using very large voxels and large diffusion‐weighting (b ∼ 2.69 × 103 sec/mm2). However, the apparent partial volume effects in white matter decreased significantly when smaller voxel dimensions were used. For diffusion tensor studies obtained using typical diffusion‐weighting values (b ∼ 1 × 103 sec/mm2) partial volume effects are much more difficult to detect and resolve. More accurate measurements of multiple diffusion compartments may lead to improved confidence in diffusion measurements for clinical applications. Magn Reson Med 45:770–780, 2001. © 2001 Wiley‐Liss, Inc.
Vis-à-Vis : Online Social Networking via Virtual Individual Ser vers

Online social networks (OSN) are hugely popular, but their centralized control of sensitive user data raises important privacy concerns. This paper presents Vis-à-Vis, a decentralized framework for online social networking based on the privacy-preserving notion of a Virtual Individual Server (VIS), a personal virtual machine running within a cloud-computing utility. In Vis-à-Vis, each person manages personal data such as friend lists, photographs, and messages through his own VIS. In addition, VISs collectively self-organize into larger groups corresponding to shared user attributes such as interests and educational background. Vis-à-Vis uses distributed hash tables to provide a decentralized, efficient, and scalable set of operations for joining, leaving, and searching a wide range of OSN groups. We have evaluated our Vis-à-Vis prototype implementation in several virtualcomputing environments using experimental parameters observed in the Facebook OSN. Our results show that our prototype’s performance is reasonable and demonstrate that Vis-à-Vis represents an attractive and feasib le alternative to today’s centralized OSNs.