Miracle-dz/UniFAR-FTUs huggingface dataset:
A comparison of the IGBP DISCover and University of Maryland 1 km global land cover products

Two global 1 km land cover data sets derived from 1992-1993 Advanced Very High Resolution Radiometer (AVHRR) data are currently available, the International Geosphere-Biosphere Programme Data and Information System (IGBP-DIS) DISCover and the University of Maryland (UMd) 1 km land cover maps. This paper makes a preliminary comparison of the methodologies and results of the two products. The DISCover methodology employed an unsupervised clustering classification scheme on a per-continent basis using 12 monthly maximum NDVI composites as inputs. The UMd approach employed a supervised classification tree method in which temporal metrics derived from all AVHRR bands and the NDVI were used to predict class membership across the entire globe. The DISCover map uses the IGBP classification scheme, while the UMd map employs a modified IGBP scheme minus the classes of permanent wetlands, cropland/natural vegetation mosaic and ice and snow. Global area totals of aggregated vegetation types are very similar and have a per-pixel agreement of 74%. For tall versus short/no vegetation, the per-pixel agreement is 84%. For broad vegetation types, core areas map similarly, while transition zones around core areas differ significantly. This results in high regional variability between the maps. Individual class agreement between the two 1 km maps is 49%. Comparison of the maps at a nominal 0.5 resolution with two global ground-based maps shows an improvement of thematic concurrency of 46% when viewing average class agreement. The absence of the cropland mosaic class creates a difficulty in comparing the maps, due to its significant extent in the DISCover map. The DISCover map, in general, has more forest, while the UMd map has considerably more area in the intermediate tree cover classes of woody savanna/ woodland and savanna/wooded grassland.
How Language Shapes Word of Mouth's Impact

Word of mouth affects consumer behavior, but how does the language used in word of mouth shape that impact? Might certain types of consumers be more likely to use certain types of language, affecting whose words have more influence? Five studies, including textual analysis of more than 1,000 online reviews, demonstrate that compared to more implicit endorsements (e.g., “I liked it,” “I enjoyed it”), explicit endorsements (e.g., “I recommend it”) are more persuasive and increase purchase intent. This occurs because explicit endorsers are perceived to like the product more and have more expertise. Looking at the endorsement language consumers actually use, however, shows that while consumer knowledge does affect endorsement style, its effect actually works in the opposite direction. Because novices are less aware that others have heterogeneous product preferences, they are more likely to use explicit endorsements. Consequently, the endorsement styles novices and experts tend to use may lead to greater persuasion by novices. These findings highlight the important role that language, and endorsement styles in particular, plays in shaping the effects of word of mouth.
Scattering by an impedance cylinder immersed halfway between dielectric-metamaterial half-spaces

The scattering of the electromagnetic field by an impedance cylinder immersed halfway between two different half-spaces is considered. One of the half-spaces is composed of a dielectric material, and the other a metamaterial. In addition to the general case, several special cases are also considered. These cases include scattering by a thin wire, the limiting values for the surface impedance on the cylinder (PEC and PMC), and the reverse isovelocity case.
Combining the Meiosis Gibbs Sampler With the Random Walk Approach for Linkage and Association Studies With a General Complex Pedigree and Multimarker Loci

A linkage analysis for finding inheritance states and haplotype configurations is an essential process for linkage and association mapping. The linkage analysis is routinely based upon observed pedigree information and marker genotypes for individuals in the pedigree. It is not feasible for exact methods to use all such information for a large complex pedigree especially when there are many missing genotypic data. Proposed Markov chain Monte Carlo approaches such as a single-site Gibbs sampler or the meiosis Gibbs sampler are able to handle a complex pedigree with sparse genotypic data; however, they often have reducibility problems, causing biased estimates. We present a combined method, applying the random walk approach to the reducible sites in the meiosis sampler. Therefore, one can efficiently obtain reliable estimates such as identity-by-descent coefficients between individuals based on inheritance states or haplotype configurations, and a wider range of data can be used for mapping of quantitative trait loci within a reasonable time.
Prediction of large esophageal varices in cirrhotic patients using classification and regression tree analysis

OBJECTIVES: Recent guidelines recommend that all cirrhotic patients should undergo endoscopic screening for esophageal varices. That identifying cirrhotic patients with esophageal varices by noninvasive predictors would allow for the restriction of the performance of endoscopy to patients with a high risk of having varices. This study aimed to develop a decision model based on classification and regression tree analysis for the prediction of large esophageal varices in cirrhotic patients. METHODS: 309 cirrhotic patients (training sample, 187 patients; test sample 122 patients) were included. Within the training sample, the classification and regression tree analysis was used to identify predictors and prediction model of large esophageal varices. The prediction model was then further evaluated in the test sample and different Child‐Pugh classes. RESULTS: The prevalence of large esophageal varices in cirrhotic patients was 50.8%. A tree model that was consisted of spleen width, portal vein diameter and prothrombin time was developed by classification and regression tree analysis achieved a diagnostic accuracy of 84% for prediction of large esophageal varices. When reconstructed into two groups, the rate of varices was 83.2% for high‐risk group and 15.2% for low‐risk group. Accuracy of the tree model was maintained in the test sample and different Child‐Pugh classes. CONCLUSIONS: A decision tree model that consists of spleen width, portal vein diameter and prothrombin time may be useful for prediction of large esophageal varices in cirrhotic patients.
Characterization of IIP2 and DC-offsets in transconductance mixers

Envelope distortion in transconductance mixers is analyzed in this paper. The analysis consists of a simplified theoretical study, simulation results, and measurements about several implemented mixers. The studied mixers are active single-balanced and double-balanced structures. A theoretical mismatch analysis is carried out instead of a complex mixer nonlinearity analysis; this gives an intuitive sense of even-order nonlinearity in balanced mixers, and provides information about the fundamental restrictions of second-order input intercept point (IIP2) and the relations of different imbalances to actual performance. The analysis uses a number of simplifying assumptions, but without losing generality. The IIP2 is sensitive to any mismatch in a differential circuit topology. It will be shown that an excellent IIP2 can be achieved in slightly imbalanced conditions by a controllable mismatch device.
Fcγ-receptor IIa-mediated Src Signaling Pathway Is Essential for the Antibody-Dependent Enhancement of Ebola Virus Infection

Antibody-dependent enhancement (ADE) of Ebola virus (EBOV) infection has been demonstrated in vitro, raising concerns about the detrimental potential of some anti-EBOV antibodies. ADE has been described for many viruses and mostly depends on the cross-linking of virus-antibody complexes to cell surface Fc receptors, leading to enhanced infection. However, little is known about the molecular mechanisms underlying this phenomenon. Here we show that Fcγ-receptor IIa (FcγRIIa)-mediated intracellular signaling through Src family protein tyrosine kinases (PTKs) is required for ADE of EBOV infection. We found that deletion of the FcγRIIa cytoplasmic tail abolished EBOV ADE due to decreased virus uptake into cellular endosomes. Furthermore, EBOV ADE, but not non-ADE infection, was significantly reduced by inhibition of the Src family protein PTK pathway, which was also found to be important to promote phagocytosis/macropinocytosis for viral uptake into endosomes. We further confirmed a significant increase of the Src phosphorylation mediated by ADE. These data suggest that antibody-EBOV complexes bound to the cell surface FcγRIIa activate the Src signaling pathway that leads to enhanced viral entry into cells, providing a novel perspective for the general understanding of ADE of virus infection.
Fuzzy clustering Approach in segmentation of T 1T 2 brain MRI

−Segmentation is a difficult and challenging problem in the magnetic resonance images, and it considered as important in computer vision and artificial intelligence. Many researchers have applied various techniques however fuzzy c-means (FCM) based algorithms is more effective compared to other methods. In this paper, we present a novel FCM algorithm for weighted bias (also called intensity in-homogeneities) estimation and segmentation of MRI. Normally, the intensity inhomogeneities are attributed to imperfections in the radio-frequency coils or to the problems associated with the image acquisition. Our algorithm is formulated by modifying the objective function of the standard FCM and it has the advantage that it can be applied at an early stage in an automated data analysis. Further this paper proposes a center knowledge method in order to reduce the running time of proposed algorithm. The proposed method can deal with the intensity in-homogeneities and image noise effectively. We have compared our results with other reported methods. The results using real MRI data show that our method provides better results compared to standard FCM based algorithms and other modified FCM-based techniques. Index Terms − Bias field, MRI, FCM, Segmentation, Data analysis.
Pan-private Algorithms: When Memory Does Not Help

Consider updates arriving online in which the $t$th input is $(i_t,d_t)$, where $i_t$'s are thought of as IDs of users. Informally, a randomized function $f$ is {\em differentially private} with respect to the IDs if the probability distribution induced by $f$ is not much different from that induced by it on an input in which occurrences of an ID $j$ are replaced with some other ID $k$ Recently, this notion was extended to {\em pan-privacy} where the computation of $f$ retains differential privacy, even if the internal memory of the algorithm is exposed to the adversary (say by a malicious break-in or by fiat by the government). This is a strong notion of privacy, and surprisingly, for basic counting tasks such as distinct counts, heavy hitters and others, Dwork et al~\cite{dwork-pan} present pan-private algorithms with reasonable accuracy. The pan-private algorithms are nontrivial, and rely on sampling. We reexamine these basic counting tasks and show improved bounds. In particular, we estimate the distinct count $\Dt$ to within $(1\pm \eps)\Dt \pm O(\polylog m)$, where $m$ is the number of elements in the universe. This uses suitably noisy statistics on sketches known in the streaming literature. We also present the first known lower bounds for pan-privacy with respect to a single intrusion. Our lower bounds show that, even if allowed to work with unbounded memory, pan-private algorithms for distinct counts can not be significantly more accurate than our algorithms. Our lower bound uses noisy decoding. For heavy hitter counts, we present a pan private streaming algorithm that is accurate to within $O(k)$ in worst case; previously known bound for this problem is arbitrarily worse. An interesting aspect of our pan-private algorithms is that, they deliberately use very small (polylogarithmic) space and tend to be streaming algorithms, even though using more space is not forbidden.
A New Mixed Gate Diffusion Input Full Adder Topology for High Speed Low Power Digital Circuits

This paper mainly presents Mixed Gate Diffusion Input Full Adder based on static CMOS inverter topology. In this proposed mixed Full Adder topology, GDI Full adders are followed by inverters in the long Full Adder chain to improve the performances as compared to conventional single topology Full adder chain. For any circuits reducing the speed and power dissipation are the important constraints. By changing the number of full adders between two consecutive inverters the delay, the dynamic and leakage power dissipation can be optimized. Delay and power has been evaluated by HSPICE simulation using TSMC 0. 35µm and 0. 18µm CMOS technologies considering minimum power design. The simulation results reveal better delay and power performance of proposed mixed full adder topology as compared to existing mixed Full Adder topologies at both 0. 35µm and 0. 18µm CMOS technologies.
Advertising keyword suggestion based on concept hierarchy

The increasing growth of the World Wide Web constantly enlarges the revenue generated by search engine advertising. Advertisers bid on keywords associated with their products to display their ads on the search result pages. Keyword suggestion methods are proposed to fill the gap between the keywords chosen by advertisers and the popular queries, through finding new relevant keywords according to some statistical information (for example, the keyword co-occurrence). However, there is little effort taking semantic information, such as concept hierarchy, into account. In this paper, we propose a novel keyword suggestion method that fully exploits the semantic knowledge among concept hierarchy. Given a keyword, we first match it with some relevant concepts. Then the relevant concepts are used with their hierarchy to fertilize the meanings of the keywords. Finally new keywords are suggested according to the concept information rather than the statistical co-occurrence of the keyword itself. Experimental results show that our proposed method can successfully provide suggestion that meets the accuracy and coverage requirements
The soft Pomeron from AdS-CFT

In previous treatments, high energy QCD was analyzed using AdS-CFT a la Polchinski-Strassler. Black hole production in AdS was responsible for power law behaviour of the total QCD cross section. Using the simplest self-consistent gravity dual assumption, that cut-off $AdS_5$ is supplemented by a 5d space $X_5$ of effective ``average'' size much larger than the scale of $AdS_5$, we find an energy behaviour just before the saturation of the Froissart bound that is $\sigma_{tot} \sim s^{1/n}= s^{1/11}\simeq s^{0.0909}$. It comes from the solution of the Laplacean on $AdS_{d+1}\times X_{\bar{d}}$ behaving like $1/r^n=1/r^{2(d-1)+\bar{d}}= 1/r^{11}$. We argue that this should be present in real QCD as well, as string corrections to the dual scattering are small, and should onset at about $N_c^2 M_{1, glueball}\sim 10 GeV$. Experimentally, one found the ``soft Pomeron'' behaviour, $\sigma_{tot}\sim s^{0.093(2)}$, that onsets at about $ 9 GeV$, that was later argued to be replaced by the unitarized Froissart + reaction-dependent constant behaviour. We argue that the soft Pomeron and the dual behaviour represent the same physics, creation of an effective field theory ``soliton''-like structure (=black hole), that then decays, and so they have to be taken seriously. We thus have an experimental prediction of string theory, literally counting the extra dimensions.
Robust adaptive passification and stabilization of linear systems

Robust adaptive strict G-passification is considered for linear time-invariant systems with norm-bounded uncertainty. First, parameter-dependent static outputfeedback and robust static output-feedback are proved to be equivalent if hyper minimum phase properties are fulfilled. Linear matrix inequality based conditions are formulated for proving these properties for all uncertain parameters. These results prove to be nonconservative with respect to complex-valued uncertainties and, illustrated on an example, only sufficient when dealing with real uncertain parameters. The second major contribution is the constructive design of adaptive controllers that are proved to guarantee the robust adaptive passivity and the robust closed loop stability under the previous conditions. An application example of robust adaptive controller design for criuse missile model is given.
Quasi–thermal comptonization and GRBs

Quasi–thermal Comptonization in internal shocks formed between relativistic shells can account for the high energy emission of gamma–ray bursts (GRB). This is the dominant cooling mechanism if the typical energy of the emitting particles is achieved either through the balance between heating and cooling or as a result of electron–positron (e) pair production. Both processes yield sub/mildly relativistic energies. In this case the synchrotron spectrum is self–absorbed, providing the seed photons for the Comptonization process, whose spectrum is flat [ const] , ending either in an exponential cutoff or a Wien peak.
Set-Based Tasks within the Singularity-Robust Multiple Task-Priority Inverse Kinematics Framework: General Formulation, Stability Analysis, and Experimental Results

Inverse kinematics algorithms are commonly used in robotic systems to transform tasks to joint references, and several methods exist to ensure the achievement of several tasks simultaneously. The multiple task-priority inverse kinematics framework allows tasks to be considered in a prioritized order by projecting task velocities through the nullspaces of higher priority tasks. This paper extends this framework to handle setbased tasks, i.e. tasks with a range of valid values, in addition to equality tasks, which have a specific desired value. Examples of set-based tasks are joint limit and obstacle avoidance. The proposed method is proven to ensure asymptotic convergence of the equality task errors and the satisfaction of all high-priority set-based tasks. The practical implementation of the proposed algorithm is discussed, and experimental results are presented where a number of both set-based and equality tasks have been implemented on a 6 degree of freedom UR5 which is an industrial robotic arm from Universal Robots. The experiments validate the theoretical results and confirm the effectiveness of the proposed approach.
GRAPH OPTIMIZATION USING FRACTAL DECOMPOSITION

We introduce a method of hierarchically decomposing graph optimization problems to obtain approximate solutions with low computation. The method uses a partition on the graph to convert the original problem to a high level problem and several lower level problems. On each level, the resulting problems are in exactly the same form as the original one, so they can be further decomposed. In this way, the problems become fractal in nature. We use best-case and worstcase instances of the decomposed problems to establish upper and lower bounds on the optimal criteria, and these bounds are achieved with significantly less computation than what is required to solve the original problem. We show that as the number of hierarchical levels increases, the computational complexity approaches O(n) at the expense of looser bounds on the optimal solution. We demonstrate this method on three example problems: all-pairs shortest path, all-pairs maximum flow, and cooperative search. Large-scale simulations show that this fractal decomposition method is computationally fast and can yield good results for practical problems.
Jamming-Resilient Secure Neighbor Discovery in Mobile Ad Hoc Networks

Secure neighbor discovery is fundamental to mobile ad hoc networks (MANETs) deployed in hostile environments and refers to the process in which two neighboring nodes exchange messages to discover and authenticate each other. It is vulnerable to the jamming attack in which the adversary intentionally transmits radio signals to prevent neighboring nodes from exchanging messages. Anti-jamming communications often rely on spread-spectrum techniques, which depend on a spreading code common to the communicating parties but unknown to the jammer. The spread code, however, is impossible to establish before the communicating parties successfully discover each other. While several elegant approaches have been recently proposed to break this circular dependence, the unique features of neighbor discovery in MANETs make them not directly applicable. In this paper, we propose JR-SND, a jamming-resilient secure neighbor discovery scheme for MANETs based on direct-sequence spread spectrum and random spread-code predistribution. JR-SND enables neighboring nodes to securely discover each other with overwhelming probability despite the presence of omnipresent jammers. Detailed theoretical and simulation results confirm the efficacy and efficiency of JR-SND.
Building Recommendation System for Hotel

As we move into the third decade of the World Wide Web (WWW), there has been a vast change in the availability of online information. Discovering information has never been more mechanized as of now, just a mouse click away. The objective of Opinion Mining can be achieved by executing a cluster of search results based on the features and quality for a given item. For rating the product and providing opinions, examination of customer evaluation is most significant-which is a challenging problem. Thus in the above context this paper attempts to discuss about the techniques and tools used by the opinion mining.
Color-Based Image Retrieval Using Perceptually Modified Hausdorff Distance

In most content-based image retrieval systems, the color information is extensively used for its simplicity and generality. Due to its compactness in characterizing the global information, a uniform quantization of colors, or a histogram, has been the most commonly used color descriptor. However, a cluster-based representation, or a signature, has been proven to be more compact and theoretically sound than a histogram for increasing the discriminatory power and reducing the gap between human perception and computer-aided retrieval system. Despite of these advantages, only few papers have broached dissimilarity measure based on the cluster-based nonuniform quantization of colors. In this paper, we extract the perceptual representation of an original color image, a statistical signature by modifying general color signature, which consists of a set of points with statistical volume. Also we present a novel dissimilarity measure for a statistical signature called Perceptually Modified Hausdorff Distance (PMHD) that is based on the Hausdorff distance. In the result, the proposed retrieval system views an image as a statistical signature, and uses the PMHD as the metric between statistical signatures. The precision versus recall results show that the proposed dissimilarity measure generally outperforms all other dissimilarity measures on an unmodified commercial image database.