Miracle-dz/UniFAR-FTUs huggingface dataset:
Personal recommendation based on a user's understanding

Current web personalization mainly focuses on extracting web pages interesting to users. However, difficulty in understanding web contents often arises for some users mainly due to the lack of background knowledge on the contents. As a solution to this problem, this paper proposes a method of personalizing web search that produces search results suitable to the understanding level of a user. Fuzzy sets and membership functions are defined in order to keep adjusting the level of a user, which is updated based on the levels of web pages on which the user shows preferences. Extensive simulation experiments are conducted to tune the system parameters optimally and to show that the proposed system outperforms not only a general search engine but also a system that updates the understanding level of a user with the mean of the web page levels. © 2009 Wiley Periodicals, Inc. Comput Appl Eng Educ 20: 62–71, 2012
Highly structured tensor identities for (2,2)-forms in four dimensions

In an n dimensional vector space, any tensor which is antisymmetric in k>n arguments must vanish; this is a trivial consequence of the limited number of dimensions. However, when other possible properties of tensors, for example trace-freeness, are taken into account, such identities may be heavily disguised. Tensor identities of this kind were first considered by Lovelock, and later by Edgar and Hoeglund. In this paper we continue their work. We obtain dimensionally dependent identities for highly structured expressions of products of (2,2)-forms. For tensors possessing more symmetries, such as block symmetry W_{abcd} = W_{cdab}, or the first Bianchi identity W_{a[bcd]} = 0, we derive identities for less structured expressions. These identities are important tools when studying super-energy tensors, and, in turn, deriving identities for them. As an application we are able to show that the Bel-Robinson tensor, the super-energy tensor for the Weyl tensor, satisfies the equation T_{abcy}T^{abcx} = 1/4g^{x}_{y}T_{abcd}T^{abcd} in four dimensions, irrespective of the signature of the space.
Top-Down Regularization of Deep Belief Networks

Designing a principled and effective algorithm for learning deep architectures is a challenging problem. The current approach involves two training phases: a fully unsupervised learning followed by a strongly discriminative optimization. We suggest a deep learning strategy that bridges the gap between the two phases, resulting in a three-phase learning procedure. We propose to implement the scheme using a method to regularize deep belief networks with top-down information. The network is constructed from building blocks of restricted Boltzmann machines learned by combining bottom-up and top-down sampled signals. A global optimization procedure that merges samples from a forward bottom-up pass and a top-down pass is used. Experiments on the MNIST dataset show improvements over the existing algorithms for deep belief networks. Object recognition results on the Caltech-101 dataset also yield competitive results.
A Review of Deep Learning Methods and Applications for Unmanned Aerial Vehicles

Deep learning is recently showing outstanding results for solving a wide variety of robotic tasks in the areas of perception, planning, localization, and control. Its excellent capabilities for learning representations from the complex data acquired in real environments make it extremely suitable for many kinds of autonomous robotic applications. In parallel, Unmanned Aerial Vehicles (UAVs) are currently being extensively applied for several types of civilian tasks in applications going from security, surveillance, and disaster rescue to parcel delivery or warehouse management. In this paper, a thorough review has been performed on recent reported uses and applications of deep learning for UAVs, including the most relevant developments as well as their performances and limitations. In addition, a detailed explanation of the main deep learning techniques is provided. We conclude with a description of the main challenges for the application of deep learning for UAV-based solutions.
Learning Locality Preserving Graph from Data

Machine learning based on graph representation, or manifold learning, has attracted great interest in recent years. As the discrete approximation of data manifold, the graph plays a crucial role in these kinds of learning approaches. In this paper, we propose a novel learning method for graph construction, which is distinct from previous methods in that it solves an optimization problem with the aim of directly preserving the local information of the original data set. We show that the proposed objective has close connections with the popular Laplacian Eigenmap problem, and is hence well justified. The optimization turns out to be a quadratic programming problem with n(n - 1)/2 variables (n is the number of data points). Exploiting the sparsity of the graph, we further propose a more efficient cutting plane algorithm to solve the problem, making the method better scalable in practice. In the context of clustering and semi-supervised learning, we demonstrated the advantages of our proposed method by experiments.
Adaptive selectivity estimation using query feedback

In this paper, we propose a novel approach for estimating the record selectivities of database queries. The real attribute value distribution is adaptively approximated by a curve-fitting function using a query feedback mechanism. This approach has the advantage of requiring no extra database access overhead for gathering statistics and of being able to continuously adapt the value distribution through queries and updates. Experimental results show that the estimation accuracy of this approach is comparable to traditional methods based on statistics gathering.
Random migration precedes stable target cell interactions of tumor-infiltrating T cells

The tumor microenvironment is composed of an intricate mixture of tumor and host-derived cells that engage in a continuous interplay. T cells are particularly important in this context as they may recognize tumor-associated antigens and induce tumor regression. However, the precise identity of cells targeted by tumor-infiltrating T lymphocytes (TILs) as well as the kinetics and anatomy of TIL-target cell interactions within tumors are incompletely understood. Furthermore, the spatiotemporal conditions of TIL locomotion through the tumor stroma, as a prerequisite for establishing contact with target cells, have not been analyzed. These shortcomings limit the rational design of immunotherapeutic strategies that aim to overcome tumor-immune evasion. We have used two-photon microscopy to determine, in a dynamic manner, the requirements leading to tumor regression by TILs. Key observations were that TILs migrated randomly throughout the tumor microenvironment and that, in the absence of cognate antigen, they were incapable of sustaining active migration. Furthermore, TILs in regressing tumors formed long-lasting (≥30 min), cognate antigen–dependent contacts with tumor cells. Finally, TILs physically interacted with macrophages, suggesting tumor antigen cross-presentation by these cells. Our results demonstrate that recognition of cognate antigen within tumors is a critical determinant of optimal TIL migration and target cell interactions, and argue against TIL guidance by long-range chemokine gradients.
A fast MQ table based merging algorithm for image segmentation

This paper presents a simple scheme to segment an image in the compressed domain. Thus the burden of decompressing computation can be avoided. In addition, there is no need to transmit the segmentation result from encoder to decoder because the proposed method is based on the MQ table of JPEG2000, which is available at both encoder and decoder. Experimental results show that the running time can be reduced significantly and the boundary displacement error (BDE) measure can be improved.
Molecular characterization of Marek's disease herpesvirus B antigen

The Marek's disease herpesvirus (MDHV) B antigen (MDHV-B) was identified and molecularly characterized as a set of three glycoproteins of 100,000, 60,000, and 49,000 apparent molecular weight (gp100, gp60, and gp49, respectively) by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) after immunoprecipitation from [35S]methionine-labeled infected cells by specific rabbit antiserum directed against MDHV-B (R alpha B), as previously determined by immunodiffusion. Further identification was accomplished by blocking this immunoprecipitation with highly purified MDHV-B. The same set of three polypeptides was also immunoprecipitated from [35S]methionine- and 14C-labeled infected cells with two other sera shown to have anti-B activity, i.e., rabbit anti-MDHV-infected-cell plasma membrane (R alpha PM) and immune chicken serum from birds naturally infected with MDHV. The three herpesvirus of turkeys (HVT) B-antigen (HVT-B) glycoproteins immunoprecipitated with all three sera containing anti-B activity were also shown to be identical in size to those of MDHV-B by immunoprecipitation and SDS-PAGE. These data serve to clarify the molecular identification of the polypeptides found in common between MDHV and HVT by linking them to MDHV-B, previously identified only by immunodiffusion, and to a similarly sized set of immunologically related common glycoproteins called gp100, gp60, and gp49, detected with monoclonal antibody by other workers. Tunicamycin inhibition of N-linked glycosylation resulted in either nonglycosylated or O-linked glycosylated putative precursors of MDHV-B and HVT-B with apparent molecular weights of 88,000, called pr88, and 44,000, tentatively called pr44, both immunoprecipitable with all three sera. However, the relationships of these two polypeptides to each other and to the overall precursor-processing relationship of the MDHV-B complex remains to be elucidated. The three fully glycosylated B-antigen polypeptides were not connected by disulfide linkage. Collectively, the data presented here and by others support the conclusion that all three glycoproteins now identified as gp100, gp60, and gp49 have MDHV-B determinants. Finally, detection of the same three polypeptides with well-absorbed R alpha PM, which was directed against purified infected-cell plasma membranes, suggests that at least one component of the B-antigen complex has a plasma membrane location in the infected cell. (ABSTRACT TRUNCATED AT 400 WORDS)
Dynamic Antiplane Shear of a Circular Piezoelectric Fiber Embedded in an Elastic Matrix with Curved Interface Cracks

ABSTRACT Based on the dynamic theory of linear piezoelectricity, we investigated the scattering of horizontally polarized shear waves from a single piezoelectric fiber partially bonded to an elastic matrix. The debonding is assumed to be curved interface cracks with non-contacting faces. The wave function expansion method is employed to reduce the problem to the solution of a pair of dual series equations. The solution of the dual series equations is then expressed in terms of a singular integral equation, introducing a dislocation density function. Numerical results for P7-epoxy and N8-epoxy composites are obtained. The effects of frequency, crack angles, and distributions on the dynamic stress intensity factor, dynamic energy release rate, scattering cross section, and electrical signal are discussed in detail.
Immune response gene control of determinant selection. I. Intramolecular mapping of the immunogenic sites on insulin recognized by guinea pig T and B cells

T-cell DNA synthesis and T-helper cell function in response to isolated insulin chains and naturally occurring insulin variants was assessed in insulin immune guinea pigs. Two distinct antigenic determinants, recognized by T cells, were defined. One localized in the B chain and the other one constituted by amino acids A8, A9, and A10 of the insulin A-chain loop. Recognition of the B-chain determinant is under the control of Ir genes linked to the strain 13 major histocompatibility complex. This was shown by studying the response to isolated insulin B chain in F1(2 x 13) guinea pigs, as well as serologically defined backcrosses and outbred animals. Insulin recognition through the A- chain loop determinant is specific for strain 2 guinea pigs. These animals recognize this region of the molecule even when displaying different amino acid sequences. The strain differences observed in those antigenic sites eliciting T-cell recognition was not found at an antibody level. No differences could be detected in the ability of the different insulin variants to inhibit the binding of 125I-labeled pork insulin to strain 2 guinea pig anti-pork insulin or to strain 13 guinea pig anti-pork insulin.
Community-based Conference Navigator

As the sheer volume of information continues to grow, information overload challenges users in many ways. Large conferences are one of the venues suffering from this overload. Faced with several parallel sessions and large volumes of papers covering diverse areas of interest, conference participants often struggle to identify the most relevant sessions to attend. We have designed a community-based conference navigator system that uses social navigation support to help conference attendees schedule the most appropriate sessions and make sure that the most important papers are not overlooked.
Wireless power transfer with artificial magnetic conductors

In this paper, a wireless power transfer system (WPT) with magnetically coupled resonators is studied. The idea to use a reflector to enhance the coupling coefficient and the transfer efficiency is proposed and analyzed. Perfect magnetic conductor (PMC) was used in numerical calculations in order to study the performance of a reflector. In the WPT system we studied, efficiency is increased from 48% without reflector to 72% with PMC reflector. With recently developed metamaterial technologies, artificial PMCs can be designed and fabricated for such reflectors.
The Permanent Requires Large Uniform Threshold Circuits

We show that the permanent cannot be computed by uniform constantdepth threshold circuits of size T (n), for any function T such that for all k, T (n) = o(2). More generally, we show that any problem that is hard for the complexity class C=P requires circuits of this size (on the uniform constant-depth threshold circuit model). In particular, this lower bound applies to any problem that is hard for the complexity classes PP or #P. This extends a recent result by Caussinus, McKenzie, Th erien, and Vollmer [CMTV96], showing that there are problems in the counting hierarchy that require superpolynomial-size uniform TC circuits. The proof in [CMTV96] uses \leaf languages" as a tool in obtaining their separations, and their proof does not immediately yield larger lower bounds for the complexity of these problems, and it also does not yield a lower bound for any particular problem at any xed level of the counting hierarchy. (It only shows that hard problems must exist at some level of the counting hierarchy.) We also present related and somewhat weaker lower bounds, extending the theorem of [CMTV96] showing that ACC is properly contained in ModPH. A preliminary version of this work appeared in Proc. 2nd International Computing and Combinatorics Conference (COCOON '96). Supported in part by NSF grant CCR-9509603.
Principal Component Analysis of High-Frequency Data

ABSTRACT We develop the necessary methodology to conduct principal component analysis at high frequency. We construct estimators of realized eigenvalues, eigenvectors, and principal components, and provide the asymptotic distribution of these estimators. Empirically, we study the high-frequency covariance structure of the constituents of the S&P 100 Index using as little as one week of high-frequency data at a time, and examines whether it is compatible with the evidence accumulated over decades of lower frequency returns. We find a surprising consistency between the low- and high-frequency structures. During the recent financial crisis, the first principal component becomes increasingly dominant, explaining up to 60% of the variation on its own, while the second principal component drives the common variation of financial sector stocks. Supplementary materials for this article are available online.
Integrating Process Inquiry And The Case Method In The Study Of IS Failure

This paper examines the integration of process inquiry and the case method in the study of IS failure. Having acknowledged the prevalence of IS failure and the need for continued inquiry in this domain the two predominant methods of enquiry, factor and process studies, are described along with the utility of both methods. The paper then examines the nature of process inquiry and notes its utility and prevalence in the study of IS phenomena, and its potential applicability for inquiry into IS failure. The case study method is then briefly described along with its potential contribution when combined with process inquiry. The final section in the paper describes how the case method can provide an overall framework for the conduct of a process inquiry, and presents an iterative six stage research process model, based on the case method, to assist with the planning, design, preparation, data collection, data analysis, and reporting of findings.
SwapGuard: A software-only solution for attesting hot-swappable devices in power grids

Hot-swappable devices play an important role for achieving high availability and easy maintainability in power grids, especially in substations. However, they also present a potential attack vector for malware to penetrate an otherwise highly closed system. We propose SwapGuard, a software-only solution that effectively guards a power grid system against swapped-in devices infected with malware. Built upon existing software-based attestation techniques, SwapGuard's design can securely handle hot-swapping event and accommodate different types of devices and networks. We evaluate SwapGuard on real-world power grid devices. The experimental results show that SwapGuard incurs low overhead to the operation of the system, in terms of the additional time required before a swapped-in device starts to function, the overhead to bootstrap the whole system, and the overhead to defend against unattested devices.
A review of FPGA-based design methodology and optimization techniques for efficient hardware realization of computation intensive algorithms

Field programmable gate arrays (FPGAs) have emerged as platform of choice for efficient hardware realization of computation intensive algorithms because of their intrinsic parallelism and flexible architecture. However, to achieve high performance, FPGA must be supported by efficient design methodology and optimization techniques. In this paper, FPGA-based design methodology and optimization techniques that can be employed to obtain area, speed and power efficient circuits are reviewed and presented.
FACE AUTHENTICATION /RECOGNITION SYSTEM FOR FORENSIC APPLICATION USING SKETCH BASED ON THE SIFT FEATURES APPROACH

Faces are highly deformable objects which may easily change their appearance over time. Not all faces areas are subject to the same variability. The SIFT approach in the context of face authentication. Several pattern recognition and classification techniques have been applied to the biometrics domain. Among them, an interesting technique is the scale invariant feature transform (sift), originally devised for object recognition. The SIFT (scale invariant feature transform) is used for extracting local features. The recognition proceeds by matching individual features to a database of features from known objects. However, the proposed algorithm is generic and is not tied up to specific features. An advantage of the proposed algorithm is that it does not impose any restrictions on the location of features. They can be extracted from any point on the face and need not be ordered. It is concerned with the problem of correctly identifying face images and assigning them to persons in a database. This paper proposes using SIFT features for the recognition process. The new technique is compared with well-established face recognition algorithms.
A fast restarting particle swarm optimizer

Particle swarm optimization (PSO) is a swarm intelligence technique that optimizes a problem by iterative exploration and exploitation in the search space. However, PSO cannot achieve the preservation of population diversity on solving multimodal optimization problems, and once the swarm falls into local convergence, it cannot jump out of the local trap. In order to solve this problem, this paper presents a fast restarting particle swarm optimization (FRPSO), which uses a novel restarting strategy based on a discrete finite-time particle swarm optimization (DFPSO). Taking advantage of frequently speeding up the swarm to converge along with a greater exploitation capability and then jumping out of the trap, this algorithm can preserve population diversity and provide a superior solution. The experiment performs on twenty-five benchmark functions which consists of single-model, multimodal and hybrid composition problems, the experimental result demonstrates that the performance of the proposed FRPSO algorithm is better than the other three representatives of the advanced PSO algorithm on most of these functions.
Multilingual Viewpoint Detection from news comments

In this paper, we investigate the task of Multilingual Viewpoint Detection (MVD) on multilingual news reader comments. To tackle the MVD task, we propose a new probabilistic graphical model called VDMC to discover latent common viewpoints from multilingual news reader comments. Our VDMC model can cope with the language gap and detect common multilingual viewpoints. To learn the model parameters, we incorporate bilingual constraints into the variational Expectation-Maximization (EM) method. Experimental results show that our VDMC model can resolve the MVD task effectively and outperform the state-of-the-art method.
Cotransporter-Mediated Intracellular Na Accumulation Affects Ca 2 Signaling in Astrocytes in an In Vitro Ischemic Model

Na–K–Cl cotransporter isoform 1 (NKCC1) plays an important role in maintenance of intracellular Na , K , and Cl levels in astrocytes. We propose that NKCC1 may contribute to perturbations of ionic homeostasis in astrocytes under ischemic conditions. After 3– 8 hr of oxygen and glucose deprivation (OGD), NKCC1-mediated Rb influx was significantly increased in astrocytes from NKCC1 wild-type (NKCC1 / ) and heterozygous mutant (NKCC1 / ) mice. Phosphorylated NKCC1 protein was increased in NKCC1 / astrocytes at 2 hr of OGD. Two hours of OGD and 1 hr of reoxygenation (OGD/REOX) triggered an 3.6-fold increase in intracellular Na concentration ([Na ]i ) in NKCC1 / astrocytes. Inhibition of NKCC1 activity by bumetanide or ablation of the NKCC1 gene significantly attenuated the rise in [Na ]i. Moreover, NKCC1 / astrocytes swelled by 10 –30% during 20 – 60 min of OGD. Either genetic ablation of NKCC1 or inhibition of NKCC1 by bumetanide-attenuated OGD-mediated swelling. An NKCC1-mediated increase in [Na ]i may subsequently affect Ca 2 signaling through the Na /Ca 2 exchanger (NCX). A rise in [Ca 2 ]i was detected after OGD/REOX in the presence of a sarcoplasmic-endoplasmic reticulum (ER) Ca 2 -ATPase inhibitor thapsigargin. Moreover, OGD/REOX led to a significant increase in Ca 2 release from ER Ca 2 stores. Furthermore, KB-R7943 (2-[2-[4(4-nitrobenzyloxy)phenyl]ethyl]isothiourea mesylate), an inhibitor of reverse-mode operation of NCX, abolished the OGD/REOX-induced enhancement in filling of ER Ca 2 stores. OGD/REOX-mediated Ca 2 accumulation in ER Ca 2 stores was absent when NKCC1 activity was ablated or pharmacologically inhibited. These findings imply that stimulation of NKCC1 activity leads to Na accumulation after OGD/REOX and that subsequent reverse-mode operation of NCX contributes to increased Ca 2 accumulation by intracellular Ca 2 stores.