Miracle-dz/UniFAR-FTUs huggingface dataset:
Scalable Video Event Retrieval by Visual State Binary Embedding

With the exponential increase of media data on the web, fast media retrieval is becoming a significant research topic in multimedia content analysis. Among the variety of techniques, learning binary embedding (hashing) functions is one of the most popular approaches that can achieve scalable information retrieval in large databases, and it is mainly used in the near-duplicate multimedia search. However, till now most hashing methods are specifically designed for near-duplicate retrieval at the visual level rather than the semantic level. In this paper, we propose a visual state binary embedding (VSBE) model to encode the video frames, which can preserve the essential semantic information in binary matrices, to facilitate fast video event retrieval in unconstrained cases. Compared with other video binary embedding models, one advantage of our proposed VSBE model is that it only needs a limited number of key frames from the training videos for hash function training, so the computational complexity is much lower in the training phase. At the same time, we apply the pairwise constraints generated from the visual states to sketch the local properties of the events at the semantic level, so accuracy is also ensured. We conducted extensive experiments on the challenging TRECVID MED dataset, and have proved the superiority of our proposed VSBE model.
Quasi-Static Response of Circular Glare Plates Subjected to Low Velocity Impact

The response of circular GLARE fiber-metal laminates subjected to low velocity impact by a hemispherical impactor is treated analytically. Using a quasi-static approach, characteristic impact variables are calculated. A force-deflection relation is determined and employed for the unloading impact stage. Delamination is also considered. Application to GLARE 4-3/2 and GLARE 5-2/1 plates is implemented. The predicted maximum impact load, its application time, and the total impact duration agree well with published experiments. The derived formulas can be used to evaluate impact response of GLARE or other similar hybrid laminates. No other solution of this problem is known to the authors.
Beyond synonymy: exploiting the UMLS semantics in mapping vocabularies

The Unified Medical Language System (UMLS) contains semantic information about terms from various sources, each concept can be understood and located by its relationships to other concepts: this is a result of the organizing principle of semantic locality. We describe a method in which the semantic relationships between concepts are used to map concepts from different vocabularies in the UMLS. Applied to mapping concepts to MeSH, this method is able to map 50 to 65% of the non-MeSH concepts to MeSH. A manual review of the mapping shows a relevance rate of 61%. Causes of failure include a lack of consistently represented relationships in the UMLS, and some inconsistencies in the categorization of the concepts. The limits of this method are discussed, as well as possible adaptations for other uses.
Algorithmes auto-stabilisants pour la construction d'arbres couvrants et la gestion d'entités autonomes. (Self-stabilizing algorithms for spanning tree construction and for the management of mobile entities)

Dans le contexte des reseaux a grande echelle, la prise en compte des pannes est une necessite evidente. Ce document s'interesse a l'approche auto-stabilisante qui vise a concevoir des algorithmes se ''reparant d'eux-meme ' en cas de fautes transitoires, c'est-a-dire de pannes impliquant la modification arbitraire de l'etat des processus. Il se focalise sur deux contextes differents, couvrant la majeure partie de mes travaux de recherche ces dernieres annees. La premiere partie du document est consacree a l'algorithmique auto-stabilisante pour les reseaux de processus. La seconde partie du document est consacree quant a elle a l'algorithmique auto-stabilisante pour des entites autonomes (agents logiciels, robots, etc.) se deplacant dans un reseau.
Design Of Torque Hysteresis DTC Controller For Squirrel Cage Induction Motor

Direct Torque Control or DTC as it is called is the very latest AC drive technology developed by ABB and is set to replace tr aditional PWM drives of the openand closed-loop type in the near future. Direct Torque Control describes the way in which the control of torque and speed are directly based on the electromagnetic state of the motor. DTC is the first technology to control the “real” motor c ontrol variables of torque and flux. Because torque and flux are motor parameters that are being directly controlled, there is no need for a modulator, as used in PWM drives, to control the frequency and voltage. This, in effect, cuts out the middle man and dramatically speeds up the response of the drive to changes in required torque. DTC also provides precise torque control without the need for a feedback device.
X- ray Diffraction and Mineralogical Study of Soil: A Review

The surface layer of soil consisting of the mixture of mineral and organic matter reflects the nature and properties of the soil. Weathering the minerals of the earth crust originally derived most of the substances including plant nutrients. Among various substances, clay is an important soil constituent that controls its properties and also influences its management and productivity. In addition to commercial applications of clay minerals, it has great potential to fix pollutants such as heavy metals organics and plays an important role in cleansing the biosphere. Despite the fact that excess clay induces unfavorable property and required more energy for tillage operation, clay immensely improves soil fertility. In this respect, it is important to carry out quantitative and qualitative analysis of clay minerals in soil. X-ray Diffraction has shown as the best tool for the identification and quantification of minerals present in soil. We report herein the work, which was published, on x-ray diffraction analysis of soil.
A New gene selection approach based on Minimum Redundancy-Maximum Relevance (MRMR) and Genetic Algorithm (GA)

Gene expression data usually contains a large number of genes (several thousand or more) but a small number of samples (usually ≪100). Among all the genes, many are irrelevant, insignificant or redundant to the discriminant problem under investigation. Hence the identification of informative genes, which have the greatest power for classification, is of fundamental and practical importance to the investigation of specific discriminant problems, such as cancer versus non-cancer or different tumor types. In this paper, we propose a two-stage selection algorithm by combining Minimum Redundancy Maximum Relevance (MRMR) and Genetic Algorithm (GA). In the first stage, MRMR is used to filter noisy and redundant genes in high dimensional microarray data. In the second stage, GA and SVM work together for selecting the highly discriminating genes. The proposed method is tested on five open datasets: NCI, Lymphoma, Lung, Leukemia and Colon. In the experiments, we compare our proposed MRMR-GA algorithm with GA-SVM wrapper and MRMR filter. The experimental results show that the proposed method has excellent selection and classification performances.
Collaborative system identification via parameter consensus

Classical schemes in system identification and adaptive control often rely on persistence of excitation to guarantee parameter convergence, which may be difficult to achieve with a single agent and a single input. Inspired by consensus systems, we extend classical parameter adaptation to the multi agent setting by combining an adaptive gradient law with consensus dynamics. The gradient law represents the main learning signal, while consensus dynamics attract each agent's parameter estimates toward those of its neighbors. We show that the resulting decentralized online parameter estimator can be used to identify the true parameters of all agents, even if no single agent employs a persistently exciting input.
Age, sex, and racial differences in the use of standard adjuvant therapy for colorectal cancer.

PURPOSE Dissemination of efficacious adjuvant therapies for resectable colorectal cancer has not been comprehensively described. Trends, patterns, and outcomes of adjuvant therapy for colorectal cancer, focusing on age, sex, and racial/ethnic differences, are reported. MATERIALS AND METHODS Population-based random samples of patients diagnosed with colorectal cancer diagnosed in nine geographic areas were collected annually between 1987 and 1991 and in 1995 (n = 4,706). Data were obtained from medical record reviews. Multiple logistic regression was used to assess the use of standard adjuvant chemotherapy for colon and rectal cancers. The Cox proportional hazards model was used to assess 9-year mortality. RESULTS From 1987 until 1995, the use of adjuvant therapy increased in all age groups. There was an increase starting in 1989 for colon and in 1988 for rectal cancer. Use of standard therapy was 78% for those younger than 55 years and 24% for those older than 80 years. White patients received standard therapy more frequently than African-Americans (odds ratio, 1.75; 95% confidence interval [CI], 1.09 to 2.83). All-cause and cancer-specific mortality exceeding 9 years were lower in those who received standard therapy (all-cause risk ratio [RR], 0.73; 95% CI, 0.61 to 0.88; cancer-specific RR, 0.87; 95% CI, 0.70 to 1.09). CONCLUSION Standard adjuvant therapies for colorectal cancer disseminated into community practices during the 1990s. However, evidence exists of differential use of therapies by older patients and by African-Americans. The use of standard therapies in the general population is associated with lower mortality. Improved dissemination of standard adjuvant therapies to all segments of the population could help reduce mortality.
Video Transcoding System based on MapReduce for Video Content Delivery

In this paper, we propose a distributed video transcoding system that transcodes various video codec formats into the MPEG-4 video format. This system makes various kinds of video content available for heterogeneous devices such as the smart phones, PCs, TVs, and pads. We design and implement our system using the MapReduce framework running on a Hadoop Distributed File System (HDFS) platform and the media processing library Xuggler. In this way, we exponentially reduce the encoding time to transcode large amounts of video content, facilitating a transcoding function. Based on experiments performed on a 28-node cluster, the proposed distributed video transcoding system provides excellent performance in terms of speed and quality.
AIS Electronic Library (AISeL)

Is information technology (IT) a substitute or complement for inventories? This study answers this question by understanding substitution and complementarity of IT and inventories insofar as they impact profitability of a firm. We use data on IT expenditures and inventories of U.S firms and find support for the substitution between IT and overall inventories. Because overall inventories comprise raw-material, work-in-process and finished goods inventories, should we take this result to imply that substitution argument applies uniformly to all types of inventories? The answer is no. Our disaggregated analyses indicate that while IT can be a substitute for raw material inventories, IT is a complement for finished goods inventories. We discuss implications of these findings for research and practice.
Leveraging Sentiment to Compute Word Similarity

In this paper, we introduce a new WordNet based similarity metric, SenSim, which incorporates sentiment content (i.e., degree of positive or negative sentiment) of the words being compared to measure the similarity between them. The proposed metric is based on the hypothesis that knowing the sentiment is beneficial in measuring the similarity. To verify this hypothesis, we measure and compare the annotator agreement for 2 annotation strategies: 1) sentiment information of a pair of words is considered while annotating and 2) sentiment information of a pair of words is not considered while annotating. Inter-annotator correlation scores show that the agreement is better when the two annotators consider sentiment information while assigning a similarity score to a pair of words. We use this hypothesis to measure the similarity between a pair of words. Specifically, we represent each word as a vector containing sentiment scores of all the content words in the WordNet gloss of the sense of that word. These sentiment scores are derived from a sentiment lexicon. We then measure the cosine similarity between the two vectors. We perform both intrinsic and extrinsic evaluation of SenSim and compare the performance with other widely usedWordNet similarity metrics.
Which magnetic resonance imaging findings accurately evaluate inflammation in small bowel Crohn's disease? A retrospective comparison with surgical pathologic analysis

Background: The aim was to evaluate the value of magnetic resonance imaging (MRI) findings in Crohn's disease (CD) in correlation with pathological inflammatory score using surgical pathology analysis as a reference method. Methods: CD patients who were to undergo bowel resection surgery underwent MR enterography before surgery. The CD pathological inflammatory score of the surgical specimens was classified into three grades: mild or nonactive CD, moderately active CD, and severely active CD; fibrosis was also classified into three grades: mild, moderate, and severe. Mural and extramural MRI findings were correlated with pathological inflammatory and fibrosis grades. Results: Fifty‐three consecutive patients were included retrospectively. The mean delay between MRI and surgery was 24 days (range 1–90, median 14). The CD pathological inflammatory score was graded as follows: grade 0 (11 patients, 21%), grade 1 (15 patients, 28%), and grade 2 (27 patients, 51%). MRI findings significantly associated with pathological inflammatory grading were wall thickness (P < 0.0001), degree of wall enhancement on delayed phase (P < 0.0001), pattern of enhancement on both parenchymatous (P = 0.02), and delayed phase, (P = 0.008), T2 relative hypersignal wall (P < 0.0001), blurred wall enhancement (P = 0.018), comb sign (P = 0.004), fistula (P < 0.0001), and abscess (P = 0.049). The inflammation score correlated with the fibrosis score (r = 0.63, P = 0.0001). Conclusions: Our study identified MRI findings significantly associated with surgical pathological inflammation. These lesions are considered potentially reversible and may be efficiently treated medically. We also showed that fibrosis was closely and positively related to inflammation. Inflamm Bowel Dis 2011
Information reconciliation for QKD with rate-compatible non-binary LDPC codes

We study the information reconciliation (IR) scheme for quantum key distribution (QKD) protocols. The IR for the QKD can be seen as the asymmetric Slepian-Wolf problem, which low-density parity-check (LDPC) codes can solve with efficient algorithms, i.e., the belief propagation. However, the LDPC codes are needed to be chosen properly from a collection of codes optimized for multiple key rates, which leads to complex decoder devices and performance degradation for unoptimized key rates. Therefore, it is desired that establish an IR scheme with a single LDPC code which supports multiple rates. To this end, in this paper, we propose an IR scheme with a rate-compatible non-binary LDPC code. Numerical results show the proposed scheme achieves IR efficiency comparable to the best know conventional IR scheme with lower decoding error rates.
Study of throughput and delay in finite-buffer line networks

In this work, we study the effects of finite buffers on the throughput and delay of line networks with erasure links. We identify the calculation of performance parameters such as throughput and delay to be equivalent to determining the stationary distribution of an irreducible Markov chain. We note that the number of states in the Markov chain grows exponentially in the size of the buffers with the exponent scaling linearly with the number of hops in a line network. We then propose a simplified iterative scheme to approximately identify the steady-state distribution of the chain by decoupling the chain to smaller chains. The approximate solution is then used to understand the effect of buffer sizes on throughput and distribution of packet delay. Further, we classify nodes based on congestion that yields an intelligent scheme for memory allocation using the proposed framework. Finally, by simulations we confirm that our framework yields an accurate prediction of the variation of the throughput and delay distribution.
Effect of Confinement on the Mechanical Response of Composite Plastic Bonded Explosives

Abstract : The mechanical properties of composite explosives are being studied as a function of mechanical confinement and three techniques for confinement were used. These are: (a) a constant confining pressure obtained by oil immersion, (b) radial confinement of a cylindrical sample by a thick walled steel cylinder that surrounds the sample (negligible radial strain), and (c) radial confinement of thin wafers by the platen-sample friction. while many energetic materials fail by crack growth when unconfined (significant surface area free of stress), with all of these forms of confinement, they appear to fail by yield and plastic flow. For crystalline explosives (e.g., TNT and Composition B), the yield strength and the modulus are independent of confining pressure so that useful results can be easily obtained by use of the steel cylinder technique. However, for materials containing polymer binders, such as plastic bonded explosives, a constant confining pressure is used because these properties are found to significantly increase with this pressure. These results indicate the very significant role of the polymer binders in determining the mechanical properties of these energetic materials.
A THEORETICAL APPROACH FOR MODELING REACTIVITY AT SOLID-LIQUID INTERFACES

We present a new general methodology capable of modeling chemical reactions at solid–liquid interfaces called CECILIA (combined embedded cluster at the interface with liquid approach). The main idea is to combine the embedded cluster molecular orbital or density functional methods for describing interactions at the surface of a solid with the dielectric continuum approach for modeling a liquid. More details are given on how to apply this methodology to model processes at the ionic solid–water interface. Geometries and adsorption binding energies of H2O, NaCl, Na+, and Cl− at the NaCl(001)–water interface are calculated using this approach and compared with those at the NaCl(001)–vacuum interface.
Semandaq: a data quality system based on conditional functional dependencies

We present Semandaq, a prototype system for improving the quality of relational data. Based on the recently proposed conditional functional dependencies (CFDs), it detects and repairs errors and inconsistencies that emerge as violations of these constraints. We demonstrate the following functionalities supported by Semandaq: (a) an interface for specifying CFDs; (b) a visual tool for automated detection of CFD violations in relational data, leveraging efficient SQL-based techniques; (c) extensive visual data exploration capabilities that provide the user with various measures of the quality of the data; (d) repair (cleaning) functionality without excess human interaction, built upon CFD-based cleaning algorithms; we show how Semandaq allows for a natural exploration of the quality of the obtained repairs. Semandaq is a promising tool that provides easy access and user-friendly data quality facilities for any relational database system.
Strong Normalization as Safe Interaction

When enriching the lambda-calculus with rewriting, union types may be needed to type all strongly normalizing terms. However, with rewriting, the elimination rule (orE) of union types may also allow to type non normalizing terms (in which case we say that (orE) is unsafe). This occurs in particular with non-determinism, but also with some confluent systems. It appears that studying the safety of (orE) amounts to the characterization, in a term, of safe interactions between some of its subterms. In this paper, we study the safety of (orE) for an extension of the lambda-calculus with simple rewrite rules. We prove that the union and intersection type discipline without (orE) is complete w.r. t. strong normalization. This allows to show that (orE) is safe if and only if an interpretation of types based on biorthogonals is sound for it. We also discuss two sufficient conditions for the safety of (orE), and study an alternative biorthogonality relation, based on the observation of the least reducibility candidate.
Lexical Event Ordering with an Edge-Factored Model

Extensive lexical knowledge is necessary for temporal analysis and planning tasks. We address in this paper a lexical setting that allows for the straightforward incorporation of rich features and structural constraints. We explore a lexical event ordering task, namely determining the likely temporal order of events based solely on the identity of their predicates and arguments. We propose an “edgefactored” model for the task that decomposes over the edges of the event graph. We learn it using the structured perceptron. As lexical tasks require large amounts of text, we do not attempt manual annotation and instead use the textual order of events in a domain where this order is aligned with their temporal order, namely cooking recipes.
A dynamical method to extract communities induced by low or middle-degree nodes

Many networks are proved to have community structure. Dense communities have been intensively investigated in recent years, oppositely seldom attention has been paid to sparse ones, which refer to those communities induced by low or middle-degree nodes rather than high-degree components. Recently, it has gradually been recognized that sparse community is also an important structure in biological networks because most disease genes and drug targets are within it. In this paper, we propose a dynamical method to extract sparse communities in complex networks by constructing local synchronization properties of phase oscillators. Compared to dense communities, sparse ones provide more general building and functional blocks in the networks without emphasis on the dominance of internal degrees over outside ones as well as the constraints of high degree connectors.
CBC: clustering based text classification requiring minimal labeled data

Semisupervised learning methods construct classifiers using both labeled and unlabeled training data samples. While unlabeled data samples can help to improve the accuracy of trained models to certain extent, existing methods still face difficulties when labeled data is not sufficient and biased against the underlying data distribution. We present a clustering based classification (CBC) approach. Using this approach, training data, including both the labeled and unlabeled data, is first clustered with the guidance of the labeled data. Some of unlabeled data samples are then labeled based on the clusters obtained. Discriminative classifiers can subsequently be trained with the expanded labeled dataset. The effectiveness of the proposed method is justified analytically. Our experimental results demonstrated that CBC outperforms existing algorithms when the size of labeled dataset is very small.