Miracle-dz/UniFAR-FTUs huggingface dataset:
Summarizing social image search results

Most existing social image search engines present search results as a ranked list of images, which cannot be consumed by users in a natural and intuitive manner. Here, we present a novel algorithm that exploits both visual features and tags of the search results to generate high quality image search result summary. The summary not only breaks the results into visually and semantically coherent clusters, but it also maximizes the coverage of the original search results. We demonstrate the effectiveness of our method against state-of-the-art image summarization and clustering algorithms.
Rate-distortion optimized streaming of fine-grained scalable video sequences

We present optimal schemes for allocating bits of fine-grained scalable video sequences among multiple senders streaming to a single receiver. This allocation problem is critical in optimizing the perceived quality in peer-to-peer and distributed multi-server streaming environments. Senders in such environments are heterogeneous in their outgoing bandwidth and they hold different portions of the video stream. We first formulate and optimally solve the problem for individual frames, then we generalize to the multiple frame case. Specifically, we formulate the allocation problem as an optimization problem, which is nonlinear in general. We use rate-distortion models in the formulation to achieve the minimum distortion in the rendered video, constrained by the outgoing bandwidth of senders, availability of video data at senders, and incoming bandwidth of receiver. We show how the adopted rate-distortion models transform the nonlinear problem to an integer linear programming (ILP) problem. We then design a simple rounding scheme that transforms the ILP problem to a linear programming (LP) one, which can be solved efficiently using common optimization techniques such as the Simplex method. We prove that our rounding scheme always produces a feasible solution, and the solution is within a negligible margin from the optimal solution. We also propose a new algorithm (FGSAssign) for the single-frame allocation problem that runs in O(nlog n) steps, where n is the number of senders. We prove that FGSAssign is optimal. Furthermore, we propose a heuristic algorithm (mFGSAssign) that produces near-optimal solutions for the multiple-frame case, and runs an order of magnitude faster than the optimal one. Because of its short running time, mFGSAssign can be used in real time. Our experimental study validates our analytical analysis and shows the effectiveness of our allocation algorithms in improving the video quality.
A refinement theory for timed-dataflow analysis with support for reordering

Real-time stream processing applications executed on embedded multiprocessor systems often have strict throughput and latency constraints. Violating these constraints is undesired and temporal analysis methods are therefore used to prevent such violations. These analysis methods use abstractions of the analyzed applications to simplify their temporal analysis. Refinement theories have enabled the creation of deterministic abstractions of stream processing applications that are executed on multiprocessor systems. Prominent examples of such abstract models are deterministic timed-dataflow models which can be efficiently analyzed because they only have one behavior. An important aspect of a stream processing application can be that it makes use of reordered data streams between tasks. An example is the bit-reversed ordered stream produced by a Fast Fourier Transform (FFT) task. However, existing abstraction/refinement theories do not support such reordering behavior or do not handle this type of behavior correctly. This is because existing refinement theories assume that the temporal behavior of applications is orthogonal to their functional behavior, whereas this orthogonality does not always hold in the case of reordered data streams. In this paper we introduce a new refinement theory in which the potential interaction between temporal and functional behavior is taken into account. The introduced theory supports reordering of data and can therefore be used to validate existing systems with such reordering. Furthermore, the theory enables showing that deterministic dataflow models that do not apply reordering can be used as valid abstractions of systems in which reordering takes place. The applicability of the refinement theory is demonstrated by creating deterministic timed-dataflow model abstractions of a Digital Video Broadcasting Terrestrial (DVB-T) application, and a communication network in which data is reordered. With these dataflow models the guaranteed throughput and buffer capacities of implementation options are compared.
Assamese spoken query system to access the price of agricultural commodities

In this work, a spoken query system developed for accessing the price of agricultural commodities in Assamese language is described. The developed system enables the user to access the latest price of the commodity by calling the system using a landline/mobile phone. The spoken query system consists of interactive voice response (IVR) and automatic speech recognition (ASR) modules which are developed using open source resources. For the development of ASR models, the task specific speech data is collected from different dialect regions of Assam. The issues in test data adaptation are highlighted and a constrained data-unseen speaker adaptation approach is implemented which is found to give a relative improvement by 8% in baseline performance.
Using subspace analysis for event detection from web click-through data

Although most of existing research usually detects events by analyzing the content or structural information of Web documents, a recent direction is to study the usage data. In this paper, we focus on detecting events from Web click-through data generated by Web search engines. We propose a novel approach which effectively detects events from click-through data based on robust subspace analysis. We first transform click-through data to the 2D polar space. Next, an algorithm based on Generalized Principal Component Analysis (GPCA) is used to estimate subspaces of transformed data such that each subspace contains query sessions of similar topics. Then, we prune uninteresting subspaces which do not contain query sessions corresponding to real events by considering both the semantic certainty and the temporal certainty of query sessions in each subspace. Finally, various events are detected from interesting subspaces by utilizing a nonparametric clustering technique. Compared with existing approaches, our experimental results based on real-life click-through data have shown that the proposed approach is more accurate in detecting real events and more effective in determining the number of events.
Delay and state observer for SISO LTI systems

This paper deals with the problem of state and delay estimation for SISO LTI systems, with unknown time-varying delay in the input. Thanks to an adequate approximation of the delayed input by the Taylor's theorem, an original approach based on observer design is proposed in order to estimate both state and delay. This new technique allows the estimation of time-varying delay. The convergence of the observer is formally proved. The efficiency of the method is widely illustrated by simulations.
Conformation and hydrogen bonding of N-formylmethionyl peptides. II. Crystal and molecular structure of N-formyl-L-methionyl-L-phenylalanine.

Crystals of N-formyl-L-methionyl-L-phenylalanine (C15H20N2O4S), grown from aqueous methanol solution are orthorhombic, space group, P2(1)2(1)2(1), with cell parameters at 294K of a = 4.900(2), b = 17.947(4), c = 18.726(4)A, V = 1646.8A3, M.W. = 324.4, Z = 4 and Dm = 1.308 g/cc, and as expected, all nearly identical to that of N-f-D-Met-D-Phe studied by Jeffs, Heald, Chodosh & Eggleston (Int. J. Peptide Protein Res. 24, 442-446, 1984). The crystal structure was solved and refined using CAD-4 data (1095 reflections greater than or equal to 3 sigma) to a final R value of 0.042. Molecules related by the alpha-translation form a parallel beta-sheet rather than anti-parallel sheet as stated in the earlier study of Jeffs et al. The formation of the parallel rather than the anti-parallel beta-sheet structure, the use of the C-H . O hydrogen bonds to stabilize the beta-sheet and the very short O-H . O hydrogen bond between the carboxyl OH and the N-acyl oxygen atom emerge as the main structural features of the chemotactic N-formyl methionyl peptides.
Language Independent Probabilistic Context-Free Parsing Bolstered by Machine Learning

Unlexicalized probabilistic context-free parsing is a general and flexible approach that sometimes reaches competitive results in multilingual dependency parsing even if a minimum of language-specific information is supplied. Furthermore, integrating parser results (good at long dependencies) and tagger results (good at short range dependencies, and more easily adaptable to treebank peculiarities) gives competitive results in all languages.
Multi-view clustering of web documents using multi-objective genetic algorithm

Clustering ensembles are a common approach to clustering problem, which combine a collection of clustering into a superior solution. The key issues are how to generate different candidate solutions and how to combine them. Common approach for generating candidate clustering solutions ignores the multiple representations of the data (i.e., multiple views) and the standard approach of simply selecting the best solution from candidate clustering solutions ignores the fact that there may be a set of clusters from different candidate clustering solutions which can form a better clustering solution. This paper presents a new clustering method that exploits multiple views to generate different clustering solutions and then selects a combination of clusters to form a final clustering solution. Our method is based on Nondominated Sorting Genetic Algorithm (NSGA-II), which is a multi-objective optimization approach. Our new method is compared with five existing algorithms on three data sets that have increasing difficulty. The results show that our method significantly outperforms other methods.
Efficient GPU-based Construction of Occupancy Girds Using several Laser Range-finders

Building occupancy grids (OGs) in order to model the surrounding environment of a vehicle implies to fusion occupancy information provided by the different embedded sensors in the same grid. The principal difficulty comes from the fact that each can have a different resolution, but also that the resolution of some sensors varies with the location in the field of view. In this article we present a new exact approach to this issue and we explain why the problem of switching coordinate systems is an instance of the texture mapping problem in computer graphics. Therefore we introduce a calculus architecture to build occupancy grids with a graphical processor unit (GPU). Thus we present computational time results that can allow to compute occupancy grids for 50 sensors at frame rate even for a very fine grid. To validate our method, the results with GPU are compared to results obtained through the exact approach
Tuning of Optimum PID Controller Parameter using Particle Swarm Optimization Algorithm Approach

In this paper, an artificial intelligence method, Particle Swarm Optimization (PSO) algorithm is presented for determining the optimal proportional-integral-derivative (PID) controller parameters of a typical servo motion system. This paper demonstrates in detail on how to employ the PSO method to search efficiently the optimal PID controller parameters of a typical servo motion system. In order to assist estimating the performance of the proposed PSO-PID controller, a new timedomain performance criterion function has been used. The proposed approach yields better solution in term of rise time, settling time, maximum overshoot and steady state error condition of the system. Compared to conventional Ziegler – Nichols method, the proposed method was indeed more efficient and robust in improving the step response of a typical servo motion system.
Mass Spectrometric Quantification of Markers for Protein Oxidation by Tyrosyl Radical, Copper, and Hydroxyl Radical in Low Density Lipoprotein Isolated from Human Atherosclerotic Plaques*

Lipoprotein oxidation has been implicated in the pathogenesis of atherosclerosis. However, the physiologically relevant pathways mediating oxidative damage have not yet been identified. Three potential mechanisms are tyrosyl radical, hydroxyl radical, and redox active metal ions. Tyrosyl radical forms o,o′-dityrosine cross-links in proteins. The highly reactive hydroxyl radical oxidizes phenylalanine residues to o-tyrosine and m-tyrosine. Metal ions oxidize low density lipoprotein (LDL) by poorly understood pathways. To explore the involvement of tyrosyl radical, hydroxyl radical, and metal ions in atherosclerosis, we developed a highly sensitive and quantitative method for measuring levels of o,o′-dityrosine, o-tyrosine, and m-tyrosine in proteins, lipoproteins, and tissue, using stable isotope dilution gas chromatography-mass spectrometry. We showed that o,o′-dityrosine was selectively produced in LDL oxidized with tyrosyl radical. Both o-tyrosine and o,o′-dityrosine were major products when LDL was oxidized with hydroxyl radical. Only o-tyrosine was formed in LDL oxidized with copper. Similar profiles of oxidation products were observed in bovine serum albumin oxidized with the three different systems. Applying these findings to LDL isolated from human atherosclerotic lesions, we detected a 100-fold increase in o,o′-dityrosine levels compared to those in circulating LDL. In striking contrast, levels of o-tyrosine and m-tyrosine were not elevated in LDL isolated from atherosclerotic tissue. Analysis of fatty streaks revealed a similar pattern of oxidation products; compared with normal aortic tissue, there was a selective increase in o,o′-dityrosine with no change in o-tyrosine. The detection of a selective increase of o,o′-dityrosine in LDL isolated from vascular lesions is consistent with the hypothesis that oxidative damage in human atherosclerosis is mediated in part by tyrosyl radical. In contrast, these observations do not support a role for free metal ions as catalysts of LDL oxidation in the artery wall.
Expression of BMP6 is associated with its methylation status in colorectal cancer tissue but lacks prognostic significance.

BACKGROUND The study aimed to evaluate the incidence of CpG island promoter methylation of BMP6, a member of the transforming growth factor beta family, in tissue samples from colorectal cancers (CRC) and look for its association with BMP6 expression and clinicopathological correlation. MATERIALS AND METHODS Methylation specific PCR for the BMP6 promoter region was performed with 85 frozen tissue samples of CRC and 45 of normal colon. Methylation status of MLH1 was also determined by the same method. Expression of BMP6 was evaluated by immunohistochemistry (IHC), using Allred's scoring system. The methylation status was analyzed against clinical and pathological parameters in CRC. RESULTS The study revealed BMP6 hypermethylation in 34 of 85 tumor specimens (40%), and 15 out of 45 normal tissue samples from CRC (33%). The incidence of hypermethylation was inversely correlated with IHC score. Allred's scores of 7 or more were correlated with lower frequency of BMP6 hypermethylation (29% compared to 50% in the remaining, p-value 0.049). However, there was no association between hypermethylation status and any clinicopathological parameters. The methylation status of BMP6 was not correlated with that of MLH1, a key methylation determinant in CRC. On survival analysis, there was no significant difference in progress-free survival (PFS) between the cases with and without hypermethylation (2-year PFS 74% and 76%, respectively). CONCLUSIONS CpG island methylation of BMP6 is found in high frequency in CRC and this epigenetic event is associated with suppressed protein expression in the tumor tissue. However, the marker is not associated with tumor progression of the disease.
A Novel Graph-Based Suboptimal Multiuser Detector for FDM-CPM Transmissions

We consider a frequency division multiplexed (FDM) system where each user employs a continuous phase modulation (CPM), serially concatenated with an outer code through an interleaver, and iterative detection/decoding. In such a system, the spectral efficiency can be increased by reducing the spacing between two adjacent channels, thus increasing the relevant interference. Hence, we address the design of low-complexity suboptimal multiuser detectors able to effectively cope with such an interference. We extend some well known multiuser detection algorithms proposed for code division multiple access (CDMA) systems. Moreover, we introduce a new detection scheme using the framework based on factor graphs (FGs) and the sum-product algorithm (SPA). The simulation results show that the described algorithms allow to effectively reduce the spacing, thus increasing the spectral efficiency, and in particular, the proposed detection scheme results to be the most effective one in terms of performance and computational complexity.
A Study on Sigmoid Kernels for SVM and the Training of non-PSD Kernels by SMO-type Methods

The sigmoid kernel was quite popular for support vector machines due to its origin from neural networks. Although it is known that the kernel matrix may not be positive semi-definite (PSD), other properties are not fully studied. In this paper, we discuss such non-PSD kernels through the viewpoint of separability. Results help to validate the possible use of non-PSD kernels. One example shows that the sigmoid kernel matrix is conditionally positive definite (CPD) in certain parameters and thus are valid kernels there. However, we also explain that the sigmoid kernel is not better than the RBF kernel in general. Experiments are given to illustrate our analysis. Finally, we discuss how to solve the non-convex dual problems by SMO-type decomposition methods. Suitable modifications for any symmetric non-PSD kernel matrices are proposed with convergence proofs.
Uncovering cross-dimension group structures in multi-dimensional networks

With the proliferation of Web 2.0 and social networking sites, people can interact with each other easily through various social media. For instance, popular sites like Del.icio. us, Flickr, and YouTube allow users to comment sharing content (bookmark, photos, videos), and users can tag her own favorite content. Users can also connect to friends, and subscribe to or become a fan of other users. These diverse individual activities result in a multi-dimensional network among actors, forming cross-dimension group structures with group members focusing on similar topics. It is challenging to effectively integrate the network information of multiple dimensions to find out the cross-dimension group structure. In this work, we propose a two-phase strategy to identify the hidden structures shared across dimensions in multi-dimensional networks. We extract structural features from each dimension of the network via modularity analysis, and then integrate them to find out a robust community structure among actors. Experiments on synthetic and real-world data validate the superiority of our strategy, enabling the analysis of collective behavior underneath diverse individual activities in a large scale.
Multi-objective artificial immune algorithm for security-constrained multi-application NoC mapping

Current SoC (System-on-Chip) are characterized by the integration of larger amount of IPs targeting a wide range of application fields. Such multi-application systems are constrained by a set of security and performance requirements. Network-on-chip (NoC) is becoming important as the communication structure of the SoC. IP mapping is one of the most critical parameters in NoC design, strongly influencing the SoC performance. IP mapping has been solved for single application systems using single and multi-objective optimization algorithms. In this paper we propose the use of a multi-objective adaptive immune algorithm (M2AIA), an evolutionary approach to solve the multi-application NoC mapping problem targeting security issues, in order to group the IPs according the security characteristics while achieving the best performance. Latency and power consumption were adopted as the target multi-objective functions constrained by the security function. To compare the efficiency of our approach, our results are compared with those of the genetic and branch-and-bound multi-objective mapping algorithms. The experimental results showed that the M2AIA achieves configurations that fulfill the security requirements while decreasing the power consumption in 27% and the latency in 42% compared to the branch-and-bound approach and 29% and 36% over the genetic approach.
The Formation of g=2.49-Species of Cytochrome P450 in the Rat Liver by PCB126 Oral Administration: Identification of Heme Axial Ligands by EPR Spectroscopy

Rat livers and microsomes were subjected to electron paramagnetic resonance (EPR) measurements at 77 K. The EPR spectra of the livers from the control group, carbon tetrachloride-, 3-methylcholanthrene-, and 3,3′,4,4′,5-pentachlorobiphenyl (PCB126)-treated rats exhibited an EPR spectrum at g=2.40, 2.24, and 1.93, which is characteristic of P450 in a resting state. The liver of the PCB126-treated rats showed an additional distinct EPR spectrum at g=2.49, 2.26, and 1.87 (g=2.49-species). The heme environmental structure of g=2.49-species was identified by crystal field analysis using three EPR g-values of the microsome treated with various chemicals. These results indicated that g=2.49-species is a hemeprotein with cysteine thiolate at the 5th coordination site, and a nitrogenous ligand at the 6th site.
Distributed Spatial Modulation for Relay Networks

A cooperative diversity protocol for multi-relay wireless networks is proposed. Its distinguishable feature is to protect the transmission of the source through distributed diversity while simultaneously serving the data traffic of the relays. Therefore, the performance of the source is improved without reducing the aggregate throughput. The protocol exploits a recently introduced concept for multiple- antennas wireless systems, which is known as Spatial Modulation (SM). The contribution of this paper is threefold: i) the optimal receiver that takes into account demodulation errors at the relays is developed; ii) its achievable diversity is studied analytically; and iii) it is shown, with the help of Monte Carlo simulations, that it outperforms state- of-the-art cooperative diversity protocols.