Miracle-dz/UniFAR-FTUs huggingface dataset:
Integrated Content-aware Image Retargeting System

In recent years, image retargeting has been an active research topic due to the rapid growth of mobile devices and display screens with different resolutions and aspect ratios. To address this problem, various content-aware image-retargeting approaches have been proposed to retarget images while preserving important regions and minimizing distortions. In our research, the goal is to propose a new integrated content-aware image retargeting system that outperforms other traditional approaches. At the beginning, an improved contextaware saliency detection approach is applied for detecting low-level saliency. Then, a high level salient feature—human faces—is also incorporated using an improved Viola-Jones face detector with a skin color detector as a post-processing stage, to construct the final importance map. Finally, a non-uniform image scaling approach is employed as the retargeting stage. Experimental study over a set of 74 benchmark images demonstrated that the proposed system overcomes most of the drawbacks of other existing approaches.
Multi-way massive MIMO relay networks with maximum-ratio processing

This paper considers a multi-way massive multiple-input multiple-output (MIMO) relaying system. The bearing-information is exchanged among multiple users with the help of a multiple-antenna relay (the base station). The maximum-ratio (MR) processing is applied at the relay under the assumption of perfect channel state information. The spectral efficiency and the asymptotic results for the signal-to-interference-plus-noise ratio (when the number of relay antennas becomes large) are derived. By using a massive number of antennas, the transmit power at both user side and/or relay can be made inversely proportional to the number of relay antennas without degradation in the system performance.
The application of Markov chain Monte Carlo methods to radiation hybrid mapping

The ordering of genetic loci is central to genetic mapping at all levels. Markov chain Monte Carlo (MCMC) techniques can provide estimates of the posterior density of orders while accounting naturally for missing data, data errors and unknown parameters. Such techniques have been proposed for mapping problems such as linkage mapping and radiation hybrid mapping. The sampling schemes tend, however, to suffer from poor mixing caused by strong correlations between the model parameters. The method described here uses simulated tempering, a modified sampling scheme with improved mixing characteristics. The method is illustrated by the analysis of radiation hybrid mapping data, but the principles are applicable to a range of mapping problems. The results show the improved mixing of simulated tempering and also show the importance of allowing for data errors in the analysis.
Smart-drug delivery system employing molecular motors

A drug delivery system has been envisioned employing actinmyosin molecular motors, liposomes, microcantilevers and a specific track formed by microfilaments. Molecular motors can pull on giant liposomes enclosing the drug to be delivered at the target site. An array of microcantilevers whose deflection in nanometers would cause the opening of the valves responsible for delivering the drug, would be implanted within the liposomes. A particular microfilament network would be designed to provide direction to the molecular motors carrying the liposomes towards the specific target site, where specific biochemical recognition occurs between the immobilized biomolecules adsorbed on the microcantilever surface and the complementary disease biomarkers. The formation of complexes between the complementary biomolecules deflects the microcantilevers causing the opening of the valves, which release the drug at the particular target site.
Spatial statistics of gaze fixations during dynamic face processing

Abstract Social interaction involves the active visual perception of facial expressions and communicative gestures. This study examines the distribution of gaze fixations while watching videos of expressive talking faces. The knowledge-driven factors that influence the selective visual processing of facial information were examined by using the same set of stimuli, and assigning subjects to either a speech recognition task or an emotion judgment task. For half of the subjects assigned to each of the tasks, the intelligibility of the speech was manipulated by the addition of moderate masking noise. Both tasks and the intelligibility of the speech signal influenced the spatial distribution of gaze. Gaze was concentrated more on the eyes when emotion was being judged as compared to when words were being identified. When noise was added to the acoustic signal, gaze in both tasks was more centralized on the face. This shows that subject's gaze is sensitive to the distribution of information on the face, but can also be influenced by strategies aimed at maximizing the amount of visual information processed.
Generation of mountain ranges by modifying a controlled terrain generation approach

The terrain generation approach proposed in dasiaParametrically controlled terrain generation, (Proceedings of GRAPHITE 2007)psila focuses on generation of terrains having mountains of prespecified heights and spreads of base regions and peaks nearly at prespecified locations. However, inability to produce mountain ranges is a shortcoming of the algorithm. In this paper, we present a modification to the original algorithm that will enable generation of whole mountain ranges while preserving the similar parametric control over the resulting terrain. Experimental results of our algorithm have also been compared with results of existing algorithms.
Ten Lessons from Finance for Commercial Sharing of IT Resources

Sharing IT resources within and among organizations is an attractive value proposition in terms of efficiency and flexibility, but despite this, commercial practice is limited. In contrast, financial and commodity markets have proved very successful at dynamic allocation of different resource types to many different organizations. Thus to understand how the potential benefits of sharing IT resources may be promoted in practice, we analyze enabling factors in successful markets. We present 10 basic lessons for IT resource sharing derived from a financial perspective and modified by considering the nature and context of IT resources. From each lesson we derive the Ten Lessons from Finance for Commercial Sharing IT Resources 241 Copyright © 2005, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. required software or process capability required to support it. We then evaluate the maturity of the respective capabilities within the peer-to-peer and grid environments using a simple framework based on the standard Capability Maturity Model approach. We conclude with a description of the largest capability gaps and the lowest hanging fruit for making IT resource sharing a more viable business proposition.
An efficient algorithm for solving word equations

We present the first DEXPTIME algorithm which solves word equations i.e. finds a finite representation of all solutions of an equation in a free semigroup. We show how to use our approach to solve two new problems in PSPACE which deal with properties of the solution set of a word equation:deciding finiteness of the solution set,deciding boundness of the set of maximal exponents of periodicity of solutions. The approach can be generalized to solve in PSPACE three problems for expressible relations, namely the emptiness of the relation, finiteness of the relation and boundness of the set of maximal exponents of periodicity of elements of the relation.
Role of Non-coding Regulatory RNA in the Virulence of Human Pathogenic Vibrios

In recent decades, the identification of small non-coding RNAs in bacteria has revealed an important regulatory mechanism of gene expression involved in the response to environmental signals and to the control of virulence. In the family Vibrionaceae, which includes several human and animal pathogens, small non-coding RNAs (sRNAs) are closely related to important processes including metabolism, quorum sensing, virulence, and fitness. Studies conducted in silico and experiments using microarrays and high-throughput RNA sequencing have led to the discovery of an unexpected number of sRNAs in Vibrios. The present review discusses the most relevant reports regarding the mechanisms of action of sRNAs and their implications in the virulence of the main human pathogens in the family Vibrionaceae: Vibrio parahaemolyticus, V. vulnificus and V. cholerae.
Source-side engineering to increase holding voltage of LDMOS in a 0.5-m 16-V BCD technology to avoid latch-up failure

To avoid latch-up failure in high voltage integrated circuits, a source-side engineering technique for on-chip ESD protection nLDMOS is proposed in this work. Experimental results have been verified in a 0.5-µm 16-V bipolar CMOS DMOS technology. Measurement results from transmission-line-pulsing system show that the proposed source-side engineering method can effectively increase the holding voltage of the nLDMOS from 10.5V to 16.2V. Transient-induced latch-up tests show that the proposed source-side engineering technique significantly improves the latch-up immunity of nLDMOS in on-chip ESD protection circuit.
Dynamic Interactions between Root NH4+ Influx and Long-Distance N Translocation in Rice: Insights into Feedback Processes

' Department of Plant Sciences, University of Western Ontario, London, ON, Canada N6A 5B7 2 International Rice Research Institute (IRRI), P.O. Box 933, 1099 Manila, Philippines 3 Plant Nutrition Laboratory, Royal Veterinary and Agricultural University, DK-1871 FC, Copenhagen, Denmark 4 Department of Citriculture, ARO The Volcani Center, Bet-Dagan 50250, Israel 5 Department of Botany, University of British Columbia, Vancouver, B.C., Canada V6T 1Z4
Regression error characteristic surfaces

This paper presents a generalization of Regression Error Characteristic (REC) curves. REC curves describe the cumulative distribution function of the prediction error of models and can be seen as a generalization of ROC curves to regression problems. REC curves provide useful information for analyzing the performance of models, particularly when compared to error statistics like for instance the Mean Squared Error. In this paper we present Regression Error Characteristic (REC) surfaces that introduce a further degree of detail by plotting the cumulative distribution function of the errors across the distribution of the target variable, i.e. the joint cumulative distribution function of the errors and the target variable. This provides a more detailed analysis of the performance of models when compared to REC curves. This extra detail is particularly relevant in applications with non-uniform error costs, where it is important to study the performance of models for specific ranges of the target variable. In this paper we present the notion of REC surfaces, describe how to use them to compare the performance of models, and illustrate their use with an important practical class of applications: the prediction of rare extreme values.
Reprogramming with Minimal Transferred Data on Wireless Sensor Network

In Wireless Sensor Networks, the preloaded program code and data on sensor nodes often need to be updated due to changes in user requirements or environmental conditions. Sensor nodes are severely restricted by energy constraints. It is especially energy consuming for sensor nodes to update code through radio packages. To efficiently update code through wireless radio, we propose an algorithm, Reprogramming with Minimal Transferred Data (RMTD), to find the optimum combination of copying from the old code image and downloading from the host machine to minimize the number of bytes needed to be transferred from the host machine to a sensor node. Our experiments show that, for small code modifications, RMTD reduces the number of bytes transferred by 93.25% over the existing Rsync-based algorithm. For normal code changes, RMTD shows an improvement of 59.82% in average.
Yield Uncertainty and Risk aversion in Duality models: a Mean-Variance approach

This paper aims at the exploration of the questions connected with the effects of uncertainty and risk in agriculture and, generally, in each economic sector where risk considerations are established. The aim of the first part is to give the key features for the estimation of production structures using dual approach and risk prevalence. In order to state risk non-neutrality into the implemented dual approach, Meanvariance utility function of revenue is introduced thereafter to be the objective function for the producer’s optimization process rather than revenue. Uncertainty was set as consequence of a weather variable variance and randomness of outputs’ quantities. The estimation is implemented on panel data of the Tunisian cereals sector from 1983 to 2005. The regional coefficients of risk aversion were highly significant and results from the Mean-Variance model shows several salient
Reduction of Sulfonylurea with the Initiation of Basal Insulin in Patients with Inadequately Controlled Type 2 Diabetes Mellitus Undergoing Long-Term Sulfonylurea-Based Treatment

Background There were a limited number of studies about β-cell function after insulin initiation in patients exposed to long durations of sulfonylurea treatment. In this study, we aimed to evaluate the recovery of β-cell function and the efficacy of concurrent sulfonylurea use after the start of long-acting insulin. Methods In this randomized controlled study, patients with type 2 diabetes mellitus (T2DM), receiving sulfonylurea for at least 2 years with glycosylated hemoglobin (HbA1c) >7%, were randomly assigned to two groups: sulfonylurea maintenance (SM) and sulfonylurea reduction (SR). Following a 75-g oral glucose tolerance test (OGTT), we administered long-acting basal insulin to the two groups. After a 6-month follow-up, we repeated the OGTT. Results Among 69 enrolled patients, 57 completed the study and were analyzed: 31 in the SM and 26 in the SR group. At baseline, there was no significant difference except for the longer duration of diabetes and lower triglycerides in the SR group. After 6 months, the HbA1c was similarly reduced in both groups, but there was little difference in the insulin dose. In addition, insulin secretion during OGTT was significantly increased by 20% to 30% in both groups. A significant weight gain was observed in the SM group only. The insulinogenic index was more significantly improved in the SR group. Conclusion Long-acting basal insulin replacement could improve the glycemic status and restore β-cell function in the T2DM patients undergoing sulfonylurea-based treatment, irrespective of the sulfonylurea dose reduction. The dose reduction of the concurrent sulfonylurea might be beneficial with regard to weight grain.
Near-Infrared Spectroscopy of 0 . 4 < z < 1 . 0 CFRS Galaxies: Oxygen Abundances, SFRs and Dust 0 metal-poor dwarf galaxies at z ∼ 0.

Using new J-band VLT-ISAAC and Keck-NIRSPEC spectroscopy, we have measured H α and [N II] λ 6584 line ﬂuxes for 0 . 47 < z < 0 . 92 CFRS galaxies which have [O II] λ 3727, H β and [O III] λ 5007 line ﬂuxes available from optical spectroscopy, to investigate how the properties of the star forming gas in galaxies evolve with redshift. We derive the extinction and oxygen abundances for the sample using a method based on a set of ionisation parameter and oxygen abundance diagnostics, simultaneously ﬁtting the [OII], H β , [OIII], H α , and [NII] line ﬂuxes. The individual reddening measurements allow us to accurately correct the H α -based star formation rate (SFR) estimates for extinction. Our most salient conclusions are: a) in all 30 CFRS galaxies the source of gas ionisation is not due to AGN activity; b) we ﬁnd a range of 0 < A V < 3, suggesting that it is important to determine the extinction for every single galaxy in order to reliably measure SFRs and oxygen abundances in high redshift galaxies; c) high values of [N II] λ 6584/H α > 0 . 1 for most (but not all) of the CFRS galaxies indicate that they lie on the high-metallicity branch of the R 23 calibration; d) about one third of the 0 . 47 < z < 0 . 92 CFRS galaxies in our sample have lower metallicities than local galaxies with similar luminosities and star formation rates; e) comparison with a chemical evolution model indicates that these low metallicity galaxies are unlikely to be the progenitors of
Relation between the Luminosity of Young Stellar Objects and Their Circumstellar Environment

We present a new model-independent method of comparison of NIR visibility data of YSOs. The method is based on scaling the measured baseline with the YSO's distance and luminosity, which removes the dependence of visibility on these two variables. We use this method to compare all available NIR visibility data and demonstrate that it distinguishes YSOs of luminosity L⋆ ≲ 103 L☉ (low L) from YSOs of L⋆ ≳ 103 L☉ (high L). This confirms earlier suggestions, based on fits of image models to the visibility data, for the difference between the NIR sizes of these two luminosity groups. When plotted against the "scaled" baseline, the visibility creates the following data clusters: low-L Herbig Ae/Be stars, T Tauri stars, and high-L Herbig Be stars. We model the shape and size of clusters with different image models and find that low-L Herbig stars are best explained by the uniform brightness ring and the halo model, T Tauri stars with the halo model, and high-L Herbig stars with the accretion disk model. However, the plausibility of each model is not well established. Therefore, we try to build a descriptive model of the circumstellar environment consistent with various observed properties of YSOs. We argue that low-L YSOs have optically thick disks with the optically thin inner dust sublimation cavity and an optically thin dusty outflow above the inner disk regions. High-L YSOs have optically thick accretion disks with high accretion rates enabling gas to dominate the NIR emission over dust. Although observations would favor such a description of YSOs, the required dust distribution is not supported by our current understanding of dust dynamics.
Creating image tags for text based image retrieval using additional corpora

Image retrieval methods mainly depend on image tags. Nonetheless, images are uploaded to WEB with noisy image tags or not a single tag at all. On the other hand, generating tags for every image on the WEB is a time consuming job. In this paper, we present a novel method to improve image retrieval problem using additional text corpora which resides in WEB. Proposed method works in two modes, each of which tries to solve following problems separately; 1) cleaning irrelevant image tags from a noisy image tag list, 2) generating a tag list for an untagged image. In this manner, we designed two models which operates on bag of visual words namely Statistical Matching Model and Variance Model. Test results show that proposed methods are very successful in terms of both image tag cleaning and tag generation.
Deadline dependent coding-a framework for wireless real-time communication

A framework for real-time communication over a wireless channel is proposed. The concept of deadline dependent coding (DDC), previously suggested by the authors, is further developed using soft decision decoding of block codes to maximize the probability of delivering information before a given deadline. The strategy of DDC is to combine different coding and decoding methods with automatic repeat request (ARQ) in order to fulfil the application requirements. These requirements are formulated as two quality of service (QoS) parameters: deadline (t/sub DL/) and probability of correct delivery before the deadline (Pd), leading to a probabilistic view of real-time communication. An application can negotiate these QoS parameters with the DDC protocol, thus creating a flexible and dependable scheme.
From Sasaki-Einstein spaces to quivers via BPS geodesics: Lp,q|r

The AdS/CFT correspondence between Sasaki-Einstein spaces and quiver gauge theories is studied from the perspective of massless BPS geodesics. The recently constructed toric Lp,q|r geometries are considered: we determine the dual superconformal quivers and the spectrum of BPS mesons. The conformal anomaly is compared with the volumes of the manifolds. The U(1)2F × U(1)R global symmetry quantum numbers of the mesonic operators are successfully matched with the conserved momenta of the geodesics, providing a test of AdS/CFT duality. The correspondence between BPS mesons and geodesics allows to find new precise relations between the two sides of the duality. In particular the parameters that characterize the geometry are mapped directly to the parameters used for a-maximization in the field theory. The analysis simplifies for the special case of the Lp,q|q models, which are shown to correspond to the known generalized conifolds. These geometries can break conformal invariance through toric deformations of the complex structure.
Delegated Portfolio Management with Ambiguity Aversion

The purpose of this paper is to consider the problem of an investor, who, aware of the effect of estimating errors on expected returns, adopts a robust optimization approach. Since the investor does not have direct access to the market and delegates the investment strategy to a portfolio manager, we consider a problem of delegated portfolio management with ambiguity aversion. More specifically, we are interested in analyzing the effect of ambiguity aversion on delegated portfolio choices and managerial fees. To this end, we set a simple model where a portfolio manager (not averse to ambiguity) is hired by an investor (averse to ambiguity) who pays him a fee on the final wealth produced by the selected portfolio strategy. To study the effect of the ambiguity on the fee, we assume that the manager decides to accept the job by comparing its utility to that of a similar employment where there are no restrictions derived by aversion to ambiguity. The increase on the fee paid by the investor to the manager is therefore a measure of the cost of ambiguity, or a premium for the ambiguity aversion.
An exploratory study in classification methods for patients' dataset

There are various methods in data mining that can be applied in classification data. This paper discusses the experiments done in classifying ICU data. The dataset consists of 25 variables for 410 patients. The goal of this experiment is to determine the survival of the patients, so the targeted output are alive and dead. Three selected data mining methods are Decision Tree, Naives Bayes and Logistics Regression. Based on mean absolute error and root-squared error, the later method provides a better result. The result of classification could be used to help hospitals in predicting their patients' status and provide better way of antibiotic treatment. Applying an intelligent tool to classify the antibiotic resistance may support the decision making to diagnose the patients in an effective way. A right treatment will make sure the patient is survived. This intelligent tool for managing medicine dosage is worthy and brings a huge impact to medical sector.