Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiplicative Noise and Blur Removal by Framelet Decomposition and  $l_{1}$ -Based L-Curve Method

This paper proposes a framelet-based convex optimization model for multiplicative noise and blur removal problem. The main idea is to employ framelet expansion to represent the original image and use the variable decomposition to solve the problem. Because of the nature of multiplicative noise, we decompose the observed data into the original image variable and the noise variable to obtain the resulting model. The original image variable is represented by framelet, and it is determined by using l1-norm in the selection and shrinkage of framelet coefficients. The noise variable is measured by using the mean and the variance of the underlying probability distribution. This framelet setting can be applied to analysis, synthesis, and balanced approaches, and the resulting optimization models are convex, such that they can be solved very efficiently by the alternating direction of a multiplier method. An another contribution of this paper is to propose to select the regularization parameter by using the l1-based L-curve method for these framelet based models. Numerical examples are presented to illustrate the effectiveness of these models and show that the performance of the proposed method is better than that by the existing methods.
Identification and Enhancement of HLA-A2.1-Restricted CTL Epitopes in a New Human Cancer Antigen-POTE

Identification of CD8+ T cell epitopes that can induce T cells to kill tumor cells is a fundamental step for development of a peptide cancer vaccine. POTE protein is a newly identified cancer antigen that was found to be expressed in a wide variety of human cancers, including prostate, colon, lung, breast, ovary and pancreas. Here, we determined HLA-A2.1-restricted cytotoxic T lymphocyte (CTL) epitopes in the POTE protein, and also designed enhanced epitopes by amino acid (AA) substitutions. Five 9-mer peptides were first selected and their binding affinity to HLA-A2 molecules was measured by the T2 binding assay. POTE 272–280 and POTE 323–331 showed the strongest HLA-A2 binding affinity. AA substituted peptides POTE 252-9V (with valine at position 9), POTE 553-1Y (with tyrosine at position 1) and POTE 323-3F (with phenylalanine at position 3) conferred higher affinity for HLA-A2, and induced CTL responses cross-reactive with wild type antigens. While POTE 252-9V was the strongest in this respect, POTE 323-3F had the greatest increase in immunogenicity compared to wild type. Importantly, two modified epitopes (POTE-553-1Y and POTE-323-3F) induced CTLs that killed NCI-H522, a POTE-expressing HLA-A2+ human non-small cell lung cancer cell line, indicating natural endogenous processing of these epitopes. In conclusion, the immunogenicity of POTE epitopes can be enhanced by peptide modification to induce T cells that kill human cancer cells. A combination of POTE 553-1Y and POTE 323-3F epitopes might be an attractive vaccine strategy for HLA-A2 cancer patients to overcome tolerance induced by tumors and prevent escape.
Regional distribution of dynorphin and neo-endorphin peptides in rat brain, spinal cord, and pituitary

Five products of the dynorphin gene--alpha-neo-endorphin, beta-neo- endorphin, dynorphin A, dynorphin A-(1-8), and dynorphin B--were measured in various regions of rat brain and in rat spinal cord and pituitary. Specific antisera were used, supplemented by gel permeation analysis and high performance liquid chromatography, confirming the presence of dynorphin-32, dynorphin A, and dynorphin B in rat brain. In whole brain, alpha-neo-endorphin, dynorphin A-(1-8), and dynorphin B are present in much greater amounts than beta-neo-endorphin or dynorphin A. Although a general parallelism was found in the distribution of the five peptides, there were also noteworthy exceptions, suggesting that differential processing may occur.
Quality of claim metrics in social sensing systems: A case study on IranDeal

There is an ongoing trend in social sensing where people act as sensors and report the events happening in their surroundings. These claims are often reported by smartphones and need to be processed to discover new patterns of events. Since these claims are not generated with consistent quality, the processing and evaluation tasks can become a challenge. In this paper, we address questions on how the quality of each claim can be evaluated, and which factors should be considered to qualify the quality of the claims. To do this, we investigate the sources of low-quality claims an propose a new form of Quality of Claim (QoC) metrics. We categorize the Quality of Claim factors into two classes of Content Measure and Feedback Measure. The study is performed on Two datasets. The main dataset is the #IranDeal extracted from Twitter. To compare the quality metrics, a second dataset is crawled from the Fouresqure social network. The metrics follow the power law pattern and are modeled by a Zipfian distribution function. The results show the power degree varies from 1.75 to 5. A number of factors are discussed as an influencer of the variation, such as the query criteria of the extracted dataset, the characteristics of the QoC metric, and the type of the social network.
Flexible plastic-based inkjet printed CPW fed dipole antenna for 60 GHz ISM applications

This study investigates an inkjet-printed millimeter wave antenna on PET (polyethylene terephthalate) substrate. The 60 GHz CPW (coplanar waveguide) fed dipole antenna, was printed with a Dimatix inkjet printer using silver nanoparticle colloidal ink. First, this process uses a plasma polymerization technique to prepare the PET surface for printing. Then, the silver ink lines are printed on the PET substrate, followed by curing in an oven, to remove excess solvent. The silver ink layer conductivity is around 4×106 Siemens/m. The measured impedance bandwidth of the printed antenna, defined by return loss at -10 dB, is 4GHz, from 58 to 62 GHz.
Dollo's law and the re–evolution of shell coiling

Gastropods have lost the quintessential snail feature, the coiled shell, numerous times in evolution. In many cases these animals have developed a limpet morphology with a cap–shaped shell and a large foot. Limpets thrive in marginal habitats such as hydrothermal vents, the high–energy rocky intertidal areas and fresh water, but they are considered to be evolutionary dead–ends, unable to re–evolve a coiled shell and therefore unable to give rise to the diversity seen among coiled snails. The re–evolution of a coiled shell, or any complex character, is considered unlikely or impossible (Dollo's law) because the loss of the character is followed by the loss of the genetic architecture and developmental mechanisms that underlie that character. Here, we quantify the level of coiling in calyptraeids, a family of mostly uncoiled limpets, and show that coiled shells have re–evolved at least once within this family. These results are the first demonstration, to our knowledge, of the re–evolution of coiling in a gastropod, and show that the developmental features underlying coiling have not been lost during 20–100 Myr of uncoiled evolutionary history. This is the first example of the re–evolution of a complex character via a change in developmental timing (heterochrony) rather than a change in location of gene expression (heterotopy).
Analysis of Failures in Power Grids

This paper focuses on line failures in the transmission system of power grids. Recent large-scale power outages demonstrated the limitations of percolation- and epidemic-based tools in modeling failures and cascades in power grids. Hence, we study failures and cascades by using computational tools and a linearized power-flow model. We first obtain results regarding the Moore-Penrose pseudoinverse of the power grid admittance matrix. Based on these results, we analytically study the impact of a single-line failure on the flows on other lines and introduce metrics to evaluate the robustness of grids to failures. We also illustrate via simulation the impact of the distance and resistance distance on the flow increase following a failure, and discuss the difference from the epidemic models. We use the pseudoinverse of admittance matrix to develop an efficient algorithm to identify the cascading failure evolution, which can be a building block for cascade mitigation. Finally, we show that finding the lines whose removal results in the minimum yield (the fraction of demand satisfied after the cascade) is NP-Hard and present a simple heuristic for finding such a set. Overall, the results demonstrate that using the resistance distance and the pseudoinverse of the admittance matrix provides important insights and can support the development of algorithms for designing robust power grids and controlling the evolution of a cascade upon failures.
INTERACTIONS BETWEEN ANTARCTIC SEA ICE AND ATMOSPHERIC CIRCULATION ON WEEKLY TO SEASONAL TIME SCALES

1. BACKGROUND AND MOTIVATION Low frequency variability of the Southern Hemisphere atmospheric circulation is known to be concentrated over the southeast Pacific, largely as a result of ENSO forcing (Renwick and Revell 1999, Renwick 2002). ENSO modulation of the South Pacific circulation acts to further modulate sea ice across the southern Pacific and Atlantic sectors (Kwok and Comiso 2002, Renwick 2002, Yuan 2004). This work has been motivated by the hypothesis that sea ice variability may feed back upon the atmospheric circulation to further enhance and perhaps to geographically anchor low frequency variations of the circulation. On interannual time scales, the forcing appears to be from the atmosphere to the sea-ice, with little evidence as yet of a feedback in the other direction. This poster presentation addresses shorter time-scale variability, through a preliminary study of weekly-scale atmospheric and Antarctic sea-ice fields. It seeks to identify the main patterns of coupled variation between atmosphere and sea ice, and to contrast observed patterns with those seen at seasonal time scales. It further seeks to identify whether sea ice variability influences the atmospheric circulation on synoptic time scales.
Suitability of speech quality evaluation measures in speech enhancement

In this paper, we discuss the suitability of speech quality evaluation measures under various noise environments in the application of spectral subtraction speech enhancement. We take three kinds of typical noise and evaluate comprehensively the speech quality under the standard of global signal-to-noise ratio of noisy speech. We take six kinds of quality measures which include mean opinion score, perceptual evaluation of speech quality, segmental signal-to-noise ratio, weighted spectral slope, log-likelihood ratio and log spectral distance. Then appropriate evaluation algorithms are chosen for speech enhancement based on spectral subtraction. The simulation results show that in the application of speech enhancement, the suitability of speech quality evaluation algorithms is limited to the SNR of noisy speech, recording people, recording content and background noise environment; with regard to three kinds of practical background noise which are taken from TIMIT database, the suitability of the segmental signal-to-noise ratio is the worst and log spectral distance is the best.
A novel memory architecture for elliptic curve cryptography with parallel modular multipliers

Parallelization of operations is of utmost importance for efficient implementations of public key cryptography algorithms. Taking a clarification of parallelization methods at different abstraction levels of public key algorithms as a foundation, we propose a novel memory architecture for elliptic curve implementations with multiple modular multiplier units. This architecture is well-suited for different algorithms over GF(P) to be implemented on FPGAs. It allows the execution time to scale with the number of modular multipliers and features nearly no overhead compared to the mere runtime of the multipliers. The advantages of this distributed memory architecture is demonstrated by means of two different EC point multiplications algorithms
Integration of concurrent localization algorithms for a planetary rover

To achieve robust autonomous long range navigation, an estimate of the rover position is required at various levels in the whole system. No single localization algorithm can be robust enough to provide a good position estimate during long range navigation: a set of different algorithms have to be developed and integrated for that purpose. In this paper, we briefly present various localization algorithms developed in the lab, establish a typology of localization algorithms, and focus on the difficulties raised by their integration on board a rover.
Multilingual corpora with coreferential annotation of person entities

This paper presents three corpora with coreferential annotation of person entities for Portuguese, Galician and Spanish. They contain coreference links between several types of pronouns (including elliptical, possessive, indefinite, demonstrative, relative and personal clitic and non-clitic pronouns) and nominal phrases (including proper nouns). Some statistics have been computed, showing distributional aspects of coreference both in journalistic and in encyclopedic texts. Furthermore, the paper shows the importance of coreference resolution for a task such as Information Extraction, by evaluating the output of an Open Information Extraction system on the annotated corpora. The corpora are freely distributed in two formats: (i) the SemEval-2010 and (ii) the brat rapid annotation tool, so they can be enlarged and improved collaboratively.
WALTy: a user behavior tailored tool for evaluating Web application performance

We present WALTy (Web application load-based testing tool), a set of tools that allows the performance analysis of Web applications by means of a scalable what-if analysis on the test bed. The proposed approach is based on a workload characterization generated from information extracted from log files. The workload is generated by using of customer behavior model graphs (CBMG), that are derived by extracting information from the Web application log files. In this manner the synthetic workload used to evaluate the Web application under test is representative of the real traffic that the Web application has to serve. One of the most common critics to this approach is that synthetic workload produced by Web stressing tools is far from being realistic. The use of the CBMGs might be useful to overcome this critic.
Association of the Functional MICA-129 Polymorphism With the Severity of Chronic Chagas Heart Disease

MICA-129 polymorphism affects the binding affinity of MICA molecules with the NKG2D receptor and influences effector cell function. The genotype met/met was associated with the severity of left ventricular systolic dysfunction (LVSD) in patients with chronic Chagas heart disease, while the val/val genotype was associated with the absence of LVSD.
Evolutionary neural networks for Non-Player Characters in Quake III

Designing and implementing the decisions of Non-Player Characters in first person shooter games becomes more difficult as the games get more complex. For every additional feature in a level potentially all decisions have to be revisited and another check made on this new feature. This leads to an explosion of the number of cases that have to be checked, which in its turn leads to situations where combinations of features are overlooked and Non-Player Characters act strange in those particular circumstances. In this paper we show how evolutionary neural networks can be used to avoid these problems and lead to good and robust behavior.
Better target detection in the presence of collinear flankers under high working memory load

There are multiple ways in which working memory can influence selective attention. Aside from the content-specific effects of working memory on selective attention, whereby attention is more likely to be directed towards information that matches the contents of working memory, the mere level of load on working memory has also been shown to have an effect on selective attention. Specifically, high load on working memory is associated with increased processing of irrelevant information. In most demonstrations of the effect to-date, this has led to impaired target performance, leaving open the possibility that the effect partly reflects an increase in general task difficulty under high load. Here we show that working memory load can result in a performance gain when processing of distracting information aids target performance. The facilitation in the detection of a low-contrast Gabor stimulus in the presence of collinear flanking Gabors was greater when load on a concurrent working memory task was high, compared to low. This finding suggests that working memory can interact with selective attention at an early stage in visual processing.
Weighted Fingerprinting Localization based on a Multichannel WiFi Map

WiFi localization based on fingerprinting method became popular during last decade. However, collecting information to construct a WiFi signal map is challenging due to high cost. We assume that it is possible to gather such information with cheap cost using crowd sourcing with smartphones. Each user’s current location is estimated by either GPS, WiFi, radio cell signals or manually set by the user and the WiFi scan information is collected into a server. In this paper, we focus on outdoor geolocalization based multichannel WiFi Map where each channel conveys different accuracy of the reference position. The proposed approach optimizes importance weight on the different channels of WiFi map using Least Squares. Gradient Descent algorithm optimizes weight parameter to minimizes localization error on the training set. The experiment shows the proposed approach outperforms localization with equal importance.
Emotion tagging for comments of online news by meta classification with heterogeneous information sources

With the rapid growth of online news services, users can actively respond to online news by making comments. Users often express subjective emotions in comments such as sadness, surprise and anger. Such emotions can help understand the preferences and perspectives of individual users, and therefore may facilitate online publishers to provide users with more relevant services. This paper tackles the task of predicting emotions for the comments of online news. To the best of our knowledge, this is the first research work for addressing the task. In particular, this paper proposes a novel Meta classification approach that exploits heterogeneous information sources such as the content of the comments and the emotion tags of news articles generated by users. The experiments on two datasets from online news services demonstrate the effectiveness of the proposed approach.
SCOPE: Scalable Composite Optimization for Learning on Spark

Many machine learning models, such as logistic regression (LR) and support vector machine (SVM), can be formulated as composite optimization problems. Recently, many distributed stochastic optimization (DSO) methods have been proposed to solve the large-scale composite optimization problems, which have shown better performance than traditional batch methods. However, most of these DSO methods might not be scalable enough. In this paper, we propose a novel DSO method, called scalable composite optimization for learning (SCOPE), and implement it on the fault-tolerant distributed platform Spark. SCOPE is both computation-efficient and communication-efficient. Theoretical analysis shows that SCOPE is convergent with linear convergence rate when the objective function is strongly convex. Furthermore, empirical results on real datasets show that SCOPE can outperform other state-of-the-art distributed learning methods on Spark, including both batch learning methods and DSO methods.
Compressing historical information in sensor networks

We are inevitably moving into a realm where small and inexpensive wireless devices would be seamlessly embedded in the physical world and form a wireless sensor network in order to perform complex monitoring and computational tasks. Such networks pose new challenges in data processing and dissemination because of the limited resources (processing, bandwidth, energy) that such devices possess. In this paper we propose a new technique for compressing multiple streams containing historical data from each sensor. Our method exploits correlation and redundancy among multiple measurements on the same sensor and achieves high degree of data reduction while managing to capture even the smallest details of the recorded measurements. The key to our technique is the base signal, a series of values extracted from the real measurements, used for encoding piece-wise linear correlations among the collected data values. We provide efficient algorithms for extracting the base signal features from the data and for encoding the measurements using these features. Our experiments demonstrate that our method by far outperforms standard approximation techniques like Wavelets. Histograms and the Discrete Cosine Transform, on a variety of error metrics and for real datasets from different domains.
Critical evaluation of the role of acarbose in the treatment of diabetes: patient considerations

The alpha-glucosidase inhibitor acarbose has been used for more than 20 years in the management of hyperglycemia. Owing to its unique mode of action in the gastrointestinal tract, its properties are very different from other antidiabetic medications. Patients on long-term treatment to control a chronic disease are not only interested in good treatment efficacy, but are also even more interested in the safety and side effects of their medications. Significant aspects of acarbose predominantly regarding safety and tolerability in the management of type 2 diabetes and prediabetes are reviewed. It is concluded that acarbose is a convenient long-term treatment option, with benefits for both type 2 diabetics and patients in a prediabetic state.
Rate-adaptive FSO communication via rate-compatible punctured LDPC codes

In this paper, rate-adaptive free-space optical (FSO) communication is studied using experimental data measured over a 1.87 km terrestrial FSO link in different weather conditions. To accommodate to the channel gain fluctuations induced by atmospheric turbulence and/or weather variations, a rate-adaptive communication system is implemented by puncturing low-density parity-check (LDPC) codes in a rate-compatible fashion. Beside the conventional random puncturing method, an optimized intentional puncturing technique is employed. Using experimental data and on-off-keying (OOK) modulation, the performance of the rate-adaptive FSO system operating at different signaling rates is evaluated. Unlike uncoded OOK, the rate-adaptive technique provides reliable and efficient FSO communication under different weather conditions and scintillation indices. Applying intentional puncturing can further improve the efficiency of the FSO system in terms of throughput.
Spatial Intuition in Elementary Arithmetic: A Neurocomputational Account

Elementary arithmetic (e.g., addition, subtraction) in humans has been shown to exhibit spatial properties. Its exact nature has remained elusive, however. To address this issue, we combine two earlier models for parietal cortex: A model we recently proposed on number-space interactions and a modeling framework of parietal cortex that implements radial basis functions for performing spatial transformations. Together, they provide us with a framework in which elementary arithmetic is based on evolutionarily more basic spatial transformations, thus providing the first implemented instance of Dehaene and Cohen's recycling hypothesis.