Miracle-dz/UniFAR-FTUs huggingface dataset:
Media Bias Characterization in Brazilian Presidential Elections

News media bias is commonly associated with framing information so as to influence readers judgments. It is not rare to find different news outlets reporting the same events under different perspectives with the intention to deliberately influence the reader. For example, making one side's ideological perspective look better than another. This may be an indication of a well known cognitive bias, the framing effect, which states that people may change their judgment based on how the information is presented (or framed). According to a 2017's survey from the Knight Foundation and Gallup, Americans believe that 62% of the news they consume is biased [1]. Still according to the survey, there is a sharp divergence of bias perception across Republicans and Democrats regarding news organizations. This implies that the perception of bias may be affected by whether one agrees (or not) with the ideological leaning (when present) of the news source. How to expose such biases in an automatic fashion from textual content only? One way to do that is by comparing different news outlets on the same stories and look for divergences. In this talk, we present an investigation on news media bias in the context of Brazilian presidential elections by comparing four popular news outlets during three consecutive election years (2010, 2014, and 2018). We analyse the textual content of news stories in search for three kinds of bias: coverage, association, and subjective language. Coverage bias is related to differences in mention rates of candidates and parties. Association bias [2] occurs when, for example, one candidate is associated with a negative concept while another not. Subjective bias [3], has to do with wording that attempts to influence the readers by appealing to emotion, stereotypes, or persuasive language. We perform a thorough analysis on a large scale news data set where several such biases are exposed.
Periodic Solutions of Branched Space from Closed Orbits under Mixed Perturbations

By combining the means of the center manifold theorem and Planar branching theory, this paper studies the sufficient conditions for general three-dimensional systems to branch out into spatial periodic solutions under mixed perturbations, and obtains two theorems for judging the periodic solutions of general three-dimensional systems branching out from closed orbits, which generalize the results of existing planar systems.
Valve status recognition method based on saliency map and improved CNN

Valves are widely used in industrial production, and real-time acquisition of valve status is of great significance for production control. Because the manual acquisition method is time-consuming and laborious, this paper proposes a valve status recognition method based on the saliency map and improved convolutional neural network using the valve image: Based on the improved homomorphic filtering method, the valve image is preprocessed to reduce the uneven illumination; The FT algorithm is used to generate the image saliency map and extract the valve body from background image; By stretching the image multi-directional and multi-scale, training set is extended. The improved CNN is constructed and trained to realize the valve status recognition. The experimental results show that the proposed method can effectively identify the status of different valves in the actual environment.
Juan Piece, a Mobile Puzzle Game about Philippine Geography: its Effects on Students' Problem Solving Skills

The study endeavored to develop a 2D infinite runner - puzzle game, called Juan Piece, in order to identify its effects to the respondents' problem-solving skills and to their Philippine geography appreciation as well. The game was deployed to Android smartphones and tablets. The researchers wanted to know if puzzle games like Juan Piece can improve one's problem-solving skills. Paired t-tests and chi-square tests of independence were used to analyze and interpret the data gathered from the respondents. The said tests, all at a 5% level of significance, revealed that 1) there is no significant difference between the problem-solving skills of the respondents before and after playing, 2) game puzzle interest (i.e. whether the respondents play puzzle games or not prior to the study) is independent from respondents' problem-solving skills before and after playing Juan Piece, and 3) there is a significant difference between the respondents' Philippine Geography appreciation before and after playing the game.
Understanding the Influences of Past Experience on Trust in Human-agent Teamwork

People use the knowledge acquired from past experiences in assessing the trustworthiness of a trustee. In a time where the agents are being increasingly accepted as partners in collaborative efforts and activities, it is critical to understand all aspects of human trust development in agent partners. For human-agent virtual ad hoc teams to be effective, humans must be able to trust their agent counterparts. To earn the humans’ trust, agents need to quickly develop an understanding of the expectation of human team members and adapt accordingly. This study empirically investigates the impact of past experience on human trust in and reliance on agent teammates. To do so, we developed a team coordination game, the Game of Trust (GoT), in which two players repeatedly cooperate to complete team tasks without prior assignment of subtasks. The effects of past experience on human trust are evaluated by performing an extensive set of controlled experiments with participants recruited from Amazon Mechanical Turk, a crowdsourcing marketplace. We collect both teamwork performance data as well as surveys to gauge participants’ trust in their agent teammates. The results show that positive (negative) past experience increases (decreases) human trust in agent teammates; lack of past experience leads to higher trust levels compared to positive past experience; positive (negative) past experience facilitates (hinders) reliance on agent teammates; the relationship between trust in and reliance on agent teammates is not always correlated. These findings provide clear and significant evidence of the influence of key factors on human trust in virtual agent teammates and enhance our understanding of the changes in human trust in peer-level agent teammates with respect to past experience.
Deep Spatial-Temporal Neural Network for Classification of EEG-Based Motor Imagery

As a challenging topic in brain-computer interface (BCI) research, motor imagery classification based on electroencephalogram (EEG) received more and more attention. Due to the low signal-noise ratio of the EEG signal, the promotion of classification result is formidable to carry on. In the current circumstances, how to extract more abstract representations and deep features becomes a research hotspot. Meanwhile, Deep learning (DL) models have been applied in various fields demonstrating great superiority in feature extraction and classification. Despite the fact that a variety of methods have been developed to the feature learning of EEG, few studies have applied deep learning to model the representations of EEG features. In this paper, we present a novel method to perform preprocessing and feature learning. By performing Morlet wavelet and cubic spline interpolation methods, we can obtain a series of spectral frames from the multi-channel EEG signals. Then we construct a hybrid model which associates modified Convolutional Neural Network (CNN) with Bidirectional Gated Recurrent Unit (BGRU). The BGRU is used to learn the contextual information from the feature maps that are extracted from the modified CNN. Finally, motor imagery classification task is performed to evaluate our method. The experimental results demonstrate the significant effectiveness of the proposed approach compared with the state-of-the-art models in classification accuracy.
Robust time‐varying formation design for multiagent systems with disturbances: Extended‐state‐observer method

Robust time‐varying formation design problems for second‐order multiagent systems subjected to external disturbances are investigated. First, by constructing an extended state observer, the disturbance compensation is estimated, which is a critical term in the proposed robust time‐varying formation control protocol. Then, an explicit expression of the formation center function is determined and impacts of disturbance compensations on the formation center function are presented. With the formation feasibility conditions, robust time‐varying formation design criteria are derived to determine the gain matrix of the formation control protocol by utilizing the algebraic Riccati equation technique. Furthermore, the tracking performance and the robustness property of multiagent systems are analyzed. Finally, the numerical simulation is provided to illustrate the effectiveness of theoretical results.
Localization with Limited Annotation

Localization of an object within an image is a common task in medical imaging. Learning to localize or detect objects typically requires the collection of data which has been labelled with bounding boxes or similar annotations, which can be very time consuming and expensive. A technique which could perform such learning with much less annotation would, therefore, be quite valuable. We present such a technique for localization with limited annotation, in which the number of images with bounding boxes can be a small fraction of the total dataset (e.g. less than 1%); all other images only possess a whole image label and no bounding box. We propose a novel loss function for tackling this problem; the loss is a continuous relaxation of a well-defined discrete formulation of weakly supervised learning and is numerically well-posed. Furthermore, we propose a new architecture which accounts for both patch dependence and shift-invariance, through the inclusion of CRF layers and anti-aliasing filters, respectively. We apply our technique to the localization of thoracic diseases in chest X-ray images and demonstrate state-of-the-art localization performance on the ChestX-ray14 dataset.
Lower Bound for (Sum) Coloring Problem

The Minimum Sum Coloring Problem is a variant of the Graph Vertex Coloring Problem, for which each color has a weight. This paper presents a new way to find a lower bound of this problem, based on a relaxation into an integer partition problem with additional constraints. We improve the lower bound for 18 graphs of standard benchmark DIMACS, and prove the optimal value for 4 graphs by reaching their known upper bound.
Dynamic Bandwidth Allocation in Small-Cell Networks: An Economics Approach

We propose and experimentally demonstrate a bandwidth allocation method based on the comparative advantage of spectral efficiency among users in a multi-tone small-cell radio access system with frequency-selective fading channels. The method allocates frequency resources by ranking the comparative advantage of the spectrum measured at the receivers ends. It improves the overall spectral efficiency of the access system with low implementation complexity and independently of power loading. In a two-user wireless transmission experiment, we observe up to 23.1% average capacity improvement by using the proposed method.
Improved stability for 2D attractive Bose gases

We study the ground-state energy of N attractive bosons in the plane. The interaction is scaled for the gas to be dilute, so that the corresponding mean-field problem is a local non-linear Schr{o}dinger (NLS) equation. We improve the conditions under which one can prove that the many-body problem is stable (of the second kind). This implies, using previous results, that the many-body ground states and dynamics converge to the NLS ones for an extended range of diluteness parameters.
Layout Simplification of Patrologia Graeca

This paper illustrates how automated image preprocessing can improve OCR results of an important 19th century series of Greek authors. The roughly 75,000 ancient Greek pages from the volumes published by J.-P. Migne known today as Patrologia Graeca have a wholly regular layout, but one that is not easily reduced to a reading order by standard layout-analysis algorithms. We use a Hough transform and k-nearest-neighbor distance rejection to locate and then remove the features that confuse modern layout analysis. This allows us to separate the valuable Greek text from the less useful Latin translation, greatly simplifying later stages of processing, including de-hyphenation, automatic spell-checking and editing. Our implementation is based upon the Gamera Python package. We make the source code of our solution freely available. 1. ANCIENT GREEK DIGITAL CORPORA AND OCR The study of ancient Greek literature from its origins in the eighth century BC to the fall of Byzantium in AD 1453 has benefited for decades from successive efforts to digitize source documents. Yet in today’s age of ‘linked’ and ‘big’ data, the limitations of these corpora, singularly or combined, impede scholarly research and publication. The admirably comprehensive Thesaurus Linguae Graecae (hereafter, TLG), which made Greek texts available on digital tape as early as 1976, includes the works of all authors listed in the Canon published supplementary to the project. The last print version of this document [1], comprises works roughly to AD 1000. According to the project website, this upper limit in recent years has been extended to the sixteenth century AD. However, the TLG corpus is not freely available: at present, the single user is charged a yearly fee of around $100 (USD) and is subject to a license agreement which, among other things, prohibits “the pubPermission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for thirdparty components of this work must be honored. For all other uses, contact the Owner/Author. Copyright is held by the owner/author(s) DaTECH May 19-2
Request Distribution Strategies in Cluster Based Network Servers

As distributed applications and services are getting popular now a days, Web servers play a central role in the telecommunications infrastructure. Cluster-based Web servers are increasingly adopted to host a variety of networkbased services. So performance improvement of these servers is necessary. The cluster based architecture consists of frontend dispatcher and several back-end servers. Front end is responsible for request distribution. The back-end nodes are responsible for request processing. They should provide reliability, availability and efficient services. So performance improvement of these servers is necessary. Load balancing is one of the best efficient methods for performance improvement of cluster system. It is often desirable to isolate the performance of different classes of requests from each other. Our objective is to deliver better services to high priority request classes without over-sacrificing low priority classes. The main objective is to minimize the response time of requests that need intra cluster communication.
econstor Make Your Publications Visible . A Service of zbw

We develop a model in which non-white individuals are defined with respect to their social environment (family, friends, neighbors) and their attachments to their culture of origin (religion, language), and in which jobs are mainly found through social networks. We find that, depending on how strong peer pressures are, nonwhites choose to adopt “oppositional” identities since some individuals may identify with the dominant culture and others may reject that culture, even if it implies adverse labor market outcomes.
SCDP: Systematic Rateless Coding for Efficient Data Transport in Data Centers

In this paper we propose SCDP, a general-purpose data transport protocol for data centres that, in contrast to all other protocols proposed to date, supports efficient one-to-many and many-to-one communication, which is extremely common in modern data centres. SCDP does so without compromising on efficiency for short and long unicast flows. SCDP achieves this by integrating RaptorQ codes with receiver-driven data transport, packet trimming and Multi-Level Feedback Queuing (MLFQ); (1) RaptorQ codes enable efficient one-to-many and many-to-one data transport; (2) on top of RaptorQ codes, receiver-driven flow control, in combination with in-network packet trimming, enable efficient usage of network resources as well as multi-path transport and packet spraying for all transport modes. Incast and Outcast are eliminated; (3) the systematic nature of RaptorQ codes, in combination with MLFQ, enable fast, decoding-free completion of short flows. We extensively evaluate SCDP in a wide range of simulated scenarios with realistic data centre workloads. For one-to-many and many-to-one transport sessions, SCDP performs significantly better compared to NDP and PIAS. For short and long unicast flows, SCDP performs equally well or better compared to NDP and PIAS.
Sparse Representation of Gaussian Molecular Surface

In this paper, we propose a model and algorithm for sparse representing Gaussian molecular surface. The original Gaussian molecular surface is approximated by a relatively small number of radial basis functions (RBFs) with rotational ellipsoid feature. The sparsity of the RBF representation is achieved by solving a nonlinear $L_1$ optimization problem. Experimental results demonstrate that the original Gaussian molecular surface is able to be represented with good accuracy by much fewer RBFs using our $L_1$ model and algorithm. The sparse representation of Gaussian molecular surface is useful in various applications, such as molecular structure alignment, calculating molecular areas and volumes, and the method in principle can be applied to sparse representation of general shapes and coarse-grained molecular modeling.
The Multi-slit Approach to Coronal Spectroscopy with the Multi-slit Solar Explorer (MUSE)

The Multi-slit Solar Explorer (MUSE) is a proposed mission aimed at understanding the physical mechanisms driving the heating of the solar corona and the eruptions that are at the foundation of space weather. MUSE contains two instruments, a multi-slit extreme ultraviolet (EUV) spectrograph and a context imager. It will simultaneously obtain EUV spectra (along 37 slits) and context images with the highest resolution in space (0.″33–0.″4) and time (1–4 s) ever achieved for the transition region (TR) and corona. The MUSE science investigation will exploit major advances in numerical modeling, and observe at the spatial and temporal scales on which competing models make testable and distinguishable predictions, thereby leading to a breakthrough in our understanding of coronal heating and the drivers of space weather. By obtaining spectra in four bright EUV lines (Fe ix 171 Å, Fe xv 284 Å, Fe xix 108Å, Fe xxi 108 Å) covering a wide range of TR and coronal temperatures along 37 slits simultaneously, MUSE will be able to “freeze” the evolution of the dynamic coronal plasma. We describe MUSE’s multi-slit approach and show that the optimization of the design minimizes the impact of spectral lines from neighboring slits, generally allowing line parameters to be accurately determined. We also describe a Spectral Disambiguation Code to resolve multi-slit ambiguity in locations where secondary lines are bright. We use simulations of the corona and eruptions to perform validation tests and show that the multi-slit disambiguation approach allows accurate determination of MUSE observables in locations where significant multi-slit contamination occurs.
Scalable Deep Unsupervised Clustering with Concrete GMVAEs

Discrete random variables are natural components of probabilistic clustering models. A number of VAE variants with discrete latent variables have been developed. Training such methods requires marginalizing over the discrete latent variables, causing training time complexity to be linear in the number clusters. By applying a continuous relaxation to the discrete variables in these methods we can achieve a reduction in the training time complexity to be constant in the number of clusters used. We demonstrate that in practice for one such method, the Gaussian Mixture VAE, the use of a continuous relaxation has no negative effect on the quality of the clustering but provides a substantial reduction in training time, reducing training time on CIFAR-100 with 20 clusters from 47 hours to less than 6 hours.
Multivariate Rank-Based Distribution-Free Nonparametric Testing Using Measure Transportation

Abstract In this article, we propose a general framework for distribution-free nonparametric testing in multi-dimensions, based on a notion of multivariate ranks defined using the theory of measure transportation. Unlike other existing proposals in the literature, these multivariate ranks share a number of useful properties with the usual one-dimensional ranks; most importantly, these ranks are distribution-free. This crucial observation allows us to design nonparametric tests that are exactly distribution-free under the null hypothesis. We demonstrate the applicability of this approach by constructing exact distribution-free tests for two classical nonparametric problems: (I) testing for mutual independence between random vectors, and (II) testing for the equality of multivariate distributions. In particular, we propose (multivariate) rank versions of distance covariance and energy statistic for testing scenarios (I) and (II), respectively. In both these problems, we derive the asymptotic null distribution of the proposed test statistics. We further show that our tests are consistent against all fixed alternatives. Moreover, the proposed tests are computationally feasible and are well-defined under minimal assumptions on the underlying distributions (e.g., they do not need any moment assumptions). We also demonstrate the efficacy of these procedures via extensive simulations. In the process of analyzing the theoretical properties of our procedures, we end up proving some new results in the theory of measure transportation and in the limit theory of permutation statistics using Stein’s method for exchangeable pairs, which may be of independent interest.
The human Imp 3 and Imp 4 proteins form a ternary complex with hMpp 10 , which only interacts with the U 3 snoRNA in 60 ± 80 S ribonucleoprotein complexes

Ribosome biogenesis requires a vast number of trans-acting factors many of which are required for the chemical modi®cation and processing of the pre-rRNA component. The U3 snoRNP complex is required for the early cleavage steps in pre-rRNA processing. We have cloned cDNAs encoding the human and mouse homologs of the yeast U3 snoRNP-associated proteins Imp3 and Imp4. Both human proteins localize to nucleoli and interact with the U3 snoRNA. The results of complementation experiments show that, in contrast to mouse Imp4, mouse Imp3 can partially alleviate the growth defect of the corresponding yeast null strain, indicating that the role of Imp3 in pre-rRNA processing is evolutionarily conserved. The results of density gradient centrifugation experiments show that, in contrast to hU3-55K, the human Imp3 and Imp4 proteins predominantly interact with the U3 snoRNA in 60±80S ribonucleoprotein complexes. In addition, we have found that hImp3, hImp4 and hMpp10 can form a stable hetero-trimeric complex in vitro, which is generated by direct interactions of both hImp3 and hImp4 with hMpp10. The analysis of hImp3 and hImp4 mutants indicated that their binding to hMpp10 correlates with their nucleolar accumulation, strongly suggesting that the formation of the ternary complex of hImp3, hImp4 and hMpp10 is required for their association with nucleolar