Miracle-dz/UniFAR-FTUs huggingface dataset:
Discriminative Learning of Selectional Preference from Unlabeled Text

We present a discriminative method for learning selectional preferences from unlabeled text. Positive examples are taken from observed predicate-argument pairs, while negatives are constructed from unobserved combinations. We train a Support Vector Machine classifier to distinguish the positive from the negative instances. We show how to partition the examples for efficient training with 57 thousand features and 6.5 million training instances. The model outperforms other recent approaches, achieving excellent correlation with human plausibility judgments. Compared to Mutual Information, it identifies 66% more verb-object pairs in unseen text, and resolves 37% more pronouns correctly in a pronoun resolution experiment.
Modeling the Impact of Clustering with Random Event Arrival on the Lifetime of WSN

Energy efficiency and network lifetime are the most critical issues for Wireless Sensor Networks (WSNs). Clustering is one of the well-known techniques to reduce the energy consumption of the network and improving its lifetime as well. However, clustering itself is time and energy-consuming. Besides, some other ultimate factors such as radio propagation models; network traffic probability are also influencing factors of energy consumption. To ensure the energy efficient network operation, it is essential to taken into account the mentioned factors. Since the number of events occurred inside the network are directly related to the node's energy consumption. For this reason based on the random event arrival; it is crucial to determine an appropriate clustering frequency so that the network lifetime is maximized. We rely on the Poisson distribution, where we can assume random event arrival inside the network. Again formulate clustering problem and sketch a guideline on acquiring an optimal number of clustering in favor of height network lifetime.
English Language Learners’ Pathways to Four-Year Colleges

Background/Context English language learners (ELLs) are the fastest growing segment of the K–12 student population in the United States, yet they encounter substantial problems entering higher education. The gap between ELLs and non-ELLs is particularly acute for four-year college access. Research has been largely silent on ELLs’ college advancement, and we know little about what inhibits ELLs’ college access. Purpose To examine the process of ELLs’ college planning in order to determine which stages of college planning present difficulties to ELLs and why. College planning is conceptualized as consisting of five milestones: (a) aspiring to college, (b) acquiring college qualifications, (c) graduating from high school, (d) applying to college, and (e) enrolling in college. Research Design Secondary data analysis of the Education Longitudinal Study of 2002. Only students who participated in all of the first three waves (2002, 2004, and 2006) of data collection were included (N = 12,450). Students were divided into three language background groups: (a) ELLs, (b) English-proficient linguistic minority students (EPs), and (c) native speakers of English (NSs). We first compared the college-access patterns of the three language groups. We then mapped out each group's pathways through the milestones. Finally, we conducted multigroup analyses to examine whether and to what extent a different set of predictors shape the groups’ college pathways. Findings/Results It is the early stages of college planning (aspirations and college qualifications stages) that are particularly challenging to ELLs, such that the majority of ELLs never reach the later milestone of applying to a four-year college. Predictors known to matter significantly for the general population's college access are not all significant for ELLs. Conclusions/Recommendations In order to enable more ELLs to reach four-year colleges, we should make a targeted effort to support them in the early stages of college planning. Racial/ ethnic minority ELLs are particularly vulnerable and need more support. We also need to invest more effort into identifying the factors and conditions that specifically influence ELLs’ college planning.
Design of Varactor-Based Tunable Matching Networks for Dynamic Load Modulation of High Power Amplifiers

In this paper, the design of varactor-based tunable matching networks for dynamic load modulation of high power amplifiers (PAs) is presented. Design guidelines to overcome the common breakdown, and tunability problems of the varactors for high power applications are proposed. Based on the guidelines, using commercially available abrupt junction silicon varactors, a tunable matching network is built and measured. The matching network is then used for load modulation of a 1-GHz 7-W class-E LDMOS PA. Static measurements of the load modulated PA show that the power-added efficiency of the PA is improved by an absolute value of 10% at 10-dB backoff. This promising result proves, for the first time, the feasibility of load modulation techniques for high-power applications in the gigahertz frequency range.
Combing Semantic and Prior Polarity Features for Boosting Twitter Sentiment Analysis Using Ensemble Learning

Twitter sentiment analysis provides organizations with real-time monitoring of public feelings towards particular products and events related to them. Most existing research is focused on extraction of sentiment features through analysis of lexical and syntactic features that are expressed explicitly through words, emoticons, exclamation marks etc. Single machine learning classifiers are usually employed by these approaches for tweet sentiment classification. In this paper, we introduce a semantic feature model that utilizes co-occurrence statistics and latent contextual semantic relationships between words in tweets. These semantic features are combined with prior polarity score features and n-grams features as a sentiment feature set of tweets. The feature set is incorporated into an ensemble classifier formed by Support Vector Machines, Logistic Regression and Random Forest for training and predicting sentiment classification labels. Five Twitter datasets are used in our evaluation and the performance of the model is compared with a word n-grams model as a baseline. Experimental results show that our method has superior performance in terms of the accuracy of the sentiment classification and that ensemble learning can be used to improve classification accuracy. Keywords— Twitter, semantic feature, sentiment analysis, ensemble learning
A Computational Framework for Infinite-Dimensional Bayesian Inverse Problems Part I: The Linearized Case, with Application to Global Seismic Inversion

We present a computational framework for estimating the uncertainty in the numerical solution of linearized infinite-dimensional statistical inverse problems. We adopt the Bayesian inference formulation: given observational data and their uncertainty, the governing forward problem and its uncertainty, and a prior probability distribution describing uncertainty in the parameter field, find the posterior probability distribution over the parameter field. The prior must be chosen appropriately in order to guarantee well-posedness of the infinite-dimensional inverse problem and facilitate computation of the posterior. Furthermore, straightforward discretizations may not lead to convergent approximations of the infinite-dimensional problem. And finally, solution of the discretized inverse problem via explicit construction of the covariance matrix is prohibitive due to the need to solve the forward problem as many times as there are parameters. Our computational framework builds on the infinite-dimensional formulation proposed by Stuart (A. M. Stuart, Inverse problems: A Bayesian perspective, Acta Numerica, 19 (2010), pp. 451-559), and incorporates a number of components aimed at ensuring a convergent discretization of the underlying infinite-dimensional inverse problem. The framework additionally incorporates algorithms for manipulating the prior, constructing a low rank approximation of the data-informed component of the posterior covariance operator, and exploring the posterior that together ensure scalability of the entire framework to very high parameter dimensions. We demonstrate this computational framework on the Bayesian solution of an inverse problem in 3D global seismic wave propagation with hundreds of thousands of parameters.
On Selfishness, Local Information, and Network Optimality: A Topology Control Example

Topology control of ad hoc and mesh networks specifies how to assign per-node transmission parameters (such as power level, frequency etc.) so as to achieve energy efficiency, while maintaining certain desirable properties such as connec- tivity. In autonomous networks, nodes may act in their self- interest and improve their performance, perhaps at the expense of other nodes', or even the overall network's, performance. Besides, nodes must also contend with limited information about the network operating state during their decision-making. We analyze the above problem using non-cooperative game theory and quantify the impact of partial network state knowl- edge that nodes possess on the network optimality. We develop a local topology control algorithm that uses the idea of maintaining connectivity of 1-hop neighborhoods. This algorithm is first shown to converge and be stable. We then examine the trade-off between network performance (energy efficiency) and the cost of having knowledge (by exchanging control messages): more information exchange makes the nodes more network-aware, and hence leads to more efficient networks, but exchange of control information itself is costly. Taking the cost of obtaining knowledge into account, we observe that when nodes can operate along the continuum of knowledge, from 1-hop to omniscience, the network consumes least energy when nodes have significantly less connectivity information. I. INTRODUCTION Ad hoc networks are touted as autonomous, self-configuring and decentralized, with an intrinsic ability to adapt and be resilient. These networks also embrace an "open" network philosophy by distributing network control to autonomous, distributed, and independent nodes. Such a radical networking paradigm can foster aberrant behavior among nodes. By not complying to protocols, nodes can act selfishly, sometimes even maliciously, and "game" the system. Improving wireless network performance, on the other hand, often requires cross- layer optimization schemes that rely on cooperation and access to information. 1 Network engineers may then be faced with a dilemma: How can a network, whose performance relies on node cooperation, come to terms with (a) selfish nodes that are trying to optimize their own objectives, and (b) the lack of sufficient information, often integral to most cooperative algorithms? In other words, how can cooperation arise when nodes are acting selfishly, or when they do not have enough
T1R3 and gustducin in gut sense sugars to regulate expression of Na+-glucose cotransporter 1

Dietary sugars are transported from the intestinal lumen into absorptive enterocytes by the sodium-dependent glucose transporter isoform 1 (SGLT1). Regulation of this protein is important for the provision of glucose to the body and avoidance of intestinal malabsorption. Although expression of SGLT1 is regulated by luminal monosaccharides, the luminal glucose sensor mediating this process was unknown. Here, we show that the sweet taste receptor subunit T1R3 and the taste G protein gustducin, expressed in enteroendocrine cells, underlie intestinal sugar sensing and regulation of SGLT1 mRNA and protein. Dietary sugar and artificial sweeteners increased SGLT1 mRNA and protein expression, and glucose absorptive capacity in wild-type mice, but not in knockout mice lacking T1R3 or α-gustducin. Artificial sweeteners, acting on sweet taste receptors expressed on enteroendocrine GLUTag cells, stimulated secretion of gut hormones implicated in SGLT1 up-regulation. Gut-expressed taste signaling elements involved in regulating SGLT1 expression could provide novel therapeutic targets for modulating the gut's capacity to absorb sugars, with implications for the prevention and/or treatment of malabsorption syndromes and diet-related disorders including diabetes and obesity.
Elastin‐like polypeptides: Biomedical applications of tunable biopolymers

Artificial repetitive polypeptides have grown in popularity as a bioinspired alternative to synthetic polymers. The genetically encoded synthesis, monodispersity, potential lack of toxicity, and biocompatibility are attractive features of these biopolymers for biological applications. Elastin‐like polypeptides (ELPs) are one such class of biopolymers that are of particular interest because of their “smart”—stimuli responsive—properties. Herein, we discuss the genetically encoded design and recombinant synthesis of ELPs that enable precise control of their physicochemical properties and which have led to a wide range of biomedical applications of these biopolymers in the last decade. © 2010 Wiley Periodicals, Inc. Biopolymers (Pept Sci) 94:60–77, 2010.
Mirror-based Camera Pose Estimation Using an Orthogonality Constraint

This paper is aimed at employing mirrors to estimate relative posture and position of camera, i.e., extrinsic parameters, against a 3D reference object that is not directly visible from the camera. The key contribution of this paper is to propose a novel formulation of extrinsic camera calibration based on orthogonality constraint which should be satisfied by all families of mirror-reflections of a single reference object. This allows us to obtain a larger number of equations which contribute to make the calibration more robust. We demonstrate the advantages of the proposed method in comparison with a state-of-the-art by qualitative and quantitative evaluations using synthesized and real data.
Understanding Delayed T-Cell Priming, Lung Recruitment, and Airway Luminal T-Cell Responses in Host Defense against Pulmonary Tuberculosis

Mycobacterium tuberculosis (M.tb), the causative bacterium of pulmonary tuberculosis (TB), is a serious global health concern. Central to M.tb effective immune avoidance is its ability to modulate the early innate inflammatory response and prevent the establishment of adaptive T-cell immunity for nearly three weeks. When compared with other intracellular bacterial lung pathogens, such as Legionella pneumophila, or even closely related mycobacterial species such as M. smegmatis, this delay is astonishing. Customarily, the alveolar macrophage (AM) acts as a sentinel, detecting and alerting surrounding cells to the presence of an invader. However, in the case of M.tb, this may be impaired, thus delaying the recruitment of antigen-presenting cells (APCs) to the lung. Upon uptake by APC populations, M.tb is able to subvert and delay the processing of antigen, MHC class II loading, and the priming of effector T cell populations. This delay ultimately results in the deferred recruitment of effector T cells to not only the lung interstitium but also the airway lumen. Therefore, it is of upmost importance to dissect the mechanisms that contribute to the delayed onset of immune responses following M.tb infection. Such knowledge will help design the most effective vaccination strategies against pulmonary TB.
Design research in the context of federal law enforcement

In recent years, design thinking and design research have proved indispensable in creating useful and usable solutions that both address human needs and create value for the client. Design research is the connective activity that helps us integrate specialized knowledge into meaningful solutions. We used a structured design process and various design research methods to facilitate the design and development of a software and hardware solution for a federal law enforcement agency to automate and standardize the processing and collection of a certain type of evidence. The methods we used were crucial in gathering the necessary evidence and presenting a synthesized, meaningful picture of the problems at hand to the client. This in turn led to a highly effective and collaborative partnership that resulted in a high-value application that helped standardize the current state of practice and advanced the mission of the federal law enforcement agency in the realm of intelligence gathering.
MEASURING SCHOOL DEMAND IN THE PRESENCE OF SPATIAL DEPENDENCE . A CONDITIONAL APPROACH

Improving educational quality is an important public policy goal. However, its success requires identifying factors associated with student achievement. At the core of these proposals lies the principle that increased public school quality can make school system more efficient, resulting in correspondingly stronger performance by students. Nevertheless, the public educational system is not devoid of competition which arises, among other factors, through the efficiency of management and the geographical location of schools. Moreover, families in Spain appear to choose a school on the grounds of location. In this environment, the objective of this paper is to analyze whether geographical space has an impact on the relationship between the level of technical quality of public schools (measured by the efficiency score) and the school demand index. To do this, an empirical application is performed on a sample of 1,695 public schools in the region of Catalonia (Spain). This application shows the effects of spatial autocorrelation on the estimation of the parameters and how these problems are addressed through spatial econometrics models. The results confirm that space has a moderating effect on the relationship between efficiency and school demand, although only in urban municipalities.
Confocal scanning laser microscopy with complementary 3D image analysis allows quantitative studies of functional state of ionoregulatory cells in the nile tilapia (Oreochromis niloticus) following salinity challenge

The development of a novel three‐dimensional image analysis technique of stacks generated by confocal laser scanning microscopy is described allowing visualization of mitochondria‐rich cells (MRCs) in the seawater‐adapted Nile tilapia in relation to their spatial location. This method permits the assessment and classification of both active and nonactive MRCs based on the distance of the top of the immunopositive cell from the epithelial surface. In addition, this technique offers the potential for informative and quantitative studies, for example, densitometric and morphometric measurements based on MRC functional state. Confocal scanning laser microscopy used with triple staining whole‐mount immunohistochemistry was used to detect integumental MRCs in the yolk‐sac larvae tail of the Nile tilapia following transfer from freshwater to elevated salinities, that is, 12.5 and 20 ppt. Mean active MRC volume was always significantly larger and displayed a greater staining intensity (GLM; P<0.05) than nonactive MRCs. Following transfer, the percentage of active MRCs was seen to increase as did MRC volume (GLM; P<0.05). Microsc. Res. Tech. 76:412–418, 2013. © 2013 Wiley Periodicals, Inc.
Analysis environment of conversational structure with nonverbal multimodal data

This paper shows the IMADE (Interaction Measurement, Analysis, and Design Environment) project to build a recording and anlyzing environment of human conversational interactions. The IMADE room is designed to record audio/visual, human-motion, eye gazing data for building interaction corpus mainly focusing on understanding of human nonverbal behaviors. In this paper, we show the notion of interaction corpus and iCorpusStudio, software environment for browsing and analyzing the interaction corpus. We also present a preliminary experiment on multiparty conversations.
RTA Algorithms on Buffer-Efficient Optical TDM Rings

Optical TDM allows multiple traffic streams to share the bandwidth of a wavelength. This paper presents a framework for determining the capacity and routing strategy needed to establish different logical topologies on an optical TDM ring network with propagation delay but no Optical Time Slot Interchange (OTSI); OTSI requires optical buffering which is not practical at the current time. We show that an optical TDM ring is characterized by a parameter called cycle length. The impact of the cycle length on the capacity of a bi-directional optical TDM ring is evaluated and joint routing and time-slot assignment strategies are developed.
An Investigation of Three Metacognitive Measures

Metacognition, or the awareness and ability to control one’s cognitions, is hypothesized to play a central role in productive problem solving (Berardi-Coletta, Buyer, Dominowski, & Rellinger, 1995) and self-regulated learning (Zepeda, Richey, Ronevich, & Nokes-Malach, 2015). To assess metacognitive skills, different measures have been developed including questionnaires, verbal protocols, and metacognitive judgments. However, there is little research on whether these measures assess the same metacognitive processes (e.g., monitoring, debugging, and evaluation) or are related to the same learning outcomes (e.g., transfer and preparation for future learning). To address these issues we investigated whether these three measures captured the same metacognitive processes during a learning task and test. The results showed that evaluation skills as measured by verbal protocols were positively related to debugging and evaluation as measured by the task-based questionnaire. There were also unexpected negative associations between monitoring skills as measured by verbal protocols and the questionnaire and metacognitive judgments. There was no association between the monitoring questionnaire and metacognitive judgments. All three measures were related to learning, but the type of metacognitive skill, the direction of the effect, and the type of learning differed among the measures. Implications for future research and applications are discussed.
Classificação de Prioridade de Tweets utilizando Máquinas de Vetor de Suporte (Tweet Priority Classification Using Support Vector Machines) [in Portuguese]

Resumo. Este artigo provê resultados iniciais sobre a tarefa de classificação automática de prioridade de tweets, como forma de amenizar a sobrecarga de informação sofrida por usuários do Twitter. Para tanto, aplicamos Máquinas de Vetor de Suporte a um extenso conjunto de exemplos contendo tweets manualmente classificados por nove usuários. Mostramos resultados promissores mesmo com a quantidade limitada de informações textuais presente em tweets.