Miracle-dz/UniFAR-FTUs huggingface dataset:
“How did you become political?”: Narratives of junior researcher-practitioners in applied linguistics

ABSTRACT This article is based on a set of short narratives from four researchers in applied linguistics, who adopt the descriptor “junior political researcher-practitioners. ” These individuals shared and analyzed these narratives relating to their personal experiences and an emerging political identity in the academy under investigation in this study. The study was methodologically inspired by collective memory-work, a research framework with transformative aspirations that integrates narrative writing with group analysis and dissolves the boundaries between theory and method as well as researcher and research participants. The study itself enacts an inquiry. The narratives are presented for readers to read and respond to. They constitute the joint object of an inquiry by the coauthors and their readers. Other language educators with goals for personal and sociopolitical development may find themselves in this study and be encouraged into embracing their own political potential.
Univariate adaptive thinning

In this paper we approximate large sets of univariate data by piecewise linear functions which interpolate subsets of the data, using adaptive thinning strategies. Rather than minimize the global error at each removal (AT0), we propose a much cheaper thinning strategy (AT1) which only minimizes errors locally. Interestingly, the two strategies are equivalent in all our numerical tests and we prove this to be true for convex data. We also compare with non-adaptive thinning strategies.
Predicting in vitro drug sensitivity using Random Forests

MOTIVATION Panels of cell lines such as the NCI-60 have long been used to test drug candidates for their ability to inhibit proliferation. Predictive models of in vitro drug sensitivity have previously been constructed using gene expression signatures generated from gene expression microarrays. These statistical models allow the prediction of drug response for cell lines not in the original NCI-60. We improve on existing techniques by developing a novel multistep algorithm that builds regression models of drug response using Random Forest, an ensemble approach based on classification and regression trees (CART). RESULTS This method proved successful in predicting drug response for both a panel of 19 Breast Cancer and 7 Glioma cell lines, outperformed other methods based on differential gene expression, and has general utility for any application that seeks to relate gene expression data to a continuous output variable. IMPLEMENTATION Software was written in the R language and will be available together with associated gene expression and drug response data as the package ivDrug at http://r-forge.r-project.org.
An Exact Model for Active P2P Worms Propagation

Peer-to-Peer(P2P) network has some closer relation-ship with Internet while the scale of P2P network increases. Active P2P worms attack neighbour peers based on hit-list and pose a severe threat to both P2P network and Internet. Due to the complexity of P2P network, the traditional worms propagation model cannot be adequate to exact model P2P worms spread. Both the dynamic characteristic of P2P network and the heterogeneity of peers could have some great impact on active P2P worms spread. A mathematical model of active P2P worms propagation is presented in this paper. We simulate active P2P worms spread after analyzing the mathematical model. The results show that many factors could affect the propagation of active P2P worms such as the state of P2P network, the scan rate of P2P worms, and the security knowledge peers have. We also study the late phase of active P2P worms spread and find that a very small space of P2P network is enough to guarantee active P2P worms self-propagation when peers cannot repair security holes in time for many reasons.
Enabling collaboration and coordination of wireless sensor networks via social networks

The Internet of Things is envisaged to deliver constant information flow from the physical objects in our world and make the sensory information about our environment available online. This paper presents the on-going work to integrate the physical world with the largely available Internet infrastructure by a model that interconnect wireless sensor networks with social networks. The paper provides a design and framework for employing social networks to facilitate the collaboration and coordination of distributed sensor networks. Seamlessly exchanging sensory measurements amongst geographically dispersed sensor networks is anticipated to enhance our capability to capture the status of the physical world and to realise a smart environment once sensory information is delivered to various machineries in the world. Additionally, sensor networks may utilise social networks for distributing the sensing responsibilities amongst sensor networks, and as a result prolonging the lifetime of the networks whilst maintaining constant monitoring and detection of the environment.
Cross-layer design of packet scheduling and resource allocation algorithms for 4 G cellular systems

Adaptive radio resource management in next generation wireless cellular networks is a key issue to attain efficiency and complexity trade-offs with full fledged flexibility. Cross-layer algorithms encompassing the view of MAC and physical layer are needed to exploit at best radio resource. Two main stochastic drivers need be considered: traffic profile, along with fairness and QoS constraint, and radio channel for mobile terminals. We consider a time division OFDMA/TDD based wireless interface where frequency carriers and OFDM symbols are grouped into resource chunks, that can be individually and dynamically assigned to active traffic flows. We take a loose coupling approach and define a clear-cut interface between the packet scheduling module, in charge of traffic policies, and the radio resource allocator, aiming at an optimized assignment of resource chunks autonomously performed within each cell. A detailed multi-cell simulation software is used to evaluate the obtained performance in a number of scenarios, as defined during the PRIMO project, where the reported research activity has been developed.
Improving Verb Phrase Extraction from Historical Text by use of Verb Valency Frames

In this paper we explore the idea of using verb valency information to improve verb phrase extraction from historical text. As a case study, we perform experiments on Early Modern Swedish data, but the approach could easily be transferred to other languages and/or time periods as well. We show that by using verb valency information in a post-processing step to the verb phrase extraction system, it is possible to remove improbable complements extracted by the parser and insert probable complements not extracted by the parser, leading to an increase in both precision and recall for the extracted complements.
Edinburgh Research Explorer Differential requirements for Gli2 and Gli3 in the regional specification of the mouse hypothalamus.

Secreted protein Sonic hedgehog (Shh) ventralizes the neural tube by modulating the crucial balance between activating and repressing functions (GliA, GliR) of transcription factors Gli2 and Gli3 . This balance—the Shh-Gli code—is species-and context-dependent and has been elucidated for the mouse spinal cord. The hypothalamus, a forebrain region regulating vital functions like homeostasis and hormone secretion, shows dynamic and intricate Shh expression as well as complex regional differentiation. Here we asked if particular combinations of Gli2 and Gli3 and of GliA and GliR functions contribute to the variety of hypothalamic regions, i.e., we wanted to approach the question of a possible hypothalamic version of the Shh-Gli code. Based on mouse mutant analysis, we show that: (1) hypothalamic regional heterogeneity is based in part on differentially stringent requirements for Gli2 or Gli3 ; (2) another source of diversity are differential requirements for Shh of neural vs. non-neural origin; (3) the medial progenitor domain known to depend on Gli2 for its development generates several essential hypothalamic nuclei plus the pituitary and median eminence; (4) the suppression of Gli3R by neural and non-neural Shh is essential for hypothalamic speciﬁcation. Finally, we have mapped our results on a recent model which considers the hypothalamus as a transverse region with alar and basal portions. Our data conﬁrm the model and are explained by it.
Insulin Resistance and Cancer-Specific and All-Cause Mortality in Postmenopausal Women: The Women's Health Initiative.

BACKGROUND Insulin resistance has been proposed as a mediator of the increased cancer incidence and mortality associated with obesity. However, prior studies included limited cancer deaths and had inconsistent findings. Therefore, we evaluated insulin resistance and cancer-specific and all-cause mortality in postmenopausal women participating in the Women's Health Initiative (WHI). METHODS Eligible were a subsample of 22 837 WHI participants aged 50-79 years enrolled at 40 US clinical centers from 1993 to 1998 who had baseline fasting glucose and insulin levels. Baseline insulin resistance was measured by the homeostasis model assessment of insulin resistance (HOMA-IR). Cancers were verified by central medical record review and deaths verified by medical record and death certificate review enhanced by National Death Index queries. Cox proportional hazards regression models were used to calculate adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) for cancer-specific and all-cause mortality. All statistical tests were two-sided. RESULTS During a median of 18.9 years of follow-up, 1820 cancer deaths and 7415 total deaths occurred. Higher HOMA-IR quartile was associated with higher cancer-specific mortality (Q4 vs Q1, HR = 1.26, 95% CI = 1.09 to 1.47; Ptrend = .003) and all-cause mortality (Q4 vs Q1, HR = 1.63, 95% CI = 1.51 to 1.76; Ptrend < .001). A sensitivity analysis for diabetes status did not change findings. Among women with body mass index less than 25 kg/m2, higher HOMA-IR quartile was associated with higher cancer mortality (Fine and Gray, P = .004). CONCLUSIONS High insulin resistance, as measured by HOMA-IR, identifies postmenopausal women at higher risk for cancer-specific and all-cause mortality who could potentially benefit from early intervention.
A Selection Criterion for a Class of Agent-Based Spatial Decision Models

A model selection method inspired by the idea of universal models is proposed for choosing between spatially explicit multi-agent decision models. Since this kind of models are often used as support in real-world decision making, and usually there are not much data available to validate the models, it is important that the selection of the best model is based on sound criteria, not merely on model's face value. In this research the best model is de ned as one with an adequate t to the current data and good generalizability to the future data. The criterion's ability to choose the best model is achieved by properly combining measures for goodness of t and complexity in order to avoid overtting, i.e., the models ability to t noise in the data, induced by its super uous complexity. The proposed model selection criterion is applied and its performance is analyzed in the context of simple spatial agent-based decision models, which are made to generate arti cial data. The preliminary results indicate that the criterion adequately balances the two measures by preferring the simpler model when there are not much data available, even when the more complex model generated the data. Contact: Tei Laine Computer Science Department Indiana University Lindley Hall 215 150 S. Woodlawn Ave. Bloomington, IN 47405-7104 Tel: +1 812 855-8702 Fax: +1 812 855-4829 Email: telaine@cs.indiana. edu
3D visual SLAM with a Time-of-Flight camera

Location and mapping are fundamental problems for a mobile robot to implement a series of high-level applications. Traditional solutions to Simultaneous Location and Mapping (SLAM) are probabilistic reasoning. This paper proposes an analytic solution to 3D visual SLAM with a Time-of-Flight (TOF) camera. According to the visual registration, 3D visual SLAM problem is decomposed into such steps as environment sensing, data matching, motion estimation, as well as location update and registration of new landmarks. First, TOF range camera enables the robot to capture images of distance and intensity of a scene. Scale-Invariant feature transform (SIFT) algorithms for visual feature extraction is applied to the captured intensity images. These visual features combined with the corresponding distance information give a full measurement of 3D landmarks. Then, the process of data association and match is developed through SIFT and the Iterative Closest Point (ICP) to minimize the relative and global error in SLAM process, while obtaining motion estimation. Finally, based on the visual theory of structure from motion (SFM), an analytic solution to location and mapping is presented to 3D Visual SLAM, instead of conventional probabilistic reasoning. We provide 3D visual SLAM experimental results from simulation and the indoor environment. It turns out that the proposed scheme is feasible.
The Mod(mdg4) Component of the Su(Hw) Insulator Inserted in the P Transposon Can Repress Its Mobility in Drosophila melanogaster

Transposable element P of Drosophila melanogaster is one of the best-characterized eukaryotic transposons. Successful transposition requires the interaction between transposase complexes at both termini of the P element. Here we found that insertion of one or two copies of the Su(Hw) insulator in the P transposon reduces the frequency of its transposition. Inactivation of a Mod(mdg4) component of the Su(Hw) insulator suppresses the insulator effect. Thus, the Su(Hw) insulator can modulate interactions between transposase complexes bound to the ends of the P transposon in germ cells.
Scalable Parallel Algorithms ForPredictive Modelling

Data Mining applications have to deal with increasingly large data sets and complexity. Only algorithms which scale linearly with data size are feasible. We present parallel regression algorithms which after a few initial scans of the data compute predictive models for data mining and do not require further access to the data. In addition, we describe various ways of dealing with the complexity (high dimensionality) of the data. Three methods are presented for three different ranges of attribute numbers. They use ideas from the finite element method and are based on penalised least squares fits using sparse grids and additive models for intermediate and very high dimensional data. Computational experiments confirm scalability both with respect to data size and number of processors.
Automatic Generation of Tamil Lyrics for Melodies

This paper presents our on-going work to automatically generate lyrics for a given melody, for phonetic languages such as Tamil. We approach the task of identifying the required syllable pattern for the lyric as a sequence labeling problem and hence use the popular CRF++ toolkit for learning. A corpus comprising of 10 melodies was used to train the system to understand the syllable patterns. The trained model is then used to guess the syllabic pattern for a new melody to produce an optimal sequence of syllables. This sequence is presented to the Sentence Generation module which uses the Dijkstra's shortest path algorithm to come up with a meaningful phrase matching the syllabic pattern.
Tempol Attenuates Excitatory Actions of Angiotensin II in the Rostral Ventrolateral Medulla During Emotional Stress

Superoxide has been shown to be an important intracellular mediator of actions of angiotensin II. Recently, we found that blockade of angiotensin II type-1 receptors in the rostral ventrolateral medulla (RVLM) abrogated the pressor effect of emotional stress in rabbits. In the present study, we examined the influence of superoxide dismutase mimetics, tempol and tiron, in RVLM on cardiovascular stress response in conscious rabbits. Air-jet stress evoked a sustained increase in blood pressure (+14±2 mm Hg), tachycardia (+52±7 bpm), and renal sympathoactivation (+58±8%). Bilateral microinjections of tempol or tiron (20 nmol) into RVLM did not alter resting cardiovascular parameters, but attenuated the pressor, sympathetic, and tachycardiac response to stress by 40% to 55%. By contrast, 3-carbamoylproxyl, which is structurally close to tempol but has a lower superoxide scavenging activity, did not alter the stress response. Neither tempol nor tiron altered the sympathoexcitatory response to glutamate microinjections into RVLM or to baroreceptor unloading. Microinjections of nitric oxide synthase inhibitor NG-nitro-L-arginine methyl ester (L-NAME; 10 nmol) into RVLM did not affect the stress response. Coinjections of tempol and L-NAME decreased the pressor response to stress by 35±3%. Tempol attenuated the pressor response to microinjection of angiotensin II into RVLM by 59±15%, whereas L-NAME did not alter this response. These results suggest that superoxide dismutase mimetics in RVLM attenuate, partially via a nitric oxide-independent mechanism, the pressor effect of emotional stress in rabbits. Together with our previous studies, these results also indicate that superoxide is a key mediator of excitatory actions of angiotensin II in RVLM during acute stress.
Design and architecture of wireless body area network using android application

Patients in hospitals have issue with health instrumentality that's connected with wires to their body. Wired health instrumentality restricts the quality of the patient. Moreover, health caretaker’s area unit compelled to work the instrumentality and take the measurements. Hence, wireless observance of patient is incredibly effective resolution thereto drawback. The most target of this study was to analysis the present trend and prospect of wireless observance of patients within the hospitals. This study conjointly aims to create the epitome system to implement wireless observance. Additionally to that, this thesis conjointly studies most fitted technique for building the foremost effective wireless observance system. The sensing element nodes and receiver of the epitome were designed. Golem phone was used as entranceway to receive the information from sensing element node and forward the information into receiver. Bluetooth Low energy was wont to communicate between sensing element nodes and golem phone. LAN is employed to speak between golem phone and also the receiver that is connected to laptop. The sensing element readings were initially ascertained in Arduino Serial Monitor so sent to sink node. The sensing element readings of a body were displayed in golem phone and yet as within the web site. Real time information of sensing element was created and with success updated within the web site. The study of results and project showed that wireless observance would be terribly effective by exploitation Interference free, short vary and extremely secure suggests that of communication. Bluetooth low energy that is appropriate choice for the system. Style of sensing element nodes ought to be terribly tiny as a result of it's to be worn round the body. Therefore smaller parts ought to be used.
Phoneme restoration in degraded speech communication

This paper presents the experimental results of investigation in a research on the phoneme restoration effects for the case of degraded speech communication. The first experiment examined the role of coarticulation in consonant restoration inside CV syllables. In the second experiment the phonological effects in initial consonant restoration around the words were demonstrated. The third experiment demonstrated consonant restoration effects inside the sentences either grammatically and semantically correct or incorrect. Finally, the forth experiment showed an integral effects of coarticulation, phonology, syntax and semantics on phoneme restoration in spontaneous speech communication.
Recognizing problem behaviors of children with developmental disabilities using smartwatch

We develop a scheme to analyze problem behaviors of children with developmental disabilities by making use of 3-axis accelerometer, gyroscope, and microphone sensors in a smartwatch. It consists of four phases: classification of behaviors, feature collection, classifier construction and behavior recognition. Real measurements show that the recognition accuracy is above 92% for all test cases.
Improving digital signal interpolation: L2-optimal kernels with kernel-invariant interpolation speed

Interpolation is responsible for digital signal resampling and can significantly degrade the original signal quality if not done properly. For many years, optimal interpolation algorithms were sought within constrained classes of interpolation kernel functions. We derive a new family of unconstrained L2-optimal interpolation kernels, and compare their properties to the previously known. Although digital images are used to illustrate this work, our L2-optimal kernels can be applied to interpolate any digital signals.
btaining external parameters of an mnidirectional camera with a fisheye lens sing contour matching

ongho Hwang yunki Hong hung-Ang University raduate School of Advanced Imaging Science, Multimedia and Film epartment of Image Engineering 21 Huksuk-dong, Dongjak-ku eoul, 156-756, Korea -mail: honghk@cau.ac. kr Abstract. This paper presents a novel omnidirectional camera calibration technique based on automatic contour matching. In this proposed method, the initial parameters, translation and rotation, are computed using the epipolar constraint from matched feature points. By using a normal vector to a 3-D plane connecting the contour with the center of the camera projection model, after contour segmentation, candidate contours are chosen to establish a correspondence between views. In order to find the corresponding end points of the contours among the views, the initial parameters and their active matching windows are utilized. Finally, the camera motions are estimated by minimizing the angular error function between the epipolar plane and the backprojected endpoint vectors of the corresponding contours. Experiments on synthetic and real images demonstrate that the proposed algorithm obtains more precise camera parameters than previous methods. © 2008 Society of PhotoOptical Instrumentation Engineers. DOI: 10.1117/1.2969129
GREEN BANK TELESCOPE ZPECTROMETER CO(1–0) OBSERVATIONS OF THE STRONGLY LENSED SUBMILLIMETER GALAXIES FROM THE HERSCHEL ATLAS

The Herschel Astrophysical Terahertz Large Area Survey (H-ATLAS) has uncovered a population of strongly lensed submillimeter galaxies (SMGs). The Zpectrometer instrument on the Green Bank Telescope (GBT) was used to measure the redshifts and constrain the masses of the cold molecular gas reservoirs for two candidate high-redshift lensed sources. We derive CO(1–0) redshifts of z = 3.042 ± 0.001 and z = 2.625 ± 0.001, and measure molecular gas masses of (1–3) ×1010 M☉, corrected for lens amplification and assuming a conversion factor of α = 0.8 M☉( K km s−1 pc2)−1. We find typical L(IR)/L′(CO) ratios of 120 ± 40 and 140 ± 50 L☉( K km s−1 pc2)−1, which are consistent with those found for local ultraluminous infrared galaxies (ULIRGs) and other high-redshift SMGs. From analysis of published data, we find no evidence for enhanced L(IR)/L′(CO(1–0)) ratios for the SMG population in comparison to local ULIRGs. The GBT results highlight the power of using the CO lines to derive blind redshifts, which is challenging for the SMGs at optical wavelengths given their high obscuration.