Miracle-dz/UniFAR-FTUs huggingface dataset:
UFZ Discussion Papers Department of Economics 13 / 2013 Investment and Adaptation as Commitment Devices in Climate Politics

It is well established that adaptation and technological investment in each case may serve as a commitment device in international climate politics. This paper for the first time analyzes the combined impact of these two strategic variables on global mitigation within a noncooperative framework where countries either decide on mitigation before or after adaptation. By investment, which is assumed to be made in the first place due to its considerable lead time, countries commit to lower national contributions to the global public good of mitigation. We find that the sequencing of adaptation before mitigation reinforces this strategic effect of technological investments at least for sufficiently similar countries. As a consequence, the subgame-perfect equilibrium yields a globally lower level of mitigation and higher global costs of climate change when adaptation is decided before mitigation. Besides this theoretical contribution, the paper proposes some strategies to combat the unfortunate rush to adaptation which can be currently observed in climate politics. JEL classification: Q54, H41, H87, C72
Multi-technology Integration Based on Low-contrast Microscopic Image Enhancement

Microscopic image enhancement is an important issue of image processing technique, which is used to improve the visual quality of image. This paper describes a novel multi resolution image segmentation algorithm for low DOF images. The algorithm is designed to separate a sharply focused object of interest from other foreground or background objects. The algorithm is fully automatic in that all parameters are image in dependent. A multiscale-approach based on high frequency wavelet coefficients and their statistics is used to perform context dependent classification of individual blocks of the image. Compared with the state of the art algorithms, this new algorithm provides better accuracy at higher speed. Copyright © 2014 IFSA Publishing, S. L.
Attention Network for Information Diffusion Prediction

In this paper, we propose an attention network for diffusion prediction problem. The developed diffusion attention module can effectively explore the implicit user-to-user diffusion dependency among information cascade users. Besides, the user-to-cascade importance and the time-decay effect are captured and utilized by the model. The superiority of the proposed model over state-of-the-art methods is demonstrated by experiments on real diffusion data.
Raising Local Density for Object Reconstruction using Auxiliary Points

This investigation proposes a novel method for identifying high-risk point sets before surface reconstruction. Given a set of points P sampled from some unknown surface M, triangular meshes are constructed to approximate the scanned model. The point sets are obtained by laser scanner sampling on the object, so increasing the variance of the object shape increases the likelihood of irregular density distribution and the occurrence of non-indigenous holes. To solve this problem, this study proposes an algorithm to raise density, and to judge whether a new sampling point is required. The add-point algorithm discovers the locations of add-points, and maps them to the MLS surface on the basis of "feature points extracted" and "non-feature points with insufficient density" point sets. The test results demonstrate that the proposed strategy of adding auxiliary points improves the point distribution, and helps prevent the formation of non-indigenous holes. Finally, the auxiliary points are removed and returned to the original point set
The Temporality of Participation in School Science: Coordination of Teacher Control and the Pace of Students’ Participation

ABSTRACT This study investigates classroom organisation and interaction focusing on phases of activity. The detailed in-depth case study is based on video recordings of 1 science unit consisting of 11 lessons about biological evolution in a Swedish ninth-grade class (aged 15). The study illuminates the temporality of student participation as a fundamental and practical concern for the science teaching profession. Through multiple scale analysis an activity pattern and students' contrasting positions are identified. Movements of control and agency between teacher and students in small-groups provide opportunities for the teacher to coordinate the teaching and the pace of students' participation. The implications point to challenges involved in providing all students with the learning opportunities that participation in the school science discourse affords.
Emotional speaker recognition in simulated and spontaneous context

An interesting issue to be considered up to now in emotional speaker recognition system is the context in which speech database used to develop and evaluate the performance of the system. So, we propose and assess an emotional speaker recognition system based on different feature extraction methods, focusing on the diversities between simulated and natural emotional speech databases(BERLIN and IEMOCAP). For the goal of this work, a common vocal tract features are extracted such Mel-Frequency Cepstral Coefficients (MFCC), Gammatone Frequency Cepstral Coefficients (GFCC), Linear Prediction Cepstral Coefficients (LPCC) and Perceptual Linear Prediction (PLP). These features are evaluated with classification system based on Hidden Markov Model (HMM). Different results reveal that the cepstral features give always the best accuracy in speaker recognition for both simulated and spontaneous context signalizing the efficiency difference of the system between the two various contexts. Trial results show also that the context of emotional speech impact significatively speaker recognition rates.
Chinese Migrant Children's Attributions to Discrimination and Its Effect on Affect

Objective:To examine the characteristics of attributions to discrimination and its effect on positive and negative affect among Chinese migrant children.Methods:On the principle of voluntary participation,1298 migrant children from 4 public schools and 4 private schools in Beijing were investigated with the inventory of attributions to discrimination,positive and negative affects scale and perceived control scale.Result:(1) Migrant children's attribution to discrimination was higher than that of urban children [(2.57±0.98) vs.(2.44±1.03),P0.01]. Compared with effort,luck and other reasons,migrant children more frequently attributed the negative outcomes to the rural registered resident [(2.57±0.98) vs.(2.17±0.69),(1.60±0.62) and(2.01±0.64),Ps0.05]. Migrant children from public school more frequently attributed the negative outcomes to discrimination compared to those from special schools for migrant children [(2.72±1.02) vs.(2.51±0.97),P0.01]. Migrant children from junior high school more frequently attributed the negative outcomes to discrimination compared to those from primary schools [grade 5:(2.44±1.00),grade 6:(2.46±1.03),vs.junior 1:(2.77±0.92) and junior 2:(2.69±0.92);Ps0.01]. Long-term and mid-term migrant children were more likely to make an attribution to discrimination than short-term ones [(2.67±0.96) and(2.64±0.99) vs.(2.47±0.98),Ps0.01]. (2) Attributions to discrimination mainly had the significant indirect effect on negative via the mediation effect of perceived control among short-term migrant children(indirect β=0.07). Conclusions: Migrant children more frequently attribute the negative outcomes to discrimination.More attention should be paid to the negative effect of attributions to discrimination of short-term migrant children.
Evidence Suggesting That Obesity Prevention Measures May Improve Prostate Cancer Outcomes Using Data from a Prospective Randomized Trial

Purpose. Increasing body mass index (BMI) is associated with higher risk prostate cancer (PC) at presentation. Whether increasing BMI also prompts earlier salvage androgen suppression therapy (sAST) is unknown. Materials and Methods. Between 1995 and 2001, 206 men with unfavorable risk PC were treated with radiation therapy (RT) or RT and six months of androgen suppression therapy in a randomized controlled trial (RCT). 108 sustained PSA failure; 51 received sAST for PSA approaching 10 ng/mL; 49 with BMI data comprised the study cohort. A multivariable Cox regression analysis identified pretreatment factors associated with earlier sAST receipt. Results. Increasing BMI prompted earlier sAST (median years: 3.7 for overweight/obese, 6.9 for normal weight; adjusted hazard ratio (AHR): 1.11; 95% CI: 1.04, 1.18; P = 0.002) as did high versus other risk PC (median: 3.2 versus 5.2 years; AHR: 2.01; 95% CI: 1.05, 3.83; P = 0.03). Increasing median time to sAST was observed for overweight/obese men with high versus other risk PC and for normal-weight men with any risk PC being 2.3, 4.6, and 6.9 years, respectively (P < 0.001 for trend). Conclusion. Increasing BMI was associated with earlier sAST. A RCT evaluating whether BMI reduction delays or eliminates need for sAST is warranted.
UMBC at SemEval-2018 Task 8: Understanding Text about Malware

We describe the systems developed by the UMBC team for 2018 SemEval Task 8, SecureNLP (Semantic Extraction from CybersecUrity REports using Natural Language Processing). We participated in three of the sub-tasks: (1) classifying sentences as being relevant or irrelevant to malware, (2) predicting token labels for sentences, and (4) predicting attribute labels from the Malware Attribute Enumeration and Characterization vocabulary for defining malware characteristics. We achieve F1 score of 50.34/18.0 (dev/test), 22.23 (test-data), and 31.98 (test-data) for Task1, Task2 and Task2 respectively. We also make our cybersecurity embeddings publicly available at http://bit.ly/cyber2vec.
The Minority Game: An Economics Perspective

This paper gives a critical account of the minority game literature. The minority game is a simple congestion game: players need to choose between two options, and those who have selected the option chosen by the minority win. The learning model proposed in this literature seems to differ markedly from the learning models commonly used in economics. We relate the learning model from the minority game literature to standard game-theoretic learning models, and show that in fact it shares many features with these models. However, the predictions of the learning model differ considerably from the predictions of most other learning models. We discuss the main predictions of the learning model proposed in the minority game literature, and compare these to experimental findings on congestion games.
Simplification Using Paraphrases and Context-Based Lexical Substitution

Lexical simplification involves identifying complex words or phrases that need to be simplified, and recommending simpler meaning-preserving substitutes that can be more easily understood. We propose a complex word identification (CWI) model that exploits both lexical and contextual features, and a simplification mechanism which relies on a word-embedding lexical substitution model to replace the detected complex words with simpler paraphrases. We compare our CWI and lexical simplification models to several baselines, and evaluate the performance of our simplification system against human judgments. The results show that our models are able to detect complex words with higher accuracy than other commonly used methods, and propose good simplification substitutes in context. They also highlight the limited contribution of context features for CWI, which nonetheless improve simplification compared to context-unaware models.
An enhanced method for privacy preservation in data publishing

Privacy-maintaining data release is one of the most important challenges in an information system, because of the wide collection of sensitive information on the internet. A number of solutions have been designed for privacy-maintaining data release. This paper provides an inspection of the state-of-the-art methods for privacy protection. The paper discusses novel and powerful privacy definitions which can be categorized into microdata anonymity methods and differential privacy methods for privacy-maintaining data release. The methods include k-anonymity, l-diversity, t-closeness and js-reduce defense. This paper proposes an enhanced method which will prevents sequential background knowledge attack and provides some anonymization also.
Typical Types and Formation Mechanisms of Haze in an Eastern Asia Megacity, Shanghai

Abstract. An intensive aerosol and gases campaign was performed at Shanghai in the Yangtze River Delta region over Eastern China from late March to early June 2009. This study provided a complementary picture of typical haze types and the formation mechanisms in megacities over China by using a synergy of ground-based monitoring, satellite and lidar observations. During the whole study period, several extreme low visibility periods were observed with distinct characteristics, and three typical haze types were identified, i.e. secondary inorganic pollution, dust, and biomass burning. Sulfate, nitrate and ammonium accounted for a major part of PM 2.5 mass during the secondary inorganic pollution, and the good correlation between SO 2 /NO x /CO and PM 2.5 indicated that coal burning and vehicle emission were the major sources. Large-scale regions with high AOD (aerosol optical depths) and low Angstrom exponent were detected by remote-sensing observation during the dust pollution episode, and this episode corresponded to coarse particles rich in mineral components such as Al and Ca contributing 76.8% to TSP. The relatively low Ca/Al ratio of 0.75 along with the air mass backward trajectory analysis suggested the dust source was from Gobi Desert. Typical tracers for biomass burning from satellite observation (column CO and HCHO) and from ground measurement (CO, particulate K + , OC, and EC) were greatly enhanced during the biomass burning pollution episode. The exclusive linear correlation between CO and PM 2.5 corroborated that organic aerosol dominated aerosol chemistry during biomass burning, and the high concentration and enrichment degree of arsenic (As) could be also partly derived from biomass burning. Aerosol optical profile observed by lidar demonstrated that aerosol was mainly constrained below the boundary layer and comprised of spheric aerosol (depolarization ratio
What Research Says about the Effect of Academic Enrichment on the Success Rate of Baccalaureate Students.

In the nursing literature, many variables have been used in research to identify students at risk, predict program success, and to indicate the best time to identify at-risk students and how to predict success on the Nursing Council Licensure Exam for Registered Nurses (NCLEX-RN). In general, American College Testing Program (ACT) scores predicted success better for associate degree students than for baccalaureate students. Grade point average in clinical nursing courses and the Mosby Assess Test appeared to be good predictors of success on NCLEX-RN for baccalaureate students. No formal research was found using the Nelson-Denny reading exam as a predictor of baccalaureate nursing success. Other than the verbal scores of the Scholastic Aptitude Tests and the ACT, none of the research used reading comprehension ability or nursing vocabulary as variables in either identifying at-risk students or predicting NCLEX-RN success. Most studies agreed that pre-admission criteria are useful for admission decisions and upholding admission standards but should not be used to prevent at-risk students from having a chance of being admitted. There is a consensus that at-risk students should be identified early and given assistance, but the exact type of assistance and who the provider should be have not been determined. Very little information was found regarding specific strategies to improve the success of at-risk nursing students other than test-taking strategies, relaxation techniques, and NCLEX-RN review. (KM) *********************************************************************** Reproductions supplied by EDRS are the best that can be made from the original document. *********************************************************************** "WHAT RESEARCH SAYS ABOUT THE EFFECT OF ACADEMIC ENRICHMENT ON THE SUCCESS RATE OF BACCALAUREATE STUDENTS"
Efficiently Determining Silhouette Consistency

Volume intersection is a frequently used technique to solve the Shape-From-Silhouette problem, which constructs a 3D object estimate from a set of silhouettes taken with calibrated cameras. It is natural to develop an efficient algorithm to determine the consistency of a set of silhouettes before performing time-consuming reconstruction, so that inaccurate silhouettes can be omitted. In this paper we first present a fast algorithm to determine the consistency of three silhouettes from known (but arbitrary) viewing directions, assuming the projection is scaled orthographic. The temporal complexity of the algorithm is linear in the number of points of the silhouette boundaries. We further prove that a set of more than three convex silhouettes are consistent if and only if any three of them are consistent. Another possible application of our approach is to determine the miscalibrated cameras in a large camera system. A consistent subset of cameras can be determined on the fly and miscalibrated cameras can also be recalibrated at a coarse scale. Real and synthesized data are used to demonstrate our results.
Building Bridges: A Case Study in Structuring Human-ML Training Interactions via UX

With the increasing ubiquity of artificial intelligence and machine learning applications, systems are emerging that require non-ML experts to interact with machine learning at the training step, not just the final system. These users may not have the skills, time, or inclination to familiarize themselves with the way machine learning works, so training systems must be developed that can communicate the necessary information and facilitate effortless collaboration with the user. We consider how to utilize techniques from qualitative coding, a human-centered approach for manual classification, and build better user experience for ML training.
Identi fi cation of water stress genes in Pinus pinaster Ait . by controlled progressive stress and suppression-subtractive hybridization

Climate change is a major challenge particularly for forest tree species, which will have to face the severe alterations of environmental conditions with their current genetic pool. Thus, an understanding of their adaptive responses is of the utmost interest. In this work we have selected Pinus pinaster as a model species. This pine is one of the most important conifers (for which molecular tools and knowledge are far more scarce than for angiosperms) in the Mediterranean Basin, which is characterised in all foreseen scenarios as one of the regions most drastically affected by climate change, mainly because of increasing temperature and, particularly, by increasing drought. We have induced a controlled, increasing water stress by adding PEG to a hydroponic culture. We have generated a subtractive library, with the aim of identifying the genes induced by this stress and have searched for the most reliable expressional candidate genes, based on their overexpression during water stress, as revealed by microarray analysis and confirmed by RT-PCR. We have selected a set of 67 candidate genes belonging to different functional groups that will be useful molecular tools for further studies on drought stress responses, adaptation, and population genomics in conifers, as well as in breeding programs. 2011 Elsevier Masson SAS. All rights reserved.
Reconstruction of Sparse Binary Signals Using Compressive Sensing

This paper has described an improved algorithm for reconstructing sparse binary signals using compressive sensing. The algorithm is based on the reweighted $l_q$ norm optimization algorithm of \cite{04}, but with the important additional operation of bounding in each round of the interior-point method iteration, and progressive reduction of $q$. Experimental results confirm that the algorithm performs well both in terms of the ability to recover an input signal as well as in terms of speed. We also found that both the progressive reduction and the bounding are integral to the improvement in performance.
A novel track-drive mobile robotic framework for conducting projects on robotics and control systems

This paper presents a novel robotic framework to help students to practically grasp the concepts of Robotics and Control Systems in a laboratory environment. The framework is centered on a robotic rover having two tank-like tracks which permit locomotion on uneven terrains. The sensory system consists of encoders for position feedback while the actuation system comprises of six precise DC motors. To enhance the learning outcomes of students and to permit readily realizaion of applications, developed software library supports three different command levels. The efficacy of the framework has been demonstrated by presenting a list of projects conducted on the framework. In particular, as a case-study, a project titled tether tracking and control of robotic rover has been detailed in the paper with presentation of experimental results. The pilot study indicated that incorporating the framework in robotics laboratory resulted in an efficient methodology of imparting interdisciplinary knowledge to engineering students. Additionally, the framework finds its potential in research of advanced robotic and control algorithms. [Jamshed Iqbal, S. Riaz un Nabi, Abdul Attayyab Khan, Hamza Khan. A Novel Track-Drive Mobile Robotic Framework for Conducting Projects on Robotics and Control Systems. Life Sci J 2013; 10(3): 130-137]. (ISSN: 1097-8135). http://www.lifesciencesite. com 21
Modeling and control of a permanent-magnet brushless DC motor drive using a fractional order proportional-integral-derivative controller

This paper deals with the speed control of a permanent-magnet brushless direct current (PMBLDC) motor. A fractional order PID (FOPID) controller is used in place of the conventional PID controller. The FOPID controller is a generalized form of the PID controller in which the order of integration and differentiation is any real number. It is shown that the proposed controller provides a powerful framework to control the PMBLDC motor. Parameters of the controller are found by using a novel dynamic particle swarm optimization (dPSO) method. The frequency domain pole-zero (p-z) interlacing method is used to approximate the fractional order operator. A three-phase inverter with four switches is used in place of the conventional six-switches inverter to suggest a cost-effective control scheme. The digital controller has been implemented using a field programmable gate array (FPGA). The control scheme is verified using the FPGA-in-the-loop (FIL) wizard of MATLAB/Simulink. Improvement in the overall performance of the system is observed using the proposed FOPID controller. The energy efficient nature of the FOPID controller is also demonstrated.
Effect of phase noise in IEEE 802.11ad MIMO-OFDM systems with common/independent local oscillators

In this paper, we show the effect of phase noise on the bit-error rate performance in multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems based on IEEE 802.11ad standards. We consider two different systems; (1) a system which has multiple antennas with independent phase noise at transmit and receive side and (2) a system which has coherent phase noise from common local oscillators. Mathematical representation of the demodulated signal with phase noise is derived and simulation results are provided to demonstrate the effects of phase noise in each system. We see from out simulations that the performance degradation becomes worse for the first case, that is, when phase noise is independent at all transmit and receive antennas.