Miracle-dz/UniFAR-FTUs huggingface dataset:
Measuring Voter Registration and Turnout in Surveys Do Official Government Records Yield More Accurate Assessments

In post-election surveys, the proportion of respondents who claim to have voted is routinely greater than officially reported turnout rates, which has often been attributed to respondent lying. Such concerns have led researchers to prefer gauging respondents’ turnout by using government records of their behavior (called “validated turnout measures”). Using data from an online survey of a nationally representative sample of American adults, we found that this latter method did produce lower apparent rates of registration and turnout than did selfreports. However, the assessments based on government records appear to be less accurate in describing each respondent than their self–reports. Results based on government records appear be distorted by two countervailing biases: (1) a “downward bias” resulting from failures to roperly match some survey respondents to their government records, and (2) an “upward bias” that occurs because survey respondents turn out to vote at a higher rate than non-respondents and report their behaviors accurately. The downward bias is greater than the upward bias, which causes the illusion that aggregate turnout rates become more accurate when generated using government records. Thus, the use of government records appears to reduce the accuracy of individual-level registration and turnout measurements rather than improving validity.
Finite-time trajectory tracking control of unmanned surface vehicle with input saturation

In this paper, a finite-time trajectory tracking control approach is proposed for an unmanned surface vehicle (USV) with unknown external disturbances and input saturation. Significant contributions of this paper are as follows: 1) an auxiliary design system is employed to address input constraint effectively, and make sure control performance ideally; 2) an disturbance observer is designed via a robust homogeneous differentiator so that the unknown external disturbances can be exactly identified in finite-time; 3) the proposed finite-time controller can achieve an excellent tracking performance and enhance the robustness of the overall control system; 4) by the proposed control scheme, tracking errors can be rendered to zero faster than conventional backstepping approach as well as all the signals of the closed-loop are bounded. The simulation studies illustrate the effectiveness of the proposed approach.
A tag-centric discriminative model for web objects classification

This paper studies web object classification problem with the novel exploration of social tags. More and more web objects are increasingly annotated with human interpretable labels (i.e., tags), which can be considered as an auxiliary attribute to assist the object classification. Automatically classifying web objects into manageable semantic categories has long been a fundamental pre-process for indexing, browsing, searching, and mining heterogeneous web objects. However, such heterogeneous web objects often suffer from a lack of easy-extractable and uniform descriptive features. In this paper, we propose a discriminative tag-centric model for web object classification by jointly modeling the objects category labels and their corresponding social tags and un-coding the relevance among social tags. Our approach is based on recent techniques for learning large-scale discriminative models. We conduct experiments to validate our approach using real-life data. The results show the feasibility and good performance of our approach.
Dysfunctional amygdala activation and connectivity with the prefrontal cortex in current cocaine users

Stimulant use is associated with increased anxiety and a single administration of dexamphetamine increases amygdala activation to biologically salient stimuli in healthy individuals. Here, we investigate how current cocaine use affects amygdala activity and amygdala connectivity with the prefrontal cortex in response to biologically salient stimuli in an emotional face matching task (EFMT).
Empirical Exploitation of Click Data for Query-Type-Based Ranking

In machine-learning-based ranking, each category of queries can be applied with a specific ranking function, which is called query-type-based ranking. Such a divideand-conquer strategy can potentially provide better ranking function for each query categories. A critical problem for the query-type-based ranking is training data insufficiency, which may be solved by using the data extracted from click log. This paper empirically studies how to appropriately exploit click data to improve rank function learning in query-type-based ranking. The main contributions are 1) the exploration on the utilities of two promising approaches for click pair extraction; 2) the analysis of the role played by the noise information which inevitably appears in click data extraction; 3) the appropriate strategy for combining training data and click data; 4) the comparison of click data which are consistent and inconsistent with baseline function.
Detecting Protein–Protein Interactions with CFP‐YFP FRET by Acceptor Photobleaching

FRET is a light microscopy method for detecting protein‐protein interactions within intact cells. The FRET protocol presented here is for CFP‐ and YFP‐tagged proteins examined with an argon laser on a scanning confocal microscope. FRET is assayed by one of the most straightforward approaches available, namely, acceptor photobleaching. In this procedure, the YFP‐tagged protein (the FRET “acceptor”) is photobleached at a cellular site of interest, and then the intensity of the CFP‐tagged protein (the FRET “donor”) at that same site is measured. In principle, FRET is detected when the CFP intensity increases after the photobleaching of YFP. This unit describes the appropriate steps to perform this measurement, as well as the necessary controls to ensure that an increase in CFP intensity, if detected, in fact reflects bona fide FRET. Successful application of the protocol will support the conclusion that the CFP‐ and YFP‐tagged proteins directly interact at the site of the photobleaching
An Overview of E-invoicing in China and the Factors Affecting Individual ' s Intention to B 2 C E-invoicing Adoption Information Systems

Objectives of the Study This study is aimed to present a clear picture of e-invoicing in China, by introducing the whole related process and work that have been carried out or are going to be done in the recent years. Also, it will explore Chinese individual’s attitude towards B2C e-invoicing and the factors that can affect individual’s intention to adoption. Based on those studies, useful suggestions for the developing of e-invoicing in China are supposed to be put forward. Academic background and methodology The theoretical part of this thesis is based on the theory of reasoned behavior (TRA) introduced by Fishbein (1967), the technology acceptance model (TAM) proposed by Davis (1986), and the unified theory of acceptance and use of technology (UTAUT) (Venkatesh et al., 2003). A framework will be adapted from the previous studies to better illustrate einvoicing in China, and a new factor—promotional effort, will be included to examine whether it is able to affect user’s intention to adoption. The empirical part will adopt an online survey conducted among Chinese citizens, and the responds data will be analyzed to test whether the framework is able to explain individuals’ attitudes and intention to adoption, whether the proposed factors are significant, and whether hypothesis about determinants related to attitudes and intention are true or not. Findings and conclusions The results attained from the analysis indicate that, PU is the most significant variable that affecting individual's attitude to e-invoicing, while the power of PEU and T are much weaker. Consistent with the hypothesis, attitude is positively related to intention. While the hypothesis regarding to social influence that it will positively affect people’s intention turned out to be false in this case. What is remarkable is that promotional effort is found to plays a role in stimulating users’ intention, and its effect is stronger for women than for men, and stronger for existed user than for those who never used e-invoicing, which is worthy for further study.
Polarization couplers in fibers with different core shapes

We report the rocking filter formation in a circular, a bow tie and in an elliptical core optical fiber by the technique of internal light exposure. The visible Argon ion laser lines in 488.0 and 514.5 nm, with circular polarization, are used as the writing beam source, and the obtained filters shown coupling efficiency between 2.5 and 60 percent. For similar exposure parameters, elliptical core fiber lead to higher efficiency couplers as compared to the stress induced birefringent fibers.
The effect of gamma and electron beam irradiation on the thermal and mechanical properties of injection-moulded high crystallinity poly(propylene)

In the present study, the effect of gamma and electron beam (EB) irradiation on the thermal and mechanical properties of high crystalline polypropylene (HPP) was studied. To study the structural modifications of HPP polymer which could occur following these treatments at different doses (20, 40, 60, 80, 100, and 120 kGy) were applied to all samples. Nonirradiated HPP were used as control samples, differential scanning calorimetry analysis, thermogravimetric analysis, and Mechanical tests were carried out to evaluate the effect of both irradiation treatments (EB and gamma irradiation) on HPP samples. Irradiated samples of HPP decreases melting temperature (Tm) of matrix in EB more than in gamma rays up to 5°C. The changes of mechanical properties exhibit different radiation stability towards 60Co-gamma radiation and EB radiation. This difference reflects much higher penetration of the gamma radiation through the polymeric material as a function of sample thickness. The degradation on polymer properties caused by gamma irradiation was more than that caused by EB irradiation. Next, we compared the effects of gamma and EB irradiation to determine which of these two processes better maintained the integrity of the irradiated product. POLYM. COMPOS. , 2008. © 2008 Society of Plastics Engineers.
Escalation of oncologic services at the end of life among patients with gynecologic cancer at an urban, public hospital.

PURPOSE Use of oncology-related services is increasingly scrutinized, yet precisely which services are actually rendered to patients, particularly at the end of life, is unknown. This study characterizes the end-of-life use of medical services by patients with gynecologic cancer at a safety-net hospital. METHODS Oncologic history and metrics of medical use (eg, hospitalizations, chemotherapy infusions, procedures) for patients with gynecologic oncology who died between December 2006 and February 2012 were evaluated. Mixed-effect regression models were used to test time effects and construct usage summaries. RESULTS Among 116 subjects, cervical cancer accounted for the most deaths (42%). The median age at diagnosis was 55 years; 63% were Hispanic, and 65% had advanced disease. Only 34% died in hospice care. The median times from do not resuscitate/do not intubate documentation and from last therapeutic intervention to death were 9 days and 55 days, respectively. Significant time effects for all services (eg, hospitalizations, diagnostics, procedures, treatments, clinic appointments) were detected during the patient's final year (P < .001), with the most dramatic changes occurring during the last 2 months. Patients with longer duration of continuity of care used significantly fewer resources toward the end of life. CONCLUSION To our knowledge, this is the first report enumerating medical services obtained by patients with gynecologic cancer in a large, public hospital during the end of life. Marked changes in interventions in the patient's final 2 months highlight the need for cost-effective, evidence-based metrics for delivering cancer care. Our data emphasize continuity of care as a significant determinant of oncologic resource use during this critical period.
Optimizing Cellular Networks Enabled with Renewal Energy via Strategic Learning

An important issue in the cellular industry is the rising energy cost and carbon footprint due to the rapid expansion of the cellular infrastructure. Greening cellular networks has thus attracted attention. Among the promising green cellular network techniques, the renewable energy-powered cellular network has drawn increasing attention as a critical element towards reducing carbon emissions due to massive energy consumption in the base stations deployed in cellular networks. Game theory is a branch of mathematics that is used to evaluate and optimize systems with multiple players with conflicting objectives and has been successfully used to solve various problems in cellular networks. In this paper, we model the green energy utilization and power consumption optimization problem of a green cellular network as a pilot power selection strategic game and propose a novel distributed algorithm based on a strategic learning method. The simulation results indicate that the proposed algorithm achieves correlated equilibrium of the pilot power selection game, resulting in optimum green energy utilization and power consumption reduction.
Energy System Contribution to Olympic Distances in Flat Water Kayaking (500 and 1,000 m) in Highly Trained Subjects

Zouhal, H, Le Douairon Lahaye, S, Ben Abderrahaman, A, Minter, G, Herbez, R, and Castagna, C. Energy system contribution to Olympic distances in flat water kayaking (500 and 1,000 m) in highly trained subjects. J Strength Cond Res 26(3): 825–831, 2012—Olympic flat water kayaking races take place over a distance of 500 and 1,000 m. This study was designed to determine the aerobic and anaerobic contributions to 500- and 1,000-m races during flat water paddling in open water, using the accumulated oxygen deficit (AOD) method. Seven internationally ranked athletes, specialized in 500-m races and familiar with 1,000-m races, participated in this study (age: 21.86 ± 1.68 years, body mass: 78.54 ± 3.41 kg, height: 1.84 ± 0.03 m, body fat%: 10.14 ± 0.69%). All the participants performed 3 track-kayaking sessions. During the first session, the maximal oxygen uptake and maximal aerobic speed were determined using a portable gas analyzer and a global positioning system. During the successive testing sessions, paddlers performed in a randomized counterbalanced order a 500- and a 1,000-m race under field conditions (open water track kayaking). The 500-m AOD was significantly higher than the 1,000-m AOD (18.16 ± 4.88 vs. 9.34 ± 1.38 ml·kg−1, p < 0.05). The aerobic contribution resulted in being higher during the 1,000 m compared with that in the 500-m condition (86.61 ± 1.86% vs. 78.30 ± 1.85%, respectively, p < 0.05). The results of this study showed that the 500- and 1,000-m races are 2 physiologically different kayaking events with a higher aerobic contribution in the 1,000 m. The training prescription for elite athletes should emphasize aerobic high-intensity training for the 1,000 m and anaerobic short-term training for the 500-m race.
Baseline Multimodal Place Classifier for the 2012 Robot Vision Task

The objective of this article is reporting the participation of the SIMD-IDIAP group in the RobotVision@ImageCLEF 2012 challenge. This challenge addresses the problem of multimodal place classification, and the 2012 edition has been organized by the members of the SIMDIDIAP team. One of the main novelties in the 2012 edition of the task has been the proposal of several techniques for features extraction and cue integration. This paper details how to use all these techniques for developing a multimodal place classifier and describes the results obtained with it. Our approach ranked 7th for task 1 and 4th for task 2. The complete results for all the participants of the 2012 RobotVision task are also reported in this article.
Evaluating machine learning methods for online game traffic identification

Online gaming is becoming more and more prominent in the Internet, in terms of both traffic volume and as a potential source of revenue. Quality of Service (QoS) requirements for highly interactive games are much stricter than for traditional Internet applications, such as web or email. For effective QoS implementations that are transparent to users and game applications, an accurate and reliable method of classifying game traffic flows in the network must be found. Current methods such as port number and payload-based identification exhibit a number of shortfalls. A potential solution is the u se of Machine Learning techniques to identify game traffic based on payload independent statistical features such as packet length distributions. In this paper we evaluate the effectiveness of the proposed approach. We compare the accuracy and performance of different Machine Learning techniques and we also use feature selection techniques to examine which features are most important in discriminating game traffic from other traffic. We find that machine learning algorithms are able to separate online game traffic from other network traffic with very high (>99%) accuracy. We also show that feature selection, while reducing accuracy, allows games to be identified with fewer features and substantial speed gains.
Compressive Deformation Behaviors of Coarse- and Ultrafine-Grained Pure Titanium at Different Temperatures: A Comparative Study

Institute of Materials Physics and Chemistry, College of Sciences, Northeastern University, P.O. Box 104, No. 3-11, Wenhua Road, Shenyang, 110004, P. R. China State Key Laboratory for Mechanical Behavior of Materials, Xi’an Jiaotong University, Xi’an 710049, P. R. China Key Laboratory for Anisotropy and Texture of Materials, Ministry of Education, Northeastern University, No. 3-11, Wenhua Road, Shenyang 110004, P. R. China
Distributed breakout revisited

Distributed breakout algorithm (DBA) is an efficient method for solving distributed constraint satisfaction problems (CSP). Inspired by its potential of being an efficient, low-overhead agent coordination method for problems in distributed sensor networks, we study DBA's properties in this paper. We specifically show that on an acyclic graph of <i>n</i> nodes, DBA can find a solution in <i>O(n<sup>2</sup>)</i> synchronized distributed steps. This completeness result reveals DBA's superiority over conventional local search on acyclic graphs and implies its potential as a simple self-stabilization method for tree-structured distributed systems. We also show a worst case of DBA in a cyclic graph where it never terminates. To overcome this problem on cyclic graphs, we propose two stochastic variations to DBA. Our experimental analysis shows that stochastic DBAs are able to avoid DBA's worst-case scenarios and has similar performance as that of DBA.
Plättchen-Biegeversuch nach VDA 238-100

Kurzfassung Zur Ermittlung der Versagensanfälligkeit von metallischen Werkstoffen bei Biege-Umformprozessen sowie von Kennwerten zur numerischen Simulation des Verformungsverhaltens dient der sogenannte Plättchen-Biegeversuch. Als Kennwert dient der beim Anriss der Probe herrschende Biegewinkel, der in der Regel aus dem aufgenommenen Stempelweg und der Geometrie der Prüfanordnung rechnerisch ermittelt wird. Bei ThyssenKrupp Steel Europe ist hierzu ein neuartiges optisches Winkelmesssystem im Einsatz, das zeitlich synchronisiert mit dem Kraft-Weg-Verlauf der Maschine den Biegewinkel kontinuierlich und direkt an der Probe bestimmt. Durch den Vergleich berechneter und gemessener Biegewinkelverläufe wird gezeigt, dass insbesondere bei Biegewinkeln über 90° deutliche Unterschiede in den Prüfergebnissen auftreten.
Novelty Detection via Answer Updating

Abstract : The detection of new and novel information in a document stream is an important component of potential applications. This paper describes an answer updating approach to novelty detection at the sentence level. Specifically, we explore the use of question-answering techniques for novelty detection. New information is defined as new/previously unseen answers to questions representing a user's information need. A sentence is treated as novel sentence if the system believes that it may contain a previously unseen answer to the question. In our answer updating approach, there are two important steps: question formulation and new answer detection. Experiments were carried out on data from the TREC 2002 novelty track using the proposed approach. The results show that novelty detection via answer updating outperforms other novelty measures reported in the literature in terms of precision at low recall.
A Wearable Fall Detector for Elderly People Based on AHRS and Barometric Sensor

Falls and their consequences are among the major health care problems affecting functional mobility and quality of life of elderly people. Even for people living independently, falls are common occurrences. In this paper, we present a waist-mounted device useful to detect possible falls in elderly people. Through data coming from a three-axis accelerometer, a three-axis gyroscope, a three-axis magnetometer, and a barometer sensor integrated into our device, we are able to obtain a highly accurate estimation about posture and altitude of the subject. By means of such information, we have developed an extremely efficient system for fall detection, reaching 100% of sensitivity in commonly adopted testing protocols. In particular, the algorithm was tested according to three different experimental protocols, where volunteers performed several scenarios, including various types of falls, falls with recovery, and daily living activities frequent in the elderly. Results show that the proper combined use of the four sensors and efficient data fusion algorithms allow to achieve noticeable better performances to those obtained with similar systems proposed in the literature.
Integrated DNN-based model adaptation technique for noise-robust speech recognition

Since the introduction of deep neural network (DNN)-based acoustic model, robust automatic speech recognition using DNN are being in research. Especially in model adaptation, the techniques utilizing auxiliary context features is known to be a promising technique. Recently, we proposed a technique which is called two-stage noise-aware training (TSNAT). The key idea of TS-NAT is to let the DNN clarify the relationship among noise estimate, noisy features and phonetic target through clean feature representation. However, although TS-NAT enhances the robustness of the DNN, we cannot be certain whether TS-NAT describes the clean feature representation sufficiently. In this paper, we extend TS-NAT using true noise feature and various DNN training techniques. It has been shown that the proposed technique outperforms the conventional DNN-based techniques on Aurora5-task and mismatched noise conditions.