Miracle-dz/UniFAR-FTUs huggingface dataset:
Patient Satisfaction: How Do Qualitative Comments Relate to Quantitative Scores on a Satisfaction Survey?

Objective This study was designed to determine whether there are differences in how patients quantitatively rate their inpatient service in a given section of a patient satisfaction survey based on the types of qualitative comments they make about that given section. Methods The population under study was patients discharged from The James Cancer Hospital and Solove Research Institute at Ohio State University from April 1 to June 30, 2006. A sample of 1468 questionnaires was mailed. A total of 446 completed questionnaires were returned for a response rate of 30%, and 1068 comments were offered by the respondents. Results An analysis of variance determined statistically significant differences between the ratings and the comment types in all 10 sections of the survey. A Bonferroni post hoc test revealed that the scale ratings were higher for patients who made positive comments than for patients who made negative comments for 8 of the 10 survey sections. A positive comment produced a higher mean score than did a negative comment. Conclusion The study results offer empirical evidence that there are differences in how patients quantitatively rate the services received in a hospital based on patient comment types. Hospital staff interpreting patient satisfaction survey results could say that negative comments produce lower quantitative ratings than do positive comments.
A Secure Testament Revealing Protocol

The testament is the message opened to the public after the message writer is gone. In this paper, after modeling the secure testament revealing protocol under three parties, the rich, the family and the lawyer, we propose a scheme for this protocol under the dened security requirements. Also we claim that this research will play one of typical practices in multiparty cryptographic protocols.
City event detection from social media with neural embeddings and topic model visualization

Social media is widely known as a rich and timely source of information on various events, from mundane daily events to larger and more significant events related with cities, regions and the world. In this paper, we introduce an interactive system based on unsupervised methods and models for the recognition of targeted city and regional events from social media data. The system combines the use of event keywords with expansions using word embeddings to identify semantically related terms and seed events, then retrieve event relevant posts from data collection. LDA topic modelling method and topic visualization method LDAvis are applied to help discover event clusters for a specific class of events based on their topic features. Our extensive experiments with the system help to shed light on the presence or absence of different types of city events on Instagram and Twitter.
Recognizing trees at a distance with discriminative deep feature learning

We investigate discriminative features that are able to improve classification accuracy on visually similar classes. To this end, we build a deep feature learning network, which learns features with discriminative constraint in each single layer module, and learns multiple levels of features for hierarchical image representation. Specifically, the network encodes the discriminative information by automatically selecting the informative features, and forcing them to be closer to the features extracted from the same class than the features from different classes. We also collect a new fine-grained dataset containing 51 common tree species in Singapore. All the images are taken at a distance with large intra class variance, which makes the tree species hard to be distinguished. Our experimental results show that we are able to achieve 78.03% in accuracy on this challenging dataset, which is 8.48% higher than general hand-designed feature.
Comparison of several parallel API for cloth modelling on modern GPUs

The paper compares three APIs for the implementation of cloth modelling on modern graphics processor units (GPU): OpenGL plus GLSL, NVIDIA CUDA and OpenCL. They are compared by programming features, platform and device portability, and performance for the purpose of dynamic cloth simulation. Results about performance are given and conclusions are drawn about use cases.
Estimation of speech acoustics from visual speech features: A comparison of linear and non-linear models

This paper examines the degree of correlation between lip and jaw con guration and speech acoustics. The lip and jaw positions are characterised by a system of measurements taken from video images of the speaker's face and pro le, and the acoustics are represented using line spectral pair parameters and a measure of RMS energy. A correlation is found between the measured acoustic parameters and a linear estimate of the acoustics recovered from the visual data. This correlation exists despite the simplicity of the mapping and is in rough agreement with correlations measured in earlier work by Yehia et al. The linear estimates are also compared to estimates made using nonlinear models. In particular it is shown that although performance of the two models is remarkably similar for static visual features, non-linear models are better able to handle dynamic features.
Template regularized sparse coding for face verification

In this paper, we propose a novel regularized sparse coding approach for template-based unconstrained face verification. Unlike traditional verification tasks, which require the evaluation on image-to-image or video-to-video pairs, template-based face verification/recognition methods can exploit training and/or gallery data containing a mixture of both images or videos from the person of interest. The proposed regularized sparse coding approach addresses the adaptation to training and gallery data using three steps. First, we construct a reference dictionary, which represents the training set. Then we learn the discriminative sparse codes of the templates for verification through the proposed template regularized sparse coding approach. Finally, we measure the similarity between templates. An efficient algorithm is employed to learn the template regularized sparse codes. Extensive experiments on the template-based verification benchmark dataset show that the proposed approach outperforms several state-of-the-art methods.
Family Ownership and Performance in New Firms

We study the relation between family ownership and performance in a large sample of new rms. We nd that new family-owned rms exhibit better performance than new non-family-owned rms in terms of higher returns on assets, wider pro t margins and greater survival rates. These ndings are especially strong in the earliest years of the rm life cycle and when two of the leading shareholders are married. A large part of the advantage of family ties between shareholders stems from an ability of shareholders and managers to commit to keep cash within the rm. The performance advantage of new family-owned rms is also larger when managers hold smaller percentages of equity. Our results suggest that family ties serve as a mechanism to improve the monitoring of managers by shareholders and the enforcement of implicit contracts between shareholders and managers. Moreover, the additional performance advantage of rms owned by married shareholders, even after controlling for managerial equity ownership, suggests family ties, in particular the ties of marriage, serve as a mechanism to enhance rm performance above and beyond mitigating the manager-owner conict. Overall, our results support e¢ ciency-based explanations for why family ownership, in particular the "mom and pop shop", is a predominant ownership structure in new rms.
Powerful mutator activity of the polA1 mutation within the histidine region of Escherichia coli K-12

We examined 122 spontaneous histidine auxotrophs accumulated in overnight cultures of polA1 strains of Escherichia coli K-12 at approximate frequencies of 10(-3). One hundred and thirteen appeared to be minus frameshifts, and nine appeared to be deletions. Of the frameshift mutations, 109 affected the hisC gene, and 4 affected genes hisD, hisH, hisA, and hisI. The lack of base substitutions supported the idea that polymerase-defective polA is a minus frameshift- and deletion-type mutator. Contrary to a previous report, we did not observe superior growth of PolA auxotrophs over their prototrophic progenitors (15 auxotrophs tested). We conclude that the polA1 mutation exerts a powerful mutator activity in this specific genetic context.
Angiotensin-converting enzyme inhibitor ramiprilat interferes with the sequestration of the B2 kinin receptor within the plasma membrane of native endothelial cells.

BACKGROUND ACE (kininase II) inhibitors have been shown to exert their beneficial cardiovascular effects via the inhibition of both angiotensin II formation and bradykinin breakdown. Because recent evidence suggests that ACE inhibitors may also interfere with B2 kinin receptor signaling and thus enhance the vascular response to bradykinin, we examined whether the distribution of B2 kinin receptors within the plasma membrane of native endothelial cells is affected by an ACE inhibitor. METHODS AND RESULTS Localization of the B2 kinin receptor in membranes prepared from native porcine aortic endothelial cells was evaluated by means of specific [3H]bradykinin binding and immunoprecipitation of the B2 receptor from isolated membranes. Effects of bradykinin and ramiprilat on intracellular signaling were determined by monitoring the activation of the extracellularly regulated kinases Erk1 and Erk2 as well as [Ca2+]i increases in fura 2-loaded endothelial cells. Stimulation of native endothelial cells with bradykinin 100 nmol/L resulted in the time-dependent sequestration of the B2 receptor to caveolin-rich (CR) membranes, which was maximal after 5 minutes. Pretreatment with ramiprilat 100 nmol/L for 15 minutes significantly attenuated the recovery of B2 kinin receptors in CR membranes while increasing that from membranes lacking caveolin. This effect was not due to the inhibition of bradykinin degradation, because no effect was seen in the presence of an inhibitory concentration of the synthetic ACE substrate hippuryl-L-histidyl-L-leucine. Ramiprilat also decreased [3H]bradykinin binding to CR membranes when applied either before or after bradykinin stimulation. Moreover, ramiprilat resulted in reactivation of the B2 receptor in bradykinin-stimulated cells and induced a second peak in [Ca2+]i and reactivation of Erk1/2. CONCLUSIONS The ACE inhibitor ramiprilat interferes with the targeting of the B2 kinin receptor to CR membrane domains in native endothelial cells. Therefore, effects other than the inhibition of kininase II may account for the effects of ramiprilat and other ACE inhibitors on the vascular system.
The Penn Discourse TreeBank 2.0.

We present the second version of the Penn Discourse Treebank, PDTB-2.0, describing its lexically-grounded annotations of discourse relations and their two abstract object arguments over the 1 million word Wall Street Journal corpus. We describe all aspects of the annotation, including (a) the argument structure of discourse relations, (b) the sense annotation of the relations, and (c) the attribution of discourse relations and each of their arguments. We list the differences between PDTB-1.0 and PDTB-2.0. We present representative statistics for several aspects of the annotation in the corpus.
Static and Dynamic Accuracy of an Innovative Miniaturized Wearable Platform for Short Range Distance Measurements for Human Movement Applications

Magneto-inertial measurement units (MIMU) are a suitable solution to assess human motor performance both indoors and outdoors. However, relevant quantities such as step width and base of support, which play an important role in gait stability, cannot be directly measured using MIMU alone. To overcome this limitation, we developed a wearable platform specifically designed for human movement analysis applications, which integrates a MIMU and an Infrared Time-of-Flight proximity sensor (IR-ToF), allowing for the estimate of inter-object distance. We proposed a thorough testing protocol for evaluating the IR-ToF sensor performances under experimental conditions resembling those encountered during gait. In particular, we tested the sensor performance for different (i) target colors; (ii) sensor-target distances (up to 200 mm) and (iii) sensor-target angles of incidence (AoI) (up to 60∘). Both static and dynamic conditions were analyzed. A pendulum, simulating the oscillation of a human leg, was used to generate highly repeatable oscillations with a maximum angular velocity of 6 rad/s. Results showed that the IR-ToF proximity sensor was not sensitive to variations of both distance and target color (except for black). Conversely, a relationship between error magnitude and AoI values was found. For AoI equal to 0∘, the IR-ToF sensor performed equally well both in static and dynamic acquisitions with a distance mean absolute error <1.5 mm. Errors increased up to 3.6 mm (static) and 11.9 mm (dynamic) for AoI equal to ±30∘, and up to 7.8 mm (static) and 25.6 mm (dynamic) for AoI equal to ±60∘. In addition, the wearable platform was used during a preliminary experiment for the estimation of the inter-foot distance on a single healthy subject while walking. In conclusion, the combination of magneto-inertial unit and IR-ToF technology represents a valuable alternative solution in terms of accuracy, sampling frequency, dimension and power consumption, compared to existing technologies.
Geo-predictive real-time media delivery in mobile environment

Multimedia streaming is one of the most popular services today. When the user is in a mobile scenario, the delivery of multimedia streaming services becomes more challenging. Mobile streaming suffers from discontinuous playback that sometimes impairs user experience. Among other factors, this is also due to the high network bandwidth variation that a user can experience along a path. In some cases, the available bandwidth is close to zero when traversing tunnels or areas where the network capacity goes below what is required for a multimedia session to be pause-less. Typically, media adaptation and rate control are used to fight against variable bandwidth. However, these are usually reactive algorithms, where an event is first detected (e.g., a drop in available bandwidth), and then an action is taken, either by the streaming client or by the server. This action may result just in a mitigation of the problem and not in the complete removal. In this paper we introduce the novel concept of Geo-Predictive mobile streaming. This is a collaborative service that makes use of prediction rather than reaction. Network coverage maps are built with the aid of mobile users; with these maps, the available network bandwidth for each location is recorded in a server, and when a user travels from point A to point B, it is possible to predict well in advance what will be the experienced bandwidth along that route. In case of bandwidth drops, these can be known in advance and media adaptation algorithms can be triggered so that a pause-less media playback experience can be guaranteed to the end users anywhere and all the time.
Towards understanding interruption and reordering in HSPA/HSPA+ networks

In this paper, we quantify reordering and connection interruption while downloading data to mobile phones in modern HSPA/HSPA+ mobile networks based on end-to-end measurements conducted in commercial operator networks. We show how these events are affected by phone model, operator, time of the day, user movement and signal strength. We find that phone model, time of day and user movement affect the number of connection interruptions, but not their length. Data loss during interruptions is influenced by operator and time of day. The number of reordering events is impacted by the operator and the time of day, the operator additionally affects the reordering extent. This improves the understanding of HSPA/HSPA+ networks and enables further research to improve the data transfer and methods for delay spike prevention.
New Generation of High - Power Semiconductor Closing Switches for Pulsed Power Applications

Summary form only given. A new generation of semiconductor closing switches based on reverse switched dynistors (RSD) has been developed to switch high-power current pulses with microsecond duration. In this report the results of theoretical and experimental investigations of RSD at peak current more than 500 kA and pulse duration up to 500 mus (at 0.1 Imax) are observed. The criteria of extreme peak current for switch taking into consideration the longtime performance under pulse-periodical mode are gave. The design and test results for switch under single pulse mode at operating voltage up to 25 kV and peak current up to 300 kA are described. The possibility of using such a switch in the ISKRA-6 capacitor bank is estimated.
A framework for defining logics

The Edinburgh Logical Framework (LF) provides a means to define (or present) logics. It is based on a general treatment of syntax, rules, and proofs by means of a typed λ-calculus with dependent types. Syntax is treated in a style similar to, but more general than, Martin-Lo¨f's system of arities. The treatment of rules and proofs focuses on his notion of a judgment. Logics are represented in LF via a new principle, the judgments as types principle, whereby each judgment is identified with the type of its proofs. This allows for a smooth treatment of discharge and variable occurence conditions and leads to a uniform treatment of rules and proofs whereby rules are viewed as proofs of higher-order judgments and proof checking is reduced to type checking. The practical benefit of our treatment of formal systems is that logic-independent tools, such as proof editors and proof checkers, can be constructed.
A KaiC-associating SasA–RpaA two-component regulatory system as a major circadian timing mediator in cyanobacteria

KaiA, KaiB, and KaiC clock proteins from cyanobacteria and ATP are sufficient to reconstitute the KaiC phosphorylation rhythm in vitro, whereas almost all gene promoters are under the control of the circadian clock. The mechanism by which the KaiC phosphorylation cycle drives global transcription rhythms is unknown. Here, we report that RpaA, a potential DNA-binding protein that acts as a cognate response regulator of the KaiC-interacting kinase SasA, mediates between KaiC phosphorylation and global transcription rhythms. Circadian transcription was severely attenuated in sasA (Synechococcus adaptive sensor A)- and rpaA (regulator of phycobilisome-associated)-mutant cells, and the phosphotransfer activity from SasA to RpaA changed dramatically depending on the circadian state of a coexisting Kai protein complex in vitro. We propose a model in which the SasA–RpaA two-component system mediates time signals from the enzymatic oscillator to drive genome-wide transcription rhythms in cyanobacteria. Moreover, our results indicate the presence of secondary output pathways from the clock to transcription control, suggesting that multiple pathways ensure a genome-wide circadian system.
A graph-matching kernel for object categorization

This paper addresses the problem of category-level image classification. The underlying image model is a graph whose nodes correspond to a dense set of regions, and edges reflect the underlying grid structure of the image and act as springs to guarantee the geometric consistency of nearby regions during matching. A fast approximate algorithm for matching the graphs associated with two images is presented. This algorithm is used to construct a kernel appropriate for SVM-based image classification, and experiments with the Caltech 101, Caltech 256, and Scenes datasets demonstrate performance that matches or exceeds the state of the art for methods using a single type of features.
Developing with lethal RA levels: genetic ablation of Rarg can restore the viability of mice lacking Cyp26a1

We have previously reported that the retinoic acid (RA) catabolizing enzyme CYP26A1 plays an important role in protecting tail bud tissues from inappropriate exposure to RA generated in the adjacent trunk tissues by RALDH2, and that Cyp26a1-null animals exhibit spina bifida and caudal agenesis. We now show that, in the absence of Cyp26a1, retinoic acid receptor gamma (RARγ) mediates ectopic RA-signaling in the tail bud. We also show that activated RARγ results in downregulation of Wnt3a and Fgf8, which integrate highly conserved signaling pathways known for their role in specifying caudal morphogenesis. Ablation of the gene for RARγ (Rarg) rescues Cyp26a1-null mutant animals from caudal regression and embryonic lethality, thus demonstrating that CYP26A1 suppresses the RA-mediated downregulation of WNT3A and FGF8 signaling pathways by eliminating ectopic RA in gastrulating tail bud mesoderm.
Pacific Asia Conference on Information Systems ( PACIS ) 9 July 2011 A Longitudinal Study Of E-Government Maturity

Extending the findings of prior cross-sectional studies, this paper presents a longitudinal analysis of the drivers of e-Government maturity. We constructed a panel dataset for the period from 2003 to 2007 using data published by various authoritative sources. We fitted a mixed-effects regression model to the data to study how the growth of e-Government around the globe is influenced by changing levels of affluence, ICT infrastructure, human capital, and governance. We found that countries’ e-Government matures as they become more affluent (in terms of GDP per capita) and as their ICT infrastructure improves. Human capital and the quality of governance have no significant effect on the development of e-Government maturity. The results suggest that countries investing in leading-edge ICT infrastructure can maintain or improve their global standing in e-Government without substantial changes to human capital or governance. We put forward plausible reasons to explain our findings, and their implications for future research and the practice of e-government.
Machine Learning Approach for Taxation Analysis using Classification Techniques

Data mining process discovers useful information from the hidden data, which can be used for future prediction. Machine learning provides methods, techniques and tools, which help to learn automatically and to make accurate predictions based on past observations. The data are retrieved from the real time environmental setup. Machine learning techniques can help in the integration of computer-based systems in predicting the dataset and to improve the efficiency of the system. The main purpose of this paper is to provide a comparison of some commonly employed classification algorithms under same conditions. Such comparison helps to provide the accurate result in algorithms. Hence comparing the algorithms for such a classifier is a tedious task, for real time dataset. The classification models were experimented by using 365 datasets with 24 attributes. The predicted values for the classifiers were evaluated and the results were compared. General Terms: Algorithms, Experimentation, Performance.