Miracle-dz/UniFAR-FTUs huggingface dataset:
Are Commercial Fishers Risk-Lovers?

Fishers are risk-averse according to most empirical studies, while expected-utility theory predicts risk neutrality even for sizable stakes. We test this prediction using data from a stated-choice experiment with Swedish commercial fishers. Our results show that only 48% of the fishers can be broadly characterized as risk-neutral, while 26% are modestly risk-averse, and 26% are strongly risk-averse. Fishers are more risk-neutral the higher the fraction of their household’s income comes from fishing. Sensitivity testing implies that modest stake decisions like a few days of fishing are not influenced by wealth level. (JEL D81; Q22)
An analysis of Internet inter-domain topology and route stability

The Internet routing fabric is partitioned into several domains. Each domain represents a region of the fabric administered by a single commercial entity. Over the past two years, the routing fabric has experienced significant growth. From more than a year's worth of inter-domain routing traces, we analyze the Internet inter-domain topology, its route stability behavior, and the effect of growth on these characteristics. Our analysis reveals several interesting results. Despite growth, the degree distribution and the diameter of the inter-domain topology have remained relatively unchanged. Furthermore, there exists a four-level hierarchy of Internet domains classified by degree. However, connectivity between domains is significantly non-hierarchical. Despite increased connectivity at higher levels in the topology, the distribution of paths to prefixes from the backbone remained relatively unchanged. There is evidence that both route availability and the mean reachability duration have degraded with Internet growth.
Domain transfer for person re-identification

Automatic person re-identification in is a crucial capability underpinning many applications in public space video surveillance. It is challenging due to intra-class variation in person appearance when observed in different views, together with limited inter-class variability. Various recent approaches have made great progress in re-identification performance using discriminative learning techniques. However, these approaches are fundamentally limited by the requirement of extensive annotated training data for every pair of views. For practical re-identification, this is an unreasonable assumption, as annotating extensive volumes of data for every pair of cameras to be re-identified may be impossible or prohibitively expensive. In this paper we move toward relaxing this strong assumption by investigating flexible multi-source transfer of re-identification models across camera pairs. Specifically, we show how to leverage prior re-identification models learned for a set of source view pairs (domains), and flexibly combine these to obtain good re-identification performance in a target view pair (domain) with greatly reduced training data requirements in the target domain.
Patient perceptions of integrated care: confused by the term, clear on the concept

PURPOSE Health care reform in the US has introduced terms such as 'the patient-centered medical home' and 'integrated care' that are often unclear and unfamiliar to patients. This study explored patient experiences with the functional domains of integrated care. THEORY AND METHODS Patients first wrote their definitions of integrated care and then participated in focus group discussions about their experiences with the health care system. Transcripts were analyzed for thematic content. RESULTS Forty-four patients participated in one of seven focus groups in San Francisco, CA in English and Spanish. Many patients were not clear about the meaning of the term integrated care. However, patients described experiences largely reflected in an existing conceptual model of integrated care and the importance of coordination within and across teams and with community resources, continuity and sharing of information, and patient engagement. Patients with high medical needs described the ubiquitous challenges they faced in experiencing coordinated care. CONCLUSIONS Patients may not understand the term integrated care but are relatively clear on what the concept of integrated care entails and support its successful implementation. Patients and their families are at the center of integrated care, and health systems need to support and empower them to successfully navigate the medical neighborhood.
“Task Trade and its determinants in Spain: a national and regional analysis”

Globalisation and technological advances have made possible to offshore specific productive tasks (that do not require physical proximity to the actual location of the work unit) to foreign countries where these are usually performed at lower costs. We analyse the effect of task trade (i.e. task offshorability) on Spanish regional and national employment levels correlating a newly built index of task-delocalisation index to key variables such as the region’s wealth, the worker’s age and level of education, the importance of the service sector and the technological level of the economic activities undertaken in that particular geographical area. We conclude that approximately 25% of Spanish occupations are potentially affected by task trade/offshoring and that this is likely to benefit Spanish economy (and the performance of specific regions, categories of workers and sectors) being Spain a potential recipient of tasks offshored from abroad. Also we obtain that Spain’s trade in tasks correlates strongly with the above variables, presenting significant regional differences.
Initializing the VA medication reference terminology using UMLS metathesaurus co-occurrences

We developed and evaluated a UMLS Metathesaurus Co-occurrence mining algorithm to connect medications and diseases they may treat. Based on 16 years of co-occurrence data, we created 977 candidate drug-disease pairs for a sample of 100 ingredients (50 commonly prescribed and 50 selected at random). Our evaluation showed that more than 80% of the candidate drug-disease pairs were rated "APPROPRIATE" by physician raters. Additionally, there was a highly significant correlation between the overall frequency of citation and the likelihood that the connection was rated "APPROPRIATE. " The drug-disease pairs were used to initialize term definitions in an ongoing effort to build a medication reference terminology for the Veterans Health Administration. Co-occurrence mining is a valuable technique for initializing term definitions in a large-scale reference terminology creation project.
SAT-based Unbounded Model Checking of Timed Automata

Symbolic model checking, based mostly on BDD graphs, is a standard technology nowadays. In the last decade, very efficient implementations of SAT solvers have been provided. Thanks to that SAT-based bounded model checking (BMC) and unbounded model checking (UMC) became feasible. The idea of UMC consists in encoding the states of a model, where a temporal formula holds, by propositional formulas in conjunctive normal form (called blocking clauses). Unfortunately, the number of these clauses can be exponential. There are several methods aiming at improving the above deficiency by using generalized blocking clauses, or for instance circuit cofactoring. In this paper we define and use timed generalized blocking clauses in UMC of timed automata for untimed temporal properties expressed in CTL-X.
Why Mate Choices are not as Reciprocal as we Assume: The Role of Personality, Flirting and Physical Attractiveness

Based on a social relations perspective on mating, the actual and assumed reciprocity of mate choices was studied in a real–life speed–dating context. A community sample involving 382 singles aged 18–54 years filled out a questionnaire for the measurement of self–perceived mate value, sociosexuality, extraversion, and shyness and participated in free speed–dating sessions. Immediately after each date, choices and assumed choices were recorded. Measures of physical attractiveness and flirting behaviour were obtained by independent observers. Results show that actual mate choices are not reciprocal although people strongly expect their choices to be reciprocated and flirting behaviour is indeed strongly reciprocal. This interesting pattern of results was explained by investigating individual and dyadic effects of flirting, self–perceived mate value and physical attractiveness on mate choices. Results have important implications for understanding mating behaviour, sex differences and the (in)accuracies of mating decisions. Copyright © 2010 John Wiley & Sons, Ltd.
Impact of Stripe Unit Size on Performance and Endurance of SSD-Based RAID Arrays

Over the past decades, Redundant Array of independent Disks (RAIDs) have been configured based on mechanical characteristics of Hard Disk Drives (HDDs). With the advent of Solid-State Drives (SSDs), such configurations such as stripe unit size can be far from the characteristics of SSDs. In this paper, we investigate the effect of stripe unit size on the endurance and the overall I/O performance of an SSD-based RAID array and compare the optimal stripe unit size with the suggested stripe unit sizes for HDD-based RAID. To this end, we first examine the number of extra page reads and writes imposed by write requests and then observe the corresponding impact on the overall throughput and the average response time of SSD-based RAID arrays. The effect of extra page writes for different stripe unit sizes and their impact on endurance has been also examined. To validate the analytical study, we have used I/O intensive traces and simulated an SSD-based RAID array using DiskSim simulator with different stripe unit sizes. The experimental results reveal that unlike HDD-based RAID arrays, a 4KB stripe unit size can significantly improve the throughput, response time, and endurance of an SSD-based RAID4 array (up to 67.6%, 52.2%, and 48.6%, respectively) as compared to 128KB stripe unit size.
Fast Semantic Image Segmentation with High Order Context and Guided Filtering

This paper describes a fast and accurate semantic image segmentation approach that encodes not only the discriminative features from deep neural networks, but also the high-order context compatibility among adjacent objects as well as low level image features. We formulate the underlying problem as the conditional random field that embeds local feature extraction, clique potential construction, and guided filtering within the same framework, and provide an efficient coarse-to-fine solver. At the coarse level, we combine local feature representation and context interaction using a deep convolutional network, and directly learn the interaction from high order cliques with a message passing routine, avoiding time-consuming explicit graph inference for joint probability distribution. At the fine level, we introduce a guided filtering interpretation for the mean field algorithm, and achieve accurate object boundaries with 100+ faster than classic learning methods. The two parts are connected and jointly trained in an end-to-end fashion. Experimental results on Pascal VOC 2012 dataset have shown that the proposed algorithm outperforms the state-of-the-art, and that it achieves the rank 1 performance at the time of submission, both of which prove the effectiveness of this unified framework for semantic image segmentation.
A Scalable Framework for Secure Multicast

The lack of security mechanisms for IP multicast has impeded the large scale commercial deployment of applications such as pay-per-view information dissemination services and real-time videoconferencing. In this report, we describe the design and implementation of a scalable mechanism for secure multicast. It relies on a trusted, centralized security manager to authenticate participants and distribute keys. To ensure that participants are unable to access session data sent before they join or after they leave the group, the security manager rekeys the participants of the session in response to group membership changes. To avoid an excess of rekeying traffic that would be caused by frequent membership changes, we introduce an epoch-based rekeying protocol, wherein rekeying takes place at most once per a fixed duration of time called an epoch. We use the SRM reliable multicast protocol to disseminate rekeying messages. To minimize disruption of the session at participants caused by loss of rekey messages (and their consequent inability to decrypt session data), the usage of new keys is delayed by an additional epoch. This technique maximizes the probability of the reliable multicast mechanism delivering the rekey message to all participants. The key distribution protocol and the corresponding objects have been implemented in the MASH toolkit as reusable modules. Performance studies reveal the bottleneck in our system to be the bandwidth consumed by rekeying traffic. Based on our observations, we propose an extension to our scheme that would go a long way towards achieving true global scalability for secure multicast groups.
On the Optimality of Classifier Chain for Multi-label Classification

To capture the interdependencies between labels in multi-label classification problems, classifier chain (CC) tries to take the multiple labels of each instance into account under a deterministic high-order Markov Chain model. Since its performance is sensitive to the choice of label order, the key issue is how to determine the optimal label order for CC. In this work, we first generalize the CC model over a random label order. Then, we present a theoretical analysis of the generalization error for the proposed generalized model. Based on our results, we propose a dynamic programming based classifier chain (CC-DP) algorithm to search the globally optimal label order for CC and a greedy classifier chain (CC-Greedy) algorithm to find a locally optimal CC. Comprehensive experiments on a number of real-world multi-label data sets from various domains demonstrate that our proposed CC-DP algorithm outperforms state-of-the-art approaches and the CC-Greedy algorithm achieves comparable prediction performance with CC-DP.
An observation-based model for fault localization

Automatic techniques for helping developers in finding the root causes of software failures are extremely important in the development cycle of software. In this paper we study a dynamic modeling approach to fault localization, which is based on logic reasoning over program traces. We present a simple diagnostic performance model to assess the influence of various parameters, such as test set size and coverage, on the debugging effort required to find the root causes of software failures. The model shows that our approach unambiguously reveals the actual faults, provided that sufficient test cases are available. This optimal diagnostic performance is confirmed by numerical experiments. Furthermore, we present preliminary experiments on the diagnostic capabilities of this approach using the single-fault Siemens benchmark set. We show that, for the Siemens set, the approach presented in this paper yields a better diagnostic ranking than other well-known techniques.
Dynamics of brain networks in the aesthetic appreciation

Neuroimage experiments have been essential for identifying active brain networks. During cognitive tasks as in, e.g., aesthetic appreciation, such networks include regions that belong to the default mode network (DMN). Theoretically, DMN activity should be interrupted during cognitive tasks demanding attention, as is the case for aesthetic appreciation. Analyzing the functional connectivity dynamics along three temporal windows and two conditions, beautiful and not beautiful stimuli, here we report experimental support for the hypothesis that aesthetic appreciation relies on the activation of two different networks, an initial aesthetic network and a delayed aesthetic network, engaged within distinct time frames. Activation of the DMN might correspond mainly to the delayed aesthetic network. We discuss adaptive and evolutionary explanations for the relationships existing between the DMN and aesthetic networks and offer unique inputs to debates on the mind/brain interaction.
A prospective randomized trial comparing patient-controlled sedation using propofol and alfentanil and physician-administered sedation using diazepam and pethidine during transvaginal ultrasound-guided oocyte retrieval.

BACKGROUND This open label study compared the effectiveness of patient-controlled sedation (PCS), with physician-administered sedation (PAS) during transvaginal ultrasound-guided oocyte retrieval (TUGOR). METHODS A total of 106 patients was randomized using a computer model to receive either PCS (n = 51) or PAS (n = 55). Intra-operative sedation and co-operation were measured on a 5-point scale while severity of pain, and doctor and patient satisfaction were assessed using a 100 mm visual analogue scale. Number of oocytes retrieved and fertilization, cleavage and clinical pregnancy rates were also recorded. RESULTS Levels of sedation and co-operation were similar between groups. The pain score was higher in the PCS group during (mean +/- SD, 53 +/- 23 versus 35 +/- 24; P < 0.01) and 2 h after the procedure (29 +/- 27 versus 17 +/- 22; P < 0.05). Doctors were less satisfied with PCS than PAS (62 +/- 25 versus 71 +/- 26; P < 0.05) while patients were highly satisfied with both methods (76 +/- 23 versus 74 +/- 21; not significant). There were no oversedation or peri-operative complications. Fertility outcomes were similar. Patients tended to prefer PCS when given the choice of sedation method. CONCLUSION Although PCS provides less analgesia then PAS during TUGOR, it is safe, satisfactory and accepted by patients.
MULTIOBJECTIVE OPTIMIZATION OF WATER DISTRIBUTION NETWORKS USING SMS-EMOA

The optimization of water distribution networks (WDNs) deals with finding cost-effective network designs that can reliably service the drinking water demands at consumer nodes, which is an inherently multiobjective problem. Using a three-objective problem formulation based on cost and reliability, we compare the well-established NSGA-II to SMS-EMOA that applies multiobjective selection based on hypervolume contribution. With respect to three classical WDN benchmarks SMS-EMOA was found to strictly outperform NSGA-II, using an integer programming formulation for optimizing the pipe diameters.
Topological considerations in isosurface generation

A popular technique for rendition of isosurfaces in sampled data is to consider cells with sample points as corners and approximate the isosurface in each cell by one or more polygons whose vertices are obtained by interpolation of the sample data. That is, each polygon vertex is a point on a cell edge, between two adjacent sample points, where the function is estimated to equal the desired threshold value. The two sample points have values on opposite sides of the threshold, and the interpolated point is called an intersection point. When one cell face has an intersection point in each of its four edges, then the correct connection among intersection points becomes ambiguous. An incorrect connection can lead to erroneous topology in the rendered surface, and possible discontinuities. We show that disambiguation methods, to be at all accurate, need to consider sample values in the neighborhood outside the cell. This paper studies the problems of disambiguation, reports on some solutions, and presents some statistics on the occurrence of such ambiguities. A natural way to incorporate neighborhood information is through the use of calculated gradients at cell corners. They provide insight into the behavior of a function in well-understood ways. We introduce two gradient consistency heuristics that use calculated gradients at the corners of ambiguous faces, as well as the function values at those corners, to disambiguate at a reasonable computational cost. These methods give the correct topology on several examples that caused problems for other methods we examined.
An adaptive filtering approach for segmentation of tuberculosis bacteria in Ziehl-Neelsen sputum stained images

Tuberculosis is a disease with one of the most leading causes of deaths in the world, however, its fatality index could be reduced if it is diagnosed and treated on time. The Ziehl-Neelsen stained sputum smear method is the most used for bacilli detection and for developing a proper diagnosis by the specialist. Nevertheless, these stained images do not always present an adequate contrast, then, the elaboration of a reliable diagnosis is a complex, time consuming and a difficult process. This research proposes an alternative method to perform automatic bacilli segmentation in Ziehl-Neelsen images using Adaptive Signal Processing techniques, like the Least Mean Squares and Reduced Rank with Eigendecomposition algorithms. The quantitative results achieved, in correlation and true positives detection, are encouraging and suggest the use of this approach as a feasible alternative, when compared with the classical segmentation techniques, for automatic bacilli segmentation in the Ziehl-Neelsen images.
A Dynamic MDS-Based Localization Algorithm for Mobile Sensor Networks

In this paper, we proposed a dynamic mobility-assisted MDS-based localization algorithms for sparse mobile sensor network. For sparse networks, the assumption of the existing MDS-based localization algorithms is not necessary valid and the network may even be nonrigid, which significantly affects the application and accuracy of the existing MDS-based algorithms. In the proposed algorithm, we utilize the mobile capability of sensors in a mobile network. By moving the sensors in a random direction and recording the distances to their neighbors during the movement, virtual nodes are added. The distances between virtual nodes and real nodes provide more information about network, which leads to significantly better localization than existing methods for sparse networks. Experiments and evaluation of the proposed algorithm are provided.
Scaling of contractile properties of catfish feeding muscles

Biomechanical models are intrinsically limited in explaining the ontogenetic scaling relationships for prey capture kinematics in aquatic vertebrates because no data are available on the scaling of intrinsic contractile properties of the muscles that power feeding. However, functional insight into scaling relationships is fundamental to our understanding of the ecology, performance and evolution of animals. In this study, in vitro contractile properties of three feeding muscles were determined for a series of different sizes of African air-breathing catfishes (Clarias gariepinus). These muscles were the mouth closer musculus adductor mandibulae A2A3′, the mouth opener m. protractor hyoidei and the hypaxial muscles responsible for pectoral girdle retraction. Tetanus and twitch activation rise times increased significantly with size, while latency time was size independent. In accordance with the decrease in feeding velocity with increasing size, the cycle frequency for maximal power output of the protractor hyoidei and the adductor mandibulae showed a negative scaling relationship. Theoretical modelling predicts a scaling relationship for in vivo muscle function during which these muscles always produced at least 80% of their maximal in vitro power. These findings suggest that the contractile properties of these feeding muscles are fine-tuned to the changes in biomechanical constraints of movement of the feeding apparatus during ontogeny. However, each muscle appears to have a unique set of contractile properties. The hypaxials, the most important muscle for powering suction feeding in clariid catfish, differed from the other muscles by generating higher maximal stress and mass-specific power output with increased size, whilst the optimum cycle frequency for maximal power output only decreased significantly with size in the larger adults (cranial lengths greater than 60 mm).