Miracle-dz/UniFAR-FTUs huggingface dataset:
Computing Complete Test Graphs for Hierarchical Systems

Conformance testing focuses on checking whether an implementation under test (IUT) behaves according to its specification. Typically, testers are interested in performing targeted tests that exercise certain features of the IUT. This intention is formalized as a test purpose. The tester needs a "strategy" to reach the goal specified by the test purpose. Also, for a particular test case, the strategy should tell the tester whether the IUT has passed, failed, or deviated from the test purpose. In (J. Jeron and P. Morel, 1999) Jeron and Morel show how to compute, for a given finite state machine specification and a test purpose automaton, a complete test graph (CTG) which represents all test strategies. In this paper, we consider the case when the specification is a hierarchical state machine and show how to compute a hierarchical CTG which preserves the hierarchical structure of the specification. We also propose an algorithm for an online test oracle which avoids a space overhead associated with the CTG
Electric Pulse Stimulation of Cultured Murine Muscle Cells Reproduces Gene Expression Changes of Trained Mouse Muscle

Adequate levels of physical activity are at the center of a healthy lifestyle. However, the molecular mechanisms that mediate the beneficial effects of exercise remain enigmatic. This gap in knowledge is caused by the lack of an amenable experimental model system. Therefore, we optimized electric pulse stimulation of muscle cells to closely recapitulate the plastic changes in gene expression observed in a trained skeletal muscle. The exact experimental conditions were established using the peroxisome proliferator-activated receptor γ coactivator 1α (PGC-1α) as a marker for an endurance-trained muscle fiber. We subsequently compared the changes in the relative expression of metabolic and myofibrillar genes in the muscle cell system with those observed in mouse muscle in vivo following either an acute or repeated bouts of treadmill exercise. Importantly, in electrically stimulated C2C12 mouse muscle cells, the qualitative transcriptional adaptations were almost identical to those in trained muscle, but differ from the acute effects of exercise on muscle gene expression. In addition, significant alterations in the expression of myofibrillar proteins indicate that this stimulation could be used to modulate the fiber-type of muscle cells in culture. Our data thus describe an experimental cell culture model for the study of at least some of the transcriptional aspects of skeletal muscle adaptation to physical activity. This system will be useful for the study of the molecular mechanisms that regulate exercise adaptation in muscle.
What are the Sources of Country and Industry Diversification?

In this paper, we develop a new framework in which one can analyze industry and country effects by examining their underlying return components. We find that the global cash flow factor explains on average 48% of the variation of industry cash flows and the global discount rates explain 43% of the variation of industry discount rates. These are more than double the explanatory power of the two factors over country cash flow and discount rate variations, which are 23% and 13% respectively. This suggests that global factors are much less important for return components at country level than at industry level. The larger benefits of diversification across countries than across industries are thus driven more by better diversification of expected returns, although better diversification of cash flows also drives the result. Moreover, emerging markets tend to have much smaller co-movements of both dividends and equity risk premiums with those of the world, suggesting a lower degree of integration with the world goods and financial markets. This appears to be the basis for emerging market diversification.
Intent and its discontents: the user at the wheel of the online video search engine

We embrace the position of the user in the driver's seat of the video search engine by proposing a principled framework for multimedia retrieval that moves beyond what users are searching for also to encompass why they search. This 'why' is understood as the reason, purpose or immediate goal behind a user information need, which we identify as the underlying user 'intent'. Breaking information needs down into a topical dimension representing 'what' and an intent dimension representing 'why' will allow online video search engines to provide users with more satisfying search results. Until now, research on intent has remained small scale, limited by the lack of a systematic method for arriving at possible dimensions of user intent that provide productive areas of inquiry for multimedia research. We demonstrate how mining information from user descriptions of video information needs on the social Web makes it possible to identify useful intent categories for online video search and carry out validation experiments showing that these categories display enough invariance to be successfully modeled by a video search engine. In a final experiment, we demonstrate the potential for these categories to improve video retrieval with a large user study confirming that users associate salient differences within topically homogenous video search engine results lists with these intent categories. This reveals the potential to refine video results list using user intent.
Diagnostic and Prognostic Performance of Myeloperoxidase Plasma Levels Compared With Sensitive Troponins in Patients Admitted With Acute Onset Chest Pain

Background—Activation of leukocytes with release of myeloperoxidase (MPO) has been linked to acute coronary disease. To date, studies investigating the diagnostic and prognostic performance of circulating MPO in patients with chest pain (CP) are mainly retrospective, of low size, and lack a cut-off value for MPO. Herein, we prospectively assess the diagnostic and prognostic properties of MPO compared with sensitive troponin I (sTNI) in patients admitted to the emergency room with CP. Methods and Results—One thousand, eight hundred and eighteen consecutive patients (mean age, 61.4±13.5 years; 33.6% female) admitted for CP underwent determination of MPO, sTnI, and B-natriuretic peptide plasma levels at admission and 3 hours and 6 hours thereafter. A cut-off for MPO was defined in 5000 population-based subjects. Baseline MPO levels were elevated in patients with acute myocardial infarction compared with patients with noncoronary CP. For all time-points accuracy of MPO was inferior to sTNI for predicting AMI. The sensitivity of MPO to diagnose AMI at presentation was 73.5% compared with 90.7% for sTNI, and the specificity of MPO was 45.5% as opposed to 90.2%. B-natriuretic peptide levels also failed to demonstrate independent diagnostic information. Both MPO and B-natriuretic peptide were predictive for increased risk of adverse events at 30 days and 6 months, whereas their predictive value was weakened after covariate adjustment. Conclusions—The data demonstrate that MPO and B-natriuretic peptide fail to provide incremental information for patients with acute onset CP when added to sensitive troponin. However, there is a potential value for both biomarkers as prognostic markers.
Modeling and controller design of a MAGLEV guiding system for application in precision positioning

We analyze the dynamics of a magnetic guiding system and derive its analytical model with full degrees-of-freedom (DOFs). Then, an adaptive controller which deals with unknown parameters is proposed to regulate the five DOFs in this system. The guiding system including sensors and drive systems is has been implemented. From the experimental results, satisfactory performance including stiffness and resolution was achieved. This validates the design of system hardware and demonstrates the feasibility of the developed controller.
Lessons learned: a pilot study on occupational therapy effectiveness for children with sensory modulation disorder.

OBJECTIVE The purpose of this pilot study was to prepare for a randomized controlled study of the effectiveness of occupational therapy using a sensory integration approach (OT-SI) with children who have sensory processing disorders (SPD). METHOD A one-group pretest, posttest design with 30 children was completed with a subset of children with SPD, those with sensory modulation disorder. RESULTS Lessons learned relate to (a) identifying a homogeneous sample with quantifiable inclusion criteria, (b) developing an intervention manual for study replication and a fidelity to treatment measure, (c) determining which outcomes are sensitive to change and relate to parents' priorities, and (d) clarifying rigorous methodologies (e.g., blinded examiners, randomization, power). CONCLUSION A comprehensive program of research is needed, including multiple pilot studies to develop enough knowledge that high-quality effectiveness research in occupational therapy can be completed. Previous effectiveness studies in OT-SI have been single projects not based on a unified long-term program of research.
Recurrent Neural State Estimation in Domains with Long-Term Dependencies

This paper presents a state estimation approach for reinforce- ment learning (RL) of a partially observable Markov decision process. It is based on a special recurrent neural network architecture, the Markov deci- sion process extraction network with shortcuts (MPEN-S). In contrast to previous work regarding this topic, we address the problem of long-term de- pendencies, which cause major problems in many real-world applications. The architecture is designed to model the reward-relevant dynamics of an environment and is capable to condense large sets of continuous observ- ables to a compact Markovian state representation. The resulting estimate can be used as input for RL methods that assume the underlying system to be a Markov decision process. Although the approach was developed with RL in mind, it is also useful for general prediction tasks.
A simplex K-means algorithm for radio-port placement in cellular networks

To increase capacities of cellular networks, large cells must be replaced by micro and/ or pico cells. Employing small cells increases the number of radio-ports required to cover the same service area. Therefore, optimal placement of radio-ports can minimize the number of radio-ports and reduce the deployment costs, and at the same time, it provides the same required coverage. This paper proposes a hybrid of the Nelder-Mead simplex and K-means algorithms. The algorithm minimizes the maximum path loss experienced by wireless portable handsets. The performance of the proposed algorithm is compared with those of the quasi-Newton algorithm, as well as the exhaustive search. Simulation results show that the simplex K-means algorithm finds better radio-port placements, especially for larger floor plans
ILLUMINATION RECONSTRUCTION FROM REAL-TIME VIDEO FOR INTERACTIVE AUGMENTED REALITY

This paper presents an interactive real-time augmented reality system with realistic illumination of integrated virtual objects. A marker and a mirror sphere are placed in a scene that is captured by a video camera. Dynamic illumination (as the marker is interactively moved) is estimated from the video signal and used for realistic real-time integration of virtual objects into the video. To achieve interactive real-time performance, we have developed a hardware-accelerated approach, where self-occlusion and form factors are precalculated and stored in a low-resolution cube map for each vertex. This data is multiplied with the real-time illumination information estimated from the video to obtain diffuse per vertex illumination. Our approach works well for small virtual objects moving around in real environments and is almost completely implemented on the GPU per performance purposes. The real environment operates as a large area light source, which casts soft shadows on our diffusely lit virtual objects.
A novel ranking algorithm based on manifold learning for CBIR system

At present, most of image retrieval applications use Principal Component Analysis (PCA) algorithm to reduce the low-level features of images and rank the similarity of images based on graph structure to enhance the retrieval accuracy with relevance feedback technique. However, there are two issues to consider in traditional image retrieval methods: (1) the feature space of images is probably highly non-linear, in this case, PCA always fails to uncover the intrinsic structure so that the performance of dimension reduction is unsatisfactory; (2) ranking algorithms based on manifold learning most likely ignore the global structure of image feature space. To address the issues above, this paper utilizes Linear Local Tangent Space Alignment (LLTSA) algorithm to uncover the non-linear structure of images feature space. At the ranking stage, we take advantage of the semi-supervised idea to sort the similarity of images. Such a strategy makes up the shortcomings of manifold ranking. Experiments on a large collection of images have shown the effectiveness of our proposed algorithm.
Air Assisted Atomization in Spiral Type Nozzles

Processes utilizing various spraying cooling are widely used in many gas treatment applications. Effective spray cooling system design optimization often depends on the approach used to investigate such gas cooling problems. The mass transfer efficiency of spray cooling problems depends on the ability of the liquid to atomize and disperse into the gas by interacting in a controlled manner. Experimental methods coupled with Computation Fluid Dynamics (CFD) can be used effectively in application design of a certain classes of spray evaporation processes. In this study, a spiral type nozzle was studied. Both water and air flow at different respective mass fractions (mass flow rate of water over mass flow rate of air) were included while keeping the water flow the same. Phase Doppler Interferometry (PDI) and Laser Sheet Imaging (LSI) were used to measure the drop size and distribution throughout the range of operation conditions. The results indicated a slight reduction in drop size as air mass flow rate was increased. Additionally the increase in air flow caused a shift of the spray plume from predefined center axis. At the same flow conditions, an internal and external two-phase flow of the nozzle was simulated with a Volume of Fluid (VOF) model which is a Computation Fluid Dynamics (CFD) method for multiphase problems. The fluids' velocity environment at the nozzle's orifice was investigated. As expected, the exit (from nozzle's orifice) velocities of the liquid increased as the mass fraction decreased due to increased total volume flow rate of both fluids. * Corresponding author: wojciech.kalata@spray.com
Is small tidal volume with low positive end expiratory pressure during one-lung ventilation an effective ventilation method for endoscopic thoracic surgery?

Background The present study will focus on the rationale for the use of small tidal volume with 6 cmH2O positive end expiratory pressure (PEEP) with the changes of arterial oxygen tension, plateau airway pressure, and static lung compliance during one lung ventilation for endoscopic thoracic surgery. Methods Forty-three patients were intubated with a double-lumen endobronchial tube. After positioning the patients in the lateral decubitus, one-lung ventilation was started with 100% oxygen, tidal volume 10 ml/kg without PEEP; arterial oxygen tension, plateau airway pressure, and static compliance were checked as baseline values (T0). Fifteen minutes later, same parameters were measured (T15). The tidal volume had changed to 6 ml/kg with 6 cmH2O PEEP. Fifteen minutes later, the same parameters were measured (T30). Results Oxygen tension had decreased at T15 (282.1 ± 83.4 mmHg) compared to T0 (477.2 ± 82.4 mmHg) (P < 0.0001), but was maintained at T30 (270.4 ± 81.9 mmHg). There was no difference in peak inspiratory pressure at T15 or T30 compared to T0, plateau airway pressure was increased at T15 and T30 (P < 0.05) and static lung compliance was decreased at T15 and T30 (P < 0.0001). Conclusions In carrying out one-lung ventilation for thoracic surgery using an endoscope, the addition of a PEEP of 6 cmH2O in the dependent lung, while reducing the tidal volume of 6 ml/kg, both oxygen tension and lung compliance are maintained without increasing the plateau airway pressure. Protective lung ventilation is useful for one lung ventilation.
Using Static and Dynamic Escape Analysis To Enable Model Reductions in Model-Checking Concurrent Object-Oriented Programs ?

This paper proposes several partial order reduction strategies for model-checking concurrent object-oriented software that are based on detecting heap objects that are thread-local, i.e., reachable from a single thread only. We show how thread-local information appropriate for driving these reductions can be obtained by static analysis (using adaptations of existing escape analyses). However, we show that in many cases, existing static analyses targetted to compiler optimizations fail to detect thread-local information that can lead to significant savings in model-checking. In fact, we show that a novel dynamic analysis carried out during model-checking is actually simpler to implement, and is more effective at reducing verification costs. These reduction strategies showing upwards of 20 times reduction on realistic programs, and can be easily incorporated into existing explicit-state software model-checkers.
Efficient Media Access Using Adaptive Array Antennas

Wireless ad hoc networks have traditionally been assumed to use omni-directional antennas. However, it has been recently recognized that the use of advanced antenna technologies could help alleviate the capacity limitations of multi-hop wireless networks, stemming from interference generated in the shared medium [1]. Adaptive array antennas have the ability to automatically respond to an unknown interference environment, in real time, by steering nulls and reducing side lobe levels in the direction of interference, while retaining some desired signal beam characteristics. However, real-time beamforming usually requires sophisticated DSP algorithms that use extra synchronization information, like training sequences, and might be quite “costly” for small wireless terminals. What is more, simply equipping each node with an adaptive antenna is not merely sufficient to improve performance. In this paper, we present a cross-layer approach that enables nodes in an ad hoc network to adapt the radiation pattern of a fully adaptive array antenna using information already available or easily obtainable at the MAC layer. We propose a protocol, called ADAPT, that extends the basic mechanisms of 802.11, and adjusts the antenna pattern, on a perpacket basis, using only MAC layer information (semi-static beamforming). Additionally, we use analytical methods to assess the effect on performance due to adapting the antenna pattern on a per-packet basis, instead of real-time, in the presence of node mobility and contenting neighboring communications. Finally, we use ns-2 simulations to extensively compare the performance of our adaptive antenna configuration against typical omnidirectional and directional configurations. Our protocol is shown to achieve up to a 3× improvement over the omni-directional case and up to a 50% improvement over the directional one, in most scenarios considered.
Comparison of post-editing productivity between professional translators and lay users

This work compares the post-editing productivity of professional translators and lay users. We integrate an English to Basque MT system within Bologna Translation Service, an end-to-end translation management platform, and perform a producitivity experiment in a real working environment. Six translators and six lay users translate or post-edit two texts from English into Basque. Results suggest that overall, post-editing increases translation throughput for both translators and users, although the latter seem to benefit more from the MT output. We observe that translators and users perceive MT differently. Additionally, a preliminary analysis seems to suggest that familiarity with the domain, source text complexity and MT quality might affect potential productivity gain.
Pareto-Optimal Motif for Predicting Peptide Binders

The peptide binding prediction methods are constantly evolving due to the accuracy gap between the computational predictions and the experimental predictions. Binding peptides are reflected by the presence of a motif that defines the binding site to a particular molecule. However, the degenerate nature of motifs and the varying chemical properties of residues at the binding sites across a wide spectrum of peptides render the detection of a motif a formidable task for specific molecules. One such molecule is MHC Class II I-A molecule. An extensive exploration of the solution space is therefore necessary to find a motif that can well represent the binding sites, to predict peptide binders. For our analysis, we express a motif by a quantitative scoring matrix representing many possible interactions among amino acids, and the positions of binding to peptides. Using the experimentally validated binding and non-binding data associated with I-A molecule, we address the issue of finding a motif that can fulfill two objectives: larger separation in prediction scores between binders and non-binders, a fewer number of false positives and false negatives. Earlier, we have attempted this problem using a single objective Genetic Algorithm (GA) by combining both objectives and presenting as a single objective [2]. In this paper, we use a Multi-Objective Genetic Algorithm (MOGA), which can facilitate the optimization of multiple objectives of our approach simultaneously. On the contrary to single objective GA where only fitness is used to compare the solution population, in MOGAs, a measure known as dominance is used to rank the solutions. A solution is said to be dominant if it is as good as or better than the solution in all objectives [1]. Often, there is more than one dominant solution, and is known as the Pareto-front representing the best solutions from a single iteration. As for our analysis, we use NSGA-II, a MOGA that can be used with multiple constraints [1]. The motif found with multi-objective GA gave an area under the receiver operator characteristic (ROC) value of 86% and better separation between binders and non-binder scores for an independent dataset outperforming all the experimentally derived motifs.
Defining the Cloud Battlefield - Supporting Security Assessments by Cloud Customers

Cloud computing is becoming more and more popular, but security concerns overshadow its technical and economic benefits. In particular, insider attacks and malicious insiders are considered as one of the major threats and risks in cloud computing. As physical boundaries disappear and a variety of parties are involved in cloud services, it is becoming harder to define a security perimeter that divides insiders from outsiders, therefore making security assessments by cloud customers more difficult. In this paper, we propose a model that combines a comprehensive system model of infrastructure clouds with a security model that captures security requirements of cloud customers as well as characteristics of attackers. This combination provides a powerful tool for systematically analyzing attacks in cloud environments, supporting cloud customers in their security assessment by providing a better understanding of existing attacks and threats. Furthermore, we use the model to construct "what-if" scenarios that could possible lead to new attacks and to raise concerns about unknown threats among cloud customers.