Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-frame Measurement Fusion for State Estimation

Multi-frame Measurement Fusion for State Estimation Simultaneous Localization and Mapping (SLAM) is a technique used by robots and autonomous vehicles to build up a map while operating in an unknown environment, while at the same time keeping track of a current position estimate. This process is not as straightforward as it may sound. Due to inherent uncertainties in tracking the robot’s motion and observing the world around it, errors accumulate. Incorporating External Localization (EL) sensors, such as Global Positioning System (GPS) receivers, which are not subject to the same type of drift over time, can allow a system to eliminate errors from the SLAM process. This, however, requires the system to work with measurements from both the SLAM and EL coordinate frames. Since the SLAM coordinate origin is dependent upon the vehicle’s starting location, which is unknown, the relationship between it and the EL frame is unknown and must be estimated. The main contributions of this thesis arise from a novel approach to integrating EL with SLAM, by estimating a transformation between the SLAM and external reference frames. The Constrained Relative Submap Filter (CRSF) SLAM is a computationally efficient SLAM filter operates on a series of local submaps, instead of one large map, as with Extended Kalman Filter (EKF) SLAM. By integrating the transformation estimation process with CRSF SLAM , a method to correct submap locations with EL is presented. This eliminates long term estimation drift, aids in loop closing and allows for accurate map generation with a reference to an external frame.
Learning to Classify Fine-Grained Categories with Privileged Visual-Semantic Misalignment

Image categorisation is an active yet challenging research topic in computer vision, which is to classify the images according to their semantic content. Recently, fine-grained object categorisation has attracted wide attention and remains difficult due to feature inconsistency caused by smaller inter-class and larger intra-class variation as well as large varying poses. Most of the existing frameworks focused on exploiting a more discriminative imagery representation or developing a more robust classification framework to mitigate the suffering. The concern has recently been paid to discovering the dependency across fine-grained class labels based on Convolutional Neural Networks. Encouraged by the success of semantic label embedding to discover the fine-grained class labels’ correlation, this paper exploits the misalignment between visual feature space and semantic label embedding space and incorporates it as a privileged information into a cost-sensitive learning framework. Owing to capturing both the variation of imagery feature representation and also the label correlation in the semantic label embedding space, such a visual-semantic misalignment can be employed to reflect the importance of instances, which is more informative that conventional cost-sensitivities. Experiment results demonstrate the effectiveness of the proposed framework on public fine-grained benchmarks with achieving superior performance to state-of-the-arts.
Intelligent Approach to Simulation Software Evaluation

In this study, an intelligent approach is presented to help any simulation practitioner to evaluate simulation software alternatives and determine the best satisfying one based on his/her needs. On the other hand, this evaluation process is a typical multiple criteria decision making (MCDM) problem in the presence of evaluation criteria and a set of possible alternatives, and there are many methods in the literature, which have been used to successfully carry out this difficult and time-consuming process. In this paper, one of these methods, the analytic network process (ANP) method integrated with alpha-cut fuzzy logic is used because it can accommodate the variety of interactions, dependencies and feedback between higher and lower level elements, rather than analytic hierarchy process (AHP). In addition, an alpha-cut fuzzy extension of ANP uses uncertain human preferences as input information in the decision-making process. Instead of using the classical eigenvector prioritization method in AHP, only employed in the prioritization stage of ANP, an alpha-cut fuzzy logic method providing more accuracy on judgments is applied. The resulting alpha-cut fuzzy ANP enhances the potential of the conventional ANP for dealing with imprecise and uncertain human comparison judgments.
Enterovirus 71 Induces Mitochondrial Reactive Oxygen Species Generation That is Required for Efficient Replication

Redox homeostasis is an important host factor determining the outcome of infectious disease. Enterovirus 71 (EV71) infection has become an important endemic disease in Southeast Asia and China. We have previously shown that oxidative stress promotes viral replication, and progeny virus induces oxidative stress in host cells. The detailed mechanism for reactive oxygen species (ROS) generation in infected cells remains elusive. In the current study, we demonstrate that mitochondria were a major ROS source in EV71-infected cells. Mitochondria in productively infected cells underwent morphologic changes and exhibited functional anomalies, such as a decrease in mitochondrial electrochemical potential ΔΨm and an increase in oligomycin-insensitive oxygen consumption. Respiratory control ratio of mitochondria from infected cells was significantly lower than that of normal cells. The total adenine nucleotide pool and ATP content of EV71-infected cells significantly diminished. However, there appeared to be a compensatory increase in mitochondrial mass. Treatment with mito-TEMPO reduced eIF2α phosphorylation and viral replication, suggesting that mitochondrial ROS act to promote viral replication. It is plausible that EV71 infection induces mitochondrial ROS generation, which is essential to viral replication, at the sacrifice of efficient energy production, and that infected cells up-regulate biogenesis of mitochondria to compensate for their functional defect.
Matched filter based spectrum sensing and power level recognition with multiple antennas

In this paper, we consider the spectrum sensing problem in a cognitive radio (CR) network with only one primary user (PU) and one secondary user (SU). PU is equipped with single antenna while SU is equipped with multiple antennas. The key different from the conventional spectrum sensing studies is that we assume PU could work on different transmit power levels to cope with different communication environments, which is a more practice-matching model. We also suppose that there is some cooperative mechanism between PU and SU so that SU could know PU's training symbols during sensing. In the propose scheme, SU needs not only to detect the presence of PU but also to identify PU's transmit power levels after it is “detected”. Hence, the whole problem is much more challenging than that in conventional CR. We design the matched filter spectrum sensing algorithm by assuming that the channel information is only partially known. Quite a few new performance metrics are provided to better testify the effectiveness of the proposed algorithm in this multiple primary transmit power (MPTP) scenario, and a number of closed-form performance analysis are also derived. Finally simulation results are presented to verify the proposed studies.
Building a Java virtual machine for server applications: The Jvm on OS/390

As the use of the JavaTM language and virtual machines proliferates beyond the sphere of applets into the space of server programs, developers are requiring better performance, availability, and transactional and scalability features. This paper describes the work done for the Operating System/390 (OS/390®) Java virtual machine to improve performance and serviceability, to introduce security and performance enhancements, and to redesign parts of the virtual machine to enable it to run server programs efficiently and safely. Although OS/390 was the motivating platform for these changes, Java server programs on any platform can benefit from these features.
Profiling executive dysfunction in adults with autism and comorbid learning disability

Executive dysfunction is thought to be primary to autism. We examined differences in executive function between 20 adults with autism and learning disability and 23 individuals with learning disabilities outside the autistic spectrum. All participants were matched for chronological age and full-scale IQ, and were given a battery of tasks assessing fluency, planning, set-shifting, inhibition and working memory. Analyses of the individual tasks revealed very few significant differences between the two groups. However, analyses of composite scores derived for each executive domain revealed that the group with autism showed impaired performance on the working memory and planning tests. Together, these two measures were sufficient to classify participants into their diagnostic groups significantly better than would be expected by chance (75% of the autism group; 65% of the control group). Executive impairments were neither universal nor exclusive to the autism group, and we suggest that an alternative cognitive theory may better explain the cognitive profile we found.
Monitoring capabilities of schedulers in model-driven development of real-time systems

Model-driven development has the potential to reduce the design complexity of real-time embedded systems by increasing the abstraction level, enabling analysis at earlier phases of development, and automatic generation of code from the models. In this context, capabilities of schedulers as part of the underlying platform play an important role. They can affect the complexity of code generators and how the model is implemented on the platform. Also, the way a scheduler monitors the timing behaviors of tasks and schedules them can facilitate the extraction of runtime information. This information can then be used as feedback to the original model in order to identify parts of the model that may need to be re-designed and modified. This is especially important in order to achieve round-trip support for model-driven development of real-time systems. In this paper, we describe our work in providing such monitoring features by introducing a second layer scheduler on top of the OSE real-time operating system's scheduler. The goal is to extend the monitoring capabilities of the scheduler without modifying the kernel. The approach can also contribute to the predictability of applications by bringing more awareness to the scheduler about the type of real-time tasks (i.e., periodic, sporadic, and aperiodic) that are to be scheduled and the information that should be monitored and logged for each type.
An Allosteric Theory of Transcription Factor Induction

Allosteric molecules serve as regulators of cellular activity across all domains of life. We present a general theory of allosteric transcriptional regulation that permits quantitative predictions for how physiological responses are tuned to environmental stimuli. To test the model's predictive power, we apply it to the specific case of the ubiquitous simple repression motif in bacteria. We measure the fold-change in gene expression at different inducer concentrations in a collection of strains that span a range of repressor copy numbers and operator binding strengths. After inferring the inducer dissociation constants using data from one of these strains, we show the broad reach of the model by predicting the induction profiles of all other strains. Finally, we derive an expression for the free energy of allosteric transcription factors which enables us to collapse the data from all of our experiments onto a single master curve, capturing the diverse phenomenology of the induction profiles.
Investigating novice programming mistakes: educator beliefs vs. student data

Educators often form opinions on which programming mistakes novices make most often - for example, in Java: "they always confuse equality with assignment", or "they always call methods with the wrong types". These opinions are generally based solely on personal experience. We report a study to determine if programming educators form a consensus about which Java programming mistakes are the most common. We used the Blackbox data set to check whether the educators' opinions matched data from over 100,000 students - and checked whether this agreement was mediated by educators' experience. We found that educators formed only a weak consensus about which mistakes are most frequent, that their rankings bore only a moderate correspondence to the students in the Blackbox data, and that educators' experience had no effect on this level of agreement. These results raise questions about claims educators make regarding which errors students are most likely to commit.
Plan validation and mixed-initiative planning in space operations

Bringing artificial intelligence planning and scheduling applications into the real world is a hard task that is receiving more attention every day by researchers and practitioners from many fields. In many cases, it requires the integration of several underlying techniques like planning, scheduling, constraint satisfaction, mixed-initiative planning and scheduling, temporal reasoning, knowledge representation, formal models and languages, and technological issues. Most papers included in this book are clear examples on how to integrate several of these techniques. Furthermore, the book also covers many interesting approaches in application areas ranging from industrial job shop to electronic tourism, environmental problems, virtual teaching or space missions. This book also provides powerful techniques that allow to build fully deployable applications to solve real problems and an updated review of many of the most interesting areas of application of these technologies, showing how powerful these technologies are to overcome the expresiveness and efficiency problems of real world problems.
Scale-Free Neural and Physiological Dynamics in Naturalistic Stimuli Processing

Abstract Neural activity recorded at multiple spatiotemporal scales is dominated by arrhythmic fluctuations without a characteristic temporal periodicity. Such activity often exhibits a 1/f-type power spectrum, in which power falls off with increasing frequency following a power-law function: P(f)∝1/fβ, which is indicative of scale-free dynamics. Two extensively studied forms of scale-free neural dynamics in the human brain are slow cortical potentials (SCPs)—the low-frequency (<5 Hz) component of brain field potentials—and the amplitude fluctuations of α oscillations, both of which have been shown to carry important functional roles. In addition, scale-free dynamics characterize normal human physiology such as heartbeat dynamics. However, the exact relationships among these scale-free neural and physiological dynamics remain unclear. We recorded simultaneous magnetoencephalography and electrocardiography in healthy subjects in the resting state and while performing a discrimination task on scale-free dynamical auditory stimuli that followed different scale-free statistics. We observed that long-range temporal correlation (captured by the power-law exponent β) in SCPs positively correlated with that of heartbeat dynamics across time within an individual and negatively correlated with that of α-amplitude fluctuations across individuals. In addition, across individuals, long-range temporal correlation of both SCP and α-oscillation amplitude predicted subjects’ discrimination performance in the auditory task, albeit through antagonistic relationships. These findings reveal interrelations among different scale-free neural and physiological dynamics and initial evidence for the involvement of scale-free neural dynamics in the processing of natural stimuli, which often exhibit scale-free dynamics.
Analysis and enhancement of software dynamic defect models

Dynamic defect models are used to estimate the number of defects in a software project, predict the release date and required effort of maintenance, and measure the progress and quality of development. The literature suggests that defects projection over time follows a Rayleigh distribution. In this paper, data concerning defects are collected from several software projects and products. Data projection showed that the previous assumption of the Rayleigh distribution is not valid for current projects which are much more complex. Empirical data collected showed that defect distribution in even simpler software projects cannot be represented by the Rayleigh curves due to the adoption of several types of testing on different phases in the project lifecycle. The findings of this paper enhance the well known Puntam's defect model and propose new performance criteria to support the changes occurred during the project. Results of fitting and predicting the collected data show the superiority of the new enhanced defect model over the original defect model.
The Feasibility of Ecological Momentary Assessment of Pain Intensity, Affect and Self-Efficacy Associated with Exercise in Women with Chronic Pain

Objective: The purpose of the following study was to test the feasibility of using an ecological momentary assessment strategy during participation in water exercise. This assessment strategy was used to collect ratings of pain intensity level, affective status and self-efficacy for engaging in regular exercise prior to, during and following participation in water exercise for women with chronic pain. Design: Participants (N=15) completed six measures assessing physical activity level and reactions to physical activity and exercise participation and participated in elicitation interviews focused on their experiences with chronic pain and physical activity and exercise. Participants reported daily pain intensity levels, affect and self-efficacy each morning by phone and used cellular phones to report momentary ratings immediately following participation in water exercise for 6 weeks. Results: Participant profiles were developed to display patterns of pain intensity, affect and self-efficacy over the course of 6 weeks. Profiles indicated a variety of levels of exercise consistency in participants. Pain intensity, affect and self-efficacy varied over the course of an exercise event and revealed varied patterns across participants. Overall, momentary self-efficacy (M1= 7.98, SD=1.65; M2= 8.29, SD=1.62; M3=8.45, SD=1.45) and affect mean ratings (M1= 2.05, SD=1.42; M2= 2.76, SD=1.22; M3=3.02, SD=1.06) increased over the course of the exercise events while pain levels decreased from pre-exercise levels (M1= 2.67, SD=2.30; M2= 1.85, SD=1.86; M3=1.95, SD=2.05). Elicitation interviews indicated themes related to the importance of enjoyment of exercise, social factors, and impact on pain level and overall physical condition. Final interviews provided information about the reactions of participants to the assessment strategy and offered insight into the acceptance of this approach
Diagnostic Accuracy and Yield of Multidetector CT Angiography in the Evaluation of Spontaneous Intraparenchymal Cerebral Hemorrhage

BACKGROUND AND PURPOSE: Multidetector CT angiography (MDCTA) is emerging as the favored initial diagnostic examination in the evaluation of patients presenting with spontaneous intraparenchymal hemorrhage (IPH). This study aims to evaluate the diagnostic accuracy and yield of MDCTA for the detection of vascular etiologies in adult patients presenting to the emergency department with IPH. MATERIALS AND METHODS: We conducted a retrospective study of 623 consecutive adult patients presenting to the emergency department with IPH, who were evaluated with MDCTA during a 9-year period. CT angiograms were reviewed by 2 neuroradiologists to determine the IPH site and the presence of a vascular etiology. Patients with associated subarachnoid hemorrhage in the basal cisterns were excluded from the study. Medical records were reviewed for risk factors and correlation with final diagnosis. The diagnostic accuracy of MDCTA compared with conventional angiography, intraoperative evaluation, and pathologic findings was determined, when available. Multiple-variable logistic regression analysis was performed to determine clinical and radiologic factors that predict a higher yield of MDCTA. RESULTS: MDCTA demonstrated a vascular etiology in 91 patients (14.6%), with a sensitivity of 96%, specificity of 99%, and diagnostic accuracy of 98%. We found independent, statistically significant higher yields of MDCTA in patients with the following characteristics: 1) age younger than 46 years (47%); 2) lobar (20%) or infratentorial (16%) IPH, especially lobar IPH with associated intraventricular hemorrhage (25%); 3) female sex (18%); or 4) neither known hypertension nor impaired coagulation at presentation (33%). CONCLUSIONS: MDCTA is an accurate diagnostic examination in the evaluation of adult patients presenting with spontaneous IPH and should be performed in all patients with the aforementioned clinical and radiologic characteristics.
Removal of Mixed Impulse noise and Gaussian noise using genetic programming

In this paper, we have put forward a nonlinear filtering method for removing mixed Impulse and Gaussian noise, based on the two step switching scheme. The switching scheme uses two cascaded detectors for detecting the noise and two corresponding estimators which effectively and efficiently filters the noise from the image. A supervised learning algorithm, Genetic programming, is employed for building the two detectors with complementary characteristics. Most of the noisy pixels are identified by the first detector. The remaining noises are searched by the second detector, which is usually hidden in image details or with amplitudes close to its local neighborhood. Both the detectors designed are based on the robust estimators of location and scale i.e. Median and Median Absolute Deviation (MAD). Unlike many filters which are specialized only for a particular noise model, the proposed filters in this paper are capable of effectively suppressing all kinds of Impulse and Gaussian noise. The proposed two-step Genetic Programming filters removes impulse and Gaussian noise very efficiently, and also preserves the image details.
Lack of Cortical Correlates of Response Inhibition in 6-Year-Olds Born Extremely Preterm – Evidence from a Go/NoGo Task in Magnetoencephalographic Recordings

Children born extremely preterm (EPT) may have difficulties in response inhibition, but the neural basis of such problems is unknown. We recorded magnetoencephalography (MEG) during a somatosensory Go/NoGo task in 6-year-old children born EPT (n = 22) and in children born full term (FT; n = 21). The children received tactile stimuli randomly to their left little (target) and index (non-target) finger and were instructed to squeeze a soft toy with the opposite hand every time they felt a stimulus on the little finger. Behaviorally, the EPT children performed worse than the FT children, both in responding to the target finger stimulation and in refraining from responding to the non-target finger stimulation. In MEG, after the non-target finger stimulation (i.e., during the response inhibition), the sensorimotor alpha oscillation levels in the contralateral-to-squeeze hemisphere were elevated in the FT children when compared with a condition with corresponding stimulation but no task (instead the children were listening to a story and not attending to the fingers). This NoGo task effect was absent in the EPT children. Further, in the sensorimotor cortex contralateral to the tactile stimulation, the post-stimulus suppression was less pronounced in the EPT than FT children. We suggest that the missing NoGo task effect and lower suppression of sensorimotor oscillations are markers of deficient functioning of the sensorimotor networks in the EPT children.