Miracle-dz/UniFAR-FTUs huggingface dataset:
Model predictive control of remotely operated underwater vehicles

This paper describes the implementation of a model predictive controller novel in an underwater robot vehicle. This work also shows the development of an underwater vehicle model that accounts for physical, hydrodynamic and restorative effects, while the damping coefficients are neglected in the prediction of the vehicle position and orientation. The vehicle kinematic and dynamic models are linearized and arranged into the state space form inside the predictive controller. The model helps to determine the future position and orientation of the vehicle to track a predefined underwater trajectory in an optimal way. The results show that the predictive controller offered significant benefits compared to PID controllers by reducing the MSE and RMS by 40% and 76% respectively.
Effects of vital exhaustion on cardiac autonomic nervous functions assessed by heart rate variability at rest in middle-aged male workers

We investigated the effects of vital exhaustion (VE) on cardiac autonomic functions in relation to working conditions such as overtime and frequent business trips, and to lifestyles such as smoking on 52 healthy middle-aged male workers. VE was evaluated by an abbreviated Maastricht Vital Exhaustion Questionnaire. Cardiac autonomic function atsupine rest was assessed by spectral analysis of heart rate variability inanannual healthcheckup.Themeanamplitudeofthehigh frequency(HF: 0.15-0.4 Hz) component was lowerinthe high-VE group, whereas no significant difference in the ratio ofthe low frequency (LF: 0.04-0.15 Hz) component powerto HF power (the LF/HF ratio) was observed among VE groups. There were significant interactive effects of VE and smoking on HF amplitude, and of VE and frequent business trips on the LF/HF ratio. VE symptoms were related to the suppression of the cardiac parasympathetic nervous functionat rest in middle-aged male workers, but not to the alteration in sympathovagal balance. Smoking and overwork such as frequent business trips may amplify the autonomic dysfunction in relation to VE among workers with a pronounced feeling of VE.
An Introduction to Formal Semantics

When people talk, they generally talk about things, events, and situations in the world. They are able to do this because they represent connections between the expressions of their language and extra-linguistic phenomena in a fully systematic way. The meaning of a sentence in a language is, to a large extent, dependent upon the ways in which the words and phrases from which it is constructed can be related to situations in the world. Speakers of a language are able to communicate effectively with each other because they have internalized the same rules for pairing the lexical items of the language with non-linguistic elements, and they use the same procedures for computing the meaning of a syntactically complex phrase from the meanings of its parts. Therefore, speakers will, in general, converge on the same sets of possible language–world connections which they assign to the sentences in their discourse. Formal semanticists seek to understand this aspect of linguistic meaning by constructing precise mathematical models of the principles that speakers use to define those relations between expressions in a natural language and the world which support meaningful discourse. Consider an example. Assume that two students in a class are discussing the class’s progress on a term paper. One student asserts (1a) and the second responds with (1b).
Sparse Representation of Multivariate Extremes with Applications to Anomaly Ranking

Extremes play a special role in Anomaly Detection. Beyond inference and simulation purposes, probabilistic tools borrowed from Extreme Value Theory (EVT), such as the angular measure, can also be used to design novel statistical learning methods for Anomaly Detection/ranking. This paper proposes a new algorithm based on multivariate EVT to learn how to rank observations in a high dimensional space with respect to their degree of 'abnormality'. The procedure relies on an original dimension-reduction technique in the extreme domain that possibly produces a sparse representation of multivariate extremes and allows to gain insight into the dependence structure thereof, escaping the curse of dimensionality. The representation output by the unsupervised methodology we propose here can be combined with any Anomaly Detection technique tailored to non-extreme data. As it performs linearly with the dimension and almost linearly in the data (in O(dn log n)), it fits to large scale problems. The approach in this paper is novel in that EVT has never been used in its multivariate version in the field of Anomaly Detection. Illustrative experimental results provide strong empirical evidence of the relevance of our approach.
Adaptive Rest Condition Potentials: First and Second Order Edge-Preserving Regularization

A new regularization formulation for inverse problems in computer vision and image processing is introduced, which allows one to reconstruct second order piece-wise smooth images, that is, images consisting of an assembly of regions with almost constant value, almost constant slope, or almost constant curvature. This formulation is based on the idea of using potential functions that correspond to springs or thin plates with an adaptive rest condition. Efficient algorithms for computing the solution and examples illustrating the performance of this scheme, compared with other known regularization schemes, are presented as well.
"Sousveillance": inverse surveillance in multimedia imaging

This is a personal narrative that began 30 years ago as a childhood hobby, of wearing and implanting various sensors, effectors, and multimedia computation in order to re-define personal space and modify sensory perception computationally. This work involved the creation of various computational seeing aids that evolved into a new kind of visual art, using multimedia cyborglogs. Becoming at one with the machine, the author was able to explore a new humanity at the nexus of cyberspace and the real world. The author presents what was discovered accidentally, as a result of facing "cyborg discrimination". In particular, over the past 30 years, peer discrimination has decreased, while institutional and organized discrimination has intensified. Most notably, it was discovered that cyborg discrimination was most intense in establishments having the most surveillance. Rather than avoid such establishments, the author was able to explore and capture unique aspects to understand surveillance in new ways. The word <i>sur-veillance</i> denotes a God's eye view from on high (i.e. French for "to watch from above"). An inverse, called sous-veillance (French for "to watch from below") explores what happens when cameras move from lamp posts and ceilings down to eye level. Finally, it is suggested that new personal multimedia technologies, like mass-produced wearable cameraphones, can be used as tools for artists to explore "equiveillance" by shifting this equilibrium between surveillance and sousveillance with inverse/reverse/accountability/recountability/continuability of continuous sur/sousveillance.
Computational Aspects of Feedback in Neural Circuits

It has previously been shown that generic cortical microcircuit models can perform complex real-time computations on continuous input streams, provided that these computations can be carried out with a rapidly fading memory. We investigate the computational capability of such circuits in the more realistic case where not only readout neurons, but in addition a few neurons within the circuit, have been trained for specific tasks. This is essentially equivalent to the case where the output of trained readout neurons is fed back into the circuit. We show that this new model overcomes the limitation of a rapidly fading memory. In fact, we prove that in the idealized case without noise it can carry out any conceivable digital or analog computation on time-varying inputs. But even with noise, the resulting computational model can perform a large class of biologically relevant real-time computations that require a nonfading memory. We demonstrate these computational implications of feedback both theoretically, and through computer simulations of detailed cortical microcircuit models that are subject to noise and have complex inherent dynamics. We show that the application of simple learning procedures (such as linear regression or perceptron learning) to a few neurons enables such circuits to represent time over behaviorally relevant long time spans, to integrate evidence from incoming spike trains over longer periods of time, and to process new information contained in such spike trains in diverse ways according to the current internal state of the circuit. In particular we show that such generic cortical microcircuits with feedback provide a new model for working memory that is consistent with a large set of biological constraints. Although this article examines primarily the computational role of feedback in circuits of neurons, the mathematical principles on which its analysis is based apply to a variety of dynamical systems. Hence they may also throw new light on the computational role of feedback in other complex biological dynamical systems, such as, for example, genetic regulatory networks.
Pixel-based partial volume correction of small animal PET images using Point Spread Function system characterization: Evaluation of effects on cardiac output, perfusion and metabolic rate using parametric images

Physiologic parameters such as glucose metabolic rate (GMR), perfusion and cardiac output (CO) can be estimated by performing quantitative analysis using PET dynamic images. The measurement of the image derived arterial input function (IF) and the tissue time activity curve (TAC) can be affected by partial volume effect (PVE). Because of partial volume, the estimate of different physiological parameters can be severely biased. The main goal of this work was to evaluate the effects of an image reconstruction based partial volume correction (PVC) method of small animal PET images on metabolic rate and perfusion on a pixel-based analysis and on cardiac output. The proposed PVC method is based on Point Spread Function (PSF) modeling in the reconstruction scheme. Dynamic mouse heart images were created using the Moby phantom. IF and TAC were simulated for one and two compartmental models and different radiotracers in order to take into account the different positron range. Images were simulated using different sets of rate constants and different noise levels. In order to obtain an estimate of the probability distribution of each kinetic parameter from each pixel value, bootstrap resampling with replacement was applied. The coefficient of variation and the bias of the mean of the distribution with respect to the theoretical value were estimated. Pre and post-correction parametric images of GMR and perfusion and the relative errors show that the image reconstruction PVC reduces errors with respect to the theoretical values in each pixel. The percentage error of CO from uncorrected and corrected images with respect to the theoretical value was 13.5% and 2.3%, respectively, for 18F-FDG study. Regarding images acquired using 82Rb, CO estimate was equal to 34.6 % and 23.2 % for non PVC and PVC images, respectively. In conclusion, pixel based PVC allows to obtain a better measure of the radiotracer concentration in the heart and a more accurate estimation of the phisiological parameters.
Directional MAC Approach for Wireless Body Area Networks

Wireless Body Area Networks (WBANs) designed for medical, sports, and entertainment applications, have drawn the attention of academia and industry alike. A WBAN is a special purpose network, designed to operate autonomously to connect various medical sensors and appliances, located inside and/or outside of a human body. This network enables physicians to remotely monitor vital signs of patients and provide real time feedback for medical diagnosis and consultations. The WBAN system can offer two significant advantages: patient mobility due to their use of portable monitoring devices and a location independent monitoring facility. With its appealing dimensions, it brings about a new set of challenges, which we do not normally consider in such small sensor networks. It requires a scalable network in terms of heterogeneous data traffic, low power consumption of sensor nodes, integration in and around the body networking and coexistence. This work presents a medium access control protocol for WBAN which tries to overcome the aforementioned challenges. We consider the use of multiple beam adaptive arrays (MBAA) at BAN Coordinator (BAN_C) node. When used as a BAN_C, an MBAA can successfully receive two or more overlapping packets at the same time. Each beam captures a different packet by automatically pointing its pattern toward one packet while annulling other contending packets. This paper describes how an MBAA can be integrated into a single hope star topology as a BAN_C. Simulation results show the performance of our proposed protocol.
Ant Foraging Revisited

Most previous artificial ant foraging algorithms have to dat e relied to some degree on a priori knowledge of the environment, in the form of explicit gradients generated by the nest , by hard-coding the nest location in an easily-discoverable place, or by imbuing the artificial ants with the knowledge of the nest direction. In contrast, the work presented solve ant foraging problems using two pheromones, one applied when searching for food and the other when returning food items to the nest. This replaces the need to use complicated nest-discovery devices with simpler mechanisms based on pheromone information, which in turn reduces the ant system complexity. The resulting algorithm is orthogonal and simple, yet ants are able to establish increasingly efficien t trails from the nest to the food in the presence of obstacles. The algorithm replaces the blind addition of new amounts of pheromones with an adjustment mechanism that resembles dynamic programming.
A Markov Random Field Model of Context for High-Level Information Fusion

This paper presents a method for inferring threat in a military campaign through matching of battle field entities to a doctrinal template. In this work the set of random variables denoting the possible template matches for the scenario entities is a realization of a Markov random field. This approach does not separate low level fusion from high level fusion but optimizes both simultaneously. The result of the added high level context is a method that is robust to false positive and false negative, or missed, sensor readings. Furthermore, the high level context helps to direct the search for the best template match. Empirical results illustrate the efficacy of the method both at identifying threats in the face of false negatives, and at negating false positives, as well as illustrating the reduced computational effort resulting from the incorporation of additional high-level context
Effect of Mg2+ on the control of Ca2+ release in skeletal muscle fibres of the toad.

1. The effect of myoplasmic Mg2+ on Ca2+ release was examined in mechanically skinned skeletal muscle fibres, in which the normal voltage‐sensor control of Ca2+ release is preserved. The voltage sensors could be activated by depolarizing the transverse tubular (T‐) system by lowering the [K+] in the bathing solution. 2. Fibres spontaneously contracted when the free [Mg2+] was decreased from 1 to 0.05 mM, with no depolarization or change of total ATP, [Ca2+] or pH (pCa 6.7, 50 microM‐EGTA). After such a ‘low‐Mg2+ response’ the sarcoplasmic reticulum (SR) was depleted of Ca2+ and neither depolarization nor caffeine (2 mM) could induce a response, unless the [Mg2+] was raised and the SR reloaded with Ca2+. Exposure to 0.05 mM‐Mg2+ at low [Ca2+] (2 mM‐free EGTA, pCa greater than 8.7) also induced Ca2+ release and depleted the SR. 3. The response to low [Mg2+] was unaffected by inactivation of the voltage sensors, but was completely blocked by 2 microM‐Ruthenium Red indicating that it involved Ca2+ efflux through the normal Ca2+ release channels. 4. In the absence of ATP (and creatine phosphate), complete removal of Mg2+ (i.e. no added Mg2+ with 1 mM‐EDTA) did not induce Ca2+ release. Depolarization in the absence of Mg2+ and ATP also did not induce Ca2+ release. 5. Depolarization in 10 mM‐Mg2+ (pCa 6.7, 50 microM‐EGTA, 8 mM‐total ATP) did not produce any response. In the presence of 1 mM‐EGTA to chelate most of the released Ca2+, depolarizations in 10 mM‐Mg2+ did not noticeably deplete the SR of Ca2+, whereas a single depolarization in 1 mM‐Mg2+ (and 1 mM‐EGTA) resulted in marked depletion. Depolarization in the presence of D600 and 10 mM‐Mg2+ produced use‐dependent ‘paralysis’, indicating that depolarization in 10 mM‐Mg2+ did indeed activate the voltage sensors. 6. Depolarization in the presence of 10 mM‐Mg2+ and 25 microM‐ryanodine neither interfered with the normal voltage control of Ca2+ release nor caused depletion of the Ca2+ in the SR even after returning to 1 mM‐Mg2+ for 1 min, indicating that few if any of the release channels had been opened by the depolarization. (ABSTRACT TRUNCATED AT 400 WORDS)
Expiration Classes for Implicit Memory Management

Expiration classes are a concept to analyze memory management systems. It reveals the costs and the source of costs. Explicit memory management, garbage collection, region-based memory management and cyclic allocation are analyzed in this master thesis. Furthermore, we present AGC, a new memory management system which combines garbage collection with cyclic allocation. Cyclic allocation has very good timing properties and predictable space consumption, but it is hard to use. On the other hand, garbage collection is very convenient, but it has some unwanted side e ects, like long pause times. AGC can use the advantage of both systems, with only small overhead. Benchmarks show that AGC is competitive to other garbage collected systems.
von Willebrand factor cleavage ADAMTS-13 cysteine-rich / spacer domains are functionally essential for

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A Completely Evolvable Genotype-Phenotype Mapping for Evolutionary Robotics

To achieve a desired global behavior for a swarm of robots where each robot has a local view and operating range in the environment is a well-known and challenging problem. Evolutionary Robotics is a self-adaptation approach which has been shown to e effectively find robot controllers for behaviors which are hard to implement by hand. There, evolvability is highly dependent on controller representation during evolution. It is known that using a genotypic controller representation which also encodes parts of the genotype-phenotype mapping (GPM) can lead to a meta-adaptation of the evolutionary operators to the search space structure, thus improving evolvability. We enhance this idea using a fully flexible GPM which is represented in the same way as the behavioral controllers are, and, therefore, can be completely evolved along with the behavior. The approach is based on finite state machines and extends an existing framework for decentralized evolution of robot behavior in swarms of mobile robots. Experiments indicate that the evolvable GPM outperforms both the extensively improved operators of the existing framework and a standard operator for the new real-valued genotypes with fixed GPM.
Allergenicity of Recombinant Troponin C from Tyrophagus putrescentiae

Background: The storage mite, Tyrophagus putrescentiae, produces potent allergens, many of which have not been characterized. This study was undertaken to characterize the allergenicity of troponin C from T. putrescentiae.Methods: A cDNA encoding 17.7 kDa troponin C, with homology to cockroach allergen Bla g 6, was identified from T. putrescentiae-expressed sequence tags. Recombinant troponin C was expressed and IgE responses to the recombinant protein were assessed in the presence and absence of 10 mM CaCl2. Cross-reactivity between T. putrescentiae troponin C and Bla g 6 was tested using an inhibition ELISA. Results: Recombinant T. putrescentiae troponin C shares 62.7–85.5% homology with troponin C from various arthropods. Sera from 5 of 47 subjects in our study group (10.6%) showed IgE binding to the recombinant protein. Interestingly, addition of 10 mM CaCl2 increased the intensity of IgE binding approximately 2-fold. In an immune-inhibition ELISA with these sera, T. putrescetiae troponin C and Bla g 6 did not cross-react significantly. Conclusions: Troponin C is a new mite allergen with calcium-dependent IgE reactivity.
Epigenetic Drugs for Multiple Sclerosis

There is increasing evidence that abnormalities in epigenetic mechanisms of gene expression contribute to the development of multiple sclerosis (MS). Advances in epigenetics have given rise to a new class of drugs, epigenetic drugs. Although many classes of epigenetic drugs are being investigated, at present most attention is being paid to two classes of epigenetic drugs: drugs that inhibit DNA methyltransferase (DNMTi) and drugs that inhibit histone deacetylase (HDACi). This paper discusses the potential use of epigenetic drugs in the treatment of MS, focusing on DNMTi and HDACi. Preclinical drug trials of DNMTi and HDACi for the treatment of MS are showing promising results. Epigenetic drugs could improve the clinical management of patients with MS.
A comparison of gap-filling approaches for Landsat-7 satellite data

ABSTRACT The purpose of this study is to assess the relative performance of four different gap-filling approaches across a range of land-surface conditions, including both homogeneous and heterogeneous areas as well as in scenes with abrupt changes in landscape elements. The techniques considered in this study include: (1) Kriging and co-Kriging; (2) geostatistical neighbourhood similar pixel interpolator (GNSPI); (3) a weighted linear regression (WLR) algorithm; and (4) the direct sampling (DS) method. To examine the impact of image availability and the influence of temporal distance on the selection of input training data (i.e. time separating the training data from the gap-filled target image), input images acquired within the same season (temporally close) as well as in different seasons (temporally far) to the target image were examined, as was the case of using information only within the target image itself. Root mean square error (RMSE), mean spectral angle (MSA), and coefficient of determination (R2) were used as the evaluation metrics to assess the prediction results. In addition, the overall accuracy (OA) and kappa coefficient (kappa) were used to assess a land-cover classification based on the gap-filled images. Results show that all of the gap-filling approaches provide satisfactory results for the homogeneous case, with R2 > 0.93 for bands 1 and 2 in all cases and R2 > 0.80 for bands 3 and 4 in most cases. For the heterogeneous example, GNSPI performs the best, with R2 > 0.85 for all tested cases. WLR and GNSPI exhibit equivalent accuracy when a temporally close input image is used (i.e. WLR and GNSPI both have an R2 equal to 0.89 for band 1). For the case of abrupt changes in scene elements or in the absence of ancillary data, the DS approach outperforms the other tested methods.