Miracle-dz/UniFAR-FTUs huggingface dataset:
Petri Net Modeling of the Reconfigurable Protocol Stack for Cloud Computing Control Systems

The Industrial Ethernet is promising for the implementation of a Cloud Computing based control system. However, numerous standard organizations and vendors have developed various Industrial Ethernets to satisfy the real-time requirements of field devices. This paper presents a real-time reconfigurable protocol stack to cope with this challenge, by introducing the architecture with a core of dynamic routing and autonomic local scheduling. It is based on the deterministic and stochastic Petri-Nets (DSPN) method to illustrate the performance of producer/consumer based application model, CSMA/CD based node accessing activities, and TDMA based resource allocation for real time and non-real time traffic. Furthermore, the predicted time distribution for evaluating the stability of a control system can be obtained from the proposed DSPN model. It is shown that the DSPN modeling yields good verification analysis and performance prediction results through a real experimentation.
Neural Direct Approaches for Geoacoustic Inversion

This paper presents different neural network approaches for geoacoustic inversion. The basic idea of neural inversion is to approximate the inverse function from a set of behaviors, i.e. relations between acoustic fields and geoacoustic parameters. In this work, such methods have been applied in two different forms: a global approach which aims to estimate all parameters from all data, and a hierarchical approach in which the most sensitive parameters are estimated before the least sensitive. The methods are tested using synthetic data. Statistical results, as well as benchmark results show that such approaches are efficient and have similar performances.
GAME THEORY APPLIED TO THE BASIC TRAFFIC CONTROL PROBLEM

Abstract Traffic congestion is an issue in every major city. Many approaches have been tried. However, a noncooperative approach which gives rise to a game, has not been studied yet. In this paper, we propose to model signalized intersections as finite controlled Markov chains. The intersection is seen as noncooperative game where each player try to minimize its queue, so Nash's equilibrium is the best solution. This paper is focused on the traffic light control problem for urban traffic, using Game Theory and Linear Programming. The examples show the effectiveness of the suggested approach.
Perception-inspired tone mapping

The display of high dynamic range images and video requires a tone mapping algorithm to depict their original appearance on existing display dev ices whose capabilities in terms of dynamic range are insufficient. The insightful appl ication of knowledge about human visual system can assure high fidelity of depiction in s uch an algorithm. In this thesis, we design new tone mapping models and improve existing algorithms by an informed use of human perception to provide a high fideli ty depiction of high dynamic range. We develop a real-time tone mapping solution which reproduces the subjective appearance of dynamic HDR contents by accountin g for perceptual effects that significantly contribute to the appearance of natural s cenes. We design a computational model of lightness perception that can be applied to h igh quality tone mapping for static images to reproduce their original HDR appearanc e i terms of lightness. We identify common distortions typical to tone mapping whic h may hinder the comprehension of image contents, we design appropriate metric s to measure the perceived magnitude of these distortions and evaluate existing tone m apping algorithms accordingly. To compensate for observed distortions, we introduc e a method which improves the tone mapping results beyond numerically optimized solu tion by using techniques strongly based on perception of contrasts. Presented solut ions can be efficiently integrated in varied HDR applications including photography , playback of HDR video, image synthesis, light simulations, predictive rendering , and computer games.
THE RATE OF BACTERICIDAL ACTION OF PENICILLIN IN VITRO AS A FUNCTION OF ITS CONCENTRATION, AND ITS PARADOXICALLY REDUCED ACTIVITY AT HIGH CONCENTRATIONS AGAINST CERTAIN ORGANISMS

1. The concentrations of penicillin G which (a) reduced the net rate of multiplication, (b) exerted a net bactericidal effect, and (c) killed the organisms at a maximal rate, have been defined for a total of 41 strains of α- and β-hemolytic streptococci, Staphylococcus aureus and Staphylococcus albus, Diplococcus pneumoniae, and the Reiter treponoma. 2. The concentration which killed the organisms at a maximal rate was 2 to 20 times the minimal effective level ("sensitivity" as ordinarily defined). With some organisms, even a 32,000-fold increase beyond this maximally effective level did not further increase the rate of its bactericidal effect. However, with approximately half the strains here studied (all 4 strains of group B β-hemolytic streptococci, 4 of 5 group C strains, 5 of 7 strains of Streptococcus fecalis, 2 of 4 other α-hemolytic streptococci, and 4 of 9 strains of staphylococci), when the concentration of penicillin was increased beyond that optimal level, the rate at which the organisms died was paradoxically reduced rather than increased, so that the maximal effect was obtained only within a relatively narrow optimal zone. 3. There were marked differences between bacterial species, and occasionally between different strains of the same species, not only with respect to the effective concentrations of penicillin, but also with respect to the maximal rate at which they could be killed by the drug in any concentration. Although there was a rough correlation between these two factors, there were many exceptions; individual strains affected only by high concentrations of penicillin might nevertheless be killed rapidly, while strains sensitive to minute concentrations might be killed only slowly. 4. Within the same bacterial suspension, individual organisms varied only to a minor degree with respect to the effective concentrations of penicillin. They varied strikingly, however, in their resistance to penicillin as measured by the times required to kill varying proportions of the cells.
Modeling latency and shape changes in trial based neuroimaging data

To overcome poor signal-to-noise ratios in neuroimaging, data sets are often acquired over repeated trials that form a three-way array of space×time×trials. As neuroimaging data contain multiple inter-mixed signal components blind signal separation and decomposition methods are frequently invoked for exploratory analysis and as a preprocessing step for signal detection. Most previous component analyses have avoided working directly with the tri-linear structure, but resorted to bi-linear models such as ICA, PCA, and NMF. Multi-linear decomposition can exploit consistency over trials and contrary to bi-linear decomposition render unique representations without additional constraints. However, they can degenerate if data does not comply with the given multi-linear structure, e.g., due to time-delays. Here we extend multi-linear decomposition to account for general temporal modeling within a convolutional representation. We demonstrate how this alleviates degeneracy and helps to extract physiologically plausible components. The resulting convolutive multi-linear decomposition can model realistic trial variability as demonstrated in EEG and fMRI data.
Collapse of a Rotating Supermassive Star to a Supermassive Black Hole: Analytic Determination of the Black Hole Mass and Spin

The collapse of a uniformly rotating, supermassive star (SMS) to a supermassive black hole (SMBH) has been followed recently by means of hydrodynamic simulations in full general relativity. The initial SMS of arbitrary mass M in these simulations rotates uniformly at the mass-shedding limit and is marginally unstable to radial collapse. The final black hole mass and spin have been determined to be Mh/M ≈ 0.9 and Jh/M ≈ 0.75. The remaining mass goes into a disk of mass Mdisk/M ≈ 0.1. Here we show that these black hole and disk parameters can be calculated analytically from the initial stellar density and angular momentum distribution. The analytic calculation thereby corroborates and provides a simple physical explanation for the computational discovery that SMS collapse inevitably terminates in the simultaneous formation of a SMBH and a rather substantial ambient disk. This disk arises even though the total spin of the progenitor star, J/M2 = 0.97, is safely below the Kerr limit. The calculation performed here applies to any marginally unstable n = 3 polytrope uniformly rotating at the breakup speed, independent of stellar mass or the source of internal pressure. It illustrates how the black hole and disk parameters can be determined for the collapse of other types of stars with different initial density and rotation profiles.
Accurate video sequences mutiple moving targets detection via background reconstruction

Firstly this paper discusses drawbacks of traditional adjacent frame difference motion detection method. Then we propose to restore video sequence background using image energy, higher order statistics theory, plus some background constrains criteria. Lastly, instead of using adjacent frame difference method, background based difference is used to detect moving regions. Our tests indicate the proposed method is more precise, and is independent of the speed of the moving target. The method is extremely useful for the scenarios where moving targets are in high mobility.
Synchronization, Coherence-Detection and Three-Dimensional Vision

A new functional role for spiking neurons is proposed, considered necessary to convert noisy sensory data into meaningful and stable perceptions. Percept creation and validation is performed by a dynamical process of coherence detection between neural signals. The crucial operational step of the network is the interaction of neural oscillators in the weak-coupling limit, which realizes coherence detection dynamically by selective synchronization of neural responses. A robust estimate of the incoming signals is transmitted as modulation frequency of the output current of the coherencedetecting layer, and a validation measure is given by the modulation depth of this current. As a real world example of these ideas, a neural network is presented solving the task of stereo vision with real image data. It combines operations of time-averaging, rate-coding neurons with integrate-and-fire-neurons, which calculate a disparity map of a scene by partially synchronizing their spike trains.
Change of Representation for Statistical Relational Learning

Statistical relational learning (SRL) algorithms learn statistical models from relational data, such as that stored in a relational database. We previously introduced view learning for SRL, in which the view of a relational database can be automatically modified, yielding more accurate statistical models. The present paper presents SAYU-VISTA, an algorithm which advances beyond the initial view learning approach in three ways. First, it learns views that introduce new relational tables, rather than merely new fields for an existing table of the database. Second, new tables or new fields are not limited to being approximations to some target concept; instead, the new approach performs a type of predicate invention. The new approach avoids the classical problem with predicate invention, of learning many useless predicates, by keeping only new fields or tables (i.e., new predicates) that immediately improve the performance of the statistical model. Third, retained fields or tables can then be used in the definitions of further new fields or tables. We evaluate the new view learning approach on three relational classification tasks.
Semantic document retrieval system using fuzzy clustering and reformulated query

In this paper, we develop an algorithm for document retrieval system through clustering process and query basis. Initially, the pre-processing is applied on whole documents to remove the unnecessary words and phrases of every document. Then the clustering process in applied to make the partition of the documents through the proposed semantic similarity measure used in the possibilistic fuzzy c means (PFCM) clustering algorithm. For each cluster, the index constructed, which contains common important keywords of the documents of cluster. Once the user enter the keyword as the input to the system, it will process the keywords with the WORDNET ontology to obtain the neighbourhood keywords and related synset keywords. From the set of keywords obtained from the WORDNET is refined and the refined keywords are matched with the index keywords of the clusters to calculate the matching score. Finally, the documents inside the cluster are released at first as the resultant related documents for the query keyword, which clusters have the maximum matching score values. The experimentation process is carried out with the help of different set of documents to achieve the results, the performance analysis of the proposed approach is estimated by precision, and we proved our proposed approach is outperformed in terms of precision.
Discrete Tabu Search for Graph Matching

Graph matching is a fundamental problem in computer vision. In this paper, we propose a novel graph matching algorithm based on tabu search [13]. The proposed method solves graph matching problem by casting it into an equivalent weighted maximum clique problem of the corresponding association graph, which we further penalize through introducing negative weights. Subsequent tabu search optimization allows for overcoming the convention of using positive weights. The method's distinct feature is that it utilizes the history of search to make more strategic decisions while looking for the optimal solution, thus effectively escaping local optima and in practice achieving superior results. The proposed method, unlike the existing algorithms, enables direct optimization in the original discrete space while encouraging rather than artificially enforcing hard one-to-one constraint, thus resulting in better solution. The experiments demonstrate the robustness of the algorithm in a variety of settings, presenting the state-of-the-art results. The code is available at http://cv.snu. ac.kr/research/~DTSGM/.
Reduced baroreflex sensitivity is associated with increased vascular calcification and arterial stiffness.

INTRODUCTION Vascular calcification is a critical determinant of cardiovascular morbidity and mortality in chronic haemodialysis (HD) patients. The pathophysiology underlying this observation remains obscure. Baroreceptor sensitivity (BRS) is important in the maintenance of an appropriate cardiovascular status both at rest and under the physiological stress of HD. BRS is determined by both the mechanical properties of the vascular wall, mediating the transfer of transmural pressure, and afferent and efferent autonomic function. We aimed to study the association between arterial structure, function and BRS in chronic HD patients. METHODS We studied 40 chronic HD patients mean age 62+/-2 (26-86) years who had received HD for a mean 40+/-4 (9-101) months. Spontaneous BRS was assessed using software studying the relationship between inter-beat variability and beat to beat changes in systolic blood pressure. Functional characteristics of conduit arteries (pulse wave analysis) were studied with applanation tonometry at the radial artery. Arterial calcification was assessed in lower limbs using reconstructed multi-slice computed tomography and quantified with volume-corrected calcification scores within the superficial femoral artery. RESULTS Mean BRS was 4.43+/-0.44 ms/mmHg, with a wide range from 1.0 to 11.5 ms/mmHg. This correlated with arterial stiffness as measured by time to shoulder calculated from the central pulse wave analysis (r = 0.4, P = 0.01). BRS was also associated with vascular calcification (P = 0.01) but not by other factors such as dialysis vintage, age or pre-dialysis systolic/diastolic blood pressure. CONCLUSION The reduction in BRS and the resulting aberrant blood pressure response to the physiological stress and volume changes of HD may be important in the further understanding of the pathophysiology of the increased mortality in HD patients with vascular calcification.
Epithelial-Derived Brain-Derived Neurotrophic Factor Is Required for Gustatory Neuron Targeting during a Critical Developmental Period

Brain-derived neurotrophic factor (BDNF) is expressed in epithelial targets of gustatory neurons (i.e., fungiform papillae) before their innervation, and BDNF overexpression in nontaste regions of the tongue misdirects gustatory axons to these sites, suggesting that BDNF is necessary for gustatory axons to locate and innervate their correct targets during development. To test this hypothesis, we examined the targeting of taste neurons in BDNF-null mice (bdnf −/−). Analysis of bdnf −/− mice using a combination of DiI labeling and electron microscopy revealed that taste regions were not innervated by gustatory axons. Instead, branching was increased and many nontaste regions were innervated. The increased branching by gustatory axons in these animals was facilitated by neurotrophin 4 (NT4), because branching was virtually eliminated in bdnf −/−/nt4 −/− mice. No abnormalities in gustatory innervation patterns and targeting were observed in nt4 −/− mice. Conditional removal of BDNF selectively in epithelial cells disrupted targeting at the tongue tip, where gene recombination removed bdnf by embryonic day 13.5 (E13.5). However, innervation patterns were normal in the midregion and caudal portions of the tongue, where gene recombination did not occur until E14.5. These findings demonstrate that BDNF derived from gustatory epithelia is required for gustatory axons to correctly locate and innervate fungiform papillae. In addition, they show that BDNF-mediated targeting is restricted to a critical period of development, on or before E13.5.
Secure Distance Bounding Verification using Physical-Channel Properties

We consider the problem of distance bounding verification (DBV), where a proving party claims a distance and a verifying party ensures that the prover is within the claimed distance. Current approaches to "secure" distance estimation use signal's time of flight, which requires the verifier to have an accurate clock. We study secure DBV using physical channel properties as an alternative to time measurement. We consider a signal propagation environment that attenuates signal as a function of distance, and then corrupts it by an additive noise. We consider three attacking scenarios against DBV, namely distance fraud (DFA), mafia fraud (MFA) and terrorist fraud (TFA) attacks. We show it is possible to construct efficient DBV protocols with DFA and MFA security, even against an unbounded adversary; on the other hand, it is impossible to design TFA-secure protocols without time measurement, even with a computationally-bounded adversary. We however provide a TFA-secure construction under the condition that the adversary's communication capability is limited to the bounded retrieval model (BRM). We use numerical analysis to examine the communication complexity of the introduced DBV protocols. We discuss our results and give directions for future research.
Hidden attractor in smooth Chua systems

The hidden oscillations (a basin of attraction of which does not contain neighborhoods of equilibria) have been obtained first in the 50-60s of 20th century in automatic control systems with scalar piecewise-linear nonlinearity. This brings up the question about the excitation nature of hidden oscillations. In the present paper it is shown that hidden oscillations can exist not only in systems with piecewise-linear nonlinearity but also in smooth systems. Here it is demonstrated the possibility of the existence of hidden chaotic attractor in modified Chua’s system with a smooth characteristic of nonlinear element.
Speech recognition in mobile environments

The growth of cellular telephony combined with recent advances in speech recognition technology results in sizeable potential opportunities for mobile speech recognition applications. Classic robustness techniques that have been previously proposed for speech recognition yield limited improvements of the degradation introduced by idiosyncrasies of the mobile networks. These sources of degradation include distortion introduced by the speech codec as well as artifacts arising from channel errors and discontinuous transmission. In this thesis we focus on characterizing the distortion introduced to the speech signal by the speech codec and we propose methods for reducing the detrimental effect of coding on recognition accuracy. The initial focus of this thesis is on the full rate GSM codec (FR-GSM). We propose a method to generate recognition features directly from codec parameters. It is shown in this work that by selectively constructing a cepstral feature vector from the GSM codec parameters it is possible to reduce the effect of coding on recognition. The later parts of this work are related to weighted acoustic modeling for robust speech recognition. The motivation for this approach is based on the observation that not all phones in a GSM-coded corpus are distorted to the same extent due to coding. We first establish a set of phonetic distortion classes through an analysis of the distribution of the log spectral distortion introduced to each phone by the codec. These classes are then employed to estimate an optimal weighted combination of acoustic models according to the average distortion encountered by the class. A relative reduction of almost 70% of the degradation introduced by the GSM codec was achieved using this method. The technique of weighted acoustic modeling based on instantaneous distortion is introduced as an alternative to the method based on average distortion information. When the extent of cepstral distortion introduced by coding is known, weighted acoustic modeling provides a reduction of about 50% in the word error rate introduced by concurrent GSM and CELP. We propose two methods to estimate the instantaneous distortion information: one based on recoding sensitivity and another based on long-term predictability. Due to the non linear relation between the time and the log-spectral domain, the proposed estimates of the instantaneous distortion do not perform as well as algorithms based on knowledge of cepstral distortion. However, we show that employing the proposed instantaneous distortion information estimates can help obtain the best recognition results established in the baseline conditions employing only 50% of the baseline Gaussian density computations.