Miracle-dz/UniFAR-FTUs huggingface dataset:
A new covariance estimate for Bayesian classifiers in biometric recognition

In many biometric pattern-recognition problems, the number of training examples per class is limited, and consequently the sample group covariance matrices often used in parametric and nonparametric Bayesian classifiers are poorly estimated or singular. Thus, a considerable amount of effort has been devoted to the design of other covariance estimators, for use in limited-sample and high-dimensional classification problems. In this paper, a new covariance estimate, called the maximum entropy covariance selection (MECS) method, is proposed. It is based on combining covariance matrices under the principle of maximum uncertainty. In order to evaluate the MECS effectiveness in biometric problems, experiments on face, facial expression, and fingerprint classification were carried out and compared with popular covariance estimates, including the regularized discriminant analysis and leave-one-out covariance for the parametric classifier, and the Van Ness and Toeplitz covariance estimates for the nonparametric classifier. The results show that, in image recognition applications whenever the sample group covariance matrices are poorly estimated or ill posed, the MECS method is faster and usually more accurate than the aforementioned approaches in both parametric and nonparametric Bayesian classifiers.
A Unified Framework for Location Privacy

We introduce a novel framework that provides a logical structure for classifying and organizing fundamental components and concepts of location privacy. Our framework models mobile networks and applications, threats, location-privacy preserving mechanisms, and metrics. We demonstrate the relevance of our framework by showing how the existing proposals in the field of location privacy are embodied appropriately in the framework. Our framework provides "the big picture" of research on location privacy and hence aims at paving the way for future research. It helps researchers to better understand this field of research, identify open problems, appropriately design new schemes, and position their work with respect to other efforts. The terminology proposed in this framework also facilitates establishing an inter-disciplinary research community on location privacy.
Time-varying delays in Networked Control Systems 1

In high-tech systems design, couplings between the domains of control engineering and real-time software need to be considered. Here, we focus on the coupling between the latency and jitter in the real-time software, i.e. time-del ay in control terms, and the stability of the controlled system . It is well known that time-delays can destabilize the system. In the area of Networked Control Systems (NCSs) [3] time-varying delays occur due to the use of a communication network. Here, we propose a stability test for NCSs with time-varying delays.
A Reselection-based Energy Efficient Routing Algorithm for Wireless Sensor Networks

Clustering is an effective topology control approach, which can prolong the lifetime and increase scalability for wireless sensor networks (WSNs). The popular criteria for clustering technique are to select cluster heads with more residual energy and to rotate them periodically. However, hot spots will emerge in real network environment at locations where they have heavy traffic load to transfer. Sensors at such locations quickly deplete their energy resources and die much faster, lending to energy hole and network partition. In this paper, we proposed our Reselection-based Energy Efficient Routing Algorithm (REERA) for WSNs, which determines proper cluster size based on the distance to the base station. After all clusters are formed, cluster heads can be reselected according to the ratio of residual energy of each node to their distance from base station. Simulation results show that our proposed algorithm can be more efficient in saving energy consumption and prolonging lifetime of WSNs than traditional LEACH algorithm.
Noise Robust Speech Parameterization using Relative Spectra and Auditory Filterbank

In the present study, a new feature extraction method based on relative spectra and gammachirp auditory filterbank is proposed for robust noisy speech recognition. The relative spectra filtering are applied to the log of the output of the gammachirp filterbank which incorporates the properties of the cochlear filter in order to remove uncorrelated additive noise components. The performances of this method have been evaluated on the isolated speech word corrupted by real-world noisy environments using the continuous Gausian-Mixture density Hidden Markov Model. The evaluation of the experimental results shows that the proposed method achieves best recognition rates compared to the conventional techniques like Perceptual Linear Prediction (PLP), Linear Predictive Cepstral Coefficients (LPCC) and Mel-Frequency Cepstral Coefficients (MFCC).
Monte Carlo Modeling of Photon Propagation Reveals Highly Scattering Coral Tissue

Corals are very efficient at using solar radiation, with photosynthetic quantum efficiencies approaching theoretical limits. Here, we investigated potential mechanisms underlying such outstanding photosynthetic performance through extracting inherent optical properties of the living coral tissue and skeleton in a massive faviid coral. Using Monte Carlo simulations developed for medical tissue optics it is shown that for the investigated faviid coral, the coral tissue was a strongly light scattering matrix with a reduced scattering coefficient of μs’ = 10 cm-1 (at 636 nm). In contrast, the scattering coefficient of the coral skeleton was μs’ = 3.4 cm-1, which facilitated the efficient propagation of light to otherwise shaded coral tissue layers, thus supporting photosynthesis in lower tissues. Our study provides a quantification of coral tissue optical properties in a massive faviid coral and suggests a novel light harvesting strategy, where tissue and skeletal optics act in concert to optimize the illumination of the photosynthesizing algal symbionts embedded within the living coral tissue.
Anaesthetic management of obese parturient

Obesity has become a ticking time bomb. The population of obese people and so also obese pregnant patients is increasing worldwide and it won’t be long before when anaesthetists will be more commonly faced with managing obese parturients with a large spectrum of comorbidities. The last confidential enquiry into maternal and child health (CEMACH) 2002 2005 report stressed obesity as a major risk factor associated with maternal mortality and following suit of its recommendation, we write this review article on management of obese parturients, highlighting the problems in obese parturients and recommending guidelines for management of such patients. As the use of regional anaesthesia in obstetrics anaesthesia has increased, the trainee anaesthetists are relatively less skilled to provide general anaesthesia. General anaesthesia with all the airway management problems has been the major reason of maternal mortality in the previous CEMACH reports. An epidural block though technically difficult, provides optimal analgesia and can be extended for caesarean section if required. Hence obese parturient should be assessed and consulted by a senior anaesthetist as early as 28 weeks of gestation in the pregnancy for formulating a plan for labour analgesia and anaesthesia for caesarean section if required. Epidural analgesia should be provided in early labour prophylactically to avoid general anaesthesia. Early anaesthetic assessment, prophylactic epidural block, ensuring its effectiveness, alternative plan for failed regional block along with preparation for general anaesthetic and difficult intubation, involving senior help in the management and multidisciplinary approach are advocated to mitigate potential anaesthetic risks.
Direct dark matter detection by observing electrons produced in neutralino-nucleus collisions

Exotic dark matter and dark energy together seem to dominate in the Universe. Supersymmetry naturally provides a candidate for the dark matter constituents via the lightest supersymmetric particle (LSP). The most important process for directly detecting dark matter is the LSP-nucleus elastic scattering by measuring the energy of the recoiling nucleus. In the present work we explore a novel process, which has definite experimental advantages, that is the detection of the dark matter constituents by observing the low energy ionization electrons. These electrons, which are produced during the LSP-nucleus collision, may be observed separately or in coincidence with the recoiling nuclei. We develop the formalism and apply it in calculating the ratio of the ionization rate to the nuclear recoil rate in a variety of atoms including 20Ne, 40Ar, 76Ge, 78Kr and 132Xe employing realistic Hartree-Fock e-mail: moustaki@ auth.gr e-mail: Vergados@ cc.uoi. gr e-mail: ejiri@ rcnp.osaka-u.ac. jp
Decision-making models for resource allocation in multi-population co- evolutionary genetic algorithms implemented on networks

Summary It is quite necessary that optimizing complicated systems using multi-population coevolutionary genetic algorithms implemented on networks. But there is no effective way to allocate computational resources to improve the performance of algorithms. A decision-making model for resource allocation in multipopulation coevolutionary genetic algorithms implemented on networks is established, considering the case that all the populations coevolve synchronously. In this model, the optimized objective is to minimize the maximal computational time for average one-step iteration of a computational node, and the constraint is that each evolutionary population is allocated to one computational node at most. A method for solving the presented model based on genetic algorithms is also given. The validity of the model is shown through comparative examples. The results obtained in this paper provide a feasible mode for resource allocation in multi-population coevolutionary genetic algorithms implemented on networks.
A Study on self-timed asynchronous subthreshold logic

This paper investigates self-timed asynchronous design techniques for subthreshold digital circuits. In this voltage range extremely high voltage-dependent delay uncertainties arise which make the use of synchronous circuits rather inefficient or their reliability doubtful. Delay-line controlled circuits face these difficulties with self-timed operation with the disadvantage of necessary timing margins for proper operation. In this paper we discuss these necessary timing overheads and present our approach to their analysis and reduction to a minimum value by the use of circuit techniques allowing completion detection. Transistor-level simulation results for an entirely delay-adaptable counter under variable supply down to 200 mV are presented. Additionally an analytical comparison and simulation of timing and energy consumption of more complex subthreshold asynchronous circuits is shown. The outcome is that a combination of delay-line based circuits with circuits using completion detection is promising for applications where the supply voltages are at extremely low levels.
Characterization of bacterial strains and their impact on plant growth promotion and yield of wheat and microbial populations of soil

The aim of this research work is to evaluate the potentiality of bacterial strains isolated from rhizosphere of various cropping systems on growth, yield and soil microbial populations at harvest of wheat under pot and field experiments. Bacterial isolates, isolated from rhizosphere of various cropping systems. Morphological, biochemical and molecular characterization of bacterial strains were done. All the bacterial strains were rod shaped and ammonia producers. Most of the strains were Gram’s +ve and showed positive to catalase, VP, MR tests and HCN production. Pseudomonas aeuroginosa showed significantly lowest pH 4.42 of broth and solubilized 160.34 µgml-1 tri-calcium phosphates. Enterobacter sp. produced maximum 27.06 µgml-1 IAA at 100 µgml-1 tryptophan. All the bacterial strains showed >97% similarity with strains already submitted to NCBI Gene bank. Arthrobacter chlorophenolicus and Enterobacter sp. showed maximum and significantly 23.60 and 28.97% plant height in pot and field experiments at 90 days after sowing. Bacillus megaterium caused maximum and significantly 51.45 and 40.47% grain and straw yield, respectively under pot while Serratia marcescens showed 14.78 and 17.04% grain and straw yield in field as compared to control. Most of the strains showed significant effect on microbial populations of post harvest soil under pot and field. It was concluded that A. chlorophenolicus, Enterobacter sp. , B. megaterium and S. marcescens are effective strains of plant growth promoting bacterial strains for wheat crop production under Indo- Gangetic plains of India. Key words: Bacterial strains, biochemical, growth, microbial population, wheat, yield.
Children's interpretations of general quantifiers, specific quantifiers and generics

Recently, several scholars have hypothesised that generics are a default mode of generalisation, and thus that young children may at first treat quantifiers as if they were generic in meaning. To address this issue, the present experiment provides the first in-depth, controlled examination of the interpretation of generics compared to both general quantifiers (‘all Xs’, ‘some Xs’) and specific quantifiers (‘all of these Xs’, ‘some of these Xs’). We provided children (3 and 5 years) and adults with explicit frequency information regarding properties of novel categories, to chart when ‘some’, ‘all’ and generics are deemed appropriate. The data reveal three main findings. First, even 3-year-olds distinguish generics from quantifiers. Second, when children make errors, they tend to be in the direction of treating quantifiers like generics. Third, children were more accurate when interpreting specific versus general quantifiers. We interpret these data as providing evidence for the position that generics are a default mode of generalisation, especially when reasoning about kinds.
Toward accurate polynomial evaluation in rounded arithmetic

Given a multivariate real (or complex) polynomial p and a domain D, we would like to decide whether an algorithm exists to evaluate p(x) accurately for all x ∈ D using rounded real (or complex) arithmetic. Here "accurately" means with relative error less than 1, i.e., with some correct leading digits. The answer depends on the model of rounded arithmetic: We assume that for any arithmetic operator op(a, b), for example a+b or ab, its computed value is op(a, b)� (1+δ), where |δ| is bounded by some constant ǫ where 0 < ǫ ≪ 1, but δ is otherwise arbitrary. This model is the traditional one used to analyze the accuracy of floating point algorithms. Our ultimate goal is to establish a decision procedure that, for any p and D, either exhibits an accurate algorithm or proves that none exists. In contrast to the case where numbers are stored and manipulated as finite bit strings (e.g., as floating point numbers or rational numbers) we show that some polynomials p are impossible to evaluate accurately. The existence of an accurate algorithm will depend not just on p and D, but on which arithmetic operators are available (perhaps beyond +, −, and � ), which constants are available to the algorithm (integers, algebraic numbers, .), and whether branching is permitted in the algorithm. For floating point computation, our model can be used to identify which accurate operators beyond +, − and � (e.g., dot products, 3x3 determinants, .) are necessary to evaluate a particular p(x). Toward this goal, we present necessary conditions on p for it to be accurately evaluable on open real or complex domains D. We also give sufficient conditions, and describe progress toward a complete decision procedure. We do present a complete decision procedure for homogeneous polynomials p with integer coefficients,D = C n , and using only the arithmetic operations +, − and � .
FUZZY VECTOR MEDIAN DEFINITION BASED ON A FUZZY VECTOR DISTANCE

In this paper, the Fuzzy Vector Median is proposed, defined as an extension of Vector Median. It is based on a novel distance definition of multidimensional fuzzy numbers (fuzzy vectors), which satisfy the property of angle decomposition. The proposed distance of two fuzzy vectors depends on the classical distance of the fuzzy set centers and on the fuzziness that every fuzzy set holds. As a result the Fuzzy Vector Median of a set of fuzzy vectors is affected by the presence of fuzziness.
Viewpoint Paper: Informatics Challenges for the Impending Patient Information Explosion

As we move toward an era when health information is more readily accessible and transferable, there are several issues that will arise. This article addresses the challenges of information filtering, context-sensitive decision support, legal and ethical guidelines regarding obligations to obtain and use the information, aligning patient and health professionals' expectations in regard to the use and usefulness of the information, and enhancing data reliability. The authors discuss the issues and offer suggestions for addressing them.
Memory in processor-supercomputer on a chip: processor design and execution semantics for massive single-chip performance

The MIP S.C. O.C was designed to overcome the Von-Neumann bottleneck and develop massive on-chip parallelism to achieve Teraflop scale single chip performance. We case study here a specific 2 MB MIP node that has a 128 bit datapath. This paper also specifies the technological reqirements and discusses the implementation strategy to support the feasibility of the project. We develop the ISA format and preview the hardware compiler COS, that will map applications and schedule the instructions on the MIP S. C. O. C. We then discuss the library specifications and COS-HLF unit interface. We develop the scalable pyramid cluster to accomodate massive node performance. We discuss briefly the programming model and an application execution to demonstrate the scalability.
A vector space analysis of swedish patent claims with different linguistic indices

The purpose of this study was twofold, first to examine if it is possible to use a general automatic retrieval model, the Vector Space Model (VSM), in order to discover similarities between Swedish patent claims; and second to examine whether an addition morphological decompounding module at the pre-processing level improves the result. In the present study, a comparison between three different topic sets consisting of patent claims was compared against an entire collection of 30,117 claims. The VSM was evaluated with and without additional morphological decompounding modules. The results indicate that decompounding will influence the performance of the retrieval model in a positive way. However, the sublanguage of patent claims and the errors made during the Optical Character Recognition (OCR) process were harmful towards the overall performance of the Natural Language Processing (NLP) applications as well as for the retrieval model.
Beyond Consciousness of External Reality: A “Who” System for Consciousness of Action and Self-Consciousness

This paper offers a framework for consciousness of internal reality. Recent PET experiments are reviewed, showing partial overlap of cortical activation during self-produced actions and actions observed from other people. This overlap suggests that representations for actions may be shared by several individuals, a situation which creates a potential problem for correctly attributing an action to its agent. The neural conditions for correct agency judgments are thus assigned a key role in self/other distinction and self-consciousness. A series of behavioral experiments that demonstrate, in normal subjects, the poor monitoring of action-related signals and the difficulty in recognizing self-produced actions are described. In patients presenting delusions, this difficulty dramatically increases and actions become systematically misattributed. These results point to schizophrenia and related disorders as a paradigmatic alteration of a "Who? " system for self-consciousness.
Responses of Amygdala Neurons to Positive Reward-predicting Stimuli Depend on Background Reward (contingency) Rather than Stimulus-reward Pairing (contiguity) Behavioral Procedures

Responses of amygdala neurons to positive reward-predicting stimuli depend on background reward (contingency) rather than stimulus-reward pairing (contiguity). Prediction about outcomes constitutes a basic mechanism underlying informed economic decision making. A stimulus constitutes a reward predictor when it provides more information about the reward than the environmental background. Reward prediction can be manipulated in two ways, by varying the reward paired with the stimulus, as done traditionally in neurophysiological studies, and by varying the background reward while holding stimulus reward pairing constant. Neuronal mechanisms involved in reward prediction should also be sensitive to changes in background reward independently of stimulus-reward pairing. We tested this assumption on a major brain structure involved in reward processing, the central and basolateral amygdala. In a 2 ϫ 2 design, we examined the influence of rewarded and unrewarded backgrounds on neuronal responses to rewarded and unrewarded visual stimuli. Indeed, responses to the unchanged rewarded stimulus depended crucially on background reward in a population of amygdala neurons. Elevating background reward to the level of the rewarded stimulus extinguished these responses, and lowering background reward again reinstated the responses without changes in stimulus-reward pairing. None of these neurons responded specifically to an inhibitory stimulus predicting less reward compared with background (negative contingency). A smaller group of amygdala neurons maintained stimulus responses irrespective of background reward, possibly reflecting stimulus-reward pairing or visual sensory processes without reward prediction. Thus in being sensitive to background reward, the responses of a population of amygdala neurons to phasic stimuli appeared to follow the full criteria for excitatory reward prediction (positive contingency) rather than reflecting simple stimulus-reward pairing (contiguity). A stimulus that conveys specific information about a reward is conventionally called a reward predictor. When there is more reward with the stimulus compared with the reward occurring irrespective of the stimulus (background), the stimulus elicits behavioral reactions and constitutes an excitatory predictor. When there is less reward with the stimulus than the background, the stimulus reduces approach behavior and constitutes an inhibitory predictor. Thus the notion of reward prediction concerns the amount of information conveyed by the stimulus that is not available without the stimulus. This information reflects the dependency , or contingency, of the reward on the stimulus (Egger and Miller 1962; Gallistel 2003). Thus it is not just the stimulus reward but the relationship between stimulus and background reward that determines the prediction. Given that reward prediction is based on the …