Miracle-dz/UniFAR-FTUs huggingface dataset:
A Hybrid Approach to Face Recognition under Varying Illumination

Face recognition algorithms are mainly divided into Holistic and Feature based approaches. Principal Component Analysis (PCA) and Fisher Discriminant Analysis (FDA) are the main holistic based approaches that overcome Feature based approach. The challenging factors in face recognition are facial expressions, illumination variations and face orientations. These existing face recognition algorithms work only to a level, that too only under well controlled laboratory setups and fail under varying illuminations. Face recognition algorithms are commonly used for authentication and authorization purposes. Since they are used in computer applications for various security purposes, the importance of the whole system is necessary. To avoid the breaches in security of the existing algorithms and to outperform those algorithms computationally a need of a better algorithm exists. Hence, a hybrid approach to human face recognition is worked out to overcome these limitations. Face recognition consists of Training and Testing phases. In the Training phase the most eminent and effective features are extracted from the database images and in the Testing phase the probe test image is selected as the input and its prominent features are extracted to be compared with the database images for any matching features. The security of the algorithm purely relies on the method of extracting the feature vectors from the digital image. The proposed method of the Hybrid algorithm classifies images of individual persons into separate classes. This technique is implemented with the YALE & ORL face databases. The database images were taken at different postures, by varying the lighting and facial expressions. The performance of the Hybrid algorithm attains 96% recognition rate under varying postures and illumination conditions and it naturally outperforms the standard Holistic algorithms. Keywords— PCA (Principal Component Analysis), FDA (Fisher Discriminant Analysis), L2 norm, Hybrid approach.
Identification of the Transmitter and Receptor Mechanisms Responsible for REM Sleep Paralysis

During REM sleep the CNS is intensely active, but the skeletal motor system is paradoxically forced into a state of muscle paralysis. The mechanisms that trigger REM sleep paralysis are a matter of intense debate. Two competing theories argue that it is caused by either active inhibition or reduced excitation of somatic motoneuron activity. Here, we identify the transmitter and receptor mechanisms that function to silence skeletal muscles during REM sleep. We used behavioral, electrophysiological, receptor pharmacology and neuroanatomical approaches to determine how trigeminal motoneurons and masseter muscles are switched off during REM sleep in rats. We show that a powerful GABA and glycine drive triggers REM paralysis by switching off motoneuron activity. This drive inhibits motoneurons by targeting both metabotropic GABAB and ionotropic GABAA/glycine receptors. REM paralysis is only reversed when motoneurons are cut off from GABAB, GABAA and glycine receptor-mediated inhibition. Neither metabotropic nor ionotropic receptor mechanisms alone are sufficient for generating REM paralysis. These results demonstrate that multiple receptor mechanisms trigger REM sleep paralysis. Breakdown in normal REM inhibition may underlie common sleep motor pathologies such as REM sleep behavior disorder.
Predicting changes in the output of OECD countries: An international network perspective

We use a simple linear regression framework to present evidence, that complex relationships between stock markets and economies may be used to predict changes in the output of 27 OECD countries. We construct new unidirectional return co-exceedance networks to account for complex relationships between stock market returns, and between real economic growths. Although there is heterogeneity between individual country level results, overall our data and analysis provides evidence that topological properties of our networks are useful for in-sample prediction of next quarter changes in the output.
A P2P streaming model based on IPv4/IPv6 coexisting network environment

Peer-to-peer (P2P) technology has become as a promising scalable solution to offer high-quality streaming multimedia to a large number of consumers at reasonable cost. Many existing systems have been deployed based on IPv4 network environment 1] ^ [2]. IPv6 protocol has been seemed as a core component in Next Generation Internet(NGI) and the P2P streaming scheme which can be implemented in a IPv4 and IPv6 coexisting network environment is rarely concerned. This paper addresses the architectural and fundamental design issues for supporting P2P streaming applications based on IPv4 and IPv6 coexist network environment. Our motivation is P2P streaming systems can be designed and implemented through a layered model and different layers in this novel model can implement different kinds of important functions. Our main focuses include the future P2P streaming applications design and their essential demands. Simulation results show that our model hold the higher efficiency than other protocol oftern in use, and can guarantee the data exchanging in a security way.
Robust Multiuser Detection for Impulse Radio In Non-Gaussian UWB Channels

The frequency-selective ultra-wideband (UWB) channels are subject to various types of electromagnetic noise, which is non-Gaussian (impulsive). The conventional linear receivers based on the additive white Gaussian noise assumption exhibit performance degradation. In this paper, a robust receiver using the M-estimation technique is introduced for multiple access UWB channels with impulsive noise, eliminating multiuser interference and impulsive noise at the same time, provided that accurate channel information about the users is available. Both the robust receiver and its simplified version are shown to outperform the linear receiver based on the Gaussian noise assumption.
A Model for the Formation of High-Density Clumps in Proto-Planetary Nebulae

The detection of NaCl at large radii in the Egg Nebula, CRL 2688, requires densities of 107-108 cm-3 in a thick shell of r ~ a few × 1017 cm. To explain these results, a mechanism is needed for producing high densities at a considerable distance from the central star. In two-dimensional simulations of the interaction of the fast wind with an ambient medium, the material becomes thermally unstable. The resulting clumps can achieve the requisite conditions for NaCl excitation. We present two-dimensional models with simple physics as proof-of-principle calculations to show that the clumping behavior is robust. Clumping is a natural outcome of cooling in the colliding wind model and comparable to that inferred from observations.
Globalizing Resistance: The Battle of Seattle and the Future of Social Movements

The massive protests at the Third Ministerial Meeting of the World Trade Organization in November 1999 resulted from broad and accelerating changes in global social and political relations. Many protesting groups had been involved in previous struggles for global economic justice that shaped their identities and strategies in Seattle. This study examines the participants, activities, and political context of the "Battle of Seattle. " It explores the transnational activist linkages and suggests that a division of labor was present whereby groups with local and national ties took on mobilization roles while groups with routinized transnational ties provided information and frames for the struggle. An examination of the tactics used in Seattle suggests that national protest "repertoires" have been adapted for use in global political arenas. There is also some evidence of protest innovation in response to global political integration and technology. While this study encompasses only a single protest episode, i.
The impact of choice on public services

It is well observed that individual behaviour can have an effect on the efficiency of queueing systems. The impact of this behaviour on the economic efficiency of public services is considered in this paper where we present results concerning the congestion related implications of decisions made by individuals when choosing between facilities. The work presented has important managerial implications at a public policy level when considering the effect of allowing individuals to choose between providers. We show that in general the introduction of choice in an already inefficient system will not have a negative effect. Introducing choice in a system that copes with demand will have a negative effect.
Understanding route directions in human-robot dialogue

This paper discusses some of the challenges in building a robot that is supposed to autonomously navigate in an urban environment by asking pedestrians for route directions. We present a novel Wizard-of-Oz setup for collecting route direction dialogues, and a novel approach for datadriven semantic interpretation of route descriptions into route graphs. The results indicate that it is indeed possible to get people to freely describe routes which can be automatically interpreted into a route graph that may be useful for robot navigation.
Video segmentation and feature co-occurrences for activity classification

Bag-of-Word scheme has almost become de rigueur for event recognition tasks due to its robustness and simplicity. Despite its effectiveness, this technique discards spatial and temporal relationships between codewords. This paper tackles the problem of building a video codeword representation that captures such relationships. We developed a new method that harnesses spatio-temporal boundaries and discriminative codeword co-occurrences. Given a set of videos and their corresponding quantized features, the video is first decomposed in spatio-temporal volumes according to a multi-scale video segmentation algorithm. Meaningful codeword co-occurrences are then extracted within each volume and videos are then represented with histograms of co-occurring features. The set of histograms is finally fed to an SVM for classification. Evaluation under the realistic TRECVID MED11 challenge database validates the approach.
Limb lengthening and peripheral nerve function—factors associated with deterioration of conduction

Background and purpose Limb lengthening is performed for a diverse range of orthopedic problems. A high rate of complications has been reported in these patients, which include motor and sensory loss as a result of nerve damage. We investigated the effect of limb lengthening on peripheral nerve function. Patients and methods 36 patients underwent electrophysiological testing at 3 points: (1) preoperatively, (2) after application of external fixator/corticotomy but before lengthening, and (3) after lengthening. The limb-length discrepancy was due to a congenital etiology (n = 19), a growth disturbance (n = 9), or a traumatic etiology (n = 8). Results 2 of the traumatic etiology patients had significant changes evident on electrophysiological testing preoperatively. They both deteriorated further with lengthening. 7 of the 21 patients studied showed deterioration in nerve function after lengthening, but not postoperatively, indicating that this was due to the lengthening process and not to the surgical procedure. All of these patients had a congenital etiology for their leg-length discrepancy. Interpretation As detailed electrophysiological tests were carried out before surgery, after surgery but before lengthening, and finally after completion of lengthening, it was possible to distinguish between the effects of the operation and the effects of lengthening on nerve function. The results indicate that the etiology, site (femur or tibia), and nerve (common peroneal or tibial) had a bearing on the risk of nerve injury and that these factors had a far greater effect than the total amount of lengthening.
A laboratory testbed for embedded computer control

There has been a tremendous growth in the use of modern embedded computers in control and other applications in the past few years. While courses offered in the electrical and computer engineering disciplines cover such topics as microprocessors, digital and analog hardware, control theory, and programming languages, there exist few courses that focus on integrating these subjects for designing embedded systems. On the other hand, there is a growing need in industry for engineers who can perform software design and system integration for various applications in embedded control. Toward filling this gap, this paper describes a laboratory testbed developed for a new course on Embedded Computer Control offered at the University of Western Ontario, London, ON, Canada. An outline of the course structure, laboratory setup, and the design aspects for implementing a modern embedded control application are presented. The embedded controller performs command, control, and user interface tasks required to operate a low-cost prototype of a thermal system. Furthermore, its network connectivity allows users to tune system parameters, start and stop running the system, and observe the status of the plant via the Internet.
The Generalized Group Lasso

In this paper the Generalized Lasso model of R. Tibshirani is extended to consider multidimensional features (or groups of features) à la Group Lasso, by substituting the ℓ1 norm of the regularizer by the ℓ2,1 norm. The resultant model is called Generalized Group Lasso (GenGL), and it contains as particular cases the already known Group Lasso and Group Fused Lasso (GFL), but also new models as the Graph-Guided Group Fused Lasso, or the trend filtering for multidimensional features. We show how to solve them efficiently combining FISTA iterations with the Proximal Operator of the corresponding regularizer, which we compute using a dual formulation. Moreover, GenGL makes possible to introduce a new approach to Group Total Variation, the regularizer of GFL, that results in a training much faster than that of previous methods.
Microbial Diversity of Acidic Hot Spring (Kawah Hujan B) in Geothermal Field of Kamojang Area, West Java-Indonesia

Microbial communities in an acidic hot spring, namely Kawah Hujan B, at Kamojang geothermal field, West Java-Indonesia was examined using culture dependent and culture independent strategies. Chemical analysis of the hot spring water showed a characteristic of acidic-sulfate geothermal activity that contained high sulfate concentrations and low pH values (pH 1.8 to 1.9). Microbial community present in the spring was characterized by 16S rRNA gene combined with denaturing gradient gel electrophoresis (DGGE) analysis. The majority of the sequences recovered from culture-independent method were closely related to Crenarchaeota and Proteobacteria phyla. However, detail comparison among the member of Crenarchaeota showing some sequences variation compared to that the published data especially on the hypervariable and variable regions. In addition, the sequences did not belong to certain genus. Meanwhile, the 16S Rdna sequences from culture-dependent samples revealed mostly close to Firmicute and gamma Proteobacteria.
Routing architectures for hierarchical field programmable gate arrays

This paper evaluates an architecture that implements a hierarchical routing structure for FPGAs, called a hierarchical FPGA (HFPGA). A set of new tools has been used to place and route several circuits on this architecture, with the goal of comparing the cost of HFPGAs to conventional symmetrical FPGAs. The results show that HFPGAs can implement circuits with fewer routing switches, and fewer switches in total, compared to symmetrical FGPAs, although they have the potential disadvantage that they may require more logic blocks due to coarser granularity. <<ETX>>
Topomers: A Validated Protocol for Their Self-Consistent Generation

The hypothesis underlying topomer development is that describing molecular structures consistently may be at least as productive as describing them more realistically but incompletely. A general protocol is detailed for deterministically generating self-consistent shapes of molecular fragments from their topologies. These and other extensions to the topomer methodology are validated by repetition of earlier benchmark studies.
Generalizing diversity detection in blog feed retrieval

The goal of a blog retrieval system is to retrieve and rank blogs, as collections of documents, in response to a given query. Previous studies have shown that diversity among the top retrieved posts from a blog is a positive feature for indicating relevance of the blog to the query. However, existing methods capture the diversity of a blog using post-level properties that limits their application to a specific category of retrieval methods. In this paper, we propose a blog-level diversity measure where there is no assumption made about the underlying blog-ranking technique. The proposed measure enables us to integrate diversity in any existing blog retrieval method. Our experimental results show that the proposed method, while being more general, produces comparable results to the post-level diversity detection methods.
Multi agent negotiation model for distributed task allocation

In the multi agent system, negotiation between different agents becomes more and more complex when the distributed tasks need to be allocated among them. Usually, this process has been done in the centralized way where agents have all the information of both the tasks and the environment. However, in the real application such as satellite scheduling problem or production scheduling problem, not all the information can be acquired, some of them even cannot be known. Therefore, in this paper, we propose a multi agent negotiation model for distributed task allocation in a priority based environment. This process is modeled as a finite Markov Decision Process (F-MDP), and a coordinated negotiation protocol is also suggested to support the model. The experiments show that tasks are allocated in an efficient way, and it can also reduce the communication cost.
Productivity and technical change: the case of Taiwan

This paper analyses productivity growth in 16 of Taiwan's manufacturing industries during the period 1978–1992. The non-parametric Data Envelopment Analysis approach is used to compute Malmquist productivity indexes. These are decomposed into efficiency change and technical change. The latter is further decomposed into an output bias, an input bias and a magnitude component. In addition, the direction of input bias is identified. Empirical results indicate that the sector's TFP increased at a rate of 2.89% per annum, which could be ascribed to a technical progress (2.56%) and an efficiency improvement (0.33%).
Finite rank commutators and semicommutators of Toeplitz operators with harmonic symbols

In this paper we completely characterize finite rank semicommutator or commutator of two Toeplitz operators with bounded harmonic symbols on the Bergman space. We show that if the product of two Toeplitz operators with bounded harmonic symbols has finite rank, then one of the Toeplitz operators must be zero.
Deep Learning with Nonparametric Clustering

Clustering is an essential problem in machine learning and data mining. One vital factor that impacts clustering performance is how to learn or design the data representation (or features). Fortunately, recent advances in deep learning can learn unsupervised features effectively, and have yielded state of the art performance in many classification problems, such as character recognition, object recognition and document categorization. However, little attention has been paid to the potential of deep learning for unsupervised clustering problems. In this paper, we propose a deep belief network with nonparametric clustering. As an unsupervised method, our model first leverages the advantages of deep learning for feature representation and dimension reduction. Then, it performs nonparametric clustering under a maximum margin framework -- a discriminative clustering model and can be trained online efficiently in the code space. Lastly model parameters are refined in the deep belief network. Thus, this model can learn features for clustering and infer model complexity in an unified framework. The experimental results show the advantage of our approach over competitive baselines.
M ay 1 99 8 MS-TPI-98-09 On the Stability of the O ( N )-Invariant and the Cubic-Invariant 3-Dimensional N-Component Renormalization Group Fixed Points in the Hierarchical Approximation

We compute renormalization group fixed points and their spectrum in an ultralocal approximation. We study a case of two competing non-trivial fixed points for a three-dimensional real N -component field: the O(N)-invariant fixed point vs. the cubic-invariant fixed point. We compute the critical value Nc of the cubic φ -perturbation at the O(N)-fixed point. The O(N) fixed point is stable under a cubic φ-perturbation belowNc, above Nc it is unstable. The critical value comes out as 2.219435 < Nc < 2.219436 in the ultralocal approximation. We also compute the critical value of N at the cubic invariant fixed point. Within the accuracy of our computations, the two values coincide.
Spatial sound rendering for dynamic virtual environments

We present the details of a virtual sound rendering engine (VSRE) that is being developed for virtual environments and serious games. The VSRE incorporates innovative graphics processing unit-based methods to allow for the approximation of acoustical occlusion/diffraction and reverberation effects at interactive rates. In addition, the VSRE includes a GPU-based method that performs the one-dimensional convolution allowing for the incorporation of head-related transfer functions also at interactive rates. The VSRE is being developed as a research tool for examining multi-modal (audio-visual) interactions through the simple manipulation of the acoustic environment and audio parameters (sound quality), that will, through a series of human-based experiments, allow for the testing of the effect of varying these parameters may have on immersion, engagement, and visual fidelity perception within a virtual environment. Finally, we also provide a running time comparison of several one-dimensional convolution implementations.
Named Entity Recognition for Indian Languages

Abstract Stub This paper talks about a new approach to recognize named entities for Indian languages. Phonetic matching tech- nique is used to match the strings of differ- ent languages on the basis of their similar sounding property. We have tested our sys- tem with a comparable corpus of English and Hindi language data. This approach is language independent and requires only a set of rules appropriate for a language.
SHARE: supporting large-scale rescue operations with communication and information services over mobile networks

This paper presents the EU project SHARE which is developing a mobile service architecture to support large-scale rescue operations with multimedia communication and information services. The task of planning and controlling large-scale rescue operations requires flexible and robust tools which help the rescue forces to do their search and rescue work with maximum efficiency. Today the main channels of communication are analog radio and paper text forms. The SHARE system introduces an advanced multimedia communication and information system to the highly mobile working environment which supports well established command hierarchies of the rescue organizations. Different operation scenarios for mobile networks using WiMAX, WLAN or UMTS are discussed regarding their usability for large-scale rescue operations.
The Case for Predicate-Oriented Debugging of Sensornets

A method of providing a protective layer for a glass sheet. The protective layer provided by depositing a solution of a polymer and water on a glass sheet at a temperature high enough to provide a layer that is insoluble in water below a temperature of about 20° C. The layer is soluble in water at a temperature above about 60° C. , so that the protective layer can be removed by rinsing in hot water.