Towards a framework for combining stochastic and deterministic descriptions of nonstationary financial time series

We present in this paper ideas to tackle the problem of analysing and forecasting nonstationary time series within the financial domain. Accepting the stochastic nature of the underlying data generator we assume that the evolution of the generator's parameters is restricted on a deterministic manifold. Therefore we propose methods for determining the characteristics of the time-localised distribution. Starting with the assumption of a static normal distribution we refine this hypothesis according to the empirical results obtained with the methods anc conclude with the indication of a dynamic non-Gaussian behaviour with varying dependency for the time series under consideration.

A heuristic approach for component scheduling on a high-speed PCB assembly machine

The collect-and-place machine is one of the most widely used placement machines for assembling electronic components on the printed circuit boards (PCBs). Nevertheless, the number of researches concerning the optimisation of the machine performance is very few. This motivates us to study the component scheduling problem for this type of machine with the objective of minimising the total assembly time. The component scheduling problem is an integration of the component sequencing problem, that is, the sequencing of component placements; and the feeder arrangement problem, that is, the assignment of component types to feeders. To solve the component scheduling problem efficiently, a hybrid genetic algorithm is developed in this paper. A numerical example is used to compare the performance of the algorithm with different component grouping approaches and different population sizes.

Associative knowledge controls deployment of visual selective attention

According to some models of visual selective attention, objects in a scene activate corresponding neural representations, which compete for perceptual awareness and motor behavior. During a visual search for a target object, top-down control exerted by working memory representations of the target's defining properties resolves competition in favor of the target. These models, however, ignore the existence of associative links among object representations. Here we show that such associations can strongly influence deployment of attention in humans. In the context of visual search, objects associated with the target were both recalled more often and recognized more accurately than unrelated distractors. Notably, both target and associated objects competitively weakened recognition of unrelated distractors and slowed responses to a luminance probe. Moreover, in a speeded search protocol, associated objects rendered search both slower and less accurate. Finally, the first saccades after onset of the stimulus array were more often directed toward associated than control items.

Distributed routing algorithms for interconnected FDDI LANS

The Fibre Distributed Data Interface (FDDI) represents the new generation of local area networks (LANs). These high speed LANs are capable of supporting up to 500 users over a 100 km distance. User traffic is expected to be as diverse as file transfers, packet voice and video. As the proliferation of FDDI LANs continues, the need to interconnect these LANs arises. FDDI LAN interconnection can be achieved in a variety of different ways. Some of the most commonly used today are public data networks, dial up lines and private circuits. For applications that can potentially generate large quantities of traffic, such as an FDDI LAN, it is cost effective to use a private circuit leased from the public carrier. In order to send traffic from one LAN to another across the leased line, a routing algorithm is required. Much research has been done on the Bellman-Ford algorithm and many implementations of it exist in computer networks. However, due to its instability and problems with routing table loops it is an unsatisfactory algorithm for interconnected FDDI LANs. A new algorithm, termed ISIS which is being standardized by the ISO provides a far better solution. ISIS will be implemented in many manufacturers routing devices. In order to make the work as practical as possible, this algorithm will be used as the basis for all the new algorithms presented. The ISIS algorithm can be improved by exploiting information that is dropped by that algorithm during the calculation process. A new algorithm, called Down Stream Path Splits (DSPS), uses this information and requires only minor modification to some of the ISIS routing procedures. DSPS provides a higher network performance, with very little additional processing and storage requirements. A second algorithm, also based on the ISIS algorithm, generates a massive increase in network performance. This is achieved by selecting alternative paths through the network in times of heavy congestion. This algorithm may select the alternative path at either the originating node, or any node along the path. It requires more processing and memory storage than DSPS, but generates a higher network power. The final algorithm combines the DSPS algorithm with the alternative path algorithm. This is the most flexible and powerful of the algorithms developed. However, it is somewhat complex and requires a fairly large storage area at each node. The performance of the new routing algorithms is tested in a comprehensive model of interconnected LANs. This model incorporates the transport through physical layers and generates random topologies for routing algorithm performance comparisons. Using this model it is possible to determine which algorithm provides the best performance without introducing significant complexity and storage requirements.

The adsorptive separation of aromatic hydrocarbon mixtures

A detailed study has been made of the feasibility of adsorptive purification of slack waxes from traces of aromatic compounds using type 13X molecular sieves to achieve 0.01% aromatics in the product. The limited literature relating to the adsorption of high molecular weight aromatic compounds by zeolites was reviewed. Equilibrium isotherms were determined for typical individual aromatic compounds. Lower molecular weight, or more compact, molecules were preferentially adsorbed and the number of molecules captured by one unit cell decreased with increasing molecular weight of the adsorbate. An increase in adsorption temperature resulted in a decrease in the adsorption value. The isosteric heat of adsorption of differnt types of aromatic compounds was determined from pairs of isotherms at 303 K to 343 K at specific coverages. The lowest heats of adsorption were for dodecylbenzene and phenanthrene. Kinetics of adsorption were studied for different aromatic compounds. The diffusivity decreased significantly when a long alkyl chain was attached to the benzene ring e.g. in dodecylbenzene; molecules with small cross-sectional diameter e.g. cumene were adsorbed most rapidly. The sorption rate increased with temperature. Apparent activation energies increased with increasing polarity. In a study of the dynamic adsorption of selected aromatic compounds from binary solutions in isooctane or n-alkanes, naphthalene exhibited the best dynamic properties followed by dibenzothiophene and finally dodecylbenzene. The dynamic adsorption of naphthalene from different n-alkane solvents increased with a decrease in solvent molecular weight. A tentative mathematical approach is proposed for the prediction of dynamic breakthrough curves from equilibrium isotherms and kinetic data. The dynamic properties of liquid phase adsorption of aromatics from slack waxes were studied at different temperatures and concentrations. The optimum operating temperature was 543 K. The best dynamic performance was achieved with feeds of low aromatic content. The studies with individual aromatic compounds demonstrated the affinity of type NaX molecular sieves to adsorb aromatics in the concentration range 3% - 5% . Wax purification by adsorption was considered promising and extension of the experimental programme was recommended.

The spectrum and severity of FUS-immunoreactive inclusions in the frontal and temporal lobes of ten cases of neuronal intermediate filament inclusion disease

Neuronal intermediate filament inclusion disease (NIFID), a rare form of frontotemporal lobar degeneration (FTLD), is characterized neuropathologically by focal atrophy of the frontal and temporal lobes, neuronal loss, gliosis, and neuronal cytoplasmic inclusions (NCI) containing epitopes of ubiquitin and neuronal intermediate filament proteins. Recently, the 'fused in sarcoma' (FUS) protein (encoded by the FUS gene) has been shown to be a component of the inclusions of familial amyotrophic lateral sclerosis with FUS mutation, NIFID, basophilic inclusion body disease, and atypical FTLD with ubiquitin-immunoreactive inclusions (aFTLD-U). To further characterize FUS proteinopathy in NIFID, and to determine whether the pathology revealed by FUS immunohistochemistry (IHC) is more extensive than a-internexin, we have undertaken a quantitative assessment of ten clinically and neuropathologically well-characterized cases using FUS IHC. The densities of NCI were greatest in the dentate gyrus (DG) and in sectors CA1/2 of the hippocampus. Anti-FUS antibodies also labeled glial inclusions (GI), neuronal intranuclear inclusions (NII), and dystrophic neurites (DN). Vacuolation was extensive across upper and lower cortical layers. Significantly greater densities of abnormally enlarged neurons and glial cell nuclei were present in the lower compared with the upper cortical laminae. FUS IHC revealed significantly greater numbers of NCI in all brain regions especially the DG. Our data suggest: (1) significant densities of FUS-immunoreactive NCI in NIFID especially in the DG and CA1/2; (2) infrequent FUS-immunoreactive GI, NII, and DN; (3) widely distributed vacuolation across the cortex, and (4) significantly more NCI revealed by FUS than a-internexin IHC.

A peak-clustering method for MEG group analysis to minimise artefacts due to smoothness

Magnetoencephalography (MEG), a non-invasive technique for characterizing brain electrical activity, is gaining popularity as a tool for assessing group-level differences between experimental conditions. One method for assessing task-condition effects involves beamforming, where a weighted sum of field measurements is used to tune activity on a voxel-by-voxel basis. However, this method has been shown to produce inhomogeneous smoothness differences as a function of signal-to-noise across a volumetric image, which can then produce false positives at the group level. Here we describe a novel method for group-level analysis with MEG beamformer images that utilizes the peak locations within each participant's volumetric image to assess group-level effects. We compared our peak-clustering algorithm with SnPM using simulated data. We found that our method was immune to artefactual group effects that can arise as a result of inhomogeneous smoothness differences across a volumetric image. We also used our peak-clustering algorithm on experimental data and found that regions were identified that corresponded with task-related regions identified in the literature. These findings suggest that our technique is a robust method for group-level analysis with MEG beamformer images.

ACARS:adaptive context - aware rate selection for DSRC in vehicular networks

Throughput plays a vital role for data transfer in Vehicular Networks which is useful for both safety and non-safety applications. An algorithm that adapts to mobile environment by using Context information has been proposed in this paper. Since one of the problems of existing rate adaptation algorithm is underutilization of link capacity in Vehicular environments, we have demonstrated that in wireless and mobile environments, vehicles can adapt to high mobility link condition and still perform better due to regular vehicles that will be out of communication range due to range checking and then de-congest the network thereby making the system perform better since fewer vehicles will contend for network resources. In this paper, we have design, implement and analyze ACARS, a more robust algorithm with significant increase in throughput performance and energy efficiency in the mist of high mobility of vehicles.

Diagnosing autism spectrum disorder:who will get a DSM-5 diagnosis?

Background Introduction of proposed criteria for DSM-5 Autism Spectrum Disorder (ASD) has raised concerns that some individuals currently meeting diagnostic criteria for Pervasive Developmental Disorder (PDD; DSM-IV-TR/ICD-10) will not qualify for a diagnosis under the proposed changes. To date, reports of sensitivity and specificity of the new criteria have been inconsistent across studies. No study has yet considered how changes at the 'sub domain' level might affect overall sensitivity and specificity, and few have included individuals of different ages and ability levels. Methods A set of DSM-5 ASD algorithms were developed using items from the Diagnostic Interview for Social and Communication Disorders (DISCO). The number of items required for each DSM-5 subdomain was defined either according to criteria specified by DSM-5 (Initial Algorithm), a statistical approach (Youden J Algorithm), or to minimise the number of false positives while maximising sensitivity (Modified Algorithm). The algorithms were designed, tested and compared in two independent samples (Sample 1, N = 82; Sample 2, N = 115), while sensitivity was assessed across age and ability levels in an additional dataset of individuals with an ICD-10 PDD diagnosis (Sample 3, N = 190). Results Sensitivity was highest in the Initial Algorithm, which had the poorest specificity. Although Youden J had excellent specificity, sensitivity was significantly lower than in the Modified Algorithm, which had both good sensitivity and specificity. Relaxing the domain A rules improved sensitivity of the Youden J Algorithm, but it remained less sensitive than the Modified Algorithm. Moreover, this was the only algorithm with variable sensitivity across age. All versions of the algorithm performed well across ability level. Conclusions This study demonstrates that good levels of both sensitivity and specificity can be achieved for a diagnostic algorithm adhering to the DSM-5 criteria that is suitable across age and ability level. © 2013 The Authors. Journal of Child Psychology and Psychiatry © 2013 Association for Child and Adolescent Mental Health.

Establishing Correspondences between Attribute Spaces and Complex Concept Spaces Using Meta-PGN Classifier

In this paper, we present one approach for extending the learning set of a classification algorithm with additional metadata. It is used as a base for giving appropriate names to found regularities. The analysis of correspondence between connections established in the attribute space and existing links between concepts can be used as a test for creation of an adequate model of the observed world. Meta-PGN classifier is suggested as a possible tool for establishing these connections. Applying this approach in the field of content-based image retrieval of art paintings provides a tool for extracting specific feature combinations, which represent different sides of artists' styles, periods and movements.

Generalized Discernibility Function Based Attribute Reduction in Incomplete Decision Systems

A rough set approach for attribute reduction is an important research subject in data mining and machine learning. However, most attribute reduction methods are performed on a complete decision system table. In this paper, we propose methods for attribute reduction in static incomplete decision systems and dynamic incomplete decision systems with dynamically-increasing and decreasing conditional attributes. Our methods use generalized discernibility matrix and function in tolerance-based rough sets.

Hogyan kezeljük a „közösségi ügyfeleket”? – Social CRM marketing és IT megközelítésben (How to manage social customer? Social CRM in marketing and IT context.)

Van egy szó, ami egyre fontosabb lesz a társadalom és a vállalatok számára is, ez a szó a közösség. A közösséghez tevékenységek tartoznak, és ezen a ponton kapcsolódik be a vállalat. A vállalkozások az elmúlt években a közösségi igényeket a CRM-(Customer Relationship Management) megoldásokkal szolgálták ki. Informatikailag a közösségi hálózatok, már nemcsak vállalkozási folyamatot, hanem ehhez kapcsoltan az emberek társadalmi igényét is megpróbálják lefedni az elektronika lehetőségeivel. Egyre inkább a közösségi vállalkozások korát éljük, melyben a folyamathoz tartozó közösségek megosztják, egymás rendelkezésére bocsátják az információkat. A korábbi klasszikus CRM-rendszerek csak begyűjtötték az információkat, ezzel ellenben a közösségi CRM-rendszerek kétirányú kommunikációt folytatnak, párbeszédet kezdeményeznek az ügyfelekkel, buzdítják őket, hogy mondják el a véleményüket. Vajon ez az új stratégia,egy teljesen új világot hoz el a vállalatok számára, vagy csak a CRM fejlődésének egy újabb fokát jelenti? A szerzők erre a kérdésre keresik a választ gyakorlati esetek és szakirodalmi publikációk feldolgozásával. ______ There is a word that begins to be more and more important for the society and the companies, and this word is community. We can talk about social networks, people seek the social demand they already had as a part of their lives for a long time, and this means that it appears in the electronic society as an essential need too. The community is not enough, activities are also needed and this is the point where the companies link in, who promote their goods and facilities to the outside world and with this they use the next stage of customer relationship management, the fulfilment of social needs. We live in the age of social shopping, communities are everywhere and everyone shares information, and up to the present classic CR M systems ran from static databases. On the contrary social CR M systems perform a two-way communication, start a conversation with customers and encourage them to tell their opinions, which always changes on social media, so they build a dynamic database and communicate with customers through response-reactions. Does this new strategy bring a whole new world to companies or is it only another step in the development and another channel of CRM?

An incremental multilinear system for human face learning and recognition

This dissertation establishes a novel system for human face learning and recognition based on incremental multilinear Principal Component Analysis (PCA). Most of the existing face recognition systems need training data during the learning process. The system as proposed in this dissertation utilizes an unsupervised or weakly supervised learning approach, in which the learning phase requires a minimal amount of training data. It also overcomes the inability of traditional systems to adapt to the testing phase as the decision process for the newly acquired images continues to rely on that same old training data set. Consequently when a new training set is to be used, the traditional approach will require that the entire eigensystem will have to be generated again. However, as a means to speed up this computational process, the proposed method uses the eigensystem generated from the old training set together with the new images to generate more effectively the new eigensystem in a so-called incremental learning process. In the empirical evaluation phase, there are two key factors that are essential in evaluating the performance of the proposed method: (1) recognition accuracy and (2) computational complexity. In order to establish the most suitable algorithm for this research, a comparative analysis of the best performing methods has been carried out first. The results of the comparative analysis advocated for the initial utilization of the multilinear PCA in our research. As for the consideration of the issue of computational complexity for the subspace update procedure, a novel incremental algorithm, which combines the traditional sequential Karhunen-Loeve (SKL) algorithm with the newly developed incremental modified fast PCA algorithm, was established. In order to utilize the multilinear PCA in the incremental process, a new unfolding method was developed to affix the newly added data at the end of the previous data. The results of the incremental process based on these two methods were obtained to bear out these new theoretical improvements. Some object tracking results using video images are also provided as another challenging task to prove the soundness of this incremental multilinear learning method.

Could Relatedness Help Explain Why Individuals Lead in Bottlenose Dolphin Groups?

In many species, particular individuals consistently lead group travel. While benefits to followers often are relatively obvious, including access to resources, benefits to leaders are often less obvious. This is especially true for species that feed on patchy mobile resources where all group members may locate prey simultaneously and food intake likely decreases with increasing group size. Leaders in highly complex habitats, however, could provide access to foraging resources for less informed relatives, thereby gaining indirect benefits by helping kin. Recently, leadership has been documented in a population of bottlenose dolphins (Tursiops truncatus) where direct benefits to leaders appear unlikely. To test whether leaders could benefit indirectly we examined relatedness between leader-follower pairs and compared these levels to pairs who associated but did not have leader-follower relationship (neither ever led the other). We found the average relatedness value for leader-follower pairs was greater than expected based on chance. The same was not found when examining non leader-follower pairs. Additionally, relatedness for leader-follower pairs was positively correlated with association index values, but no correlation was found for this measure in non leader-follower pairs. Interestingly, haplotypes were not frequently shared between leader-follower pairs (25%). Together, these results suggest that bottlenose dolphin leaders have the opportunity to gain indirect benefits by leading relatives. These findings provide a potential mechanism for the maintenance of leadership in a highly dynamic fission-fusion population with few obvious direct benefits to leaders.

Optimization of three-dimensional branching networks of microchannels for thermal management of microelectronics

The aim of this work is to present a methodology to develop cost-effective thermal management solutions for microelectronic devices, capable of removing maximum amount of heat and delivering maximally uniform temperature distributions. The topological and geometrical characteristics of multiple-story three-dimensional branching networks of microchannels were developed using multi-objective optimization. A conjugate heat transfer analysis software package and an automatic 3D microchannel network generator were developed and coupled with a modified version of a particle-swarm optimization algorithm with a goal of creating a design tool for 3D networks of optimized coolant flow passages. Numerical algorithms in the conjugate heat transfer solution package include a quasi-ID thermo-fluid solver and a steady heat diffusion solver, which were validated against results from high-fidelity Navier-Stokes equations solver and analytical solutions for basic fluid dynamics test cases. Pareto-optimal solutions demonstrate that thermal loads of up to 500 W/cm2 can be managed with 3D microchannel networks, with pumping power requirements up to 50% lower with respect to currently used high-performance cooling technologies.