Embedding HMMs-based models in a Euclidean space: the topological hidden Markov models

Current extensions of hidden Markov models such as structural, hierarchical, coupled, and others have the power to classify complex and highly organized patterns. However, one of their major limitations is the inability to cope with topology: When applied to a visible observation (VO) sequence, the traditional HMM-based techniques have difficulty predicting the n-dimensional shape formed by the symbols of the VO sequence. To fulfill this need, we propose a novel paradigm named “topological hidden Markov models” (THMMs) that classifies VO sequences by embedding the nodes of an HMM state transition graph in a Euclidean space. This is achieved by modeling the noise embedded in the shape generated by the VO sequence. We cover the first and second level topological HMMs. We describe five basic problems that are assigned to a second level topological hidden Markov model: (1) sequence probability evaluation, (2) statistical decoding, (3) structural decoding, (4) topological decoding, and (5) learning. To show the significance of this research, we have applied the concept of THMMs to: (i) predict the ASCII class assigned to a handwritten numeral, and (ii) map protein primary structures to their 3D folds. The results show that the second level THMMs outperform the SHMMs and the multi-class SVM classifiers significantly.     

Traffic analysis and synthetic models of second life

Virtual Worlds (VW), such as Massive Multiplayer Online Social Games, have been gaining increasing attention in the last few years, mainly due to the new way users interact with them. However, little effort has been devoted to understand their traffic profile and the implications to the traffic management area. With the current growing rate of VWs’ usage, their traffic demand could eventually impose a significant burden on the operation of a typical Internet Service Provider (ISP) network. In this paper, we seek to understand the traffic behavior of an increasingly popular VW application, namely Second Life (SL), from both the connection and network level perspectives. We also show results of a traffic analysis of a SL client, when an avatar performs different actions in the VW, at different places and under different network conditions. Our results show that SL makes intensive use of network resources (mostly bandwidth), since the capacity needed for having a full SL experience (listening to live music) may reach 500 kbps. We then extend the traffic analysis work on SL by providing analytical models for its traffic profile. We aim at helping the network management and planning area to estimate the impact of an intense use of such VW on access links or core networks. Such synthetic models are also useful to the networking research community for the use in network simulations.     

Adherence and bouncing of liquid droplets impacting on dry surfaces

The paper explores liquid drop dynamics over a solid surface, focusing on adherence and bouncing phenomena. The study relies on detailed interface tracking simulations using the Level Set approach incorporated within a Navier–Stokes solver. The investigation deals with moderate Reynolds number droplet flows, for which two-dimensional axisymmetric simulations can be performed. The modelling approach has been validated against experiments for axisymmetric and full three-dimensional impact upon dry surfaces. A drop-impact regime map is generated for axisymmetric conditions, in which the impact dynamics is characterized as a function of Weber number and equilibrium contact angle, based on about 60 simulations. The detailed simulations also helped validate a new mechanistic model based on energy-balance analysis, delimiting the boundary between adherence and bouncing zones at low Weber numbers. The mechanistic model is only valid for moderate droplet Reynolds numbers and it complements existing models for higher Reynolds numbers.     

SPIDER: An availability-aware framework for the service function chain placement in distributed scenarios

The network function virtualization (NFV) paradigm replaces hardware-dependent network functions by virtual network functions (VNFs) that can be deployed in commodity hardware, including legacy servers. Consequently, the use of NFV is expected to reduce operating and capital expenses, as well as improve service deployment operation and management flexibility. For many use cases, the VNFs must be visited and invoked following a specific order of execution in order to compose a complete network service, named service function chain (SFC). Nonetheless, despite the benefits from NFV and SFC virtualization technologies, their introduction must not harm network performance and service availability. On the one hand, redundancy is seen by network service planners as a mechanism well established to combat availability issues. At same time, there is a goal to optimize resource utilization in order to reduce operational expenditure. In this article, we share our experience in the design use of a framework, named SPIDER, focused on SFC placement that considers the network infrastructure condition and the required SFC availability to define the placement strategy. The SPIDER monitors the status of infrastructure nodes and links and defines which servers the VNFs should be placed on and the number of redundant replicas needed. We present a proof-of-concept of SPIDER using Kubernetes to launch the VNFs as containers. We also use Kubernetes to forward the traffic between the VNFs, composing the service chain. We perform experiments to evaluate the runtime of SPIDER and the SFC delay under different network conditions.     

A 3D unsplit-advection volume tracking algorithm with planarity-preserving interface reconstruction

A new volume tracking method is introduced for tracking interfaces in three-dimensional (3D) geometries partitioned with orthogonal hexahedra. The method approximates interface geometries as piecewise planar, and advects volumes in a single unsplit step using fully multidimensional fluxes that have their definition based in backward-trajectory remapping. By using multidimensional unsplit advection, the expense of high-order interface reconstruction is incurred only once per timestep. Simple departures from strict backward-trajectory remapping remove any need for consideration of volume computations involving shapes consisting of non-planar ruled surfaces. Second-order accuracy of the method is demonstrated even for vigorous 3D deformations.     

Structural hidden Markov models for biometrics: Fusion of face and fingerprint

The goal of this paper is threefold: (i) propose a novel face and fingerprint feature modeling using the structural hidden Markov models (SHMMs) paradigm, (ii) explore the use of some feature extraction techniques such as ridgelet transform, discrete wavelet transform with various classifiers for biometric identification, and (iii) determine the best method for classifier combination. The experimental results reported in both fingerprint and face recognition reveal that the SHMMs concept is promising since it has outperformed several state-of-the-arts classifiers when combined with the discrete wavelet transform. Besides, this study has shown that the ridgelet transform without principal components analysis (PCA) dimension reduction fits better with the support vector machines (SVMs) classifier than it does with the SHMMs in the fingerprint recognition task. Finally, these results also reveal a small improvement of the bimodal biometric system over unimodal systems; which suggest that a most effective fusion scheme is necessary.     

Amino acid composition,foaming, emulsifying properties and surface hydrophobicity of mustard protein isolate as affected by pH and NaCl

Amino acid composition, protein hydrophobicity, foaming and emulsifying properties of mustard protein isolate at pH 3, 5, 7 and in 0.05 and 0.1 m NaCl were studied. Glutamic (19.18 ± 0.30%) and aspartic (7.49 ± 0.11%) acids were the dominants. Foaming ability was enhanced by NaCl. Time to reach 75 mL foam was 23% higher in water than in NaCl. Drained volume after 10 min was concentration dependent and was the lowest in 0.05 and 0.1 m NaCl at protein concentration of 2.5% and 5%. The emulsifying properties were pH and concentration dependent, and the best results were obtained at pH 3, corresponding to the highest positive charge density of the protein surface. The highest emulsion stability (90.22 ± 3.52%) was obtained in 0.05 m NaCl and 5% protein concentration, whereas the lowest (63.00 ± 1.06%) was in water at all protein concentrations. Protein hydrophobicity was low and depended of pH but not of NaCl.     

Approaches to resource reservation for migrating real-time sessions in future mobile wireless networks

This paper presents two novel frameworks for session admission control and resource reservation in the context of next generation mobile and cellular networks. We also devised a special scheme that avoids per-user reservation signaling overhead in order to meet scalability requirements needed for next generation multi-access networks. The first proposal, Distributed Call Admission Control with Aggregate Resource Reservation (VR), uses mobility prediction based on mobile positioning system location information and takes into account the expected bandwidth to be used by calls handing off to and from neighboring cells within a configurable estimation time window. In conjunction, a novel concept called virtual reservation has been devised to prevent per-user reservation. Our second proposal, Local Call Admission Control and Time Series-based Resource Reservation, takes into account the expected bandwidth to be used by calls handed off from neighboring cells based only on local information stored into the current cell a user is seeking admission to. To this end, we suggest the use of two time series-based models for predicting handoff load: the Trigg and Leach (TL), which is an adaptive exponential smoothing technique, and Autoregressive Integrated Moving Average (ARIMA) that uses the Box and Jenkins methodology. It is worth to emphasize that the use of bandwidth prediction based on ARIMA technique still exist for wireless networks. The novelty of our approach is to build an adaptive framework based on ARIMA technique that takes into account the measured handoff dropping probability in order to tuning the prediction time window size so increasing the prediction accuracy. The proposed schemes are compared through simulations with the fixed guard channel (GC) and other optimized dynamic reservation-based proposals present in the literature. The results show that our schemes outperform many others and that the simpler local proposal based on TL can grant nearly similar levels of handoff dropping probability as compared to those from more the complex distributed approach.     

Combined microwave and enzymatic treatments for ��-lactoglobulin and bovine whey proteins and their effect on the IgE immunoreactivity

Effect of combined microwave (MW) and enzymatic hydrolysis on the human immunoglobulin E (IgE)-binding properties of ??-lactoglobulin (??-lg) and other whey proteins (WP) was investigated. Separated ??-lg and full whey protein isolate (WPI) were hydrolyzed with trypsin, chymotrypsin, mixture of trypsin/chymotrypsin, and pepsin at three microwave power levels: 50?W during 1 and 5?min, 100 and 200?W during 1 and 3?min. The immunoreactivity of the obtained hydrolysates resulting from combined microwave protease treatment was assessed using sera of young patients allergic to bovine whey proteins. The application of microwave treatment at 200?W enhances the hydrolysis of ??-lg by pepsin in 3?min and decreases significantly its immunoreactivity. The extensive hydrolysis of the microwave-treated ??-lg and WPI with trypsin, chymotrypsin, and the mixture of trypsin with chymotrypsin did not have an impact on the IgE binding of the products obtained in all the studied conditions.