Particle Competition and Cooperation in Networks for Semi-Supervised Learning with Concept Drift

Concept drift is a problem of increasing importance in machine learning and data mining. Data sets under analysis are no longer only static databases, but also data streams in which concepts and data distributions may not be stable over time. However, most learning algorithms produced so far are based on the assumption that data comes from a fixed distribution, so they are not suitable to handle concept drifts. Moreover, some concept drifts applications requires fast response, which means an algorithm must always be (re) trained with the latest available data. But the process of labeling data is usually expensive and/or time consuming when compared to unlabeled data acquisition, thus only a small fraction of the incoming data may be effectively labeled. Semi-supervised learning methods may help in this scenario, as they use both labeled and unlabeled data in the training process. However, most of them are also based on the assumption that the data is static. Therefore, semi-supervised learning with concept drifts is still an open challenge in machine learning. Recently, a particle competition and cooperation approach was used to realize graph-based semi-supervised learning from static data. In this paper, we extend that approach to handle data streams and concept drift. The result is a passive algorithm using a single classifier, which naturally adapts to concept changes, without any explicit drift detection mechanism. Its built-in mechanisms provide a natural way of learning from new data, gradually forgetting older knowledge as older labeled data items became less influent on the classification of newer data items. Some computer simulation are presented, showing the effectiveness of the proposed method.

Pattern recognition issues on anisotropic smoothed particle hydrodynamics

This is a preliminary theoretical discussion on the computational requirements of the state of the art smoothed particle hydrodynamics (SPH) from the optics of pattern recognition and artificial intelligence. It is pointed out in the present paper that, when including anisotropy detection to improve resolution on shock layer, SPH is a very peculiar case of unsupervised machine learning. On the other hand, the free particle nature of SPH opens an opportunity for artificial intelligence to study particles as agents acting in a collaborative framework in which the timed outcomes of a fluid simulation forms a large knowledge base, which might be very attractive in computational astrophysics phenomenological problems like self-propagating star formation.

Validation of a stability-indicating HPLC method with diode array detection for the determination of beclomethasone dipropionate in aqueous suspension for nebulizer

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Antennas for the detection of radio emission pulses from cosmic-ray induced air showers at the Pierre Auger Observatory

The Pierre Auger Observatory is exploring the potential of the radio detection technique to study extensive air showers induced by ultra-high energy cosmic rays. The Auger Engineering Radio Array (AERA) addresses both technological and scientific aspects of the radio technique. A first phase of AERA has been operating since September 2010 with detector stations observing radio signals at frequencies between 30 and 80 MHz. In this paper we present comparative studies to identify and optimize the antenna design for the final configuration of AERA consisting of 160 individual radio detector stations. The transient nature of the air shower signal requires a detailed description of the antenna sensor. As the ultra-wideband reception of pulses is not widely discussed in antenna literature, we review the relevant antenna characteristics and enhance theoretical considerations towards the impulse response of antennas including polarization effects and multiple signal reflections. On the basis of the vector effective length we study the transient response characteristics of three candidate antennas in the time domain. Observing the variation of the continuous galactic background intensity we rank the antennas with respect to the noise level added to the galactic signal.

Chemically Resolved Particle Fluxes Over Tropical and Temperate Forests

Chemically resolved submicron (PM1) particlemass fluxes were measured by eddy covariance with a high resolution time-of-flight aerosolmass spectrometer over temperate and tropical forests during the BEARPEX-07 and AMAZE-08 campaigns. Fluxes during AMAZE-08 were small and close to the detection limit (<1 ng m−2 s−1) due to low particle mass concentrations (<1 μg m−3). During BEARPEX-07, concentrations were five times larger, with mean mid-day deposition fluxes of −4.8 ng m−2 s−1 for total nonrefractory PM1 (Vex,PM1 = −1 mm s−1) and emission fluxes of +2.6 ng m−2 s−1 for organic PM1 (Vex,org = +1 mm s−1). Biosphere–atmosphere fluxes of different chemical components are affected by in-canopy chemistry, vertical gradients in gas-particle partitioning due to canopy temperature gradients, emission of primary biological aerosol particles, and wet and dry deposition. As a result of these competing processes, individual chemical components had fluxes of varying magnitude and direction during both campaigns. Oxygenated organic components representing regionally aged aerosol deposited, while components of fresh secondary organic aerosol (SOA) emitted. During BEARPEX-07, rapid incanopy oxidation caused rapid SOA growth on the timescale of biosphere-atmosphere exchange. In-canopy SOA mass yields were 0.5–4%. During AMAZE-08, the net organic aerosol flux was influenced by deposition, in-canopy SOA formation, and thermal shifts in gas-particle partitioning.Wet deposition was estimated to be an order ofmagnitude larger than dry deposition during AMAZE-08. Small shifts in organic aerosol concentrations from anthropogenic sources such as urban pollution or biomass burning alters the balance between flux terms. The semivolatile nature of the Amazonian organic aerosol suggests a feedback in which warmer temperatures will partition SOA to the gas-phase, reducing their light scattering and thus potential to cool the region.

Multiple pathogen detection using nanoplasmonic sensing

Opportunistic diseases caused by Human Immunodeficiency Virus (HIV) and Hepatitis B Virus (HBV) is an omnipresent global challenge. In order to manage these epidemics, we need to have low cost and easily deployable platforms at the point-of-care in high congestions regions like airports and public transit systems. In this dissertation we present our findings in using Localized Surface Plasmon Resonance (LSPR)-based detection of pathogens and other clinically relevant applications using microfluidic platforms at the point-of-care setting in resource constrained environment. The work presented here adopts the novel technique of LSPR to multiplex a lab-on-a-chip device capable of quantitatively detecting various types of intact viruses and its various subtypes, based on the principle of a change in wavelength occurring when metal nano-particle surface is modified with a specific surface chemistry allowing the binding of a desired pathogen to a specific antibody. We demonstrate the ability to detect and quantify subtype A, B, C, D, E, G and panel HIV with a specificity of down to 100 copies/mL using both whole blood sample and HIV-patient blood sample discarded from clinics. These results were compared against the gold standard Reverse Transcriptase Polymerase Chain Reaction (RT-qPCR). This microfluidic device has a total evaluation time for the assays of about 70 minutes, where 60 minutes is needed for the capture and 10 minutes for data acquisition and processing. This LOC platform eliminates the need for any sample preparation before processing. This platform is highly multiplexable as the same surface chemistry can be adapted to capture and detect several other pathogens like dengue virus, E. coli, M. Tuberculosis, etc.

Fast frontend electronics for high rate particle detectors

Future experiments in nuclear and particle physics are moving towards the high luminosity regime in order to access rare processes. In this framework, particle detectors require high rate capability together with excellent timing resolution for precise event reconstruction. In order to achieve this, the development of dedicated FrontEnd Electronics (FEE) for detectors has become increasingly challenging and expensive. Thus, a current trend in R&D is towards flexible FEE that can be easily adapted to a great variety of detectors, without impairing the required high performance. This thesis reports on a novel FEE for two different detector types: imaging Cherenkov counters and plastic scintillator arrays. The former requires high sensitivity and precision for detection of single photon signals, while the latter is characterized by slower and larger signals typical of scintillation processes. The FEE design was developed using high-bandwidth preamplifiers and fast discriminators which provide Time-over-Threshold (ToT). The use of discriminators allowed for low power consumption, minimal dead-times and self-triggering capabilities, all fundamental aspects for high rate applications. The output signals of the FEE are readout by a high precision TDC system based on FPGA. The performed full characterization of the analogue signals under realistic conditions proved that the ToT information can be used in a novel way for charge measurements or walk corrections, thus improving the obtainable timing resolution. Detailed laboratory investigations proved the feasibility of the ToT method. The full readout chain was investigated in test experiments at the Mainz Microtron: high counting rates per channel of several MHz were achieved, and a timing resolution of better than 100 ps after walk correction based on ToT was obtained. Ongoing applications to fast Time-of-Flight counters and future developments of FEE have been also recently investigated.

ACETYL RADICAL IN TOBACCO SMOKE: DETECTION, QUANTIFICATION AND SIMULATION

Free radicals are present in cigarette smoke and can have a negative effect on human health by attacking lipids, nucleic acids, proteins and other biologically important species. However, because of the complexity of the tobacco smoke system and the dynamic nature of radicals, little is known about the identity of the radicals, and debate continues on the mechanisms by which those radicals are produced. In this study, acetyl radicals were trapped from the gas phase using 3-amino-2, 2, 5, 5- tetramethyl-proxyl (3AP) on solid support to form stable 3AP adducts for later analysis by high performance liquid chromatography (HPLC), mass spectrometry/tandem mass spectrometry (MS-MS/MS) and liquid chromatography- mass spectrometry (LC-MS). Simulations of acetyl radical generation were performed using Matlab and the Master Chemical Mechanism (MCM) programs. A range of 10- 150 nmol/cigarette of acetyl radical was measured from gas phase tobacco smoke of both commerial and research cigarettes under several different smoking conditions. More radicals were detected from the puff smoking method compared to continuous flow sampling. Approximately twice as many acetyl radicals were trapped when a GF/F particle filter was placed before the trapping zone. Computational simulations show that NO/NO2 reacts with isoprene, initiating chain reactions to produce a hydroxyl radical, which abstracts hydrogen from acetaldehyde to generate acetyl radical. With initial concentrations of NO, acetaldehyde, and isoprene in a real-world cigarette smoke scenario, these mechanisms can account for the full amount of acetyl radical detected experimentally. This study contributes to the overall understanding of the free radical generation in gas phase cigarette smoke.

Detection efficiency of microchannel plates for e(-) and pi(-) in the momentum range from 17.5 to 345 MeV/c

High-energy e(-) and pi(-) were measured by the multichannel plate (MCP) detector at the PiM1 beam line of the High Intensity Proton Accelerator Facilities located at the Paul Scherrer Institute, Villigen, Switzerland. The measurements provide the absolute detection efficiencies for these particles: 5.8% +/- 0.5% for electrons in the beam momenta range 17.5-300 MeV/c and 6.0% +/- 1.3% for pions in the beam momenta range 172-345 MeV/c. The pulse height distribution determined from the measurements is close to an exponential function with negative exponent, indicating that the particles penetrated the MCP material before producing the signal somewhere inside the channel. Low charge extraction and nominal gains of the MCP detector observed in this study are consistent with the proposed mechanism of the signal formation by penetrating radiation. A very similar MCP ion detector will be used in the Neutral Ion Mass (NIM) spectrometer designed for the JUICE mission of European Space Agency (ESA) to the Jupiter system, to perform measurements of the chemical composition of the Galilean moon exospheres. The detection efficiency for penetrating radiation determined in the present studies is important for the optimisation of the radiation shielding of the NIM detector against the high-rate and high-energy electrons trapped in Jupiter's magnetic field. Furthermore, the current studies indicate that MCP detectors can be useful to measure high-energy particle beams at high temporal resolution. (C) 2015 AIP Publishing LLC.

Particle flux studies of sediment traps from the northern and equatorial Indian Ocean

The Asian monsoon system governs seasonality and fundamental environmental characteristics in the study area from which two distinct peculiarities are most notable: upwelling and convective mixing in the Arabian Sea and low surface salinity and stratification in the Bay of Bengal due to high riverine input and monsoonal precipitation. The respective oceanography sets the framework for nutrient availability and productivity. Upwelling ensures high nitrate concentration with temporal/spatial Si limitation; freshwater-induced stratification leads to reduced nitrogen input from the subsurface but Si enrichment in surface waters. Ultimately, both environments support high abundance of diatoms, which play a central role in the export of organic matter. It is speculated that, additional to eddy pumping, nitrogen fixation is a source of N in stratified waters and contributes to the low-d15N signal in sinking particles formed under riverine impact. Organic carbon fluxes are best correlated to opal but not to carbonate, which is explained by low foraminiferal carbonate fluxes within the river-impacted systems. This observation points to the necessity of differentiating between carbonate sources for carbon flux modeling. As evident from a compilation of previously published and new data on labile organic matter composition (amino acids and carbohydrates), organic matter fluxes are mainly driven by direct input from marine production, except the site off Pakistan where sedimentary input of (marine) organic matter is dominant during the NE monsoon. The explanation of apparently different organic carbon export efficiency calls for further investigations of, for example, food web structure and water column processes.

Video analysis based vehicle detection and tracking using an MCMC sampling framework

This article presents a probabilistic method for vehicle detection and tracking through the analysis of monocular images obtained from a vehicle-mounted camera. The method is designed to address the main shortcomings of traditional particle filtering approaches, namely Bayesian methods based on importance sampling, for use in traffic environments. These methods do not scale well when the dimensionality of the feature space grows, which creates significant limitations when tracking multiple objects. Alternatively, the proposed method is based on a Markov chain Monte Carlo (MCMC) approach, which allows efficient sampling of the feature space. The method involves important contributions in both the motion and the observation models of the tracker. Indeed, as opposed to particle filter-based tracking methods in the literature, which typically resort to observation models based on appearance or template matching, in this study a likelihood model that combines appearance analysis with information from motion parallax is introduced. Regarding the motion model, a new interaction treatment is defined based on Markov random fields (MRF) that allows for the handling of possible inter-dependencies in vehicle trajectories. As for vehicle detection, the method relies on a supervised classification stage using support vector machines (SVM). The contribution in this field is twofold. First, a new descriptor based on the analysis of gradient orientations in concentric rectangles is dened. This descriptor involves a much smaller feature space compared to traditional descriptors, which are too costly for real-time applications. Second, a new vehicle image database is generated to train the SVM and made public. The proposed vehicle detection and tracking method is proven to outperform existing methods and to successfully handle challenging situations in the test sequences.

Vision-based vehicle detection and tracking with a mobile camera using a statistical framework

En esta tesis se aborda la detección y el seguimiento automático de vehículos mediante técnicas de visión artificial con una cámara monocular embarcada. Este problema ha suscitado un gran interés por parte de la industria automovilística y de la comunidad científica ya que supone el primer paso en aras de la ayuda a la conducción, la prevención de accidentes y, en última instancia, la conducción automática. A pesar de que se le ha dedicado mucho esfuerzo en los últimos años, de momento no se ha encontrado ninguna solución completamente satisfactoria y por lo tanto continúa siendo un tema de investigación abierto. Los principales problemas que plantean la detección y seguimiento mediante visión artificial son la gran variabilidad entre vehículos, un fondo que cambia dinámicamente debido al movimiento de la cámara, y la necesidad de operar en tiempo real. En este contexto, esta tesis propone un marco unificado para la detección y seguimiento de vehículos que afronta los problemas descritos mediante un enfoque estadístico. El marco se compone de tres grandes bloques, i.e., generación de hipótesis, verificación de hipótesis, y seguimiento de vehículos, que se llevan a cabo de manera secuencial. No obstante, se potencia el intercambio de información entre los diferentes bloques con objeto de obtener el máximo grado posible de adaptación a cambios en el entorno y de reducir el coste computacional. Para abordar la primera tarea de generación de hipótesis, se proponen dos métodos complementarios basados respectivamente en el análisis de la apariencia y la geometría de la escena. Para ello resulta especialmente interesante el uso de un dominio transformado en el que se elimina la perspectiva de la imagen original, puesto que este dominio permite una búsqueda rápida dentro de la imagen y por tanto una generación eficiente de hipótesis de localización de los vehículos. Los candidatos finales se obtienen por medio de un marco colaborativo entre el dominio original y el dominio transformado. Para la verificación de hipótesis se adopta un método de aprendizaje supervisado. Así, se evalúan algunos de los métodos de extracción de características más populares y se proponen nuevos descriptores con arreglo al conocimiento de la apariencia de los vehículos. Para evaluar la efectividad en la tarea de clasificación de estos descriptores, y dado que no existen bases de datos públicas que se adapten al problema descrito, se ha generado una nueva base de datos sobre la que se han realizado pruebas masivas. Finalmente, se presenta una metodología para la fusión de los diferentes clasificadores y se plantea una discusión sobre las combinaciones que ofrecen los mejores resultados. El núcleo del marco propuesto está constituido por un método Bayesiano de seguimiento basado en filtros de partículas. Se plantean contribuciones en los tres elementos fundamentales de estos filtros: el algoritmo de inferencia, el modelo dinámico y el modelo de observación. En concreto, se propone el uso de un método de muestreo basado en MCMC que evita el elevado coste computacional de los filtros de partículas tradicionales y por consiguiente permite que el modelado conjunto de múltiples vehículos sea computacionalmente viable. Por otra parte, el dominio transformado mencionado anteriormente permite la definición de un modelo dinámico de velocidad constante ya que se preserva el movimiento suave de los vehículos en autopistas. Por último, se propone un modelo de observación que integra diferentes características. En particular, además de la apariencia de los vehículos, el modelo tiene en cuenta también toda la información recibida de los bloques de procesamiento previos. El método propuesto se ejecuta en tiempo real en un ordenador de propósito general y da unos resultados sobresalientes en comparación con los métodos tradicionales. ABSTRACT This thesis addresses on-road vehicle detection and tracking with a monocular vision system. This problem has attracted the attention of the automotive industry and the research community as it is the first step for driver assistance and collision avoidance systems and for eventual autonomous driving. Although many effort has been devoted to address it in recent years, no satisfactory solution has yet been devised and thus it is an active research issue. The main challenges for vision-based vehicle detection and tracking are the high variability among vehicles, the dynamically changing background due to camera motion and the real-time processing requirement. In this thesis, a unified approach using statistical methods is presented for vehicle detection and tracking that tackles these issues. The approach is divided into three primary tasks, i.e., vehicle hypothesis generation, hypothesis verification, and vehicle tracking, which are performed sequentially. Nevertheless, the exchange of information between processing blocks is fostered so that the maximum degree of adaptation to changes in the environment can be achieved and the computational cost is alleviated. Two complementary strategies are proposed to address the first task, i.e., hypothesis generation, based respectively on appearance and geometry analysis. To this end, the use of a rectified domain in which the perspective is removed from the original image is especially interesting, as it allows for fast image scanning and coarse hypothesis generation. The final vehicle candidates are produced using a collaborative framework between the original and the rectified domains. A supervised classification strategy is adopted for the verification of the hypothesized vehicle locations. In particular, state-of-the-art methods for feature extraction are evaluated and new descriptors are proposed by exploiting the knowledge on vehicle appearance. Due to the lack of appropriate public databases, a new database is generated and the classification performance of the descriptors is extensively tested on it. Finally, a methodology for the fusion of the different classifiers is presented and the best combinations are discussed. The core of the proposed approach is a Bayesian tracking framework using particle filters. Contributions are made on its three key elements: the inference algorithm, the dynamic model and the observation model. In particular, the use of a Markov chain Monte Carlo method is proposed for sampling, which circumvents the exponential complexity increase of traditional particle filters thus making joint multiple vehicle tracking affordable. On the other hand, the aforementioned rectified domain allows for the definition of a constant-velocity dynamic model since it preserves the smooth motion of vehicles in highways. Finally, a multiple-cue observation model is proposed that not only accounts for vehicle appearance but also integrates the available information from the analysis in the previous blocks. The proposed approach is proven to run near real-time in a general purpose PC and to deliver outstanding results compared to traditional methods.

Moving Object Detection for Real-Time High-Quality Lightweight Applications on Smart Cameras

Here, a novel and efficient strategy for moving object detection by non-parametric modeling on smart cameras is presented. Whereas the background is modeled using only color information, the foreground model combines color and spatial information. The application of a particle filter allows the update of the spatial information and provides a priori information about the areas to analyze in the following images, enabling an important reduction in the computational requirements and improving the segmentation results