Query: Learning Face Representation from Scratch

Relevant passages:
Improving neural networks by preventing co-adaptation of feature detectors <=SEP=> When a large feedforward neural network is trained on a small training set, it typically performs poorly on held-out test data. This overfitting is greatly reduced by randomly omitting half of the feature detectors on each training case. This prevents complex co-adaptations in which a feature detector is only helpful in the context of several other specific feature detectors. Instead, each neuron learns to detect a feature that is generally helpful for producing the correct answer given the combinatorially large variety of internal contexts in which it must operate. Random “dropout” gives big improvements on many benchmark tasks and sets new records for speech and object recognition. A feedforward, artificial neural network uses layers of non-linear hidden units between its inputs and its outputs. By adapting the weights on the incoming connections of these hidden units it learns feature detectors that enable it to predict the correct output when given an input vector [15]. If the relationship between the input and the correct output is complicated and the network has enough hidden units to model it accurately, there will typically be many different settings of the weights that can model the training set almost perfectly, especially if there is only a limited amount of labeled training data. Each of these weight vectors will make different predictions on held-out test data and almost all of them will do worse on the test data than on the training data because the feature detectors have been tuned to work well together on the training data but not on the test data. Overfitting can be reduced by using “dropout” to prevent complex co-adaptations on the training data. On each presentation of each training case, each hidden unit is randomly omitted from the network with a probability of 0.5, so a hidden unit cannot rely on other hidden units being present. Another way to view the dropout procedure is as a very efficient way of performing model averaging with neural networks. A good way to reduce the error on the test set is to average the predictions produced by a very large number of different networks. The standard way to do this is to train many separate networks and then to apply each of these networks to the test data, but this is computationally expensive during both training and testing. Random dropout makes it possible to train a huge number of different networks in a reasonable time. There is almost certainly a different network for each presentation of each training case but all of these networks share the same weights for the hidden units that are present. We use the standard, stochastic gradient descent procedure for training the dropout neural networks on mini-batches of training cases, but we modify the penalty term that is normally used to prevent the weights from growing too large. Instead of penalizing the squared length (L2 norm) of the whole weight vector, we set an upper bound on the L2 norm of the incoming weight vector for each individual hidden unit. If a weight-update violates this constraint, we renormalize the weights of the hidden unit by division. Using a constraint rather than a penalty prevents weights from growing very large no matter how large the proposed weight-update is. This makes it possible to start with a

The CMU Pose, Illumination, and Expression (PIE) Database <=SEP=> Between October 2000 and December 2000 we collected a database of over 40,000 facial images of 68 people. Using the CMU 3D Room we imaged each person across 13 different poses, under 43 different illumination conditions, and with 4 different expressions. We call this database the CMU Pose, Illumination, and Expression (PIE) database. In this paper we describe the imaging hardware, the collection procedure, the organization of the database, several potential uses of the database, and how to obtain the database.

Face recognition in unconstrained videos with matched background similarity <=SEP=> Recognizing faces in unconstrained videos is a task of mounting importance. While obviously related to face recognition in still images, it has its own unique characteristics and algorithmic requirements. Over the years several methods have been suggested for this problem, and a few benchmark data sets have been assembled to facilitate its study. However, there is a sizable gap between the actual application needs and the current state of the art. In this paper we make the following contributions. (a) We present a comprehensive database of labeled videos of faces in challenging, uncontrolled conditions (i.e., ‘in the wild’), the ‘YouTube Faces’ database, along with benchmark, pair-matching tests1. (b) We employ our benchmark to survey and compare the performance of a large variety of existing video face recognition techniques. Finally, (c) we describe a novel set-to-set similarity measure, the Matched Background Similarity (MBGS). This similarity is shown to considerably improve performance on the benchmark tests.

Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition <=SEP=> This paper introduces a novel Gabor-Fisher (1936) classifier (GFC) for face recognition. The GFC method, which is robust to changes in illumination and facial expression, applies the enhanced Fisher linear discriminant model (EFM) to an augmented Gabor feature vector derived from the Gabor wavelet representation of face images. The novelty of this paper comes from 1) the derivation of an augmented Gabor feature vector, whose dimensionality is further reduced using the EFM by considering both data compression and recognition (generalization) performance; 2) the development of a Gabor-Fisher classifier for multi-class problems; and 3) extensive performance evaluation studies. In particular, we performed comparative studies of different similarity measures applied to various classifiers. We also performed comparative experimental studies of various face recognition schemes, including our novel GFC method, the Gabor wavelet method, the eigenfaces method, the Fisherfaces method, the EFM method, the combination of Gabor and the eigenfaces method, and the combination of Gabor and the Fisherfaces method. The feasibility of the new GFC method has been successfully tested on face recognition using 600 FERET frontal face images corresponding to 200 subjects, which were acquired under variable illumination and facial expressions. The novel GFC method achieves 100% accuracy on face recognition using only 62 features.

Probabilistic Visual Learning for Object Representation <=SEP=> We present an unsupervised technique for visual learning, which is based on density estimation in high-dimensional spaces using an eigenspace decomposition. Two types of density estimates are derived for modeling the training data: a multivariate Gaussian (for unimodal distributions) and a Mixture-of-Gaussians model (for multimodal distributions). These probability densities are then used to formulate a maximum-likelihood estimation framework for visual search and target detection for automatic object recognition and coding. Our learning technique is applied to the probabilistic visual modeling, detection, recognition, and coding of human faces and nonrigid objects, such as hands.

Introspection-based memory de-duplication and migration <=SEP=> Memory virtualization abstracts a physical machine's memory resource and presents to the virtual machines running on it a piece of physical memory that could be shared, compressed and moved. To optimize the memory resource utilization by fully leveraging the flexibility afforded by memory virtualization, it is essential that the hypervisor have some sense of how the guest VMs use their allocated physical memory. One way to do this is virtual machine introspection (VMI), which interprets byte values in a guest memory space into semantically meaningful data structures. However, identifying a guest VM's memory usage information such as free memory pool is non-trivial. This paper describes a bootstrapping VM introspection technique that could accurately extract free memory pool information from multiple versions of Windows and Linux without kernel version-specific hard-coding, how to apply this technique to improve the efficiency of memory de-duplication and memory state migration, and the resulting improvement in memory de-duplication speed, gain in additional memory pages de-duplicated, and reduction in traffic loads associated with memory state migration.

Deep LSTM based Feature Mapping for Query Classification <=SEP=> Traditional convolutional neural network (CNN) based query classification uses linear feature mapping in its convolution operation. The recurrent neural network (RNN), differs from a CNN in representing word sequence with their ordering information kept explicitly. We propose using a deep long-short-term-memory (DLSTM) based feature mapping to learn feature representation for CNN. The DLSTM, which is a stack of LSTM units, has different order of feature representations at different depth of LSTM unit. The bottom LSTM unit equipped with input and output gates, extracts the first order feature representation from current word. To extract higher order nonlinear feature representation, the LSTM unit at higher position gets input from two parts. First part is the lower LSTM unit’s memory cell from previous word. Second part is the lower LSTM unit’s hidden output from current word. In this way, the DLSTM captures the nonlinear nonconsecutive interaction within n-grams. Using an architecture that combines a stack of the DLSTM layers with a tradition CNN layer, we have observed new state-of-the-art query classification accuracy on benchmark data sets for query classification.

Robust Nighttime Vehicle Detection by Tracking and Grouping Headlights <=SEP=> Nighttime traffic surveillance is difficult due to insufficient and unstable appearance information and strong background interference. We present in this paper a robust nighttime vehicle detection system by detecting, tracking, and grouping headlights. First, we train AdaBoost classifiers for headlights detection to reduce false alarms caused by reflections. Second, to take full advantage of the complementary nature of grouping and tracking, we alternately optimize grouping and tracking. For grouping, motion features produced by tracking are used by headlights pairing. We use a maximal independent set framework for effective pairing, which is more robust than traditional pairing-by-rules methods. For tracking, context information provided by pairing is employed by multiple object tracking. The experiments on challenging datasets and quantitative evaluation show promising performance of our method.

Compact Microstrip-to-CPS Transition for UWB Application <=SEP=> A novel ultra-wideband (UWB) microstrip-to-CPS (coplanar stripline) transition has been developed. This transition or balun structure has several attractive advantages such as good impedance transformation, compact size and wide bandwidth. After the parallel-coupled line section between the microstrip line and CPS is investigated under varied traversal dimensions, a wide transmitting band is well achieved with the emergence of two transmission poles. Next, such a single transition circuit is optimally designed to cover the whole UWB band (3.1 GHz to 10.6 GHz). To verify the predicted results in experiment, the two back-to-back transitions with the same 50 Omega microstrip feed lines are fabricated and tested. Measured results exhibit the return loss close to 10.0 dB over a band from 3.5 GHz to 10.0 GHz.

A Reinforcement Learning-driven Translation Model for Search-Oriented Conversational Systems <=SEP=> Search-oriented conversational systems rely on information needs expressed in natural language (NL). We focus here on the understanding of NL expressions for building keywordbased queries. We propose a reinforcementlearning-driven translation model framework able to 1) learn the translation from NL expressions to queries in a supervised way, and, 2) to overcome the lack of large-scale dataset by framing the translation model as a word selection approach and injecting relevance feedback as a reward in the learning process. Experiments are carried out on two TREC datasets. We outline the effectiveness of our approach.

Key technology and application of millimeter wave communications for 5G: a survey <=SEP=> As mobile communication technology continues to develop, millimeter-wave communication for 5G mobile networks is attracting widespread public attention. Many studies have appeared in the literatures related to millimeter wave mobile broadband communication systems. This paper briefly introduces the development of 5G and millimeter wave communications, analyses several practical designs and implementations of millimeter wave communications for 5G, and proposes some future applications and challenges for 5G.

Five-Level Reduced-Switch-Count Boost PFC Rectifier With Multicarrier PWM <=SEP=> A multilevel boost power factor correction (PFC) rectifier is presented in this paper controlled by cascaded controller and multicarrier pulse width modulation technique. The presented topology has less active semiconductor switches compared to similar ones reducing the number of required gate drives that would shrink the manufactured box significantly. A simple controller has been implemented on the studied converter to generate a constant voltage at the output while generating a five-level voltage waveform at the input without connecting the load to the neutral point of the dc bus capacitors. Multicarrier pulse-width modulation technique has been used to produce switching pulses from control signal at a fixed switching frequency. Multilevel voltage waveform harmonics has been analyzed comprehensively which affects the harmonic contents of input current and the size of required filters directly. Full experimental results confirm the good dynamic performance of the proposed five-level PFC boost rectifier in delivering power from ac grid to the dc loads while correcting the power factor at the ac side as well as reducing the current harmonics remarkably.

Stock market prediction system with modular neural networks <=SEP=> This paper discusses a buying and selling timing prediction system for stocks on the Tokyo Stock Exchange and analysis of intemal representation. It is based on modular neural networks[l][2]. We developed a number of learning algorithms and prediction methods for the TOPIX(Toky0 Stock Exchange Prices Indexes) prediction system. The prediction system achieved accurate predictions and the simulation on stocks tradmg showed an excellent profit. The prediction system was developed by Fujitsu and Nikko Securities.

Subspace Learning and Imputation for Streaming Big Data Matrices and Tensors <=SEP=> Extracting latent low-dimensional structure from high-dimensional data is of paramount importance in timely inference tasks encountered with “Big Data” analytics. However, increasingly noisy, heterogeneous, and incomplete datasets, as well as the need for real-time processing of streaming data, pose major challenges to this end. In this context, the present paper permeates benefits from rank minimization to scalable imputation of missing data, via tracking low-dimensional subspaces and unraveling latent (possibly multi-way) structure from incomplete streaming data. For low-rank matrix data, a subspace estimator is proposed based on an exponentially weighted least-squares criterion regularized with the nuclear norm. After recasting the nonseparable nuclear norm into a form amenable to online optimization, real-time algorithms with complementary strengths are developed, and their convergence is established under simplifying technical assumptions. In a stationary setting, the asymptotic estimates obtained offer the well-documented performance guarantees of the batch nuclear-norm regularized estimator. Under the same unifying framework, a novel online (adaptive) algorithm is developed to obtain multi-way decompositions of low-rank tensors with missing entries and perform imputation as a byproduct. Simulated tests with both synthetic as well as real Internet and cardiac magnetic resonance imagery (MRI) data confirm the efficacy of the proposed algorithms, and their superior performance relative to state-of-the-art alternatives.

Prevalence of autism in a US metropolitan area. <=SEP=> CONTEXT
Concern has been raised about possible increases in the prevalence of autism. However, few population-based studies have been conducted in the United States.


OBJECTIVES
To determine the prevalence of autism among children in a major US metropolitan area and to describe characteristics of the study population.


DESIGN, SETTING, AND POPULATION
Study of the prevalence of autism among children aged 3 to 10 years in the 5 counties of metropolitan Atlanta, Ga, in 1996. Cases were identified through screening and abstracting records at multiple medical and educational sources, with case status determined by expert review.


MAIN OUTCOME MEASURES
Autism prevalence by demographic factors, levels of cognitive functioning, previous autism diagnoses, special education eligibility categories, and sources of identification.


RESULTS
A total of 987 children displayed behaviors consistent with Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition criteria for autistic disorder, pervasive developmental disorder-not otherwise specified, or Asperger disorder. The prevalence for autism was 3.4 per 1000 (95% confidence interval [CI], 3.2-3.6) (male-female ratio, 4:1). Overall, the prevalence was comparable for black and white children (black, 3.4 per 1000 [95% CI, 3.0-3.7] and white, 3.4 per 1000 [95% CI, 3.2-3.7]). Sixty-eight percent of children with IQ or developmental test results (N = 880) had cognitive impairment. As severity of cognitive impairment increased from mild to profound, the male-female ratio decreased from 4.4 to 1.3. Forty percent of children with autism were identified only at educational sources. Schools were the most important source for information on black children, children of younger mothers, and children of mothers with less than 12 years of education.


CONCLUSION
The rate of autism found in this study was higher than the rates from studies conducted in the United States during the 1980s and early 1990s, but it was consistent with those of more recent studies.

A direct fingerprint minutiae extraction approach based on convolutional neural networks <=SEP=> Minutiae, as the essential features of fingerprints, play a significant role in fingerprint recognition systems. Most existing minutiae extraction methods are based on a series of hand-defined preprocesses such as binarization, thinning and enhancement. However, these preprocesses require strong prior knowledge and are always lossy operations. And that will lead to dropped or false extractions of minutiae. In this paper, a novel minutiae extraction approach based on deep convolutional neural networks is proposed, which directly extract minutiae on raw fingerprint images without any preprocess since we tactfully take the advantage of the strong representative capacity of deep convolutional neural networks. Minutiae can be effectively extracted due to the well designed architectures. Furthermore, the accuracy is guaranteed in that the comprehensive estimate is made to eliminate spurious minutiae. Moreover, a number of implement skills are employed both to avoid overfitting and to improve the robustness. This approach makes a good performance because it not only makes all use of information in fingerprint images but also learns the minutiae patterns from large amounts of data. Comparisons are made with previous works and a widely applied commercial fingerprint identification system. Results show that our approach performs better both in accuracy and robustness.

VOCALOID - commercial singing synthesizer based on sample concatenation <=SEP=> The song submitted here to the “Synthesis of Singing Challenge” is synthesized by the latest version of the singing synthesizer “Vocaloid”, which is commercially available now. In this paper, we would like to present the overview of Vocaloid, its product lineups, description of each component, and the synthesis technique used in Vocaloid.

PWM regenerative rectifiers: state of the art <=SEP=> New regulations impose more stringent limits on current harmonics injected by power converters that are achieved with pulsewidth-modulated (PWM) rectifiers. In addition, several applications demand the capability of power regeneration to the power supply. This work presents the state of the art in the field of regenerative rectifiers with reduced input harmonics and improved power factor. Regenerative rectifiers are able to deliver energy back from the dc side to the ac power supply. Topologies for single- and three-phase power supplies are considered with their corresponding control strategies. Special attention is given to the application of voltage- and current-source PWM rectifiers in different processes with a power range from a few kilowatts up to several megawatts. This paper shows that PWM regenerative rectifiers are a highly developed and mature technology with a wide industrial acceptance.

Design of an endoluminal NOTES robotic system <=SEP=> Natural orifice transluminal endoscopic surgery, or NOTES, allows for exceedingly minimally invasive surgery but has high requirements for the dexterity and force capabilities of the tools. An overview of the ViaCath System is presented. This system is a first generation teleoperated robot for endoluminal surgery and consists of a master console with haptic interfaces, slave drive mechanisms, and 6 degree-of-freedom, long-shafted flexible instruments that run alongside a standard gastroscope or colonoscope. The system was validated through animal studies. It was discovered that the devices were difficult to introduce into the GI tract and manipulation forces were insufficient. The design of a second generation system is outlined with improvements to the instrument articulation section and a steerable overtube. Results of basic evaluation tests performed on the tools are also presented.

Adaptive control of systems in cascade with saturation <=SEP=> Some systems may be approximated as block cascades structures where each block of the cascade can be approximately feedback linearized. In such systems, the states of the lower subsystem blocks affect the dynamics of the upper subsystems. In generating an inverse for a given block, a subset of its lower subsystem states can be treated as virtual actuators in addition to actual direct actuation that may be available. The desired virtual actuator signal arising from the upper subsystem's inverse now appears as a command to the lower subsystem. This paper introduces an adaptive element that is capable of canceling modeling errors arising due to feedback linearization. It also introduces reference models that include a pseudocontrol hedging signal which protects the adaptive element from lower subsystem's dynamics.

Selecting Coherent and Relevant Plots in Large Scatterplot Matrices <=SEP=> The scatterplot matrix (SPLOM) is a well-established technique to visually explore high-dimensional data sets. It is characterized by the number of scatterplots (plots) of which it consists of. Unfortunately, this number quadratically grows with the number of the data set’s dimensions. Thus, a SPLOM scales very poorly. Consequently, the usefulness of SPLOMs is restricted to a small number of dimensions. For this, several approaches already exist to explore such “small” SPLOMs. Those approaches address the scalability problem just indirectly and without solving it. Therefore, we introduce a new greedy approach to manage “large” SPLOMs with more than hundred dimensions. We establish a combined visualization and interaction scheme that produces intuitively interpretable SPLOMs by combining known quality measures, a pre-process reordering, and a perception-based abstraction. With this scheme, the user can interactively find large amounts of relevant plots in large SPLOMs.

Using galvanic skin response for cognitive load measurement in arithmetic and reading tasks <=SEP=> Galvanic Skin Response (GSR) has recently attracted researchers' attention as a prospective physiological indicator of cognitive load and emotions. However, it has commonly been investigated through single or few measures and in one experimental scenario. In this research, aiming to perform a comprehensive study, we have assessed GSR data captured from two different experiments, one including text reading tasks and the other using arithmetic tasks, each imposing multiple cognitive load levels. We have examined temporal and spectral features of GSR against different task difficulty levels. ANOVA test was applied for the statistical evaluation. Obtained results show the strong significance of the explored features, especially the spectral ones, in cognitive workload measurement in the two studied experiments.

A Blockchain-based Security-Oriented Framework for Cloud Federation <=SEP=> Cloud federations have been formed to share the services, prompt and support cooperation, as well as interoperability among their already deployed cloud systems. However, the creation and management of the cloud federations lead to various security issues such as confidentially, integrity and availability of the data. Despite the access control policies in place, an attacker may compromise the communication channel processing the access requests and the decisions between the access control systems and the members(users) and vice-versa. In cloud federation, the rating of the services offered by different cloud members becomes integral to providing the users with the best quality services. Hence, we propose an innovative blockchainbased framework that on the one hand permits secure communication between the members of the federation and the access control systems, while on the other hand provides the quality services to the members by considering the service constraints imposed by them.

Total Recall: System Support for Automated Availability Management <=SEP=> Goal: Highly available data storage in large-scale distributed systems in which * Hosts are transiently inaccessible * Individual host failures are common Current peer-to-peer systems are prime examples * Highly dynamic, challenging environment * hosts join and leave frequently in short-term * Hosts leave permanently over long-term * Workload varies in terms of popularity, access patterns, file size These systems require automated availability management.

CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes <=SEP=> We propose a network for Congested Scene Recognition called CSRNet to provide a data-driven and deep learning method that can understand highly congested scenes and perform accurate count estimation as well as present high-quality density maps. The proposed CSRNet is composed of two major components: a convolutional neural network (CNN) as the front-end for 2D feature extraction and a dilated CNN for the back-end, which uses dilated kernels to deliver larger reception fields and to replace pooling operations. CSRNet is an easy-trained model because of its pure convolutional structure. We demonstrate CSRNet on four datasets (ShanghaiTech dataset, the UCF_CC_50 dataset, the WorldEXPO'10 dataset, and the UCSD dataset) and we deliver the state-of-the-art performance. In the ShanghaiTech Part_B dataset, CSRNet achieves 47.3% lower Mean Absolute Error (MAE) than the previous state-of-the-art method. We extend the targeted applications for counting other objects, such as the vehicle in TRANCOS dataset. Results show that CSRNet significantly improves the output quality with 15.4% lower MAE than the previous state-of-the-art approach.

The role of character strengths in adolescent romantic relationships: an initial study on partner selection and mates' life satisfaction. <=SEP=> The present study investigated the role of 24 character strengths in 87 adolescent romantic relationships focusing on their role in partner selection and their role in mates' life satisfaction. Measures included the Values in Action Inventory of Strengths for Youth, the Students' Life Satisfaction Scale, and an Ideal Partner Profiler for the composition of an ideal partner. Honesty, humor, and love were the most preferred character strengths in an ideal partner. Hope, religiousness, honesty, and fairness showed the most substantial assortment coefficients. Hierarchical regression analyses revealed targets' character strengths as explaining variance in targets' life satisfaction. Furthermore, to a lesser degree, specific character strengths of partners and couples' similarity in certain character strengths explained variance in targets' life satisfaction beyond targets' character strengths. This first research on this topic showed that character strengths play a significant role in adolescent romantic relationships.

LDA-AdaBoost.MH: Accelerated AdaBoost.MH based on latent Dirichlet allocation for text categorization <=SEP=> AdaBoost.MH is a boosting algorithm that is considered to be one of the most accurate algorithms for multilabel classification. It works by iteratively building a committee of weak hypotheses of decision stumps. To build the weak hypotheses, in each iteration, AdaBoost.MH obtains the whole extracted features and examines them one by one to check their ability to characterize the appropriate category. Using Bag-Of-Words for text representation dramatically increases the computational time of AdaBoost.MH learning, especially for large-scale datasets. In this paper we demonstrate how to improve the efficiency and effectiveness of AdaBoost.MH using latent topics, rather than words. A well-known probabilistic topic modelling method, Latent Dirichlet Allocation, is used to estimate the latent topics in the corpus as features for AdaBoost.MH. To evaluate LDA-AdaBoost.MH, the following four datasets have been used: Reuters-21578-ModApte, WebKB, 20-Newsgroups and a collection of Arabic news. The experimental results confirmed that representing the texts as a small number of latent topics, rather than a large number of words, significantly decreased the computational time of AdaBoost.MH learning and improved its performance for text categorization.

Adapting instance weights for unsupervised domain adaptation using quadratic mutual information and subspace learning <=SEP=> Domain adaptation (DA) algorithms utilize a label-rich old dataset (domain) to build a machine learning model (classification, detection etc.) in a label-scarce new dataset with different data distribution. Recent approaches transform cross-domain data into a shared subspace by minimizing the shift between their marginal distributions. In this paper, we propose a novel iterative method to learn a common subspace based on non-parametric quadratic mutual information (QMI) between data and corresponding class labels. We extend a prior work of discriminative subspace learning based on maximization of QMI and integrate instance weighting into the QMI formulation. We propose an adaptive weighting model to identify relevant samples that share underlying similarity across domains and ignore irrelevant ones. Due to difficulty of applying cross-validation, an alternative strategy is integrated with the proposed algorithm to setup model parameters. A set of comprehensive experiments on benchmark datasets is conducted to prove the efficacy of our proposed framework over state-of-the-art approaches.

Outlier Detection over Massive-Scale Trajectory Streams <=SEP=> The detection of abnormal moving objects over high-volume trajectory streams is critical for real-time applications ranging from military surveillance to transportation management. Yet this outlier detection problem, especially along both the spatial and temporal dimensions, remains largely unexplored. In this work, we propose a rich taxonomy of novel classes of neighbor-based trajectory outlier definitions that model the anomalous behavior of moving objects for a large range of real-time applications. Our theoretical analysis and empirical study on two real-world datasets—the Beijing Taxi trajectory data and the Ground Moving Target Indicator data stream—and one generated Moving Objects dataset demonstrate the effectiveness of our taxonomy in effectively capturing different types of abnormal moving objects. Furthermore, we propose a general strategy for efficiently detecting these new outlier classes called the <underline>m</underline>inimal <underline>ex</underline>amination (MEX) framework. The MEX framework features three core optimization principles, which leverage spatiotemporal as well as the predictability properties of the neighbor evidence to minimize the detection costs. Based on this foundation, we design algorithms that detect the outliers based on these classes of new outlier semantics that successfully leverage our optimization principles. Our comprehensive experimental study demonstrates that our proposed MEX strategy drives the detection costs 100-fold down into the practical realm for applications that analyze high-volume trajectory streams in near real time.

Deep Robust Kalman Filter <=SEP=> A Robust Markov Decision Process (RMDP) is a sequential decision making model that accounts for uncertainty in the parameters of dynamic systems. This uncertainty introduces difficulties in learning an optimal policy, especially for environments with large state spaces. We propose two algorithms, RTD-DQN and Deep-RoK, for solving large-scale RMDPs using nonlinear approximation schemes such as deep neural networks. The RTD-DQN algorithm incorporates the robust Bellman temporal difference error into a robust loss function, yielding robust policies for the agent. The Deep-RoK algorithm is a robust Bayesian method, based on the Extended Kalman Filter (EKF), that accounts for both the uncertainty in the weights of the approximated value function and the uncertainty in the transition probabilities, improving the robustness of the agent. We provide theoretical results for our approach and test the proposed algorithms on a continuous state domain.