This paper proposes a novel quantum key distribution (QKD) set-up designed to enhance the security of a free-space optical (FSO) quantum communication system. By incorporating channel ordering information, the proposed QPSK-QKD (P-QPSK-QKD) system leverages the inherent randomness of FSO channels, providing dual protection against eavesdroppers (Eve). The first layer of security is achieved through the QPSK-QKD mechanism, while the second arises from the randomness of the FSO channels. To ensure a comprehensive investigation, the performance of P-QPSK-QKD and the existing QPSK-QKD (E-QPSK-QKD) system is analyzed using a Málaga distributed atmospheric turbulence channel model incorporating pointing errors. Novel closed-form expressions are derived for different performance metrics, including probability of sifted keys, quantum bit error rate, probability of Eve (Peve), and ergodic secret key rate (ESKR), under both perfect and imperfect feedback conditions. Intensive examination indicates that, unlike E-QPSK-QKD, wherePeveshows a water-falling trend with increasing signal-to-noise ratios (SNR) under dual thresholds, P-QPSK-QKD achieves a saturatedPeve, enhancing security. Moreover, under the single threshold,Peveof P-QPSK-QKD is twice that of E-QPSK-QKD. Additionally, due to channel randomness, the mutual information between Alice and Eve remains zero (for both single and dual thresholds), even at infinite SNR for P-QPSK-QKD, providing consistently higher ESKR than E-QPSK-QKD system under both perfect and imperfect feedback conditions.

This work analyzes a terrestrial-based free-space quantum key distribution system employing quadrature-phase shift keying signaling (QKD-QPSK). An analytical framework is proposed to determine various joint probabilities under the gamma-gamma turbulence channel model without utilizing the Gaussian–Laguerre (GL) method. Although GL-based method provides ease of computation of integral forms, but it is not always stable. Using derived joint probabilities, novel analytical expressions for the probability of sifted keys (Psift), quantum bit error rate (QBER), error probability for the eavesdropper (Peve), and the ergodic secret-key rate (ESKR) are determined for QKD-QPSK system. This work also reveals that the QKD-QPSK system can be transformed into QKD-based binary phase-shift keying (QKD-BPSK) system. Furthermore, using the proposed analytical framework, all the considered performance metrics are also evaluated for the QKD-BPSK system. Finally, QKD-QPSK and QKD-BPSK systems are compared under moderate and strong turbulence regimes. Comparative results indicate that QKD-QPSK outperforms QKD-BPSK in terms of Peve. However, QKD-BPSK is superior to QKD-QPSK for Psiftand QBER. Furthermore, a positive ESKR can be achieved for both systems with the proper selection of threshold coefficient (ρ) and transmitted power (Pt).

This study presents a social analytics approach to the study of public toilet cleanliness in Singapore. From popular social media platforms, our system automatically gathers and analyzes relevant public posts that mention about toilet cleanliness in highly frequented locations across the Singapore island - from busy shopping malls to food `hawker' centers.

In this work we demonstrate how Finsler geometry---and specifically the related geodesic tracto-graphy---can be levied to analyze structural connections between different brain regions. We present new theoretical developments which support the definition of a novel Finsler metric and associated connectivity measures, based on closely related works on the Riemannian framework for diffusion MRI. Using data from the Human Connectome Project, as well as population data from an autism spectrum disorder study, we demonstrate that this new Finsler metric, together with the new connectivity measures, results in connectivity maps that are much closer to known tract anatomy compared to previous geodesic connectivity methods. Our implementation can be used to compute geodesic distance and connectivity maps for segmented areas and is publicly available.

The affective state of people changes in the course of conversations and these changes are expressed externally in a variety of channels, including facial expressions, voice, and spoken words. Recent advances in automatic sensing of affect, through cues in individual modalities, have been remarkable; yet emotion recognition is far from a solved problem. Recently, researchers have turned their attention to the problem of multimodal affect sensing in the hope that combining different information sources would provide great improvements. However, reported results fall short of the expectations, indicating only modest benefits and occasionally even degradation in performance. We develop temporal Bayesian fusion for continuous real-value estimation of valence, arousal, power, and expectancy dimensions of affect by combining video, audio, and lexical modalities. Our approach provides substantial gains in recognition performance compared to previous work. This is achieved by the use of a powerful temporal prediction model as prior in Bayesian fusion as well as by incorporating uncertainties about the unimodal predictions. The temporal prediction model makes use of time correlations on the affect sequences and employs estimated temporal biases to control the affect estimations at the beginning of conversations. In contrast to other recent methods for combination of modalities our model is simpler, since it does not model relationships between modalities and involves only a few interpretable parameters to be estimated from the training data.

People convey their emotional state in their face and voice. We present an audio-visual data set uniquely suited for the study of multi-modal emotion expression and perception. The data set consists of facial and vocal emotional expressions in sentences spoken in a range of basic emotional states (happy, sad, anger, fear, disgust, and neutral). 7,442 clips of 91 actors with diverse ethnic backgrounds were rated by multiple raters in three modalities: audio, visual, and audio-visual. Categorical emotion labels and real-value intensity values for the perceived emotion were collected using crowd-sourcing from 2,443 raters. The human recognition of intended emotion for the audio-only, visual-only, and audio-visual data are 40.9%, 58.2% and 63.6% respectively. Recognition rates are highest for neutral, followed by happy, anger, disgust, fear, and sad. Average intensity levels of emotion are rated highest for visual-only perception. The accurate recognition of disgust and fear requires simultaneous audio-visual cues, while anger and happiness can be well recognized based on evidence from a single modality. The large dataset we introduce can be used to probe other questions concerning the audio-visual perception of emotion.

Automatic recognition of emotion using facial expressions in the presence of speech poses a unique challenge because talking reveals clues for the affective state of the speaker but distorts the canonical expression of emotion on the face. We introduce a corpus of acted emotion expression where speech is either present (talking) or absent (silent). The corpus is uniquely suited for analysis of the interplay between the two conditions. We use a multimodal decision level fusion classifier to combine models of emotion from talking and silent faces as well as from audio to recognize five basic emotions: anger, disgust, fear, happy and sad. Our results strongly indicate that emotion prediction in the presence of speech from action unit facial features is less accurate when the person is talking. Modeling talking and silent expressions separately and fusing the two models greatly improves accuracy of prediction in the talking setting. The advantages are most pronounced when silent and talking face models are fused with predictions from audio features. In this multi-modal prediction both the combination of modalities and the separate models of talking and silent facial expression of emotion contribute to the improvement.

We present experiments on fusing facial video, audio and lexical indicators for affect estimation during dyadic conversations. We use temporal statistics of texture descriptors extracted from facial video, a combination of various acoustic features, and lexical features to create regression based affect estimators for each modality. The single modality regressors are then combined using particle filtering, by treating these independent regression outputs as measurements of the affect states in a Bayesian filtering framework, where previous observations provide prediction about the current state by means of learned affect dynamics. Tested on the Audio-visual Emotion Recognition Challenge dataset, our single modality estimators achieve substantially higher scores than the official baseline method for every dimension of affect. Our filtering-based multi-modality fusion achieves correlation performance of 0.344 (baseline: 0.136) and 0.280 (baseline: 0.096) for the fully continuous and word level sub challenges, respectively.

We introduce a novel emotion recognition approach which integrates ranking models. The approach is speaker independent, yet it is designed to exploit information from utterances from the same speaker in the test set before making predictions. It achieves much higher precision in identifying emotional utterances than a conventional SVM classifier. Furthermore we test several possibilities for combining conventional classification and predictions based on ranking. All combinations improve overall prediction accuracy. All experiments are performed on the FAU AIBO database which contains realistic spontaneous emotional speech. Our best combination system achieves 6.6 % absolute improvement over the Interspeech 2009 emotion challenge baseline system on the 5-class classification tasks. Index Terms: emotion classification, ranking models, spontaneous speech

This paper proposes a novel nonrigid inter-subject multichannel image registration method which combines information from different modalities/channels to produce a unified joint registration. Multichannel images are created using co-registered multimodality images of the same subject to utilize information across modalities comprehensively. Contrary to the existing methods which combine the information at the image/intensity level, the proposed method uses feature-level information fusion method to spatio-adaptively combine the complementary information from different modalities that characterize different tissue types, through Gabor wavelets transformation and Independent Component Analysis (ICA), to produce a robust inter-subject registration. Experiments on both simulated and real multichannel images illustrate the applicability and robustness of the proposed registration method that combines information across modalities. This inter-subject registration is expected to pave the way for subsequent unified population-based multichannel studies.

Traditional approaches to automatic emotion recognition from speech typically make use of utterance level prosodic features. Still, a great deal of useful information about expressivity and emotion can be gained from segmental spectral features, which provide a more detailed description of the speech signal, or from measurements from specific regions of the utterance, such as the stressed vowels. Here we introduce a novel set of spectral features for emotion recognition: statistics of Mel-Frequency Spectral Coefficients computed over three phoneme type classes of interest: stressed vowels, unstressed vowels and consonants in the utterance. We investigate performance of our features in the task of speaker-independent emotion recognition using two publicly available datasets. Our experimental results clearly indicate that indeed both the richer set of spectral features and the differentiation between phoneme type classes are beneficial for the task. Classification accuracies are consistently higher for our features compared to prosodic features or utterance-level spectral features. Combination of our phoneme class features with prosodic features leads to even further improvement. Index Terms: emotion recognition

Diffusion tensor magnetic resonance imaging (DT-MRI) provides a comprehensive characterization of white matter (WM) in the brain and therefore, plays a crucial role in the investigation of diseases in which WM is suspected to be compromised such as multiple sclerosis and neuropsychiatric disorders like schizophrenia. However changes induced by pathology may be subtle and affected regions of the brain can only be revealed by a group-based analysis of patients in comparison with healthy controls. This in turn requires voxel-based statistical analysis of spatially normalized brain DT images, as in the case of conventional MR images. However this process is rendered extremely challenging in DT-MRI due to the high dimensionality of the data and its inherent non-linearity that causes linear component analysis methods to be inapplicable. We therefore propose a novel framework for the statistical analysis of DT-MRI data using manifold-based techniques such as isomap and kernel PCA that determine the underlying manifold structure of the data, embed it to a manifold and help perform high dimensional statistics on the manifold to determine regions of difference between the groups of patients and controls. The framework has been successfully applied to DT-MRI data from patients with schizophrenia, as well as to study developmental changes in small animals, both of which identify regional changes, indicating the need for manifold-based methods for the statistical analysis of DTI.

Most of current computer-based facial expression analysis methods focus on the recognition of perfectly posed expressions, and hence are incapable of handling the individuals with expression impairments. In particular, patients with schizophrenia usually have impaired expressions in the form of "flat" or "inappropriate" affects, which make the quantification of their facial expressions a challenging problem. This paper presents methods to quantify the group differences between patients with schizophrenia and healthy controls, by extracting specialized features and analyzing group differences on a feature manifold. The features include 2D and 3D geometric features, and the moment invariants combining both 3D geometry and 2D textures. Facial expression recognition experiments on actors demonstrate that our combined features can better characterize facial expressions than either 2D geometric or texture features. The features are then embedded into an ISOMAP manifold to quantify the group differences between controls and patients. Experiments show that our results are strongly supported by the human rating results and clinical findings, thus providing a framework that is able to quantify the abnormality in patients with schizophrenia.

Although it is usually assumed in many pattern recognition problems that different patterns are distinguishable, some patterns may have inseparable overlap. For example, some facial expressions involve subtle muscle movements, and are difficult to separate from other expressions or neutral faces. In this paper, we consider such overlapped patterns as "clusters", and present a novel method to quantify cluster overlap based on the Bayes error estimation on manifolds. Our method first applies a manifold learning method, ISOMAP, to discover the intrinsic structure of data, and then measures the overlap of different clusters using the k-NN Bayes error estimation on the learned manifolds. Due to the ISOMAP"s capability of preserving geodesic distances and k-NN's localized estimation, the method can provide an accurate measure of the overlap between clusters, as demonstrated by our simulation experiments. The method is further applied for an analysis of a specific type of facial expression impairment in schizophrenia, i.e.,"flat effect", which refers to a severe reduction in emotional expressiveness. In this study, we capture facial expressions of individuals, and quantify their expression flatness by estimating overlap between different facial expressions. The experimental results show that the patient group has much larger facial expression overlap than the control group, and demonstrate that the flat affect is an important symptom in diagnosing schizophrenia patients.

We propose a novel framework for tissue abnormality characterization in normal appearing brain tissue (NABT) that is progressively deteriorating, using affinity propagation applied to multi-parametric data created using a combination of magnetic resonance (MR) protocols. While traditional tissue segmentation and clustering can reveal clusters pertaining to healthy and diseased tissue easily, a complete characterization of the effect of pathology requires the study of heterogeneity of NABT. The problem is rendered challenging by the fact that there are no training samples available for such tissue and hence classification based techniques cannot be used and neither can traditional clustering techniques since the number of clusters are not known a priori. Our framework for the automated clustering of tissue types employs a combination of a) manifold learning, that determines the underlying non-linear structure and embeds it into a lower dimensional space and b) affinity propagation (AP), which is a novel clustering technique that combines model- and similarity- based clustering, to automatically obtain exemplar-based clustering. We also define a novel probabilistic clustering technique. The number of clusters associated with a tissue type is indicative of its heterogeneity. By computing the overlap of these clusters in each of the MR protocols, we obtain a measure of the degree of abnormality in a tissue type and the protocol most sensitive in providing that classification. This general framework is applied towards the characterization of NABT in patients with multiple sclerosis. Results demonstrate a greater heterogeneity in NABT surrounding the lesions along with a greater overlap between the NABT and lesion tissue.

Methods of computational anatomy are typically based on a spatial transformation that maps a template to an individual anatomy and vice versa. However, important morphological characteristics are frequently not captured by this transformation, thereby leading to lossy representations. We extend this formulation by incorporating residual anatomical information, i.e., information that is not captured by the shape transformation but is necessary in order to fully and exactly reconstruct the anatomy under measurement. We, therefore, arrive at a lossless morphological representation. By virtue of being lossless, this representation allows us to represent the same anatomy by an infinite number of pairs [transformation, residual], since different residuals correspond to different transformations. We treat these pairs as members of an anatomical equivalence class (AEC), which we approximate using principal component analysis. We show that projection onto the orthogonal to the AEC subspace produces measurements that allow us to better detect morphological abnormalities by eliminating variation in the data that is irrelevant and confounds underlying subtle morphological characteristics. Finally, we show that higher classification rates between a group of normal brains and a group of brains with localized atrophy are obtained if we use nonmetric distances between AECs instead of conventional Euclidean distances between individual morphological measurements. The results confirm that this representation can improve the results compared to conventional analysis, but also highlight limitations of the current approach and point to directions of further development of this general morphological analysis framework.

This paper addresses the problem of statistical analysis of diffusion tensor magnetic resonance images (DT-MRI). DT-MRI cannot be analyzed by commonly used linear methods, due to the inherent nonlinearity of tensors, which are restricted to lie on a nonlinear submanifold of the space in which they are defined, namely R6. We estimate this submanifold using the Isomap manifold learning technique and perform tensor calculations using geodesic distances along this manifold. Multivariate statistics used in group analyses also use geodesic distances between tensors, thereby warranting that proper estimates of means and covariances are obtained via calculations restricted to the proper subspace of R6. Experimental results on data with known ground truth show that the proposed statistical analysis method properly captures statistical relationships among tensor image data, and it identifies group differences. Comparisons with standard statistical analyses that rely on Euclidean, rather than geodesic distances, are also discussed.