Towards non-blinking colloidal quantum dots

At a single-molecule level, fluorophore emission intensity fluctuates between bright and dark states. These fluctuations, known as blinking, limit the use of fluorophores in single-molecule experiments. The dark-state duration shows a universal heavy-tailed power-law distribution characterized by the occurrence of long non-emissive periods. Here we have synthesized novel CdSe-CdS core-shell quantum dots with thick crystalline shells, 68% of which do not blink when observed individually at 33 Hz for 5 min. We have established a direct correlation between shell thickness and blinking occurrences. Importantly, the statistics of dark periods that appear at high acquisition rates (1 kHz) are not heavy tailed, in striking contrast with previous observations. Blinking statistics are thus not as universal as thought so far. We anticipate that our results will help to better understand the physico-chemistry of single-fluorophore emission and rationalize the design of other fluorophores that do not blink.     

Residual-Based robust estimation and image-motion analysis

We explain that the task of multiple rigid motion segmentation and estimation from image feature point correspondence demands an estimator of high robustness. We show that a heuristics-based partial modeling approach can be used to develop a highly robust estimator called the MF estimator for general regression, where “MF” represents an abbreviation of Model Fitting. Finally, we provide experimental results in estimating single rigid motion from a mixture of 2D-2D (or image-image), 3D-2D (or range-image) and 3D-3D (or range-range) corresponding point data by using the proposed MF estimator. As will be seen, only four well matched corresponding point pairs are needed to get a good estimate of motion parameters no matter how many mismatched corresponding point pairs (or outliers) occur. The article represents an initial effort towards robust image-motion analysis.     

Distributed robust optimization (DRO), part I: framework and example

Robustness of optimization models for network problems in communication networks has been an under-explored topic. Most existing algorithms for solving robust optimization problems are centralized, thus not suitable for networking problems that demand distributed solutions. This paper represents a first step towards a systematic theory for designing distributed and robust optimization models and algorithms. We first discuss several models for describing parameter uncertainty sets that can lead to decomposable problem structures and thus distributed solutions. These models include ellipsoid, polyhedron, and D-norm uncertainty sets. We then apply these models in solving a robust rate control problem in wireline networks. Three-way tradeoffs among performance, robustness, and distributiveness are illustrated both analytically and through simulations. In Part II of this two-part paper, we will present applications to wireless power control using the framework of distributed robust optimization.     

Mental Illness Disorder Diagnosis Using Emotion Variation Detection from Continuous English Speech

Automatic recognition of human emotions in a continuous dialog model remains challenging where a speaker’s utterance includes several sentences that may not always carry a single emotion. Limited work with standalone speech emotion recognition (SER) systems proposed for continuous speech only has been reported. In the recent decade, various effective SER systems have been proposed for discrete speech, i.e., short speech phrases. It would be more helpful if these systems could also recognize emotions from continuous speech. However, if these systems are applied directly to test emotions from continuous speech, emotion recognition performance would not be similar to that achieved for discrete speech due to the mismatch between training data (from training speech) and testing data (from continuous speech). The problem may possibly be resolved if an existing SER system for discrete speech is enhanced. Thus, in this work the author’s existing effective SER system for multilingual and mixed-lingual discrete speech is enhanced by enriching the cepstral speech feature set with bi-spectral speech features and a unique functional set of Mel frequency cepstral coefficient features derived from a sine filter bank. Data augmentation is applied to combat skewness of the SER system toward certain emotions. Classification using random forest is performed. This enhanced SER system is used to predict emotions from continuous speech with a uniform segmentation method. Due to data scarcity, several audio samples of discrete speech from the SAVEE database that has recordings in a universal language, i.e., English, are concatenated resulting in multi-emotional speech samples. Anger, fear, sad, and neutral emotions, which are vital during the initial investigation of mentally disordered individuals, are selected to build six categories of multi-emotional samples. Experimental results demonstrate the suitability of the proposed method for recognizing emotions from continuous speech as well as from discrete speech.     

Assessing Lévy walks as models of animal foraging

The hypothesis that the optimal search strategy is a Lévy walk (LW) or Lévy flight, originally suggested in 1995, has generated an explosion of interest and controversy. Long-standing empirical evidence supporting the LW hypothesis has been overturned, while new models and data are constantly being published. Statistical methods have been criticized and new methods put forward. In parallel with the empirical studies, theoretical search models have been developed. Some theories have been disproved while others remain. Here, we gather together the current state of the art on the role of LWs in optimal foraging theory. We examine the body of theory underpinning the subject. Then we present new results showing that deviations from the idealized one-dimensional search model greatly reduce or remove the advantage of LWs. The search strategy of an LW with exponent μ = 2 is therefore not as robust as is widely thought. We also review the available techniques, and their potential pitfalls, for analysing field data. It is becoming increasingly recognized that there is a wide range of mechanisms that can lead to the apparent observation of power-law patterns. The consequence of this is that the detection of such patterns in field data implies neither that the foragers in question are performing an LW, nor that they have evolved to do so. We conclude that LWs are neither a universal optimal search strategy, nor are they as widespread in nature as was once thought.     

Critical scaling laws and a classical equation of state

In this paper we present a method which modifies a classical equation of state by incorporating the nonclassical critical behavior. As an example we have applied our procedure to the Carnahan-Starling-DeSantis (CSD) equation of state. The resulting equation reproduces the universal scaling behavior near the critical point and reduces to the universal ideal-gas behavior at low densities. We show that the renormalized CSD equation yields an improved and consistent representation of both mechanical and caloric thermodynamic properties. In addition, the suppression of the critical temperature due to the critical fluctuations is clearly demonstrated.     

Robustness of quantum dot power-law blinking

Photon emission from quantum dots (QDs) and other quantum emitters is characterized by abrupt jumps between an "on" and an "off" state. In contrast to ions and atoms however, the durations of bright and dark periods in colloidal QDs curiously defy a characteristic time scale and are best described by a power-law probability distribution, i.e., ρ(τ) ∝ τ(-α). We controllably couple a single colloidal QD to a single gold nanoparticle and find that power-law blinking is preserved unaltered even as the gold nanoparticle drastically modifies the excitonic decay rate of the QD. This resilience of the power law to change provides evidence that blinking statistics are not swayed by environment-induced variations in kinetics and provides clues toward the mechanism responsible for universal fluorescence intermittency.     

The effect of microbial selection on the occurrence-abundance patterns of microbiomes

Theoretical models are useful to investigate the drivers of community dynamics. In the simplest case of neutral models, the events of death, birth and immigration of individuals are assumed to only depend on their abundance—thus, all types share the same parameters. The community level expectations arising from these simple models and their agreement to empirical data have been discussed extensively, often suggesting that in nature, rates might indeed be neutral or their differences might not be important. However, how robust are these model predictions to type-specific rates? Also, what are the consequences at the level of types? Here, we address these questions moving from simple neutral communities to heterogeneous communities. For this, we build a model where types are differently adapted to the environment. We compute the equilibrium distribution of the abundances. Then, we look into the occurrence-abundance pattern often reported in microbial communities. We observe that large immigration and biodiversity—common in microbial systems—lead to such patterns, regardless of whether the rates are neutral or non-neutral. We conclude by discussing the implications to interpret and test empirical data.     

Modelling control of epidemics spreading by long-range interactions

We have studied the spread of epidemics characterized by a mixture of local and non-local interactions. The infection spreads on a two-dimensional lattice with the fixed nearest neighbour connections. In addition, long-range dynamical links are formed by moving agents (vectors). Vectors perform random walks, with step length distributed according to a thick-tail distribution. Two distributions are considered in this paper, an α-stable distribution describing self-similar vector movement, yet characterized by an infinite variance and an exponential power characterized by a large but finite variance. Such long-range interactions are hard to track and make control of epidemics very difficult. We also allowed for cryptic infection, whereby an infected individual on the lattice can be infectious prior to showing any symptoms of infection or disease. To account for such cryptic spread, we considered a control strategy in which not only detected, i.e. symptomatic, individuals but also all individuals within a certain control neighbourhood are treated upon the detection of disease. We show that it is possible to eradicate the disease by using such purely local control measures, even in the presence of long-range jumps. In particular, we show that the success of local control and the choice of the optimal strategy depend in a non-trivial way on the dispersal patterns of the vectors. By characterizing these patterns using the stability index of the α-stable distribution to change the power-law behaviour or the exponent characterizing the decay of an exponential power distribution, we show that infection can be successfully contained using relatively small control neighbourhoods for two limiting cases for long-distance dispersal and for vectors that are much more limited in their dispersal range.     

Modeling of atmospheric temperature fluctuations by translations of oscillatory random processes with application to spacecraft atmospheric re-entry

The presence of random fluctuations of air temperature within the Earth’s atmosphere is a well-documented phenomenon. During the past seventy years there have been numerous experimental efforts to accurately measure air temperature as a function of altitude and, through careful data analysis, provide statistics describing these fluctuations and the associated fluctuations in temperature gradients. In addition, several researchers suggest the presence of atmospheric layers or “sheets” where the statistics describing fluctuations in air temperature can vary significantly from layer to layer. Herein, we propose a model to represent fluctuations of air temperature within a layered atmosphere. The model is a special type of inhomogeneous non-Gaussian differentiable random process and can be calibrated to available data on the marginal statistics and spectral content of the fluctuating temperature field, as well as the associated first derivative of the process representing fluctuations in temperature gradients. Properties of the proposed model are presented, and statistical realizations of the fluctuating temperature field and its gradient are computed and presented for illustration. The random vibration response of a spacecraft falling to Earth through these fluctuating conditions is then considered to demonstrate the usefulness of the proposed model.     

一种利用主曲线的说话人自适应方法

为了克服噪声对语音识别系统的影响,提出了一种基于主曲线的说话人自适应方法,这种方法可以通过一组主曲线描述所有状态的特征统计信息和码本参数之间的关系,并利用特征统计量在主曲线上的投影更新码本.当有背景噪声存在时,这种方法可以有效修正状态的特征统计信息以减弱或去掉噪声的影响.在863大词汇量连续语音识别数据库上的实验结果证明:这种方法相对于基线以及本征音说话人自适应算法,系统识别性能都有明显的提高.     

Dynamical Quantum Phase Transitions

A sweep through a quantum phase transition by means of a time-dependent external parameter (e.g., pressure) entails non-equilibrium phenomena associated with a break-down of adiabaticity: At the critical point, the energy gap vanishes and the response time diverges (in the thermodynamic limit). Consequently, the external time-dependence inevitably drives the system out of equilibrium, i.e., away from the ground state, if we assume zero temperature initially. In this way, the initial quantum fluctuations can be drastically amplified and may become observable—especially for symmetry-breaking (restoring) transitions. By means of several examples, possible effects of these amplified quantum fluctuations are studied and universal features (such as freezing) are discussed.     

Development of Robust Design Under Contaminated and Non-normal Data

The usual assumptions behind robust design are that the distribution of experimental data is approximately normal and that there is no major contamination due to outliers in the data. Under these assumptions, sample mean and variance are often used to estimate process mean and variance. In this article, we first show simulation results indicating that sample mean and variance may not be the best choice when one or both assumptions are not met. The results further show that sample median and median absolute deviation or sample median and interquartile range are indeed more resistant to departures from normality and to contaminated data. We then show how to incorporate this observation into robust design modeling and optimization. A case study is presented.     

基于深度学习检测器的多角度人脸关键点检测

针对人脸关键点检测(人脸对齐)在应用场景下的速度和精度需求,首先在SSD基础之上融合更多分布均匀的特征层,对人脸框坐标进行级联预测,形成对于多尺度人脸信息均具有更加鲁棒响应的深度学习检测器MR-SSD。其次在局部二值特征LBF的级联形状回归方法基础上,提出了基于面部像素差值的多角度初始化算法。采用端正人脸正负90°倾斜范围内的五组特征点形状进行初始化,求取每组回归后形状的眼部特征点像素均方差值并以最大者对应方案作为最终回归形状,从而实现对多角度倾斜人脸优异的拟合效果。本文所提出的最优架构可以实时获得极具鲁棒性的人脸框坐标并且可实现对于多角度倾斜人脸的关键点检测。     

Conceptual robustness in simultaneous engineering: An extension of Taguchi's parameter design

Simultaneous engineering processes involve multifunctional teams; team members simultaneously make decisions about many parts of the product-production system and aspects of the product life cycle. This paper argues that such simultaneous distributed decisions should be based on communications about sets of possibilities rather than single solutions. By extending Taguchi's parameter design concepts, we develop a robust and distributed decision-making procedure based on such communications. The procedure shows how a member of a design team can make appropriate decisions based on incomplete information from the other members of the team. More specifically, it (1) treats variations among the designs considered by other members of the design team asconceptual noise; (2) shows how to incorporate such noises into decisions that are robust against these variations; (3) describes a method for using the same data to provide preference information back to the other team members; and (4) provides a procedure for determining whether to release theconceptually robust design or to wait for further decisions by others. The method is demonstrated by part of a distributed design process for a rotary CNC milling machine. While Taguchi's approach is used as a starting point because it is widely known, these results can be generalized to use other robust decision techniques.     

Strength statistics of adhesive contact between a fibrillar structure and a rough substrate

Equal distribution of load among fibrils in contact with a substrate is an important characteristic of fibrillar structures used by many small animals and insects for contact and adhesion. This is in contrast with continuum systems where stress concentration dominates interfacial failure. In this work, we study how adhesion strength of a fibrillar system depends on substrate roughness and variability of the fibril structure, which are modelled using probability distributions for fibril length and fibril attachment strength. Monte Carlo simulations are carried out to determine the adhesion strength statistics where fibril length follows normal or uniform distribution and attachment strength has a power-law form. Our results indicate that the strength distribution is Gaussian (normal) for both the uniform and the normal distributions for length. However, the fibrillar structure having normally distributed lengths has higher strength and lower toughness than one having uniformly distributed lengths. Our simulations also show that an increase in the compliance of the fibrils can compensate for both the substrate roughness and the attachment strength variation. We also show that, as the number of fibrils n increases, the load-carrying efficiency of each fibril goes down. For large n, this effect is found to be small. Furthermore, this effect is compensated by the fact that the standard deviation of the adhesive strength decreases as 1/ square root n.     

Comparison of pure and “Latinized” centroidal Voronoi tessellation against various other statistical sampling methods

A recently developed centroidal Voronoi tessellation (CVT) sampling method is investigated here to assess its suitability for use in statistical sampling applications. CVT efficiently generates a highly uniform distribution of sample points over arbitrarily shaped M-dimensional parameter spaces. On several 2-D test problems CVT has recently been found to provide exceedingly effective and efficient point distributions for response surface generation. Additionally, for statistical function integration and estimation of response statistics associated with uniformly distributed random-variable inputs (uncorrelated), CVT has been found in initial investigations to provide superior points sets when compared against latin-hypercube and simple-random Monte Carlo methods and Halton and Hammersley quasi-random sequence methods. In this paper, the performance of all these sampling methods and a new variant (“Latinized” CVT) are further compared for non-uniform input distributions. Specifically, given uncorrelated normal inputs in a 2-D test problem, statistical sampling efficiencies are compared for resolving various statistics of response: mean, variance, and exceedence probabilities.     

Quantum coherence and phase transitions in granular superconductors with dissipation. I. Ordered arrays

We investigate the effect of dissipative currents on a superconducting phase transition in two- and three-dimensional granular arrays. We find the corresponding phase diagrams and show that, depending on dissipation, both long-range and local quantum fluctuations may be effective in destroying the global phase coherence. For large values of charging-to-Josephson coupling energy ratio and atT=0, we find the universal critical resistanceR _c only for three dimensional arrays while for two dimensional onesR _c ^–1 diverges logarithmically with increasing of the grain charging energy. For large values of a Josephson coupling energy, quantum phase slip effects play the main role and lead to the universal critical resistance in both cases. We also investigate the effect of dissipation on the critical temperature of theXY model phase transition and show that strong dissipation is extremely effective in suppressing quantum effects.     

Robust multi-planar region detection for image mosaic based on hierarchical clustering mechanism

Abstract

Planar structures exist widely in the images of various scenes, and the detection of planar regions is important in many applications related to computer vision, such as image mosaic and three-dimensional reconstruction. In this paper, a robust detection method for multi-planar regions is proposed. After the feature point pairs are extracted, their preference vectors are generated in similar conceptual space. By introducing the shared nearest neighbour in clustering procedure, the feature point pairs with smaller Jaccard distance and more shared nearest neighbours simultaneously are clustered into the same planar region. Because the relationship between the feature point pairs is considered, the accuracy of the inlier probability is high. Our method can detect multi-planar regions correctly without pre-determining the number of regions, and the corresponding clustered feature point pairs can be easily utilised for image mosaic. The experimental results show the effectiveness of the proposed method.