A Mobile App Illustrating Sensory Neural Coding Through an Efficient Coding of Collected Images and Sounds

Sensory neuroscience in the early auditory and visual systems appears distinct not only to outside observers, but to many trained neuroscientists as well. However, to a computational neuroscientist, both sensory systems represent an efficient neural coding of information. In fact, on a computational level it appears the brain is using the same processing strategy for both senses - the same algorithm with just a change in inputs. Insights like this can greatly simplify our understanding of the brain, but require a significant computational background to fully appreciate. How can such illuminating results of computational neuroscience be made more accessible to the entire neuroscience community? We built an Android mobile app that simulates the neural coding process in the early visual and auditory system. The app demonstrates the type of visual or auditory codes that would develop depending on the images or sounds that an evolving species would be exposed to over evolutionary time. This is done by visually displaying the derived image and sound filters based on an optimal encoding that information, and comparing them to visual representations of neural receptive fields in the brain. Image patches (or equivalently, sound clips) are efficiently encoded using Independent Components Analysis (ICA) as a proxy for the coding objective of the early visual system. As has been observed for the past two decades, the resulting code from natural images resembles the 2D Gabor filter receptive fields measured from neurons in primary visual cortex (V1). Similarly, this efficient encoding demonstration has been done for a mixture of natural sounds to create linear filters resembling the gammatone filters of the spiral ganglia from the cochlea. The app demonstrates the relationship between efficient codes of images and sounds and related sensory neural coding in an intuitive, accessible way. This enables budding neuroscientists, and even the general public, to appreciate how an understanding of computational tools (like ICA or sparse coding) can bridge research across seemingly distinct areas of the brain. This enables a more parsimonious view of how the brain processes information, and may encourage early-program neuroscientists to consider improving their computational skills

Essays on financial econometrics:variance and covariance estimation using price durations

Asset variance and covariance are fundamental for financial risk management and many finance applications. With the advent of tick-by-tick high-frequency data, the estimation of univariate variances and multivariate covariance matrices has attracted more attention from econometricians. Many of the proposed high-frequency variance and covariance estimators are based on time-domain measurements. In this thesis, we investigate variance and covariance estimators constructed on the price domain: the price duration based variance and covariance estimators. A price event occurs when the absolute cumulative price change equals or exceeds a pre-specified threshold value. The time taken between two consecutive price events is a price duration. Intuitively, shorter durations are indicative of higher volatility. The duration-based approach provides a new angle to look at the high-frequency data, additionally, the duration based variance and covariance estimators are shown to be more efficient than competing time-domain high-frequency estimators. The information advantage of the duration based approach is demonstrated through two empirical applications, a volatility forecasting exercise and an out-of-sample globalminimum-variance portfolio allocation problem. The duration based estimators are shown to provide both better forecasting performance and better portfolio allocation results. The paper in Chapter 2 is under the first round Revise&Resubmit to the Journal of Business & Economic Statistics. In Chapter 2, we discuss the estimation of univariate variance using price durations. Variance estimation using high-frequency data needs to take into account the effect of market microstructure (MMS) noise, including discrete transaction times, discrete price levels, and bid/ask spreads, as well as price jumps. The price duration estimator has a built-in feature to be robust to large price jumps, while its robustness against the MMS noise is achieved through a careful selection of the threshold value that defines a price event. We discuss the selection of this optimal threshold value through both simulation and empirical evidence. We devise both a non-parametric and a parametric estimator. For the estimation of integrated variance at a daily frequency, the non-parametric duration based variance estimator suffices, while the parametric estimator additionally provides us with an instantaneous variance estimator. As an empirical application to 20 DJIA stocks, we compare the volatility forecasting performance of three classes of volatility estimators, including the realized volatility, the option implied volatility, and the price duration based volatility estimators, on one-day, one-week, and one-month horizons. Forecasting comparisons among individual estimators, as well as in a combination setup, are considered. The duration based estimators, especially the parametric price duration volatility estimator, are found to provide more accurate out-of-sample forecasts. In Chapter 3, we introduce a covariance matrix estimator using price durations. In the multivariate setting, there is the additional issue of nonsynchronous trade arrival times when estimating a high-dimensional variance-covariance matrix using tick-by-tick transaction data. Through simulation, we assess the effects of the lasttick time-synchronization method and MMS noise on the duration based covariance estimator, and compare its accuracy and efficiency with other candidate covariance estimators. Since the covariance matrix is estimated on a pairwise basis, it is not guaranteed to be positive semi-definite (psd). To reduce the number of negative eigenvalues produced by a non-psd matrix, we devise an averaging estimator which is the average of a wide range of duration based covariance matrix estimators. This estimator is applied to a portfolio of 19 DJIA stocks on an out-of-sample global minimum variance portfolio allocation problem where the objective is to minimize the one-day ahead portfolio variance. A simple shrinkage technique is used to improve non-psd and ill-conditioned matrices. The price duration covariance matrix estimator is shown to provide a comparably low portfolio variance while yielding considerably lower portfolio turnover rates than previous estimators

G2C: A Generator-to-Classifier Framework Integrating Multi-Stained Visual Cues for Pathological Glomerulus Classification

Pathological glomerulus classification plays a key role in the diagnosis of nephropathy. As the difference between different subcategories is subtle, doctors often refer to slides from different staining methods to make decisions. However, creating correspondence across various stains is labor-intensive, bringing major difficulties in collecting data and training a vision-based algorithm to assist nephropathy diagnosis. This paper provides an alternative solution for integrating multi-stained visual cues for glomerulus classification. Our approach, named generator-to-classifier (G2C), is a two-stage framework. Given an input image from a specified stain, several generators are first applied to estimate its appearances in other staining methods, and a classifier follows to combine visual cues from different stains for prediction (whether it is pathological, or which type of pathology it has). We optimize these two stages in a joint manner. To provide a reasonable initialization, we pre-train the generators in an unlabeled reference set under an unpaired image-to-image translation task, and then fine-tune them together with the classifier. We conduct experiments on a glomerulus type classification dataset collected by ourselves (there are no publicly available datasets for this purpose). Although joint optimization slightly harms the authenticity of the generated patches, it boosts classification performance, suggesting more effective visual cues are extracted in an automatic way. We also transfer our model to a public dataset for breast cancer classification, and outperform the state-of-the-arts significantly.Comment: Accepted by AAAI 201

Research on the Architecture Model of Volatile Data Forensics

AbstractThis paper proposed a new architecture model of volatile data forensic. The model applied to all the volatile data sources is a general model. It can rebuild the evidence data fragment to chains of evidence which contains the behavior characteristics, so as to assist investigators to do case analysis. With the accumulated experience, the model can help judicial officers to intelligently analyze the same type of computer crimes, and based on currently available information to predict the impending crimes

A Study of Trait Anhedonia in Non-Clinical Chinese Samples: Evidence from the Chapman Scales for Physical and Social Anhedonia

Background: Recent studies suggest that anhedonia, an inability to experience pleasure, can be measured as an enduring trait in non-clinical samples. In order to examine trait anhedonia in a non-clinical sample, we examined the properties of a range of widely used questionnaires capturing anhedonia. Methods: 887 young adults were recruited from colleges. All of them were administered a set of checklists, including Chapman Scale for Social Anhedonia (CRSAS) and the Chapman Scale for Physical Anhedonia Scale (CPAS), The Temporal Experience of Pleasure Scale(TEPS), and The Schizotypal Personality Questionnaire (SPQ). Results: Males showed significantly higher level of physical (F = 5.09, p<0.001) and social (F = 4.38, p<0.005) anhedonia than females. As expected, individuals with schizotypal personality features also demonstrated significantly higher scores of physical (t = 3.81, p<0.001) and social (t = 7.33, p<0.001) trait anhedonia than individuals without SPD features, but no difference on self-report anticipatory and consummatory pleasure experience. Conclusions: Concerning the comparison on each item of physical and social anhedonia, the results indicated that individuals with SPD feature exhibited higher than individuals without SPD features on more items of social anhedonia than physical anhedonia scale. These preliminary findings suggested that trait anhedonia can be identified a non-clinical sample. Exploring the demographic and clinical correlates of trait anhedonia in the general population may provide clues to the pathogenesis of psychotic disorder.China. Ministry of Science and Technology. National Key Technologies R&D Program (2012BAI36B01)National Science Fund China (Grant no. 81088001)National Science Fund China (Grant no. 91132701)Chinese Academy of Sciences. Knowledge Innovation Project (KSCX2-EW-J-8

Characterizing Membership Privacy in Stochastic Gradient Langevin Dynamics

Bayesian deep learning is recently regarded as an intrinsic way to characterize the weight uncertainty of deep neural networks~(DNNs). Stochastic Gradient Langevin Dynamics~(SGLD) is an effective method to enable Bayesian deep learning on large-scale datasets. Previous theoretical studies have shown various appealing properties of SGLD, ranging from the convergence properties to the generalization bounds. In this paper, we study the properties of SGLD from a novel perspective of membership privacy protection (i.e., preventing the membership attack). The membership attack, which aims to determine whether a specific sample is used for training a given DNN model, has emerged as a common threat against deep learning algorithms. To this end, we build a theoretical framework to analyze the information leakage (w.r.t. the training dataset) of a model trained using SGLD. Based on this framework, we demonstrate that SGLD can prevent the information leakage of the training dataset to a certain extent. Moreover, our theoretical analysis can be naturally extended to other types of Stochastic Gradient Markov Chain Monte Carlo (SG-MCMC) methods. Empirical results on different datasets and models verify our theoretical findings and suggest that the SGLD algorithm can not only reduce the information leakage but also improve the generalization ability of the DNN models in real-world applications.Comment: Under review of AAAI 202

Excitatory nucleo-olivary pathway shapes cerebellar outputs for motor control

The brain generates predictive motor commands to control the spatiotemporal precision of high-velocity movements. Yet, how the brain organizes automated internal feedback to coordinate the kinematics of such fast movements is unclear. Here we unveil a unique nucleo-olivary loop in the cerebellum and its involvement in coordinating high-velocity movements. Activating the excitatory nucleo-olivary pathway induces well-timed internal feedback complex spike signals in Purkinje cells to shape cerebellar outputs. Anatomical tracing reveals extensive axonal collaterals from the excitatory nucleo-olivary neurons to downstream motor regions, supporting integration of motor output and internal feedback signals within the cerebellum. This pathway directly drives saccades and head movements with a converging direction, while curtailing their amplitude and velocity via the powerful internal feedback mechanism. Our finding challenges the long-standing dogma that the cerebellum inhibits the inferior olivary pathway and provides a new circuit mechanism for the cerebellar control of high-velocity movements.</p