Supporting Regularized Logistic Regression Privately and Efficiently

As one of the most popular statistical and machine learning models, logistic regression with regularization has found wide adoption in biomedicine, social sciences, information technology, and so on. These domains often involve data of human subjects that are contingent upon strict privacy regulations. Increasing concerns over data privacy make it more and more difficult to coordinate and conduct large-scale collaborative studies, which typically rely on cross-institution data sharing and joint analysis. Our work here focuses on safeguarding regularized logistic regression, a widely-used machine learning model in various disciplines while at the same time has not been investigated from a data security and privacy perspective. We consider a common use scenario of multi-institution collaborative studies, such as in the form of research consortia or networks as widely seen in genetics, epidemiology, social sciences, etc. To make our privacy-enhancing solution practical, we demonstrate a non-conventional and computationally efficient method leveraging distributing computing and strong cryptography to provide comprehensive protection over individual-level and summary data. Extensive empirical evaluation on several studies validated the privacy guarantees, efficiency and scalability of our proposal. We also discuss the practical implications of our solution for large-scale studies and applications from various disciplines, including genetic and biomedical studies, smart grid, network analysis, etc

Real-Time Traffic Light Recognition Based on C-HOG Features

This paper proposes a real-time traffic light detection and recognition algorithm that would allow for the recognition of traffic signals in intelligent vehicles. This algorithm is based on C-HOG features (Color and HOG features) and Support Vector Machine (SVM). The algorithm extracted red and green areas in the video accurately, and then screened the eligible area. Thereafter, the C-HOG features of all kinds of lights could be extracted. Finally, this work used SVM to build a classifier of corresponding category lights. This algorithm obtained accurate real-time information based on the judgment of the decision function. Furthermore, experimental results show that this algorithm demonstrated accuracy and good real-time performance

K-Means Clustering on Multiple Correspondence Analysis Coordinates

On April 18, 2017, the International Federation of Classification Societies (IFCS) issued a challenge to its members and the classification community to analyze a data set of 928 low back pain patients. In this paper, we present our contribution in terms of a cluster analysis of this data set. We will discuss our data cleaning process, which we view as a two-pronged approach: inferring values that are missing not at random and imputing values that are missing at random. We will also discuss the challenges in clustering mixed data types and the required data transformation prior to applying a clustering algorithm. We call our proposed data transformation process split-then-join. Finally, we offer our interpretation of the clustering results with respect to validation variables and we present some thoughts on selecting important variables to classify new observations

Implication of cholinergic transmission in rat model of spinal cord injury: A potential therapeutic target

Purpose: To assess the involvement of cholinergic transmission in the etiology of spinal cord injury (SCI) in a rat model. Methods: Male adult rats (Wistar) with body weight ranging from 200 to 250 g were equally allocated into 2 groups: test (SCI) and control (non-SCI). Clipping method was used to induce SCI. Thereafter, motor function was measured using rotarod. Each rat was sacrificed by decapitation, and the cortex was excised for use in the study of the involvement of cholinergic transmission in SCI using real time quantitative polymerase chain reaction (RT-PCR) and western blot analysis (WBA). Results: Significant upregulation in acetylcholine esterase (AChE) was observed in the cortex of SCI rats, relative to non-SCI rats (p < 0.005). Results from cholinergic receptor binding studies revealed significantly decreased maximum binding (Bmax) and dissociation constant (kd) values for muscarinic receptors in SCI rats, when compared to non-SCI rats. Moreover, the reduction in intensity of cholinergic receptors was significantly greater in the cerebral cortex of SCI group compared to non-SCI group. Conclusion: The results of this study suggested that the reduction in cortical cholinergic transmission impairs motor functions in SCI, and plays a major role in motor deficits in SCI

Laboratory Study of the Effects of Flexible Vegetation on Solute Diffusion in Unidirectional Flow

Background Flexible vegetation is an important part of the riverine ecosystem, which can reduce flow velocity, change turbulence structure, and affect the processes of solute transport. Compared with the flow with rigid vegetation, which has been reported in many previous studies, bending of flexible vegetation increases the complexity of the flow-vegetation-solute interactions. In this study, laboratory experiments are carried out to investigate the influence of flexible vegetation on solute transport, and methods for estimating the lateral and longitudinal diffusion coefficients in the rigid vegetated flow are examined for their applications to the flow with flexible vegetation. Results The experimental observations find that vegetation can significantly reduce flow velocity, and the Manning coefficient increases with increasing vegetation density and decreases with inflow discharge. Under all the cases, the vertical peak of the solute concentration moves towards the bottom bed along the flow, and the values of vertical peak concentration longitudinally decreases from the injection point. The lateral diffusion coefficients Dy increase with vegetation density, while the longitudinal diffusion coefficients DL are opposite. Both Dy and DL increase with the inflow discharge. To estimate the Dy and DL in the flow with flexible vegetation, an effective submerged vegetation height considering vegetation bending is incorporated in the methods proposed for flow with rigid vegetation (Lou et al. Environ Sci Eur 32:15, 2020). The modified approach can well predict the diffusion coefficients in the experiments with the relative errors in the range of 5%-12%. Conclusions The methods proposed in this study can be used to estimate the lateral and longitudinal diffusion coefficients in flows through both rigid and flexible vegetations using the effective submerged vegetation height

Stimulus Pulse-Based Distributed Control for the Locomotion of a UBot Modular Robot

A distributed control algorithm based on a stimulus pulse signal is proposed in this paper for the locomotion of a Modular Self-reconfigurable Robot (MSRR). This approach can adapt effectively to the dynamic changes in the MSRR's topological configuration: the functional role of the configuration can be recognized through local topology detection, dynamic ID address allocation and local topology matching, such that the features of the entire configuration can be identified and thereby the corresponding stimulus signals can be chosen to control the whole system for coordinated locomotion. This approach has advantages over centralized control in terms of flexibility and robustness, and communication efficiency is not limited by the module number, which can realize coordinated locomotion control conveniently (especially for configurations made up of massive modules and characterized by a chain style or a quadruped style)

A Set of Novel Microsatellite Markers Developed for Luculia yunnanensis (Rubiaceae), an Endangered Plant Endemic to Yunnan, China

The genus Luculia Sweet contains about five species of small trees or shrubs and is a member of the family Rubiaceae (tribe Cinchoneae). Luculia yunnanensis is an endangered ornamental shrub endemic to southwest China. Only two natural populations of L. yunnanensis exist in the wild according to our field investigation. It can be inferred that L. yunnanensis is facing a very high risk of extinction in the wild and an urgent conservation strategy is required. By using a modified biotin-sterptavidin capture method, 24 primer sets were identified in two wild populations. Of these primers, 11 displayed polymorphisms and 13 were monomorphic. The number of alleles per locus ranged from two to four, values for observed and expected heterozygosities ranged from 0.000 to 0.833 and from 0.431 to 0.771, with averages of 0.389 and 0.614, respectively. These markers will be useful for further investigation of conservation of resources, selecting parental types in cross-breeding, evolution of this species at the molecular level and related research in Luculia species

Dual functional states of working memory realized by memristor-based neural network

Working memory refers to the brain's ability to store and manipulate information for a short period. It is disputably considered to rely on two mechanisms: sustained neuronal firing, and “activity-silent” working memory. To develop a highly biologically plausible neuromorphic computing system, it is anticipated to physically realize working memory that corresponds to both of these mechanisms. In this study, we propose a memristor-based neural network to realize the sustained neural firing and activity-silent working memory, which are reflected as dual functional states within memory. Memristor-based synapses and two types of artificial neurons are designed for the Winner-Takes-All learning rule. During the cognitive task, state transformation between the “focused” state and the “unfocused” state of working memory is demonstrated. This work paves the way for further emulating the complex working memory functions with distinct neural activities in our brains