Predictive Encoding of Contextual Relationships for Perceptual Inference, Interpolation and Prediction

We propose a new neurally-inspired model that can learn to encode the global relationship context of visual events across time and space and to use the contextual information to modulate the analysis by synthesis process in a predictive coding framework. The model learns latent contextual representations by maximizing the predictability of visual events based on local and global contextual information through both top-down and bottom-up processes. In contrast to standard predictive coding models, the prediction error in this model is used to update the contextual representation but does not alter the feedforward input for the next layer, and is thus more consistent with neurophysiological observations. We establish the computational feasibility of this model by demonstrating its ability in several aspects. We show that our model can outperform state-of-art performances of gated Boltzmann machines (GBM) in estimation of contextual information. Our model can also interpolate missing events or predict future events in image sequences while simultaneously estimating contextual information. We show it achieves state-of-art performances in terms of prediction accuracy in a variety of tasks and possesses the ability to interpolate missing frames, a function that is lacking in GBM

Making the Invisible Visible: Action Recognition Through Walls and Occlusions

Understanding people's actions and interactions typically depends on seeing them. Automating the process of action recognition from visual data has been the topic of much research in the computer vision community. But what if it is too dark, or if the person is occluded or behind a wall? In this paper, we introduce a neural network model that can detect human actions through walls and occlusions, and in poor lighting conditions. Our model takes radio frequency (RF) signals as input, generates 3D human skeletons as an intermediate representation, and recognizes actions and interactions of multiple people over time. By translating the input to an intermediate skeleton-based representation, our model can learn from both vision-based and RF-based datasets, and allow the two tasks to help each other. We show that our model achieves comparable accuracy to vision-based action recognition systems in visible scenarios, yet continues to work accurately when people are not visible, hence addressing scenarios that are beyond the limit of today's vision-based action recognition.Comment: ICCV 2019. The first two authors contributed equally to this pape

Comparative Analysis of Static Loading Performance of Rigid and Flexible Road Wheel based on Finite Element Method

To overcome the shortcomings of traditional rigid road wheel, such as poor damping effect and low load-bearing efficiency, a new type of flexible road wheel, having a unique suspension-bearing mode, was introduced. The three-dimensional nonlinear finite element model of rigid and flexible road wheel, considering the triple nonlinear characteristics of geometry, material and contact, is established for numerical investigation of static loading performance. The accuracy of the finite element model of the rigid and flexible road wheel is verified by static loading experiment. The static loading performance of the rigid and flexible road wheels is numerically analyzed. The influence of vertical load on maximum stress and deformation of the rigid and flexible wheels is also studied. The results show that the contact pressure uniformity of the flexible road wheel is better than that of the rigid road wheel under the static vertical load, but the maximum stress and deformation of the flexible road wheel are greater than that of the rigid road wheel. However, this problem can be solved by increasing the number of hinge sets and optimising the joints. The research results provide theoretical basis for replacing rigid road wheel with flexible road wheel, and also provide reference for structural optimisation of flexible road wheel

Equivalent stiffness and dynamic response of new mechanical elastic wheel

To investigate the stiffness characteristics of the new mechanical elastic wheel (MEW), the elastic foundation closed circle curved beam model of MEW was established by curved beam theory. With the Laplace transformation and boundary conditions of the governing differential equations, the analytical relations among the radial deformation, bending stiffness of elastic wheel, the elastic foundation stiffness of hinges, elastic wheel laminated structure parameters and excitation frequency were analyzed. The correctness of the curved beam model was validated by the finite element method. Curved beam model validation and the application of the nonlinear finite element model show that the influence of elastic wheel laminated structure and deformation on dynamic response is equal to the equivalent stiffness. The results indicate that the equivalent stiffness and dynamic response of MEW become increased nonlinearly with component content of elastic bead ring, moreover, the equivalent stiffness and dynamic response of MEW increase nonlinearly with the deformation amount of MEW, and the dynamic response significantly decreases with the increase of excitation frequency, under this circumstance that the laminated structure of elastic wheel has been unchanged

Bidirectional Inference Networks: A Class of Deep Bayesian Networks for Health Profiling

We consider the problem of inferring the values of an arbitrary set of variables (e.g., risk of diseases) given other observed variables (e.g., symptoms and diagnosed diseases) and high-dimensional signals (e.g., MRI images or EEG). This is a common problem in healthcare since variables of interest often differ for different patients. Existing methods including Bayesian networks and structured prediction either do not incorporate high-dimensional signals or fail to model conditional dependencies among variables. To address these issues, we propose bidirectional inference networks (BIN), which stich together multiple probabilistic neural networks, each modeling a conditional dependency. Predictions are then made via iteratively updating variables using backpropagation (BP) to maximize corresponding posterior probability. Furthermore, we extend BIN to composite BIN (CBIN), which involves the iterative prediction process in the training stage and improves both accuracy and computational efficiency by adaptively smoothing the optimization landscape. Experiments on synthetic and real-world datasets (a sleep study and a dermatology dataset) show that CBIN is a single model that can achieve state-of-the-art performance and obtain better accuracy in most inference tasks than multiple models each specifically trained for a different task.Comment: Appeared at AAAI 201

REST: Robust and Efficient Neural Networks for Sleep Monitoring in the Wild

In recent years, significant attention has been devoted towards integrating deep learning technologies in the healthcare domain. However, to safely and practically deploy deep learning models for home health monitoring, two significant challenges must be addressed: the models should be (1) robust against noise; and (2) compact and energy-efficient. We propose REST, a new method that simultaneously tackles both issues via 1) adversarial training and controlling the Lipschitz constant of the neural network through spectral regularization while 2) enabling neural network compression through sparsity regularization. We demonstrate that REST produces highly-robust and efficient models that substantially outperform the original full-sized models in the presence of noise. For the sleep staging task over single-channel electroencephalogram (EEG), the REST model achieves a macro-F1 score of 0.67 vs. 0.39 achieved by a state-of-the-art model in the presence of Gaussian noise while obtaining 19x parameter reduction and 15x MFLOPS reduction on two large, real-world EEG datasets. By deploying these models to an Android application on a smartphone, we quantitatively observe that REST allows models to achieve up to 17x energy reduction and 9x faster inference. We open-source the code repository with this paper: https://github.com/duggalrahul/REST.Comment: Accepted to WWW 202

Visibility Video Detection with Dark Channel Prior on Highway

Dark channel prior (DCP) has advantages in image enhancement and image haze removal and is explored to detect highway visibility according to the physical relationship between transmittance and extinction coefficient. However, there are three major error sources in calculating transmittance. The first is that sky regions do not satisfy the assumptions of DCP algorithm. So the optimization algorithms combined with region growing and coefficient correction method are proposed. When extracting atmospheric brightness, different values lead to the second error. Therefore, according to different visibility conditions, a multimode classification method is designed. Image blocky effect causes the third error. Then guided image filtering is introduced to obtain accurate transmittance of each pixel of image. Next, according to the definition meteorological optical visual range and the relationship between transmittance and extinction coefficient of Lambert-Beer’s Law, accurate visibility value can be calculated. A comparative experimental system including visibility detector and video camera was set up to verify the accuracy of these optimization algorithms. Finally, a large number of highway section videos were selected to test the validity of DCP method in different models. The results indicate that these detection visibility methods are feasible and reliable for the smooth operation of highways

Equivalent stiffness and dynamic response of new mechanical elastic wheel

To investigate the stiffness characteristics of the new mechanical elastic wheel (MEW), the elastic foundation closed circle curved beam model of MEW was established by curved beam theory. With the Laplace transformation and boundary conditions of the governing differential equations, the analytical relations among the radial deformation, bending stiffness of elastic wheel, the elastic foundation stiffness of hinges, elastic wheel laminated structure parameters and excitation frequency were analyzed. The correctness of the curved beam model was validated by the finite element method. Curved beam model validation and the application of the nonlinear finite element model show that the influence of elastic wheel laminated structure and deformation on dynamic response is equal to the equivalent stiffness. The results indicate that the equivalent stiffness and dynamic response of MEW become increased nonlinearly with component content of elastic bead ring, moreover, the equivalent stiffness and dynamic response of MEW increase nonlinearly with the deformation amount of MEW, and the dynamic response significantly decreases with the increase of excitation frequency, under this circumstance that the laminated structure of elastic wheel has been unchanged

Whole-genome analysis revealed the growth-promoting and biological control mechanism of the endophytic bacterial strain Bacillus halotolerans Q2H2, with strong antagonistic activity in potato plants

IntroductionEndophytes are colonizers of healthy plants and they normally exhibit biocontrol activities, such as reducing the occurrence of plant diseases and promoting plant growth. The endophytic bacterium Bacillus halotolerans Q2H2 (Q2H2) was isolated from the roots of potato plants and was found to have an antagonistic effect on pathogenic fungi.MethodsQ2H2 was identified by morphological observations, physiological and biochemical identification, and 16S rRNA gene sequence analysis. Genes related to the anti-fungal and growth-promoting effects were analyzed using whole-genome sequencing and comparative genomic analysis. Finally, we analyzed the growth-promoting and biocontrol activities of Q2H2 in potato plants using pot experiments.ResultsAntagonism and non-volatile substance plate tests showed that Q2H2 had strong antagonism against Fusarium oxysporum, Fusarium commune, Fusarium graminearum, Fusarium brachygibbosum, Rhizoctonia solani and Stemphylium solani. The plate test showed that Q2H2 had the ability to produce proteases, cellulases, β-1,3-glucanase, dissolved organic phosphate, siderophores, indole-3-acetic acid (IAA), ammonia and fix nitrogen. The suitable growth ranges of Q2H2 under different forms of abiotic stress were pH 5–9, a temperature of 15–30°C, and a salt concentration of 1–5%. Though whole-genome sequencing, we obtained sequencing data of approximately 4.16 MB encompassed 4,102 coding sequences. We predicted 10 secondary metabolite gene clusters related to antagonism and growth promotion, including five known products surfactin, bacillaene, fengycin, bacilysin, bacillibactin, and subtilosin A. Average nucleotide identity and comparative genomic analyses revealed that Q2H2 was Bacillus halotolerans. Through gene function annotation, we analyzed genes related to antagonism and plant growth promotion in the Q2H2 genome. These included genes involved in phosphate metabolism (pstB, pstA, pstC, and pstS), nitrogen fixation (nifS, nifU, salA, and sufU), ammonia production (gudB, rocG, nasD, and nasE), siderophore production (fhuC, fhuG, fhuB, and fhuD), IAA production (trpABFCDE), biofilm formation (tasA, bslA, and bslB), and volatile compound production (alsD, ilvABCDEHKY, metH, and ispE), and genes encoding hydrolases (eglS, amyE, gmuD, ganB, sleL, and ydhD). The potato pot test showed that Q2H2 had an obvious growth-promoting effect on potato roots and better control of Fusarium wilt than carbendazim.ConclusionThese findings suggest that the strain-specific genes identified in bacterial endophytes may reveal important antagonistic and plant growth-promoting mechanisms

A recombinant Fasciola gigantica 14-3-3 epsilon protein (rFg14-3-3e) modulates various functions of goat peripheral blood mononuclear cells

Background The molecular structure of Fasciola gigantica 14-3-3 protein has been characterized. However, the involvement of this protein in parasite pathogenesis remains elusive and its effect on the functions of innate immune cells is unknown. We report on the cloning and expression of a recombinant F. gigantica 14-3-3 epsilon protein (rFg14-3-3e), and testing its effects on specific functions of goat peripheral blood mononuclear cells (PBMCs). Methods rFg14-3-3e protein was expressed in Pichia pastoris. Western blot and immunofluorescence assay (IFA) were used to examine the reactivity of rFg14-3-3e protein to anti-F. gigantica and anti-rFg14-3-3e antibodies, respectively. Various assays were used to investigate the stimulatory effects of the purified rFg14-3-3e protein on specific functions of goat PBMCs, including cytokine secretion, proliferation, migration, nitric oxide (NO) production, phagocytosis, and apoptotic capabilities. Potential protein interactors of rFg14-3-3e were identified by querying the databases Intact, String, BioPlex and BioGrid. A Total Energy analysis of each of the identified interaction was performed. Gene Ontology (GO) enrichment analysis was conducted using Funcassociate 3.0. Results Sequence analysis revealed that rFg14-3-3e protein had 100% identity to 14-3-3 protein from Fasciola hepatica. Western blot analysis showed that rFg14-3-3e protein is recognized by sera from goats experimentally infected with F. gigantica and immunofluorescence staining using rat anti-rFg14-3-3e antibodies demonstrated the specific binding of rFg14-3-3e protein to the surface of goat PBMCs. rFg14-3-3e protein stimulated goat PBMCs to produce interleukin-10 (IL-10) and transforming growth factor beta (TGF-β), corresponding with low levels of IL-4 and interferon gamma (IFN-γ). Also, this recombinant protein promoted the release of NO and cell apoptosis, and inhibited the proliferation and migration of goat PBMCs and suppressed monocyte phagocytosis. Homology modelling revealed 65% identity between rFg14-3-3e and human 14-3-3 protein YWHAE. GO enrichment analysis of the interacting proteins identified terms related to apoptosis, protein binding, locomotion, hippo signalling and leukocyte and lymphocyte differentiation, supporting the experimental findings. Conclusions Our data suggest that rFg14-3-3e protein can influence various cellular and immunological functions of goat PBMCs in vitro and may be involved in mediating F. gigantica pathogenesis. Because of its involvement in F. gigantica recognition by innate immune cells, rFg14-3-3e protein may have applications for development of diagnostics and therapeutic interventions