DNA strand displacement (DSD) is an experimental technology based on DNA molecules, which has a wide range of prospects for application in the fields of molecular biology, nanotechnology and biomedicine. In this paper, classical conditioned reflexes and operant conditioning based on DSD are researched and applied to path planning of unmanned aerial vehicles (UAVs). Firstly, classical conditioned reflexes and four kinds of operant conditioning are studied. Secondly, the process of using the integral system principle to train dogs is modeled based on DSD. Thirdly, the combination of classical conditioned reflexes and operant conditioning is investigated. Finally, the application of conditioned reflexes in path planning of UAVs is proposed. All experiments in this paper are validated using Visual DSD software. The combination of DSD technology and conditioned reflexes provides a research idea for intelligent IoT systems.

While automatically generated polynomial elimination templates have sparked great progress in the field of 3D computer vision, there remain many problems for which the degree of the constraints or the number of unknowns leads to intractability. In recent years, homotopy continuation has been introduced as a plausible alternative. However, the method currently depends on expensive parallel tracking of all possible solutions in the complex domain, or a classification network for starting problem-solution pairs trained over a limited set of real-world examples. Our innovation lies in a novel approach to finding solution-problem pairs, where we only need to predict a rough initial solution, with the corresponding problem generated by an online simulator. Subsequently, homotopy continuation is applied to track that single solution back to the original problem. We apply this elegant combination to generalized camera resectioning, and also introduce a new solution to the challenging generalized relative pose and scale problem. As demonstrated, the proposed method successfully compensates the raw error committed by the regressor alone, and leads to state-of-the-art efficiency and success rates.

In the field of biocomputing and neural networks, deoxyribonucleic acid (DNA) strand displacement (DSD) technology performs well in computation, programming, and information processing. In this article, the multiplication gate, addition gate, and threshold gate based on DSD are used to cascade into a single DNA neuron. Multiple DNA neurons can be cascaded to form different neural networks. The DNA neural networks are designed to implement seven classical conditioned reflexes from Pavlovian associative memory experiments. A classical conditioned reflex is a combination of a conditioned stimulus (CS) and another un CS with a reward or punishment. So that the individual develops a conditioned reflex that is similar to an unconditioned reflex in the use of CS alone. The seven classical conditioned reflexes include acquisition and forgetting, interstimulus interval effect, blocking, conditioned inhibition, overshadowing, generation, and differentiation. The simulations are verified by the software visual DSD. This article provides a direction for the integration of biology and psychology.

Despite the promise of superior performance under challenging conditions, event-based motion estimation remains a hard problem owing to the difficulty of extracting and tracking stable features from event streams. In order to robustify the estimation, it is generally believed that fusion with other sensors is a requirement. In this work, we demonstrate reliable, purely event-based visual odometry on planar ground vehicles by employing the constrained non-holonomic motion model of Ackermann steering platforms. We extend single feature n-linearities for regular frame-based cameras to the case of quasi time-continuous event-tracks, and achieve a polynomial form via variable degree Taylor expansions. Robust averaging over multiple event tracks is simply achieved via histogram voting. As demonstrated on both simulated and real data, our algorithm achieves accurate and robust estimates of the vehicle’s instantaneous rotational velocity, and thus results that are comparable to the delta rotations obtained by frame-based sensors under normal conditions. We furthermore significantly outperform the more traditional alternatives in challenging illumination scenarios. The code is available at https://github.com/gowanting/NHEVO.

The optimization of therapeutic antibodies through traditional techniques, such as candidate screening via hybridoma or phage display, is resource-intensive and time-consuming. In recent years, computational and artificial intelligence-based methods have been actively developed to accelerate and improve the development of therapeutic antibodies. In this study, we developed an end-to-end sequence-based deep learning model, termed AttABseq, for the predictions of the antigen-antibody binding affinity changes connected with antibody mutations. AttABseq is a highly efficient and generic attention-based model by utilizing diverse antigen-antibody complex sequences as the input to predict the binding affinity changes of residue mutations. The assessment on the three benchmark datasets illustrates that AttABseq is 120% more accurate than other sequence-based models in terms of the Pearson correlation coefficient between the predicted and experimental binding affinity changes. Moreover, AttABseq also either outperforms or competes favorably with the structure-based approaches. Furthermore, AttABseq consistently demonstrates robust predictive capabilities across a diverse array of conditions, underscoring its remarkable capacity for generalization across a wide spectrum of antigen-antibody complexes. It imposes no constraints on the quantity of altered residues, rendering it particularly applicable in scenarios where crystallographic structures remain unavailable. The attention-based interpretability analysis indicates that the causal effects of point mutations on antibody-antigen binding affinity changes can be visualized at the residue level, which might assist automated antibody sequence optimization. We believe that AttABseq provides a fiercely competitive answer to therapeutic antibody optimization.

Convolutional neural networks (CNNs) have emerged as a primary method for seismic data interpolation. These networks are trained on a large dataset, and interpolation can be accomplished by inputting incomplete data into the learned network without the need for parameter selection, as in traditional method. However, most studies have focused on two-dimension (2D)/ three-dimension (3D) interpolation cases, leaving five-dimension (5D) situations not properly handled. To address this issue, we propose a novel 5D convolutional block designed to capture features in seismic records in five dimensions, leading to the construction of a simple and effective 5D fully convolution network (5D-FCN). Specifically, leveraging the linearity of convolution, we represent 5D convolution operator with the aid of a number of 3D convolution operators. This allows for efficient implementation of 5D convolution using 3D convolution on popular deep learning platforms such as Pytorch and Tensorflow. We conduct experiments on both synthetic and field examples. Compared with 3D-CNN-based (3D-Unet) method, which interpolates 5D data split into a combination of 3D blocks, and the damped rank-reduction method (DRR), the 5D-FCN demonstrates superior interpolation performance. In the synthetic example, the signal-to-noise ratio (SNR) values for data recovered by DRR, 3D-Unet, and 5D-FCN are 13.54 dB, 12.08 dB, and 15.00 dB, respectively. In the field example, our method exhibits superior spatial coherence in the reconstruction results.

Branch-and-bound-based consensus maximization stands out due to its important ability of retrieving the globally optimal solution to outlier-affected geometric problems. However, while the discovery of such solutions caries high scientific value, its application in practical scenarios is often prohibited by its computational complexity growing exponentially as a function of the dimensionality of the problem at hand. In this work, we convey a novel, general technique that allows us to branch over an n-1 dimensional space for an n-dimensional problem. The remaining degree of freedom can be solved globally optimally within each bound calculation by applying the efficient interval stabbing technique. While each individual bound derivation is harder to compute owing to the additional need for solving a sorting problem, the reduced number of intervals and tighter bounds in practice lead to a significant reduction in the overall number of required iterations. Besides an abstract introduction of the approach, we present applications to four fundamental geometric computer vision problems: camera resectioning, relative camera pose estimation, point set registration, and rotation and focal length estimation. Through our exhaustive tests, we demonstrate significant speed-up factors at times exceeding two orders of magnitude, thereby increasing the viability of globally optimal consensus maximizers in online application scenarios.

Traditional visual-inertial state estimation targets absolute camera poses and spatial landmark locations while first-order kinematics are typically resolved as an implicitly estimated substate. However, this poses a risk in velocity-based control scenarios, as the quality of the estimation of kinematics depends on the stability of absolute camera and landmark coordinates estimation. To address this issue, we propose a novel solution to tight visual–inertial fusion directly at the level of first-order kinematics by employing a dynamic vision sensor instead of a normal camera. More specifically, we leverage trifocal tensor geometry to establish an incidence relation that directly depends on events and camera velocity, and demonstrate how velocity estimates in highly dynamic situations can be obtained over short-time intervals. Noise and outliers are dealt with using a nested two-layer random sample consensus (RANSAC) scheme. In addition, smooth velocity signals are obtained from a tight fusion with preintegrated inertial signals using a sliding window optimizer. Experiments on both simulated and real data demonstrate that the proposed tight event-inertial fusion leads to continuous and reliable velocity estimation in highly dynamic scenarios independently of absolute coordinates. Furthermore, in extreme cases, it achieves more stable and more accurate estimation of kinematics than traditional, point-position-based visual-inertial odometry.

Vision-based localization is a cost-effective and thus attractive solution for many intelligent mobile platforms. However, its accuracy and especially robustness still suffer from low illumination conditions, illumination changes, and aggressive motion. Event-based cameras are bio-inspired visual sensors that perform well in HDR conditions and have high temporal resolution, and thus provide an interesting alternative in such challenging scenarios. While purely event-based solutions currently do not yet produce satisfying mapping results, the present work demonstrates the feasibility of purely event-based tracking if an alternative sensor is permitted for mapping. The method relies on geometric 3D-2D registration of semi-dense maps and events, and achieves highly reliable and accurate cross-modal tracking results. Practically relevant scenarios are given by depth camera-supported tracking or map-based localization with a semi-dense map prior created by a regular image-based visual SLAM or structure-from-motion system. Conventional edge-based 3D-2D alignment is extended by a novel polarity-aware registration that makes use of signed time-surface maps (STSM) obtained from event streams. We furthermore introduce a novel culling strategy for occluded points. Both modifications increase the speed of the tracker and its robustness against occlusions or large view-point variations. The approach is validated on many real datasets covering the above-mentioned challenging conditions, and compared against similar solutions realised with regular cameras.

We present a convex optimization approach for generalized essential matrix (GEM) estimation. The six-point minimal solver for the GEM has poor numerical stability and applies only for a minimal number of points. Existing non-minimal solvers for GEM estimation rely on either local optimization or relinearization techniques, which impedes high accuracy in common scenarios. Our proposed non-minimal solver minimizes the sum of squared residuals by reformulating the problem as a quadratically constrained quadratic program. The globally optimal solution is thus obtained by a semidefinite relaxation. The algorithm retrieves certifiably globally optimal solutions to the original non-convex problem in polynomial time. We also provide the necessary and sufficient conditions to recover the optimal GEM from the relaxed problems. The improved performance is demonstrated over experiments on both synthetic and real multi-camera systems.

Over the past decade, the Gröbner basis theory and automatic solver generation have lead to a large number of solutions to geometric vision problems. In practically all cases, the derived solvers apply a fixed elimination template to calculate the Groebner basis and thereby identify the zero-dimensional variety of the original polynomial constraints. However, it is clear that different variable or monomial orderings lead to different elimination templates, and we show that they may present a large variability in accuracy for a certain instance of a problem. The present paper has two contributions. We first show that for a common class of problems in geometric vision, variable reordering simply translates into a permutation of the columns of the initial coefficient matrix, and that-as a result-one and the same elimination template can be reused in different ways, each one leading to potentially different accuracy. We then prove that the original set of coefficients may contain sufficient information to train a classifier for online selection of a good solver, most notably at the cost of only a small computational overhead. We demonstrate wide applicability at the hand of generic dense polynomial problem solvers, as well as a concrete solver from geometric vision.