搜索结果：Laterality

A mathematical model for wire rolling is developed, focusing on predicting the lateral spread. This provides, for the first time, an analytic model of lateral spread without any fitting parameters. The model is derived directly from the governing equations, assuming a rigid, perfectly plastic material and exploiting the thinness of the wire (in thickness and width) relative to the roller size. Results are compared against experiments performed on stainless steel wire using 100mm diameter rolls, demonstrating accurate predictions of lateral spread across a wide range of wire diameters (2.96mm-7.96mm) and reduction ratios (20%-60%), all without the need for fitting parameters. Since the model requires only seconds to compute, the model's valid range is explored for varying roll diameter, wire diameter, and reduction ratio, and their effects on the resulting lateral spread characterized. The model can serve as a robust tool for validating FE results, guiding process design, and laying the foundation for future improved models. Matlab code to evaluate the model is provided in the supplementary material.

Lateral connection is a fundamental feature of biological neural circuits, facilitating local information processing and adaptive learning. In this work, we integrate lateral connections with a substructure selection network to develop a novel diffusion model based on spiking neural networks (SNNs). Unlike conventional artificial neural networks, SNNs employ an intrinsic spiking inner loop to process sequential binary spikes. We leverage this spiking inner loop alongside a lateral connection mechanism to iteratively refine the substructure selection network, enhancing model adaptability and expressivity. Specifically, we design a lateral connection framework comprising a learnable lateral matrix and a lateral mapping function, both implemented using spiking neurons, to dynamically update lateral connections. Through mathematical modeling, we establish that the proposed lateral update mechanism, under a well-defined local objective, aligns with biologically plausible synaptic plasticity principles. Extensive experiments validate the effectiveness of our approach, analyzing the role of substructure selection and lateral connection during training. Furthermore, quantitative comparison

Semiconducting transition metal dichalcogenides (TMDs), such as MoSe$_2$ and WSe$_2$, exhibit unique optical and electronic properties. Vertical stacking of layers of one or more TMDs, to create heterostructures, has expanded the fields of moiré physics and twistronics. Bottom-up fabrication techniques, such as chemical vapor deposition, have advanced the creation of heterostructures beyond what was possible with mechanical exfoliation and stacking. These techniques now enable the fabrication of lateral heterostructures, where two or more monolayers are covalently bonded in the plane of their atoms. At their atomically sharp interfaces, lateral heterostructures exhibit additional phenomena, such as the formation of charge-transfer excitons, in which the electron and hole reside on opposite sides of the interface. Due to the energy landscape created by differences in the band structures of the constituent materials, unique effects such as unidirectional exciton transport and excitonic lensing can be observed in lateral heterostructures. This review outlines recent progress in exciton dynamics and spectroscopy of TMD-based lateral heterostructures and offers an outlook on future deve

Automated parking requires accurate localization for quick and precise maneuvering in tight spaces. While the longitudinal velocity can be measured using wheel encoders, the estimation of the lateral velocity remains a key challenge due to the absence of dedicated sensors in consumer-grade vehicles. Existing approaches often rely on simplified vehicle models, such as the zero-slip model, which assumes no lateral velocity at the rear axle. It is well established that this assumption does not hold during low-speed driving and researchers thus introduce additional heuristics to account for differences. In this work, we analyze real-world data from parking scenarios and identify a systematic deviation from the zero-slip assumption. We provide explanations for the observed effects and then propose a lateral velocity model that better captures the lateral dynamics of the vehicle during parking. The model improves estimation accuracy, while relying on only two parameters, making it well-suited for integration into consumer-grade applications.

The lateral diffusion of lipids within membrane is of paramount importance, serving as a central mechanism in numerous physiological processes including cell signaling, membrane trafficking, protein activity regulation, and energy transduction pathways. This review offers a comprehensive overview of lateral lipid diffusion in model biomembrane systems explored through the lens of neutron scattering techniques. We examine diverse models of lateral diffusion and explore the various factors influencing this fundamental process in membrane dynamics. Additionally, we offer a thorough summary of how different membrane-active compounds, including drugs, antioxidants, stimulants, and membrane proteins, affect lipid lateral diffusion. Our analysis unveils the intricate interplay between these additives and membranes, shedding light on their dynamic interactions. We elucidate that this interaction is governed by a complex combination of multiple factors including the physical state and charge of the membrane, the concentration of additives, the molecular architecture of the compounds, and their spatial distribution within the membrane. In conclusion, we briefly discuss the future directions

Brain-computer interfaces (BCIs) enable users to interact with the external world using brain activity. Despite their potential in neuroscience and industry, BCI performance remains inconsistent in noninvasive applications, often prioritizing algorithms that achieve high classification accuracies while masking the neural mechanisms driving that performance. In this study, we investigated the interpretability of features derived from brain network lateralization, benchmarking against widely used techniques like power spectrum density (PSD), common spatial pattern (CSP), and Riemannian geometry. We focused on the spatial distribution of the functional connectivity within and between hemispheres during motor imagery tasks, introducing network-based metrics such as integration and segregation. Evaluating these metrics across multiple EEG-based BCI datasets, our findings reveal that network lateralization offers neurophysiological plausible insights, characterized by stronger lateralization in sensorimotor and frontal areas contralateral to imagined movements. While these lateralization features did not outperform CSP and Riemannian geometry in terms of classification accuracy, they dem

This paper addresses the lateral control of Autonomous and Connected Vehicles (ACVs) in a platoon executing an Emergency Lane Change (ELC) maneuver. These maneuvers are typically triggered by emergency signals from the front or rear of the platoon in response to the need to avoid obstacles or allow other vehicles to pass. The study assumes that ACVs maintain reliable connectivity, enabling each following vehicle to access GPS position traces of both the lead and immediately preceding vehicles in the platoon. We demonstrate that lateral string stability in the ACV platoon can be achieved using communicated information solely from the lead and preceding vehicles. Additionally, we present a lateral control framework for ACVs, which helps track a discretized preview of the trajectory constructed from the communicated data. This framework involves constructing two distinct trajectories based on the preview data from the lead and preceding vehicles, calculating the associated errors and lateral control actions for each, and then integrating these to generate a steering command. Numerical results validate the effectiveness of the proposed lateral control scheme.

The majority of computer vision algorithms fail to find higher-order (abstract) patterns in an image so are not robust against adversarial attacks, unlike human lateralized vision. Deep learning considers each input pixel in a homogeneous manner such that different parts of a ``locality-sensitive hashing table'' are often not connected, meaning higher-order patterns are not discovered. Hence these systems are not robust against noisy, irrelevant, and redundant data, resulting in the wrong prediction being made with high confidence. Conversely, vertebrate brains afford heterogeneous knowledge representation through lateralization, enabling modular learning at different levels of abstraction. This work aims to verify the effectiveness, scalability, and robustness of a lateralized approach to real-world problems that contain noisy, irrelevant, and redundant data. The experimental results of multi-class (200 classes) image classification show that the novel system effectively learns knowledge representation at multiple levels of abstraction making it more robust than other state-of-the-art techniques. Crucially, the novel lateralized system outperformed all the state-of-the-art deep le

Body tides reveal information about planetary interiors and affect their evolution. Most models to compute body tides rely on the assumption of a spherically-symmetric interior. However, several processes can lead to lateral variations of interior properties. We present a new spectral method to compute the tidal response of laterally-heterogeneous bodies. Compared to previous spectral methods, our approach is not limited to small-amplitude lateral variations; compared to finite element codes, the approach is more computationally-efficient. While the tidal response of a spherically-symmetric body has the same wave-length as the tidal force; lateral heterogeneities produce an additional tidal response with an spectra that depends on the spatial pattern of such variations. For Mercury, the Moon and Io the amplitude of this signal is as high as $1\%-10\%$ the main tidal response for long-wavelength shear modulus variations higher than $\sim 10\%$ the mean shear modulus. For Europa, Ganymede and Enceladus, shell-thickness variations of $50\%$ the mean shell thickness can cause an additional signal of $\sim 1\%$ and $\sim 10\%$ for the Jovian moons and Encelaudus, respectively. Future mi

Current research on energy related problems such as eco-routing, eco-driving and range prediction for electric vehicles (EVs) primarily considers the effect of longitudinal dynamics on EV energy consumption. However, real-world driving includes longitudinal as well as lateral motion. Therefore, it is important to understand the effects of lateral dynamics on battery energy consumption. This paper conducts an analysis of the stated effect and validates its significance through simulations. Specifically, this study demonstrates that inclusion of the effect of lateral dynamics can improve accuracy and reliability of solutions in eco-routing, eco-driving and range prediction applications.

The success of language models has inspired the NLP community to attend to tasks that require implicit and complex reasoning, relying on human-like commonsense mechanisms. While such vertical thinking tasks have been relatively popular, lateral thinking puzzles have received little attention. To bridge this gap, we devise BRAINTEASER: a multiple-choice Question Answering task designed to test the model's ability to exhibit lateral thinking and defy default commonsense associations. We design a three-step procedure for creating the first lateral thinking benchmark, consisting of data collection, distractor generation, and generation of adversarial examples, leading to 1,100 puzzles with high-quality annotations. To assess the consistency of lateral reasoning by models, we enrich BRAINTEASER based on a semantic and contextual reconstruction of its questions. Our experiments with state-of-the-art instruction- and commonsense language models reveal a significant gap between human and model performance, which is further widened when consistency across adversarial formats is considered. We make all of our code and data available to stimulate work on developing and evaluating lateral thin

Lateralization is ubiquitous in vertebrate brains which, as well as its role in locomotion, is considered an important factor in biological intelligence. Lateralization has been associated with both poor and good performance. It has been hypothesized that lateralization has benefits that may counterbalance its costs. Given that lateralization is ubiquitous, it likely has advantages that can benefit artificial intelligence. In turn, lateralized artificial intelligent systems can be used as tools to advance the understanding of lateralization in biological intelligence. Recently lateralization has been incorporated into artificially intelligent systems to solve complex problems in computer vision and navigation domains. Here we describe and test two novel lateralized artificial intelligent systems that simultaneously represent and address given problems at constituent and holistic levels. The experimental results demonstrate that the lateralized systems outperformed state-of-the-art non-lateralized systems in resolving complex problems. The advantages arise from the abilities, (i) to represent an input signal at both the constituent level and holistic level simultaneously, such that

Sequential lateration is a class of methods for multidimensional scaling where a suitable subset of nodes is first embedded by some method, e.g., a clique embedded by classical scaling, and then the remaining nodes are recursively embedded by lateration. A graph is a lateration graph when it can be embedded by such a procedure. We provide a stability result for a particular variant of sequential lateration. We do so in a setting where the dissimilarities represent noisy Euclidean distances between nodes in a geometric lateration graph. We then deduce, as a corollary, a perturbation bound for stress minimization. To argue that our setting applies broadly, we show that a (large) random geometric graph is a lateration graph with high probability under mild conditions, extending a previous result of Aspnes et al (2006).

This paper studies the design of a Model Predictive Controller (MPC) for integrated lateral stability, traction/braking control, and rollover prevention of electric vehicles intended for very high speed (VHS) racing applications. We first identify the advantages of a state-of-the-art dynamic model in that it includes rollover prevention into the MPC (a total of 8 states) and also linearizes the tire model prior to solving the MPC problem to save computation time. Then the design of a novel model predictive controller for lateral stability control is proposed aimed for achieving stable control at top speed significantly greater than typical highway speed limits. We have tested the new solution in simulation environments associated with the Indy Autonomous Challenge, where its real-world racing conditions include significant road banking angles, lateral position tracking, and a different suspension model of its Dallara Indy Lights chassis. The results are very promising with a low solver time in Python, as low as 50 Hz, and a lateral error of 30 cm at speeds of 45 m/s. Our open source code is available at: https: //github.com/jadyahya/Roll-Yaw-and-Lateral-Velocity-MPC/.

Ultrathin lateral heterostructures of monolayer MoS2 and WS2 have successfully been realized with the metal-organic chemical vapor deposition method. Atomic-resolution HAADF-STEM observations have revealed that the junction widths of lateral heterostructures range from several nanometers to single-atom thickness, the thinnest heterojunction in theory. The interfaces are atomically flat with minimal mixing between MoS2 and WS2, originating from rapid and abrupt switching of the source supply. Due to one-dimensional interfaces and broken rotational symmetry, the resulting ultrathin lateral heterostructures, 1~2 mixed-dimensional structures, can show emergent optical/electronic properties. The MOCVD growth developed in this work allows us to access various ultrathin lateral heterostructures, leading to future exploration of their emergent properties absent in each component alone.

The interlock drive system generates traction by penetrating narrow articulated spikes into the ground and by using the strength of the deeper soil layers to resist horizontal draft forces. The system promises good tractive performance in low gravity environments where tires generate little traction due to low vehicle weight. Possible applications include heavy-duty vehicles for civil engineering tasks like earthmoving or mining excavation. Safe vehicle operation in complex terrain geometry requires lateral vehicle stability to prevent vehicle rollover. Good lateral stability is a particular requirement for excavation and piling operations where the margins of safety define the terrain geometry that can be worked in, and it is a major constraint in operational planning. An earthmoving vehicle that can operate at a high roll angle reduces the need to maintain ramps in pits and on piles and can shorten and simplify the paths for individual maneuvers. Here we report on several field trials on the lateral stability of an earthmoving vehicle that uses the interlock drive systems. We find that the vehicle can work well at roll angles of up to 20°, but that it needs further improvement if

Atmospheric refraction modifies the apparent position of objects in the sky. We computed the lateral translation that is to be considered for short-range applications, such as wavefront sensing and meteor trajectories. We aim to calculate the lateral shift at each altitude and study its variation according to meteorological conditions and the location of the observation site. We also pay special attention to the chromatism of this lateral shift. We extracted the variation equations of refraction from the geometric tracing of a light ray path. A numerical method and a dry atmosphere model allowed us to numerically integrate the system of coupled equations. In addition to this, based on Taylor expansions, we established three analytic approximations of the lateral shift, one of which is the one already known in the literature. We compared the three approximations to the numerical solution. All these estimators are included in a Python 3.2 package, which is available online. Using the numerical integration estimator, we calculated the lateral shift values for any zenith angle including low elevations. The shift is typically around 3 m at a zenith angle of 45°, 10 m at 65°, and even 30

Lateral Movement refers to methods by which threat actors gain initial access to a network and then progressively move through said network collecting key data about assets until they reach the ultimate target of their attack. Lateral Movement intrusions have become more intricate with the increasing complexity and interconnected nature of enterprise networks, and require equally sophisticated detection mechanisms to proactively detect such threats in near real-time at enterprise scale. In this paper, the authors propose a novel, lightweight method for Lateral Movement detection using user behavioral analysis and machine learning. Specifically, this paper introduces a novel methodology for cyber domain-specific feature engineering that identifies Lateral Movement behavior on a per-user basis. Furthermore, the engineered features have also been used to develop two supervised machine learning models for Lateral Movement identification that have demonstrably outperformed models previously seen in literature while maintaining robust performance on datasets with high class imbalance. The models and methodology introduced in this paper have also been designed in collaboration with securi