Local Field Research Articles

Reducing Bandwidth Usage in CVSLAM: A Novel Approach to Map Point Selection and Efficient Data Compression

In the field of collaborative visual simultaneous localization and mapping (CVSLAM), efficient data communication poses a significant challenge, particularly in environments with limited bandwidth. To address this issue, we introduce a method aimed at reducing communication consumption. Our approach starts with a strategic culling of map points, aiming at maximizing pose-visibility and expanding spatial diversity to effectively eliminate redundant data in CVSLAM. We achieve this by formulating the problem of maximizing pose-visibility and spatial diversity as a minimum-cost maximum-flow graph optimization problem. Subsequently, we apply finite state entropy encoding for the compression of visual information, further alleviating bandwidth constraints. To verify the proposed method, we implement it within a centralized collaborative monocular simultaneous localization and mapping (SLAM) system. Our approach has been tested on publicly available datasets and in real-world scene. The results show a prominent reduction in bandwidth usage by 49% while maintaining mapping accuracy and without introducing additional latency, confirming its effectiveness in a multi-agent system setting.

Local high chirality near exceptional points based on asymmetric backscattering

Abstract We investigate local high chirality inside a microcavity near exceptional points (EPs) achieved via asymmetric backscattering by two internal weak scatterers. At EPs, coalescent eigenmodes exhibit position-dependent and symmetric high chirality characteristics for a large azimuthal angle between the two scatterers. However, asymmetric mode field features appear near EPs. Two azimuthal regions in the microcavity classified by the scatterers exhibit different wave types and chirality. Such local mode field features are attributed to the symmetries of backscattering in direction and spatial distribution. The connections between the wave types, the symmetry of mode field distribution and different symmetries of backscattering near EPs are also analyzed and discussed. Benefiting from the small size of weak scatterers, such microcavities with a high Q/V near EPs can be used to achieve circularly polarized quantum light sources and explore EP modified quantum optical effects in cavity quantum electrodynamics systems.

Interaction of motor behaviour, cortical oscillations and deep brain stimulation in Parkinson disease

Abstract Recent progress in the study of Parkinson's disease (PD) has highlighted the pivotal role of beta oscillations within the basal ganglia-thalamo-cortical network in modulating motor symptoms. Predominantly manifesting as transient bursts, these beta oscillations are central to the pathophysiology of PD motor symptoms, especially bradykinesia. Our central hypothesis is that increased bursting duration in cortex, coupled with kinematics of movement, disrupts the typical flow of neural information, leading to observable changes in motor behavior in PD. To explore this hypothesis, we employed an integrative approach, analyzing the interplay between moment-to-moment brain dynamics and movement kinematics, and the modulation of these relationships by therapeutic deep brain stimulation (DBS). Local field potentials were recorded from the hand motor (M1) and premotor cortical (PM) areas, and internal Globus Pallidus (GPi) in 26 PD patients undergoing DBS implantation surgery. Participants executed rapid alternating hand movements in 30-second blocks, both with and without therapeutic pallidal stimulation. Behaviorally, the analysis revealed bradykinesia, with hand movement cycle width increasing linearly over time during DBS-OFF blocks. Crucially, there was a moment-to-moment correlation between M1 low beta burst duration and movement cycle width, a relationship that dissipated with therapeutic DBS. Further analyses suggest that high gamma activity correlates with enhanced motor performance with DBS-ON. Regardless of the nature of coupling, DBS’s modulation of cortical bursting activity appeared to amplify the brain signals’ informational content regarding instantaneous movement changes. Our findings underscore that DBS significantly reshapes the interaction between motor behavior and neural signals in PD, not only modulating specific bands but also expanding the system's capability to process and relay information for motor control. These insights shed light on the possible network mechanisms underlying DBS therapeutic effects, suggesting a profound impact on both neural and motor domains.

Nitrogen Fixation by Atmospheric Microwave Plasma with Local Electric Field Enhancement

Abstract Nitrogen fixation by atmospheric microwave plasma is a promising green technology. However, the energy consumption of nitrogen fixation driven by microwave plasma is still too high in existing reports, and there is a lack of effective methods to reduce it. In this paper, a method of local electric field enhancement in atmospheric microwave plasma is reported, which is achieved in an optimized metal array structure that can increase the electric field intensity by 385 times. The experimental results show that the reduction of energy consumption reaches up to 20% and the nitrogen oxide production increases by up to 26% with the local electric field enhancement. The achieved energy consumption is as low as 1.78 MJ/mol with a corresponding nitrogen oxide production of 3.53%. To the best of our knowledge, this is the first study to explore the role of local electric field enhancement on nitrogen fixation, and the energy consumption in this study is the lowest among the existing studies of nitrogen fixation by atmospheric pressure microwave air plasma.

Toward 3D magnetic force microscopy: Simultaneous torsional cantilever excitation to access a second, orthogonal stray field component

Magnetic force microscopy (MFM) is long established as a powerful tool for probing the local stray fields of magnetic nanostructures across a range of temperatures and applied stimuli. A major drawback of the technique, however, is that the detection of stray fields emanating from a sample’s surface rely on a uniaxial vertical cantilever oscillation, and thus are only sensitive to vertically oriented stray field components. The last two decades have shown an ever-increasing literature fascination for exotic topological windings where particular attention to in-plane magnetic moment rotation is highly valuable when identifying and understanding such systems. Here, we present a method of detecting in-plane magnetic stray field components, by utilizing a split-electrode excitation piezo that allows the simultaneous excitation of a cantilever at its fundamental flexural and torsional modes. This allows for the joint acquisition of traditional vertical mode images and a lateral MFM where the tip–cantilever system is only sensitive to stray fields acting perpendicular to the torsional axis of the cantilever.

Advanced pyro-/piezoelectric catalytic disinfection of water via porous spontaneously polarized ceramic with strong local electric field induced by micro-nano bubbles

In-situ electroporation disinfection technology inactivates microorganisms by local electric field penetrating antioxidant membranes without producing by-products, which reduces the consumption of oxidants. However, this individual technology exhibits weak disinfection performance for high water fluxes. Herein, a disinfection system based on the synergy of porous spontaneously polarized ceramic (PSPC) and micro-nano bubbles (MNB) (PSPC/MNB disinfection system, PMDS) was constructed for efficient disinfection of drinking water. Pyro-/piezoelectric effects of PSPC induced by MNB collapse constructed a 1.92 × 108 V/m of local electric field (simulated by COMSOL Multiphysics) and generated reactive oxygen species (ROS; •OH, 1O2, •O2−, and H2O2), resulting in electroporation damage on microorganisms and subsequent fatal oxidation of the intracellular contents by ROS. PMDS completely inactivated 107 CFU/mL of E. coli within 45 min, about 10 times faster than the MNB process, which is attributed to that the abundant porous and rough surface in PSPC increased the specific surface area and dipoles for pyro-/piezoelectric catalysis. Moreover, MNB collapse enriched •OH and induced pyro-/piezoelectric effects of PSPC through local temperature and pressure gradients. Synergistic interaction of PSPC/MNB collected additional thermal and mechanical energies besides the collapse of MNB, and the pyro-/piezoelectricity-induced in-situ electroporation–ROS deep oxidation avoided oxidant consumption and hazardous by-products. PMDS presented favorable disinfection efficiency in actual drinking water source, expected to be a practical system for eliminating microbial hazards in drinking water.

A Comparison of Local and Global Strategies for Exploiting Field Inversion on Separated Flows at Low Reynolds Number

The prediction of separated flows at low Reynolds numbers is crucial for several applications in aerospace and energy fields. Reynolds-averaged Navier–Stokes (RANS) equations are widely used but their accuracy is limited in the presence of transition or separation. In this work, two different strategies for improving RANS simulations by means of field inversion are discussed. Both strategies require solving an optimization problem to identify a correction field by minimizing the error on some measurable data. The obtained correction field is exploited with two alternative strategies. The first strategy aims to the identification of a relation that allows to express the local correction field as a function of some local flow features. However, this regression can be difficult or even impossible because the relation between the assumed input variables and the local correction could not be a function. For this reason, an alternative is proposed: a U-Net model is trained on the original and corrected RANS results. In this way, it is possible to perform a prediction with the original RANS model and then correct it by means of the U-Net. The methodologies are evaluated and compared on the flow around the NACA0021 and the SD7003 airfoils.

Equivalence of regular spinor fields

In the Lounesto classification, there are three types of regular spinors. They are classified by the condition that at least one of the scalar or pseudo scalar norms are non-vanishing. The Dirac spinors are regular spinors because their scalar and pseudo scalar norms are non-zero and zero respectively. We construct local and Lorentz-covariant fermionic fields from all three classes of regular spinors. By computing the invariants and bilinear covariants of the regular spinor fields, we show that they are physically equivalent to the Dirac fields in the sense that whatever interactions one writes down using the regular spinor fields, they can always be expressed in terms of the Dirac fields.

MDFA-Net: Multi-Scale Differential Feature Self-Attention Network for Building Change Detection in Remote Sensing Images

Building change detection (BCD) from remote sensing images is an essential field for urban studies. In this well-developed field, Convolutional Neural Networks (CNNs) and Transformer have been leveraged to empower BCD models in handling multi-scale information. However, it is still challenging to accurately detect subtle changes using current models, which has been the main bottleneck to improving detection accuracy. In this paper, a multi-scale differential feature self-attention network (MDFA-Net) is proposed to effectively integrate CNN and Transformer by balancing the global receptive field from the self-attention mechanism and the local receptive field from convolutions. In MDFA-Net, two innovative modules were designed. Particularly, a hierarchical multi-scale dilated convolution (HMDConv) module was proposed to extract local features with hybrid dilation convolutions, which can ameliorate the effect of CNN’s local bias. In addition, a differential feature self-attention (DFA) module was developed to implement the self-attention mechanism at multi-scale difference feature maps to overcome the problem that local details may be lost in the global receptive field in Transformer. The proposed MDFA-Net achieves state-of-the-art accuracy performance in comparison with related works, e.g., USSFC-Net, in three open datasets: WHU-CD, CDD-CD, and LEVIR-CD. Based on the experimental results, MDFA-Net significantly exceeds other models in F1 score, IoU, and overall accuracy; the F1 score is 93.81%, 95.52%, and 91.21% in WHU-CD, CDD-CD, and LEVIR-CD datasets, respectively. Furthermore, MDFA-Net achieved first or second place in precision and recall in the test in all three datasets, which indicates its better balance in precision and recall than other models. We also found that subtle changes, i.e., small-sized building changes and irregular boundary changes, are better detected thanks to the introduction of HMDConv and DFA. To this end, with its better ability to leverage multi-scale differential information than traditional methods, MDFA-Net provides a novel and effective avenue to integrate CNN and Transformer in BCD. Further studies could focus on improving the model’s insensitivity to hyper-parameters and the model’s generalizability in practical applications.

Experience Dependence of Alpha Rhythms and Neural Dynamics in the Mouse Visual Cortex.

The role of experience in the development and maintenance of emergent network properties such as cortical oscillations and states is poorly understood. To define how early-life experience affects cortical dynamics in the visual cortex of adult, head-fixed mice, we examined the effects of two forms of blindness initiated before eye opening and continuing through recording: (1) bilateral loss of retinal input (enucleation) and (2) degradation of visual input (eyelid suture). Neither form of deprivation fundamentally altered the state-dependent regulation of firing rates or local field potentials. However, each deprivation caused unique changes in network behavior. Laminar analysis revealed two different generative mechanisms for low-frequency synchronization: one prevalent during movement and the other during quiet wakefulness. The former was absent in enucleated mice, suggesting a mouse homolog of human alpha oscillations. In addition, neurons in enucleated animals were less correlated and fired more regularly, but no change in mean firing rate. Eyelid suture decreased firing rates during quiet wakefulness, but not during movement, with no effect on neural correlations or regularity. Sutured animals showed a broadband increase in depth EEG power and an increased occurrence, but reduced central frequency, of narrowband gamma oscillations. The complementary-rather than additive-effects of lid suture and enucleation suggest that the development of emergent network properties does not require vision but is plastic to modified input. Our results suggest a complex interaction of internal set points and experience determines mature cortical activity, with low-frequency synchronization being particularly susceptible to early deprivation.

A Novel Multi-Scale Gated Convolutional Neural Network based on Informer for Predicting the Remaining Useful Life of Rotating Machinery

Abstract Recently, neural networks based on deep learning techniques have been employed for Remaining Useful Life (RUL) prediction of rotating machinery. However, there are some limitations: 1) CNNs primarily use local receptive fields to extract features, which leads to a relative weakness in modeling long-term dependencies on a global scale; 2) Transformers face difficulties when dealing with uncertainties such as anomalies in input sequences, missing data, or variations in sampling frequency. This paper introduces a informer-based multi-scale gated convolutional network (MSGCN-Informer). Initially, a multi-scale gated convolution module is constructed to effectively extract features across various levels, adeptly capturing temporal patterns and long-term dependencies within the dataset. Subsequently, the derived multi-scale degradation features are utilized in predicting the RUL through an informer network, thereby enhancing the efficiency of parallel computing. To validate the effectiveness and superiority of this method, comparative experiments were conducted using two publicly available bearing datasets and various model approaches.

Contribution of basal ganglia activity to REM sleep disorder in Parkinson’s disease

BackgroundRapid eye movement (REM) sleep behaviour disorder (RBD) is one of the most common sleep problems and represents a key prodromal marker in Parkinson’s disease (PD). It remains unclear whether...

Exploring the Spatial Distribution of Local Field Gradients by Tip-Enhanced Raman Mapping of a Single-Molecule Probe

Exploring the Spatial Distribution of Local Field Gradients by Tip-Enhanced Raman Mapping of a Single-Molecule Probe

Electrophysiological and Behavioral Markers of Hyperdopaminergia in DAT-KO Rats

Background/Objectives: Dopamine dysfunction (DA) is a hallmark of many neurological disorders. In this case, the mechanism of changes in dopamine transmission on behavior remains unclear. This study is a look into the intricate link between disrupted DA signaling, neuronal activity patterns, and behavioral abnormalities in a hyperdopaminergic animal model. Methods: To study the relationship between altered DA levels, neuronal activity, and behavioral deficits, local field potentials (LFPs) were recorded during four different behaviors in dopamine transporter knockout rats (DAT-KO). At the same time, local field potentials were recorded in the striatum and prefrontal cortex. Correlates of LFP and accompanying behavioral patterns in genetically modified (DAT-KO) and control animals were studied. Results: DAT-KO rats exhibited desynchronization between LFPs of the striatum and prefrontal cortex, particularly during exploratory behavior. A suppressive effect of high dopamine levels on the striatum was also observed. Wild-type rats showed greater variability in LFP patterns across certain behaviors, while DAT-KO rats showed more uniform patterns. Conclusions: The decisive role of the synchrony of STR and PFC neurons in the organization of motor acts has been revealed. The greater variability of control animals in certain forms of behavior probably suggests greater adaptability. More uniform patterns in DAT-KO rats, indicating a loss of striatal flexibility when adapting to specific motor tasks. It is likely that hyperdopaminergy in the DAT-KO rat reduces the efficiency of information processing due to less synchronized activity during active behavior.

Sensing data and methodology from the Adaptive DBS Algorithm for Personalized Therapy in Parkinson’s Disease (ADAPT-PD) clinical trial

Adaptive deep brain stimulation (aDBS) is an emerging advancement in DBS technology; however, local field potential (LFP) signal rate detection sufficient for aDBS algorithms and the methods to set-up aDBS have yet to be defined. Here we summarize sensing data and aDBS programming steps associated with the ongoing Adaptive DBS Algorithm for Personalized Therapy in Parkinson’s Disease (ADAPT-PD) pivotal trial (NCT04547712). Sixty-eight patients were enrolled with either subthalamic nucleus or globus pallidus internus DBS leads connected to a Medtronic PerceptTM PC neurostimulator. During the enrollment and screening procedures, a LFP (8–30 Hz, ≥1.2 µVp) control signal was identified by clinicians in 84.8% of patients on medication (65% bilateral signal), and in 92% of patients off medication (78% bilateral signal). The ADAPT-PD trial sensing data indicate a high LFP signal presence in both on and off medication states of these patients, with bilateral signal in the majority, regardless of PD phenotype.

Attitude determination for multirotor aerial vehicles using a prescribed‐time super‐twisting algorithm

AbstractThis paper is concerned with the prescribed‐time robust attitude determination (AD) of multirotor aerial vehicles (MAVs) using vector measurements from the local magnetic field and local gravity. To address this problem, we first introduce a novel modified super‐twisting algorithm endowed with the prescribed‐time convergence property. The state of the proposed algorithm is governed by an unbounded time‐varying gain up to the prescribed settling time (PST) and by a function after that. Therefore, after the PST, the new algorithm coincides with the conventional super‐twisting, thus showing robust stability at the origin. This prescribed‐time super‐twisting algorithm (PTSTA) is then applied to the formulation of a three‐stage gyro‐free attitude determination method for MAVs. In the first stage, the classical QUEST algorithm is used to compute a Wahba‐optimal attitude estimate from the vector measurements. In the second stage, the PTSTA is employed in the formulation of a robust state estimator that provides estimates of the attitude Gibbs vector and its rate. Finally, in the third stage, these state estimates as well as the attitude kinematic equation are immediately used to compute the MAV angular velocity. The proposed robust prescribed‐time gyro‐free AD method is evaluated numerically, showing invariance with respect to disturbance and model uncertainty.

An Ultrasensitive Programmable 2D Photoelectric Synaptic Transistor

AbstractThe burgeoning advancement of information technology has engendered a discernible surge in the examination of neuromorphic devices, notably drawing broader attention to artificial vision systems endowed with sensory recognition capabilities. Current photoelectric synapse devices employed in artificial vision systems are generally well‐suited for well‐illuminated conditions, yet exhibit diminished sensitivity in weak‐light scenarios, resulting in a pronounced deterioration of recognition accuracy. Here, an ultrasensitive photoelectric synaptic transistor based on negative quantum capacitance effect resulted from the 2D semi‐metallic graphene layer that partially enclosed within the gate dielectric layer, which manifests a noteworthy reduction in device control voltage and exhibits perception and storage capabilities for weak light of 39.4 nW cm−2 with detectivity above 1016 cm Hz1/2 W−1 is demonstrated. The voltage amplification effect and the concomitant formation of an equivalent local electrostatic field induced by the negative quantum capacitance effect engenders a robust programmable synaptic plasticity for extremely weak light by modifying the control gate. These results represent the inaugural integration of the negative quantum capacitance effect into optoelectronic devices and furnish a robust hardware foundation for developing vision systems in weak‐light environments.

Tailoring charge transport in BaTiO3 crystals through dislocation engineering

AbstractDislocations in oxide ceramics significantly influence their physical properties by creating substantial local strain fields, new electronic states, and space‐charge layers. In this study, we investigated the effects of mechanically introduced dislocations on the electrical conductivity of BaTiO3 single crystals. High‐temperature plastic deformation was employed to introduce a high dislocation density with a {100}〈100〉 slip system. Impedance measurements revealed a significant anisotropy in the conductivity due to the presence of oriented dislocation structures. The crystals with dislocation lines aligned parallel to the measurement axis ([001] crystallographic direction) exhibited 16‐fold higher conductivity compared to those measured across the dislocations. Compared to the pristine crystals, this means an increase in conductivity when the measurements were carried out parallel to dislocation lines and a decrease in perpendicular measurements. Our study demonstrates that not only ferroelectric properties but also charge transport can be modified by dislocation introduction in BaTiO3.

Mourre Theory and Asymptotic Observables in Local Relativistic Quantum Field Theory

We prove the convergence of Araki–Haag detectors in any Haag–Kastler quantum field theory with an upper and lower mass gap. We cover the case of a single Araki–Haag detector on states of bounded energy, which are selected from the absolutely continuous part of the energy-momentum spectrum sufficiently close to the lower boundary of the multi-particle spectrum. These states essentially encompass those states in the multi-particle spectrum lying below the three-particle threshold. In our proof, we draw on insights from proofs of asymptotic completeness in quantum mechanics. Notably, we apply Mourre’s conjugate operator method for the first time within the framework of Haag–Kastler quantum field theory. Furthermore, we discuss applications of our findings for the problem of asymptotic completeness in local relativistic quantum field theory.

Local Magnetic Field Inversion Based on the Modified L-Curve Model Function Method

Local Magnetic Field Inversion Based on the Modified L-Curve Model Function Method