Symmetric Architecture Research Articles

Abstract B053: NucAE: Autoencoder-based enhancement of nucleosome occupancy signals from low-coverage cfDNA sequencing

Abstract Introduction: Cell-free DNA (cfDNA) is emerging as a valuable cancer biomarker, enabling both the detection and epigenetic characterization of malignancies through sequencing. cfDNA sequencing can reveal cancer-specific nucleosome occupancy patterns, which provide insights into tumor type and differentiation status, which define prognosis and therapy recommendations. Low-coverage whole-genome cfDNA sequencing is a cost-efficient approach to assay multiple cfDNA samples, however, the low coverage limits its sensitivity. We developed NucAE, a denoising autoencoder model, to enhance nucleosome occupancy signals from low-coverage cfDNA-sequencing. Methods: We designed NucAE to estimate nucleosome occupancy genome- wide from low-coverage sequencing data. To train the model, we undersampled high-coverage cfDNA-sequencing data and produced high- and low-coverage sample pairs at varying coverages (1x-90x). Inputs to NucAE include cfDNA fragment data and the corresponding genomic nucleotide sequence, with the model leveraging a symmetrical architecture comprising two convolutional layers in both the encoder and decoder, connected by ReLU activations. Mean squared error relative to the high-coverage signal was employed as the loss function. To evaluate the model independently, we performed peak detection from the nucleosome occupancy signals. We conducted a grid search to optimize hyperparameters. Results: We found that NucAE enhances the signal to noise ratio by detecting periodicities in nucleosome occupancy data. Denoising was improved by supplementing the cfDNA fragment signals with the genomic nucleotide sequences as input, which demonstrates that the model learned the nucleotide- sequence dependence of nucleosome positioning. Independent validation through peak detection on previously unseen samples demonstrated a significant improvement (p<0.001) in accuracy compared to raw signals across all tested coverages (1x-45x). Remarkably, at extremely low coverages (1x-2x), denoised signals allowed for more accurate peak detection than raw signals from 10-20x deeper sequencing. Conclusions: By using advanced machine-learning, we were able to enhance low-coverage cfDNA sequencing data to assay nucleosome positions at a nucleotide resolution. NucAE achieves an order of magnitude improvement over the low- coverage signals and greatly improves the utility of cfDNA-sequencing data to identify cancer- specific nucleosome occupancy, while preserving its low cost. Our model is the first to use autoencoders for genome-wide signal enhancement with potential use cases in many other areas of genomics. Citation Format: Zsolt Balázs, Jean Radig, Noah Wolford, Manuel Schürch, Michael Krauthammer. NucAE: Autoencoder-based enhancement of nucleosome occupancy signals from low-coverage cfDNA sequencing [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr B053.

Building Extraction from Unmanned Aerial Vehicle (UAV) Data in a Landslide-Affected Scattered Mountainous Area Based on Res-Unet

Building extraction in landslide-affected scattered mountainous areas is essential for sustainable development, as it improves disaster risk management, fosters sustainable land use, safeguards the environment, and bolsters socio-economic advancement; however, this process entails considerable challenges. This study proposes a Res-Unet-based model to extract landslide-affected buildings from unmanned aerial vehicle (UAV) data in scattered mountain regions, leveraging the feature extraction capabilities of ResNet and the precise localization abilities of U-Net. A landslide-affected, scattered mountainous region within the Three Gorges Reservoir area was selected as a case study to validate the model’s performance. Experimental results indicate that Res-Unet displays high accuracy and robustness in building recognition, attaining accuracy (ACC), intersection-over-union (IOU), and F1-score values of 0.9849, 0.9785, and 0.9892, respectively. This enhancement can be attributed to the combined model, which amalgamates the skip connections, the symmetric architecture of U-Net, and the residual blocks of ResNet. This integration preserves low-level detail during recovery at higher levels, facilitating the extraction of multi-scale features while also mitigating the vanishing gradient problem prevalent in deep network training through the residual block structure, thus enabling the extraction of more complex features. The proposed Res-Unet approach shows significant potential for the accurate recognition and extraction of buildings in complex terrains through the efficient processing of remote sensing images.

Symmetric Connected U-Net with Multi-Head Self Attention (MHSA) and WGAN for Image Inpainting

This study presents a new image inpainting model based on U-Net and incorporating the Wasserstein Generative Adversarial Network (WGAN). The model uses skip connections to connect every encoder block to the corresponding decoder block, resulting in a strictly symmetrical architecture referred to as Symmetric Connected U-Net (SC-Unet). By combining SC-Unet with a GAN, the study aims to reconstruct images more effectively and seamlessly. The traditional discriminators only differentiate the entire image as true or false. In this study, the discriminator calculated the probability of each pixel belonging to the hole and non-hole regions, which provided the generator with more gradient loss information for image inpainting. Additionally, every block of SC-Unet incorporated a Dilated Convolutional Neural Network (DCNN) to increase the receptive field of the convolutional layers. Our model also integrated Multi-Head Self-Attention (MHSA) into selected blocks to enable it to efficiently search the entire image for suitable content to fill the missing areas. This study adopts the publicly available datasets CelebA-HQ and ImageNet for evaluation. Our proposed algorithm demonstrates a 10% improvement in PSNR and a 2.94% improvement in SSIM compared to existing representative image inpainting methods in the experiment.

Target-specified reference-based deep learning network for joint image deblurring and resolution enhancement in surgical zoom lens camera calibration

Background and objectiveFor the augmented reality of surgical navigation, which overlays a 3D model of the surgical target on an image, accurate camera calibration is imperative. However, when the checkerboard images for calibration are captured using a surgical microscope having high magnification, blur owing to the narrow depth of focus and blocking artifacts caused by limited resolution around the fine edges occur. These artifacts strongly affect the localization of corner points of the checkerboard in these images, resulting in inaccurate calibration, which leads to a large displacement in augmented reality. To solve this problem, in this study, we proposed a novel target-specific deep learning network that simultaneously enhances both the blur and spatial resolution of an image for surgical zoom lens camera calibration. MethodsAs a scheme of an end-to-end convolutional deep neural network, the proposed network is specifically intended for the checkerboard image enhancement used in camera calibration. Through the symmetric architecture of the network, which consists of encoding and decoding layers, the distinctive spatial features of the encoding layers are transferred and merged with the output of the decoding layers. Additionally, by integrating a multi-frame framework including subpixel motion estimation and ideal reference image with the symmetric architecture, joint image deblurring and enhanced resolution were efficiently achieved. ResultsFrom experimental comparisons, we verified the capability of the proposed method to improve the subjective and objective performances of surgical microscope calibration. Furthermore, we confirmed that the augmented reality overlap ratio, which quantitatively indicates augmented reality accuracy, from calibration with the enhanced image of the proposed method is higher than that of the previous methods. ConclusionsThese findings suggest that the proposed network provides sharp high-resolution images from blurry low-resolution inputs. Furthermore, we demonstrate superior performance in camera calibration by using surgical microscopic images, thus showing its potential applications in the field of practical surgical navigation.

Design and implementation of optimized 2D FIR symmetric filter architecture using modified McClellan transformation and CSD-CSE

Design and implementation of optimized 2D FIR symmetric filter architecture using modified McClellan transformation and CSD-CSE

Protecting unauthenticated messages in LTE/5G mobile networks: A two-level Hierarchical Identity-Based Signature (HIBS) solution

As an essential public infrastructure, the security and reliability of mobile networks have a profound impact on people’s production and life. Although the security of LTE/5G networks has been improved a lot with the evolution of standards, there are still some unprotected messages being transmitted between the cellular network and device due to the symmetric key-based security architecture and the trade-off between security and other criteria like network availability. By exploiting these messages, various security attacks have been proposed and demonstrated against commercial mobile networks and devices in existing literature, such as user location tracking, bidding-down, and DoS attacks. To address this security issue, in this paper, we aim to protect these unauthenticated messages in mobile networks using digital signatures. Based on the idea of Hierarchical Identity-Based Signature (HIBS) in existing work, we analyse and design a two-level HIBS solution in detail in terms of different aspects such as keys generation and provisioning procedures, replay mitigation, and cell selection. Unlike previous work, our proposed solution also supports the protection of individual vulnerable RRC and NAS layer signalling in addition to authenticating the base station. We evaluated the efficiency and feasibility of several existing HIBS schemes and implemented the most efficient one in the 5G standalone network setup using open-source software. The implementation results further proved the feasibility of the solution in practice.

Image harmonization with Simple Hybrid CNN-Transformer Network

Image harmonization seeks to transfer the illumination distribution of the background to that of the foreground within a composite image. Existing methods lack the ability of establishing global–local pixel illumination dependencies between foreground and background of composite images, which is indispensable for sharp and color-consistent harmonized image generation. To overcome this challenge, we design a novel Simple Hybrid CNN-Transformer Network (SHT-Net), which is formulated into an efficient symmetrical hierarchical architecture. It is composed of two newly designed light-weight Transformer blocks. Firstly, the scale-aware gated block is designed to capture multi-scale features through different heads and expand the receptive fields, which facilitates to generate images with fine-grained details. Secondly, we introduce a simple parallel attention block, which integrates the window-based self-attention and gated channel attention in parallel, resulting in simultaneously global–local pixel illumination relationship modeling capability. Besides, we propose an efficient simple feed forward network to filter out less informative features and allow the features to contribute to generating photo-realistic harmonized results passing through. Extensive experiments on image harmonization benchmarks indicate that our method achieve promising quantitative and qualitative results. The code and pre-trained models are available at https://github.com/guanguanboy/SHT-Net.

Explicit–implicit symmetric diffeomorphic deformable image registration with convolutional neural network

AbstractMedical image registration is essential and a key step in many advanced medical image tasks. In recent years, medical image registration has been applied to many clinical diagnoses, but large deformation registration is still a challenge. Deep learning‐based methods typically have higher accuracy but do not involve spatial transformation, which ignores some desirable properties, including topology preservation and the invertibility of transformation, for medical imaging studies. On the other hand, diffeomorphic registration methods achieve a differentiable spatial transformation, which guarantees topology preservation and invertibility of transformation, but registration accuracy is low. Therefore, a diffeomorphic deformation registration with CNN is proposed, based on a symmetric architecture, simultaneously estimating forward and inverse deformation fields. CNN with Efficient Channel Attention is used to better capture the spatial relationship. Deformation fields are optimized explicitly and implicitly to enhance the invertibility of transformations. An extensive experimental evaluation is performed using two 3D datasets. The proposed method is compared with different state‐of‐the‐art methods. The experimental results show excellent registration accuracy while better guaranteeing the diffeomorphic transformation.

Self-Exfoliating Benzotristriazine Macrocyclic Network: A New 2D Material for High-Performance Electrochemical Energy Storage.

Aza-fused aromatic π-conjugated networks are an important class of 2D graphitic analogs, which are generally constructed using aromatic precursors. Herein, the study describes a new synthetic approach and electrochemical properties of a self-exfoliating benzotristriazine 2D network (BTTN) constructed using aliphatic precursors, under relatively mild conditions. The obtained BTTN exhibits a nanodisc-like morphology, the self-exfoliation tendency of which is ascribed to the presence of structurally different macrocycles with high electronic repulsion between the layers. The benzotristriazine repeat units of BTTN is electroactive and holds higher carbon/nitrogen ratio when compared with the conventional graphitic aza-fused π-conjugated networks. The self-exfoliated BTTN nanodiscs show excellent electrochemical energy storage of 485 and 333 F g-1 at 1 A g-1 in three-electrode and two-electrode measurements, respectively. BTTN in a symmetric coin-cell architecture exhibits a high specific energy value of 46Wh kg-1 at a power density of 1kW kg-1 and shows excellent cyclic stability of 96% for 10000 and 90% for 30000 charge-discharge cycles at a higher current density of 5 A g-1, surpassing the device performance of most of the reported all-organic pseudocapacitive 2D networks.

Electrochemical Conversion of CO2 to Fuels and Useful Chemicals over Metal Supported Solid Oxide Electrolyzers

Electrochemical conversion of CO2 and H2O to fuels and chemicals provides a promising approach to simultaneously mitigate greenhouse emission and store surplus energy, which reduces electricity network variation due to increased intermittent renewable energy (such as solar and wind) utilization. Metal supported solid oxide electrolyzers (MS-SOEs) developed at Lawrence Berkeley National Laboratory provide advantages of rapid start-up, mechanical ruggedness, redox tolerance, dynamic operation, and low-cost materials. A relatively high operating temperature range of 600 to 750°C promotes energy efficiency and allows utilization of thermal energy such as steam.In this work, efficient and stable catalysts developed at the University of South Carolina have been infiltrated in the symmetrical and porous MS-SOE architecture. The phase purity has been analyzed by X-day diffraction. The morphologies of these infiltrated and posttest catalysts have been examined by scanning electron microscopy. Optimization of cell structure, processing temperature, cell voltage, and catalyst infiltration cycles for better cell performance will be presented. The stability of MS-SOEs has been evaluated during continuous operation for >200 hours. The cell degradation mechanisms revealed by electrochemical impedance spectroscopy and high-resolution scanning electron microscopy techniques will be presented.In addition to pure CO2 electrochemical conversion, effects of additional water and hydrogen in CO2 feedstock will be analyzed. Faradic efficiency and chemical selectivity calculated from gas chromatograph and electrochemical analyses will be presented.

Enhanced In‐Plane Omnidirectional Energy Harvesting from Extremely Weak Magnetic Fields via Fourfold Symmetric Magneto‐Mechano‐Electric Coupling

AbstractMagneto‐mechano‐electric (MME) energy harvesters (EHs) have emerged as a promising solution for powering Internet of Things (IoT) sensor networks by capturing ambient stray magnetic fields in urban circumstance. Despite significant advancements, achieving milliwatt‐level power generation from extremely weak (<1 Oe) and randomly oriented magnetic fields remains challenging. Drawing inspiration from the configuration of cross‐shaped spider legs, this study introduces an innovative X‐shaped MME‐EH featuring fourfold symmetric architecture with symmetrically distributed magnets at each end, enabling near‐isotropic in‐plane omnidirectional energy harvesting. Under extremely weak magnetic fields 0.75 and 0.3 Oe at 60 Hz, the device achieves a record‐high output power of 7.31 and 1.67 mWRMS, respectively, representing a 362% improvement in normalized power over the state‐of‐the‐art results. The theoretical model attributes these gains to the synergistic enhancement effect of the second bending mode and the dual‐mode structure, collectively improving both magnetomechanical and electromechanical coupling by augmenting the magnetic moments and suppressing the clamp loss. The device further demonstrates robust real‐world applications, efficiently powering a wireless IoT sensor system by harvesting multidirectional magnetic field energy from household appliances. This work opens new avenues for developing efficient multimodal MME‐EHs capable of harnessing omnidirectional, weak magnetic fields for widespread IoT applications.

Creating LEGO Figurines from Single Images

This paper presents a computational pipeline for creating personalized, physical LEGO ®1 figurines from user-input portrait photos. The generated figurine is an assembly of coherently-connected LEGO ® bricks detailed with uv-printed decals, capturing prominent features such as hairstyle, clothing style, and garment color, and also intricate details such as logos, text, and patterns. This task is non-trivial, due to the substantial domain gap between unconstrained user photos and the stylistically-consistent LEGO ® figurine models. To ensure assemble-ability by LEGO ® bricks while capturing prominent features and intricate details, we design a three-stage pipeline: (i) we formulate a CLIP-guided retrieval approach to connect the domains of user photos and LEGO ® figurines, then output physically-assemble-able LEGO ® figurines with decals excluded; (ii) we then synthesize decals on the figurines via a symmetric U-Nets architecture conditioned on appearance features extracted from user photos; and (iii) we next reproject and uv-print the decals on associated LEGO ® bricks for physical model production. We evaluate the effectiveness of our method against eight hundred expert-designed figurines, using a comprehensive set of metrics, which include a novel GPT-4V-based evaluation metric, demonstrating superior performance of our method in visual quality and resemblance to input photos. Also, we show our method's robustness by generating LEGO ® figurines from diverse inputs and physically fabricating and assembling several of them.

Lightweight attention mechanisms for EEG emotion recognition for brain computer interface

BackgroundIn the realm of brain-computer interfaces (BCI), identifying emotions from electroencephalogram (EEG) data is a difficult endeavor because of the volume of data, the intricacy of the signals, and the several channels that make up the signals. New methodsUsing dual-stream structure scaling and multiple attention mechanisms (LDMGEEG), a lightweight network is provided to maximize the accuracy and performance of EEG-based emotion identification. Reducing the number of computational parameters while maintaining the current level of classification accuracy is the aim. This network employs a symmetric dual-stream architecture to assess separately time-domain and frequency-domain spatio-temporal maps constructed using differential entropy features of EEG signals as inputs. ResultThe experimental results show that after significantly lowering the number of parameters, the model achieved the best possible performance in the field, with a 95.18 % accuracy on the SEED dataset. Comparison with existing methodsMoreover, it reduced the number of parameters by 98 % when compared to existing models. ConclusionThe proposed method distinct channel-time/frequency-space multiple attention and post-attention methods enhance the model's ability to aggregate features and result in lightweight performance.

Symmetry Molecular Design Strategy for Highly Efficient Blue Electroluminescence with Hot Exciton Mechanisms

AbstractEmitters with a hot exciton mechanism are regarded as one of the most promising candidates for organic light‐emitting diodes (OLEDs). In this study, a deep‐blue emitter with the hot exciton mechanism is reported, namely 2An‐PCz, by integrating a pair of carbazole groups with a 9,9′‐bi‐anthracene nucleus. Owing to the symmetric molecular architecture and intrinsic local excited state character, multiple high‐lying reverse intersystem cross (hRISC) channels and large overlaps of frontier molecular orbits (FMOs) can be formed, facilitating rapid hRISC processes as well as enhancement of radiative transition rates simultaneously. Combined with the strong luminescence properties brought by the unique X‐packing mode, a high photoluminescence quantum yield of 60.5% is achieved in the non‐doped state. Strikingly, non‐doped deep‐blue OLEDs exhibited a maximum external quantum efficiency (EQE) of 10.50% with minimal efficient roll‐off, which is one of the highest values for deep‐blue organic light‐emitting devices based on hot exciton emitters thus far. The magneto‐electroluminescence (MEL) experiment and transient electroluminescence measurements corroborated that both the high EQE and suppressed efficiency roll‐off are attributable to the rapid “hot exciton” channels.

Dual-mode dual-band symmetrical Doherty power amplifier employing reciprocal gate bias for high-efficiency range enhancement

This article presents an in-depth exploration of the circuit architecture and design approach for broadening the high-efficiency range of dual-mode symmetrical Doherty power amplifiers (DPAs). To enhance performance across a broader spectrum, a modified dual-mode load modulation network (LMN) is introduced, leveraging the reciprocal gate bias configuration to support an expanded high-efficiency range exceeding 6 dB across two distinct operational bands. To corroborate the efficacy of our proposed dual-mode symmetrical DPA architecture, we designed and constructed a prototype power amplifier (PA) utilizing commercially available GaN transistors. The implemented PA demonstrates exceptional performance, achieving two distinct Doherty operation bands, specifically 1.85–2.0 GHz in Mode I and 1.5–1.55 GHz in Mode II. Remarkably, the high-efficiency range is extended to 8 dB in both bands. Furthermore, the drain efficiency achieves levels of 48.5%–51.8% in Mode I and 47.7%–50.4% in Mode II at 8 dB back-off, with a saturated output power exceeding 43 dBm.

Implementation of block-based diagonal and quadrantal symmetry type 2D-FIR filter architectures using DA technique

The efficient architectures of Two-Dimensional (2D) Finite Impulse Response (FIR) filters are proposed for image processing applications. The performance metrics such as power consumption, area, and delay of the architectures can be optimized using VLSI design. The 2D FIR filter architectures can be implemented using parallel or block processing to increase the filter throughput and to prune the number of clock cycles required for image processing. The symmetry in the filter coefficients decreases the number of multipliers, whereas the multiplier block is very complex and a power-consuming block in the filter architecture. In this work, two types of symmetric architectures such as diagonal symmetry and quadrantal symmetry filters are proposed. The remaining multipliers required for the filter architecture are replaced by Distributed Arithmetic (DA) logic. The memory-based DA reduces the LUT size and hence the area, power, and delay are reduced in the filter architecture. Block processing, symmetry, and DA concepts are introduced here to optimize the 2D FIR filter architecture. The proposed architectures are implemented in 45 nm CMOS technology using Cadence Genus Synthesis tools and area, delay, power, Area-Delay Product (ADP), and Power-Delay Product (PDP) results are obtained and compared with state-of-the-art works. The ADP value of the proposed diagonal symmetry architecture is decreased by a maximum of 73.34 %, and a minimum of 20.39 %, and the PDP value is decreased by a maximum of 90.28 %, and a minimum of 21.27 % when compared to the existing works. The ADP and PDP values of the proposed quadrantal symmetry are decreased by a minimum of 23.62 %, and 27.74 % when compared to existing works, respectively.

A Heterogeneous Group CNN for Image Super-Resolution.

Convolutional neural networks (CNNs) have obtained remarkable performance via deep architectures. However, these CNNs often achieve poor robustness for image super-resolution (SR) under complex scenes. In this article, we present a heterogeneous group SR CNN (HGSRCNN) via leveraging structure information of different types to obtain a high-quality image. Specifically, each heterogeneous group block (HGB) of HGSRCNN uses a heterogeneous architecture containing a symmetric group convolutional block and a complementary convolutional block in a parallel way to enhance the internal and external relations of different channels for facilitating richer low-frequency structure information of different types. To prevent the appearance of obtained redundant features, a refinement block (RB) with signal enhancements in a serial way is designed to filter useless information. To prevent the loss of original information, a multilevel enhancement mechanism guides a CNN to achieve a symmetric architecture for promoting expressive ability of HGSRCNN. Besides, a parallel upsampling mechanism is developed to train a blind SR model. Extensive experiments illustrate that the proposed HGSRCNN has obtained excellent SR performance in terms of both quantitative and qualitative analysis. Codes can be accessed at https://github.com/hellloxiaotian/HGSRCNN.

Photoelectron Imaging Signature for Selective Formation of Icosahedral Anionic Silver Cages Encapsulating Group 5 Elements: M@Ag12- (M = V, Nb, and Ta).

An assembly of 13 atoms can form highly symmetric architectures like those belonging to D3h, Oh, D5h, and Ih point groups. Here, using photoelectron imaging spectroscopy in combination with density functional theory (DFT) calculations, we present a simple yet convincing experimental signature for the selective formation of icosahedral cages of anionic silver clusters encapsulating a dopant atom of group 5 elements: M@Ag12- (M = V, Nb, and Ta). Their photoelectron images obtained at 4 eV closely resemble one another: only a single ring is observed, which is assignable to photodetachment signals from a 5-fold degenerate superatomic 1D electronic shell in the 1S21P61D10 configuration of valence electrons. The perfect degeneracy represents an unambiguous fingerprint of an icosahedral symmetry, which would otherwise be lifted in all of the other structural isomers. DFT calculations confirm that Ih forms are the most stable and that D5h, Oh, and D3h structures are not found even in metastable states.

Survey on Power and Area efficient FIR Filter Architecture in Digital Encephalography System

In the process of Electroencephalogram (EEG) acquisition, the electrical signals from the brain are contaminated by numerous noise sources and artifacts. American Clinical Neurophysiology Society recommends digital filtering of acquired EEG signals with an upper cut-off frequency of 70 Hz before display in digital encephalography systems. We propose a linear symmetrical FIR filter architecture for filtering EEG signals using approximate computation techniques. Approximate computation techniques result in lower hardware resources and lower power consumption with some compromise in quality. Software tools used for this study include MATLAB (and its FDA tool) and Xilinx ISE 14.7. The chosen target platform is the Spartan-3e starter FPGA board. We propose the architecture for an Approximate Dadda Tree Multiplier. The proposed approximate multiplier consumes 25% lesser slices and 35.18% lower dynamic power than state-of-the-art approximate multipliers. The proposed FIR filter consumes 19.77% lesser LUTs and 34.97% lower power than state-of-the-art symmetric FIR filter architectures.

NEAR-ZERO PARASITIC SHIFT FLEXURE PIVOTS BASED ON COUPLED N-RRR PLANAR PARALLEL MECHANISMS

Abstract Flexure pivots, which are widely used for precision mechanisms, generally have the drawback of presenting parasitic shifts accompanying their rotation. The known solutions for canceling these undesirable parasitic translations usually induce a loss in radial stiffness, a reduction of the angular stroke, and nonlinear moment-angle characteristics. This article introduces a novel family of kinematic structures based on coupled n-RRR planar parallel mechanisms which presents exact zero parasitic shifts, while alleviating the drawbacks of some known pivoting structures. Based on this invention, three symmetrical architectures have been designed and implemented as flexure-based pivots. The performance of the newly introduced pivots has been compared with two known planar flexure pivots having theoretically zero parasitic shift via Finite Element models and experiments performed on plastic mockups. The results show that the newly introduced flexure pivots are an order of magnitude radially stiffer than the considered pivots from the state of the art, while having equivalent angular strokes. To experimentally evaluate the parasitic shift of the novel pivots, one of the architectures was manufactured in titanium alloy using wire-cut electrical discharge machining. This prototype exhibits a parasitic shift under 1.5 µm over a rotation stroke of ±15°, validating the near-zero parasitic shift properties of the presented designs. These advantages are key to applications such as mechanical time bases, surgical robotics, or optomechanical mechanisms.