Recent bcoe_ece_oapolicydeposits items

Near-field electrospinning of polymer/phage whispering gallery mode microfiber resonators for label-free biosensing

Fri, 27 Feb 2026 00:00:00 +0000

Whispering gallery mode (WGM) resonators have attracted attention as optical biosensors due to their capacity for rapid label-free detection. Separately, the M13 bacteriophage has been investigated as an alternative biorecognition element. In this work, a novel polymer WGM biosensor functionalized with filamentous virus biocapture agents was fabricated using near-field electrospinning (NFES) in a single step. Polyvinyl alcohol (PVA) was mixed with streptavidin-binding M13 bacteriophage and rhodamine 6 G in aqueous solution for blend electrospinning of active cavity microfiber-based resonators. Confocal fluorescence microscopy revealed alignment of M13 bioreceptors along the fiber axis, while x-ray photoelectron spectroscopy (XPS) and streptavidin-conjugated nanoparticle binding studies indicated that bioactive M13 bacteriophage were on the fiber surface. Dye-doped, crosslinked PVA/M13 fibers supported moderate quality (Q) factor first order WGM resonances within their cross-sections...

M13 bacteriophage spheroids as scaffolds for directed synthesis of spiky gold nanostructures

Fri, 27 Feb 2026 00:00:00 +0000

The spherical form (s-form) of a genetically-modified gold-binding M13 bacteriophage was investigated as a scaffold for gold synthesis. Repeated mixing of the phage with chloroform caused a 15-fold contraction from a nearly one micron long filament to an approximately 60 nm diameter spheroid. The geometry of the viral template and the helicity of its major coat protein were monitored throughout the transformation process using electron microscopy and circular dichroism spectroscopy, respectively. The transformed virus, which retained both its gold-binding and mineralization properties, was used to assemble gold colloid clusters and synthesize gold nanostructures. Spheroid-templated gold synthesis products differed in morphology from filament-templated ones. Spike-like structures protruded from the spherical template while isotropic particles developed on the filamentous template. Using inductively coupled plasma-mass spectroscopy (ICP-MS), gold ion adsorption was found to be comparatively...

Bifunctional M13 Bacteriophage Nanospheroids for the Synthesis of Hybrid Noncentrosymmetric Nanoparticles

Thu, 26 Feb 2026 00:00:00 +0000

Noncentrosymmetric nanostructures are composed of two different materials displayed on spatially distinct surface regions. These nontrivial building blocks facilitate self-assembly of hierarchical nanostructures with increased complexity and support cooperative material behaviors. The arrangement of M13 bacteriophage structural proteins within its capsid creates a promising monodisperse and readily modifiable template for these low-symmetry nanomaterials. Here, a 9-mer ZnS-binding peptide was inserted into the p3 minor coat protein (capsid tip) of an M13 bacteriophage with Au-binding motif fused to its p8 major coat protein (capsid body). Multiple vortex/rest cycles with chloroform were used to convert this bifunctional, Au/ZnS (p8/p3)-binding phage from filament to spheroid. The shape transformation was studied with transmission electron microscopy, circular dichroism spectroscopy, and fluorescence spectroscopy. The effects of the p3 peptide fusion and conversion temperature...

Whispering gallery mode emission from dye-doped polymer fiber cross-sections fabricated by near-field electrospinning

Thu, 26 Feb 2026 00:00:00 +0000

Whispering gallery mode (WGM) resonators demonstrate great potential for photonic and sensing applications. Yet, these devices are often disadvantaged by costly materials or complex fabrication approaches, in addition to lack of manufacturing scalability. Near-field electrospinning (NFES), a recently emerged facile fiber fabrication method, offers a solution. Here, WGM resonances are reported in Rhodamine 6G-doped poly(vinyl) alcohol (PVA) microfibers via NFES. Diameters are tuned over a range of more than 10 μm by varying substrate stage speed. Fibers display uniform distribution of dye, smooth surfaces, and circular cross-sections, all critical for supporting WGMs. High quality (Q) resonances are confirmed within fiber cross-sections through polarization experiments, free-spectral range analysis, and Mie-theory-derived mode assignment. In addition to WGMs, groups of associated spiral or conical modes are observed due to taper-induced weak optical confinement along the fiber...

Gold-Decorated M13 I‑Forms and S‑Forms for Targeted Photothermal Lysis of Bacteria

Thu, 26 Feb 2026 00:00:00 +0000

With the emergence of multidrug-resistant bacteria, photothermal therapy has been proposed as an alternative to antibiotics for targeting and killing pathogens. In this study, two M13 bacteriophage polymorphs were studied as nanoscaffolds for plasmonic bactericidal agents. Receptor-binding proteins found on the pIII minor coat protein targeted Escherichia coli bacteria with F-pili (F⁺ strain), while a gold-binding peptide motif displayed on the pVIII major coat protein templated Au nanoparticles. Temperature-dependent exposure to a chloroform-water interface transformed the native filamentous phage into either rod-like or spheroid structures. The morphology, geometry, and size of the polymorphs, as well as the receptor-binding protein and host cell receptor interaction were studied using electron microscopy. Au/template structures were formed through incubation with Au colloid, and optical absorbance was measured. Despite the closely packed Au nanoparticle layer...

Electric Field Polarity Controls Distribution of Viral Bioreceptors within Near-Field Electrospun Biohybrid Microfiber Optical Biosensors

Thu, 26 Feb 2026 00:00:00 +0000

Microorganisms (e.g., bacteria, fungi, and viruses) add indispensable functionality to a range of electrospun polymer materials and devices. The optimal distribution of bioactive agents on either the interior or exterior of the fiber is application-specific. Current microbe surface immobilization strategies and core-confinement techniques continue to pose a number of challenges. Here, we explore a simple strategy, utilizing electrostatic forces, to control the migration and surface concentration of the M13 bacteriophage within near-field electrospun poly(vinyl alcohol) (PVA) microfibers. Both the surface charge of the electrospun virus and the applied electric field polarity altered microbe placement. When doped with Rhodamine 6G (R6G), the circular microfiber cross sections formed active whispering gallery mode (WGM) resonators. These relatively high-quality (Q) optical cavities enabled us to sensitively probe the virus content of their outer layer, while functioning as...

Fuel-driven filamentous phage nanomotors

Thu, 26 Feb 2026 00:00:00 +0000

Virus-based nanocarriers have shown great potential for noninvasive delivery of drugs, diagnostics, and imaging agents to hard-to-reach anatomical locations. Yet, they largely depend on diffusion for transport, often lacking the force to actively penetrate biological barriers, and navigation to guide therapeutic agents. In these studies, the M13 bacteriophage, a linearly shaped virus, was converted from passive nanocarrier to actively propelled, fuel-driven nanomotor. Using the distinctive low symmetry of its capsid, a single Pt nanoparticle was added to one end of the M13 virus to form a tadpole-like structure. The Pt/M13 head/tail nanomotors exhibited notably enhanced diffusion in the presence of hydrogen peroxide fuel, and significantly improved uptake by SVOK3 ovarian cancer cells in vitro. Given the successes of the M13 bacteriophage as a nanocarrier, the demonstration of this simple, but comparatively mobile M13-based nanomotor platform represents an important step...

On Secret-Message Transmission by Echoing Encrypted Probes

Thu, 29 Jan 2026 00:00:00 +0000

A scheme for secure communications, called “Secret-message Transmission by Echoing Encrypted Probes (STEEP)”, is revisited. STEEP is a round-trip scheme with a probing phase from one user to another and an echoing phase in the reverse direction. STEEP is shown to be broadly applicable to yield a positive secrecy rate in bits per channel use even if the receive channels at eavesdropper (Eve) are stronger than those between legitimate users in both forward and reverse directions. This paper focuses on STEEP in the following settings: using Gaussian probing signal and Gaussian linear encryption over MIMO Gaussian channel (G-STEEP); using phase-shift-keying probing signal and a nonlinear encryption over SISO channel (P-STEEP); and a variation of G-STEEP for multiple access communication (M-STEEP). In each of the settings, Eve is assumed to have any given number of antennas, and STEEP is shown to yield a positive secrecy rate subject to a sufficiently large power in the echoing phase,...

Néel spin-orbit torque in antiferromagnetic quantum spin and anomalous Hall insulators

Fri, 29 Aug 2025 00:00:00 +0000

Interplay between topological electrons and magnetic ordering enables efficient electrical control of magnetism. We extend the Kane-Mele model to include the exchange coupling to a collinear antiferromagnetic (AFM) order, which allows the system to exhibit the quantum anomalous Hall and quantum spin Hall effects in the absence of a net magnetization. These topological phases support a staggered Edelstein effect through which an applied electric field can generate opposite non-equilibrium spins on the two AFM sublattices, realizing the Néel-type spin-orbit torque (NSOT). Contrary to known NSOTs in AFM metals driven by conduction currents, our NSOT arises from pure adiabatic currents devoid of Joule heating, while being a bulk effect not carried by the edge currents. By virtue of the NSOT, the electric field of a microwave can drive the AFM resonance with a remarkably high efficiency, outpacing the magnetic field-induced AFM resonance by orders of magnitude in terms of power absorption.

A Case Study of Environmental Footprints for Generative AI Inference: Cloud versus Edge

Mon, 30 Jun 2025 00:00:00 +0000

The rapid growth of generative AI has placed significant strain on traditional data center infrastructures and existing power grids, leading to soaring energy demands and environmental burdens that may disproportionately affect the local communities. Shifting AI inference from the cloud to edge devices could potentially reduce the reliance on network connections, enhance user privacy, and alleviate the escalating pressure data centers impose on the local electricity grid. In this work, we present a case study examining the environmental footprint and energy consumption when deploying a generative AI model on cloud and edge platforms. To this end, we model and evaluate the water consumption and carbon emissions associated with AI inference across these deployment scenarios. Our empirical results demonstrate that, for several state-of-the-art generative AI models deployable on both cloud and edge devices, a reduced environmental footprint is observed for edge platform deployments....

Interposer-Based ESD Protection: A Potential Solution for μ-Packaging Reliability of 3D Chips

Thu, 22 May 2025 00:00:00 +0000

The ending of Moore's Law calls for innovations in integrated circuit (IC) technologies and chip designs. Heterogeneous integration (HI) emerges as a pathway towards smart future chips for more Moore time and for beyond-Moore time, featuring systems-on-integrated-chiplets (SoICs) and advanced micro-packaging (μ-packaging). Reliability, particularly with regard to electrostatic charge (ESD) failure, is a major challenge for 3D SoIC chips in μ-packaging, which is an emerging design-for-reliability challenge for future chips. This perspective article articulates that interposer-based ESD protection will be an important potential solution for 3D SoIC chips in μ-packaging against the devastating ESD failure problem.

Reconciling the contrasting narratives on the environmental impact of large language models

Thu, 21 Nov 2024 00:00:00 +0000

The recent proliferation of large language models (LLMs) has led to divergent narratives about their environmental impacts. Some studies highlight the substantial carbon footprint of training and using LLMs, while others argue that LLMs can lead to more sustainable alternatives to current practices. We reconcile these narratives by presenting a comparative assessment of the environmental impact of LLMs vs. human labor, examining their relative efficiency across energy consumption, carbon emissions, water usage, and cost. Our findings reveal that, while LLMs have substantial environmental impacts, their relative impacts can be dramatically lower than human labor in the U.S. for the same output, with human-to-LLM ratios ranging from 40 to 150 for a typical LLM (Llama-3-70B) and from 1200 to 4400 for a lightweight LLM (Gemma-2B-it). While the human-to-LLM ratios are smaller with regard to human labor in India, these ratios are still between 3.4 and 16 for a typical LLM and between...

Optimal Training Design and Individual Channel Estimation for MIMO Two-Way Relay Systems in Colored Environment

Thu, 21 Nov 2024 00:00:00 +0000

In this paper, while considering the impact of antenna correlation and the interference from neighboring users, we study the problem of channel estimation and training sequence design in multi-input multi-output (MIMO) two-way relaying (TWR) systems. To this end, we propose to decompose the bidirectional transmission links into two phases, i.e., the multiple access (MAC) and the broadcast (BC) phases. By deriving the optimal linear minimum mean-square-error estimators, the corresponding training design problems for the MAC and BC phases are formulated and solved. Subsequently, algorithms and, in some special cases, closed-form solutions for obtaining the optimal training sequences for channel estimation in TWR systems are derived. Moreover, to further reduce channel estimation overhead, the minimum required length of the training sequences are determined. Simulation results verify the effectiveness of the proposed training designs in improving channel estimation performance in...

Unveiling the spin evolution in van der Waals antiferromagnets via magneto-exciton effects

Tue, 29 Oct 2024 00:00:00 +0000

Among the fascinating phenomena observed in two-dimensional (2D) magnets, the magneto-exciton effect stands out as a pivotal link between optics and magnetism. Although the excitonic effect has been revealed and exhibits a considerable correlation with the spin structures in certain 2D magnets, the underlying mechanism of the magneto-exciton effect remains underexplored, especially under high magnetic fields. Here we perform a systematic investigation of the spin-exciton coupling in 2D antiferromagnetic NiPS3 under high magnetic fields. When an in-plane magnetic field is applied, the exceptional sharp excitonic emission at ~1.4756 eV exhibits a Zeeman-like splitting with g ≈ 2.0, experimentally identifying the exciton as an excitation of dominant triplet-singlet character. By examining the polarization of excitonic emission and simulating the spin evolution, we further verify the correlation between excitonic emission and Néel vector in NiPS3. Our work elucidates the mechanism...

Manipulating chiral spin transport with ferroelectric polarization

Tue, 20 Aug 2024 00:00:00 +0000

A magnon is a collective excitation of the spin structure in a magnetic insulator and can transmit spin angular momentum with negligible dissipation. This quantum of a spin wave has always been manipulated through magnetic dipoles (that is, by breaking time-reversal symmetry). Here we report the experimental observation of chiral spin transport in multiferroic BiFeO3 and its control by reversing the ferroelectric polarization (that is, by breaking spatial inversion symmetry). The ferroelectrically controlled magnons show up to 18% modulation at room temperature. The spin torque that the magnons in BiFeO3 carry can be used to efficiently switch the magnetization of adjacent magnets, with a spin–torque efficiency comparable to the spin Hall effect in heavy metals. Utilizing such controllable magnon generation and transmission in BiFeO3, an all-oxide, energy-scalable logic is demonstrated composed of spin–orbit injection, detection and magnetoelectric control. Our observations open...

NeuroSim Simulator for Compute-in-Memory Hardware Accelerator: Validation and Benchmark

Fri, 19 Jul 2024 00:00:00 +0000

Compute-in-memory (CIM) is an attractive solution to process the extensive workloads of multiply-and-accumulate (MAC) operations in deep neural network (DNN) hardware accelerators. A simulator with options of various mainstream and emerging memory technologies, architectures, and networks can be a great convenience for fast early-stage design space exploration of CIM hardware accelerators. DNN+NeuroSim is an integrated benchmark framework supporting flexible and hierarchical CIM array design options from a device level, to a circuit level and up to an algorithm level. In this study, we validate and calibrate the prediction of NeuroSim against a 40-nm RRAM-based CIM macro post-layout simulations. First, the parameters of a memory device and CMOS transistor are extracted from the foundry's process design kit (PDK) and employed in the NeuroSim settings; the peripheral modules and operating dataflow are also configured to be the same as the actual chip implementation. Next, the area,...

Enhanced Noise-Resilient Pressure Mat System Based on Hyperdimensional Computing

Fri, 12 Apr 2024 00:00:00 +0000

Traditional systems for indoor pressure sensing and human activity recognition (HAR) rely on costly, high-resolution mats and computationally intensive neural network-based (NN-based) models that are prone to noise. In contrast, we design a cost-effective and noise-resilient pressure mat system for HAR, leveraging Velostat for intelligent pressure sensing and a novel hyperdimensional computing (HDC) classifier that is lightweight and highly noise resilient. To measure the performance of our system, we collected two datasets, capturing the static and continuous nature of human movements. Our HDC-based classification algorithm shows an accuracy of 93.19%, improving the accuracy by 9.47% over state-of-the-art CNNs, along with an 85% reduction in energy consumption. We propose a new HDC noise-resilient algorithm and analyze the performance of our proposed method in the presence of three different kinds of noise, including memory and communication, input, and sensor noise. Our system...

Non-Abelian braiding of graph vertices in a superconducting processor

Mon, 1 Apr 2024 00:00:00 +0000

Indistinguishability of particles is a fundamental principle of quantum mechanics1. For all elementary and quasiparticles observed to date—including fermions, bosons and Abelian anyons—this principle guarantees that the braiding of identical particles leaves the system unchanged2,3. However, in two spatial dimensions, an intriguing possibility exists: braiding of non-Abelian anyons causes rotations in a space of topologically degenerate wavefunctions4–8. Hence, it can change the observables of the system without violating the principle of indistinguishability. Despite the well-developed mathematical description of non-Abelian anyons and numerous theoretical proposals9–22, the experimental observation of their exchange statistics has remained elusive for decades. Controllable many-body quantum states generated on quantum processors offer another path for exploring these fundamental phenomena. Whereas efforts on conventional solid-state platforms typically involve Hamiltonian dynamics...

Direct observation of confined acoustic phonon polarization branches in free-standing semiconductor nanowires

Sun, 24 Dec 2023 00:00:00 +0000

Similar to electron waves, the phonon states in semiconductors can undergo changes induced by external boundaries. However, despite strong scientific and practical importance, conclusive experimental evidence of confined acoustic phonon polarization branches in individual free-standing nanostructures is lacking. Here we report results of Brillouin—Mandelstam light scattering spectroscopy, which reveal multiple (up to ten) confined acoustic phonon polarization branches in GaAs nanowires with a diameter as large as 128 nm, at a length scale that exceeds the grey phonon mean-free path in this material by almost an order-of-magnitude. The dispersion modification and energy scaling with diameter in individual nanowires are in excellent agreement with theory. The phonon confinement effects result in a decrease in the phonon group velocity along the nanowire axis and changes in the phonon density of states. The obtained results can lead to more efficient nanoscale control of acoustic...

Text-Based Localization of Moments in a Video Corpus

Thu, 26 Oct 2023 00:00:00 +0000

Prior works on text-based video moment localization focus on temporally grounding the textual query in an untrimmed video. These works assume that the relevant video is already known and attempt to localize the moment on that relevant video only. Different from such works, we relax this assumption and address the task of localizing moments in a corpus of videos for a given sentence query. This task poses a unique challenge as the system is required to perform: 2) retrieval of the relevant video where only a segment of the video corresponds with the queried sentence, 2) temporal localization of moment in the relevant video based on sentence query. Towards overcoming this challenge, we propose Hierarchical Moment Alignment Network (HMAN) which learns an effective joint embedding space for moments and sentences. In addition to learning subtle differences between intra-video moments, HMAN focuses on distinguishing inter-video global semantic concepts based on sentence queries. Qualitative...

Learning Person Re-Identification Models From Videos With Weak Supervision

Thu, 26 Oct 2023 00:00:00 +0000

Most person re-identification methods, being supervised techniques, suffer from the burden of massive annotation requirement. Unsupervised methods overcome this need for labeled data, but perform poorly compared to the supervised alternatives. In order to cope with this issue, we introduce the problem of learning person re-identification models from videos with weak supervision. The weak nature of the supervision arises from the requirement of video-level labels, i.e. person identities who appear in the video, in contrast to the more precise frame-level annotations. Towards this goal, we propose a multiple instance attention learning framework for person re-identification using such video-level labels. Specifically, we first cast the video person re-identification task into a multiple instance learning setting, in which person images in a video are collected into a bag. The relations between videos with similar labels can be utilized to identify persons, on top of that, we introduce...

Bidding strategy for wireless charging roads with energy storage in real-time electricity markets

Thu, 28 Sep 2023 00:00:00 +0000

The combination of wireless charging roads and energy storage systems is a promising option for electric vehicle charging because of their capabilities in mitigating range anxiety of electric vehicle drivers. Wireless charging road operators can purchase electric energy by submitting price-sensitive demand bids in real-time electricity markets. Efficient bidding strategies are crucial to minimizing the energy costs for providing wireless charging services. In this study, we first propose a composite statistical model based on graph signal processing and linear regression to forecast the future locational marginal prices (LMPs) in a power network. Then an estimate of future electric load on each wireless charging road is derived by simulating its traffic flow using a point queue-based traffic flow model. An efficient price-sensitive bidding strategy for each individual wireless charging road is developed based on its LMP forecast, wireless charging load estimate, and a model predictive...

Model-free voltage control of active distribution system with PVs using surrogate model-based deep reinforcement learning

Thu, 28 Sep 2023 00:00:00 +0000

Accurate knowledge of the distribution system topology and parameters is required to achieve good voltage control performance, but this is difficult to obtain in practice. This paper proposes a physical-model-free voltage control method based on a surrogate-model-enabled deep reinforcement learning approach. Specifically, a surrogate model is trained in a supervised manner using the recorded limited number of historical data to learn the relationship between the power injections and voltage fluctuations of each node. Then, the deep reinforcement learning algorithm is applied to learn an optimal control strategy from the experiences obtained by continuous interactions with the surrogate model. The proposed method can achieve physical-model-free control of unbalanced distribution network and inform real-time decisions to deal with fast voltage fluctuations caused by the rapid variation of PV generation. Simulation results on an unbalance IEEE 123-bus system show that the proposed...

DeephESC 2.0: Deep Generative Multi Adversarial Networks for improving the classification of hESC

Wed, 20 Sep 2023 00:00:00 +0000

Human embryonic stem cells (hESC), derived from the blastocysts, provide unique cellular models for numerous potential applications. They have great promise in the treatment of diseases such as Parkinson's, Huntington's, diabetes mellitus, etc. hESC are a reliable developmental model for early embryonic growth because of their ability to divide indefinitely (pluripotency), and differentiate, or functionally change, into any adult cell type. Their adaptation to toxicological studies is particularly attractive as pluripotent stem cells can be used to model various stages of prenatal development. Automated detection and classification of human embryonic stem cell in videos is of great interest among biologists for quantified analysis of various states of hESC in experimental work. Currently video annotation is done by hand, a process which is very time consuming and exhaustive. To solve this problem, this paper introduces DeephESC 2.0 an automated machine learning approach consisting...

Automated detection of brain abnormalities in neonatal hypoxia ischemic injury from MR images

Sun, 17 Sep 2023 00:00:00 +0000

We compared the efficacy of three automated brain injury detection methods, namely symmetry-integrated region growing (SIRG), hierarchical region splitting (HRS) and modified watershed segmentation (MWS) in human and animal magnetic resonance imaging (MRI) datasets for the detection of hypoxic ischemic injuries (HIIs). Diffusion weighted imaging (DWI, 1.5T) data from neonatal arterial ischemic stroke (AIS) patients, as well as T2-weighted imaging (T2WI, 11.7T, 4.7T) at seven different time-points (1, 4, 7, 10, 17, 24 and 31 days post HII) in rat-pup model of hypoxic ischemic injury were used to assess the temporal efficacy of our computational approaches. Sensitivity, specificity, and similarity were used as performance metrics based on manual ('gold standard') injury detection to quantify comparisons. When compared to the manual gold standard, automated injury location results from SIRG performed the best in 62% of the data, while 29% for HRS and 9% for MWS. Injury severity detection...

Safely catching aerial micro-robots in mid-air using an open-source aerial robot with soft gripper

Fri, 1 Sep 2023 00:00:00 +0000

This work focuses on catching safely an aerial micro-robot in mid-air using another aerial robot that is equipped with a universal soft gripper. To avoid aerodynamic disturbances such as downwash, that would push the target robot away, we follow a horizontal grasping approach. To this end, the article introduces a gripper design based on soft actuators that can stay horizontally straight with a single fixture and maintain sufficiently compliance in order to bend when air pressure is applied. Further, we develop the Soft Aerial Gripper (SoAG), an open-source aerial robot equipped with the developed soft end-effector and that features an onboard pneumatic regulation system. Experimental results show that the developed low-cost soft gripper has fast opening and closing responses despite being powered by lightweight air pumps, responses that are comparable to those of a commercially available end-effector tested we test against. Static grasping tests study the soft gripper's robustness...

Strain-tunable Berry curvature in quasi-two-dimensional chromium telluride

Fri, 1 Sep 2023 00:00:00 +0000

Magnetic transition metal chalcogenides form an emerging platform for exploring spin-orbit driven Berry phase phenomena owing to the nontrivial interplay between topology and magnetism. Here we show that the anomalous Hall effect in pristine Cr2Te3 thin films manifests a unique temperature-dependent sign reversal at nonzero magnetization, resulting from the momentum-space Berry curvature as established by first-principles simulations. The sign change is strain tunable, enabled by the sharp and well-defined substrate/film interface in the quasi-two-dimensional Cr2Te3 epitaxial films, revealed by scanning transmission electron microscopy and depth-sensitive polarized neutron reflectometry. This Berry phase effect further introduces hump-shaped Hall peaks in pristine Cr2Te3 near the coercive field during the magnetization switching process, owing to the presence of strain-modulated magnetic layers/domains. The versatile interface tunability of Berry curvature in Cr2Te3 thin films...

RAIM and Failure Mode Slope: Effects of Increased Number of Measurements and Number of Faults

Sat, 5 Aug 2023 00:00:00 +0000

This article provides a comprehensive analysis of the impact of the increasing number of measurements and the possible increase in the number of faults in multi-constellation Global Navigation Satellite System (GNSS) Receiver Autonomous Integrity Monitoring (RAIM). Residual-based fault detection and integrity monitoring techniques are ubiquitous in linear over-determined sensing systems. An important application is RAIM, as used in multi-constellation GNSS-based positioning. This is a field in which the number of measurements, m, available per epoch is rapidly increasing due to new satellite systems and modernization. Spoofing, multipath, and non-line of sight signals could potentially affect a large number of these signals. This article fully characterizes the impact of measurement faults on the estimation (i.e., position) error, the residual, and their ratio (i.e., the failure mode slope) by analyzing the range space of the measurement matrix and its orthogonal complement....

Hydrous Ruthenium Oxide Nanoparticles Anchored to Graphene and Carbon Nanotube Hybrid Foam for Supercapacitors

Fri, 21 Jul 2023 00:00:00 +0000

In real life applications, supercapacitors (SCs) often can only be used as part of a hybrid system together with other high energy storage devices due to their relatively lower energy density in comparison to other types of energy storage devices such as batteries and fuel cells. Increasing the energy density of SCs will have a huge impact on the development of future energy storage devices by broadening the area of application for SCs. Here, we report a simple and scalable way of preparing a three-dimensional (3D) sub-5 nm hydrous ruthenium oxide (RuO2) anchored graphene and CNT hybrid foam (RGM) architecture for high-performance supercapacitor electrodes. This RGM architecture demonstrates a novel graphene foam conformally covered with hybrid networks of RuO2 nanoparticles and anchored CNTs. SCs based on RGM show superior gravimetric and per-area capacitive performance (specific capacitance: 502.78 F g−1, areal capacitance: 1.11 F cm−2) which leads to an exceptionally high energy...

Cryogenic characteristics of graphene composites—evolution from thermal conductors to thermal insulators

Tue, 18 Jul 2023 00:00:00 +0000

The development of cryogenic semiconductor electronics and superconducting quantum computing requires composite materials that can provide both thermal conduction and thermal insulation. We demonstrated that at cryogenic temperatures, the thermal conductivity of graphene composites can be both higher and lower than that of the reference pristine epoxy, depending on the graphene filler loading and temperature. There exists a well-defined cross-over temperature—above it, the thermal conductivity of composites increases with the addition of graphene; below it, the thermal conductivity decreases with the addition of graphene. The counter-intuitive trend was explained by the specificity of heat conduction at low temperatures: graphene fillers can serve as, both, the scattering centers for phonons in the matrix material and as the conduits of heat. We offer a physical model that explains the experimental trends by the increasing effect of the thermal boundary resistance at cryogenic...

Role of turgor-pressure induced boundary tension in the maintenance of the shoot apical meristem of Arabidopsis thaliana

Tue, 18 Jul 2023 00:00:00 +0000

In plants, the robust maintenance of tissue structure is crucial to supporting its functionality. The multi-layered shoot apical meristem (SAM) of Arabidopsis, containing stem cells, is an approximately radially symmetric tissue whose shape and structure is maintained throughout the life of the plant. In this paper, a new biologically calibrated pseudo-three-dimensional (P3D) computational model of a longitudinal section of the SAM is developed. It includes anisotropic expansion and division of cells out of the cross-section plane, as well as representation of tension experienced by the SAM epidermis. Results from the experimentally calibrated P3D model provide new insights into maintenance of the structure of the SAM epidermal cell monolayer under tension and quantify dependence of epidermal and subepidermal cell anisotropy on the amount of tension. Moreover, the model simulations revealed that out-of-plane cell growth is important in offsetting cell crowding and...

Formation of robust bound states of interacting microwave photons

Sat, 8 Jul 2023 00:00:00 +0000

Systems of correlated particles appear in many fields of modern science and represent some of the most intractable computational problems in nature. The computational challenge in these systems arises when interactions become comparable to other energy scales, which makes the state of each particle depend on all other particles1. The lack of general solutions for the three-body problem and acceptable theory for strongly correlated electrons shows that our understanding of correlated systems fades when the particle number or the interaction strength increases. One of the hallmarks of interacting systems is the formation of multiparticle bound states2–9. Here we develop a high-fidelity parameterizable fSim gate and implement the periodic quantum circuit of the spin-½ XXZ model in a ring of 24 superconducting qubits. We study the propagation of these excitations and observe their bound nature for up to five photons. We devise a phase-sensitive method for constructing the few-body...

Generalized Channel Probing and Generalized Pre-Processing for Secret Key Generation

Mon, 26 Jun 2023 00:00:00 +0000

The paper considers secret key generation (SKG) from a channel between Alice and Bob against Eve all with multiple antennas. A generalized channel probing (GCP) method and a generalized pre-processing (GPP) method are proposed as two major steps before quantization, information reconciliation and privacy amplification for SKG. The degree-of-freedom (DoF) of the secret key capacity (SKC), relative to power or signal-to-noise ratio, based on the data sets at Alice, Bob and Eve after the execution of GCP is shown. The SKC-DoF is also shown to be the same as that based the new data sets after the application of GPP except for modifications chosen for computational efficiency. In particular, if Eve has a less number of antennas than either Alice and/or Bob, the SKC-DoF grows with the number of random transmissions from either Alice and/or Bob within each channel coherence period. This SKC-DoF property for GCP and GPP does not require the reciprocity of the channel. A reciprocal channel...

Secrecy Throughput of ANECE Assisted Transmission of Information in Finite Blocklength

Mon, 26 Jun 2023 00:00:00 +0000

Anti-eavesdropping channel estimation (ANECE) among two or more cooperative full-duplex radio devices allows these devices to obtain consistent estimates of their receive channel state information (CSI) with respect to each other but at the same time prevents eavesdropper (Eve) from obtaining a consistent estimate of its receive CSI, which improves the secrecy of subsequent transmission of information between the devices. This paper presents an analysis of secrecy throughput of ANECE assisted transmission of information between such single-antenna devices against Eve with multiple antennas. The analysis is based on finite blocklength coding and assumes that Eve applies a standard approach for information detection. Easy-to-compute analytical expressions of secrecy throughput in terms of various controllable parameters are obtained. Numerical results are presented and discussed.

Continuous Encryption Functions for Security Over Networks

Mon, 26 Jun 2023 00:00:00 +0000

This paper presents a study of continuous encryption functions (CEFs) of secret feature vectors for security over networks, which include physical layer encryption for wireless communications and biometric template security for online Internet applications. While CEFs are defined here to include all prior continuous one-way functions, a good CEF is defined to be a continuous function that turns a random feature vector of limited dimension into a long sequence of numbers in such a way that it is hard to invert and hard to substitute, it has no or little amplification of noise, and its output samples have zero or near-zero correlations and have identical or nearly identical distributions. A number of prior CEFs, such as dynamic random projection, index-of-max hashing and higher-order polynomials, are all shown to fail on these criteria. Based on selected components of singular value decomposition (SVD) of randomly modulated matrices of the feature vector, a family of SVD-CEFs is...

Exponential suppression of bit or phase errors with cyclic error correction

Fri, 23 Jun 2023 00:00:00 +0000

Realizing the potential of quantum computing requires sufficiently low logical error rates1. Many applications call for error rates as low as 10−15 (refs. 2–9), but state-of-the-art quantum platforms typically have physical error rates near 10−3 (refs. 10–14). Quantum error correction15–17 promises to bridge this divide by distributing quantum logical information across many physical qubits in such a way that errors can be detected and corrected. Errors on the encoded logical qubit state can be exponentially suppressed as the number of physical qubits grows, provided that the physical error rates are below a certain threshold and stable over the course of a computation. Here we implement one-dimensional repetition codes embedded in a two-dimensional grid of superconducting qubits that demonstrate exponential suppression of bit-flip or phase-flip errors, reducing logical error per round more than 100-fold when increasing the number of qubits from 5 to 21. Crucially, this error...

Noncured Graphene Thermal Interface Materials for High-Power Electronics: Minimizing the Thermal Contact Resistance

Fri, 23 Jun 2023 00:00:00 +0000

We report on experimental investigation of thermal contact resistance, R_C, of the noncuring graphene thermal interface materials with the surfaces characterized by different degree of roughness, S_q. It is found that the thermal contact resistance depends on the graphene loading, ξ, non-monotonically, achieving its minimum at the loading fraction of ξ ~15 wt %. Decreasing the surface roughness by S_q~1 μm results in approximately the factor of ×2 decrease in the thermal contact resistance for this graphene loading. The obtained dependences of the thermal conductivity, KTIM, thermal contact resistance, R_C, and the total thermal resistance of the thermal interface material layer on ξ and S_q can be utilized for optimization of the loading fraction of graphene for specific materials and roughness of the connecting surfaces. Our results are important for the thermal management...

A Magnetometer Based on a Spin Wave Interferometer

Fri, 12 May 2023 00:00:00 +0000

We describe a magnetic field sensor based on a spin wave interferometer. Its sensing element consists of a magnetic cross junction with four micro-antennas fabricated at the edges. Two of these antennas are used for spin wave excitation while two other antennas are used for detection of the inductive voltage produced by the interfering spin waves. Two waves propagating in the orthogonal arms of the cross may accumulate significantly different phase shifts depending on the magnitude and direction of the external magnetic field. This phenomenon is utilized for magnetic field sensing. The sensitivity attains its maximum under the destructive interference condition, where a small change in the external magnetic field results in a drastic increase of the inductive voltage, as well as in the change of the output phase. We report experimental data obtained for a micrometer scale Y3Fe2(FeO4)3 cross structure. The change of the inductive voltage near the destructive interference point...

Metal-induced rapid transformation of diamond into single and multilayer graphene on wafer scale

Thu, 4 May 2023 00:00:00 +0000

The degradation of intrinsic properties of graphene during the transfer process constitutes a major challenge in graphene device fabrication, stimulating the need for direct growth of graphene on dielectric substrates. Previous attempts of metal-induced transformation of diamond and silicon carbide into graphene suffers from metal contamination and inability to scale graphene growth over large area. Here, we introduce a direct approach to transform polycrystalline diamond into high-quality graphene layers on wafer scale (4 inch in diameter) using a rapid thermal annealing process facilitated by a nickel, Ni thin film catalyst on top. We show that the process can be tuned to grow single or multilayer graphene with good electronic properties. Molecular dynamics simulations elucidate the mechanism of graphene growth on polycrystalline diamond. In addition, we demonstrate the lateral growth of free-standing graphene over micron-sized pre-fabricated holes, opening exciting opportunities...

Properties for Thermally Conductive Interfaces with Wide Band Gap Materials

Wed, 19 Oct 2022 00:00:00 +0000

The goal of this study is to determine how bulk vibrational properties and interfacial structure affect thermal transport at interfaces in wide band gap semiconductor systems. Time-domain thermoreflectance measurements of thermal conductance G are reported for interfaces between nitride metals and group IV (diamond, SiC, Si, and Ge) and group III-V (AlN, GaN, and cubic BN) materials. Group IV and group III-V semiconductors have systematic differences in vibrational properties. Similarly, HfN and TiN are also vibrationally distinct from each other. Therefore, comparing G of interfaces formed from these materials provides a systematic test of how vibrational similarity between two materials affects interfacial transport. For HfN interfaces, we observe conductances between 140 and 300 MW m^-2 K^-1, whereas conductances between 200 and 800 MW m^-2 K^-1 are observed for TiN interfaces. TiN forms exceptionally conductive interfaces...

All-optical dissipative discrete time crystals

Tue, 11 Oct 2022 00:00:00 +0000

Time crystals are periodic states exhibiting spontaneous symmetry breaking in either time-independent or periodically-driven quantum many-body systems. Spontaneous modification of discrete time-translation symmetry in periodically-forced physical systems can create a discrete time crystal (DTC) constituting a state of matter possessing properties like temporal rigid long-range order and coherence, which are inherently desirable for quantum computing and information processing. Despite their appeal, experimental demonstrations of DTCs are scarce and significant aspects of their behavior remain unexplored. Here, we report the experimental observation and theoretical investigation of DTCs in a Kerr-nonlinear optical microcavity. Empowered by the self-injection locking of two independent lasers with arbitrarily large frequency separation simultaneously to two same-family cavity modes and a dissipative Kerr soliton, this versatile platform enables realizing long-awaited phenomena such...

Total Secrecy From Anti-Eavesdropping Channel Estimation

Wed, 14 Sep 2022 00:00:00 +0000

Anti-eavesdropping channel estimation (ANECE) is useful for a network of cooperative full-duplex radio devices/users. Using ANECE, a secret key can be generated by each pair of users, and additional secret information can be transmitted between a pair of users. This paper analyzes the capacity of the secret key based on ANECE, and compares it with the conventional method for channel training. The paper also analyzes the secrecy capacity of information transmission using an one-way scheme, and compares it with a two-way scheme. It is shown that the total amount of secrecy generated from ANECE can be substantially larger than that based on the conventional method especially when an eavesdropper may have an unlimited number of antennas. The paper also formulates a total secure degree of freedom (TSDoF) of the ANECE based scheme, and compares it with a prior scheme of secret information transmission from a multi-antenna node to another against a multi-antenna eavesdropper where channel...

Secrecy Analyses of a Full-Duplex MIMOME Network

Wed, 14 Sep 2022 00:00:00 +0000

This paper presents secrecy analyses of a full-duplex MIMOME network which consists of two full-duplex multi-antenna users (Alice and Bob) and an arbitrarily located multi-antenna eavesdropper (Eve). The paper assumes that Eve's channel state information (CSI) is completely unknown to Alice and Bob except for a small radius of secured zone. The first part of this paper aims to optimize the powers of jamming noises from both users. To handle Eve's CSI being unknown to users, the focus is placed on Eve at the most harmful location, and the large matrix theory is applied to yield a hardened secrecy rate to work on. The performance gain of the power optimization in terms of maximum tolerable number of antennas on Eve is shown to be significant. The second part of this paper shows two analyses of anti-eavesdropping channel estimation (ANECE) that can better handle Eve with any number of antennas. One analysis assumes that Eve has a prior statistical knowledge of its CSI, which yields...

Secrecy of Multi-Antenna Transmission With Full-Duplex User in the Presence of Randomly Located Eavesdroppers

Wed, 14 Sep 2022 00:00:00 +0000

This paper considers the secrecy performance of several schemes for multi-antenna transmission to single-antenna users with full-duplex (FD) capability against randomly distributed single-antenna eavesdroppers (EDs). These schemes and related scenarios include transmit antenna selection (TAS), transmit antenna beamforming (TAB), artificial noise (AN) from the transmitter, user selection based their distances to the transmitter, and colluding and non-colluding EDs. The locations of randomly distributed EDs and users are assumed to be distributed as Poisson Point Process (PPP). We derive closed form expressions for the secrecy outage probabilities (SOP) of all these schemes and scenarios. The derived expressions are useful to reveal the impacts of various environmental parameters and user’s choices on the SOP, and hence useful for network design purposes. Examples of such numerical results are discussed.

Optimal Pilots for Anti-Eavesdropping Channel Estimation

Wed, 14 Sep 2022 00:00:00 +0000

Anti-eavesdropping channel estimation (ANECE) is a method that uses specially designed pilot signals to allow two or more full-duplex radio devices each with one or more antennas to estimate their channel state information (CSI) consistently and at the same time prevent eavesdropper (Eve) with any number of antennas from obtaining its CSI consistently. This paper presents optimal designs of the pilots for ANECE based on two criteria. The first is the mean squared error (MSE) of channel estimation for the users, and the second is the mutual information (MI) between the pilot-driven signals observed by the users. Closed-form optimal pilots are shown under the sum-MSE and sum-MI criteria subject to a symmetric and isotropic condition. Algorithms for computing the optimal pilots are shown for general cases. Fairness issues for three or more users are discussed. The performances of different designs are compared.

Secret Key Generation by Continuous Encryption Before Quantization

Wed, 14 Sep 2022 00:00:00 +0000

Secret key generation (SKG) is a fundamental signal processing problem for security applications including wireless network security and biometric template security. For SKG, a pair (or more) of highly correlated secret vectors (SVs) need to be respectively quantized into a pair (or more) of almost identical sequences of binary bits (i.e., keys). The literatures on SKG in the wireless community almost uniformly default on a direct quantization on SVs. On the other hand, many works on biometric template security advocate a quantization on the output of a one-way function of SVs. In this paper, we present a generalized approach for SKG called continuous encryption before quantization (CEbQ). By CEbQ, a pair of SVs of limited dimension are first transformed by a continuous encryption function into a pair of sequences of quasi-continuous pseudorandom numbers (QCPRNs) of any desired length, and then these QCPRNs are quantized into keys. We show that CEbQ can yield a much lower key...

Combined computational modeling and experimental analysis integrating chemical and mechanical signals suggests possible mechanism of shoot meristem maintenance

Sat, 23 Jul 2022 00:00:00 +0000

Stem cell maintenance in multilayered shoot apical meristems (SAMs) of plants requires strict regulation of cell growth and division. Exactly how the complex milieu of chemical and mechanical signals interact in the central region of the SAM to regulate cell division plane orientation is not well understood. In this paper, simulations using a newly developed multiscale computational model are combined with experimental studies to suggest and test three hypothesized mechanisms for the regulation of cell division plane orientation and the direction of anisotropic cell expansion in the corpus. Simulations predict that in the Apical corpus, WUSCHEL and cytokinin regulate the direction of anisotropic cell expansion, and cells divide according to tensile stress on the cell wall. In the Basal corpus, model simulations suggest dual roles for WUSCHEL and cytokinin in regulating both the direction of anisotropic cell expansion and cell division plane orientation. Simulation results are...

Current-induced Néel order switching facilitated by magnetic phase transition

Fri, 22 Jul 2022 00:00:00 +0000

Terahertz (THz) spin dynamics and vanishing stray field make antiferromagnetic (AFM) materials the most promising candidate for the next-generation magnetic memory technology with revolutionary storage density and writing speed. However, owing to the extremely large exchange energy barriers, energy-efficient manipulation has been a fundamental challenge in AFM systems. Here, we report an electrical writing of antiferromagnetic orders through a record-low current density on the order of 106 A cm−2 facilitated by the unique AFM-ferromagnetic (FM) phase transition in FeRh. By introducing a transient FM state via current-induced Joule heating, the spin-orbit torque can switch the AFM order parameter by 90° with a reduced writing current density similar to ordinary FM materials. This mechanism is further verified by measuring the temperature and magnetic bias field dependences, where the X-ray magnetic linear dichroism (XMLD) results confirm the AFM switching besides the electrical...

Generative Adversarial Networks for Morphological–Temporal Classification of Stem Cell Images

Tue, 14 Jun 2022 00:00:00 +0000

Frequently, neural network training involving biological images suffers from a lack of data, resulting in inefficient network learning. This issue stems from limitations in terms of time, resources, and difficulty in cellular experimentation and data collection. For example, when performing experimental analysis, it may be necessary for the researcher to use most of their data for testing, as opposed to model training. Therefore, the goal of this paper is to perform dataset augmentation using generative adversarial networks (GAN) to increase the classification accuracy of deep convolutional neural networks (CNN) trained on induced pluripotent stem cell microscopy images. The main challenges are: 1. modeling complex data using GAN and 2. training neural networks on augmented datasets that contain generated data. To address these challenges, a temporally constrained, hierarchical classification scheme that exploits domain knowledge is employed for model learning. First, image patches...

SweepCam — Depth-Aware Lensless Imaging Using Programmable Masks

Wed, 6 Oct 2021 00:00:00 +0000

Lensless cameras, while extremely useful for imaging in constrained scenarios, struggle with resolving scenes with large depth variations. To resolve this, we propose imaging with a set of mask patterns displayed on a programmable mask, and introduce a computational focusing operator that helps to resolve the depth of scene points. As a result, the proposed imager can resolve dense scenes with large depth variations, allowing for more practical applications of lensless cameras. We also present a fast reconstruction algorithm for scene at multiple depths that reduces reconstruction time by two orders of magnitude. Finally, we build a prototype to show the proposed method improves both image quality and depth resolution of lensless cameras.

A Dual Camera System for High Spatiotemporal Resolution Video Acquisition

Wed, 6 Oct 2021 00:00:00 +0000

This paper presents a dual camera system for high spatiotemporal resolution (HSTR) video acquisition, where one camera shoots a video with high spatial resolution and low frame rate (HSR-LFR) and another one captures a low spatial resolution and high frame rate (LSR-HFR) video. Our main goal is to combine videos from LSR-HFR and HSR-LFR cameras to create an HSTR video. We propose an end-to-end learning framework, AWnet, mainly consisting of a FlowNet and a FusionNet that learn an adaptive weighting function in pixel domain to combine inputs in a frame recurrent fashion. To improve the reconstruction quality for cameras used in reality, we also introduce noise regularization under the same framework. Our method has demonstrated noticeable performance gains in terms of both objective PSNR measurement in simulation with different publicly available video and light-field datasets and subjective evaluation with real data captured by dual iPhone 7 and Grasshopper3 cameras. Ablation...

A pneumatic random-access memory for controlling soft robots

Mon, 9 Aug 2021 00:00:00 +0000

Pneumatically-actuated soft robots have advantages over traditional rigid robots in many applications. In particular, their flexible bodies and gentle air-powered movements make them more suitable for use around humans and other objects that could be injured or damaged by traditional robots. However, existing systems for controlling soft robots currently require dedicated electromechanical hardware (usually solenoid valves) to maintain the actuation state (expanded or contracted) of each independent actuator. When combined with power, computation, and sensing components, this control hardware adds considerable cost, size, and power demands to the robot, thereby limiting the feasibility of soft robots in many important application areas. In this work, we introduce a pneumatic memory that uses air (not electricity) to set and maintain the states of large numbers of soft robotic actuators without dedicated electromechanical hardware. These pneumatic logic circuits use normally-closed...

Tunable room-temperature ferromagnetism in Co-doped two-dimensional van der Waals ZnO

Mon, 26 Jul 2021 00:00:00 +0000

The recent discovery of ferromagnetism in two-dimensional van der Waals crystals has provoked a surge of interest in the exploration of fundamental spin interaction in reduced dimensions. However, existing material candidates have several limitations, notably lacking intrinsic room-temperature ferromagnetic order and air stability. Here, motivated by the anomalously high Curie temperature observed in bulk diluted magnetic oxides, we demonstrate room-temperature ferromagnetism in Co-doped graphene-like Zinc Oxide, a chemically stable layered material in air, down to single atom thickness. Through the magneto-optic Kerr effect, superconducting quantum interference device and X-ray magnetic circular dichroism measurements, we observe clear evidences of spontaneous magnetization in such exotic material systems at room temperature and above. Transmission electron microscopy and atomic force microscopy results explicitly exclude the existence of metallic Co or cobalt oxides clusters....

Task Planning on Stochastic Aisle Graphs for Precision Agriculture

Wed, 9 Jun 2021 00:00:00 +0000

This work addresses task planning under uncertainty for precision agriculture applications whereby task costs are uncertain and the gain of completing a task is proportional to resource consumption (such as water consumption in precision irrigation). The goal is to complete all tasks while prioritizing those that are more urgent, and subject to diverse budget thresholds and stochastic costs for tasks. To describe agriculture-related environments that incorporate stochastic costs to complete tasks, a new Stochastic-Vertex-Cost Aisle Graph (SAG) is introduced. Then, a task allocation algorithm, termed Next-Best-Action Planning (NBA-P), is proposed. NBA-P utilizes the underlying structure enabled by SAG, and tackles the task planning problem by simultaneously determining the optimal tasks to perform and an optimal time to exit (i.e. return to a base station), at run-time. The proposed approach is tested with both simulated data and real-world experimental datasets collected in a...

Hybrid LSTM and Encoder–Decoder Architecture for Detection of Image Forgeries

Wed, 30 Dec 2020 00:00:00 +0000

With advanced image journaling tools, one can easily alter the semantic meaning of an image by exploiting certain manipulation techniques such as copy clone, object splicing, and removal, which mislead the viewers. In contrast, the identification of these manipulations becomes a very challenging task as manipulated regions are not visually apparent. This paper proposes a high-confidence manipulation localization architecture that utilizes resampling features, long short-term memory (LSTM) cells, and an encoder-decoder network to segment out manipulated regions from non-manipulated ones. Resampling features are used to capture artifacts, such as JPEG quality loss, upsampling, downsampling, rotation, and shearing. The proposed network exploits larger receptive fields (spatial maps) and frequency-domain correlation to analyze the discriminative characteristics between the manipulated and non-manipulated regions by incorporating the encoder and LSTM network. Finally, the decoder network...

Error-correcting Bacon-Shor code with continuous measurement of noncommuting operators

Mon, 1 Jun 2020 00:00:00 +0000

We analyze the continuous operation of the nine-qubit error-correcting Bacon-Shor code with all noncommuting gauge operators measured at the same time. The error syndromes are continuously monitored using cross correlations of sets of three measurement signals. We calculate the logical error rates due to X, Y, and Z errors in the physical qubits and compare the continuous implementation with the discrete operation of the code. We find that both modes of operation exhibit similar performances when the measurement strength from continuous measurements is sufficiently strong. We also estimate the value of the crossover error rate of the physical qubits, below which continuous error correction gives smaller logical error rates. Continuous operation has the advantage of passive monitoring of errors and avoids the need for additional circuits involving ancilla qubits.

Genetically Encoded Fluorescent Redox Probes