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Universal Quantum Computing with Twist-Free and Temporally Encoded Lattice Surgery
No full textLattice-surgery protocols allow for the efficient implementation of universal gate sets with two-dimensional topological codes where qubits are constrained to interact with one another locally. In this work, we first introduce a decoder capable of correcting spacelike and timelike errors during lattice-surgery protocols. Subsequently, we compute the logical failure rates of a lattice-surgery protocol for a biased circuit-level noise model. We then provide a protocol for performing twist-free lattice surgery, where we avoid twist defects in the bulk of the lattice. Our twist-free protocol eliminates the extra circuit components and gate-scheduling complexities associated with the measurement of higher weight stabilizers when using twist defects. We also provide a protocol for temporally encoded lattice surgery that can be used to reduce both the run times and the total space-time costs of quantum algorithms. Lastly, we propose a layout for a quantum processor that is more efficient for rectangular surface codes exploiting noise bias and that is compatible with the other techniques mentioned above
Microbial communities of Auka hydrothermal sediments shed light on vent biogeography and the evolutionary history of thermophily
No full textHydrothermal vents have been key to our understanding of the limits of life, and the metabolic and phylogenetic diversity of thermophilic organisms. Here we used environmental metagenomics combined with analysis of physicochemical data and 16S rRNA gene amplicons to characterize the sediment-hosted microorganisms at the recently discovered Auka vents in the Gulf of California. We recovered 325 metagenome assembled genomes (MAGs) representing 54 phyla, over 30% of those currently known, showing the microbial community in Auka hydrothermal sediments is highly diverse. 16S rRNA gene amplicon screening of 224 sediment samples across the vent field indicates that the MAGs retrieved from a single site are representative of the microbial community in the vent field sediments. Metabolic reconstruction of a vent-specific, deeply branching clade within the Desulfobacterota suggests these organisms metabolize sulfur using novel octaheme cytochrome-c proteins related to hydroxylamine oxidoreductase. Community-wide comparison between Auka MAGs and MAGs from Guaymas Basin revealed a remarkable 20% species-level overlap, suggestive of long-distance species transfer over 400 km and subsequent sediment colonization. Optimal growth temperature prediction on the Auka MAGs, and thousands of reference genomes, shows that thermophily is a trait that has evolved frequently. Taken together, our Auka vent field results offer new perspectives on our understanding of hydrothermal vent microbiology
Caltech Conte Center, a multimodal data resource for exploring social cognition and decision-making
No full textThis data release of 117 healthy community-dwelling adults provides multimodal high-quality neuroimaging and behavioral data for the investigation of brain-behavior relationships. We provide structural MRI, resting-state functional MRI, movie functional MRI, together with questionnaire-based and task-based psychological variables; many of the participants have multiple datasets from retesting over the course of several years. Our dataset is distinguished by utilizing open-source data formats and processing tools (BIDS, FreeSurfer, fMRIPrep, MRIQC), providing data that is thoroughly quality checked, preprocessed to various extents and available in multiple anatomical spaces. A customizable denoising pipeline is provided as open-source code that includes tools for the generation of functional connectivity matrices and initialization of individual difference analyses. Behavioral data include a comprehensive set of psychological assessments on gold-standard instruments encompassing cognitive function, mood and personality, together with exploratory factor analyses. The dataset provides an in-depth, multimodal resource for investigating associations between individual differences, brain structure and function, with a focus on the domains of social cognition and decision-making
Safety and tolerability of AAV8 delivery of a broadly neutralizing antibody in adults living with HIV: a phase 1, dose-escalation trial
No full textAdeno-associated viral vector-mediated transfer of DNA coding for broadly neutralizing anti-HIV antibodies (bnAbs) offers an alternative to attempting to induce protection by vaccination or by repeated infusions of bnAbs. In this study, we administered a recombinant bicistronic adeno-associated virus (AAV8) vector coding for both the light and heavy chains of the potent broadly neutralizing HIV-1 antibody VRC07 (AAV8-VRC07) to eight adults living with HIV. All participants remained on effective anti-retroviral therapy (viral load (VL) 1 µg ml⁻¹ in three individuals. In four individuals, VRC07 serum concentrations remained stable near maximal concentration for up to 3 years of follow-up. In exploratory analyses, neutralizing activity of in vivo produced VRC07 was similar to that of in vitro produced VRC07. Three of eight participants showed a non-idiotypic anti-drug antibody (ADA) response directed against the Fab portion of VRC07. This ADA response appeared to decrease the production of serum VRC07 in two of these three participants. These data represent a proof of concept that adeno-associated viral vectors can durably produce biologically active, difficult-to-induce bnAbs in vivo, which could add valuable new tools to the fight against infectious diseases
Predicting electron spin decoherence with a many-body first-principles approach
No full textDeveloping a microscopic understanding of spin decoherence is essential to advancing quantum technologies. Electron spin decoherence due to atomic vibrations (phonons) plays a special role as it sets an intrinsic limit to the performance of spin-based quantum devices. Two main sources of phonon-induced spin decoherence - the Elliott-Yafet (EY) and Dyakonov-Perel (DP) mechanisms - have distinct physical origins and theoretical treatments. Here we show calculations that unify their modeling and enable accurate predictions of spin relaxation and precession in semiconductors. We compute the phonon-dressed vertex of the spin-spin correlation function, with a treatment analogous to the calculation of the anomalous electron magnetic moment in QED. We find that the vertex correction provides a giant renormalization of the electron spin dynamics in solids, greater by many orders of magnitude than the corresponding correction in vacuum. Our work demonstrates a general approach for quantitative analysis of spin decoherence in materials, advancing the quest for spin-based quantum technologies
Estimate of OH trends over one decade in North American cities
No full textThe hydroxyl radical (OH) is the most important oxidant on global and local scales in the troposphere. Urban OH controls the removal rate of primary pollutants and triggers the production of ozone. Interannual trends of OH in urban areas are not well documented or understood due to the short lifetime and high spatial heterogeneity of OH. We utilize machine learning with observational inputs emphasizing satellite remote sensing observations to predict surface OH in 49 North American cities from 2005 to 2014. We observe changes in the summertime OH over one decade, with wide variation among different cities. In 2014, compared to the summertime OH in 2005, 3 cities show a significant increase of OH, whereas, in 27 cities, OH decreases in 2014. The year-to-year variation of OH is mapped to the decline of the NO₂ column. We conclude that these cities in this analysis are either in the NOₓ-limited regime or at the transition from a NOₓ suppressed regime to a NOₓ-limited regime. The result emphasizes that, in the future, controlling NOₓ emissions will be most effective in regulating the ozone pollution in these cities
GWSkyNet-Multi: A Machine-learning Multiclass Classifier for LIGO–Virgo Public Alerts
No full textCompact object mergers which produce both detectable gravitational waves and electromagnetic (EM) emission can provide valuable insights into the neutron star equation of state, the tension in the Hubble constant, and the origin of the r-process elements. However, EM follow-up of gravitational wave sources is complicated by false-positive detections, and the transient nature of the associated EM emission. GWSkyNet-Multi is a machine learning model that attempts facilitate EM follow-up by providing real-time predictions of the source of a gravitational wave detection. The model uses information from Open Public Alerts (OPAs) released by LIGO–Virgo within minutes of a gravitational wave detection. GWSkyNet was introduced in Cabero et al. as a binary classifier and uses the OPA skymaps to classify sources as either astrophysical or as glitches. In this paper, we introduce GWSkyNet-Multi, an extension of GWSkyNet which further distinguishes sources as binary black hole mergers, mergers involving a neutron star, or non-astrophysical glitches. GWSkyNet-Multi is a sequence of three one-versus-all classifiers trained using a class-balanced and physically motivated source mass distribution. Training on this data set, we obtain test set accuracies of 93.7% for binary black hole-versus-all, 94.4% for neutron star-versus-all, and 95.1% for glitch-versus-all. We obtain an overall accuracy of 93.4% using a hierarchical classification scheme. Furthermore, we correctly identify 36 of the 40 gravitational wave detections from the first half of LIGO–Virgo’s third observing run (O3a) and present predictions for O3b sources. As gravitational wave detections increase in number and frequency, GWSkyNet-Multi will be a powerful tool for prioritizing successful EM follow-up
A Spatially-resolved Large Cavity of the J0337 Protoplanetary Disk in Perseus
No full textWe present Keck/NIRC2 KₚLₚ high-contrast imaging observations of a J0337 protoplanetary disk. The data discover the spatially-resolved large cavity, which is the second report among protoplanetary disks in the Perseus star-forming region after the LkHα 330 system. Our data and forward modeling using RADMC-3D suggests ∼80 au for the cavity radius. There is discrepancy between J0337's spectral energy distribution (SED) and the modeled SED at ∼10 μm and this suggests an unseen inner disk. We also searched for companions around J0337 but did not detect any companion candidates at separations between 0farcs1 and 2farcs5. The Lp-band detection limit corresponds to ∼20Mⱼᵤₚ at 60 au, ∼9–10Mⱼᵤₚ at 90 au, and ∼3Mⱼᵤₚ at >120 au. Compared with other young systems with large cavities such as PDS 70 and RX J1604, multiple Jovian planets, a single eccentric Jovian planet, or a massive brown dwarf at an inner separation could exist within the cavity
A convolutional neural network for spatial downscaling of satellite-based solar-induced chlorophyll fluorescence (SIFnet)
No full textGross primary productivity (GPP) is the sum of leaf photosynthesis and represents a crucial component of the global carbon cycle. Space-borne estimates of GPP typically rely on observable quantities that co-vary with GPP such as vegetation indices using reflectance measurements (e.g., normalized difference vegetation index, NDVI, near-infrared reflectance of terrestrial vegetation, NIRv, and kernel normalized difference vegetation index, kNDVI). Recent work has also utilized measurements of solar-induced chlorophyll fluorescence (SIF) as a proxy for GPP. However, these SIF measurements are typically coarse resolution, while many processes influencing GPP occur at fine spatial scales. Here, we develop a convolutional neural network (CNN), named SIFnet, that increases the resolution of SIF from the TROPOspheric Monitoring Instrument (TROPOMI) on board of the satellite Sentinel-5P by a factor of 10 to a spatial resolution of 500 m. SIFnet utilizes coarse SIF observations together with high-resolution auxiliary data. The auxiliary data used here may carry information related to GPP and SIF. We use training data from non-US regions between April 2018 until March 2021 and evaluate our CNN over the conterminous United States (CONUS). We show that SIFnet is able to increase the resolution of TROPOMI SIF by a factor of 10 with a r² and RMSE metrics of 0.92 and 0.17 mW m⁻² sr⁻¹ nm⁻¹, respectively. We further compare SIFnet against a recently developed downscaling approach and evaluate both methods against independent SIF measurements from Orbiting Carbon Observatory 2 and 3 (together OCO-2/3). SIFnet performs systematically better than the downscaling approach (r = 0.78 for SIFnet, r = 0.72 for downscaling), indicating that it is picking up on key features related to SIF and GPP. Examination of the feature importance in the neural network indicates a few key parameters and the spatial regions in which these parameters matter. Namely, the CNN finds low-resolution SIF data to be the most significant parameter with the NIRv vegetation index as the second most important parameter. NIRv consistently outperforms the recently proposed kNDVI vegetation index. Advantages and limitations of SIFnet are investigated and presented through a series of case studies across the United States. SIFnet represents a robust method to infer continuous, high-spatial-resolution SIF data
Unsupervised ECG Analysis: A Review
No full textElectrocardiography is the gold standard technique for detecting abnormal heart conditions. Automatic analysis of electrocardiogram (ECG) can help physicians in the interoperation of the large amount of data produced daily by cardiac monitors. As the successful application of supervised machine learning algorithms relies on unprecedented amounts of labeled training data, there is a growing need for unsupervised algorithms for ECG analysis. Unsupervised learning aims to partition ECG into distinct abnormality classes without cardiologist-supplied labelsa process referred to as ECG clustering. In addition to abnormality detection, ECG clustering can discover inter and intra-individual patterns that carry valuable information about the whole body and mind, such as emotions and mental disorders. ECG clustering can also resolve specific challenges facing supervised learning systems, such as the imbalanced data problem, and can enhance biometric systems. While several reviews exist on supervised ECG analysis, a comprehensive review of unsupervised ECG analysis techniques is still lacking. This study reviews recent ECG clustering techniques with the focus on machine learning and deep learning algorithms. We critically review and compare these techniques, discuss their applications and limitations, and provide future research directions. This review provides further insights into ECG clustering and presents the necessary information required to adopt the appropriate algorithm for a specific application