Showing papers by "Apple Inc. published in 2018"

VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection

Abstract: Accurate detection of objects in 3D point clouds is a central problem in many applications, such as autonomous navigation, housekeeping robots, and augmented/virtual reality. To interface a highly sparse LiDAR point cloud with a region proposal network (RPN), most existing efforts have focused on hand-crafted feature representations, for example, a bird's eye view projection. In this work, we remove the need of manual feature engineering for 3D point clouds and propose VoxelNet, a generic 3D detection network that unifies feature extraction and bounding box prediction into a single stage, end-to-end trainable deep network. Specifically, VoxelNet divides a point cloud into equally spaced 3D voxels and transforms a group of points within each voxel into a unified feature representation through the newly introduced voxel feature encoding (VFE) layer. In this way, the point cloud is encoded as a descriptive volumetric representation, which is then connected to a RPN to generate detections. Experiments on the KITTI car detection benchmark show that VoxelNet outperforms the state-of-the-art LiDAR based 3D detection methods by a large margin. Furthermore, our network learns an effective discriminative representation of objects with various geometries, leading to encouraging results in 3D detection of pedestrians and cyclists, based on only LiDAR.

Rateless Codes for Near-Perfect Load Balancing in Distributed Matrix-Vector Multiplication

Abstract: Large-scale machine learning and data mining applications require computer systems to perform massive matrix-vector and matrix-matrix multiplication operations that need to be parallelized across multiple nodes. The presence of straggling nodes -- computing nodes that unpredictably slowdown or fail -- is a major bottleneck in such distributed computations. Ideal load balancing strategies that dynamically allocate more tasks to faster nodes require knowledge or monitoring of node speeds as well as the ability to quickly move data. Recently proposed fixed-rate erasure coding strategies can handle unpredictable node slowdown, but they ignore partial work done by straggling nodes thus resulting in a lot of redundant computation. We propose a \emph{rateless fountain coding} strategy that achieves the best of both worlds -- we prove that its latency is asymptotically equal to ideal load balancing, and it performs asymptotically zero redundant computations. Our idea is to create linear combinations of the $m$ rows of the matrix and assign these encoded rows to different worker nodes. The original matrix-vector product can be decoded as soon as slightly more than $m$ row-vector products are collectively finished by the nodes. We conduct experiments in three computing environments: local parallel computing, Amazon EC2, and Amazon Lambda, which show that rateless coding gives as much as $3\times$ speed-up over uncoded schemes.

CRISPR-Cas9-mediated genome editing in apple and grapevine.

Abstract: The CRISPR-Cas9 genome-editing tool and the availability of whole-genome sequences from plant species have revolutionized our ability to introduce targeted mutations into important crop plants, both to explore genetic changes and to introduce new functionalities. Here, we describe protocols adapting the CRISPR-Cas9 system to apple and grapevine plants, using both plasmid-mediated genome editing and the direct delivery of CRISPR-Cas9 ribonucleoproteins (RNPs) to achieve efficient DNA-free targeted mutations in apple and grapevine protoplasts. We provide a stepwise protocol for the design and transfer of CRISPR-Cas9 components to apple and grapevine protoplasts, followed by verification of highly efficient targeted mutagenesis, and regeneration of plants following the plasmid-mediated delivery of components. Our plasmid-mediated procedure and the direct delivery of CRISPR-Cas9 RNPs can both be utilized to modulate traits of interest with high accuracy and efficiency in apple and grapevine, and could be extended to other crop species. The complete protocol employing the direct delivery of CRISPR-Cas9 RNPs takes as little as 2-3 weeks, whereas the plasmid-mediated procedure takes >3 months to regenerate plants and study the mutations.

A 60-GHz 144-Element Phased-Array Transceiver for Backhaul Application

Abstract: A 144-element phased array transceiver is realized using a modular tiled approach that supports 802.11ad, MCS12 single carrier 16-quadratic-amplitude modulation (QAM) 4.6 Gbps, in the 60-GHz band. It consists of a system-on-a chip (SOC) (MAC/PHY/BB to IF) in 28-nm CMOS, and one IF-to-60-GHz transceiver master chip driving twelve 60-GHz phased array transceiver slave chips fabricated in a 40-nm CMOS. Using the master-slave configuration, the 60-GHz transceiver with 12 phase-controlled TX/RX slices is expanded to 144 phase-controlled slices. Each final TX/RX slice is then connected to two patch antennas on LTCC substrate. A tiled approach is used to create the 288 patch antenna array out of six identical tiles each with two slave 60-GHz transceivers connected to a 48-element antenna array. The single tile phased array with 48 antennas has a measured beam steering scan angle of 60° in azimuth and 10° in elevation. The full phased array transceiver with 288 antennas has a measured over the air (OTA) max effective isotropic radiated power (EIRP) of 51 dBm at saturated power (PSAT), and EIRP of 44.8 dBm with −22 dB EVM for MCS12 (16QAM-4.6 Gbps) at broadside. It has an OTA measured sensitivity of −87.3 and −80.4 dBm for MCS9 (QPSK-2.5 Gbps) and MCS12, respectively, at broadside. A packet error rate of 10−5 was measured for MCS9 and MCS12 with an OTA input power of −85 and −77.5 dBm, respectively, for the full phased array transceiver at broadside.

Automatic emotion and attention analysis of young children at home: a ResearchKit autism feasibility study

Abstract: Current tools for objectively measuring young children’s observed behaviors are expensive, time-consuming, and require extensive training and professional administration. The lack of scalable, reliable, and validated tools impacts access to evidence-based knowledge and limits our capacity to collect population-level data in non-clinical settings. To address this gap, we developed mobile technology to collect videos of young children while they watched movies designed to elicit autism-related behaviors and then used automatic behavioral coding of these videos to quantify children’s emotions and behaviors. We present results from our iPhone study Autism & Beyond, built on ResearchKit’s open-source platform. The entire study—from an e-Consent process to stimuli presentation and data collection—was conducted within an iPhone-based app available in the Apple Store. Over 1 year, 1756 families with children aged 12–72 months old participated in the study, completing 5618 caregiver-reported surveys and uploading 4441 videos recorded in the child’s natural settings. Usable data were collected on 87.6% of the uploaded videos. Automatic coding identified significant differences in emotion and attention by age, sex, and autism risk status. This study demonstrates the acceptability of an app-based tool to caregivers, their willingness to upload videos of their children, the feasibility of caregiver-collected data in the home, and the application of automatic behavioral encoding to quantify emotions and attention variables that are clinically meaningful and may be refined to screen children for autism and developmental disorders outside of clinical settings. This technology has the potential to transform how we screen and monitor children’s development.

Microwave-mediated synthesis, photocatalytic degradation and antibacterial activity of α-Bi2O3 microflowers/novel γ-Bi2O3 microspindles

Abstract: The present article portray the microwave (MW) synthesis of alpha bismuth oxide microflowers ( α -Bi2O3 MFs) and novel gamma bismuth oxide microspindles ( γ -Bi2O3 MSs) using bismuth nitrate pentahydrate [Bi(NO3)3.5H2O], and polyvinylpyrrolidone (PVP) as a surfactant. The structural, BET surface area, morphological, photocatalytic activity under ultraviolet (UV) light and antibacterial performance was investigated with varying MW irradiation time. XRD study confirmed the formation of monoclinic ( α -phase) and body-centered cubic ( γ -phase) of Bi2O3 with variation of MW irradiation time for 10 min and 15 min respectively. SEM images revealed the formation of microflowers for α -Bi2O3 and microspindles for γ -Bi2O3. Further HR-TEM illustrated the appearance of nanoflakes (19–35 nm) and the rose-flowers, and back-bending man (15–30 nm) like structures for α -Bi2O3 MFs and γ -Bi2O3 MSs correspondingly. Interestingly, the comparative photocatalytic activity of α -Bi2O3 MFs and γ -Bi2O3 MSs were evaluated under UV-light irradiation for the degradation of five different dyes viz. malachite green (MG), eriochrome black-T (EBT), methyl thymol blue (MTB), bromophenol blue (BPB), and congo-red (CR). The α -Bi2O3 MFs show enhanced photocatalytic activity than γ -Bi2O3 MSs under UV light without decreasing efficiency up to four consecutive cycles and fallows the pseudo-first order reaction kinetics and degradation order: MTB>MG>EBT>CR>BPB. Furthermore, antibacterial assay of α -Bi2O3 MFs and γ -Bi2O3 MSs was carried out against gram-positive (Staphylococcus aureus (S a) and Pseudomonas aeruginosa (P a)) and gram-negative (Escherichia coli (E c), and Klebsiella Pneumonia (K p)) human pathogenic bacteria using agar well diffusion method. The α -Bi2O3 MFs show comparatively better antibacterial activity against P a than γ -Bi2O3 MSs. The current study provides a simple approach to design the α -Bi2O3 MFs and novel γ -Bi2O3 MSs as a photocatalyst for the environmental remediation of organic pollutants (OPs).

Anthocyanin Biosynthesis in Apple Fruit

Abstract: Anthocyanin accumulation is responsible for the red color of the skin and flesh of apple fruits (Malus × domestica Borkh.), and redder fruits are more marketable. Pigmentation in the skin of apple fruit varies among different cultivars and is influenced by environmental factors, including temperature conditions and the level of sunlight irradiation. Because warmer temperatures suppress anthocyanin synthesis in the skin of apple fruit, there are increasing concerns that global warming may be detrimental to fruit pigmentation. Recent molecular studies have revealed that the MdMYB1 gene, which encodes a transcription factor, plays a critical role in regulating anthocyanin synthesis in both the skin and flesh of apple fruits. A marker-assisted selection process has been developed to identify MdMYB1 genotypes and predict those fruits that will develop redder skin. These apples may be better adapted to a warmer global climate. The application of hormones can also increase the level of pigmentation in fruit skin, and plant growth regulators such as ethylene and jasmonate are commercially available. The mechanisms that regulate anthocyanin biosynthesis in the flesh of red-fleshed apple fruit appear to partially differ from those that function in the skin of red-skinned fruit. In the flesh of red-fleshed fruit, the pigment accumulates under dark conditions, whereas no anthocyanin is synthesized in the skin of bagged apple fruit. Conversely, in both red-skinned and red-fleshed apple fruits, warmer temperatures inhibit anthocyanin accumulation. Further studies on the regulation of anthocyanin synthesis in the flesh of red-fleshed apple fruit are necessary.

Modular 128-Channel $\Delta$ - $\Delta \Sigma$ Analog Front-End Architecture Using Spectrum Equalization Scheme for 1024-Channel 3-D Neural Recording Microsystems

Abstract: We report an area- and energy-efficient integrated circuit architecture of a 128-channel $\Delta$ -modulated $\Delta \Sigma$ analog front-end ( $\Delta$ - $\Delta \Sigma$ AFE) for 1024-channel 3-D massive-parallel neural recording microsystems. Our platform has adopted a modularity of 128 channels and consists of eight multi-shank neural probes connected to individual AFEs through interposers in a small form factor. In order to reduce both area and energy consumption in the recording circuits, we implemented a spectrum equalization scheme to take advantage of the inherent spectral characteristics of neural signals, where most of the energy is confined in low frequencies and follows a ~1/f curve in the spectrum. This allows us to implement the AFE with a relaxed dynamic range by ~30 dB, thereby contributing to the significant reduction of both energy and area without sacrificing signal integrity. The $\Delta$ - $\Delta \Sigma$ AFE was fabricated using 0.18- $\mu \text{m}$ CMOS processes. The single-channel AFE consumes 3.05 $\mu \text{W}$ from 0.5 and 1.0 V supplies in an area of 0.05 mm2 with 63.8-dB signal-to-noise-and-distortion ratio, 3.02 noise efficiency factor (NEF), and 4.56 NEF2VDD. We also have achieved an energy-area product, a figure-of-merit most critical for massive-parallel neural recording systems, of 6.34 fJ/ $\text{C}\cdot \text{s}\cdot$ mm2.

Global Pose Estimation with an Attention-Based Recurrent Network

Abstract: The ability for an agent to localize itself within an environment is crucial for many real-world applications. For unknown environments, Simultaneous Localization and Mapping (SLAM) enables incremental and concurrent building of and localizing within a map. We present a new, differentiable architecture, Neural Graph Optimizer, progressing towards a complete neural network solution for SLAM by designing a system composed of a local pose estimation model, a novel pose selection module, and a novel graph optimization process. The entire architecture is trained in an end-to-end fashion, enabling the network to automatically learn domain-specific features relevant to the visual odometry and avoid the involved process of feature engineering. We demonstrate the effectiveness of our system on a simulated 2D maze and the 3D ViZ-Doom environment.

Gated Path Planning Networks

Abstract: Value Iteration Networks (VINs) are effective differentiable path planning modules that can be used by agents to perform navigation while still maintaining end-to-end differentiability of the entire architecture. Despite their effectiveness, they suffer from several disadvantages including training instability, random seed sensitivity, and other optimization problems. In this work, we reframe VINs as recurrent-convolutional networks which demonstrates that VINs couple recurrent convolutions with an unconventional max-pooling activation. From this perspective, we argue that standard gated recurrent update equations could potentially alleviate the optimization issues plaguing VIN. The resulting architecture, which we call the Gated Path Planning Network, is shown to empirically outperform VIN on a variety of metrics such as learning speed, hyperparameter sensitivity, iteration count, and even generalization. Furthermore, we show that this performance gap is consistent across different maze transition types, maze sizes and even show success on a challenging 3D environment, where the planner is only provided with first-person RGB images.

Point cloud compression

Abstract: A system comprises an encoder configured to compress attribute information for a point cloud and/or a decoder configured to decompress compressed attribute information for the point cloud. Attribute values for at least one starting point are included in a compressed attribute information file and attribute correction values used to correct predicted attribute values are included in the compressed attribute information file. Attribute values are predicted based, at least in part, on attribute values of neighboring points and distances between a particular point for whom an attribute value is being predicted and the neighboring points. The predicted attribute values are compared to attribute values of a point cloud prior to compression to determine attribute correction values. A decoder follows a similar prediction process as an encoder and corrects predicted values using attribute correction values included in a compressed attribute information file.

Active Neural Localization

Abstract: Localization is the problem of estimating the location of an autonomous agent from an observation and a map of the environment. Traditional methods of localization, which filter the belief based on the observations, are sub-optimal in the number of steps required, as they do not decide the actions taken by the agent. We propose "Active Neural Localizer", a fully differentiable neural network that learns to localize accurately and efficiently. The proposed model incorporates ideas of traditional filtering-based localization methods, by using a structured belief of the state with multiplicative interactions to propagate belief, and combines it with a policy model to localize accurately while minimizing the number of steps required for localization. Active Neural Localizer is trained end-to-end with reinforcement learning. We use a variety of simulation environments for our experiments which include random 2D mazes, random mazes in the Doom game engine and a photo-realistic environment in the Unreal game engine. The results on the 2D environments show the effectiveness of the learned policy in an idealistic setting while results on the 3D environments demonstrate the model's capability of learning the policy and perceptual model jointly from raw-pixel based RGB observations. We also show that a model trained on random textures in the Doom environment generalizes well to a photo-realistic office space environment in the Unreal engine.

Ultra High-Dimensional Nonlinear Feature Selection for Big Biological Data

Abstract: Machine learning methods are used to discover complex nonlinear relationships in biological and medical data. However, sophisticated learning models are computationally unfeasible for data with millions of features. Here, we introduce the first feature selection method for nonlinear learning problems that can scale up to large, ultra-high dimensional biological data. More specifically, we scale up the novel Hilbert-Schmidt Independence Criterion Lasso (HSIC Lasso) to handle millions of features with tens of thousand samples. The proposed method is guaranteed to find an optimal subset of maximally predictive features with minimal redundancy, yielding higher predictive power and improved interpretability. Its effectiveness is demonstrated through applications to classify phenotypes based on module expression in human prostate cancer patients and to detect enzymes among protein structures. We achieve high accuracy with as few as 20 out of one million features—a dimensionality reduction of 99.998 percent. Our algorithm can be implemented on commodity cloud computing platforms. The dramatic reduction of features may lead to the ubiquitous deployment of sophisticated prediction models in mobile health care applications.

Efficient Voice Trigger Detection for Low Resource Hardware.

Abstract: We describe the architecture of an always-on keyword spotting (KWS) system for battery-powered mobile devices used to initiate an interaction with the device. An always-available voice assistant needs a carefully designed voice keyword detector to satisfy the power and computational constraints of battery powered devices. We employ a multi-stage system that uses a low-power primary stage to decide when to run a more accurate (but more power-hungry) secondary detector. We describe a straightforward primary detector and explore variations that result in very useful reductions in computation (or increased accuracy for the same computation). By reducing the set of target labels from three to one per phone, and reducing the rate at which the acoustic model is operated, the compute rate can be reduced by a factor of six while maintaining the same accuracy.

Exit conditions for conditional handovers and beam based mobility state estimation

Abstract: Methods, systems, and storage media are provided for exiting conditional handovers and for estimating a user equipment mobility state. Other embodiments may be described and/or claimed.

Local Private Hypothesis Testing: Chi-Square Tests

Abstract: The local model for differential privacy is emerging as the reference model for practical applications collecting and sharing sensitive information while satisfying strong privacy guarantees. In the local model, there is no trusted entity which is allowed to have each individual's raw data as is assumed in the traditional curator model for differential privacy. So, individuals' data are usually perturbed before sharing them. We explore the design of private hypothesis tests in the local model, where each data entry is perturbed to ensure the privacy of each participant. Specifically, we analyze locally private chi-square tests for goodness of fit and independence testing, which have been studied in the traditional, curator model for differential privacy.

Knowledge-based Word Sense Disambiguation using Topic Models

Abstract: Word Sense Disambiguation is an open problem in Natural Language Processing which is particularly challenging and useful in the unsupervised setting where all the words in any given text need to be disambiguated without using any labeled data. Typically WSD systems use the sentence or a small window of words around the target word as the context for disambiguation because their computational complexity scales exponentially with the size of the context. In this paper, we leverage the formalism of topic model to design a WSD system that scales linearly with the number of words in the context. As a result, our system is able to utilize the whole document as the context for a word to be disambiguated. The proposed method is a variant of Latent Dirichlet Allocation in which the topic proportions for a document are replaced by synset proportions. We further utilize the information in the WordNet by assigning a non-uniform prior to synset distribution over words and a logistic-normal prior for document distribution over synsets. We evaluate the proposed method on Senseval-2, Senseval-3, SemEval-2007, SemEval-2013 and SemEval-2015 English All-Word WSD datasets and show that it outperforms the state-of-the-art unsupervised knowledge-based WSD system by a significant margin.

A Sub-mW Fractional- ${N}$ ADPLL With FOM of −246 dB for IoT Applications

Abstract: This paper presents a sub-mW fractional- ${N}$ all-digital phase-locked loop (ADPLL) with scalable power consumption, which achieves an figure of merit (FOM) of −246 dB. The proposed 10-b ultralow-power isolated constant-slope digital-to-time converter (DTC) achieves a 580-fs resolution and a measured integral nonlinearity (INL) of 870 fs with 0.14-mW power consumption at 52 MS/s. A narrow-range time amplifier (TA)-time-to-digital converter (TDC) with gain calibration minimizes both the in-band phase noise degradation and the loop-bandwidth variation. In addition, a coarse-DPLL is introduced with dead-zone function, which reduces the phase lock time to 4.2 $\mu \text{s}$ at a 13-MHz frequency error. The coarse-DPLL monitors large frequency and phase jump in the background while consuming almost zero power. In an ultralow power mode, the proposed fractional- ${N}$ ADPLL consumes a 0.65-mW power with a 26-MHz reference. A rms jitter of 1.00 ps and −50-dBc in-band fractional spur are achieved with a −242-dB FOM. In high-performance mode, a reference doubler is utilized, the jitter and spurs can be improved to 535 fs and −56 dBc, respectively, while consuming 0.98 mW. The proposed ADPLL with scalable power and jitter performance can be utilized for Internet-of-Things (IoT) applications, such as Bluetooth low energy (BLE) and Wi-Fi networks.

Devices, Methods, and Graphical User Interfaces For Navigating Between User Interfaces and Interacting with Control Objects

Abstract: An electronic device with a display and a touch-sensitive surface displays a user interface of an application. The device detects a first portion of an input including a contact on the touch-sensitive surface, and then detects a second portion of the input including movement of the contact across the touch-sensitive surface. The device displays, during the movement, application views including an application view that corresponds to the user interface of the application and another application view that corresponds to a different user interface of a different application. The device then detects a third portion of the input, including a liftoff of the contact from the touch-sensitive surface. In response, the device, upon determining that application-switcher-display criteria are met, displays an application-switcher user interface, and upon determining that home-display criteria are met, the device displays a home screen user interface that includes application launch icons.

The Effect of Promoter Methylation on MdMYB1 Expression Determines the Level of Anthocyanin Accumulation in Skins of Two Non-Red Apple Cultivars

Abstract: Fruit color in apple (Malus domestica Borkh.) is ascribed mainly to the accumulation of anthocyanin pigments, and is an important trait for determining fruit market acceptance. Bagging is a commonly used treatment to enhance the red pigmentation in apple skin. The MdMYB1 transcription factor gene plays an important role in the biosynthesis of anthocyanin in apple after bag removal, but little is known about how MdMYB1 transcription is regulated. In this study, we investigated pigmentation in the non-red skinned cultivars ‘Granny Smith’ and ‘Golden Delicious’ after bag removal. The fruit skins of the two cultivars showed red/pink pigmentation after bag treatment. Transcript levels of MdMYB1, the master regulator of anthocyanin biosynthesis in apple, increased, and showed a correlation with anthocyanin content in both cultivars after bag removal. The MdMYB1 genomic sequences were compared in the two cultivars, which showed that the green-fruited cultivar ‘Granny Smith’ harbors the MdMYB1–1 and MdMYB1–2 alleles, while the yellow-fruited cultivar ‘Golden Delicious’ harbors only MdMYB1–2. A comparison of methylation levels in the 2 kb region upstream of the MdMYB1 ATG between the bag-treated fruits after removal from the bags and the unbagged fruits showed a correlation between hypomethylation and the red-skin phenotype in ‘Granny Smith’. Moreover, ‘Granny Smith’ fruits responded to treatment with 5-aza-2′-deoxycytidine, an inducer of DNA demethylation. An investigation of the MdMYB1 promoter in ‘Granny Smith’ showed reduced methylation in the regions − 2026 to − 1870 bp, − 1898 to − 1633 bp, and − 541 to − 435 bp after bag removal and 5-aza-2′-deoxycytidine treatments. Differences in anthocyanin levels between ‘Granny Smith’ and ‘Golden Delicious’ can be explained by differential accumulation of MdMYB1-specific mRNA. Different levels of MdMYB1 transcripts in the two cultivars are associated with methylation levels in the promoter region. Hypomethylation of the MdMYB1 promoter is correlated with the formation of red pigmentation in ‘Granny Smith’ fruit skins. As a result, red pigmentation in Granny Smith’ was more intense than in ‘Golden Delicious’ fruits after bag removal.

Active Neural Localization

Abstract: Localization is the problem of estimating the location of an autonomous agent from an observation and a map of the environment. Traditional methods of localization, which filter the belief based on the observations, are sub-optimal in the number of steps required, as they do not decide the actions taken by the agent. We propose "Active Neural Localizer", a fully differentiable neural network that learns to localize accurately and efficiently. The proposed model incorporates ideas of traditional filtering-based localization methods, by using a structured belief of the state with multiplicative interactions to propagate belief, and combines it with a policy model to localize accurately while minimizing the number of steps required for localization. Active Neural Localizer is trained end-to-end with reinforcement learning. We use a variety of simulation environments for our experiments which include random 2D mazes, random mazes in the Doom game engine and a photo-realistic environment in the Unreal game engine. The results on the 2D environments show the effectiveness of the learned policy in an idealistic setting while results on the 3D environments demonstrate the model's capability of learning the policy and perceptual model jointly from raw-pixel based RGB observations. We also show that a model trained on random textures in the Doom environment generalizes well to a photo-realistic office space environment in the Unreal engine.

Implementation of biometric authentication

Abstract: An electronic device performs techniques related generally to implementing biometric authentication. In some examples, a device provides user interfaces for a biometric enrollment process tutorial. In some examples, a device provides user interfaces for aligning a biometric feature for enrollment. In some examples, a device provides user interfaces for enrolling a biometric feature. In some examples, a device provides user interfaces for providing hints during a biometric enrollment process. In some examples, a device provides user interfaces for application-based biometric authentication. In some examples, a device provides user interfaces for autofilling biometrically secured fields. In some examples, a device provides user interfaces for unlocking a device using biometric authentication. In some examples, a device provides user interfaces for retrying biometric authentication. In some examples, a device provides user interfaces for managing transfers using biometric authentication. In some examples, a device provides interstitial user interfaces during biometric authentication. In some examples, a device provides user interfaces for preventing retrying biometric authentication. In some examples, a device provides user interfaces for cached biometric authentication. In some examples, a device provides user interfaces for autofilling fillable fields based on visibility criteria. In some examples, a device provides user interfaces for automatic log-in using biometric authentication.

Instrument Calibration of the Interface Region Imaging Spectrograph (IRIS) Mission

Abstract: The Interface Region Imaging Spectrograph (IRIS) is a NASA small explorer mission that provides high-resolution spectra and images of the Sun in the 133 – 141 nm and 278 – 283 nm wavelength bands. The IRIS data are archived in calibrated form and made available to the public within seven days of observing. The calibrations applied to the data include dark correction, scattered light and background correction, flat fielding, geometric distortion correction, and wavelength calibration. In addition, the IRIS team has calibrated the IRIS absolute throughput as a function of wavelength and has been tracking throughput changes over the course of the mission. As a resource for the IRIS data user, this article describes the details of these calibrations as they have evolved over the first few years of the mission. References to online documentation provide access to additional information and future updates.

Methods and interfaces for home media control

Abstract: The present disclosure generally relates to interfaces and techniques for media playback on one or more devices In accordance with some embodiments, an electronic device includes a display, one or more processors, and memory The electronic device receives user input and, in response to receiving the user input, displays, on the display, a multi-device interface that includes: one or more indicators associated with a plurality of available playback devices that are connected to the device and available to initiate playback of media from the device, and a media playback status of the plurality of available playback devices

Dynamic Power Reduction in Scalable Neural Recording Interface Using Spatiotemporal Correlation and Temporal Sparsity of Neural Signals

Abstract: We report a scalable neural recording interface with embedded lossless compression to reduce dynamic power consumption ( $\text{P}_{D}$ ) for data transmission in high-density neural recording systems. We investigated the characteristics of neural signals and implemented effective lossless compression for local field potential (LFP) and extracellular action potential (EAP or spike) in separate signal paths. For LFP, spatial–temporal (spatiotemporal) correlation of the LFP signals is exploited in a $\Delta $ -modulated $\Delta \Sigma $ analog-to-digital converter ( $\Delta -\Delta \Sigma $ ADC) and a dedicated digital difference circuit. Then, statistical redundancy is further eliminated through entropy encoding without information loss. For spikes, only essential parts of waveforms in the spikes are extracted from the raw data by using spike detectors and reconfigurable analog memories. The prototype chip was fabricated using 180-nm CMOS processes, incorporating 128 channels into a modular architecture that is easily scalable and expandable for high-density neural recordings. The fabricated chip achieved the data rate reduction for the LFPs and spikes by a factor of 5.35 and 10.54, respectively, from the proposed compression scheme. Consequently, $P_{D}$ was reduced by 89%, when compared to the uncompressed case. We also achieved the state-of-the-art recording performance of 3.37 $\mu \text{W}$ per channel, 5.18 $\mu V_{\mathrm {rms}}$ noise, and 3.41 ${\text {NEF}}^{2}V_{\mathrm {DD}}$ .

Electronic device with millimeter wave antennas on printed circuits

Abstract: An electronic device may be provided with wireless circuitry. The wireless circuitry may include one or more antennas and transceiver circuitry such as millimeter wave transceiver circuitry. The antennas may be formed from metal traces on printed circuits. A flexible printed circuit may have an area on which the transceiver circuitry is mounted. Protruding portions may extend from the area on which the transceiver circuitry is mounted and may be separated from the area on which the transceiver circuitry is mounted by bends. Antenna resonating elements such as patch antenna resonating elements and dipole resonating elements may be formed on the protruding portions and may be used to transmit and receive millimeter wave antenna signals through dielectric-filled openings in a metal electronic device housing or a dielectric layer such as a display cover layer formed from glass or other dielectric.

Characterizing Mode-Dependent Loss and Gain in Multimode Components

Abstract: Mode-dependent loss and gain (MDL and MDG) of multimode components are fundamental impairments that reduce the capacity of mode-division-multiplexed (MDM) systems. MDL of components is commonly quantified either in terms of the root mean square (rms) or peak-to-peak (P-P) gain and loss variations. It is incorrect to specify only the P-PMDL of components if they are to be used in an MDM system with nonnegligible mode coupling, because the system's overall coupled gains are random variables whose statistics cannot be determined from the P-P MDL values. On the other hand, measurements of the rms MDL of components are sufficient to determine the rms value of the system's overall coupled MDL, regardless of whether the system has weak or strong coupling. We propose novel algorithms based on convex optimization, which can efficiently measure all modal gains of any multimode component using low-cost direct-detection hardware. In particular, we propose an efficient algorithm that produces accurate measurements of all modal gains by estimating a high-dimensional MDL ellipse using a sequence of power measurements.

Emoji recording and sending

Abstract: The present disclosure generally relates to generating and modifying virtual avatars. An electronic device having a camera and a display apparatus displays a virtual avatar that changes appearance in response to changes in a face in a field of view of the camera. In response to detecting changes in one or more physical features of the face in the field of view of the camera, the electronic device modifies one or more features of the virtual avatar.