An electronic device with a display and a fingerprint sensor may authenticate a user for a respective function. While a respective function of the device is in a locked state, the device displays a graphical element on the display, the graphical element indicating a first direction of finger movement that enables unlocking of the respective function. While displaying the graphical element, the device detects an input that includes movement of a finger in the first direction over the fingerprint sensor and determines whether the input meets unlock criteria based at least in part on fingerprint information of the finger detected by the fingerprint sensor during the input. In accordance with a determination that the input meets the unlock criteria, the device unlocks the respective function; and in accordance with a determination that the input does not meet the unlock criteria, the device maintains the respective function in the locked state.

Embedded authentication systems in an electronic device

Training large deep neural networks on massive datasets is computationally very challenging. There has been recent surge in interest in using large batch stochastic optimization methods to tackle this issue. The most prominent algorithm in this line of research is LARS, which by employing layerwise adaptive learning rates trains ResNet on ImageNet in a few minutes. However, LARS performs poorly for attention models like BERT, indicating that its performance gains are not consistent across tasks. In this paper, we first study a principled layerwise adaptation strategy to accelerate training of deep neural networks using large mini-batches. Using this strategy, we develop a new layerwise adaptive large batch optimization technique called LAMB; we then provide convergence analysis of LAMB as well as LARS, showing convergence to a stationary point in general nonconvex settings. Our empirical results demonstrate the superior performance of LAMB across various tasks such as BERT and ResNet-50 training with very little hyperparameter tuning. In particular, for BERT training, our optimizer enables use of very large batch sizes of 32868 without any degradation of performance. By increasing the batch size to the memory limit of a TPUv3 Pod, BERT training time can be reduced from 3 days to just 76 minutes (Table 1). The LAMB implementation is available at this https URL

Large Batch Optimization for Deep Learning: Training BERT in 76 minutes

A mobile terminal and a control method thereof, which can obtain an image, are provided. A mobile terminal (100) includes a camera unit (121), a pictogram extraction unit (182) and a controller (180). The camera unit (121) obtains image information corresponding to at least one of a still image and a moving image. The pictogram extraction unit (182) extracts at least one pictogram from the obtained image information. The controller (180) detects information related to the extracted pictogram, and displays the detected information to be overlapped with the obtained image information. In the mobile terminal, the controller (180) includes, as the detected information, at least one of previously recorded information and currently searched information related to the extracted pictogram.

Mobile terminal and control method thereof

An electronic device with a display and a fingerprint sensor displays a fingerprint enrollment interface and detects, on the fingerprint sensor, a plurality of finger gestures performed with a finger. The device collects fingerprint information from the plurality of finger gestures performed with the finger. After collecting the fingerprint information, the device determines whether the collected fingerprint information is sufficient to enroll a fingerprint of the finger. When the collected fingerprint information for the finger is sufficient to enroll the fingerprint of the finger, the device enrolls the fingerprint of the finger with the device. When the collected fingerprint information for the finger is not sufficient to enroll the fingerprint of the finger, the device displays a message in the fingerprint enrollment interface prompting a user to perform one or more additional finger gestures on the fingerprint sensor with the finger.

Device, method, and graphical user interface for manipulating user interfaces based on fingerprint sensor inputs

Apparatus, methods, and systems for centrally and uniformly controlling the operation of a variety of devices, such as communication, consumer electronic, audio-video, analog, digital, 1394, and the like, over a variety of protocols within a network system and, more particularly, a control system and uniform user interface for centrally controlling these devices in a manner that appears seamless and transparent to the user. In a preferred embodiment, a command center or hub of a network system includes a context and connection permutation sensitive control system that enables centralized and seamless integrated control of all types of input devices. The control system preferably includes a versatile icon based graphical user interface that provides a uniform, on-screen centralized control system for the network system. The user interface, which includes a visual recognition system, enables the user to transparently control multiple input devices over a variety of protocols while operating on a single control layer of an input command device. In an alternative embodiment, the control system also enables gated signal pass-through control while avoiding signal jamming.

Control system and user interface for network of input devices

Provided are an infrared (Ir) face authentication apparatus, and a portable terminal and a security apparatus including the Ir face authentication apparatus. The Ir face authentication apparatus includes: a light source irradiating Ir light having a wavelength of 760 nm or higher onto a face; an imaging unit detecting reflected light of the Ir light to output an Ir image; and an authenticator performing authentication of the face using the Ir image. The Ir face authentication apparatus further includes a display displaying the Ir image output from the imaging unit, and the image unit and the display are installed on the same surface.

Infrared face authenticating apparatus, and portable terminal and security apparatus including the same

Scaling the distributed deep learning to a massive GPU cluster level is challenging due to the instability of the large mini-batch training and the overhead of the gradient synchronization. We address the instability of the large mini-batch training with batch-size control and label smoothing. We address the overhead of the gradient synchronization with 2D-Torus all-reduce. Specifically, 2D-Torus all-reduce arranges GPUs in a logical 2D grid and performs a series of collective operation in different orientations. These two techniques are implemented with Neural Network Libraries (NNL). We have successfully trained ImageNet/ResNet-50 in 122 seconds without significant accuracy loss on ABCI cluster.

Massively Distributed SGD: ImageNet/ResNet-50 Training in a Flash

ImageNet/ResNet-50 Training in 224 Seconds.

In a metadata collection system comprising a plurality of content management servers each capable of managing content and content metadata as local content and managing content metadata stored in another content management server as reference content and a metadata collection apparatus connected with the plurality of content management servers to collect metadata from the plurality of content management servers, the metadata collection apparatus collects metadata from the individual content management servers based upon server information held at the content management servers and category identifiers appended to tree structures generated at the content management servers by individually referencing the plurality of content management servers.

Metadata collection system, content management server, metadata collection apparatus, metadata collection method and program

There is provided a wireless communication device including a storage unit for storing authentication information distributed to a plurality of users including a user of the wireless communication device belonging to a community on a network; an authentication unit for authenticating a peripheral wireless communication device based on the authentication information stored in the storage unit; and a control unit for forming a communication group with the peripheral wireless communication device when the authentication of the peripheral wireless communication device by the authentication unit is successful.

Yuichi Kageyama

Papers

Infrared face authenticating apparatus, and portable terminal and security apparatus including the same

Massively Distributed SGD: ImageNet/ResNet-50 Training in a Flash

ImageNet/ResNet-50 Training in 224 Seconds.

Metadata collection system, content management server, metadata collection apparatus, metadata collection method and program

Wireless communication device, wireless communication method, and program