Several of 2022's most anticipated novels offer unique perspectives on society's thorniest issues, from racism to workplace harassment. Call it a summer fiction reading list for the socially engaged. Ms. Shibata is never officially assigned the menial tasks of her workplace--making coffee, tidying, answering the phones--but since she's the only woman on staff, her colleagues expect her to oversee them. Annoyed by the tedious sexism, Shibata announces that she is pregnant and unable to continue the extra work. We follow her fake pregnancy week by week, and though there is no child, something real grows within Shibata.

As pandemic-led isolation triggers an epidemic of loneliness, Japanese are increasingly turning to "social robots" for solace and mental healing. At the city's Penguin Cafe, proud owners of the electronic dog Aibo gathered recently with their cyber-pups in Snuglis and fancy carryalls. From camera-embedded snouts to their sensor-packed paws, these high-tech hounds are nothing less than members of the family, despite a price tag of close to $3,000 -- mandatory cloud plan not included. It's no wonder Aibo has pawed its way into hearts and minds. Re-launched in 2017, Aibo's artificial intelligence-driven personality is minutely shaped by the whims and habits of its owner, building the kind of intense emotional attachments usually associated with kids, or beloved pets. Noriko Yamada rushed to order one, when her mother-in-law began showing signs of dementia several years ago.

Pet-like robots are attracting attention in Japan as companions for people spending time at home amid the spread of coronavirus infections. Some such robots, which are designed to comfort and relax users but do not have specific functions to help them, have been sent to care facilities to alleviate the loneliness of residents who have less in-person contact than before due to the pandemic. The cushion-shaped Qoobo was released by robot maker Yukai Engineering Inc. in 2018. The robot wags its "tail" in line with how strongly it is rubbed by the user. Inspired by the tails of dogs and cats, the company developed the robot for people who cannot have pets.

During the Covid-19 crisis, Shibata has corresponded with people all over the world who have recently turned to Paro robots as a therapeutic tool. In addition to their increased prominence in elderly and memory care, Shibata says the pandemic has created some novel use cases. Workers at a high-volume call center in Tokyo who dealt with calls about coronavirus testing were given a Paro as a stress-relief tool this May. And Shibata has been emailing with a 34-year-old nurse in an Atlanta intensive care unit who started using a Paro this April as a way to cope with being isolated from his loved ones and pet. "He used to live with his family and a dog at the home, but in order to avoid any risks of infection from him to them, they moved to a different house," Shibata says.

We propose an approach that connects recurrent networks with different orders of hidden interaction with regular grammars of different levels of complexity. We argue that the correspondence between recurrent networks and formal computational models gives understanding to the analysis of the complicated behaviors of recurrent networks. We introduce an entropy value that categorizes all regular grammars into three classes with different levels of complexity, and show that several existing recurrent networks match grammars from either all or partial classes. As such, the differences between regular grammars reveal the different properties of these models. We also provide a unification of all investigated recurrent networks. Our evaluation shows that the unified recurrent network has improved performance in learning grammars, and demonstrates comparable performance on a real-world dataset with more complicated models.

Global chip makers have lately been trying to stake out positions in next-generation vehicle technology. Last year, Intel Corp. bought Mobileye, an Israeli company known for chip-based camera systems that power automated driving features, for about $15 billion. Renesas Chief Executive Bunsei Kure said adding IDT would strengthen his company's presence as a supplier of system-on-a-chip products for vehicles equipped with advanced driver-assistance systems and autonomous driving technology. "Competition in the semiconductor industry has become like mixed martial arts," Mr. Kure said. Renesas was the second-largest semiconductor supplier to the automotive industry by revenue in 2017 with $3.5 billion, following No. 1 NXP Semiconductors NV of the Netherlands at $4.4 billion, according to data from research firm IHS Markit .

A mixed-signal image filtering VLSI has been developed aiming at real-time generation of edge-based image vectors for robust image recognition. A four-stage asynchronous median detection architecture basedon analog digital mixed-signal circuits has been introduced todetermine the threshold value of edge detection, the key processing parameter in vector generation. As a result, a fully seamless pipeline processing from threshold detection to edge feature mapgeneration has been established. A prototype chip was designed in a 0.35-µm double-polysilicon three-metal-layer CMOS technology and the concept was verified by the fabricated chip. The chip generates a 64-dimension feature vector from a 64x64-pixel gray scale image every 80µsec.

A mixed-signal image filtering VLSI has been developed aiming at real-time generation of edge-based image vectors for robust image recognition. A four-stage asynchronous median detection architecture based on analog digital mixed-signal circuits has been introduced to determine the threshold value of edge detection, the key processing parameter in vector generation. As a result, a fully seamless pipeline processing from threshold detection to edge feature map generation has been established. A prototype chip was designed in a 0.35-µm double-polysilicon three-metal-layer CMOS technology and the concept was verified by the fabricated chip. The chip generates a 64-dimension feature vector from a 64x64-pixel gray scale image every 80µsec.

A mixed-signal image filtering VLSI has been developed aiming at real-time generation of edge-based image vectors for robust image recognition. A four-stage asynchronous median detection architecture based on analog digital mixed-signal circuits has been introduced to determine the threshold value of edge detection, the key processing parameter in vector generation. As a result, a fully seamless pipeline processing from threshold detection to edge feature map generation has been established. A prototype chip was designed in a 0.35-µm double-polysilicon three-metal-layer CMOS technology and the concept was verified by the fabricated chip. The chip generates a 64-dimension feature vector from a 64x64-pixel gray scale image every 80µsec.

A flexible pattern-matching analog classifier is presented in conjunction with a robust image representation algorithm called Principal Axes Projection (PAP). In the circuit, the functional form of matching is configurable in terms of the peak position, the peak height and the sharpness of the similarity evaluation. The test chip was fabricated in a 0.6-µm CMOS technology and successfully applied to handwritten pattern recognition and medical radiograph analysis using PAP as a feature extraction pre-processing step for robust image coding. The separation and classification of overlapping patterns is also experimentally demonstrated.