Major Japanese nonfinancial companies project a 1.1 percent drop in their group net profit for the year ending in March 2020, a Jiji Press survey has found. The survey covered 243 companies listed on the Tokyo Stock Exchange's first section that have released earnings results for the fiscal year which ended last month. Their combined market value represents 36 percent of the total in the section. Industrial robot maker Fanuc Corp. estimates a 60 percent net profit fall, anticipating continued weakness in capital investment in China. Advantest Corp. forecasts a drop of 50 percent or more in its net profit amid expectations that sluggish memory-chip demand will weigh on sales of its semiconductor testing devices.

This week, I interviewed Yuval Noah Harari, the author of three best-selling books about the history and future of our species, and Fei-Fei Li, one of the pioneers in the field of artificial intelligence. The event was hosted by the Stanford Center for Ethics and Society, the Stanford Institute for Human-Centered Artificial Intelligence, and the Stanford Humanities Center. A transcript of the event follows, and a video is posted below. Nicholas Thompson: Thank you, Stanford, for inviting us all here. I want this conversation to have three parts: First, lay out where we are; then talk about some of the choices we have to make now; and last, talk about some advice for all the wonderful people in the hall. Yuval, the last time we talked, you said many, many brilliant things, but one that stuck out was a line where you said, "We are not just in a technological crisis. We are in a philosophical crisis." So explain what you meant and explain how it ties to AI. Let's get going with a note of ...

Neural architectures inspired by our own human cognitive system, such as the recently introduced world models, have been shown to outperform traditional deep reinforcement learning (RL) methods in a variety of different domains. Instead of the relatively simple architectures employed in most RL experiments, world models rely on multiple different neural components that are responsible for visual information processing, memory, and decision-making. However, so far the components of these models have to be trained separately and through a variety of specialized training methods. This paper demonstrates the surprising finding that models with the same precise parts can be instead efficiently trained end-to-end through a genetic algorithm (GA), reaching a comparable performance to the original world model by solving a challenging car racing task. An analysis of the evolved visual and memory system indicates that they include a similar effective representation to the system trained through gradient descent. Additionally, in contrast to gradient descent methods that struggle with discrete variables, GAs also work directly with such representations, opening up opportunities for classical planning in latent space. This paper adds additional evidence on the effectiveness of deep neuroevolution for tasks that require the intricate orchestration of multiple components in complex heterogeneous architectures.

Mobile and ubiquitous sensing of urban air quality (AQ) has received increased attention as an economically and operationally viable means to survey atmospheric environment with high spatial-temporal resolution. A necessary and value-added step towards data-driven sustainable urban management is fine-granular AQ inference, which estimates grid-level pollutant concentrations at every instance of time using AQ data collected from fixed-location and mobile sensors. We present the Mobile Air Pollution Sensing (MAPS) framework, which consists of data preprocessing, urban feature extraction, and AQ inference. This is applied to a case study in Beijing (3,025 square km, 19 June - 16 July 2018), where PM2.5 concentrations measured by 28 fixed monitoring stations and 15 vehicles are fused to infer hourly PM2.5 concentrations in 3,025 1km-by-1km grids. Two machine learning structures, namely Deep Feature Spatial-Temporal Tree (DFeaST-Tree) and Deep Feature Spatial-Temporal Network (DFeaST-Net), are proposed to infer PM2.5 concentrations supported by 62 types of urban data that encompass geography, land use, traffic, public, and meteorology. This allows us to infer fine-granular PM2.5 concentrations based on sparse AQ measurements (less than 5% coverage) with good accuracy (SMAPE<15%, R-square>0.9), while accounting for the regional transport of air pollutants outside the study area. In-depth discussions are provided on the heterogeneity of fixed and mobile data sources, spatial coverage of mobile sensing, and importance of urban features for inferring PM2.5 concentrations.

In this paper, we introduce a novel combined reward cum penalty loss function to handle the regression problem. The proposed combined reward cum penalty loss function penalizes the data points which lie outside the $\epsilon$-tube of the regressor and also assigns reward for the data points which lie inside of the $\epsilon$-tube of the regressor. The combined reward cum penalty loss function based regression (RP-$\epsilon$-SVR) model has several interesting properties which are investigated in this paper and are also supported with the experimental results.

Thanks to the recent advances in processing speed and data acquisition and storage, machine learning (ML) is penetrating every facet of our lives, and transforming research in many areas in a fundamental manner. Wireless communications is another success story -- ubiquitous in our lives, from handheld devices to wearables, smart homes, and automobiles. While recent years have seen a flurry of research activity in exploiting ML tools for various wireless communication problems, the impact of these techniques in practical communication systems and standards is yet to be seen. In this paper, we review some of the major promises and challenges of ML in wireless communication systems, focusing mainly on the physical layer. We present some of the most striking recent accomplishments that ML techniques have achieved with respect to classical approaches, and point to promising research directions where ML is likely to make the biggest impact in the near future. We also highlight the complementary problem of designing physical layer techniques to enable distributed ML at the wireless network edge, which further emphasizes the need to understand and connect ML with fundamental concepts in wireless communications.

The Immigration Services Agency plans to strengthen its eligibility standards for Japanese-language schools, it was learned Saturday. The move comes as Japanese-language schools have been under fire for accepting many foreign students whose purpose is to work in Japan. The number of Japanese-language schools recognized by the government grew 1.6 times over the past five years to 749 as of April 2. The government late last year outlined plans to improve the quality of Japanese-language schools as part of efforts to bring in more foreign workers to the country. Under the agency's plan, the requirement for the average student attendance rate would be revised from the current 50 percent or more in a month to 70 percent or more in a period of seven months. Schools failing to meet the requirement would not be allowed to accept foreign students.

If you're looking for some mundane distractions to get you through the holiday period, Shukan Taishu (May 6-13) has got just the thing. Its "Reiwa Commemorative Edition" introduces unusual rides. Not to be outdone, a park in Tochigi has camels for the same purpose, as does another in Chiba offering elephant rides. At Hakkeijima Sea Paradise in Yokohama, visitors from age 10 (who can prove they can swim for a distance of 25 meters) may emulate the "boy on a dolphin" theme and ride atop a friendly beluga whale. Two-wheeled Segway personal transporters are available for inexpensive rental at Showa Memorial Park in the city of Tachikawa.

Meditation and mindfulness have been around for thousands of years. But the advent of smartphones and computers led to a new phenomenon: the mindfulness app. There are a few to choose from, including the punchy, assertive 10% Happier, the elegant and placid Calm and the first app that really brought mindfulness to our phones, Headspace. Andy Puddicombe, a former Buddhist monk who went on to run a meditation clinic in London, met a new business partner, Richard Pierson, and launched Headspace in 2010. The company began as an events organisation and led to the now-ubiquitous app in 2012.

Worker activists at Google held a "town hall" on Friday where they alleged that the company regularly retaliates against employees who speak out about workplace problems and announced plans for a "company-wide day of action" on 1 May. The meeting, livestreamed for Google employees in offices around the world, was announced after two of the organizers of the November 2018 global walkout circulated a letter internally alleging they were being punished for their activism. The two employees, Meredith Whittaker and Claire Stapleton, provided further details of their cases during the Friday event. Their statements, along with anonymous reports of retaliation of 11 other Google employees, were published in internal documents seen by the Guardian. "I didn't walk out because I'm against Google, I walked out because I'm for it – because I wanted to make it better," Stapleton said in her written statement.