The continued scaling of flash memory technology into smaller process nodes, combined with the increased information capacity of each flash cell (i.e, storing more bits per cell), has placed NAND flash memory at the forefront of modern storage technology.

Quarter level cell (QLC) 3D NAND flash memory is emerging as the predominant storage solution in the era of artificial intelligence. QLC 3D NAND flash stores 4 bit per cell to expand the storage density, resulting in narrower read margins. Constrained to read margins, QLC always suffers from lateral charge migration (LCM), which caused by non-uniform charge density across adjacent memory cells. To suppress charge density gap between cells, there are some algorithm in form of intra-page data mapping such as WBVM, DVDS. However, we observe inter-page data arrangements also approach the suppression. Thus, we proposed an intelligent model PDA-LSTM to arrange intra-page data for LCM suppression, which is a physics-knowledge-driven neural network model. PDA-LSTM applies a long-short term memory (LSTM) neural network to compute a data arrangement probability matrix from input page data pattern. The arrangement is to minimize the global impacts derived from the LCM among wordlines. Since each page data can be arranged only once, we design a transformation from output matrix of LSTM network to non-repetitive sequence generation probability matrix to assist training process. The arranged data pattern can decrease the bit error rate (BER) during data retention. In addition, PDA-LSTM do not need extra flag bits to record data transport of 3D NAND flash compared with WBVM, DVDS. The experiment results show that the PDA-LSTM reduces the average BER by 80.4% compared with strategy without data arrangement, and by 18.4%, 15.2% compared respectively with WBVM and DVDS with code-length 64.

Prompt-based methods have gained increasing attention on NLP and shown validity on many downstream tasks. Many works have focused on mining these methods' potential for knowledge extraction, but few explore their ability to make logical reasoning. In this work, we focus on the effectiveness of the prompt-based methods on first-order logical reasoning and find that the bottleneck lies in logical negation. Based on our analysis, logical negation tends to result in spurious correlations to negative answers, while propositions without logical negation correlate to positive answers. To solve the problem, we propose a simple but effective method, Negation Augmenting and Negation Debiasing (NAND), which introduces negative propositions to prompt-based methods without updating parameters. Specifically, these negative propositions can counteract spurious correlations by providing "not" for all instances so that models cannot make decisions only by whether expressions contain a logical negation. Experiments on three datasets show that NAND not only solves the problem of calibrating logical negation but also significantly enhances prompt-based methods of logical reasoning without model retraining.

The industry ministry said on Tuesday it would extend subsidies worth as much as 242.9 billion ( 1.64 billion) for Bain Capital-backed Kioxia and Western Digital to expand memory chip production in Mie and Iwate prefectures. The funding provides underpinning for the two companies, which have been hammered by a slump in the market for NAND flash chips and whose merger talks stalled late last year following opposition from Kioxia investor SK Hynix. Japan's powerful industry ministry aims to reclaim the country's lost position as a major chip center by extending subsidies to domestic and foreign chipmakers and secure chip supply amid trade tensions between China and the United States. "The memory market is expected to grow significantly in the future, including for generative AI (artificial intelligence)," industry minister Ken Saito told reporters. "The joint investment by Kioxia and Western Digital brings together Japan and the U.S. to fulfill our responsibility to supply the memory the world needs," he said.

In this paper an efficient model based diagnostic process is described for systems whose components possess a causal relation between their inputs and their outputs. In this diagnostic process, firstly, a set of focuses on likely broken components is determined. Secondly, for each focus the most informative probing point within the focus can be determined. Both these steps of the diagnostic process have a worst case time complexity of ${\cal O}(n^2)$ where $n$ is the number of components. If the connectivity of the components is low, however, the diagnostic process shows a linear time complexity. It is also shown how the diagnostic process described can be applied in dynamic systems and systems containing loops. When diagnosing dynamic systems it is possible to choose between detecting intermitting faults or to improve the diagnostic precision by assuming non-intermittency.

From cars to planes, the future of transportation is already here--and is changing rapidly. Software engineering is increasingly central to both the development and maintenance of all kinds of vehicles. That means more people need to start thinking like systems engineers. Dale Tutt, vice president of aerospace and defense industry for Siemens Software, says this means companies must offer more training and planning for those designing and developing vehicles of the future. "As you try to address the talent gap, there's a lot you can do to help make the tools easier to use. By better integrating the tools and by bringing in technologies like AI to help automate the generation of different design concepts and the analysis of those concepts using simulation tools, you can extend the capabilities of the system so that it helps empower your engineers," says Tutt. "Companies that are the most successful at adopting systems engineering are doing it because systems engineering, and the tools being used are becoming almost like the DNA of their engineering organization. Everyone is starting to think a bit like a systems engineer, even in their normal job. The tools and the ecosystem that you use to do systems engineering has a large role in facilitating adoption." Nand Kochhar, the vice president of automotive and transportation for Siemens Software, says a systems engineering approach can extend more broadly, as engineers think about how cars and vehicles connect to everything else in their environments. "In a smart city, the system has become the city itself. Take a vehicle in the city, for example. The definition of the system has moved from the single vehicle to include the flow of traffic in the city and to how the traffic lights operate. You can extend that expansive ecosystem to other aspects like building management, for example, into the smart city environment," he says.

In the coming years, mobility solutions--or how we get from point A to point B--will bridge the gap between ground and air transportation--yes, that means flying cars. Technological advancements are transforming mobility for people and, leading to unprecedented change. Nand Kochhar, vice president of automotive and transportation for Siemens Software says this transformation extends beyond transportation to society in general. "The future of mobility is going to be multimodal to meet consumer demands, to offer a holistic experience in a frictionless way, which offers comfort, convenience, and safety to the end consumer." Thinking about transportation differently is part of a bigger trend, Kochhar notes: "Look at few other trends like sustainability and emissions, which are not just a challenge for the automotive industry but to society as a whole." The advances in technology will have benefits beyond shipping and commute improvements--these technological advancements, Kochhar argues, are poised to drive an infrastructure paradigm shift that will bring newfound autonomy to those who, today, aren't able to get around by themselves. Kochhar explains, "Just imagine people in our own families who are in that stage where they're not able to drive today. Now, you're able to provide them freedom." Laurel Ruma: From Technology Review, I'm Laurel Ruma, and this is Business Lab, the show that helps business leaders make sense of new technologies coming out of the lab and into the marketplace. Our topic today is the future of mobility. In 2011, Marc Andreessen famously said, "Software is eating the world."

According to Kaggle's 2020 edition of the State of Machine Learning and Data Science report -- which includes insights gathered from a survey of 20,036 Kaggle members -- more than 55 per cent of data scientists have less than three years of experience, and six per cent of professionals pursuing data science have been using machine learning for more than a decade. The study further revealed that machine learning has become more rooted in the companies where Kaggle scientists work. Nearly 31 %of data scientists claimed well-established machine learning methods, up from 28% in 2019 and 25 % in 2018. Though Kaggle competitions are great to practice data science skills, are they really that different from real-world data science and machine learning work? This article will unveil the difference between the two, especially when solving machine learning problems on Kaggle vs real life.

It's marketed directly at Chia cryptocurrency plotting, a very high-bandwidth sustained write task. If you want some info on how much data Chia requires, you can find it here. But if your workload involves something similar, such as continuous large-scale backup, video encoding, or anything else that involves writing lots and lots of data, it might also be of interest. The LX3030 is the fastest PCIe 3.0-based sustained writer we've tested and its TBW (TeraBytes that can be Written) ratings are astounding: 27,000TBW per 1TB of NAND. Seagate's scorching fast FireCuda 530 is rated for 1,250TBW per terabyte--a lot of data by normal standards, but shy one zero compared to the PNY's rated durability.